DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-19 are presented for examination based on the amended claims in the application filed on September 3, 2026.
Claims 1-12 and 14-19 are rejected under 35 U.S.C. § 103 as being unpatentable over US 2014/0316838 A1 Zhou, Yu et al. [herein “Zhou”] and in view of Yürüşen, Nurseda Y., Paul N. Rowley, Simon J. Watson, and Julio J. Melero. "Automated wind turbine maintenance scheduling." Reliability Engineering & System Safety 200 (2020): 106965 [herein “Yürüşen”] and in view of US 2017/0089325 A1 Timbus, Adrian et al. [herein “Timbus”].
Claim 13 is rejected under 35 U.S.C. § 103 as being unpatentable over Zhou, Yürüşen, and Timbus, as applied to claim 1 above, and further in view of Ma, Kuichao, Jiangsheng Zhu, M. Soltani, Amin Hajizadeh, Peng Hou, and Zhe Chen. "Active power dispatch strategy of wind farms under generator faults." In Safety and Reliability–Safe Societies in a Changing World, pp. 2147-2152. CRC Press, 2018 [herein “Ma”].
This action is made Final.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment, filed on September 3, 2026, has been entered. Claims 1-19 remain pending in the application. Applicant’s amendments to the Drawings and Claims have overcome every objection previously set forth in the Non-Final Office Action mailed June 5, 2026 with the exception of the objections provide in the “Claim Objections” section below.
Claim Objections
Claims 1-19 are objected to because of the following informality: recitations of elements with no previous recitations. For example, claim 1, “the basis of the monitoring data” in Ln. 16-17, is improper because there has been no previous recitation of “the basis of the monitoring data”. For the purpose of examination, “the basis of the monitoring data” will be interpreted as “a basis of the monitoring data”. Claims 2-19 are also objected to for incorporating the deficiency of its independent claim 1. Similarly, the following are objected under similar rationale:
Claim 1, “the basis of the obtained weather data” in Ln. 17-18, should be “a basis of the obtained weather data”.
Claim 8, “the period of time” in Ln. 2, should be “a period of time”.
Claim 14, “integrity of the wind turbine” in Ln. 28, should be “the integrity of the wind turbine”.
All claims dependent on an objected base claim are objected based on their dependency.
Appropriate correction is required.
Claim Interpretation
The term “low forecast wind conditions” in claim 17 is a relative term which renders the claim indefinite. The term “low” is not defined by the claim; however, the specification does provide a standard for ascertaining the requisite degree of the term for which the term has be interpreted as meaning (see Pg. 2 Ln. 33-34, “The control scheme will for example be determined such that the shutdown period is scheduled at low wind conditions (lower than average)” ). Further, claim 18, which is dependent on claim 17, recites “wherein the low forecast wind conditions are forecast wind conditions that are lower than average forecast wind conditions”. Therefore, one of ordinary skill in the art could be reasonably apprised of the scope of the invention (see MPEP § 2173.05(b)). To improve the legibility of the claim, the examiner recommends amending claim 17 to incorporate the limitation of claim 18 or similar, as supported by the specification, to confine the claim into a clear meaning and eliminate the subjectivity of the claim language. Claims 18-19, which are dependent on claim 17, have been interpreted under similar rationale.
Claim Rejections - 35 U.S.C. § 103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. § 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1-12 and 14-19 are rejected under 35 U.S.C. § 103 as being unpatentable over US 2014/0316838 A1 Zhou, Yu et al. [herein “Zhou”] and in view of Yürüşen, Nurseda Y., Paul N. Rowley, Simon J. Watson, and Julio J. Melero. "Automated wind turbine maintenance scheduling." Reliability Engineering & System Safety 200 (2020): 106965 [herein “Yürüşen”] and in view of US 2017/0089325 A1 Timbus, Adrian et al. [herein “Timbus”].
As per claim 1, Zhou teaches “A method of controlling operation of a wind turbine, comprising: determining a control scheme for the wind turbine, wherein the control scheme specifies for a future period of time at least a first operating period in which the wind turbine is operated to provide an output of electrical power to a power grid and at least one shutdown period in which the wind turbine is shut down, the shutdown period being arranged temporally after the first operating period”. (Para. 0013, “The invention is particularly, but not exclusively, advantageous for obtaining a decision support system that may potentially change the traditional concept of reactive and predictive maintenance technique for renewable energy generators, such as wind turbine generators” [A method of controlling operation of a wind turbine, comprising: determining a control scheme for the wind turbine]. Para. 0059, “The wind turbine condition monitoring module may output the wind turbine component failure alert, failure severity (alert level) and estimated remaining usage of component lifetime” and Para. 0068, “The proposal shows the conditions for the situation where 5, 2, or 0 WTGs should be stopped if required” [e.g., wherein the control scheme specifies for a future period of time at least a first operating period in which the wind turbine is operated]. Para. 0056, “FIG. 1 schematically illustrates elements of an embodiment of the present invention. The Figure schematically illustrates an overall control system 6 between a power output 2 from one or more wind turbine generators 11 and a power grid 4” [wind turbine is operated to provide an output of electrical power to a power grid]. Para. 0067, “A list of best time slots, and allowed shut down turbine number for service or maintenance will be provided” [at least one shutdown period in which the wind turbine is shut down, the shutdown period being arranged temporally after the first operating period]. Further see Para. 0013, 0023, 0056-0060, and 0067-0069. The examiner has interpreted that obtaining a decision support system to change the reactive and predictive maintenance technique for wind turbines based on continuing to run the wind turbines for estimated usage of its lifetime and providing the best time slots for allowed shut down of the turbine for service or maintenance as a method of controlling operation of a wind turbine, comprising: determining a control scheme for the wind turbine, wherein the control scheme specifies for a future period of time at least a first operating period in which the wind turbine is operated to provide an output of electrical power to a power grid and at least one shutdown period in which the wind turbine is shut down, the shutdown period being arranged temporally after the first operating period.)
Zhou teaches “wherein determining the control scheme comprises: obtaining input data, wherein obtaining input data includes at least monitoring operation of the wind turbine to obtain monitoring data related to integrity of the wind turbine, and obtaining weather data indicating weather conditions”. (Para, 0059, “A wind turbine generator condition module WT-CON 23 is further arranged for storing and/or receiving condition data from the plurality of wind turbine generators 11 WTG_i, i={1, 2, i . . . n} in the wind power plant 10, and processing said condition data into maintenance recommendations (REC-MAN) for one or more renewable energy generators” [wherein determining the control scheme comprises: obtaining input data, wherein obtaining input data includes at least monitoring operation of the wind turbine to obtain monitoring data related to integrity of the wind turbine]. Para. 0057, “The module FM receives data either from an internal storage/data base 25a and/or from one or more external sources of data 25b, e.g. weather data, demand data, price data etc.” [obtaining weather data indicating weather conditions]. Further see Para. 0057-0059. The examiner has interpreted that receiving weather data and condition data from the wind turbines to be processed into maintenance recommendations as wherein determining the control scheme comprises: obtaining input data, wherein obtaining input data includes at least monitoring operation of the wind turbine to obtain monitoring data related to integrity of the wind turbine, and obtaining weather data indicating weather conditions.)
Zhou teaches “forecasting, by the processing unit, two or more operating parameters of the wind turbine, a forecasting including at least the forecasting of wind turbine health on the basis of the monitoring data, and a forecasting of wind conditions on the basis of the obtained weather data”. (Para. 0059, “A wind turbine generator condition module WT-CON 23 is further arranged for storing and/or receiving condition data from the plurality of wind turbine generators 11 WTG_i, i={1, 2, i . . . n} in the wind power plant 10, and processing said condition data into maintenance recommendations (REC-MAN) for one or more renewable energy generators. The wind turbine condition monitoring module may output the wind turbine component failure alert, failure severity (alert level) and estimated remaining usage of component lifetime. The wind turbine generator maintenance recommendation may thus set the wind turbine service/maintenance priority list according to the failure severity” [forecasting of wind turbine health on the basis of the monitoring data]. Para. 0057, “The system comprises a forecasting module FM, 21 arranged for outputting a plurality of wind power plant relevant parameters PF in a predefined prediction window of time TW, i.e. the parameters PF are estimated or forecasted in the prediction window. The module FM receives data either from an internal storage/data base 25a and/or from one or more external sources of data 25b, e.g. weather data, demand data, price data etc.” and Para. 0063, “The forecasting module will output the average price of the spot price, wind speed and power loading in the, for example, next 7 days (short-term) and 2 to 3 months (long-term) as parameters PF” [e.g., a forecasting of wind conditions on the basis of the obtained weather data]. Para. 0072, “The invention can be implemented by means of hardware, software, firmware or any combination of these. The invention or some of the features thereof can also be implemented as software running on one or more data processors and/or digital signal processors” [by the processing unit]. Further see Para. 0057-0063 and 0072. The examiner has interpreted that receiving condition data from the wind turbines to output an estimated lifetime and receiving weather data to output a forecasted wind speed using a digital signal processor as forecasting, by the processing unit, two or more operating parameters of the wind turbine, a forecasting including at least the forecasting of wind turbine health on the basis of the monitoring data, and a forecasting of wind conditions on the basis of the obtained weather data.)
