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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/07/2026 has been considered. The submission is in compliance with the provisions of 37 CFR 1.97. Form PTO-1449 is signed and attached hereto.
Response to Amendment
The amendment filed on 06/24/2026 has been entered and considered by the examiner. By the
amendment, claims 1-3, 6-12 and 14-20. Following Applicants arguments and amendments made, Examiner modify the prior art rejections. And the 101 rejection is withdrawn since the newly added limitation “the adaptive PSS is configured to adjust an automatic voltage regulator (AVR) coupled to an electric generator to inject a control signal to the electric generator based on the derived electric generator parameter to dampen oscillations of the electric generator." solve a technical problem of oscillations of an electric generator [See Spec [0015-0016]). See office action.
Response to Applicant 103 arguments
Following Applicants arguments and amendments, the 103 rejections of the claims is Modified. New
reference YOUSEF, Shao and Wang are added, and the Maybeck reference is withdrawn. See updated 103 below that is necessitated by applicant’s amendment.
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, 6-7, 10-12, 14, 16, 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) and further in view of YOUSEF et al. ("Improved power system stabilizer by applying LQG controller." Advances in Electrical and Computer Engineering, Proceeding of 17th international conference on automatic control modeling and simulation. 2015.)
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.)
( 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, and each of the plurality of models is configured to receive the derived IB value as input and to output a 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 a first estimator configured to receive a plurality of sensor measurements as input and to output a derived infinite bus (IB) value; a plurality of models having different sets of state variables to vary a complexity of the plurality of models, wherein the plurality of models comprise a first model having a first set of state variables and a second model having a second set of state variables at least partially different from the first set of state variables and wherein the adaptive PSS is configured to adjust an automatic voltage regulator (AVR) coupled to an electric generator to inject a control signal to the electric generator based on the derived electric generator parameter to dampen oscillations of electric generator.
In the related field of invention, Takashi further teaches a first estimator configured to receive a plurality of sensor measurements as input and 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 and fig 2-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 a first estimator configured to receive a plurality of sensor measurements as input and 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)
The combination of Akhlaghi and Takashi does not teach a plurality of models having different sets of state variables to vary a complexity of the plurality of models, wherein the plurality of models comprise a first model having a first set of state variables and a second model having a second set of state variables at least partially different from the first set of state variables and wherein the adaptive PSS is configured to adjust an automatic voltage regulator (AVR) coupled to an electric generator to inject a control signal to the electric generator based on the derived electric generator parameter to dampen oscillations of the electric generator.
In the related field of invention, YOUSEF teaches a plurality of models having different sets of state variables to vary a complexity of the plurality of models, wherein the plurality of models comprise a first model having a first set of state variables and a second model having a second set of state variables at least partially different from the first set of state variables; (see abstract- The first model represents only the electrical control part of power system by means synchronous generator connected to infinite bus, while the second model, adding a turbine and governor to the model 1. See section 2.1-Model I and 2.2-Model II)
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and wherein the adaptive PSS is configured to adjust an automatic voltage regulator (AVR) coupled to an electric generator to inject a control signal to the electric generator based on the derived electric generator parameter to dampen oscillations of the electric generator.(see abstract- Power system stabilizers (PSSs) are traditionally used to provide damping torque for the synchronous generators to suppress the oscillations by generating supplementary control signals for the generator excitation system. see introduction- The basic objective of the control system is the ability to measure the output of the system, and to take corrective action if its value deviates from some desired value. The voltage regulator is the intelligence of the system and controls the output of the exciter so that the generated voltage and reactive power change in the desired way. As the number of power plants with automatic voltage regulators grew, it became apparent that the high performance of these voltage regulators had a destabilizing effect on the power system. Power oscillations of small magnitude and low frequency often persisted for long periods of time. In some cases, this presented a limitation on the amount of power able to be transmitted within the system. Power system stabilizers were developed to aid in damping of these power oscillations by modulating the excitation supplied to the synchronous machine)
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 a plurality of models having different sets of state variables to vary a complexity of the plurality of models, wherein the plurality of models comprise a first model having a first set of state variables and a second model having a second set of state variables at least partially different from the first set of state variables and wherein the adaptive PSS is configured to adjust an automatic voltage regulator (AVR) coupled to an electric generator to inject a control signal to the electric generator based on the derived electric generator parameter to dampen oscillations of the electric generator as taught by YOUSEF in the system of Akhlaghi and Takashi in order to achieve a power system stabilizer (PSS) design for synchronous generator excitation systems that assures damping of the power system transient processes under various operating conditions. (see Introduction, YOUSEF)
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 anda plurality of models having different sets of state variables to vary a complexity of the plurality of models, wherein the plurality of models comprise a first model having a first set of state variables and a second model having a second set of state variables at least partially different from the first set of state variables and adjusting an automatic voltage regulator (AVR) coupled to an electric generator to inject a control signal to the electric generator based on the derived electric generator parameter to dampen oscillations of the electric generator.
