DETAILED ACTIONS
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/05/2026 has been entered.
Response to Amendment
This office action is in response to the amendments/arguments submitted by the Applicant(s) on 06/05/2026.
Status of the Claims
Claims 1-24 are pending.
Claims 2-6, 8-12,14-18, and 20-24 are amended.
Response to Arguments
Rejections Under 35 U.S.C.§103
Applicant's arguments, see remarks page 12-19 filed 06/05/2026 with respect to the rejection(s) of Claims under 35 U.S.C. §103 has been considered and are moot because the amendment has necessitated a new ground of rejections. The new rejections are set forth below.
Claim Rejections - 35 USC § 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.
Claims 1-5, and 7-11, 13-15, 17, and 19-24 are rejected under 35 U.S.C. 103 as being unpatentable over Jeffrey D. Taft. (US 2015/0002186 A1, hereinafter Taft) and in view of Yan Yuehao et al. (CN 105021952 A, hereinafter Yuehao, a preview translation combined with original copy is uploaded by the examiner)
Regarding Claim 1, Taft teaches,
A power line event determination process implemented in a power grid (Taft, Figure 1,8 [0003], “The present invention relates generally to a system and method for managing a power grid, and more particularly to a system and method for managing outage and fault conditions in a power grid”) comprising:
implementing a plurality of implementations of a power grid event monitor; implementing at least one implementation of a power grid event analytics system (Taft, Figure 2, Grid data/ analytics service 123, Table 1, Grid data/ analytics service 123, Services (such as Sensor Data Services 124 and Analytics Management Services 125) to support access to grid data and grid analytics; management of analytics., and , Figure 5B, Event Analysis, 518, Table 3, Event analysis and triggers 518, Processing of all analytics for event detection); and
connecting the plurality of implementations of the power grid event monitor to the power grid (Taft, Figure 2, Centralized Grid Analytics Applications 139),
wherein the power grid event monitor is a voltage monitor, a current monitor, and/or a
voltage and current monitor (Taft, [0074], “local analytics processing on a real time (such as a sub-second) basis. Processing may include digital signal processing of voltage and current waveforms, detection and classification processing,
including event stream processing. Figure 5A-5B, Ring buffer 502, Table 3, Local circular buffer storage for digital waveforms sampled from analog transducers (voltage and current waveforms for example) which may be used hold the data for waveform at different time periods so that if ru1 event is detected, the waveform data leading up to the event may also be stored”);
wherein the plurality of implementations of the power grid event monitor are located in
and electrically connected to certain respective implementations of a power grid component of the power grid (Taft, Figure; and Figure 2, connectivity warehouse 131, [0058] Network location data may include the information about the grid component on the communication network. This information may be used to send messages and information to the particular grid component. Network location data may be either entered manually into the Smart Grid database as new Smart Grid components are installed or is extracted from an Asset Management System if this information is maintained externally”); and
wherein the power grid component comprises one of the following: power stations, electrical substations, electric power transmission components, powerlines, electric power distribution components, electricity generation components, generators, high-voltage substations, local substations, high voltage transmission lines, step-up substations, step-down substations, distribution substations, transformers, circuit breakers, switches, lightning arresters, capacitors, electric power distribution components, distribution lines, distribution transformers, and/or feeders. .(Taft, Table 1, The data in the connectivity warehouse 131 may describe the hierarchical information about all the components of the grid (substation, feeder, section, segment, branch, t-section, circuit breaker, recloser, switch, etc -basically all the assets” NOTE: Power grid assets are included but not limited to electricity generation, electric power transmission, and electricity distribution. Power plants, transmission lines etc. see [0005]).
Taft teaches a PMU receiving power grid data with time stamps and PMU analyzed the data to determine grid events and its location locations. See (Taft figure 1C INDE distributed grid device, signal/event analytics. device 188, Figure 5A). However, Taft did not give detailed steps of event determination analysis. Therefore,
Taft is silent on determine an estimate of a fault location within the power grid based on times of arrival of a fault signature at each of a first one of the plurality of implementations of the power grid event monitor and a second one of the plurality of implementations of the power grid event monitor
However, Yuehao teaches determine an estimate of a fault location within the power grid based on times of arrival of a fault signature at each of a first one of the plurality of implementations of the power grid event monitor and a second one of the plurality of implementations of the power grid event monitor (Yuehao, Abstract, “The method comprises a frequency change arrival time on-line obtaining method based on PMU measurement information, a reference arrival time matrix rapid construction method and a power grid fault source locating method based on ordinal pattern recognizer.” Page 6, bottom paragraph “A kind of bulk power grid Fault Locating Method based on wide area phase angle measurement information, the frequency change caused by measurement fault after fault occurs arrives the time of each generator node, form time arrow to be checked, the reference time matrix that the time arriving each generator node by the specified point fault constructed with offline mode is formed contrasts, the node of its correspondence is the abort situation detected,).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Taft PMU analyzing method in view of Yuehao to include time of arrival matrix and time series measurement data and identify fault location to determine power grid event at different locations as taught by Yuehao with the benefit of accurate event detection and identification performance. (Yuehao, abstract).