Zhou teaches “based on the at least two or more forecasted operating parameters of the wind turbine, determining the control scheme for the wind turbine such that one or more optimization parameters are optimized, wherein the optimization of the one or more optimization parameters includes at least one of maximizing a wind turbine energy production, minimizing a risk of wind turbine failure, maximizing a usable energy production by maximizing the electric energy produced by the wind turbine at times of higher than average energy demand on the power grid, minimizing wind turbine cost, maximizing a revenue from electric power generated by the wind turbine, maximizing a wind turbine availability, and maximizing a wind turbine utilization.” (Para. 0059, “A wind turbine generator condition module WT-CON 23 is further arranged for storing and/or receiving condition data from the plurality of wind turbine generators 11 WTG_i, i={1, 2, i . . . n} in the wind power plant 10, and processing said condition data into maintenance recommendations (REC-MAN) for one or more renewable energy generators. The wind turbine condition monitoring module may output the wind turbine component failure alert, failure severity (alert level) and estimated remaining usage of component lifetime. The wind turbine generator maintenance recommendation may thus set the wind turbine service/maintenance priority list according to the failure severity” [forecasted operating parameters]. Para. 0057, “The system comprises a forecasting module FM, 21 arranged for outputting a plurality of wind power plant relevant parameters PF in a predefined prediction window of time TW, i.e. the parameters PF are estimated or forecasted in the prediction window. The module FM receives data either from an internal storage/data base 25a and/or from one or more external sources of data 25b, e.g. weather data, demand data, price data etc.” and Para. 0063, “The forecasting module will output the average price of the spot price, wind speed and power loading in the, for example, next 7 days (short-term) and 2 to 3 months (long-term) as parameters PF” [forecasted operating parameters]. Para. 0058, “An optimization module OPT 22 is capable of receiving said plurality of wind power plant WPP 10 relevant parameters PF, such as wind speed and demand, and processing therefrom a proposed maintenance schedule PROP-MAN for the wind power plant WPP in order to optimize the produced energy with respect to the demand in the predefined prediction window TW, e.g. the next 3 or 7 days whatever the desire for prediction length may be, though for example reliability of weather forecast are typically critically dependent on the length of the prediction window” [based on the at least two or more forecasted operating parameters of the wind turbine, determining the control scheme for the wind turbine such that one or more optimization parameters are optimized, wherein the optimization of the one or more optimization parameters includes maximizing a wind turbine energy production]. Further see Para. 0057-0065. The examiner has interpreted that estimating wind turbines lifetime and forecasting wind speed to proposed a maintenance schedule to optimize the produced energy as based on the at least two or more forecasted operating parameters of the wind turbine, determining the control scheme for the wind turbine such that one or more optimization parameters are optimized, wherein the optimization of the one or more optimization parameters includes at least one of maximizing a wind turbine energy production.)
Zhou does not specifically teach “wherein determining the control scheme for the wind turbine to optimize the one or more optimization parameters comprises determining a priority for scheduling the shutdown period in the control scheme of the wind turbine based on at least the forecasted wind turbine health and the forecasted wind conditions”, “based on the priority, determining the control scheme that comprises the shutdown period within a predetermined time period in the future that is selected in dependence on the determined priority”, “wherein the priority is represented by a priority value, and wherein the priority value determines the predetermined time period”, and “wherein within the predetermined time period, the shutdown period is scheduled such that the one or more optimization parameters are optimized”.
However, in the same field of endeavor namely optimizing wind turbine maintenance plans, Yürüşen teaches “wherein determining the control scheme for the wind turbine to optimize the one or more optimization parameters comprises determining a priority for scheduling the shutdown period in the control scheme of the wind turbine based on at least the forecasted wind turbine health and the forecasted wind conditions” and “wherein within the predetermined time period, the shutdown period is scheduled such that the one or more optimization parameters are optimized”. (Abstract, “While many operation and maintenance (O&M) decision support systems (DSS) have been already proposed, a serious research need still exists for wind farm O&M scheduling. O&M planning is a challenging task, as maintenance teams must follow specific procedures when performing their service, which requires working at height in adverse weather conditions. Here, an automated maintenance programming framework is proposed based on real case studies considering available wind speed and wind gust data. The methodology proposed consists on finding the optimal intervention time and the most effective execution order for maintenance tasks and was built on information from regular maintenance visit tasks and a corrective maintenance visit. The objective is to find possible schedules where all work orders can be performed without breaks, and to find out when to start in order to minimise revenue losses” [wherein determining the control scheme for the wind turbine to optimize the one or more optimization parameters and wherein within the predetermined time period, the shutdown period is scheduled such that the one or more optimization parameters are optimized]. Pg. 11, Sect. 6, “It must be noted that in order to use the proposed DSS in the field, one must use as input: wind speed, wind gust and electricity market price forecasts to be able to asses, which day is preferable to schedule maintenance activities in near future” [based on at least the forecasted wind conditions]. Pg. 9 Sect. 5, “Considering the hourly electricity market price, it is possible to combine it with the energy losses for each plan, estimated from measured wind speed values and manufacturer’s power curve, to obtain the revenue prioritised decision pools” [based on at least the forecasted wind turbine health and the forecasted wind conditions]. Pg. 10 Sect. 5.1, “Fig. 12 shows the corresponding revenue losses of the prioritized maintenance plans, labelled as “low” when they are below the mean of the revenue losses estimated for the day under consideration. When they are lesser than the third quartile and greater than the mean, the label is “medium”. Lastly, for the plans with the revenue losses greater than the third quartile, the label is “high”… According to Fig. 12, the early hours of the day are more preferable in order to perform preventative intervention considering the revenue losses. Although electricity market prices are high during these hours, the limited wind resource availability, reduces power production losses and corresponding revenue losses [determining a priority for scheduling the shutdown period in the control scheme of the wind turbine based on at least the forecasted wind turbine health and the forecasted wind conditions]. Further see Sect. 5-6. The examiner has interpreted that determining wind farm operation and maintenance scheduling to find the optimal intervention time and the most effective execution order for maintenance tasks by inputting the forecasts of wind speed, electricity market price, and estimated energy losses to order to generated a prioritized maintenance plan to minimize revenue loss by performing maintenance early as wherein determining the control scheme for the wind turbine to optimize the one or more optimization parameters comprises determining a priority for scheduling the shutdown period in the control scheme of the wind turbine based on at least the forecasted wind turbine health and the forecasted wind conditions and wherein within the predetermined time period, the shutdown period is scheduled such that the one or more optimization parameters are optimized.)
Yürüşen teaches “based on the priority, determining the control scheme that comprises the shutdown period within a predetermined time period in the future that is selected in dependence on the determined priority” and “wherein the priority is represented by a priority value, and wherein the priority value determines the predetermined time period”,”. (Abstract, “The methodology proposed consists on finding the optimal intervention time and the most effective execution order for maintenance tasks and was built on information from regular maintenance visit tasks and a corrective maintenance visit. The objective is to find possible schedules where all work orders can be performed without breaks, and to find out when to start in order to minimise revenue losses” [wherein the priority is represented by a priority value, and wherein the priority value determines the predetermined time period]. Pg. 10 Sect. 5.1, “Fig. 12 shows the corresponding revenue losses of the prioritized maintenance plans, labelled as “low” when they are below the mean of the revenue losses estimated for the day under consideration. When they are lesser than the third quartile and greater than the mean, the label is “medium”. Lastly, for the plans with the revenue losses greater than the third quartile, the label is “high”… According to Fig. 12, the early hours of the day are more preferable in order to perform preventative intervention considering the revenue losses. Although electricity market prices are high during these hours, the limited wind resource availability, reduces power production losses and corresponding revenue losses [based on the priority, determining the control scheme that comprises the shutdown period within a predetermined time period in the future that is selected in dependence on the determined priority]. Further see Sect. 5-6. The examiner has interpreted that determining wind farm operation and maintenance scheduling to find the optimal intervention time and the most effective execution order for maintenance tasks to minimize revenue loss by performing maintenance by starting them early for high revenue losses as based on the priority, determining the control scheme that comprises the shutdown period within a predetermined time period in the future that is selected in dependence on the determined priority and wherein the priority is represented by a priority value, and wherein the priority value determines the predetermined time period.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein determining the control scheme for the wind turbine to optimize the one or more optimization parameters comprises determining a priority for scheduling the shutdown period in the control scheme of the wind turbine based on at least the forecasted wind turbine health and the forecasted wind conditions”, “based on the priority, determining the control scheme that comprises the shutdown period within a predetermined time period in the future that is selected in dependence on the determined priority”, “wherein the priority is represented by a priority value, and wherein the priority value determines the predetermined time period”, and “wherein within the predetermined time period, the shutdown period is scheduled such that the one or more optimization parameters are optimized” as conceptually seen from the teaching of Yürüşen, into that of Zhou because this modification of prioritizing the maintenance schedule using evaluated values from the combination of forecasted parameters for the advantageous purpose of minimizing the revenue lost due to maintaining the wind farm (Yürüşen, Pg. 10 Sect. 5 and the Abstract). Further motivation to combine be that Zhou and Yürüşen are analogous art to the current claim directed to optimizing wind turbine maintenance plans.
