Notice of Pre-AIA or AIA Status
Claims 1-20 are currently presented for Examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. FR2211107, filed on 10/26/2022.
Claim Rejections - 35 USC §101
4. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. These claims are directed to an abstract idea without significantly more.
(Step 1) Is the claims to a process, machine, manufacture, or composition of matter?
Claims: 1-9 are directed to system or machine that falls on one of statutory category.
Claims: 10-15 are directed to method or process that falls on one of statutory category.
Claim: 16-20 is directed to a non-transitory computer-readable storage medium that falls on one of statutory category that is manufacture.
Step 2A Prong 1
Claim 1, 10 and 16 recites
a first estimator configured to receive a plurality of sensor measurements as input and to output a derived infinite bus (IB) value; (this limitation directed to a mathematical calculation (using sensor inputs to update state estimates). Estimator is a Kalman filter (see spec [0025-0027]) that uses recursive mathematical modeling (state space representation) to process sensor data. Because it is a method of processing data using mathematical equations. The infinite bus calculation system 62 calculates a "derived infinite bus value, such as an infinite bus voltage and/or frequency" based on inputs [0026]. Based on the specification provided and MPEP § 2106.04(a)(2) regarding abstract idea groupings, the described first estimator, single state estimator (Kalman filter), and infinite bus calculation system comprise mathematical concepts (abstract ideas) because they are directed to mathematical formulas, relationships, and calculations.)
and a second estimator disposed downstream of the first estimator and configured to switch between a plurality of models, wherein each of the plurality of models is configured to receive the derived IB value as input and to output a derived electric generator parameter, (A "second estimator" that switches between a "plurality of models" is, a selection of mathematical formulas (e.g., Kalman filters) used to calculate a result. Under MPEP § 2106.04(a)(2), this is considered a mathematical concept because it defines a system that takes in data (value) and applies mathematical models to calculate a new value)
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application. In particular, claim 1, 10 and 16 recites the additional elements of “receive a plurality of sensor measurement via a sensor network” it is simply "a computer/generic sensor receives data" without adding "meaningful limitations" (e.g., a specific improvement to the functioning of the computer, a specific algorithm that is not conventional, or a specific, non-generic data source), it is merely using a computer as a tool to perform the abstract process faster. See MPEP 2106.05(f)(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. The additional elements of a power generation system, comprising: an adaptive power system stabilizer (PSS) which are mere instructions to implement an abstract idea on a computer, or merely using a generic computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); The additional elements of wherein the adaptive PSS is configured to use the derived electric generator parameter to provide stabilization of an electric generator, is further adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) or generally linking the use of a judicial exception to a particular technological environment or field of use (power generation system), as discussed in MPEP § 2106.05(h). The claim merely applies mathematical estimation in the field of power generation. Thus, the additional elements merely apply the abstract idea in the field of power generation using generic components such as sensors and generators. The additional elements of a non-transitory computer-readable medium having computer executable code stored thereon, the code comprising instructions in claim 16 is mere instructions to implement an abstract idea on a computer, or merely using a generic computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
In view of Step 2B, the claim as a whole does not amount to significantly more than the recited exception,
i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim In particular, claim 1, 8 and 15 recites the additional elements of “receive a plurality of sensor measurement” it is simply "a computer/generic sensor receives data" without adding "meaningful limitations" (e.g., a specific improvement to the functioning of the computer, a specific algorithm that is not conventional, or a specific, non-generic data source), it is merely using a computer as a tool to perform the abstract process faster. See MPEP 2106.05(f)(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. The additional elements of a power generation system, comprising: an adaptive power system stabilizer (PSS) which are mere instructions to implement an abstract idea on a computer, or merely using a generic computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); The additional elements of wherein the adaptive PSS is configured to use the derived electric generator parameter to provide stabilization of an electric generator, is further adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) or generally linking the use of a judicial exception to a particular technological environment or field of use (power generation system), as discussed in MPEP § 2106.05(h). The claim merely applies mathematical estimation in the field of power generation. Thus, the additional elements merely apply the abstract idea in the field of power generation using generic components such as sensors and generators. The claim is directed to an abstract idea. The additional elements of a non-transitory computer-readable medium having computer executable code stored thereon, the code comprising instructions in claim 16 is mere instructions to implement an abstract idea on a computer, or merely using a generic computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); Thus, claim 1, 10 and 16 are not patent eligible.
