DETAILED ACTION
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 Arguments
Applicant’s arguments, see pages 7-8, filed 01/22/2026, with respect to the rejections under 35 U.S.C. §101 have been fully considered and are persuasive. The 35 U.S.C. §101 rejections of 01/06/2026 have been withdrawn.
Applicant's arguments filed 01/22/2026 have been fully considered but they are not persuasive.
Regarding the argument that EP 3,772,652 A1 (‘652 hereinafter) does not disclose,
“determining data directly indicative of the operational parameter based on data related to the operational parameter received from the reliable sensors and not the unreliable sensors;
determining, using the mathematical model, an estimation of an actual value of the operational parameter based on the data directly indicative of the operational parameter;
correcting the estimation of the actual value with the data directly indicative of the operational parameter”
The Examiner respectfully reproduces paragraphs [0022]-[0025] and [0043]-[0044] of ‘652 below.
“As has been stated above, the exact sensor positioning parameters and/or sensor detection ranges and/or target positioning parameters and/or target size parameters may be unknown when the method is started. However, the values of these parameters may be estimated based on the redundant measurement of pulses by multiple sensors originating from multiple detection targets. For example, a particular detection target may be measured by the multiple proximity sensor in a redundant manner, allowing to deduce or derive the values of the sensor positioning parameters and/or sensor detection ranges and/or target positioning parameters and/or target size parameters.
The model may be represented by a set of equations relating the unknown parameters and rotor operational characteristics to the measured rising times and falling times. The set of equations may be solved by the method of least squares, in particular in a recursive manner, in particular by an iteration.
According to an embodiment of the present invention, estimating the rotor speed and/or the rotor azimuth and/or the rotation direction and/or the values of parameters, in particular specifically for each of the sensors and/or targets, includes applying an adaptive filter to the measurement model relating these quantities to the measured rising times and falling times.”
“According to an embodiment of the present invention, the method further comprises validating of at least one of the sensors by making a (one or several) consistency check of measurement results of this sensor and the estimated rotor speed and/or estimated rotor azimuth; and disqualifying of one sensor, if it does not pass the consistency check.
The redundancy of measurement enables the consistency check during the validation. Thereby, one or more sensors may be identified which provide erroneous results due to one or more problems, such as damage, improper function/positioning, or the like. The disqualified sensor may not be utilized in the method any more. In particular, measurement results provided or detected by this sensor may not be utilized for estimation of the rotor operational characteristics.”
The Examiner respectfully notes that the above paragraphs indicate that the method of ‘652 uses a mathematical model to estimate and correct (by validating and then by not utilizing faulty sensors) of the operational parameters of the wind turbine as claimed.
Regarding the argument that EP 3,760,861 (‘861 hereinafter) does not disclose,
“determining data directly indicative of the operational parameter based on data related to the operational parameter received from the reliable sensors and not the unreliable sensors;
determining, using the mathematical model, an estimation of an actual value of the operational parameter based on the data directly indicative of the operational parameter;
correcting the estimation of the actual value with the data directly indicative of the operational parameter”
The Examiner respectfully reproduces below paragraphs [0039] and [0041] of ‘861, “In case of two or more valid sensor signals, the second statistic unit 42 determines a set of statistical values, such as mean wind speed, wind speed variance, and a wind direction variance, for each signal and compares the statistical values with each other to determine a status of each sensor (such as "properly working sensor", "defective wind sensor", "partially blocked sensor" or "completely blocked sensor") in the second state determining unit 44. The results of the statistical value comparison and of the status determination is output to the decision unit 38 which combines the information to generate and output a status signal 60, a wind parameter value signal 62, and a quality and source signal 64. The status signal 60 indicates a status of each sensor, the wind parameter value signal 62 indicates a wind speed and/or wind direction value, and the quality and source signal 64 indicates an estimated quality of the wind parameter value signal 62 as well as the sensors used to obtain it.”