Zhou and Yürüşen do not specifically teach “operating the wind turbine in accordance with the control scheme by a control system including a processing unit, wherein the control system provides control commands for the wind turbine, and wherein operating the wind turbine in accordance with the control scheme includes shutting down the wind turbine during the shutdown period”.
However, in the same field of endeavor namely optimizing wind turbine maintenance plans, Timbus teaches “operating the wind turbine in accordance with the control scheme by a control system including a processing unit, wherein the control system provides control commands for the wind turbine, and wherein operating the wind turbine in accordance with the control scheme includes shutting down the wind turbine during the shutdown period”. (Para. 0040, “The wind farm management system ultimately may include a production and maintenance planning module for determining production and maintenance schedules and for issuing corresponding turbine control inputs including set-points for active or reactive power to the individual wind turbine control modules. The maintenance planning module is aware of possible maintenance intervals and constraints” [i.e., operating the wind turbine in accordance with the control scheme, wherein the control system provides control commands for the wind turbine, and wherein operating the wind turbine in accordance with the control scheme includes shutting down the wind turbine during the shutdown period]. Para. 0048, “The features of the method of operating a wind farm and the wind farm controller as described herein may be performed by way of hardware components, firmware, and/or a computing device having processing means programmed by appropriate software. For instance, the wind farm controller can include any known general purpose processor or integrated circuit such as a central processing unit (CPU), microprocessor, field programmable gate array (FPGA), Application Specific Integrated Circuit (ASIC), or other suitable programmable processing or computing device or circuit as desired. The processor can be programmed or configured to include and perform features of the exemplary embodiments of the present disclosure such as a method of operating a wind farm” [i.e., by a control system including a processing unit]. Further see Para. 0035-0048. The examiner has interpreted operating a wind farm with a wind farm controller of a wind farm management system that issues turbine control for maintenance intervals as operating the wind turbine in accordance with the control scheme by a control system including a processing unit, wherein the control system provides control commands for the wind turbine, and wherein operating the wind turbine in accordance with the control scheme includes shutting down the wind turbine during the shutdown period.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “operating the wind turbine in accordance with the control scheme by a control system including a processing unit, wherein the control system provides control commands for the wind turbine, and wherein operating the wind turbine in accordance with the control scheme includes shutting down the wind turbine during the shutdown period” as conceptually seen from the teaching of Timbus, into that of Zhou and Yürüşen because this modification of controlling the wind turbines using a maintenance plan for the advantageous purpose of maximizing revenue earnings, minimizing maintenance cost, and maximize power production for a wind farm (Timbus Para. 0025-0026). Further motivation to combine be that Zhou, Yürüşen, and Timbus are analogous art to the current claim directed to optimizing wind turbine maintenance plans.
As per claim 2, Zhou teaches “wherein obtaining input data further comprising obtaining power grid data indicative of an energy demand of the power grid; wherein forecasting the two or more operating parameters of the wind turbine further comprising a forecasting of energy demand based on the obtained power grid data; and wherein the optimization of the one or more optimization parameters includes at least maximizing the usable energy production.” (Para. 0057, “The system comprises a forecasting module FM, 21 arranged for outputting a plurality of wind power plant relevant parameters PF in a predefined prediction window of time TW, i.e. the parameters PF are estimated or forecasted in the prediction window. The module FM receives data either from an internal storage/data base 25a and/or from one or more external sources of data 25b, e.g. weather data, demand data, price data etc.” [wherein obtaining input data further comprising obtaining power grid data indicative of an energy demand of the power grid]. Para. 0063, “forecasting on the weather, power generation forecasting and electricity loading (power demand) will be factored in…forecasting module will output the average price of the spot price, wind speed and power loading in the, for example, next 7 days (short-term) and 2 to 3 months (long-term) as parameters PF” [wherein forecasting the two or more operating parameters of the wind turbine further comprising the forecasting of energy demand based on the obtained power grid data]. Para. 0064, “The optimization module OPT is capable of processing the proposed maintenance schedule PROP-MAN for the wind power plant WPP 10, as a function of the parameters PF, in order to optimize the produced energy with respect to the demand for energy in subunit 22a, price of energy in subunit 22b and/or capability of produced energy in subunit 22c in said predefined prediction window TW” [wherein the optimization of the one or more optimization parameters includes at least maximizing the usable energy production]. Further see 0017, 0024, 0057, and 0063-0064. The examiner has interpreted that receiving electrical loading demand data to forecast the power loading to propose a maintenance schedule for the wind turbine plant to optimize the produced energy for the demand and capability of the produced energy as wherein obtaining input data further comprising obtaining power grid data indicative of an energy demand of the power grid; wherein forecasting the two or more operating parameters of the wind turbine further comprising a forecasting of energy demand based on the obtained power grid data; and wherein the optimization of the one or more optimization parameters includes at least maximizing the usable energy production.)
As per claim 3, Zhou teaches “wherein obtaining input data further comprising obtaining electricity price information for electric power supplied by the wind turbine; wherein forecasting the two or more operating parameters of the wind turbine further comprising the forecasting of electricity price; and wherein the optimization of the one or more optimization parameters includes the maximization of revenue from electric power generated by the wind turbine.” (Para. 0057, “The system comprises a forecasting module FM, 21 arranged for outputting a plurality of wind power plant relevant parameters PF in a predefined prediction window of time TW, i.e. the parameters PF are estimated or forecasted in the prediction window. The module FM receives data either from an internal storage/data base 25a and/or from one or more external sources of data 25b, e.g. weather data, demand data, price data etc.” [wherein obtaining input data further comprising obtaining electricity price information for electric power supplied by the wind turbine]. Para. 0063, “forecasting on the weather, power generation forecasting and electricity loading (power demand) will be factored in…forecasting module will output the average price of the spot price, wind speed and power loading in the, for example, next 7 days (short-term) and 2 to 3 months (long-term) as parameters PF” [wherein forecasting the two or more operating parameters of the wind turbine further comprising the forecasting of electricity price]. Para. 0016, “the forecasting module may be arranged for outputting at least one renewable power plant relevant parameter (PF) related to demand for energy and/or price on energy in said predefined prediction window of time (TW) so as to optimise production of energy versus the need for energy and/or maximize possible revenue for energy production. The relevant parameter(s) is thus forecasted or estimated” [the optimization of the one or more optimization parameters includes the maximization of revenue from electric power generated by the wind turbine]. Further see 0016, 0024, 0057, and 0063-0067. The examiner has interpreted that receiving price data to forecast the price to proposed a maintenance schedule for the wind turbine plant to optimize the maximize possible revenue for energy production as wherein obtaining input data further comprising obtaining electricity price information for electric power supplied by the wind turbine; wherein forecasting the two or more operating parameters of the wind turbine further comprising the forecasting of electricity price; and wherein the optimization of the one or more optimization parameters includes the maximization of revenue from electric power generated by the wind turbine.)
As per claim 4, Zhou does not specifically teach “wherein the determining of the priority comprises an application of an evaluation matrix to at least the two forecasted operating parameters, wherein the evaluation matrix assigns to a combination of values of the at least two forecasted operating parameters an output value, the output value being the priority value that indicates the priority of scheduling the shutdown period in the control scheme of the wind turbine”.