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 and fig 2-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)
The combination of Akhlaghi and Takashi does not teach a plurality of models having different sets of state variables to vary a complexity of the plurality of models, wherein the plurality of models comprise a first model having a first set of state variables and a second model having a second set of state variables at least partially different from the first set of state variables and adjusting an automatic voltage regulator (AVR) coupled to an electric generator to inject a control signal to the electric generator based on the derived electric generator parameter to dampen oscillations of the electric generator.
In the related field of invention, YOUSEF teaches a plurality of models having different sets of state variables to vary a complexity of the plurality of models, wherein the plurality of models comprise a first model having a first set of state variables and a second model having a second set of state variables at least partially different from the first set of state variables; (see abstract- The first model represents only the electrical control part of power system by means synchronous generator connected to infinite bus, while the second model, adding a turbine and governor to the model 1. See section 2.1-Model I and 2.2-Model II)
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adjusting an automatic voltage regulator (AVR) coupled to an electric generator to inject a control signal to the electric generator based on the derived electric generator parameter to dampen oscillations of the electric generator.(see abstract- Power system stabilizers (PSSs) are traditionally used to provide damping torque for the synchronous generators to suppress the oscillations by generating supplementary control signals for the generator excitation system. see introduction- The basic objective of the control system is the ability to measure the output of the system, and to take corrective action if its value deviates from some desired value. The voltage regulator is the intelligence of the system and controls the output of the exciter so that the generated voltage and reactive power change in the desired way. As the number of power plants with automatic voltage regulators grew, it became apparent that the high performance of these voltage regulators had a destabilizing effect on the power system. Power oscillations of small magnitude and low frequency often persisted for long periods of time. In some cases, this presented a limitation on the amount of power able to be transmitted within the system. Power system stabilizers were developed to aid in damping of these power oscillations by modulating the excitation supplied to the synchronous machine)
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 a plurality of models having different sets of state variables to vary a complexity of the plurality of models, wherein the plurality of models comprise a first model having a first set of state variables and a second model having a second set of state variables at least partially different from the first set of state variables and adjusting an automatic voltage regulator (AVR) coupled to an electric generator to inject a control signal to the electric generator based on the derived electric generator parameter to dampen oscillations of the electric generator as taught by YOUSEF in the system of Akhlaghi and Takashi in order to achieve a power system stabilizer (PSS) design for synchronous generator excitation systems that assures damping of the power system transient processes under various operating conditions. (see Introduction, YOUSEF)
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; a plurality of models having different sets of state variables to vary a complexity of the plurality of models, wherein the plurality of models comprise a first model having a first set of state variables and a second model having a second set of state variables at least partially different from the first set of state variables and adjust an automatic voltage regulator (AVR) coupled to an electric generator to inject a control signal to the electric generator based on the derived electric generator parameter to dampen oscillations of the electric generator.