“As discussed above, the present invention combines the wind speed and wind direction measurements provided by each of the wind sensors in order to provide the most accurate and robust measured values for these quantities at all times. To perform this fusion, the algorithm monitors over time the statistics of the measurements from each valid wind sensor in order to detect faults in one or more sensors. When more than one wind sensor is valid (i.e., not experiencing major hardware/communication failures that prevent the sensor measurements from being available), the statistics of the sensor measurements (i.e., their means and standard deviations) are compared to each other by the second detector unit 40. Weights and statuses are assigned to the sensors based on the magnitude and direction of the relative differences between their statistics. For example, when a sensor mounted at one location on the nacelle provides wind speed and/or direction measurements that appear noisier than those from a sensor mounted at another location, the relative difference in the standard deviations between the corresponding measurements increases, and the sensor with lower standard deviation measurements is progressively weighed higher than the sensor with higher standard deviation measurements. Similarly, when a sensor provides wind speed measurements that appear lower than those from another sensor, the relative difference in the means between the wind speed measurements increases, and the sensor with higher wind speed mean is weighed higher than the sensor with lower wind speed mean.”
The Examiner respectfully asserts that adjusting the weighting of sensors inputs into a mathematical model is “correcting an estimation of an actual value of an operational parameter of a wind turbine” as the correction itself as the mathematical model outputs a quality and source signal, and better the character of the quality signal 64 the truer to the actual value of the operational parameter of the wind turbine through the wind parameter value signal 62.
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 16-20, 24, 26-29, and 31-34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Frank et al. (EP 3,772,652 A1; hereinafter ‘652) in view of Nielsen (US PGPUB 2018/0363625 A1).
Regarding claim 16, ‘652 a method for controlling operation of a wind turbine ([0003]), the method comprising:
receiving data related to an operational parameter from a plurality of sensors (S_A1, S R1, S R2; [0041]-[0042]) in the wind turbine;
from the received data, determining which of the plurality of sensors are reliable sensors ([0041]-[0042], “validating at least one of the sensors by making a (one or several) consistency check measurement results of this sensor”) and which of the plurality of sensors are unreliable sensors ([0041]-[0042], “disqualified sensor may not be utilized in the method any more”);
determining data directly indicative of the operational parameter based on data related to the operational parameter received from the reliable sensors and not the unreliable sensors ([0022]-[0025]);
determining, using the mathematical model, an estimation of an actual value of the operational parameter based on the data directly indicative of the operational parameter (([0022]-[0025]);
correcting, using the mathematical model, the estimation of the actual value with the data directly indicative of the operational parameter ([0025]);
However, ‘652 does not disclose, “determining a setpoint for controlling the wind turbine based on the corrected estimation of the actual value of the operational parameter, wherein the setpoint comprises at least one of a pitch angle and a torque; and
controlling the operation of the wind turbine using the setpoint by adjusting one or more components of the wind turbine so as to achieve the setpoint”
Nielsen teaches, in the field of controlling wind turbines, "Most modern wind turbines are
controlled and regulated continuously with the purpose of ensuring maximum power extraction from
the wind under the current wind and weather conditions, while at the same time ensuring that the loads
on the different components of the wind turbine are at any time kept within acceptable limits. [0002]",
"pre-setting a number of intervals of a wind power parameter, the wind power parameter determined
as one of a power, a torque, or a blade load of the wind turbine; [0017]", "determining a control
parameter of the wind turbine as a function of the adjusted relative wind direction; and [0024]
controlling the wind turbine according to the control parameter."
It would have been obvious to one of ordinary skill in the art to modify the wind turbine
controller of '652 to also control the operation of the wind turbine using a value of an operational
parameter (which already disqualifies sensors and no longer uses them in their method), wherein the
operational parameter is a setpoint for controlling the wind turbine, and wherein the setpoint comprises
at least one of a patch angle of torque as taught by Nielsen, as both references are in the same field of
endeavor, and one of ordinary skill would appreciate both, "Most modern wind turbines are controlled
and regulated continuously with the purpose of ensuring maximum power extraction from the wind
under the current wind and weather conditions, while at the same time ensuring that the loads on the
different components of the wind turbine are at any time kept within acceptable limits. [0002]" and
"pre-setting a number of intervals of a wind power parameter, the wind power parameter determined
as one of a power, a torque, or a blade load of the wind turbine; [0017]."
Regarding claim 17, the combination of '652 and Nielsen teach all of claim 16 as above, wherein
determining which of the plurality of sensor are reliable sensors and which of the plurality of sensors are
the unreliable sensor further comprises analyzing noise in a range of measurements of the plurality of
sensors, or analyzing noise in a range of intermediate values from the plurality of sensors ('652; [0041]-
[0042] discloses performing a consistency check on the sensors).