However, in the same field of endeavor namely optimizing wind turbine maintenance plans, Yürüşen teaches “wherein the determining of the priority comprises an application of an evaluation matrix to at least the two forecasted operating parameters, wherein the evaluation matrix assigns to a combination of values of the at least two forecasted operating parameters an output value, the output value being the priority value that indicates the priority of scheduling the shutdown period in the control scheme of the wind turbine.” (Abstract, “While many operation and maintenance (O&M) decision support systems (DSS) have been already proposed, a serious research need still exists for wind farm O&M scheduling. O&M planning is a challenging task, as maintenance teams must follow specific procedures when performing their service, which requires working at height in adverse weather conditions. Here, an automated maintenance programming framework is proposed based on real case studies considering available wind speed and wind gust data. The methodology proposed consists on finding the optimal intervention time and the most effective execution order for maintenance tasks and was built on information from regular maintenance visit tasks and a corrective maintenance visit. The objective is to find possible schedules where all work orders can be performed without breaks, and to find out when to start in order to minimise revenue losses” [wherein the determining of the priority comprises an application of an evaluation matrix]. Pg. 11, Sect. 6, “It must be noted that in order to use the proposed DSS in the field, one must use as input: wind speed, wind gust and electricity market price forecasts to be able to asses, which day is preferable to schedule maintenance activities in near future” [at least the two forecasted operating parameters]. Pg. 9 Sect. 5, “Considering the hourly electricity market price, it is possible to combine it with the energy losses for each plan, estimated from measured wind speed values and manufacturer’s power curve, to obtain the revenue prioritised decision pools” [at least the two forecasted operating parameters]. Pg. 10 Sect. 5.1, “Fig. 12 shows the corresponding revenue losses of the prioritized maintenance plans, labelled as “low” when they are below the mean of the revenue losses estimated for the day under consideration. When they are lesser than the third quartile and greater than the mean, the label is “medium”. Lastly, for the plans with the revenue losses greater than the third quartile, the label is “high”… According to Fig. 12, the early hours of the day are more preferable in order to perform preventative intervention considering the revenue losses. Although electricity market prices are high during these hours, the limited wind resource availability, reduces power production losses and corresponding revenue losses [assigns to a combination of values of the at least two forecasted operating parameters an output value, the output value being the priority value that indicates the priority of scheduling the shutdown period in the control scheme of the wind turbine]. Further see Sect. 5-6. The examiner has interpreted that determining wind farm operation and maintenance scheduling to find the optimal intervention time and the most effective execution order for maintenance tasks by inputting the forecasts of wind speed, electricity market price, and estimated energy losses to order to generated a prioritized maintenance plan to minimize revenue loss by performing maintenance early as wherein the determining of the priority comprises an application of an evaluation matrix to at least the two forecasted operating parameters, wherein the evaluation matrix assigns to a combination of values of the at least two forecasted operating parameters an output value, the output value being the priority value that indicates the priority of scheduling the shutdown period in the control scheme of the wind turbine.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the determining of the priority comprises an application of an evaluation matrix to at least the two forecasted operating parameters, wherein the evaluation matrix assigns to a combination of values of the at least two forecasted operating parameters an output value, the output value being the priority value that indicates the priority of scheduling the shutdown period in the control scheme of the wind turbine” as conceptually seen from the teaching of Yürüşen, into that of Zhou because this modification of prioritizing the maintenance schedule using evaluated values from the combination of forecasted parameters for the advantageous purpose of minimizing the revenue lost due to maintaining the wind farm (Yürüşen, Pg. 10 Sect. 5 and the Abstract). Further motivation to combine be that Zhou and Yürüşen are analogous art to the current claim directed to optimizing wind turbine maintenance plans.
As per claim 5, Zhou does not specifically teach “wherein the evaluation matrix employs as an input at least three operating parameters including the forecasted wind turbine health, the forecasted wind conditions and one of a forecasted energy demand on the power grid or a forecasted electricity price for electric power produced by the wind turbine, wherein the evaluation matrix assigns to the combination of values of the three operating parameters a respective priority value.”
However, Yürüşen teaches “wherein the evaluation matrix employs as an input at least three operating parameters including the forecasted wind turbine health, the forecasted wind conditions and one of a forecasted energy demand on the power grid or a forecasted electricity price for electric power produced by the wind turbine, wherein the evaluation matrix assigns to the combination of values of the three operating parameters a respective priority value.” (Abstract, “While many operation and maintenance (O&M) decision support systems (DSS) have been already proposed, a serious research need still exists for wind farm O&M scheduling. O&M planning is a challenging task, as maintenance teams must follow specific procedures when performing their service, which requires working at height in adverse weather conditions. Here, an automated maintenance programming framework is proposed based on real case studies considering available wind speed and wind gust data. The methodology proposed consists on finding the optimal intervention time and the most effective execution order for maintenance tasks and was built on information from regular maintenance visit tasks and a corrective maintenance visit. The objective is to find possible schedules where all work orders can be performed without breaks, and to find out when to start in order to minimise revenue losses” [wherein the evaluation matrix employs as an input at least operating parameters]. Pg. 11, Sect. 6, “It must be noted that in order to use the proposed DSS in the field, one must use as input: wind speed, wind gust and electricity market price forecasts to be able to asses, which day is preferable to schedule maintenance activities in near future” [at least two forecasted operating parameters, the forecasted wind conditions and a forecasted electricity price for electric power produced by the wind turbine ]. Pg. 9 Sect. 5, “Considering the hourly electricity market price, it is possible to combine it with the energy losses for each plan, estimated from measured wind speed values and manufacturer’s power curve, to obtain the revenue prioritised decision pools” [three operating parameters including the forecasted wind turbine health, the forecasted wind conditions a forecasted electricity price for electric power produced by the wind turbine]. Pg. 10 Sect. 5.1, “Fig. 12 shows the corresponding revenue losses of the prioritized maintenance plans, labelled as “low” when they are below the mean of the revenue losses estimated for the day under consideration. When they are lesser than the third quartile and greater than the mean, the label is “medium”. Lastly, for the plans with the revenue losses greater than the third quartile, the label is “high”… According to Fig. 12, the early hours of the day are more preferable in order to perform preventative intervention considering the revenue losses. Although electricity market prices are high during these hours, the limited wind resource availability, reduces power production losses and corresponding revenue losses” [wherein the evaluation matrix assigns to the combination of values of the three operating parameters a respective priority value]. Further see Sect. 5-6. The examiner has interpreted that determining wind farm operation and maintenance scheduling to find the optimal intervention time and the most effective execution order for maintenance tasks by inputting the forecasts of wind speed, electricity market price, and estimated energy losses to order to generated a prioritized maintenance plan to minimize revenue loss by performing maintenance early as wherein the evaluation matrix employs as an input at least three operating parameters including the forecasted wind turbine health, the forecasted wind conditions and one of a forecasted energy demand on the power grid or a forecasted electricity price for electric power produced by the wind turbine, wherein the evaluation matrix assigns to the combination of values of the three operating parameters a respective priority value.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the evaluation matrix employs as an input at least three operating parameters including the forecasted wind turbine health, the forecasted wind conditions and one of a forecasted energy demand on the power grid or a forecasted electricity price for electric power produced by the wind turbine, wherein the evaluation matrix assigns to the combination of values of the three operating parameters a respective priority value” as conceptually seen from the teaching of Yürüşen, into that of Zhou because this modification of prioritizing the maintenance schedule using evaluated values from the combination of forecasted parameters for the advantageous purpose of minimizing the revenue lost due to maintaining the wind farm (Yürüşen, Pg. 10 Sect. 5 and the Abstract). Further motivation to combine be that Zhou and Yürüşen are analogous art to the current claim directed to optimizing wind turbine maintenance plans.
As per claim 6, Zhou does not specifically teach “wherein the priority value determines the priority to schedule the shutdown period in the control scheme to allow servicing of the wind turbine, wherein control scheme is determined to comprise a shorter first operating period for a higher priority value.”
However, Yürüşen teaches “wherein the priority value determines the priority to schedule the shutdown period in the control scheme to allow servicing of the wind turbine, wherein control scheme is determined to comprise a shorter first operating period for a higher priority value.” (Abstract, “The methodology proposed consists on finding the optimal intervention time and the most effective execution order for maintenance tasks and was built on information from regular maintenance visit tasks and a corrective maintenance visit. The objective is to find possible schedules where all work orders can be performed without breaks, and to find out when to start in order to minimise revenue losses” [wherein the priority value determines the priority to schedule the shutdown period in the control scheme to allow servicing of the wind turbine]. Pg. 10 Sect. 5.1, “Fig. 12 shows the corresponding revenue losses of the prioritized maintenance plans, labelled as “low” when they are below the mean of the revenue losses estimated for the day under consideration. When they are lesser than the third quartile and greater than the mean, the label is “medium”. Lastly, for the plans with the revenue losses greater than the third quartile, the label is “high”… According to Fig. 12, the early hours of the day are more preferable in order to perform preventative intervention considering the revenue losses. Although electricity market prices are high during these hours, the limited wind resource availability, reduces power production losses and corresponding revenue losses” [wherein control scheme is determined to comprise a shorter first operating period for a higher priority value]. Further see Sect. 5-6. The examiner has interpreted that determining wind farm operation and maintenance scheduling to find the optimal intervention time and the most effective execution order for maintenance tasks to minimize revenue loss by performing maintenance by starting them early for high revenue losses as wherein the priority value determines the priority to schedule the shutdown period in the control scheme to allow servicing of the wind turbine, wherein control scheme is determined to comprise a shorter first operating period for a higher priority value.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the priority value determines the priority to schedule the shutdown period in the control scheme to allow servicing of the wind turbine, wherein control scheme is determined to comprise a shorter first operating period for a higher priority value” as conceptually seen from the teaching of Yürüşen, into that of Zhou because this modification of prioritizing the maintenance schedule using evaluated values from the combination of forecasted parameters for the advantageous purpose of minimizing the revenue lost due to maintaining the wind farm (Yürüşen, Pg. 10 Sect. 5 and the Abstract). Further motivation to combine be that Zhou and Yürüşen are analogous art to the current claim directed to optimizing wind turbine maintenance plans.