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)
The combination of Akhlaghi and Takashi does not teach a plurality of models having different sets of state variables to vary a complexity of the plurality of models, wherein the plurality of models comprise a first model having a first set of state variables and a second model having a second set of state variables at least partially different from the first set of state variables and adjust an automatic voltage regulator (AVR) coupled to an electric generator to inject a control signal to the electric generator based on the derived electric generator parameter to dampen oscillations of the electric generator.
In the related field of invention, YOUSEF teaches a plurality of models having different sets of state variables to vary a complexity of the plurality of models, wherein the plurality of models comprise a first model having a first set of state variables and a second model having a second set of state variables at least partially different from the first set of state variables; (see abstract- The first model represents only the electrical control part of power system by means synchronous generator connected to infinite bus, while the second model, adding a turbine and governor to the model 1. See section 2.1-Model I and 2.2-Model II)
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adjust an automatic voltage regulator (AVR) coupled to an electric generator to inject a control signal to the electric generator based on the derived electric generator parameter to dampen oscillations of the electric generator.(see abstract- Power system stabilizers (PSSs) are traditionally used to provide damping torque for the synchronous generators to suppress the oscillations by generating supplementary control signals for the generator excitation system. see introduction- The basic objective of the control system is the ability to measure the output of the system, and to take corrective action if its value deviates from some desired value. The voltage regulator is the intelligence of the system and controls the output of the exciter so that the generated voltage and reactive power change in the desired way. As the number of power plants with automatic voltage regulators grew, it became apparent that the high performance of these voltage regulators had a destabilizing effect on the power system. Power oscillations of small magnitude and low frequency often persisted for long periods of time. In some cases, this presented a limitation on the amount of power able to be transmitted within the system. Power system stabilizers were developed to aid in damping of these power oscillations by modulating the excitation supplied to the synchronous machine)
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 a plurality of models having different sets of state variables to vary a complexity of the plurality of models, wherein the plurality of models comprise a first model having a first set of state variables and a second model having a second set of state variables at least partially different from the first set of state variables and adjusting an automatic voltage regulator (AVR) coupled to an electric generator to inject a control signal to the electric generator based on the derived electric generator parameter to dampen oscillations of the electric generator as taught by YOUSEF in the system of Akhlaghi and Takashi in order to achieve a power system stabilizer (PSS) design for synchronous generator excitation systems that assures damping of the power system transient processes under various operating conditions. (see Introduction, YOUSEF)
Regarding claim 2 and 11
Akhlaghi, Takashi and YOUSEF teach the power generation system of claim 1. Akhlaghi, Takashi and YOUSEF teach the method of claim 10. Akhlaghi further teaches 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, Takashi and YOUSEF teach the power generation system of claim 2. Akhlaghi, Takashi and YOUSEF teach the method of claim 11. Akhlaghi, Takashi and YOUSEF teach the non-transitory computer-readable medium of claim 16. 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; (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)
Akhlaghi does not teach an electric generator internal voltage E', and a flux in the electric generator.
However, YOUSEF further teaches an electric generator internal voltage E', and a flux in the electric generator; (see section 2.2-∆E/q :Transient Voltage proportional to q axis flux linkage)
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 an electric generator internal voltage E', and a flux in the electric generator as taught by YOUSEF in the system of Akhlaghi and Takashi in order to achieve a power system stabilizer (PSS) design for synchronous generator excitation systems that assures damping of the power system transient processes under various operating conditions. (see Introduction, YOUSEF)
Regarding claim 4
Akhlaghi, Takashi and YOUSEF 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 6
Akhlaghi, Takashi and YOUSEF teach the power generation system of claim 2. Akhlaghi does not teach wherein the second set of state variables of the second model comprises the first set of state variables of the first model and additional state variable not part of the first model.
However, YOUSEF further teaches wherein the second set of state variables of the second model comprises the first set of state variables of the first model and additional state variable not part of the first model. (see abstract- The first model represents only the electrical control part of power system by means synchronous generator connected to infinite bus, while the second model, adding a turbine and governor to the model 1. See section 2.1-Model 1 and 2.2-Model 2)
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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 YOUSEF in the system of Akhlaghi and Takashi in order to achieve a power system stabilizer (PSS) design for synchronous generator excitation systems that assures damping of the power system transient processes under various operating conditions. (see Introduction, YOUSEF)
Regarding claim 7
Akhlaghi, Takashi and YOUSEF teach the power generation system of claim 6. Akhlaghi does not teach wherein the additional state variable comprises one more internal variables of the first estimator, the second estimator, or both.