Regarding claim 18, the combination of '652 and Nielsen teach all of claim 16 as above,
wherein determining which of the plurality of sensor are reliable sensors and which of the plurality of
sensors are the unreliable sensor further comprises comparing two or more values directly indicative of
the operational parameter based on measurements from different ones of the plurality of sensors with
an actual value of the operational parameter ('652; The Examiner notes that during calibration there is a
plurality of relative sensors S_RN and a reference sensor S_A1 [0035] and a consistency check is
performed [0041]-[0042] where the sensors are determined to be disqualified or not).
Regarding claim 19, the combination of '652 and Nielsen teach all of claim 16 as above, further
comprising using data related to the operational parameter from two or more sensors of the plurality of
sensors to determine which of the plurality of sensors are the reliable sensors and which of the plurality
of sensors are the unreliable sensors ('652; The Examiner notes that during calibration there is a
plurality of relative sensors S_RN and a reference sensor S_A1 [0035] and a consistency check is
performed [0041]-[0042] where the sensors are determined to be disqualified or not).
Regarding claim 20, the combination of '652 and Nielsen teach all of claim 16 as above, further
comprising determining at least one intermediate value of the operational parameter based on the data
related to the operational parameter from at least one sensor of the plurality of sensors ('652; Fig. 2, the outputs from SR1, SR2), and wherein estimating the actual value of the operational parameter is based
on the at least one intermediate value (The Examiner notes that these intermediate values are inputted
into the estimator).
Regarding claim 24, the combination of '652 and Nielsen teach all of claim 16 as above, stopping
measuring the data with the unreliable sensors after determining which of the plurality of sensors are
reliable sensors and which of the plurality of sensors are unreliable sensors ('652; [0041], "disqualified
sensor may not be utilized in the method any more".
Regarding claim 26, the combination of '652 and Nielsen teach all of claim 16 as above, wherein
the operational parameter is one of rotor speed or azimuth angle (claims 1, 4, 5).
Regarding claim 27, the combination of '652 and Nielsen teach all of claim 16 as above, wherein
at least one of the plurality of sensors is an encoder ('652; Fig. 1, [0053]).
Regarding claim 28, ‘652 discloses a wind turbine controller comprising a processor and a
memory, wherein the memory comprises instructions that, when executed by the processor, cause the
processor to ([0009]):
receive data related to an operational parameter from a plurality of sensors in a wind turbine
(S_A1, S_R1, SF [0041]-[0042));
from the received data, determine which of the plurality of sensors are reliable sensors ([0041]-
[0042], "validating at least one of the sensors by making a (one or several) consistency check
measurement results of this sensor") and which of the plurality of sensors are unreliable sensors
({0041]-[0042], "disqualified sensor may not be utilized in the method any more");
determine data directly indicative of the operational parameter based on data related to the operational parameter received from the reliable sensors and not the unreliable sensors ([0022]-[0025]);
determine, using the mathematical model, an estimation of an actual value of the operational parameter based on the data directly indicative of the operational parameter ([0022]-[0025]);
correct, using the mathematical model, the estimation of the actual value with the data directly indicative of the operational parameter ([0025]);
However, ‘652 does not disclose, “determining a setpoint for controlling the wind turbine based on the corrected estimation of the actual value of the operational parameter, wherein the setpoint comprises at least one of a pitch angle and a torque; and
controlling the operation of the wind turbine using the setpoint by adjusting one or more components of the wind turbine so as to achieve the setpoint”
Nielsen teaches, in the field of controlling wind turbines, "Most modern wind turbines are
controlled and regulated continuously with the purpose of ensuring maximum power extraction from
the wind under the current wind and weather conditions, while at the same time ensuring that the loads
on the different components of the wind turbine are at any time kept within acceptable limits. [0002]",
"pre-setting a number of intervals of a wind power parameter, the wind power parameter determined
as one of a power, a torque, or a blade load of the wind turbine; [0017]", "determining a control
parameter of the wind turbine as a function of the adjusted relative wind direction; and [0024]
controlling the wind turbine according to the control parameter."