Regarding Claim 2, combination of Taft and Yuehao teaches the power line event determination process according to claim 1,
Taft further teaches wherein the first implementation of the power grid event monitor is located in and/or connected to a first implementation of the power grid component of the power grid (Taft, Figure 4, 0071] FIG. 4 illustrates an example of the high-level architecture for the INDE SUBSTATION 180 group. This group may comprise elements that are actually hosted in the substation 170 at a substation control house on one or more servers co-located with the substation electronics and systems); wherein a second implementation of the power grid event monitor is located in and/or connected to a second implementation of the power grid component of the power grid (Taft, Figure 15A-15B, Data collected from different assets, each asset contains analytic).and _and wherein data from the first and second implementations are temporally aligned using synchronized timestamps prior to determining the estimate of the fault location. (Taft, Figure 5A, Time domain signal analytics 510, GPS data frame Timestamp 526, Table 3, GPS timing 524, Provides high resolution timing to coordinate applications and synchronize data collection
across a wide geographic area. The generated data may include a GPS data frame time stamp 526” [0126] The fault intelligence process may also classify and categorize faults. [0128] The fault intelligence may further raise fault events. Specifically, this process may create and publish fault events to the events bus once a fault has been detected, classified, categorized, characterized and isolated. This process may also be responsible for collecting, filtering, collating and deduplicating faults so that an individual fault event is raised rather than a deluge based on the raw events that are typical during a failure. Finally, the fault intelligence may log fault events to the event log database” NOTE: each first second and more individual fault has been classified, and location is identified).
Regarding Claim 3, combination of Taft and Yuehao teaches the power line event determination process according to claim 1,
Taft further teaches wherein the power grid event monitor is configured to detect a fault-criteria (Taft, Table 5, Fault Identification Criteria) and generate fault data that includes a time of fault and fault criteria (Taft, Figure 8, Fault Intelligence, characterize faults, determine fault location and log fault data); and wherein the power grid event monitor is configured to send the fault data to the power grid event analytics system. (Taft, Figure [0056], Grid components like grid devices (smart power sensors (such as a sensor with an embedded processor that can be programmed for digital processing capability) temperature sensors, etc.), power system components that includes additional embedded processing (RTU s, etc), smart meter networks (meter health, meter readings, etc), and mobile field force devices (outage events, work order completions, etc) may generate event data, operational and non-operational data. The event data generated within the smart grid may be transmitted via an event bus 147 (Figure 1B)).
and wherein the fault criteria are determined based on features extracted from time- series measurement data and including statistical and frequency-domain features. (Taft, 5A, Time domain 510, and Frequency domain 512, Figure 5B, event analysis 518, and figure 14BFault Intelligence,)
Regarding Claim 4, combination of Taft and Yuehao teaches the power line event determination process according to claim 3,
Taft further teaches wherein the power grid event monitor uses a GPS (Global Positioning System) or other timing methodology to generate the time of fault (Taft, Figure 5A, GPS timing 524, GPS Data Frame Time Stamp 526, [0067], Geographic Information System 149 (Figure 6C) is a database that contains information about where assets are located geographically and how the assets are connected together); and
wherein the power grid event monitor is configured to sample instantaneous voltage waveforms and/or current waveforms at a location of a respective implementation of the power grid component within the power grid that a particular implementation of the power grid event monitor is located to generate the fault criteria (Taft, [0074], local analytics processing on a real time (such as a sub-second) basis. Processing may include digital signal processing of voltage and current waveforms, detection and classification processing, including event stream processing”). and
Taft is silent on wherein the sampled waveforms are processed over sliding time windows to generate temporally evolving features representative of power system events.
However, Yuehao teaches wherein the sampled waveforms are processed over sliding time windows to generate temporally evolving features representative of power system events. (Yuehao, Abstract “According to the invention, a frequency change arrival time on-line identification method based on frequency change arrival time of a sliding data window is brought forward; a to-be checked time vector can be rapidly set on-line; targeted at an undirected weighted graph corresponding a framework structural model”).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Taft PMU analyzing method in view of Yuehao to include time of arrival matrix and time series measurement data and identify fault location to determine power grid event at different locations as taught by Yuehao with the benefit of accurate event detection and identification performance. (Yuehao, abstract).
Regarding Claim 5, combination of Taft and Yuehao teaches the power line event determination process according to claim 1,
Taft further teaches wherein the power grid event monitor is configured to sample instantaneous voltage waveforms and/or current waveforms with sub-millisecond timing accuracy. (Taft, [0074], local analytics processing on a real time (such as a sub-second) basis. Processing may include digital signal processing of voltage and current waveforms, detection and classification processing, including event stream processing”. NOTE: “a sub-second” refers to milli or micro seconds, less than a second). And
wherein feature values are derived from consecutive samples to form temporal-dependent embeddings of the sampled waveforms. (Taft, Figure 1C, signal/waveform processing, 185, and see figure 4, Claim 5, and claim 16, “determining a fault type comprises selecting the fault type from a group of fault types based on the comparison of the phase and magnitude data for each phase in the multi-phase power grid to the first set of predetermined criteria, in response to the fault type being determined over a predetermined number of sequentially received phase and magnitude data”).