Claim 2, 11 and 17 further recites wherein the second estimator comprises a first model included in the plurality of models, and wherein the first model is configured to model one or more internal states of the electric generator. The act of modeling "internal states of an electric generator" via this model (Kalman filter) constitutes a mathematical calculation or algorithm. It involves applying, for example, state-space equations (formula) to measured inputs to calculate a new state (calculation). Thus, it falls under the “Mathematical Concepts” of abstract ideas. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 3, 12 and 18 further recites wherein the one or more internal states comprise an angle δ between a generator electromagnetic field (EMF) and a reference voltage vector, an electric generator speed w; an electric generator internal voltage E', a flux in the electric generator, or a combination thereof. The act of modeling "internal states of an electric generator" via this model (Kalman filter) constitutes a mathematical calculation or algorithm. It involves applying, for example, state-space equations (formula) to measured inputs to calculate a new state (calculation). The claim recites "math" because it utilizes a "Kalman filter model" to manipulate data regarding voltage, speed, and flux, which MPEP 2106 identifies as a "mathematical concept". Thus, it falls under the “Mathematical Concepts” of abstract ideas. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 4 and 13 further recites wherein the first model is part of an Extended Kalman filter. It falls under the “Mathematical Concepts” of abstract ideas. The claim recites "math" because it utilizes a "Kalman filter model" to manipulate data regarding voltage, speed, and flux, which MPEP 2106 identifies as a "mathematical concept". Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 5, 14 and 19 further recites
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It falls under the “Mathematical Concepts” since it recites mathematical equation or formula of abstract ideas. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 6 and 14 further recites wherein the second estimator comprises a second model included in the plurality of models, and wherein the second model comprises the first model and an additional state variable. This limitation describes a method for creating a more complex model from a simpler one by adding a state variable. This is a mathematical manipulation designed to estimate system parameters, which falls under mathematical concepts. Thus, it falls under the “Mathematical Concepts” of abstract ideas. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 7 further recites wherein the additional state variable comprises one more internal variable. This limitation describes a method for creating a more complex model from a simpler one by adding a state variable. This is a mathematical manipulation designed to estimate system parameters, which falls under mathematical concepts. Thus, it falls under the “Mathematical Concepts” of abstract ideas. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 8, 15 and 20 further recites wherein the second estimator is configured to switch between the plurality of models either by waiting for a time to elapse and then switching, or by switching based on an error threshold, or a combination thereof. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. The process of switching between models based on an "error threshold" constitutes a mathematical, statistical, or logic-based algorithm. The steps involve calculating error, comparing it to a threshold, and switching calculations. These are considered mental processes or mathematical steps. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 9 further recites wherein the adaptive PSS is configured to use an automatic voltage regulator based on the derived electric generator parameter to provide stabilization of the electric generator. It is further adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) or generally linking the use of a judicial exception to a particular technological environment or field of use (power generation system), as discussed in MPEP § 2106.05(h). Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. It is merely using the calculated electric generator parameter without specify g how is the generation is physical controlled. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
5. Claim(s) 1-4, 8, 10-12, 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akhlaghi et al. ("A multi-model adaptive Kalman filtering approach to power system dynamic state estimation." 2019 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2019) in view of Takashi et al. (PUB NO: EP4007106A41)
Regarding claim 1
Akhlaghi teaches a power generation system, comprising: an adaptive power system stabilizer (PSS), (see abstract and fig 1-2-In this paper, a multi-model adaptive Kalman filtering (MMAKF) approach is proposed to accurately and robustly estimate power system dynamic states. See section IV and page 3-To evaluate the performance of the proposed MMAKF approach, the two-area four-machine system shown in Fig. 2 is used. Two sets of simulation data are generated to evaluate the estimation accuracy and robustness as follows. 1) Well-damped scenario is set up as a benchmark to compare the proposed approach with all the individual filters under the study. The simulation is performed for 480 s (i.e., 8 min). The fault is applied at 60.1 s. The power system stabilizers (PSSs) of all the generators are turned on.)
comprising: a first estimator configured to receive a plurality of sensor measurements as input; ( see Abstract- This approach consists of three major steps: (i) multiple Kalman filtering approaches, i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and cubature Kalman filter (CKF), are run concurrently in parallel to estimate the dynamic states of a synchronous generator using phasor measurement unit data; (ii)probability indexes, which quantify the likelihood of each estimation model, are determined at each time step using hypothesis testing based on the measurement innovation; (iii) the a posteriori estimate of states is obtained using the best-fix approach. See section II-To estimate the dynamic states, a general discrete-time state space model together with a measurement equation shown in (1) is used. Here, xk,uk, and zk are the state, known input and measurement vectors, respectively; Vectors wk and vk are the process and measurement noises. see section V-Assume that all the generation buses have PMUs. To simulate outliers, noise on the order of 15 times of the standard deviation of the voltage magnitudes and angles is added to the voltage phasor measurements between 31.0 and 31.5 s. The power system stabilizers (PSSs) of all the generators are turned on.)
and a second estimator disposed downstream of the first estimator ( see Abstract and fig 1- This approach consists of three major steps: (i) multiple Kalman filtering approaches, i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and cubature Kalman filter (CKF), are run concurrently in parallel to estimate the dynamic states of a synchronous generator using phasor measurement unit data; (ii)probability indexes, which quantify the likelihood of each estimation model, are determined at each time step using hypothesis testing based on the measurement innovation; (iii) the a posteriori estimate of states is obtained using the best-fix approach. See Introduction- To achieve the goal, four KF approaches, i.e., EKF, UKF, EnKF, and CKF, are run concurrently in parallel to estimate the dynamic states of a synchronous machine. Then, probability indexes, which quantify the probability of each estimation filter, are determined at each time step using hypothesis testing through the multi-model adaptive estimation (MMAE) algorithm based on the measurement innovation. The conditional probability is estimated and used to quantify the probability of each filter. Finally, using the best-fix approach, the estimated states from the EKF, UKF, EnKF, and CKF approaches with the highest probability index are selected to determine the a posteriori estimates of the states. The goal is to achieve high accuracy and robustness in estimating the dynamic states.)
