A state estimation method and system for a distributed multi-regional power system

Abstract

The present invention discloses a state estimation method and system for a distributed multi-regional power system, which relates to the field of power management technology. The method comprises: obtaining local measurement data, neighbor measurement signals and neighbor state estimation value signals of a power system to be measured in a target area of ​​a distributed multi-regional power system, and then calculating a priori estimated value of the state of the power system to be measured at the next moment, an upper bound of the state error covariance matrix and a neighbor gain matrix, thereby obtaining an upper bound of the calculated error covariance and a neighbor gain parameter, and then obtaining a weighted error covariance through Cholesky decomposition, calculating a state error covariance weight matrix and a measurement error covariance weight matrix through a preset Cauchy kernel function, and calculating a gain matrix of a local estimator through a fixed point iteration method, thereby completing the state estimation of the power system to be measured. By designing a reasonable Cauchy kernel maximum relative entropy condition to replace the minimum mean square error condition, the state estimation in a non-Gaussian noise environment is improved.

Description

Technical Field

[0001] The present application relates to the field of state estimation in power systems, and in particular to a state estimation method and system for a distributed multi-regional power system. Background Art

[0002] With the improvement of living standards, people's demand for electricity continues to increase. In order to provide safe and reliable power supply, the power system needs to operate safely and stably. Among them, the state estimation of the power system is the basis for ensuring the stable operation of the power system. With the addition of new energy sources such as wind power generation, hydropower generation and photovoltaic power generation, the power grid has gradually evolved into a multi-regional interconnected development model. The traditional centralized connection method has also gradually changed to a distributed connection method, thereby reducing the computing and communication burdens of the central system and meeting the plug-and-play requirements of the power grid.

[0003] In previous studies on system noise issues, most literature assumes that system noise satisfies a Gaussian distribution. Based on this assumption, the state estimator designs gain coefficients based on the minimum mean square error, such as the common Kalman filter and its variants. These estimators consider signals of different amplitudes to be of equal importance when considering each sensor information, so when subjected to large-amplitude interference signals, their estimation performance will be reduced. However, most of the noise encountered in the power system is considered to belong to a non-Gaussian distribution, such as a thick-tailed distribution. This may cause the traditional estimator based on the minimum mean square error condition to have a large degree of loss in estimation performance. In order to solve this problem, the present invention designs a value condition based on the maximum relative entropy of the Cauchy kernel to design a state estimator, so that the state estimator can set thresholds according to signals of different amplitudes, thereby reducing the impact of non-Gaussian noise on the power system state estimation and improving the accuracy of the power system state estimation. Summary of the invention

[0004] In view of this, the present invention provides a state estimation method and system for a distributed multi-regional power system.

[0005] A state estimation method for a distributed multi-region power system is provided, comprising:

[0006] Acquire local measurement data, neighbor measurement signals, and neighbor state estimation value signals of a power system to be measured in a target area of ​​a distributed multi-area power system;

[0007] Calculate the target a posteriori state estimation value equation, the state error covariance matrix upper bound and the neighbor gain matrix of the power system to be measured at the next moment according to the local measurement data, the neighbor measurement signal and the neighbor state estimation value signal;

[0008] According to the state error covariance matrix upper bound and the neighbor gain matrix, a state error covariance decomposition matrix and a measurement error covariance decomposition matrix are obtained by Cholesky decomposition;

[0009] The state error covariance weight matrix and the measurement error covariance weight matrix of the power system to be tested at the next moment are calculated by a preset Cauchy kernel function, and a weighted state error covariance matrix and a weighted measurement error covariance matrix are calculated based on the state error covariance weight matrix, the measurement error covariance weight matrix, the state error covariance decomposition matrix and the measurement error covariance decomposition matrix, wherein the preset Cauchy kernel function is a Cauchy kernel function that minimizes the maximum relative entropy;

[0010] Through the fixed point iteration method, under preset conditions, the gain matrix of the local estimator is calculated according to the weighted state error covariance matrix and the weighted measurement error covariance matrix, and the posterior state estimate of the power system to be tested at the current moment is updated based on the gain matrix to complete the state estimation of the power system to be tested.

[0011] Optionally, the step of calculating a target a posteriori state estimation value equation, an upper bound of a state error covariance matrix and a neighbor gain matrix of the power system to be measured at the next moment according to the local measurement data, the neighbor measurement signal and the neighbor state estimation value signal comprises:

[0012] The target a posteriori state estimation value equation of the power system to be measured at the next moment is calculated according to the local measurement data, the neighbor measurement signal and the neighbor state estimation value signal through a preset a posteriori state estimation value equation, wherein the preset a posteriori state estimation value equation is:

[0013]

[0014] in, and Indicated in The prior state estimate at time t is the prior state estimate, Indicated in The posterior state estimate at time , and For the state estimator The local gain matrix and neighbors in the local measurement data The neighbor gain matrix in the neighbor measurement signal, and Represents the power system The measurement matrix in the local measurement data and the measurement matrix in the neighbor measurement signal, and The local data measurement value in the local measurement data and the neighbor data measurement value in the neighbor measurement signal.