However, YOUSEF further teaches wherein the additional state variable comprises one more internal variables of the first estimator, the second estimator, or both. (see section 2.2 Model II- [Symbol font/0x44]Tm and [Symbol font/0x44]Pg are additional state variables and see section 4-Kalman filter design -For designing a control system, Therefore, an observer is required for estimating the state-vector, based upon a measurement of the output)
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 the additional state variable comprises one more internal variables of the first estimator, the second estimator, or both as taught by YOUSEF in the system of Akhlaghi and Takashi in order to achieve a power system stabilizer (PSS) design for synchronous generator excitation systems that assures damping of the power system transient processes under various operating conditions. (see Introduction, YOUSEF)
Regarding claim 14
Akhlaghi, Takashi and YOUSEF teach the method of claim 11. Akhlaghi does not teach wherein the second set of state variables of the second model comprises the first set of state variables of the first model and additional state variable not part of the first mode, wherein the additional state variable comprises one more internal variables of the first estimator, the second estimator, or both.
However, YOUSEF further teaches wherein the second set of state variables of the second model comprises the first set of state variables of the first model and additional state variable not part of the first model, wherein the additional state variable comprises one more internal variables of the first estimator, the second estimator, or both. (see abstract- The first model represents only the electrical control part of power system by means synchronous generator connected to infinite bus, while the second model, adding a turbine and governor to the model 1. See section 2.1-Model 1 and 2.2-Model 2)
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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, wherein the additional state variable comprises one more internal variables of the first estimator, the second estimator, or both as taught by YOUSEF in the system of Akhlaghi and Takashi in order to achieve a power system stabilizer (PSS) design for synchronous generator excitation systems that assures damping of the power system transient processes under various operating conditions. (see Introduction, YOUSEF)
6. Claim(s) 5, 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 YOUSEF et al. ("Improved power system stabilizer by applying LQG controller." Advances in Electrical and Computer Engineering, Proceeding of 17th international conference on automatic control modeling and simulation. 2015.) and still 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, Takashi and YOUSEF teach the power generation system of claim 4. Akhlaghi, Takashi and YOUSEF teach the method of claim 11. Akhlaghi, Takashi and YOUSEF teach the non-transitory computer-readable medium of claim 16. 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, Takashi and YOUSEF 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, Takashi and YOUSEF 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, Takashi and YOUSEF 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) 9 and 17 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 YOUSEF et al. ("Improved power system stabilizer by applying LQG controller." Advances in Electrical and Computer Engineering, Proceeding of 17th international conference on automatic control modeling and simulation. 2015.) and still further in view of Shao, Hongbo. Adaptive three-stage controlled islanding to prevent imminent wide-area blackouts. Diss. Durham University, 2016.
Regarding claim 9
Akhlaghi, Takashi and YOUSEF teach the power generation system of claim 1. Akhlaghi teaches the plurality of models (see fig 1). Akhlaghi does not teach wherein the plurality of models comprise a third model having a third set of state variables at least partially different from the first set of state variables and the second set of state variables, the first model is configured to model the first set of state variables, the second model is configured to model the second set of state variables including the first set of state variables and one or more additional state variables not part of the first model, and the third model is configured to model the third set of state variables including the second set of state variables and one or more additional state variables not part of the first and second models.