As per claim 7, Zhou teaches “wherein optimizing the one or more optimization parameters comprises estimating the one or more optimization parameters for different candidate control schemes that includes differences in a timing of the shutdown period and/or differences in a limitation of electrical power generated by the wind turbine during the first operating period, and selecting the control scheme for which the one or more optimization parameters is maximized or minimized to meet a respective optimization target.” (Para. 0020, “Preferably, the forecasting module (FM) may comprises at least one of: artificial intelligence unit, a time series model unit, a probabilistic forecasting unit, and a game theory based unit, for outputting a plurality of renewable power plant relevant parameters (PF) in said predefined prediction window of time (TW). Possible variants that can be used for forecasting are: game theory, time series model like autoregressive model, integrated model, moving average models, or combinations of these models, probabilistic forecasting, and artificial intelligent technologies like artificial neural network, fuzzy neural network etc. The predefined prediction window of time (TW) is chosen from the group consisting of: 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 10 hours, 15 hours, 20 hours, 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, 8 days, 9 days, 10 days, 15 days, or days, or other suitable periods there between, or longer. It is contemplated that more than one prediction window may be used” [e.g., wherein optimizing the one or more optimization parameters comprises estimating the one or more optimization parameters for different candidate control schemes that includes differences in a timing of the shutdown period]. Para. 0016 “the forecasting module may be arranged for outputting at least one renewable power plant relevant parameter (PF) related to demand for energy and/or price on energy in said predefined prediction window of time (TW) so as to optimise production of energy versus the need for energy and/or maximize possible revenue for energy production” and Para. 0021, “Advantageously, the optimization module (OPT) may further capable of processing the proposed maintenance schedule (PROP-MAN) for the renewable power plant (WPP) in order to optimize the produced energy with respect to the capability of produced energy in said predefined prediction window (TW). Thus, what is the capability of the renewable power plant in the period and how can that be exploited most beneficially” [selecting the control scheme for which the one or more optimization parameters is maximized to meet a respective optimization target]. Further see Para. 0020-0021 and 0052-0065. The examiner has interpreted that using a forecasting module that uses a time series model and probabilistic forecasting to output the wind turbine plant parameters over multiple prediction windows to optimize the produced energy with respect to the capability of produced energy in the suggest prediction window to maximize possible revenue for energy production as wherein optimizing the one or more optimization parameters comprises estimating the one or more optimization parameters for different candidate control schemes that includes differences in a timing of the shutdown period, and selecting the control scheme for which the one or more optimization parameters is maximized to meet a respective optimization target.)
As per claim 8, Zhou does not specifically teach “wherein the priority value determines the period of time within which the shutdown period is scheduled in the control scheme, wherein the period of time is determined to end closer to a current date the higher the priority value is, wherein different candidate control schemes vary the timing of the shutdown period within a respective time period that corresponds to the priority value obtained from the evaluation matrix.”
However, Yürüşen teaches “wherein the priority value determines the period of time within which the shutdown period is scheduled in the control scheme, wherein the period of time is determined to end closer to a current date the higher the priority value is, wherein different candidate control schemes vary the timing of the shutdown period within a respective time period that corresponds to the priority value obtained from the evaluation matrix.” (Abstract, “The methodology proposed consists on finding the optimal intervention time and the most effective execution order for maintenance tasks and was built on information from regular maintenance visit tasks and a corrective maintenance visit. The objective is to find possible schedules where all work orders can be performed without breaks, and to find out when to start in order to minimise revenue losses” and Pg. 10 Sect. 5.1, “Fig. 12 shows the corresponding revenue losses of the prioritized maintenance plans, labelled as “low” when they are below the mean of the revenue losses estimated for the day under consideration. When they are lesser than the third quartile and greater than the mean, the label is “medium”. Lastly, for the plans with the revenue losses greater than the third quartile, the label is “high”… In Fig. 12, only selected alternative maintenance plans are plotted when a cost-wise clear separation can be observed among the 1093 alternatives (14 of 1093) for the analysed summer day. Each alternative represents a program, which confirms that weather related downtime is minimised. According to Fig. 12, the early hours of the day are more preferable in order to perform preventative intervention considering the revenue losses. Although electricity market prices are high during these hours, the limited wind resource availability, reduces power production losses and corresponding revenue losses” [wherein the priority value determines the period of time within which the shutdown period is scheduled in the control scheme, wherein the period of time is determined to end closer to a current date the higher the priority value is, wherein different candidate control schemes vary the timing of the shutdown period within a respective time period that corresponds to the priority value obtained from the evaluation matrix]. Further see Sect. 5-6. The examiner has interpreted that determining wind farm operation and maintenance scheduling to find the optimal intervention time and the most effective execution order for maintenance tasks to minimize revenue loss by performing maintenance by starting them early for high revenue losses and analyzing the proposed maintenance plans with alternatives as wherein the priority value determines a period of time within which the shutdown period is scheduled in the control scheme, wherein the period of time is determined to end closer to a current date the higher the priority value is, wherein the candidate control schemes vary the timing of the shutdown period within the respective time period that corresponds to the priority value obtained from the evaluation matrix.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the priority value determines the period of time within which the shutdown period is scheduled in the control scheme, wherein the period of time is determined to end closer to a current date the higher the priority value is, wherein different candidate control schemes vary the timing of the shutdown period within a respective time period that corresponds to the priority value obtained from the evaluation matrix” as conceptually seen from the teaching of Yürüşen, into that of Zhou because this modification of prioritizing the maintenance schedule using evaluated values from the combination of forecasted parameters for the advantageous purpose of minimizing the revenue lost due to maintaining the wind farm (Yürüşen, Pg. 10 Sect. 5 and the Abstract). Further motivation to combine be that Zhou and Yürüşen are analogous art to the current claim directed to optimizing wind turbine maintenance plans.
As per claim 9, Zhou teaches “wherein optimizing the one or more optimization parameters comprises estimating one or more further operating parameters that depend on a respective candidate control scheme, wherein the one or more further operating parameters include at least a forecasted electric energy generation by the wind turbine, and using the one or more further operating parameters to estimate the one or more optimization parameters for the respective candidate control scheme.” (Para. 0063, “forecasting on the weather, power generation forecasting and electricity loading (power demand) will be factored in” [wherein optimizing the one or more optimization parameters comprises estimating one or more further operating parameters that depend on a respective candidate control scheme, wherein the one or more further operating parameters include at least a forecasted electric energy generation by the wind turbine]. Para. 0058, “An optimization module OPT 22 is capable of receiving said plurality of wind power plant WPP 10 relevant parameters PF, such as wind speed and demand, and processing therefrom a proposed maintenance schedule PROP-MAN for the wind power plant WPP in order to optimize the produced energy with respect to the demand in the predefined prediction window TW, e.g. the next 3 or 7 days whatever the desire for prediction length may be, though for example reliability of weather forecast are typically critically dependent on the length of the prediction window” [using the one or more further operating parameters to estimate the one or more optimization parameters for a respective candidate control scheme]. Further see Para. 0057-0065. The examiner has interpreted that forecasting power generation to propose a maintenance schedule to optimize the produced energy as wherein optimizing the one or more optimization parameters comprises estimating one or more further operating parameters that depend on a respective candidate control scheme, wherein the one or more further operating parameters include at least a forecasted electric energy generation by the wind turbine, and using the one or more further operating parameters to estimate the one or more optimization parameters for the respective candidate control scheme.)
As per claim 10, Zhou teaches “wherein the one or more of the optimization parameters and/or one or more of the operating parameters are estimated based on a correlation with other optimization parameters and/or operating parameters, wherein the method further comprising training the correlation based on historical data for the wind turbine or for a corresponding wind turbine and/or based on input of a wind turbine operator.” (Para. 0018, “the forecasting module may be arranged for receiving input based on data indicative of demand for energy and/or price on energy having a historic similarity with the predefined prediction window of time (TW), e.g. same time period last week, same time period last month, same time period last year, same time period last year including that holiday or other particular event, etc.” [wherein the one or more of the optimization parameters and/or one or more of the operating parameters are estimated based on a correlation with other optimization parameters and/or operating parameters]. Para. 0063, “in the forecasting module FM 21, tariff price forecasting may forecast the short-term (from a day to one week). Middle-term (two to three weeks) and long-term (one month to years) tariff price based on the historical data and electricity market forces mechanisms supply internally 25a or externally 25b. Similarly, forecasting on the weather, power generation forecasting and electricity loading (power demand) will be factored in” [training the correlation based on historical data for the wind turbine]. Further see Para. 0018 and 0063. The examiner has interpreted that using the demand for energy and price on energy having a historic similarity based on the historical data to forecast with weather, power generation, and demand as wherein the one or more of the optimization parameters and/or one or more of the operating parameters are estimated based on a correlation with other optimization parameters and/or operating parameters, wherein the method further comprising training the correlation values based on historical data for the wind turbine.)