It would have been obvious to one of ordinary skill in the art to modify the wind turbine
controller of '652 to also control the operation of the wind turbine using a value of an operational
parameter (which already disqualifies sensors and no longer uses them in their method), wherein the
operational parameter is a setpoint for controlling the wind turbine, and wherein the setpoint comprises
at least one of a patch angle of torque as taught by Nielsen, as both references are in the same field of
endeavor, and one of ordinary skill would appreciate both, "Most modern wind turbines are controlled
and regulated continuously with the purpose of ensuring maximum power extraction from the wind
under the current wind and weather conditions, while at the same time ensuring that the loads on the
different components of the wind turbine are at any time kept within acceptable limits. [0002]" and
"pre-setting a number of intervals of a wind power parameter, the wind power parameter determined
as one of a power, a torque, or a blade load of the wind turbine; [0017]."
Regarding claim 29, the combination of '652 and Nielsen teach all of claim 28 as above, wherein
the wind turbine controller is configured to estimate the actual value of the operational parameter by
performing an initial estimation (The Examiner notes that these intermediate values are inputted into
the estimator) of the actual value of the operational parameter with the mathematical model and
correcting the initial estimation with the data directly indicative of the operational parameter based on
data received from the reliable sensors ('652; Claim 14).
Regarding claim 31, ‘652 discloses a method for estimating a value of an operational parameter
of a wind turbine, the method comprising:
measuring data related to the operational parameter with a plurality of sensors (S_A1, S R1, S R2; [0041]-[0042]);
determining which of the plurality of sensors are reliable sensors ([0041]-[0042], "validating at
least one of the sensors by making a (one or several) consistency check measurement results of this
sensor") and which of the sensors are faulty or excessively noisy sensors ([0041]-[0042], "disqualified
sensor may not be utilized in the method any more"):
determining data directly indicative of the operational parameter based on data related to the operational parameter received from the reliable sensors and not the unreliable sensors ([0022]-[0025]);
determining, using the mathematical model, an estimation of an actual value of the operational parameter based on the data directly indicative of the operational parameter ([0022]-[0025]);
correcting, using the mathematical model, the estimation of the actual value based on a weighted combination of the estimation of the actual value and the data directly indicative of the operational parameter ([0022]-[0025]);
However, ‘652 does not disclose, “determining a setpoint for controlling the wind turbine based on the corrected estimation of the actual value of the operational parameter, wherein the setpoint comprises at least one of a pitch angle and a torque; and
controlling operation of the wind turbine using the setpoint by adjusting one or more components of the wind turbine so as to achieve the setpoint”
Nielsen teaches, in the field of controlling wind turbines, "Most modern wind turbines are controlled and regulated continuously with the purpose of ensuring maximum power extraction from the wind under the current wind and weather conditions, while at the same time ensuring that the loads on the different components of the wind turbine are at any time kept within acceptable limits. [0002]", "pre-setting a number of intervals of a wind power parameter, the wind power parameter determined as one of a power, a torque, or a blade load of the wind turbine; [0017]", "determining a control parameter of the wind turbine as a function of the adjusted relative wind direction; and [0024] controlling the wind turbine according to the control parameter."
It would have been obvious to one of ordinary skill in the art to modify the wind turbine
controller of '652 to also control the operation of the wind turbine using a value of an operational
parameter (which already disqualifies sensors and no longer uses them in their method), wherein the
operational parameter is a setpoint for controlling the wind turbine, and wherein the setpoint comprises
at least one of a patch angle of torque as taught by Nielsen, as both references are in the same field of
endeavor, and one of ordinary skill would appreciate both, "Most modern wind turbines are controlled
and regulated continuously with the purpose of ensuring maximum power extraction from the wind
under the current wind and weather conditions, while at the same time ensuring that the loads on the
different components of the wind turbine are at any time kept within acceptable limits. [0002]" and
"pre-setting a number of intervals of a wind power parameter, the wind power parameter determined
as one of a power, a torque, or a blade load of the wind turbine; [0017]."
Regarding claim 32, the combination of '652 and Nielsen teach all of claim 31 as above, wherein
at least two or more of the plurality of sensors are configured to determine the data directly indicative
of the operational parameter (652; The Examiner notes that during calibration there is a plurality of
relative sensors S_RN and a reference sensor S_A1 [0035] and a consistency check is performed [0041]-
[0042] where the sensors are determined to be disqualified or not).
Regarding claim 33, the combination of '652 and Nielsen teach all of claim 31 as above, wherein
determining which of the plurality of sensors are reliable sensors and which of the plurality of sensors
are faulty or excessively noisy sensors further comprises determining that signals from the faulty or
excessively noisy sensors reach or exceed a noise threshold ('652; Claim 12).