Regarding Claim 7, combination of Taft and Yuehao teaches the power line event determination process according to claim 1,
A power line event determination process implemented in a power grid (Taft, Figure 1,8 [0003], “The present invention relates generally to a system and method for managing a power grid, and more particularly to a system and method for managing outage and fault conditions in a power grid”) comprising:
implementing a plurality of implementations of a power grid event monitor; implementing at least one implementation of a power grid event analytics system (Taft, Figure 2, Grid data/ analytics service 123, Table 1, Grid data/ analytics service 123, Services (such as Sensor Data Services 124 and Analytics Management Services 125) to support access to grid data and grid analytics; management of analytics., and , Figure 5B, Event Analysis, 518, Table 3, Event analysis and triggers 518, Processing of all analytics for event detection); and
connecting the plurality of implementations of the power grid event monitor to the power grid (Taft, Figure 2, Centralized Grid Analytics Applications 139),
wherein the power grid event monitor is a voltage monitor, a current monitor, and/or a
voltage and current monitor (Taft, [0074], “local analytics processing on a real time (such as a sub-second) basis. Processing may include digital signal processing of voltage and current waveforms, detection and classification processing,
including event stream processing”. Figure 5A-5B, Ring buffer 502, Table 3, Local circular buffer storage for digital waveforms sampled from analog transducers (voltage and current waveforms for example) which may be used hold the data for waveform at different time periods so that if ru1 event is detected, the waveform data leading up to the event may also be stored”);
wherein the plurality of implementations of the power grid event monitor are located in
and electrically connected to certain respective implementations of a power grid component of the power grid (Taft, Figure; and Figure 2, connectivity warehouse 131, [0058] Network location data may include the information about the grid component on the communication network. This information may be used to send messages and information to the particular grid component. Network location data may be either entered manually into the Smart Grid database as new Smart Grid components are installed or is extracted from an Asset Management System if this information is maintained externally”); and
wherein the power grid component comprises one of the following: power stations, electrical substations, electric power transmission components, powerlines, electric power distribution components, electricity generation components, generators, high-voltage substations, local substations, high voltage transmission lines, step-up substations, step-down substations, distribution substations, transformers, circuit breakers, switches, lightning arresters, capacitors, electric power distribution components, distribution lines, distribution transformers, and/or feeders. .(Taft, Table 1, The data in the connectivity warehouse 131 may describe the hierarchical information about all the components of the grid (substation, feeder, section, segment, branch, t-section, circuit breaker, recloser, switch, etc -basically all the assets” NOTE: Power grid assets are included but not limited to electricity generation, electric power transmission, and electricity distribution. Power plants, transmission lines etc. see [0005]).
Taft teaches a PMU receiving power grid data with time stamps and PMU analyzed the data to determine grid events and its location locations. See (Taft figure 1C INDE distributed grid device, signal/event analytics. device 188, Figure 5A). However, Taft did not give detailed steps of event determination analysis. Therefore,
Taft is silent on determine an estimate of a fault location within the power grid based on times of arrival of a fault signature at each of a first one of the plurality of implementations of the power grid event monitor and a second one of the plurality of implementations of the power grid event monitor. wherein the times of arrival include 1) a first time of arrival of the fault signature at the first one of the plurality of implementations of the power grid event monitor, and 2) a second time of arrival of the fault signature at the second one of the plurality of implementations of the power grid event monitor, and wherein the power grid event analytics system is configured to determine the estimate of the fault location within the power grid based 1) a first distance from the first one of the plurality of implementations of the power grid event monitor, wherein the first distance is determined based on a speed of light and the first time of arrival, and 2) a second distance from the second one of the plurality of implementations of the power grid event monitor, wherein the second distance is determined based on the speed of light and the second time of arrival.
However, Yuehao teaches determine an estimate of a fault location within the power grid based on times of arrival of a fault signature at each of a first one of the plurality of implementations of the power grid event monitor and a second one of the plurality of implementations of the power grid event monitor (Yuehao, Abstract, “The method comprises a frequency change arrival time on-line obtaining method based on PMU measurement information, a reference arrival time matrix rapid construction method and a power grid fault source locating method based on ordinal pattern recognizer.” Page 6, bottom paragraph “A kind of bulk power grid Fault Locating Method based on wide area phase angle measurement information, the frequency change caused by measurement fault after fault occurs arrives the time of each generator node, form time arrow to be checked, the reference time matrix that the time arriving each generator node by the specified point fault constructed with offline mode is formed contrasts, the node of its correspondence is the abort situation detected,).wherein the times of arrival include 1) a first time of arrival of the fault signature at the first one of the plurality of implementations of the power grid event monitor, and 2) a second time of arrival of the fault signature at the second one of the plurality of implementations of the power grid event monitor (Yuehao, page 4, See equation from original copy,
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(Yuehao, page 4, See equation from original copy, uploaded by the Examiner)
wherein the power grid event analytics system is configured to determine the estimate of the fault location within the power grid based 1) a first distance from the first one of the plurality of implementations of the power grid event monitor, wherein the first distance is determined based on a speed of light and the first time of arrival, and 2) a second distance from the second one of the plurality of implementations of the power grid event monitor, wherein the second distance is determined based on the speed of light and the second time of arrival. (Yuehao, Page 3 bottom paragraph, frequency, according to the Changing Pattern of generator frequency fundamental function, based on frequency change discrimination method time of arrival of frequecy characteristic function slip data window, for kth point practical frequency, get M point data composition data window W on the left of this point 1, right side N point data composition data window W 2, define W respectively 1 and W 2on function L 1 and L 2for: B, reference arrive time matrix fast construction method:
(1) will make original frequency change propagation time matrix D 0=(d ), wherein:
ij0 Pattern-recognition is a kind of statistical recognition method of carrying out pattern classification according to the distance of pattern and all kinds of representative sample, and the distance being identified pattern and affiliated pattern class sample is minimum; Assuming that c classification represents the proper vector R of pattern 1..., R c represent, x is the proper vector being identified pattern, | x-R i| be x and R i(i=1,2 ..., the distance c), if | x-R i| minimum, x is divided into the i-th class;”)
It would have been obvious to a person of ordinary skill before the effective filing date to modify Taft PMU analyzing method in view of Yuehao to include time of arrival matrix and time series measurement data and identify fault location to determine power grid event at different locations as taught by Yuehao with the benefit of accurate event detection and identification performance. (Yuehao, abstract).
Regarding Claim 8, combination of Taft and Yuehao teaches the power line event determination process according to claim 1,
Taft further teaches wherein an implementation of the power grid event monitor is placed at an electrical substation at a head of each implementation of a feeder line, and an implementation of the power grid event monitor is placed at an end of the feeder line (Taft, Figure 21, [0169] FIG. 21-24 is an operational flow diagram of the outage intelligence application configured to determine outage conditions associated w;H1 Ene sensors ;n the power grid. ln one example, the line sensors may also include the feeder meters that are electrically coupled to the line sensors to provide information concerning line sensor activity”).
and wherein signature identification indicative of the fault is performed at multiple hierarchical levels including a system level, a subsystem level, and a local level based on measurements from multiple implementations of the power grid event monitor.(Taft, Table 1, The data in the connectivity warehouse 131 may describe the hierarchical information about all the components of the grid (substation, feeder, section, segment, branch, t-section, circuit breaker, recloser, switch, etc -basically all the assets” NOTE: Power grid assets are included but not limited to electricity generation, electric power transmission, and electricity distribution. Power plants, transmission lines etc. see [0005]).