and configured to switch between a plurality of models, wherein each of the plurality of models is configured to receive the derived IB value as input and to output a derived electric generator parameter, and wherein the adaptive PSS is configured to use the derived electric generator parameter to provide stabilization of an electric generator. (see abstract and see fig 1- Accurate information about dynamic states (such as rotor angle and speed of a synchronous machine) is important for monitoring and controlling power system rotor-angle stability. In this paper, a multi-model adaptive Kalman filtering (MMAKF) approach is proposed to accurately and robustly estimate power system dynamic states. This approach consists of three major steps: (i) multiple Kalman filtering approaches, i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and cubature Kalman filter (CKF), are run concurrently in parallel to estimate the dynamic states of a synchronous generator using phasor measurement unit data. (iii) the a posteriori estimate of states is obtained using the best-fix approach. See Introduction- Then, probability indexes, which quantify the probability of each estimation filter, are determined at each time step using hypothesis testing through the multi-model adaptive estimation (MMAE) algorithm based on the measurement innovation. The conditional probability is estimated and usednto quantify the probability of each filter. Finally, using the best-fix approach, the estimated states from the EKF, UKF, EnKF, and CKF approaches with the highest probability index are selected to determine the a posteriori estimates of the states. The goal is to achieve high accuracy and robustness in estimating the dynamic states. See section II- Here, xk,uk, and zk are the state, known input and measurement vectors, respectively; see section V-Assume that all the generation buses have PMUs. To simulate outliers, noise on the order of 15 times of the standard deviation of the voltage magnitudes and angles is added to the voltage phasor measurements between 31.0 and 31.5 s. The power system stabilizers (PSSs) of all the generators are turned on.)
Akhlaghi does not teach output a derived infinite bus (IB) value.
In the related field of invention, Takashi further teaches to output a derived infinite bus (IB) value; (see para 16-As illustrated in FIG. 1, the distributed power source system 2 includes a power conversion device 10, a distributed power source 6, and an electric power system 4 connected to an infinite bus power system 3. The electrical power of the electric power system 4 is alternating current power. The electrical power of the electric power system 4 is, for example, three-phase alternating current power. See para 28-Based on the active power value P, the reactive power value Q, and the voltage value Vs input from the measuring device 22, the estimated value calculator 50 calculates an estimated value ^ R of a resistance component R of the system impedance of the electric power system 4, an estimated value ^ X of a reactance component X of the system impedance of the electric power system 4, and an estimated value ^ Vr of a voltage value Vr of the infinite bus power system 3.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Multi-Model Adaptive Kalman Filtering Approach to Power System Dynamic State Estimation as disclosed by Akhlaghi to include to output a derived infinite bus (IB) value as taught by Takashi in the system of Akhlaghi in order to compensate for the voltage fluctuation at the interconnection point of the distributed power supply, thus controlling the voltage at the interconnection point of a distributed power supply to a specified value. (see Abstract, Takashi)
Regarding claim 10
Akhlaghi teaches a method, comprising: procuring, via a sensor network, a plurality of sensor measurements; ( see Abstract- This approach consists of three major steps: (i) multiple Kalman filtering approaches, i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and cubature Kalman filter (CKF), are run concurrently in parallel to estimate the dynamic states of a synchronous generator using phasor measurement unit data; (ii)probability indexes, which quantify the likelihood of each estimation model, are determined at each time step using hypothesis testing based on the measurement innovation; (iii) the a posteriori estimate of states is obtained using the best-fix approach. See section II-To estimate the dynamic states, a general discrete-time state space model together with a measurement equation shown in (1) is used. Here, xk,uk, and zk are the state, known input and measurement vectors, respectively; Vectors wk and vk are the process and measurement noises. see section V-Assume that all the generation buses have PMUs. To simulate outliers, noise on the order of 15 times of the standard deviation of the voltage magnitudes and angles is added to the voltage phasor measurements between 31.0 and 31.5 s. The power system stabilizers (PSSs) of all the generators are turned on.)
deriving, via a second estimator disposed downstream of the first estimator ( see Abstract and fig 1- This approach consists of three major steps: (i) multiple Kalman filtering approaches, i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and cubature Kalman filter (CKF), are run concurrently in parallel to estimate the dynamic states of a synchronous generator using phasor measurement unit data; (ii)probability indexes, which quantify the likelihood of each estimation model, are determined at each time step using hypothesis testing based on the measurement innovation; (iii) the a posteriori estimate of states is obtained using the best-fix approach. See Introduction- To achieve the goal, four KF approaches, i.e., EKF, UKF, EnKF, and CKF, are run concurrently in parallel to estimate the dynamic states of a synchronous machine. Then, probability indexes, which quantify the probability of each estimation filter, are determined at each time step using hypothesis testing through the multi-model adaptive estimation (MMAE) algorithm based on the measurement innovation. The conditional probability is estimated and used to quantify the probability of each filter. Finally, using the best-fix approach, the estimated states from the EKF, UKF, EnKF, and CKF approaches with the highest probability index are selected to determine the a posteriori estimates of the states. The goal is to achieve high accuracy and robustness in estimating the dynamic states.)