Regarding claim 34, the combination of '652 and Nielsen teach all of claim 31 as above, wherein
the operational parameter is rotational speed ('652; claims 1, 4, 5).
Claims 16, 18-21, 28, and 31-34 are rejected under 35 U.S.C. 103 as being unpatentable over Frank et al. (EP 3,760,681 A1; hereinafter ‘861) in view of Nielsen (US PGPUB 2018/0363625 A1).
Regarding claim 16, '861 discloses a method for controlling operation of a wind turbine (claim 1), the method comprising:
receiving data related to an operational parameter from a plurality of sensor of the wind turbine (claim 1);
determining which sensors of the plurality of sensors are reliable and which sensors of the plurality of sensors are unreliable (claims 2, 3; [0039]);
determining data directly indicative of the operational parameter based on data related to the operational parameter received from the reliable sensors and not the unreliable sensors ([0039]);
determining, using the mathematical model, an estimation of an actual value of the operational parameter based on the data directly indicative of the operational parameter ([0039], “The results of the statistical value comparison and of the status determination is output to the decision unit 38 which combines the information to generate and output a status signal 60, a wind parameter value signal 62, and a quality and source signal 64.”);
correcting, using the mathematical model, the estimation of the actual value with the data directly indicative of the operational parameter ([0039], “The results of the statistical value comparison and of the status determination is output to the decision unit 38 which combines the information to generate and output a status signal 60, a wind parameter value signal 62, and a quality and source signal 64.”);
However, ‘861 does not disclose, “determining a setpoint for controlling the wind turbine based on the corrected estimation of the actual value of the operational parameter, wherein the setpoint comprises at least one of a pitch angle and a torque; and
controlling the operation of the wind turbine using the setpoint by adjusting one or more components of the wind turbine so as to achieve the setpoint”
Nielsen teaches, in the field of controlling wind turbines, "Most modern wind turbines are controlled and regulated continuously with the purpose of ensuring maximum power extraction from the wind under the current wind and weather conditions, while at the same time ensuring that the loads on the different components of the wind turbine are at any time kept within acceptable limits. [0002]", "pre-setting a number of intervals of a wind power parameter, the wind power parameter determined as one of a power, a torque, or a blade load of the wind turbine; [0017]", "determining a control parameter of the wind turbine as a function of the adjusted relative wind direction; and [0024] controlling the wind turbine according to the control parameter."
It would have been obvious to one of ordinary skill in the art to modify the wind turbine controller of '861 to also control the operation of the wind turbine using a value of an operational parameter (which already disqualifies sensors and no longer uses them in their method), wherein the operational parameter is a setpoint for controlling the wind turbine, and wherein the setpoint comprises at least one of a patch angle of torque as taught by Nielsen, as both references are in the same field of endeavor, and one of ordinary skill would appreciate both, "Most modern wind turbines are controlled and regulated continuously with the purpose of ensuring maximum power extraction from the wind under the current wind and weather conditions, while at the same time ensuring that the loads on the different components of the wind turbine are at any time kept within acceptable limits. [0002]" and "pre-setting a number of intervals of a wind power parameter, the wind power parameter determined as one of a power, a torque, or a blade load of the wind turbine; [0017]."
Regarding claim 18, the combination of '861 and Nielsen teach all of claim 16 as above, wherein determining the plurality of sensors are reliable sensors and which of the plurality of sensors are unreliable sensors further comprises comparing two or more values directly indicative of the operational parameter based on measurements from different ones of the plurality of sensors with an actual value of the operational parameter ('861; claim 9).
Regarding claim 19, the combination of '861 and Nielsen teach all of claim 16 as above, further
comprising using the data related to the operational parameter from two or more sensors of the
plurality of sensors to determine which of the plurality of sensors are the reliable sensors and which of the plurality of sensors are the unreliable sensors ('861; [0041], [0047]).
Regarding claim 20, the combination of '861 and Nielsen teach all of claim 16 as above, further
comprising determining at least one intermediate value of the operational parameter based on the data
related to the operational parameter from at least one sensor of the plurality of sensors, and wherein
estimating the actual value of the operational parameter is based on the at least one intermediate value
('861; [0041], [0047]).