Regarding Claim 9, combination of Taft and Yuehao teaches the power line event determination process according to claim 1,
Taft further teaches wherein the power grid event monitor includes a voltage transducer, a current transducer, and an A/D (analog to digital) converter. (Taft, Figure 5A, 502, sensors, [0056], Grid components like grid devices (smart power sensors (such as a sensor with an embedded processor that can be programmed for digital processing capability) temperature sensors, etc.), power system components that includes additional embedded processing (RTU s, etc), smart meter networks (meter health, meter readings, etc), and mobile field force devices (outage events, work order completions, etc) may generate event data”)
and wherein data from the transducers is combined with supervisory control and data acquisition (SCADA) data to form multi-modal input data for fault analysis. (Taft, Table 1, operational data previously was transmitted to the SCADA (Supervisory Control And Data)
Regarding Claim 10, combination of Taft and Yuehao teaches the power line event determination process according to claim 9,
Taft further teaches wherein the current transducer is configured to measure a current associated with the power grid component of the power grid (Taft, figure 5A,502, Ia, Ib, Ic, see Figure 8, Grid state measurement); and wherein the voltage transducer is configured to measure a voltage associated with the power grid component of the power grid. (Taft, see Figure 8, Grid state measurement ,figure 5A,502, Va, Vb, Vc, Table 3- storage for digital waveforms sampled from analog transducers (voltage and current waveforms for example) which may be used hold the data for wavefonns at different time periods so that if ru1 event is detected, the waveform data leading up to the event may also be stored).
and wherein measurements from different data sources having different sampling rates are processed separately and then combined to generate a unified feature set. (Taft, Figure 15A-15B,[0088] The substation may analyze the fault determined by the device and may take corrective action depending on the fault ( such as reducing the power supplied to the feeder circuit). In the example of the device sending data indicating a fault (based on analysis of waveforms), the substation may alter the power supplied to the feeder circuit without input from the operations control
center 116. Or, the substation may combine the data indicating the fault with information from other sensors to further refine the analysis of the fault. The substation may further communicate with the operations control center 116, such as the outage intelligence application (such as discussed FIG. 13) and/or the fault intelligence application (such as discussed in FIG. 14)
Regarding Claim 11, combination of Taft and Yuehao teaches the power line event determination process according to claim 1,
Taft further teaches wherein the power grid event monitor is configured to collect sensor readings and provide the sensor readings to the power grid event analytics system. (Taft, Figure 9A, Line sensors RTUs, collect operational Data, [0056], The event data generated within the smart grid may be transmitted via an event bus 147 ) .and wherein the sensor readings include multi-source time-series data that is preprocessed to remove outliers and missing values prior to analysis. (Taft, Figure 8, [0102] As discussed above, one functionality of the application services may include observability processes. The observability processes may allow the utility to "observe" the grid. These processes may be responsible for interpreting the
raw data received from all the sensors and devices on the grid and turning them into actionable information. FIG. 8 includes a listing of some examples of the observability processes”).
Regarding Claim 13, Taft teaches,
A power line event determination system implemented in a power grid comprising:
a plurality of implementations of a power grid event monitor (Taft, Figure 2, Grid data/ analytics service 123, Table 1, Grid data/ analytics service 123, Services (such as Sensor Data Services 124 and Analytics Management Services 125) to support access to grid data and grid analytics; management of analytics., and , Figure 5B, Event Analysis, 518, Table 3, Event analysis and triggers 518, Processing of all analytics for event detection);
; and the plurality of implementations of the power grid event monitor connected to the power grid, (Taft, Figure 2, Centralized Grid Analytics Applications 139),
wherein the power grid event monitor is a voltage monitor, a current monitor, and/or a voltage and current monitor (Taft, [0074], “local analytics processing on a real time (such as a sub-second) basis. Processing may include digital signal processing of voltage and current waveforms, detection and classification processing,
including event stream processing”. Figure 5A-5B, Ring buffer 502, Table 3, Local circular buffer storage for digital waveforms sampled from analog transducers (voltage and current waveforms for example) which may be used hold the data for waveform at different time periods so that if ru1 event is detected, the waveform data leading up to the event may also be stored”) ;
wherein the plurality of implementations of the power grid event monitor are located in and electrically connected to certain respective implementations of a power grid component of the power grid (Taft, Figure; and Figure 2, connectivity warehouse 131, [0058] Network location data may include the information about the grid component on the communication network. This information may be used to send messages and information to the particular grid component. Network location data may be either entered manually into the Smart Grid database as new Smart Grid components are installed or is extracted from an Asset Management System if this information is maintained externally”); ; and
wherein the power grid component comprises one of the following: power stations, electrical substations, electric power transmission components, powerlines, electric power distribution components, electricity generation components, generators, high-voltage substations, local substations, high voltage transmission lines, step-up substations, step-down substations, distribution substations, transformers, circuit breakers, switches, lightning arresters, capacitors, electric power distribution components, distribution lines, distribution transformers, and/or feeders. .(Taft, Table 1, The data in the connectivity warehouse 131 may describe the hierarchical information about all the components of the grid (substation, feeder, section, segment, branch, t-section, circuit breaker, recloser, switch, etc -basically all the assets” NOTE: Power grid assets are included but not limited to electricity generation, electric power transmission, and electricity distribution. Power plants, transmission lines etc. see [0005]).