[0015] The target posterior state estimation equation is:

[0016]

[0017] in, and Indicated in The prior state estimation error and the posterior state estimation error calculated at all times based on all the collected data, is the identity matrix of the corresponding dimension, and Represents the power system exist The process noise and measurement noise at each moment, ;

[0018] The neighbor gain matrix is ​​obtained according to the target posterior state estimation value equation, wherein the neighbor gain matrix is:

[0019]

[0020] Obtaining an estimated error covariance matrix according to the neighbor gain matrix, and simplifying the estimated error covariance matrix by an approximation method of Young's inequality to obtain a simplified matrix;

[0021] The upper bound of the state error covariance matrix of the power system to be measured at the next moment is obtained according to the simplified matrix, wherein the upper bound of the state error covariance matrix is:

[0022]

[0023] in, For the proposed power system exist The upper bound of the estimation error covariance at time , Indicates the preset power system area The number of neighbors of is a positive real number, , is the identity matrix of the corresponding dimension, Power system area calculated for the local area The estimator gain matrix, For local power system area The output matrix of For local power system area The system matrix, ,in Electricity system area for neighbors The output matrix of Electricity system area for neighbors and Region The coupling matrix is ​​used to characterize the physical connection characteristics. , is the system noise covariance matrix, is the output noise covariance matrix.

[0024] Optionally, the step of obtaining a state error covariance decomposition matrix and a measurement error covariance decomposition matrix by Cholesky decomposition includes:

[0025] The state error covariance matrix is ​​determined according to the upper bound of the state error covariance matrix, and the state error covariance matrix and the measurement error covariance matrix are decomposed by Cholesky to obtain a state error covariance decomposition matrix and a measurement error covariance decomposition matrix.

[0026] Optionally, the step of calculating the state error covariance weight matrix and the measurement error covariance weight matrix of the power system to be measured at the next moment by a preset Cauchy kernel function, and calculating the weighted state error covariance matrix and the weighted measurement error covariance matrix based on the state error covariance weight matrix, the measurement error covariance weight matrix, the state error covariance decomposition matrix and the measurement error covariance decomposition matrix includes:

[0027] Calculating a posteriori estimate of the power system to be measured at the next moment by using the target posteriori state estimate equation according to the local measurement data, the neighbor measurement signal and the neighbor state estimate signal;

[0028] Calculate a target a priori state estimation error equation of the power system to be tested at the next moment through a preset a priori state estimation error equation according to the local measurement data, the neighbor measurement signal and the neighbor state estimation value signal, and calculate a priori estimation value of the power system to be tested at the next moment through the target a priori state estimation error equation;

[0029] Calculate a performance indicator vector of a preset Cauchy kernel function according to the posterior estimation value and the prior estimation value;

[0030] Calculating a state error covariance weight matrix and a measurement error covariance weight matrix according to the performance indicator vector;

[0031] A weighted state error covariance matrix and a weighted measurement error covariance matrix are calculated based on the state error covariance weight matrix, the measurement error covariance weight matrix, the state error covariance decomposition matrix, and the measurement error covariance decomposition matrix.

[0032] Optionally, the fixed point iteration method is used, under preset conditions, to calculate a gain matrix of a local estimator according to the weighted state error covariance matrix and the weighted measurement error covariance matrix, and based on the gain matrix, the posterior state estimate of the power system to be measured at the current moment is updated to complete the step of state estimation for the power system to be measured, including:

[0033] When the preset condition is a value condition, the gain matrix of the local estimator when the partial derivative of the value condition is zero is calculated according to the weighted state error covariance matrix and the weighted measurement error covariance matrix by a fixed point iteration method, wherein the mathematical representation of the value condition is:

[0034]

[0035] in, Represents vector No. Dimensional data, For variables The Cauchy kernel function is for The bandwidth parameter at the time.