Regarding claim 21, the combination of '861 and Nielsen teach all of claim 16 as above, wherein
determining, using the mathematical model, the estimation of the actual value of the operational parameter based on the data directly indicative of the operational parameter further comprises combining, using the mathematical model, the data directly indicative of the operational parameter to determine the estimation of the actual value (‘861; [0041], [0047]).
Regarding claim 28, '861 discloses a wind turbine controller comprising a processor and a memory, wherein the memory comprises instructions that, when executed by the processor, cause the processor to (claim 10);
receive data related to an operational parameter from a plurality of sensor of the wind turbine (claim 1);
determine which sensors of the plurality of sensors are reliable and which sensors of the plurality of sensors are unreliable (claims 2, 3; [0039]);
determine data directly indicative of the operational parameter based on data related to the operational parameter received from the reliable sensors and not the unreliable sensors ([0039]);
determine, using the mathematical model, an estimation of an actual value of the operational parameter based on the data directly indicative of the operational parameter ([0039], “The results of the statistical value comparison and of the status determination is output to the decision unit 38 which combines the information to generate and output a status signal 60, a wind parameter value signal 62, and a quality and source signal 64.”);
correct, using the mathematical model, the estimation of the actual value with the data directly indicative of the operational parameter ([0039], “The results of the statistical value comparison and of the status determination is output to the decision unit 38 which combines the information to generate and output a status signal 60, a wind parameter value signal 62, and a quality and source signal 64.”);
However, ‘861 does not disclose, “determining a setpoint for controlling the wind turbine based on the corrected estimation of the actual value of the operational parameter, wherein the setpoint comprises at least one of a pitch angle and a torque; and
controlling the operation of the wind turbine using the setpoint by adjusting one or more components of the wind turbine so as to achieve the setpoint”
Nielsen teaches, in the field of controlling wind turbines, "Most modern wind turbines are controlled and regulated continuously with the purpose of ensuring maximum power extraction from the wind under the current wind and weather conditions, while at the same time ensuring that the loads on the different components of the wind turbine are at any time kept within acceptable limits. [0002]", "pre-setting a number of intervals of a wind power parameter, the wind power parameter determined as one of a power, a torque, or a blade load of the wind turbine; [0017]", "determining a control parameter of the wind turbine as a function of the adjusted relative wind direction; and [0024] controlling the wind turbine according to the control parameter."
It would have been obvious to one of ordinary skill in the art to modify the wind turbine controller of '861 to also control the operation of the wind turbine using a value of an operational parameter (which already disqualifies sensors and no longer uses them in their method), wherein the operational parameter is a setpoint for controlling the wind turbine, and wherein the setpoint comprises at least one of a patch angle of torque as taught by Nielsen, as both references are in the same field of endeavor, and one of ordinary skill would appreciate both, "Most modern wind turbines are controlled and regulated continuously with the purpose of ensuring maximum power extraction from the wind under the current wind and weather conditions, while at the same time ensuring that the loads on the different components of the wind turbine are at any time kept within acceptable limits. [0002]" and "pre-setting a number of intervals of a wind power parameter, the wind power parameter determined as one of a power, a torque, or a blade load of the wind turbine; [0017]."
Regarding claim 31, '861 discloses a method for estimating a value of an operational parameter
of a wind turbine, the method comprising:
measuring data related to the operational parameter with a plurality of sensors (claim 1-3);
determining which of the plurality of sensors are reliable sensors and which of the plurality of sensors are faulty or excessively noisy sensors (claim 1-3);
determine data directly indicative of the operational parameter based on data related to the operational parameter received from the reliable sensors and not the unreliable sensors ([0039]);
determine, using the mathematical model, an estimation of an actual value of the operational parameter based on the data directly indicative of the operational parameter ([0039], “The results of the statistical value comparison and of the status determination is output to the decision unit 38 which combines the information to generate and output a status signal 60, a wind parameter value signal 62, and a quality and source signal 64.”);
correct, using the mathematical model, the estimation of the actual value with the data directly indicative of the operational parameter ([0039], “The results of the statistical value comparison and of the status determination is output to the decision unit 38 which combines the information to generate and output a status signal 60, a wind parameter value signal 62, and a quality and source signal 64.”);
However, ‘861 does not disclose, “determining a setpoint for controlling the wind turbine based on the corrected estimation of the actual value of the operational parameter, wherein the setpoint comprises at least one of a pitch angle and a torque; and
controlling the operation of the wind turbine using the setpoint by adjusting one or more components of the wind turbine so as to achieve the setpoint”
Nielsen teaches, in the field of controlling wind turbines, "Most modern wind turbines are controlled and regulated continuously with the purpose of ensuring maximum power extraction from the wind under the current wind and weather conditions, while at the same time ensuring that the loads on the different components of the wind turbine are at any time kept within acceptable limits. [0002]", "pre-setting a number of intervals of a wind power parameter, the wind power parameter determined as one of a power, a torque, or a blade load of the wind turbine; [0017]", "determining a control parameter of the wind turbine as a function of the adjusted relative wind direction; and [0024] controlling the wind turbine according to the control parameter."