a derived electric generator parameter, wherein the second estimator is configured to switch between a plurality of models, and wherein each of the plurality of models is configured to use the IB value as input to output the derived electric generator parameter; and stabilizing an electric generator via an adaptive power system stabilizer (PSS) based on the derived electric generator parameter. (see abstract and see fig 1- Accurate information about dynamic states (such as rotor angle and speed of a synchronous machine) is important for monitoring and controlling power system rotor-angle stability. In this paper, a multi-model adaptive Kalman filtering (MMAKF) approach is proposed to accurately and robustly estimate power system dynamic states. This approach consists of three major steps: (i) multiple Kalman filtering approaches, i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and cubature Kalman filter (CKF), are run concurrently in parallel to estimate the dynamic states of a synchronous generator using phasor measurement unit data. (iii) the a posteriori estimate of states is obtained using the best-fix approach. See Introduction- Then, probability indexes, which quantify the probability of each estimation filter, are determined at each time step using hypothesis testing through the multi-model adaptive estimation (MMAE) algorithm based on the measurement innovation. The conditional probability is estimated and usednto quantify the probability of each filter. Finally, using the best-fix approach, the estimated states from the EKF, UKF, EnKF, and CKF approaches with the highest probability index are selected to determine the a posteriori estimates of the states. The goal is to achieve high accuracy and robustness in estimating the dynamic states. See section II- Here, xk,uk, and zk are the state, known input and measurement vectors, respectively; see section V-Assume that all the generation buses have PMUs. To simulate outliers, noise on the order of 15 times of the standard deviation of the voltage magnitudes and angles is added to the voltage phasor measurements between 31.0 and 31.5 s. The power system stabilizers (PSSs) of all the generators are turned on.)
Akhlaghi does not teach deriving, via a first estimator, an infinite bus (IB) value; wherein the first estimator is configured to use the plurality of sensor measurements as input to output the IB value;
In the related field of invention, Takashi further teaches deriving, via a first estimator, an infinite bus (IB) value; wherein the first estimator is configured to use the plurality of sensor measurements as input to output the IB value; (see para 16-As illustrated in FIG. 1, the distributed power source system 2 includes a power conversion device 10, a distributed power source 6, and an electric power system 4 connected to an infinite bus power system 3. The electrical power of the electric power system 4 is alternating current power. The electrical power of the electric power system 4 is, for example, three-phase alternating current power. See para 28-Based on the active power value P, the reactive power value Q, and the voltage value Vs input from the measuring device 22, the estimated value calculator 50 calculates an estimated value ^ R of a resistance component R of the system impedance of the electric power system 4, an estimated value ^ X of a reactance component X of the system impedance of the electric power system 4, and an estimated value ^ Vr of a voltage value Vr of the infinite bus power system 3.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Multi-Model Adaptive Kalman Filtering Approach to Power System Dynamic State Estimation as disclosed by Akhlaghi to include deriving, via a first estimator, an infinite bus (IB) value; wherein the first estimator is configured to use the plurality of sensor measurements as input to output the IB value as taught by Takashi in the system of Akhlaghi in order to compensate for the voltage fluctuation at the interconnection point of the distributed power supply, thus controlling the voltage at the interconnection point of a distributed power supply to a specified value. (see Abstract, Takashi)
Regarding claim 16
Akhlaghi teaches a non-transitory computer-readable medium having computer executable code stored thereon, (see section V-Power system toolbox) the code comprising instructions to: procuring, via a sensor network, a plurality of sensor measurements; ( see Abstract- This approach consists of three major steps: (i) multiple Kalman filtering approaches, i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and cubature Kalman filter (CKF), are run concurrently in parallel to estimate the dynamic states of a synchronous generator using phasor measurement unit data; (ii)probability indexes, which quantify the likelihood of each estimation model, are determined at each time step using hypothesis testing based on the measurement innovation; (iii) the a posteriori estimate of states is obtained using the best-fix approach. See section II-To estimate the dynamic states, a general discrete-time state space model together with a measurement equation shown in (1) is used. Here, xk,uk, and zk are the state, known input and measurement vectors, respectively; Vectors wk and vk are the process and measurement noises. see section V-Assume that all the generation buses have PMUs. To simulate outliers, noise on the order of 15 times of the standard deviation of the voltage magnitudes and angles is added to the voltage phasor measurements between 31.0 and 31.5 s. The power system stabilizers (PSSs) of all the generators are turned on.)
derive, via a second estimator disposed downstream of the first estimator ( see Abstract and fig 1- This approach consists of three major steps: (i) multiple Kalman filtering approaches, i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and cubature Kalman filter (CKF), are run concurrently in parallel to estimate the dynamic states of a synchronous generator using phasor measurement unit data; (ii)probability indexes, which quantify the likelihood of each estimation model, are determined at each time step using hypothesis testing based on the measurement innovation; (iii) the a posteriori estimate of states is obtained using the best-fix approach. See Introduction- To achieve the goal, four KF approaches, i.e., EKF, UKF, EnKF, and CKF, are run concurrently in parallel to estimate the dynamic states of a synchronous machine. Then, probability indexes, which quantify the probability of each estimation filter, are determined at each time step using hypothesis testing through the multi-model adaptive estimation (MMAE) algorithm based on the measurement innovation. The conditional probability is estimated and used to quantify the probability of each filter. Finally, using the best-fix approach, the estimated states from the EKF, UKF, EnKF, and CKF approaches with the highest probability index are selected to determine the a posteriori estimates of the states. The goal is to achieve high accuracy and robustness in estimating the dynamic states.)