Taft teaches a PMU receiving power grid data with time stamp and analyzing data to determine a grid event location,
Taft is silent on at least one implementation of a power grid event analytics system that is configured to determine an estimate of a fault location within the power grid based on times of arrival of a fault signature at each of a first one of the plurality of implementations of the power grid event monitor and a second one of the plurality of implementations of the power grid event monitor.
However, Yuehao teaches determine an estimate of a fault location within the power grid based on times of arrival of a fault signature at each of a first one of the plurality of implementations of the power grid event monitor and a second one of the plurality of implementations of the power grid event monitor (Yuehao, Abstract, “The method comprises a frequency change arrival time on-line obtaining method based on PMU measurement information, a reference arrival time matrix rapid construction method and a power grid fault source locating method based on ordinal pattern recognizer.” Page 6, bottom paragraph “A kind of bulk power grid Fault Locating Method based on wide area phase angle measurement information, the frequency change caused by measurement fault after fault occurs arrives the time of each generator node, form time arrow to be checked, the reference time matrix that the time arriving each generator node by the specified point fault constructed with offline mode is formed contrasts, the node of its correspondence is the abort situation detected,).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Taft PMU analyzing method in view of Yuehao to include time of arrival matrix and time series measurement data and identify fault location to determine power grid event at different locations as taught by Yuehao with the benefit of accurate event detection and identification performance. (Yuehao, abstract).
Regarding Claim 14, combination of Taft and Yuehao teaches the power line event determination system according to claim 13,
Taft further teaches wherein the first implementation of the power grid event monitor is located in and/or connected to a first implementation of the power grid component of the power grid (Taft, Figure 4, 0071] FIG. 4 illustrates an example of the high-level architecture for the INDE SUBSTATION 180 group. This group may comprise elements that are actually hosted in the substation 170 at a substation control house on one or more servers co-located with the substation electronics and systems); and wherein a second implementation of the power grid event monitor is located in and/or connected to a second implementation of the power grid component of the power grid (Taft, Figure 15A-15B, Data, Data collected from different assets, each asset contains analytics).
_and wherein measurement data from the first and second implementations is aligned using synchronized timing prior to fault analysis. (Taft, Figure 5A, Time domain signal analytics 510, GPS data frame Timestamp 526, Table 3, GPS timing 524, Provides high resolution timing to coordinate applications and synchronize data collection across a wide geographic area. The generated data may include a GPS data frame time stamp 526” [0126] The fault intelligence process may also classify and categorize faults. [0128] The fault intelligence may further raise fault events. Specifically, this process may create and publish fault events to the events bus once a fault has been detected, classified, categorized, characterized and isolated. This process may also be responsible for collecting, filtering, collating and deduplicating faults so that an individual fault event is raised rather than a deluge based on the raw events that are typical during a failure. Finally, the fault intelligence may log fault events to the event log database” NOTE: each first second and more individual fault has been classified and location is identified).
Regarding Claim 15, combination of Taft and Yuehao teaches the power line event determination system according to claim 13,
Taft further teaches wherein the power grid event monitor is configured to detect a fault-criteria (Taft, Table 5, Fault Identification Criteria) and generate fault data that includes a time of fault and fault criteria (Taft, Figure 8, Fault Intelligence, characterize faults, determine fault location and log fault data); and wherein the power grid event monitor is configured to send the fault data to the power grid event analytics system. (Taft, Figure [0056], Grid components like grid devices (smart power sensors (such as a sensor with an embedded processor that can be programmed for digital processing capability) temperature sensors, etc.), power system components that includes additional embedded processing (RTU s, etc), smart meter networks (meter health, meter readings, etc), and mobile field force devices (outage events, work order completions, etc) may generate event data, operational and non-operational data. The event data generated within the smart grid may be transmitted via an event bus 147 (Figure 1B)).
and wherein the fault criteria correspond to a signature associated with a contributing factor of a power system event for a specific grid asset. (Taft, Figure 14 A-14B, Figure 18A, Asset I, [0009] According to another aspect of the disclosure, a fault intelligence application executable on at least one processor may be configured to receive phasor data (magnitude and phase angle) to identify fault types upon detection of fault conditions of a fault in a power grid. The fault intelligence application may apply a set of predetermined criteria to the phasor data. The fault intelligence application may apply various categories of criteria to the phasor data to systematically eliminate any fault types from consideration based on the application of the criteria”)
Regarding Claim 17, combination of Taft and Yuehao teaches the power line event determination system according to claim 13,
Taft further teaches wherein the power grid event monitor is configured to sample instantaneous voltage waveforms and/or current waveforms with sub-millisecond timing accuracy. (Taft, [0074], local analytics processing on a real time (such as a sub-second) basis. Processing may include digital signal processing of voltage and current waveforms, detection and classification processing, including event stream processing”. NOTE: “a sub-second” refers to milli or microseconds, less than a second.).
Taft is silent on wherein sampled data is used to generate features over sliding time windows and combined into a feature matrix representing a system state.
However, Yuehao teaches wherein sampled data is used to generate features over sliding time windows and combined into a feature matrix representing a system state (Yuehao, Abstract “According to the invention, a frequency change arrival time on-line identification method based on frequency change arrival time of a sliding data window is brought forward; a to-be checked time vector can be rapidly set on-line; targeted at an undirected weighted graph corresponding a framework structural model”).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Taft PMU analyzing method in view of Yuehao to include time of arrival matrix and time series measurement data and identify fault location to determine power grid event at different locations as taught by Yuehao with the benefit of accurate event detection and identification performance. (Yuehao, abstract).