It would have been obvious to one of ordinary skill in the art to modify the wind turbine controller of '861 to also control the operation of the wind turbine using a value of an operational parameter (which already disqualifies sensors and no longer uses them in their method), wherein the operational parameter is a setpoint for controlling the wind turbine, and wherein the setpoint comprises at least one of a patch angle of torque as taught by Nielsen, as both references are in the same field of endeavor, and one of ordinary skill would appreciate both, "Most modern wind turbines are controlled and regulated continuously with the purpose of ensuring maximum power extraction from the wind under the current wind and weather conditions, while at the same time ensuring that the loads on the different components of the wind turbine are at any time kept within acceptable limits. [0002]" and "pre-setting a number of intervals of a wind power parameter, the wind power parameter determined as one of a power, a torque, or a blade load of the wind turbine; [0017]."
Regarding claim 32, the combination of '861 and Nielsen teach all of claim 31 as above, wherein at least two or more of the plurality of sensors are configured to determine the data directly indicative of the operational parameter ('861; Claim 1).
Regarding claim 33, the combination of '861 and Nielsen teach all of claim 31 as above, wherein
the determining the fault or excessively noisy sensors comprises determining that signals from the fault
or excessively noisy sensor sensors reach or exceed a noise threshold ('861; Claim 3).
Regarding claim 34, the combination of '861 and Nielsen teach all of claim 31 as above, wherein
the operational parameters is rotational speed ('861; [0037]).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Frank et al. (EP 3,772,652
A1; '652 hereinafter) and Nielsen (US PGPUB 2018/0363625 A1) as applied to claim 16 as above, and
in further view of Feldman et al. (US Patent 5,626,140 A).
Regarding claim 23, the combination of '652 and Nielsen teach all of claim 16 as above.
However, the combination of '652 and Nielsen do not teach or suggest, "wherein the mathematical model comprises at least a Kalman filter."
Feldman et al. discloses; in the field of conditioning noisy sensor data (e.g., "sensor fusion" Col.
3:38), using a Kalman filter circuit (24) comprising individual Kalman filters that are each supplied with
past fused estimate, the current reading, to produce a parameter estimate and confidence level of said parameter estimate, for generally "noisy data" such as heart rate (Col. 3:21-35).
It would have been obvious to one skill in the art before the effective filing date to modify the
wind turbine control system created by the combination of '652 and Nielsen to have a Kalman filter circuit as taught by Feldman et al., and one of ordinary would appreciate that "The system is also adaptive in that the statistical model can be updated following the selection of a parameter estimate by the confidence calculator. The statistical filter circuit may be a Kalman filter for each of the possible sensor combinations wherein the Kalman filters use the previous parameter estimate and the statistical model to produce the parameter estimates. (12) The system is also susceptible to artifact interference that causes a particular sensor measurement to be considered unacceptable. The confidence calculator analyzes the parameter estimates generated by the statistical filter circuit and determines a statistical probability of error of each of the parameter estimates caused by the artifactual measurement. Thus, the system can generate a fused estimate for sensors contaminated by a nominal error, as characterized by the statistical model, as well as sensor combinations in which a sensor is affected by artifact. The system may also combine the sensor error statistical model and the statistical probability of contamination by artifact to determine a parameter estimate. In one sensor combination, all sensor measurements are contaminated by artifact and considered to be unacceptable. The system calculates a minimum confidence level for this sensor combination and further selects the parameter estimate the highest probability of contamination by artifact while simultaneously having a minimum probability of the confidence level exceeding the calculated minimum confidence level (Col. 2:46-3:5)."