a derived electric generator parameter, wherein the second estimator is configured to switch between a plurality of models, and wherein each of the plurality of models is configured to use the IB value as input to output the derived electric generator parameter; and stabilizing an electric generator via an adaptive power system stabilizer (PSS) based on the derived electric generator parameter. (see abstract and see fig 1- Accurate information about dynamic states (such as rotor angle and speed of a synchronous machine) is important for monitoring and controlling power system rotor-angle stability. In this paper, a multi-model adaptive Kalman filtering (MMAKF) approach is proposed to accurately and robustly estimate power system dynamic states. This approach consists of three major steps: (i) multiple Kalman filtering approaches, i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and cubature Kalman filter (CKF), are run concurrently in parallel to estimate the dynamic states of a synchronous generator using phasor measurement unit data. (iii) the a posteriori estimate of states is obtained using the best-fix approach. See Introduction- Then, probability indexes, which quantify the probability of each estimation filter, are determined at each time step using hypothesis testing through the multi-model adaptive estimation (MMAE) algorithm based on the measurement innovation. The conditional probability is estimated and usednto quantify the probability of each filter. Finally, using the best-fix approach, the estimated states from the EKF, UKF, EnKF, and CKF approaches with the highest probability index are selected to determine the a posteriori estimates of the states. The goal is to achieve high accuracy and robustness in estimating the dynamic states. See section II- Here, xk,uk, and zk are the state, known input and measurement vectors, respectively; see section V-Assume that all the generation buses have PMUs. To simulate outliers, noise on the order of 15 times of the standard deviation of the voltage magnitudes and angles is added to the voltage phasor measurements between 31.0 and 31.5 s. The power system stabilizers (PSSs) of all the generators are turned on.)
Akhlaghi does not teach derive, via a first estimator, an infinite bus (IB) value; wherein the first estimator is configured to use the plurality of sensor measurements as input to output the IB value;
In the related field of invention, Takashi further teaches derive, via a first estimator, an infinite bus (IB) value; wherein the first estimator is configured to use the plurality of sensor measurements as input to output the IB value; (see para 16-As illustrated in FIG. 1, the distributed power source system 2 includes a power conversion device 10, a distributed power source 6, and an electric power system 4 connected to an infinite bus power system 3. The electrical power of the electric power system 4 is alternating current power. The electrical power of the electric power system 4 is, for example, three-phase alternating current power. See para 28-Based on the active power value P, the reactive power value Q, and the voltage value Vs input from the measuring device 22, the estimated value calculator 50 calculates an estimated value ^ R of a resistance component R of the system impedance of the electric power system 4, an estimated value ^ X of a reactance component X of the system impedance of the electric power system 4, and an estimated value ^ Vr of a voltage value Vr of the infinite bus power system 3.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Multi-Model Adaptive Kalman Filtering Approach to Power System Dynamic State Estimation as disclosed by Akhlaghi to include derive, via a first estimator, an infinite bus (IB) value; wherein the first estimator is configured to use the plurality of sensor measurements as input to output the IB value as taught by Takashi in the system of Akhlaghi in order to compensate for the voltage fluctuation at the interconnection point of the distributed power supply, thus controlling the voltage at the interconnection point of a distributed power supply to a specified value. (see Abstract, Takashi)
Regarding claim 2, 11 and 17
Akhlaghi and Takashi teach the power generation system of claim 1. Akhlaghi and Takashi teach the method of claim 10. Akhlaghi, Takashi and Marchi teach the non-transitory computer-readable medium of claim 16. Akhlaghi further teaches wherein the second estimator comprises a first model included in the plurality of models, and wherein the first model is configured to model one or more internal states of the electric generator. (see section II-To estimate the dynamic states, a general discrete-time state space model together with a measurement equation shown in (1) is used. Here, xk,uk, and zk are the state, known input and measurement vectors, respectively; Vectors wk and vk are the process and measurement noises. see section IV (iii) adoptive estimation-This step combines the estimation results from different filtering approaches using the multiple-hypothesis testing. To perform the multiple hypothesis testing, three commonly used approaches are the best-fix, weighted fix and multi-hypothesis filtering. The best-fix approach accepts the estimated states with the highest probability score at each step of time and rejects the others. This approach is simple and can be effective when one hypothesis is clearly dominant on most iterations. Fig. 1 shows the best-fix approach used in this paper. In this step, the measurement innovation associated with each filter is utilized to calculate the probability indexes corresponding to each filter (i.e., and EKF UKF EnKF CKF). Here, for example, EKF stands for the probability index corresponding to the EKF and so on. To estimate the states using the four KFs, the a posteriori estimated states corresponding to the highest probability index are selected.)