[0036] On the other hand, the present application provides a state estimation system for a distributed multi-regional power system, comprising:

[0037] A data acquisition module, used for acquiring local measurement data, neighbor measurement signals and neighbor state estimation value signals of the power system to be measured in the target area of ​​the distributed multi-area power system;

[0038] A first calculation module, used for calculating the target a posteriori state estimation value equation, the upper bound of the state error covariance matrix and the neighbor gain matrix of the power system to be measured at the next moment according to the local measurement data, the neighbor measurement signal and the neighbor state estimation value signal;

[0039] A decomposition module, used for obtaining a state error covariance decomposition matrix and a measurement error covariance decomposition matrix by Cholesky decomposition according to the upper bound of the state error covariance matrix and the neighbor gain matrix;

[0040] A second calculation module is used to calculate the state error covariance weight matrix and the measurement error covariance weight matrix of the power system to be measured at the next moment by a preset Cauchy kernel function, and calculate the weighted state error covariance matrix and the weighted measurement error covariance matrix based on the state error covariance weight matrix, the measurement error covariance weight matrix, the state error covariance decomposition matrix and the measurement error covariance decomposition matrix, wherein the preset Cauchy kernel function is the Cauchy kernel function that minimizes the maximum relative entropy;

[0041] An updating module is used to calculate the gain matrix of the local estimator according to the weighted state error covariance matrix and the weighted measurement error covariance matrix under preset conditions through a fixed point iteration method, and to update the posterior state estimate of the power system to be tested at the current moment based on the gain matrix to complete the state estimation of the power system to be tested.

[0042] In a third aspect, the present application provides an electronic device comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the steps of the state estimation method of the distributed multi-regional power system as described above are performed.

[0043] In a fourth aspect, the present application provides a computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the state estimation method of the distributed multi-regional power system as described above are executed.

[0044] This application replaces the minimum mean square error condition with a reasonable Cauchy kernel maximum relative entropy condition to improve state estimation in a non-Gaussian noise environment. Compared with the traditional Kalman filter based on the least squares condition, the method of this patent has a better estimation effect on the power system state estimation in a non-Gaussian noise environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] in:

[0047] Figure 1 is a flow chart of a state estimation method for a distributed multi-region power system in one embodiment;

[0048] Figure 2 is a structural block diagram of a state estimation device for a distributed multi-region power system in one embodiment;

[0049] Figure 3 1 is a structural block diagram of a state estimation system for a distributed multi-regional power system in one embodiment. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0052] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0053] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0054] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0055] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0056] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0057] The present invention is described in detail below through specific embodiments.

[0058] Operation symbol description: represents the transpose operation of the matrix, represents the matrix inversion operation, represents the trace of the matrix, Represents vector No. Dimensional data, represents the sum operation, is a collection of multi-regional power systems. For the The set of neighbors of a region.

[0059] In the specific embodiment, the load frequency control system model is adopted, then The regional models are:

[0060]

[0061] The parameter annotations in the equations are shown in Table 1.

[0062] Table 1 Regional load frequency control system parameters and notes

[0063]

[0064] Among them is the load disturbance deviation of the system, which is assumed to be ,in is an arbitrary constant. Select the state variable , then the above system model becomes

[0065]

[0066] in, Represents the control input of the system. Different controllers can be used to keep the system stable.

[0067]

[0068] Please refer to Figure 1 , Figure 1 A state estimation method for a distributed multi-regional power system provided in an embodiment of the present invention includes:

[0069] S101, obtaining local measurement data, neighbor measurement signals, and neighbor state estimation value signals of a power system to be measured in a target area in a distributed multi-area power system;

[0070] For example, sensors in the power system can measure part of the system state, and sensor data needs to be collected before state estimation. Phase measurement units and power meters can measure the frequency and power of the local power system. Area measurement signal Expressed as:

[0071]

[0072] in

[0073]

[0074] Most of the data in modern power systems are transmitted in the form of digital signals. Ordinary intelligent sensors transmit the measurement data of each area in the form of data packets through remote terminal units to the data acquisition and monitoring control system. It is used to monitor the operating status of power systems in each area. By designing a suitable output feedback controller, the system equation is rewritten into a discrete state form for easy computer application.

[0075]

[0076] in, Represents each sampling moment. is the system noise, which has a mean of zero and a covariance of The output equation for a non-Gaussian distribution is:

[0077]

[0078] is the measurement noise, which has a mean of zero and a covariance of Non-Gaussian distribution.

[0079] S102, calculating the target a posteriori state estimation value equation, the upper bound of the state error covariance matrix and the neighbor gain matrix of the power system to be measured at the next moment according to the local measurement data, the neighbor measurement signal and the neighbor state estimation value signal;

[0080] For example, at the initial moment of the power system operation, the parameters and state estimation values ​​in the system are initialized. The parameters that need to be initialized are: a priori state estimation value and a posteriori state estimation value. , the initial value of the upper bound of the estimated error covariance , a parameter used as a criterion in the fixed point iteration algorithm and .

[0081] The priori state estimation equation and the a posteriori state estimation equation of the multi-regional interconnected power system are designed as follows:

[0082]

[0083]

[0084] in and Respectively The regional power system at time The prior state estimate and the posterior state estimate of . The matrix and are the local gain and neighbor gain of the distributed estimator.