Claim 30 is rejected under 35 U.S.C. 103 as being unpatentable over Frank et al. (EP 3,772,652
A1; hereinafter) and Nielsen (US PGPUB 2018/0363625 A1) as applied to claim 28 as above, and in
further view of Feldman et al. (US Patent 5,626,140 A).
Regarding claim 30, the combination of '652 and Nielsen teach all of claim 28 as above.
However, the combination of ‘652 and Nielsen do not teach or suggest, “wherein the mathematical model comprises at least a Kalman filter.”
Feldman et al. discloses; in the field of conditioning noisy sensor data (e.g., "sensor fusion" Col.
3:38), using a Kalman filter circuit (24) comprising individual Kalman filters that are each supplied with
past fused estimate, the current reading, to produce a parameter estimate and confidence level of said parameter estimate, for generally "noisy data" such as heart rate (Col. 3:21-35).
It would have been obvious to one skill in the art before the effective filing date to modify the
wind turbine control system created by the combination of '652 and Nielsen to have a Kalman filter circuit as taught by Feldman et al., and one of ordinary would appreciate that "The system is also adaptive in that the statistical model can be updated following the selection of a parameter estimate by the confidence calculator. The statistical filter circuit may be a Kalman filter for each of the possible sensor combinations wherein the Kalman filters use the previous parameter estimate and the statistical model to produce the parameter estimates. (12) The system is also susceptible to artifact interference that causes a particular sensor measurement to be considered unacceptable. The confidence calculator analyzes the parameter estimates generated by the statistical filter circuit and determines a statistical probability of error of each of the parameter estimates caused by the artifactual measurement. Thus, the system can generate a fused estimate for sensors contaminated by a nominal error, as characterized by the statistical model, as well as sensor combinations in which a sensor is affected by artifact. The system may also combine the sensor error statistical model and the statistical probability of contamination by artifact to determine a parameter estimate. In one sensor combination, all sensor measurements are contaminated by artifact and considered to be unacceptable. The system calculates a minimum confidence level for this sensor combination and further selects the parameter estimate the highest probability of contamination by artifact while simultaneously having a minimum probability of the confidence level exceeding the calculated minimum confidence level (Col. 2:46-3:5)."
Claim 35 is rejected under 35 U.S.C. 103 as being unpatentable over Frank et al. (EP 3,772,652 A1; hereinafter) and Nielsen (US PGPUB 2018/0363625 A1) as applied to claim 31 as above, and in further view of Feldman et al. (US Patent 5,626,140 A).
Regarding claim 35, the combination of '652 and Nielsen teach all of claim 31 as above.
However, the combination of ‘652 and Nielsen do not teach or suggest, “wherein the mathematical model comprises at least a Kalman filter.”
Feldman et al. discloses; in the field of conditioning noisy sensor data (e.g., "sensor fusion" Col.
3:38), using a Kalman filter circuit (24) comprising individual Kalman filters that are each supplied with
past fused estimate, the current reading, to produce a parameter estimate and confidence level of said parameter estimate, for generally "noisy data" such as heart rate (Col. 3:21-35).
It would have been obvious to one skill in the art before the effective filing date to modify the
wind turbine control system created by the combination of '652 and Nielsen to have a Kalman filter circuit as taught by Feldman et al., and one of ordinary would appreciate that "The system is also adaptive in that the statistical model can be updated following the selection of a parameter estimate by the confidence calculator. The statistical filter circuit may be a Kalman filter for each of the possible sensor combinations wherein the Kalman filters use the previous parameter estimate and the statistical model to produce the parameter estimates. (12) The system is also susceptible to artifact interference that causes a particular sensor measurement to be considered unacceptable. The confidence calculator analyzes the parameter estimates generated by the statistical filter circuit and determines a statistical probability of error of each of the parameter estimates caused by the artifactual measurement. Thus, the system can generate a fused estimate for sensors contaminated by a nominal error, as characterized by the statistical model, as well as sensor combinations in which a sensor is affected by artifact. The system may also combine the sensor error statistical model and the statistical probability of contamination by artifact to determine a parameter estimate. In one sensor combination, all sensor measurements are contaminated by artifact and considered to be unacceptable. The system calculates a minimum confidence level for this sensor combination and further selects the parameter estimate the highest probability of contamination by artifact while simultaneously having a minimum probability of the confidence level exceeding the calculated minimum confidence level (Col. 2:46-3:5)."
Conclusion
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.
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/RYAN C CLARK/Examiner, Art Unit 3745
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