In the related field of invention, YOUSEF teaches the first model is configured to model the first set of state variables, the second model is configured to model the second set of state variables including the first set of state variables and one or more additional state variables not part of the first model,; (see abstract- The first model represents only the electrical control part of power system by means synchronous generator connected to infinite bus, while the second model, adding a turbine and governor to the model 1. See section 2.1-Model I and 2.2-Model II)
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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 the first model is configured to model the first set of state variables, the second model is configured to model the second set of state variables including the first set of state variables and one or more additional state variables not part of the first model as taught by YOUSEF in the system of Akhlaghi and Takashi in order to achieve a power system stabilizer (PSS) design for synchronous generator excitation systems that assures damping of the power system transient processes under various operating conditions. (see Introduction, YOUSEF)
The combination of Akhlaghi, Takashi and YOUSEF does not teach a third model having a third set of state variables at least partially different from the first set of state variables and the second set of state variables, and the third model is configured to model the third set of state variables including the second set of state variables and one or more additional state variables not part of the first and second models.
In the related field of invention, Shao teaches a third model having a third set of state variables at least partially different from the first set of state variables and the second set of state variables, the first model is configured to model the first set of state variables, the second model is configured to model the second set of state variables including the first set of state variables and one or more additional state variables not part of the first model, and the third model is configured to model the third set of state variables including the second set of state variables and one or more additional state variables not part of the first and second models. (see Appendix A Shao page 146-148- Different orders of generator models represent different numbers of state variables that are used to describe the generator’s dynamic behavior. (Sixth-order generator model is the third model that models the third set of state variables including the second set of state variables and one or more additional state variables not part of the first (second-order) and second models(fourth-order))
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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, YOUSEF and Takashi to include the first model is configured to model the first set of state variables, the second model is configured to model the second set of state variables including the first set of state variables and one or more additional state variables not part of the first model as taught by Shao in the system of Akhlaghi, YOUSEF and Takashi in order to use the machine equivalent (SIME) method to predict transient stability during cascading outages that would shortly lead to blackouts, giving support in decisions about when to island in terms of transient instability. SIME also evaluates dynamic stability after islanding and ensures that the selected island candidates are stable before action is taken. Moreover, in this thesis, the power flow tracing-based method provides all possible islanding cut sets, and SIME helps to identify the one that has the best transient stability and minimal power flow disruption. If no possible island cut set exists, corrective actions through tripping critical generators or load shedding are undertaken in each island. (see Abstract, Shao)
Regarding claim 17
Akhlaghi, Takashi and YOUSEF teach the non-transitory computer-readable medium of claim 16. Akhlaghi further teaches the first model of the second estimator is configured to model the first set of state variables including 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.)
Takashi further teaches wherein the first estimator comprises a single state estimator configured to output an external reactance XE and an infinite bus calculation system configured to derive 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 and fig 2-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.)
The combination of Akhlaghi, Takashi and YOUSEF does not teach the second model of the second estimator is configured to model the second set of state variables including the first set of state variables and the external reactance XE, and a third model of the plurality of models of the second estimator is configured to model a third set of state variables including the second set of state variables and a synchronous reactance.
In the related field of invention, Shao teaches the second model of the second estimator is configured to model the second set of state variables including the first set of state variables and the external reactance XE, and a third model of the plurality of models of the second estimator is configured to model a third set of state variables including the second set of state variables and a synchronous reactance. (see Appendix A Shao page 146-148- Different orders of generator models represent different numbers of state variables that are used to describe the generator’s dynamic behavior. Fourth-order generator model is the second model that models the second set of state variables including the first set of state variables and a synchronous reactance) (Sixth-order generator model is the third model that models the third set of state variables including the second set of state variables and a synchronous reactance))
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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, YOUSEF and Takashi to include the second model of the second estimator is configured to model the second set of state variables including the first set of state variables and the external reactance XE, and a third model of the plurality of models of the second estimator is configured to model a third set of state variables including the second set of state variables and a synchronous reactance as taught by Shao in the system of Akhlaghi, YOUSEF and Takashi in order to use the machine equivalent (SIME) method to predict transient stability during cascading outages that would shortly lead to blackouts, giving support in decisions about when to island in terms of transient instability. SIME also evaluates dynamic stability after islanding and ensures that the selected island candidates are stable before action is taken. Moreover, in this thesis, the power flow tracing-based method provides all possible islanding cut sets, and SIME helps to identify the one that has the best transient stability and minimal power flow disruption. If no possible island cut set exists, corrective actions through tripping critical generators or load shedding are undertaken in each island. (see Abstract, Shao)
7. Claim(s) 8 and 15 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 Wang et al. (PUB NO:US 20200379424 A1)
Regarding claim 8 and 15
Akhlaghi, YOUSEF and Takashi teach the power generation system of claim 1. Akhlaghi and YOUSEF, Takashi teach the method of claim 11. Akhlaghi does not teach wherein the adaptive PSS is configured to: determine a first value of an external reactance via the first estimator; determine a second value of the external reactance via the second estimator; compare the first and second values of the external reactance to obtain a comparison indicative of an error; and switch between the plurality of models in response to the error not meeting an error threshold.