Regarding Claim 19, combination of Taft and Yuehao teaches the power line event determination system according to claim 13,
Taft further teaches the power grid event analytics system is configured to utilize relative times of arrival a fault signature to estimate a fault location within the power grid. (Taft, [0205], one example, fault types may be determined by the fault intelligence application based on the fault identification criteria categories of Table 5. Synchrophasor data for each phase within the power grid may be obtained from a phasor measurement unit (PMU) data collection head
located in the INDE SUBSTATION 180 group or may be located centrally in a central authority to the power grid. The PMU measures and may provide phase information including the synchrophasor data, such as phasor magnitude and phasor angle data, for each phase, A, B, and C, may be generated and
analyzed to determine if a fault is present and determine the type of fault. Phase information may be received by the fault intelligence application at block 2600.A detem1ination that a possible fault may be present may be performed at block
2602. In one example, the fault intelligence application may make the determination at block 2602 based on thresholds associated with phasor magnitude and phasor angle data for each phase being analyzed”).
Regarding Claim 20, combination of Taft and Yuehao teaches the power line event determination system according to claim 13,
Taft further teaches wherein an implementation of the power grid event monitor is placed at an electrical substation at a head of each implementation of a feeder line, and an implementation of the power grid event monitor is placed at an end of the feeder line (Taft, Figure 21, [0169] FIG. 21-24 is an operational flow diagram of the outage intelligence application configured to determine outage conditions associated with the sensors in the power grid. ln one example, the line sensors may also include the feeder meters that are electrically coupled to the line sensors to provide information concerning line sensor activity”).
and wherein data from monitors at a head and an end of a feeder line is jointly analyzed to perform correlation-based event characterization. (Taft, Figure 1B, Meta data collection head end 153, Figure 18A, Feeders.)
Regarding Claim 21, combination of Taft and Yuehao teaches the power line event determination system according to claim 13,
Taft further teaches wherein the power grid event monitor includes a voltage transducer, a current transducer, and an A/D (analog to digital) converter. (Taft, Figure 5A, 502, sensors, [0056], Grid components like grid devices (smart power sensors (such as a sensor with an embedded processor that can be programmed for digital processing capability) temperature sensors, etc.), power system components that includes additional embedded processing (RTU s, etc), smart meter networks (meter health, meter readings, etc), and mobile field force devices (outage events, work order completions, etc) may generate event data”).
wherein the monitor further includes processing circuitry configured to generate
both raw measurements and derived feature values for use in analytics (Taft, [0102]” As discussed above, one functionality of the application services may include observability processes. The observability processes may allow the utility to "observe" the grid. These processes may be responsible for interpreting the
raw data received from all the sensors and devices on the grid and turning them into actionable information. FIG. 8 includes a listing of some examples of the observability processes”).
Regarding Claim 22, combination of Taft and Yuehao teaches the power line event determination system according to claim 21,
Taft further teaches wherein the current transducer is configured to measure a current associated with the power grid component of the power grid (Taft, figure 5A,502, Ia, Ib, Ic, see Figure 8, Grid state measurement); and wherein the voltage transducer is configured to measure a voltage associated with the power grid component of the power grid. (Taft, see Figure 8, Grid state measurement, figure 5A,502, Va, Vb, Vc, Table 3- storage for digital waveforms sampled from analog transducers (voltage and current waveforms for example) which may be used hold the data for waveforms at different time periods so that if ru1 event is detected, the waveform data leading up to the event may also be stored).
wherein the measurements are transformed into both time-domain and frequency-domain representations prior to analysis. (Taft, 5A, Time domain 510, and Frequency domain 512 , Figure 5B, event analysis 518, and figure 14BFault Intelligence,).
Regarding Claim 23, combination of Taft and Yuehao teaches the power line event determination system according to claim 13,
Taft further teaches wherein the power grid event monitor is configured to collect sensor readings and provide the sensor readings to the power grid event analytics system. (Taft, Figure 9A, Line sensors RTUs, collect operational Data, [0056], The event data generated within the smart grid may be transmitted via an event bus 147) .
Taft is silent on and wherein the sensor readings are processed to generate feature vectors that capture interactions among multiple power grid components.
However, Yuehao teaches wherein the sensor readings are processed to generate feature vectors that capture interactions among multiple power grid components (Yuehao, Page 4, time vectors see page 3, bottom paragraph, B, reference arrive time matrix fast construction method:(1) will make original frequency change propagation time matrix D 0=(d ), wherein:ij Pattern-recognition is a kind of statistical recognition method of carrying out pattern classification according to the distance of pattern and all kinds of representative sample, and the distance being identified pattern and affiliated pattern class sample is minimum; Assuming that c classification represents the proper vector R of pattern 1..., R crepresent, x is the proper vector being identified pattern, | x-R i| be x and R i(i=1,2 ..., the distance c), if | x-R i| minimum, x is divided into the i-th class;)
It would have been obvious to a person of ordinary skill before the effective filing date to modify Taft PMU analyzing method in view of Yuehao to include time of arrival matrix and time series measurement data and identify fault location to determine power grid event at different locations as taught by Yuehao with the benefit of accurate event detection and identification performance. (Yuehao, abstract).