As per claim 11, Zhou does not specifically teach “obtaining one or more supplementary parameters and determining the control scheme under consideration of the one or more supplementary parameters, the supplementary parameters including one or more of: a capacity of wind turbines of a wind farm of which the wind turbine forms part to compensate an output power limitation of the wind turbine, a forecasted availability of a spare part, a forecasted availability of a service technician, and a skill level of an available service technician.”
However, Yürüşen teaches “obtaining one or more supplementary parameters and determining the control scheme under consideration of the one or more supplementary parameters, the supplementary parameters including one or more of: a capacity of wind turbines of a wind farm of which the wind turbine forms part to compensate an output power limitation of the wind turbine, a forecasted availability of a spare part, a forecasted availability of a service technician, and a skill level of an available service technician.” (Pg. 2 Sect. 2, “The normal practice depends on two weeks ahead maintenance service team booking with a single call entailing which alarm is activated for which turbine” and Pg. 2 Sect. 2, “A typical flow diagram of this type of advanced planning is shown in Fig. 1a where account needs to be taken of the weather and electricity market prices as well as the availability of a maintenance team” [obtaining one or more supplementary parameters and determining the control scheme under consideration of the one or more supplementary parameters, the supplementary parameters including a forecasted availability of a service technician]. Further see Sect. 2. The examiner has interpreted that looking ahead two weeks for the maintenance service team booking and taking into account the availability of the maintenance team for the advanced planning as obtaining one or more supplementary parameters and determining the control scheme under consideration of the one or more supplementary parameters, the supplementary parameters including a forecasted availability of a service technician.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “obtaining one or more supplementary parameters and determining the control scheme under consideration of the one or more supplementary parameters, the supplementary parameters including one or more of: a capacity of wind turbines of a wind farm of which the wind turbine forms part to compensate an output power limitation of the wind turbine, a forecasted availability of a spare part, a forecasted availability of a service technician, and a skill level of an available service technician” as conceptually seen from the teaching of Yürüşen, into that of Zhou because this modification of taking into account the maintenance team availability in the future for the advantageous purpose of generating a detailed planning maintenance schedule with other factors (Yürüşen, Pg. 2 Sect. 1 and the Abstract). Further motivation to combine be that Zhou and Yürüşen are analogous art to the current claim directed to optimizing wind turbine maintenance plans.
As per claim 12, Zhou teaches “wherein determining a control schedule by optimizing the one or more optimization parameters comprises employing a decision logic that determines the control schedule by making a decision on a scheduling of the shutdown period and/or on a limitation of the output of electrical power of the wind turbine during the first operating period, wherein the decision logic makes the respective decision on the basis of one or more of the forecasted operating parameters, wherein decision stages of the decision logic are configured such that the resulting control scheme optimizes the one or more optimization parameters.” (Para. 0068, “FIG. 6 is an illustrative embodiment of a final maintenance decision proposal FIN-PROP-MAN according to the present invention with hypothesis 1, 2, and 3 based on three different situations with various inputs and corresponding outputs. The proposal shows the conditions for the situation where 5, 2, or 0 WTGs should be stopped if required. Depending on the wind speed (7 or 10 m/s) and the tariff forecast (50 AUD or 100 AUD limits), the service technician is aided or assisted in making an improved decision based on the related revenue loss while respecting the wind turbine need for maintenance based on the shown alerts with corresponding severity levels” [wherein determining a control schedule by optimizing the one or more optimization parameters comprises employing a decision logic that determines the control schedule by making a decision on a scheduling of the shutdown period and/or on a limitation of the output of electrical power of the wind turbine during the first operating period, wherein the decision logic makes the respective decision on the basis of one or more of the forecasted operating parameters, wherein decision stages of the decision logic are configured such that the resulting control scheme optimizes the one or more optimization parameters]. Further see Para. 0068 and Fig. 6. The examiner has interpreted that providing a maintenance decision proposal that shows conditions based on various inputs to stop wind turbines for maintenance based on the forecasted wind speed, price/tariff, and severity levels to make an improved decision based on revenue loss as wherein determining a control schedule by optimizing the one or more optimization parameters comprises employing a decision logic that determines the control schedule by making a decision on a scheduling of the shutdown period and/or on a limitation of the output of electrical power of the wind turbine during the first operating period, wherein the decision logic makes the respective decision on the basis of one or more of the forecasted operating parameters, wherein decision stages of the decision logic are configured such that the resulting control scheme optimizes the one or more optimization parameters.)
Re Claim 14, it is a system claim, having similar limitations of claim 1. Thus, claim 14 is also rejected under the similar rationale as cited in the rejection of claim 1.
Furthermore, regarding claim 14, Zhou teaches “A control system for controlling the operation of the wind turbine, wherein the control system is coupled to one or more sensors of the wind turbine to obtain monitoring data related to integrity of the wind turbine and is further coupled to a data source for obtaining weather data indicating weather conditions, wherein the control system comprises a processing unit and a memory, the memory storing control instructions which when executed by the processing unit of the control system, perform the method according to claim 1.” (Para. 0013, “The invention is particularly, but not exclusively, advantageous for obtaining a decision support system that may potentially change the traditional concept of reactive and predictive maintenance technique for renewable energy generators, such as wind turbine generators” [controlling the operation of the wind turbine]. Para. 0056, “FIG. 1 schematically illustrates elements of an embodiment of the present invention. The Figure schematically illustrates an overall control system 6 between a power output 2 from one or more wind turbine generators 11 and a power grid 4” [A control system for controlling the operation of the wind turbine]. Para. 0013, “The one or more wind turbine generators 11 may be in the form of a wind power plant WPP comprising a number of wind turbines generators WTG, here 9 wind turbines. The SCADA system 6 is communicating with the WPP 10, as indicated with arrow 13 from the WPP 10 and arrow 12 back to WPP 10, and in turn the SCADA system 6 is communicating with the DSS 1 as illustrated with the double arrow. The power grid 4 may be any type of grid, such as a typical large-scale grid for distributing electricity to residential areas, industrial areas, etc.” and Para. 0059, “The module may for example receive the condition data through the SCADA system 5 as illustrated in connection with FIG. 1” [wherein the control system is coupled to one or more sensors of the wind turbine to obtain monitoring data related to integrity of the wind turbine]. Fig. 1 shows the control system 6 includes Supervisory Control And Data Acquisition (SCADA) system 5 and decision support system (DSS) 1. Para. 0057, “FIG. 2 is schematic illustration of the decision support system DSS 5 according to the present invention. The system comprises a forecasting module FM, 21 arranged for outputting a plurality of wind power plant relevant parameters PF in a predefined prediction window of time TW, i.e. the parameters PF are estimated or forecasted in the prediction window. The module FM receives data either from an internal storage/data base 25a and/or from one or more external sources of data 25b, e.g. weather data, demand data, price data etc.” [is further coupled to a data source for obtaining weather data indicating weather conditions]. Para. 0072, “The invention can be implemented by means of hardware, software, firmware or any combination of these. The invention or some of the features thereof can also be implemented as software running on one or more data processors and/or digital signal processors” [wherein the control system comprises a processing unit and a memory, the memory storing control instructions which when executed by the processing unit of the control system]. Further see Para. 0113, 0057-0059, 0072 and Fig. 1-2. The examiner has interpreted that obtaining a decision support system to change the reactive and predictive maintenance technique for wind turbines in a control system having a Supervisory Control And Data Acquisition system that communicates with the wind turbines to receive condition data and having a forecasting module that receives weather data from databases carried out through means of hardware, software, firmware, and processors as a control system for controlling the operation of the wind turbine, wherein the control system is coupled to one or more sensors of the wind turbine to obtain monitoring data related to integrity of the wind turbine and is further coupled to a data source for obtaining weather data indicating weather conditions, wherein the control system comprises a processing unit and a memory, the memory storing control instructions which when executed by the processing unit of the control system, perform the method according to claim 1.)
Re Claim 15, it is an articles of manufacture claim, having similar limitations of claim 14. Thus, claim 15 is also rejected under the similar rationale as cited in the rejection of claim 14.
As per claim 16, Zhou does not specifically teach “wherein the predetermined time period ends closer to a current date for a higher priority value.”