Takashi further teaches wherein the adaptive PSS is configured to: determine a first value of an external reactance via the first estimator; determine a second value of the external reactance via the second estimator;(see para 90 and fig 6-FIG. 6, the simulation examines the case where two systems of first and second systems (the power conversion devices 10) are connected to the electric power system 4.)
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The combination of Akhlaghi, Takashi and YOUSEF does not teach compare the first and second values of the external reactance to obtain a comparison indicative of an error; and switch between the plurality of models in response to the error not meeting an error threshold.
In the related field of invention, Wang teaches compare the first and second values of the external reactance to obtain a comparison indicative of an error; switch between the plurality of models in response to the error not meeting an error threshold. (see para 73-79-In regards to the model check, a typical synchronous generator model has four parts: machine model, turbine-governor model, excitation model and power system stabilizer (PSS) model. The model check is based on a collection of published NERC List of Acceptable Models, user preferences, and historical data. Excitation model such as EX2000 model in Siemens PSS/E shall be replaced with AC7B in PSSE and ex21br in PSLF. In the parameter check, the key parameter values and relative relationship between parameters is evaluated against the NERC Case Quality Dynamic Metrics. Below are several parameters that may be evaluated in the exemplary embodiment. First, consistent generator reactance may be evaluated, where D-axis synchronous reactance (Xd) should not be less than d-axis transient reactance (Xd′), D-axis transient reactance (Xd′) should not be less than d-axis subtransient reactance (Xd″), subtransient reactance (Xd″) should not be less than stator leakage reactance (Xl), Q-axis synchronous reactance (Xq) should not be less than q-axis transient reactance (Xq′), and Q-axis transient reactance (Xq′) should not be less than q-axis subtransient reactance (Xq″).) See para 86-87-If the relative error between simulated metric and measured metric goes below the predefined threshold, then the model's response curve is determined as Pass; otherwise, it is Fail. A default could set at 10% (relative error). See para 106- In other embodiments, the Model Check Module 1005 provides the user with the option to automatically convert the obsolete model to a recommended model.)
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, YOUSEF and Takashi to include compare the first and second values of the external reactance to obtain a comparison indicative of an error; and switch between the plurality of models in response to the error not meeting an error threshold as taught by Shao in the system of Akhlaghi, YOUSEF and Takashi in order to validate the dynamic models of the generators frequently at different operating conditions. (see Abstract, Wang)
10. 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 Wang et al. (PUB NO:US 20200379424 A1) and still further in view of Shao, Hongbo. Adaptive three-stage controlled islanding to prevent imminent wide-area blackouts. Diss. Durham University, 2016.
Regarding claim 20
Akhlaghi, YOUSEF and Takashi teach non-transitory computer-readable medium of claim 16. Akhlaghi does not teach determine a first value of an external reactance via the first estimator; determine a second value of the external reactance via the second estimator; compare the first and second values of the external reactance to obtain a comparison indicative of an error; and switch between the plurality of models in response to the error not meeting an error threshold, wherein the plurality of models comprise a third model having a third set of state variables at least partially different from both the first and second set of state variables.