Regarding claim 3, 12 and 18
Akhlaghi and Takashi teach the power generation system of claim 2. Akhlaghi and Takashi teach the method of claim 11. Akhlaghi, Takashi and Marchi teach the non-transitory computer-readable medium of claim 17. Akhlaghi further teaches wherein the one or more internal states comprise an angle δ between a generator electromagnetic field (EMF) and a reference voltage vector, an electric generator speed w; an electric generator internal voltage E', a flux in the electric generator, or a combination thereof. (See Abstract-Accurate information about dynamic states (such as rotor angle and speed of a synchronous machine) is important for monitoring and controlling power system rotor-angle stability)
Regarding claim 4
Akhlaghi and Takashi teach the power generation system of claim 2. Akhlaghi further teaches wherein the first model is part of an Extended Kalman filter. (see abstract and see fig 1- In this paper, a multi-model adaptive Kalman filtering (MMAKF) approach is proposed to accurately and robustly estimate power system dynamic states. This approach consists of three major steps: (i) multiple Kalman filtering approaches, i.e., the extended Kalman filter (EKF))
Regarding claim 8 and 15
Akhlaghi and Takashi teach the power generation system of claim 2. Akhlaghi and Takashi teach the method of claim 11. Akhlaghi further teaches wherein the second estimator is configured to switch between the plurality of models either by waiting for a time to elapse and then switching, or by switching based on an error threshold, or a combination thereof. (see page 3 and section V- To estimate the states using the four KFs, the a posteriori estimated states corresponding to the highest probability index are selected. Based on the MC simulation, the mean squared error (MSE) is defined in (8) to quantify the estimation errors at the kth time-step. The Mmse is defined in (9) to quantify the overall estimation errors. The estimated states of generator 1 using the EKF, UKF, EnKF, CKF, and MMAKF approaches with 200 MC simulations are compared in Fig. 3. The results of other generators have similar trends and are not shown here to be concise. The normalized mMSE (n-mMSE) is defined by (10) to quantify the estimation error of all the states. See Section VI-The probability indexes corresponding to the EKF, UKF, EnKF, and CKF are shown in Fig. 4. From Fig. 4, it can be seen that during the steady-state responses (before 60 s), the four estimation approaches have probability indexes around 0.25, which indicates that their levels of measurement innovation are similar. During the transient responses (zoomin plot (I) in Fig. 4), the probability index corresponding to the UKF dominates the other filters; and after a while, the EnKF is dominant. Accordingly, at each time step, the filter with the highest probability index should produce the estimates with the smallest estimation error. For example, in zoom-in plot (I) in Fig. 4, the UKF has the highest probability index, and its corresponding estimate has the smallest estimation errors. To reveal the overall estimation accuracy, the n-mMSEs of the five approaches are summarized in Fig. 5. Fig. 5 (a) compares the n-mMSEs of the five approaches. From Fig. 5 (a), it can be observed that the proposed MMAKF approach has the lowest n-mMSE among all the five approaches) (zoom-in plot (I) in Fig. 3).
6. Claim(s) 5, 9, 13 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akhlaghi et al. ("A multi-model adaptive Kalman filtering approach to power system dynamic state estimation." 2019 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2019) in view of Takashi et al. "(PUB NO: EP4007106A41) and further in view of Marchi et al. ("Location Method for Forced Oscillation Sources Caused by Synchronous Generators." arXiv preprint arXiv:2110.02692 (2021).)
Regarding claim 5, 13 and 19
Akhlaghi and Takashi teach the power generation system of claim 2. Akhlaghi and Takashi teach the method of claim 11. Akhlaghi, Takashi and Marchi teach the non-transitory computer-readable medium of claim 17. Akhlaghi further teaches wherein the Extended Kalman filter comprises a multi-state Kalman filter modeling X[k + 1] = fState(X[k],u[k]) +p[k], Z[k + 1]= hMeasure(X[k + 1], u[k]) + v[k], µ is a model noise and v is a sensor noise of one or more sensors in the sensor network. (see section II)
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The combination of Akhlaghi and Takashi does not teach u = [Efd; Pmec; IB]T where Efd is an electric generator field voltage, Pmec is a mechanical power of a turbine mechanically coupled to the electric generator, IB is the network voltage value.
In the related field of invention, Marchi teaches u =[Efd; Pmec; IB]Twhere Efd is an electric generator field voltage, Pmec is a mechanical power of a turbine mechanically coupled to the electric generator, IB is the network voltage value (see page 1, 4 and fig 1, 3)
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Examiner note: Marchi teaches the state transition equation (1) and measurement model (equation 2). Marchi further teaches unknown input vector d= [Pmech, Efd]. Marchi further teaches bus voltages phasors measured by PMUs see fig 3 at the generator connection point.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Multi-Model Adaptive Kalman Filtering Approach to Power System Dynamic State Estimation as disclosed by Akhlaghi and Takashi to include u =[Efd; Pmec; IB]Twhere Efd is an electric generator field voltage, Pmec is a mechanical power of a turbine mechanically coupled to the electric generator, IB is the network voltage value as taught by Marchi in the system of Akhlaghi and Takashi in order to identify which generator is causing the FO(force oscillations). After that, the identification of the control loop associated with synchronous generator is required. Another motivation to develop a new procedure which uses the information that the dynamic state estimation techniques are capable of obtaining and combine it with a dissipating energy flow method to find the oscillation source and evaluate the performance on simulated data considering different scenarios. (see Abstract, Marchi)
Regarding claim 9
Akhlaghi and Takashi teach the power generation system of claim 8. The combination of Akhlaghi and Takashi does not teach wherein the adaptive PSS is configured to use an automatic voltage regulator based on the derived electric generator parameter to provide stabilization of the electric generator.