However, Yürüşen teaches “wherein the predetermined time period ends closer to a current date for a higher priority value.” (Abstract, “The methodology proposed consists on finding the optimal intervention time and the most effective execution order for maintenance tasks and was built on information from regular maintenance visit tasks and a corrective maintenance visit. The objective is to find possible schedules where all work orders can be performed without breaks, and to find out when to start in order to minimise revenue losses” and Pg. 10 Sect. 5.1, “Fig. 12 shows the corresponding revenue losses of the prioritized maintenance plans, labelled as “low” when they are below the mean of the revenue losses estimated for the day under consideration. When they are lesser than the third quartile and greater than the mean, the label is “medium”. Lastly, for the plans with the revenue losses greater than the third quartile, the label is “high”… In Fig. 12, only selected alternative maintenance plans are plotted when a cost-wise clear separation can be observed among the 1093 alternatives (14 of 1093) for the analysed summer day. Each alternative represents a program, which confirms that weather related downtime is minimised. According to Fig. 12, the early hours of the day are more preferable in order to perform preventative intervention considering the revenue losses. Although electricity market prices are high during these hours, the limited wind resource availability, reduces power production losses and corresponding revenue losses” [wherein the predetermined time period ends closer to a current date for a higher priority value]. Further see Sect. 5-6. The examiner has interpreted that determining wind farm operation and maintenance scheduling to find the optimal intervention time and the most effective execution order for maintenance tasks to minimize revenue loss by performing maintenance by starting them early for high revenue losses and analyzing the proposed maintenance plans with alternatives as wherein the predetermined time period ends closer to a current date for a higher priority value.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the predetermined time period ends closer to a current date for a higher priority value” as conceptually seen from the teaching of Yürüşen, into that of Zhou because this modification of prioritizing the maintenance schedule using evaluated values from the combination of forecasted parameters for the advantageous purpose of minimizing the revenue lost due to maintaining the wind farm (Yürüşen, Pg. 10 Sect. 5 and the Abstract). Further motivation to combine be that Zhou and Yürüşen are analogous art to the current claim directed to optimizing wind turbine maintenance plans.
As per claim 17, Zhou does not specifically teach “wherein, within the predetermined time period, the shutdown period is scheduled at low forecast wind conditions.”
However, Yürüşen teaches “wherein, within the predetermined time period, the shutdown period is scheduled at low forecast wind conditions.” (Abstract, “The methodology proposed consists on finding the optimal intervention time and the most effective execution order for maintenance tasks and was built on information from regular maintenance visit tasks and a corrective maintenance visit. The objective is to find possible schedules where all work orders can be performed without breaks, and to find out when to start in order to minimise revenue losses” and Pg. 10 Sect. 5.1, “Fig. 12 shows the corresponding revenue losses of the prioritized maintenance plans, labelled as “low” when they are below the mean of the revenue losses estimated for the day under consideration. When they are lesser than the third quartile and greater than the mean, the label is “medium”. Lastly, for the plans with the revenue losses greater than the third quartile, the label is “high”… In Fig. 12, only selected alternative maintenance plans are plotted when a cost-wise clear separation can be observed among the 1093 alternatives (14 of 1093) for the analysed summer day. Each alternative represents a program, which confirms that weather related downtime is minimised. According to Fig. 12, the early hours of the day are more preferable in order to perform preventative intervention considering the revenue losses. Although electricity market prices are high during these hours, the limited wind resource availability, reduces power production losses and corresponding revenue losses” [wherein, within the predetermined time period, the shutdown period is scheduled at low forecast wind conditions]. Further see Sect. 5-6. The examiner has interpreted that determining wind farm operation and maintenance scheduling to find the optimal intervention time and the most effective execution order for maintenance tasks to minimize power production and revenue losses from available wind resource by performing maintenance by starting them early for high revenue losses and analyzing the proposed maintenance plans with alternatives as wherein, within the predetermined time period, the shutdown period is scheduled at low forecast wind conditions.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein, within the predetermined time period, the shutdown period is scheduled at low forecast wind conditions” as conceptually seen from the teaching of Yürüşen, into that of Zhou because this modification of prioritizing the maintenance schedule using evaluated values from the combination of forecasted parameters for the advantageous purpose of minimizing the revenue lost due to maintaining the wind farm (Yürüşen, Pg. 10 Sect. 5 and the Abstract). Further motivation to combine be that Zhou and Yürüşen are analogous art to the current claim directed to optimizing wind turbine maintenance plans.
As per claim 18, Zhou does not specifically teach “wherein the low forecast wind conditions are forecast wind conditions that are lower than average forecast wind conditions.”
However, Yürüşen teaches “wherein the low forecast wind conditions are forecast wind conditions that are lower than average forecast wind conditions.” (Abstract, “Here, an automated maintenance programming framework is proposed based on real case studies considering available wind speed and wind gust data The methodology proposed consists on finding the optimal intervention time and the most effective execution order for maintenance tasks and was built on information from regular maintenance visit tasks and a corrective maintenance visit. The objective is to find possible schedules where all work orders can be performed without breaks, and to find out when to start in order to minimise revenue losses”, Pg. 2 Sect. 2, “The maximum permissible wind gust speed for crane usage depends on various factors such as mean wind speed, intervention height and weight of the load [20]. Therefore, corresponding wind gust restriction for any intervention requires timely and case based controls. Moreover, high gust values cause more restrictive wind turbine component specific accessibility rules reducing the highest allowed mean wind speed.”, Pg. 10 Sect. 5.1, “Fig. 12 shows the corresponding revenue losses of the prioritized maintenance plans, labelled as “low” when they are below the mean of the revenue losses estimated for the day under consideration. When they are lesser than the third quartile and greater than the mean, the label is “medium”. Lastly, for the plans with the revenue losses greater than the third quartile, the label is “high”… In Fig. 12, only selected alternative maintenance plans are plotted when a cost-wise clear separation can be observed among the 1093 alternatives (14 of 1093) for the analysed summer day. Each alternative represents a program, which confirms that weather related downtime is minimised. According to Fig. 12, the early hours of the day are more preferable in order to perform preventative intervention considering the revenue losses. Although electricity market prices are high during these hours, the limited wind resource availability, reduces power production losses and corresponding revenue losses”, and Pg. 10 Sect. 5.2, “In this case study, the generator replacement is investigated for the proposed scheduling process. To replace the generator, a crane must be used. Firstly, the nacelle cover must be removed and then the failed generator must be taken out. These removals are followed by installation of the new generator and re-installation of the original nacelle cover. In other words, this intervention requires two types of lifting /unloading tasks. Safety requirements with regards to wind speed vary due to the gust values. The mean wind speed limit for safe working has to be decreased by 2 m/s when the wind speed gust is above 5 m/s for operation requiring a crane usage [16], from 10 m/s, for a gust lower than 5 m/s, to 8 m/s for a gust higher than 5 m/s in the case of nacelle cover and from 8 m/s to 6 m/s, for the same gust values, in the case of the generator” [wherein the low forecast wind conditions are forecast wind conditions that are lower than average forecast wind conditions]. Further see Sect. 2 and 5-6. The examiner has interpreted that determining wind farm operation and maintenance scheduling to find the optimal intervention time and the most effective execution order for maintenance tasks to minimize power production and revenue losses from available wind resource in a case scenario where the mean wind speed limit is decreased due to wind gust and thereby reducing the highest allowed mean wind speed by performing maintenance by starting them early for high revenue losses and analyzing the proposed maintenance plans with alternatives as wherein the low forecast wind conditions are forecast wind conditions that are lower than average forecast wind conditions.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the low forecast wind conditions are forecast wind conditions that are lower than average forecast wind conditions” as conceptually seen from the teaching of Yürüşen, into that of Zhou because this modification of prioritizing the maintenance schedule using evaluated values from the combination of forecasted parameters for the advantageous purpose of minimizing the revenue lost due to maintaining the wind farm (Yürüşen, Pg. 10 Sect. 5 and the Abstract). Further motivation to combine be that Zhou and Yürüşen are analogous art to the current claim directed to optimizing wind turbine maintenance plans.
As per claim 19, Zhou does not specifically teach “wherein determining the priority comprises applying an evaluation matrix to at least the forecasted wind turbine health and the forecasted wind conditions, wherein the evaluation matrix assigns the priority value indicating the priority of scheduling the shutdown period”.