Regarding Claim 24, combination of Taft and Yuehao teaches the power line event determination system according to claim 15
Taft further teaches wherein the power grid event analytics system is implemented with a processor (Taft, Figure 5, [0083], The smart grid device may include an embedded processor).
wherein the power grid event analytics system is configured to receive and parse the fault data from one or more implementations of the power grid event monitor (Taft, Figure 14A-B, receive event data 1420); and wherein the power grid event analytics system is configured process alerts from the power grid event monitor (Taft, Figure 14, [0123]. The various fault data, grid state, connectivity data, and switch state may be sent to the substation analytics for event detection and characterization,
as shown at block 1430. The event bus may also receive event messages (block 1434) and send the event messages to the substation analytics (block 1436). The substation analytics may determine the type of event, as shown at block 1432”).
and wherein the system ranks multiple candidate fault signatures based on deviation from baseline statistical distributions of normal system behavior. (Taft, [0128] The fault intelligence may further raise fault events. Specifically, this process may create and publish fault events to the events bus once a fault has been detected, classified, categorized, characterized and isolated
Claims 6,12, 16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Taft. and in view of Yuehao as applied to claim 1 and in further view of Yan et al (US 2021/0173462 A1 hereinafter Yan, previously cited)
Regarding Claim 6, combination of Taft and Yuehao teaches the power line event determination process according to claim 3,
Taft further teaches wherein the fault criteria (Taft, Table 5, Fault Identification Criteria) includes specific signatures, traditional waveshape triggering, such as RMS (root mean square) or peak value change or trigger (Taft, Figure 5A,Vrms, Irms, Table 3, Frequency domain signal analysis 512, processing of the signals in the frequency domain; extraction of RMS and power parameters, waveshape change based on THD (total harmonic distortion) and/or mean square difference from previous cycle, level trigger after high pass filtering, power line frequency comb filtering, or 60Hz comb filtering (Taft, Figure 5A, Waveform streaming service 522, Ring buffer(60 cycles)).
Both Taft and Yuehao are silent on wherein the power grid event monitor is configured to sample continuously, with triggering based on prior machine learning training information to match the fault criteria; wherein the fault criteria further includes features generated from a combination of statistical descriptors, frequency-domain features, and machine learning-derived features that are fused into a unified feature representation and reduced via dimensionality reduction prior to fault classification.
However, Yan teaches wherein the power grid event monitor is configured to sample continuously, with triggering based on prior machine learning training information to match the fault criteria;(Yan, figure 2-3, 0051] FIG. 2, “the process may be trained in an off line phase or mode and may subsequently be implemented in an online monitoring and diagnosis phase or application. [0025] Signature identification using data-driven machine learning techniques may be treated as a feature selection problem. [0030] In accordance with one or more embodiments, a machine learning-based power substation asset monitoring
system is provided which may determine various signatures corresponding to different power system events. For example, such a machine learning-based power substation asset monitoring system may receive and process data from
various sources, such a system may include components to perform operations such as feature generation or extraction, auto-associative model building, residual generation, residual generation, and signature identification, Features may comprise individual quantities extracted from one or more measured data streams”). wherein the fault criteria further includes features generated from a combination of statistical descriptors, frequency-domain features, and machine learning-derived features that are fused into a unified feature representation and reduced via dimensionality reduction prior to fault classification (Yan, [0077] To consider temporal-spatial dependence of the multimodal time-series data in comparing two data instance, the system 300 transforms the data instances to a common set of features. This transformation and comparison of features is shown in Steps 320 and 345 (both shown in FIG. 3) and also in Step 415.
[0078] The features comprise univariate-based and multivariate-based features. Univariate-based features include time-domain, frequency domain and time-frequency domain features”)
It would have been obvious to a person of ordinary skill before the effective filing date to modify Taft analyzing method in view of Yan to include machine learning data to include a machine learning method to process the power grid state data as taught by Yan and obtain a more stable and more accurate classification of a fault data with the benefit of better event detection and identification performance. (Yan, [0030-[0037]).
Regarding Claim 12, combination of Taft and Yuehao teaches the power line event determination process according to claim 3,
Taft further teaches wherein the power grid event analytics system is implemented with a processor (Taft, Figure 5, [0083], The smart grid device may include an embedded processor);
wherein the power grid event analytics system is configured to receive and parse the fault data from one or more implementations of the power grid event monitor (Taft, Figure 14A-B, receive event data 1420); and wherein the power grid event analytics system is configured process alerts from the power grid event monitor (Taft, Figure 14, [0123]. The various fault data, grid state, connectivity data, and switch state may be sent to the substation analytics for event detection and characterization,
as shown at block 1430. The event bus may also receive event messages (block 1434) and send the event messages to the substation analytics (block 1436). The substation analytics may determine the type of event, as shown at block 1432 “).
Taft and Yuehao are silent on and wherein the power grid event analytics system determines normalized residuals of event data relative to baseline residual statistics derived from normal operation data.
However, Tan teaches wherein the power grid event analytics system determines normalized residuals of event data relative to baseline residual statistics derived from normal operation data. (Yan, [0047] The event detection and identification engine 230 determines 325 the best similarity measure to use for each event or event category. Similarity measures are used to determine how similar the features of different events are. Similarity measures may include distance-based measures (e.g., Euclidean distance and Manhattan distance) statistical-based measures (e.g., correlation coefficient), and/or information-based measures (e.g., normalized information distance). [0050] A Manhattan distance includes a distance between two points measured along axes at right angles. A sum of absolute errors (SAE) comprises a sum of the absolute values of the vertical "residuals" between points generated by a function and corresponding points in the data”).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Taft analyzing method in view of Yan to include machine learning data to include a machine learning method to process the power grid state data as taught by Yan and obtain a more stable and more accurate classification of a fault data with the benefit of better event detection and identification performance. (Yan, [0030-[0037]).
Regarding Claim 16, combination of Taft and Yuehao teaches the power line event determination system according to claim 15,
Taft further teaches wherein the power grid event monitor uses a GPS (Global Positioning System) or other timing methodology to generate the time of fault (Taft, Figure 5A, GPS timing 524, GPS Data Frame Time Stamp 526, [0067], Geographic Information System 149 (Figure 6C) is a database that contains information about where assets are located geographically and how the assets are connected together); and
wherein the power grid event monitor is configured to sample instantaneous voltage waveforms and/or current waveforms at a location of a respective implementation of the power grid component within the power grid that a particular implementation of the power grid event monitor is located to generate the fault criteria (Taft, [0074], local analytics processing on a real time (such as a sub-second) basis. Processing may include digital signal processing of voltage and current waveforms, detection and classification processing, including event stream processing”).