In the related field of invention, Marchi teaches wherein the adaptive PSS is configured to use an automatic voltage regulator based on the derived electric generator parameter to provide stabilization of the electric generator. (See abstract and page 1, fig 1- The measurements that the phasor measurement units (PMUs) provide are used to estimate the internal states of different generators connected to the same bus. Fig. 1 shows the general structure of a power plant. The system consists of a synchronous generator, a turbine governor (TG), an automatic voltage regulator (AVR) and a power system stabilizer (PSS). Here, we consider that a phasor measurement unit (PMU) is installed at the connection point and that it provides high quality magnitude and phase measurements to monitor the generator unit.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Multi-Model Adaptive Kalman Filtering Approach to Power System Dynamic State Estimation as disclosed by Akhlaghi and Takashi to include wherein the adaptive PSS is configured to use an automatic voltage regulator based on the derived electric generator parameter to provide stabilization of the electric generator as taught by Marchi in the system of Akhlaghi and Takashi in order to identify which generator is causing the FO(force oscillations). After that, the identification of the control loop associated with synchronous generator is required. Another motivation to develop a new procedure which uses the information that the dynamic state estimation techniques are capable of obtaining and combine it with a dissipating energy flow method to find the oscillation source and evaluate the performance on simulated data considering different scenarios. (see Abstract, Marchi)
7. Claim(s) 6-7 and 14 and is/are rejected under 35 U.S.C. 103 as being unpatentable over Akhlaghi et al. ("A multi-model adaptive Kalman filtering approach to power system dynamic state estimation." 2019 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2019) in view of Takashi et al. "(PUB NO: EP4007106A41) and further in view of Maybeck et al. ("Performance enhancement of a multiple model adaptive estimator." IEEE Transactions on Aerospace and Electronic Systems 31.4 (2002): 1240-1254. (Year: 2002))
Regarding claim 6 and 14
Akhlaghi and Takashi teach the power generation system of claim 2. Akhlaghi and Takashi teach the method of claim 11.. Akhlaghi further teaches wherein the second estimator comprises a second model included in the plurality of models. (see abstract and fig 1-In this paper, a multi-model adaptive Kalman filtering (MMAKF) approach is proposed to accurately and robustly estimate power system dynamic states. This approach consists of three major steps: (i) multiple Kalman filtering approaches, i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and cubature Kalman filter (CKF). See section II-To estimate the dynamic states, a general discrete-time state space model together with a measurement equation shown in (1) is used. Here, xk,uk, and zk are the state, known input and measurement vectors, respectively; Vectors wk and vk are the process and measurement noises. See Section III-Assuming linearity, the KF provides unbiased minimum variance estimates of states through a recursive approach.)
The combination of Akhlaghi and Takashi does not teach wherein the second model comprises the first model and an additional state variable.
In the related field of invention, Marchi teaches wherein the second model comprises the first model and an additional state variable. (see page 1241 and equation 4-5)
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Examiner note: The final, updated state estimate (see equation 5) referred to as the "second model" is a refined version of the propagated state estimate (the "first model" see equation 4), incorporating new data. Both are state equations representing KF models.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Multi-Model Adaptive Kalman Filtering Approach to Power System Dynamic State Estimation as disclosed by Akhlaghi and Takashi to include wherein the adaptive PSS is configured to use an automatic voltage regulator based on the derived electric generator parameter to provide stabilization of the electric generator as taught by Marchi in the system of Akhlaghi and Takashi in order to describe performance improvement techniques for a multiple model adaptive estimator (MMAE) used to detect and identify control surface and sensor failures on an unmanned flight vehicle. Initially, failure identification was accomplished within 4 s of onset, but by removing the dominance” effects, bounding the hypothesis conditional probabilities, retuning the Kalman filters, increasing the penalty for measurement residuals, decreasing the probability smoothing, and increasing residual propagation, the identification time was reduced to 2 s. (see Abstract, Maybeck)
Regarding claim 7
Akhlaghi, Takashi and Maybeck teach the power generation system of claim 6. Akhlaghi further teaches wherein the additional state variable comprises one more internal variable. (see abstract and fig 1- Accurate information about dynamic states (such as rotor angle and speed of a synchronous machine) is important for monitoring and controlling power system rotor-angle stability. In this paper, a multi-model adaptive Kalman filtering (MMAKF) approach is proposed to accurately and robustly estimate power system dynamic states. This approach consists of three major steps: (i) multiple Kalman filtering approaches, i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and cubature Kalman filter (CKF). See section II-To estimate the dynamic states, a general discrete-time state space model together with a measurement equation shown in (1) is used. Here, xk,uk, and zk are the state, known input and measurement vectors, respectively; Vectors wk and vk are the process and measurement noises)
8. Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akhlaghi et al. ("A multi-model adaptive Kalman filtering approach to power system dynamic state estimation." 2019 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2019) in view of Takashi et al. "(PUB NO: EP4007106A41) and further in view of Marchi et al. ("Location Method for Forced Oscillation Sources Caused by Synchronous Generators." arXiv preprint arXiv:2110.02692 (2021).) and further in view of Maybeck et al. ("Performance enhancement of a multiple model adaptive estimator." IEEE Transactions on Aerospace and Electronic Systems 31.4 (2002): 1240-1254. (Year: 2002))
Regarding claim 20
Akhlaghi, Takashi and Marchi teach the non-transitory computer-readable medium of claim 19. Akhlaghi further teaches wherein the second estimator comprises a second model included in the plurality of models. (see abstract and fig 1-In this paper, a multi-model adaptive Kalman filtering (MMAKF) approach is proposed to accurately and robustly estimate power system dynamic states. This approach consists of three major steps: (i) multiple Kalman filtering approaches, i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and cubature Kalman filter (CKF). See section II-To estimate the dynamic states, a general discrete-time state space model together with a measurement equation shown in (1) is used. Here, xk,uk, and zk are the state, known input and measurement vectors, respectively; Vectors wk and vk are the process and measurement noises. See Section III-Assuming linearity, the KF provides unbiased minimum variance estimates of states through a recursive approach.)