However, in the same field of endeavor namely optimizing wind turbine maintenance plans, Yürüşen teaches “wherein determining the priority comprises applying an evaluation matrix to at least the forecasted wind turbine health and the forecasted wind conditions, wherein the evaluation matrix assigns the priority value indicating the priority of scheduling the shutdown period.” (Abstract, “While many operation and maintenance (O&M) decision support systems (DSS) have been already proposed, a serious research need still exists for wind farm O&M scheduling. O&M planning is a challenging task, as maintenance teams must follow specific procedures when performing their service, which requires working at height in adverse weather conditions. Here, an automated maintenance programming framework is proposed based on real case studies considering available wind speed and wind gust data. The methodology proposed consists on finding the optimal intervention time and the most effective execution order for maintenance tasks and was built on information from regular maintenance visit tasks and a corrective maintenance visit. The objective is to find possible schedules where all work orders can be performed without breaks, and to find out when to start in order to minimise revenue losses” [wherein the determining of priority comprises apply an evaluation matrix]. Pg. 11, Sect. 6, “It must be noted that in order to use the proposed DSS in the field, one must use as input: wind speed, wind gust and electricity market price forecasts to be able to asses, which day is preferable to schedule maintenance activities in near future” [at least the forecasted wind conditions]. Pg. 9 Sect. 5, “Considering the hourly electricity market price, it is possible to combine it with the energy losses for each plan, estimated from measured wind speed values and manufacturer’s power curve, to obtain the revenue prioritised decision pools” [at least the forecasted wind turbine health and the forecasted wind conditions]. Pg. 10 Sect. 5.1, “Fig. 12 shows the corresponding revenue losses of the prioritized maintenance plans, labelled as “low” when they are below the mean of the revenue losses estimated for the day under consideration. When they are lesser than the third quartile and greater than the mean, the label is “medium”. Lastly, for the plans with the revenue losses greater than the third quartile, the label is “high”… According to Fig. 12, the early hours of the day are more preferable in order to perform preventative intervention considering the revenue losses. Although electricity market prices are high during these hours, the limited wind resource availability, reduces power production losses and corresponding revenue losses [wherein the evaluation matrix assigns the priority value indicating the priority of scheduling the shutdown period]. Further see Sect. 5-6. The examiner has interpreted that determining wind farm operation and maintenance scheduling to find the optimal intervention time and the most effective execution order for maintenance tasks by inputting the forecasts of wind speed, electricity market price, and estimated energy losses to order to generated a prioritized maintenance plan to minimize revenue loss by performing maintenance early as wherein determining the priority comprises applying an evaluation matrix to at least the forecasted wind turbine health and the forecasted wind conditions, wherein the evaluation matrix assigns the priority value indicating the priority of scheduling the shutdown period.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein determining the priority comprises applying an evaluation matrix to at least the forecasted wind turbine health and the forecasted wind conditions, wherein the evaluation matrix assigns the priority value indicating the priority of scheduling the shutdown period” as conceptually seen from the teaching of Yürüşen, into that of Zhou because this modification of prioritizing the maintenance schedule using evaluated values from the combination of forecasted parameters for the advantageous purpose of minimizing the revenue lost due to maintaining the wind farm (Yürüşen, Pg. 10 Sect. 5 and the Abstract). Further motivation to combine be that Zhou and Yürüşen are analogous art to the current claim directed to optimizing wind turbine maintenance plans.
Claim 13 is rejected under 35 U.S.C. § 103 as being unpatentable over Zhou, Yürüşen, and Timbus, as applied to claim 1 above, and further in view of Ma, Kuichao, Jiangsheng Zhu, M. Soltani, Amin Hajizadeh, Peng Hou, and Zhe Chen. "Active power dispatch strategy of wind farms under generator faults." In Safety and Reliability–Safe Societies in a Changing World, pp. 2147-2152. CRC Press, 2018 [herein “Ma”].
As per claim 13, Zhou, Yürüşen, and Timbus does not specifically teach “wherein the wind turbine is part of a wind farm, wherein, if a higher than average priority to schedule the shutdown period is determined on a basis of the two or more forecasted operating parameters, the method comprises determining if wind turbines of the wind farm are capable of compensating a output power limitation of the wind turbine, and in an affirmative, including in the control scheme a limitation of the output power of the wind turbine during the first operating period and including in the control scheme a control of the other wind turbines so as to compensate for the output power limitation during the first operating period.”
However, in the same field of endeavor namely controlling a wind farm in cases of degraded wind turbines, Ma teaches “wherein the wind turbine is part of a wind farm, wherein, if a higher than average priority to schedule the shutdown period is determined on a basis of the two or more forecasted operating parameters, the method comprises determining if wind turbines of the wind farm are capable of compensating a output power limitation of the wind turbine, and in an affirmative, including in the control scheme a limitation of the output power of the wind turbine during the first operating period and including in the control scheme a control of the other wind turbines so as to compensate for the output power limitation during the first operating period.” (Pg. 2148 Sect. 3, “WF consists of several WTs” [e.g., wherein the wind turbine is part of a wind farm]. Abstract, “When the fault level is high, the strategy gives priority to protect the generator” and Pg. 2148 Sect. 2, “Fault level 2 (FL2): this fault level includes the early inter-turn short-circuit faults of the stator and rotor, as well as the initial fault of the cooling system. The characteristic of this level is that the overheating caused by fault can have a more serious effect on the generator. However, down regulation operation can reduce generator heating by decreasing the current. For protecting generator, there is a limit value of power Plimit,f . This value depends on the specific fault mode and means that the load of WT should not exceed it. The specific value of Plimit,f can be estimated according to the fault mechanism and lifetime estimation” [wherein, if a higher than average priority to schedule the shutdown period is determined on a basis of the two or more forecasted operating parameters]. Pg. 2150 Sect. 4, “If the sum of the power of healthy WTs cannot follow the Power demand. The minimum power reference of the faulty WT that can fulfil the power demand should be found by the exhaustive method. There are two possible reasons why the power demand can still be followed by reducing the power of a particular WT. One is that the available power of WF is higher than power demand. The other reason is that, due to the wake effect, the down regulation of upwind WT will increase the wind speed of the downwind WTs, and the power loss because of down-regulation will be compensated by the downwind WTs” [determining if wind turbines of the wind farm are capable of compensating a output power limitation of the wind turbine, and in an affirmative, including in the control scheme a limitation of the output power of the wind turbine during the first operating period and including in the control scheme a control of the other wind turbines so as to compensate for the output power limitation during the first operating period]. Further see Sect. 2-4. The examiner has interpreted that identifying a fault level in a wind turbine of a wind farm that has a serious effect on wind turbine generator so priority is given to the wind turbine to protect the generator through estimating the power produced by and lifetime of the wind turbine generator and down regulating the wind turbine to reduce generator heating to not exceed a load and causing the wind speed of the downwind wind turbines to increase to compensate for the power loss of the down regulated wind turbine as wherein the wind turbine is part of a wind farm, wherein, if a higher than average priority to schedule the shutdown period is determined on a basis of the two or more forecasted operating parameters, the method comprises determining if wind turbines of the wind farm are capable of compensating a output power limitation of the wind turbine, and in an affirmative, including in the control scheme a limitation of the output power of the wind turbine during the first operating period and including in the control scheme a control of the other wind turbines so as to compensate for the output power limitation during the first operating period.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the wind turbine is part of a wind farm, wherein, if a higher than average priority to schedule the shutdown period is determined on a basis of the two or more forecasted operating parameters, the method comprises determining if wind turbines of the wind farm are capable of compensating a output power limitation of the wind turbine, and in an affirmative, including in the control scheme a limitation of the output power of the wind turbine during the first operating period and including in the control scheme a control of the other wind turbines so as to compensate for the output power limitation during the first operating period” as conceptually seen from the teaching of Ma, into that of Zhou, Yürüşen, and Timbus because this modification of limit the power produced by a critical wind turbine and increasing the power produced by the other wind turbines for the advantageous purpose of maintaining a balance of power produced in the wind farm when a degradation occurs in a wind turbine (Ma Pg. 2150 Sect 4 and Pg. 2152 Sect. 6). Further motivation to combine be that Zhou and Ma are analogous art to the current claim directed to controlling a wind farm in cases of degraded wind turbines.
Response to Arguments
Applicant’s arguments, see Pg. 12-17, filed September 3, 2026, with respect to the rejection(s) of claims 1-15 under 35 U.S.C. § 101 have been fully considered and are persuasive with regards to the amended independent claim 1 that integrates the claimed invention into a practical application. Therefore, the rejection has been withdrawn.
Applicant’s arguments, see Pg. 18-25, filed September 3, 2026, with respect to the rejection(s) of 1-3, 7, 9-10, 12, and 14-15 under 35 U.S.C. 102 as anticipated by Zhou has been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of the amended claim and previously cited art, necessitated by the applicant’s amendment, as detailed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2010/0332272 A1 Ong, Jiun Keat et al. teaches a method for determining one or more control schemes to control the operation of the wind turbine to maximize the power production yield of a wind farm.
US 2010/0138060 A1 Gao, Meng et al. teach a method for collecting operation data of a wind turbine to control the operation of the machine and identify the need for maintenance.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Examiner’s Note: The examiner has cited particular columns and line numbers in the reference that applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. In the case of amending the claimed invention, the applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for the proper interpretation and also to verify and ascertain the metes and bound of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Simeon P Drapeau whose telephone number is (571)-272-1173. The examiner can normally be reached Monday - Friday, 8 a.m. - 5 p.m. ET.
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/SIMEON P DRAPEAU/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