Takashi further teaches determine a first value of an external reactance via the first estimator; determine a second value of the external reactance via the second estimator;(see para 90 and fig 6-FIG. 6, the simulation examines the case where two systems of first and second systems (the power conversion devices 10) are connected to the electric power system 4.)
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The combination of Akhlaghi, Takashi and YOUSEF does not teach compare the first and second values of the external reactance to obtain a comparison indicative of an error; and switch between the plurality of models in response to the error not meeting an error threshold, wherein the plurality of models comprise a third model having a third set of state variables at least partially different from both the first and second set of state variables
In the related field of invention, Wang teaches compare the first and second values of the external reactance to obtain a comparison indicative of an error; switch between the plurality of models in response to the error not meeting an error threshold. (see para 73-79-In regards to the model check, a typical synchronous generator model has four parts: machine model, turbine-governor model, excitation model and power system stabilizer (PSS) model. The model check is based on a collection of published NERC List of Acceptable Models, user preferences, and historical data. Excitation model such as EX2000 model in Siemens PSS/E shall be replaced with AC7B in PSSE and ex21br in PSLF. In the parameter check, the key parameter values and relative relationship between parameters is evaluated against the NERC Case Quality Dynamic Metrics. Below are several parameters that may be evaluated in the exemplary embodiment. First, consistent generator reactance may be evaluated, where D-axis synchronous reactance (Xd) should not be less than d-axis transient reactance (Xd′), D-axis transient reactance (Xd′) should not be less than d-axis subtransient reactance (Xd″), subtransient reactance (Xd″) should not be less than stator leakage reactance (Xl), Q-axis synchronous reactance (Xq) should not be less than q-axis transient reactance (Xq′), and Q-axis transient reactance (Xq′) should not be less than q-axis subtransient reactance (Xq″).) See para 86-87-If the relative error between simulated metric and measured metric goes below the predefined threshold, then the model's response curve is determined as Pass; otherwise, it is Fail. A default could set at 10% (relative error). See para 106- In other embodiments, the Model Check Module 1005 provides the user with the option to automatically convert the obsolete model to a recommended model.)
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, YOUSEF and Takashi to include compare the first and second values of the external reactance to obtain a comparison indicative of an error; and switch between the plurality of models in response to the error not meeting an error threshold as taught by Shao in the system of Akhlaghi, YOUSEF and Takashi in order to validate the dynamic models of the generators frequently at different operating conditions. (see Abstract, Wang)
The combination of Akhlaghi, Takashi, Wang and YOUSEF does not teach wherein the plurality of models comprise a third model having a third set of state variables at least partially different from both the first and second set of state variables
In the related field of invention, Shao teaches wherein the plurality of models comprise a third model having a third set of state variables at least partially different from both the first and second set of state variables. (see Appendix A Shao page 146-148- Different orders of generator models represent different numbers of state variables that are used to describe the generator’s dynamic behavior. (Sixth-order generator model is the third model that models the third set of state variables including the second set of state variables and one or more additional state variables not part of the first (second-order) and second models(fourth-order))
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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, YOUSEF and Takashi to include wherein the plurality of models comprise a third model having a third set of state variables at least partially different from both the first and second set of state variables as taught by Shao in the system of Akhlaghi, YOUSEF, Wang and Takashi in order to use the machine equivalent (SIME) method to predict transient stability during cascading outages that would shortly lead to blackouts, giving support in decisions about when to island in terms of transient instability. SIME also evaluates dynamic stability after islanding and ensures that the selected island candidates are stable before action is taken. Moreover, in this thesis, the power flow tracing-based method provides all possible islanding cut sets, and SIME helps to identify the one that has the best transient stability and minimal power flow disruption. If no possible island cut set exists, corrective actions through tripping critical generators or load shedding are undertaken in each island. (see Abstract, Shao)
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang et al. US 20210124854 A1
i. Discussing the system for modeling power systems based on multiple events.
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.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, RENEE CHAVEZ can be reached at 5712701104. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PURSOTTAM GIRI/
Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
Prosecution Insights