wherein the second estimator is configured to switch between the plurality of models either by waiting for a time to elapse and then switching, or by switching based on an error threshold, or a combination thereof. (see page 3 and section V- To estimate the states using the four KFs, the a posteriori estimated states corresponding to the highest probability index are selected. Based on the MC simulation, the mean squared error (MSE) is defined in (8) to quantify the estimation errors at the kth time-step. The Mmse is defined in (9) to quantify the overall estimation errors. The estimated states of generator 1 using the EKF, UKF, EnKF, CKF, and MMAKF approaches with 200 MC simulations are compared in Fig. 3. The results of other generators have similar trends and are not shown here to be concise. The normalized mMSE (n-mMSE) is defined by (10) to quantify the estimation error of all the states. See Section VI-The probability indexes corresponding to the EKF, UKF, EnKF, and CKF are shown in Fig. 4. From Fig. 4, it can be seen that during the steady-state responses (before 60 s), the four estimation approaches have probability indexes around 0.25, which indicates that their levels of measurement innovation are similar. During the transient responses (zoomin plot (I) in Fig. 4), the probability index corresponding to the UKF dominates the other filters; and after a while, the EnKF is dominant. Accordingly, at each time step, the filter with the highest probability index should produce the estimates with the smallest estimation error. For example, in zoom-in plot (I) in Fig. 4, the UKF has the highest probability index, and its corresponding estimate has the smallest estimation errors. To reveal the overall estimation accuracy, the n-mMSEs of the five approaches are summarized in Fig. 5. Fig. 5 (a) compares the n-mMSEs of the five approaches. From Fig. 5 (a), it can be observed that the proposed MMAKF approach has the lowest n-mMSE among all the five approaches) (zoom-in plot (I) in Fig. 3).
The combination of Akhlaghi , Marchi and Takashi does not teach wherein the second model comprises the first model and an additional state variable.
In the related field of invention, Marchi teaches wherein the second model comprises the first model and an additional state variable. (see page 1241 and equation 4-5)
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Examiner note: The final, updated state estimate (see equation 5) referred to as the "second model" is a refined version of the propagated state estimate (the "first model" see equation 4), incorporating new data. Both are state equations representing KF models.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Multi-Model Adaptive Kalman Filtering Approach to Power System Dynamic State Estimation as disclosed by Akhlaghi, Marchi and Takashi to include wherein the adaptive PSS is configured to use an automatic voltage regulator based on the derived electric generator parameter to provide stabilization of the electric generator as taught by Maybeck in the system of Akhlaghi, Takashi and Marchi in order to describe performance improvement techniques for a multiple model adaptive estimator (MMAE) used to detect and identify control surface and sensor failures on an unmanned flight vehicle. Initially, failure identification was accomplished within 4 s of onset, but by removing the dominance” effects, bounding the hypothesis conditional probabilities, retuning the Kalman filters, increasing the penalty for measurement residuals, decreasing the probability smoothing, and increasing residual propagation, the identification time was reduced to 2 s. (see Abstract, Maybeck)
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang et al. US 20200379424 A1
ii. Discussing the method for enhanced power system model validation is provided. The system includes a computing device including at least one processor in communication with at least one memory device. The at least one processor is programmed to store a plurality of models for a plurality of devices and a plurality of input files associated with the plurality of models, receive, from a user, a selection of model of the plurality of models to simulate, retrieve one or more input files of the plurality of input files, perform a model validity check on the selected model, if the selected model passed the model validity check, perform a model calibration on the selected model, and if the selected model passed the model calibration, perform a post evaluation on the selected model.
10. All claims 1-20 are rejected.
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PURSOTTAM GIRI whose telephone number is (469)295-9101. The examiner can normally be reached 7:30-5:30 PM, Monday to Friday.
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/PURSOTTAM GIRI/
Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186