Taft and Yuehao are silent on wherein sampled data is processed to extract spatial-temporal relationships among measurements collected from multiple locations in the power grid.
However, Yan teaches wherein sampled data is processed to extract spatial-temporal relationships among measurements collected from multiple locations in the power grid. (Yan, 0075] The data of the events provided may include multiple,
heterogeneous time series measurements from different sensors. A data instance in this case is either a multivariate time series data representing a known power system event or multivariate time series data whose event is to be identified. Comparing or calculating the similarity between two such data instances is challenging. Firstly, the two data instances on which the similarity is calculated can have different lengths (or number of samples) and even potentially have different number of sensors. More importantly, the data instances include strong temporal-spatial dependence and thus requiring taking into account such temporal spatial dependence in comparing data instance”)
It would have been obvious to a person of ordinary skill before the effective filing date to modify Taft analyzing method in view of Yan to include machine learning data to include a machine learning method to process the power grid state data as taught by Yan and obtain a more stable and more accurate classification of a fault data with the benefit of better event detection and identification performance. (Yan, [0030-[0037]).
Regarding Claim 18, combination of Taft, Yuehao and Yan teaches the power line event determination system according to claim 15,
Taft further teaches wherein the fault criteria (Taft, Table 5, Fault Identification Criteria) includes specific signatures, traditional waveshape triggering, such as RMS (root mean square) or peak value change or trigger (Taft, Figure 5A,Vrms, Irms, Table 3, Frequency domain signal analysis 512, processing of the signals in the frequency domain; extraction of RMS and power parameters, waveshape change based on THD (total harmonic distortion) and/or mean square difference from previous cycle, level trigger after high pass filtering, power line frequency comb filtering, or 60Hz comb filtering (Taft, Figure 5A, Waveform streaming service 522, Ring buffer(60 cycles)).
Taft is silent on wherein the power grid event monitor is configured to sample continuously, with triggering based on prior machine learning training information to match the fault criteria; and wherein machine learning-based processing includes generating residuals using an auto-associative model and ranking candidate signatures based on normalized residual values.
However, Yan teaches wherein the power grid event monitor is configured to sample continuously, with triggering based on prior machine learning training information to match the fault criteria;(Yan, figure 2-3, 0051] FIG. 2, “the process may be trained in an off line phase or mode and may subsequently be implemented in an online monitoring and diagnosis phase or application. [0025] Signature identification using data-driven machine learning techniques may be treated as a feature selection problem. [0030] In accordance with one or more embodiments, a machine learning-based power substation asset monitoring
system is provided which may determine various signatures corresponding to different power system events. For example, such a machine learning-based power substation asset monitoring system may receive and process data from
various sources, such a system may include components to perform
operations such as feature generation or extraction, auto-associative model building, residual generation, residual generation, and signature identification, Features may comprise individual quantities extracted from one or more measured data streams”). wherein machine learning-based processing includes generating residuals using an auto-associative model and ranking candidate signatures based on normalized residual values (Yan, [0047] The event detection and identification engine 230 determines 325 the best similarity measure to use for each event or event category. Similarity measures are used to determine how similar the features of different events are. Similarity measures may include distance-based measures (e.g., Euclidean distance and Manhattan distance) (…). [0050] A Manhattan distance includes a distance between two points measured along axes at right angles. A sum of absolute errors (SAE) comprises a sum of the absolute values of the vertical "residuals" between points generated
by a function and corresponding points in the data”).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Taft analyzing method in view of Yan to include machine learning data to include a machine learning method to process the power grid state data as taught by Yan and obtain a more stable and more accurate classification of a fault data with the benefit of better event detection and identification performance. (Yan, [0030-[0037]).
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li et al. (CN 108375713 A) recites “The invention discloses a kind of novel power grid functional failure travelling wave positioning method and systems. Fault point is recorded after certain transmission line malfunction of this method in power grid generates the time that transient state travelling wave signal reaches each substation, settling time matrix The shortest path that fault traveling wave is propagated is calculated using dijkstra's algorithm, shortest path distance matrix is established, calculating matrix is obtained after amendment Fault distance is calculated using calculating matrix and time matrix, establishes fault distance matrix Validity identification is carried out to the element in fault distance matrix, and comprehensive all effective fault distances obtain the exact position of fault point on transmission line of electricity. The system includes traveling wave detector device, the first structure module, both-end locating module, the second structure module and effective identification module. The method and system of the present invention, fault location precision are higher In the case that the wave number that can be expert at is adopted according to leakage and accidentally adopted, remains to realize exact failure positioning, realize that process is simple and practicable, have broad application prospects” (Abstract).
HUNTE et al. (US 2025/0283930 A1) The invention provides “An electrical grid fault localization system including: a plurality of current sensors (measurement devices) electrically connected to the electrical grid; a memory for storing
a representation of the electrical grid as a plurality of geographical markers, and for storing a plurality of sets of expected time data, each one of the plurality of sets of expected time data associated with each one of the plurality of geographical markers, each member of the set of expected time data based on an expected signal propagation time from, at least one of the plurality of geographical markers to at least one current sensor; a processor communicative with the plurality of current sensors and the memory, the processor configured to receive measured time data of a fault event from, the plurality of sensors to generate a set of fault event time data and to match the set of fault event time data to at least one of the plurality of sets of expected time data and identify in g/outputting at least one matched geographical marker. Methods and computer-readable media directed to electrical grid fault localization are also described”(Abstract).
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/DILARA SULTANA/Examiner, Art Unit 2858
09/04/2026
/SON T LE/Primary Examiner, Art Unit 2858
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