[0085] S103, obtaining a state error covariance decomposition matrix and a measurement error covariance decomposition matrix by Cholesky decomposition according to the upper bound of the state error covariance matrix and the neighbor gain matrix;

[0086] For the convenience of description, the following symbols are defined.

[0087]

[0088] in and For the state estimator The priori estimation error and the posterior estimation error of is the state estimation error covariance, For the state estimator and By substituting the state equation of the system and the prior state estimation equation, the following prior estimation error equation and a posteriori estimation error equation can be derived:

[0089]

[0090]

[0091] in, , is the identity matrix of the corresponding dimension, is the local gain matrix of the state estimator.

[0092] In order to reduce the coupling degree between the posterior estimation error and the neighbor estimation error, the coupling part of the posterior estimation error is abbreviated as :

[0093]

[0094] Among them, due to is not full column rank, so it is impossible to find the matrix inverse so that In order to quantify the range of the coupling matrix, the following matrix F norm is defined:

[0095]

[0096] in, Represents the trace of the matrix. In order to minimize the impact of neighbor estimation error, let And the following equation is obtained:

[0097]

[0098] By sorting, we can get the neighbor gain matrix:

[0099]

[0100] Further, the estimated error covariance matrix is ​​obtained by deducing for:

[0101]

[0102] in, Indicated in Time, area With area The cross error covariance of .

[0103] In multi-region power systems, it is very difficult to obtain the cross-estimation error covariance matrix. In order to meet the real-time requirements of the power system, an approximate method of Young's inequality is used to calculate the cross-estimation error covariance matrix. . The inequality is scaled as follows:

[0104]

[0105]

[0106]

[0107] in, is a positive scalar. The estimated error covariance matrix can be further simplified as:

[0108]

[0109] Therefore, for any distributed maximum relative entropy filter based on the Cauchy kernel, an upper bound on the error covariance is The calculation equation is:

[0110]

[0111] By using the Cholesky decomposition method, the state error covariance decomposition matrix and the measurement error covariance decomposition matrix The state error covariance matrix can be decomposed and the measurement error covariance matrix To obtain, decompose the equation as follows:

[0112]

[0113] in, is the upper bound of the estimated error covariance, is the measurement noise covariance matrix.

[0114] Calculate the Cauchy kernel function performance index vector and , its specific implementation form is:

[0115]

[0116] Calculate the state error covariance weight matrix and the measurement error covariance weight matrix , its specific implementation form is:

[0117]

[0118] The function represents the diagonalized matrix, Represents vector No. Dimensional data, For variables The Cauchy kernel function is is the bandwidth parameter.

[0119] Calculate the weighted state error covariance and the weighted measurement error covariance matrix The specific implementation form is:

[0120]

[0121] in Indicates that in S303 Matrix inverse operation.

[0122] S104, calculating the state error covariance weight matrix and the measurement error covariance weight matrix of the power system to be tested at the next moment by a preset Cauchy kernel function, and calculating the weighted state error covariance matrix and the weighted measurement error covariance matrix based on the state error covariance weight matrix, the measurement error covariance weight matrix, the state error covariance decomposition matrix and the measurement error covariance decomposition matrix, wherein the preset Cauchy kernel function is the Cauchy kernel function that minimizes the maximum relative entropy;

[0123] For example, in order to deal with the impact of non-Gaussian noise on system state estimation in distributed multi-regional power systems, this patent proposes a maximum relative entropy condition based on the Cauchy kernel to replace the traditional minimum mean square error condition, thereby improving the estimation performance of the distributed estimator. As shown below:

[0124]

[0125] in Represents vector No. Dimensional data.

[0126] By designing a suitable estimator for the local gain , thereby minimizing the maximum relative entropy condition based on the Cauchy kernel and making the partial derivative of the value function zero, that is, , we can get the following formula:

[0127]

[0128] By adding the weight matrix, the above formula can be written as:

[0129]

[0130] By increasing Item, we can get the following equation:

[0131]

[0132] By sorting out, the optimal local gain parameters can be obtained:

[0133]

[0134] It should be noted that the designed filter gain and the posterior state estimate is through and Mutually coupled.

[0135] S105. Through the fixed point iteration method, under preset conditions, the gain matrix of the local estimator is calculated according to the weighted state error covariance matrix and the weighted measurement error covariance matrix, and the posterior state estimate of the power system to be tested at the current moment is updated based on the gain matrix to complete the state estimation of the power system to be tested.

[0136] For example, a fixed point iteration algorithm is proposed to obtain the optimal gain parameter and state estimation value through continuous iterative calculation. The specific implementation method of the method is as follows:

[0137] Setting the estimation accuracy parameters and iteration time parameters The initialization parameters make , When satisfied and The following program cycle is performed:

[0138] Calculate the Cauchy kernel function performance index vector and ;

[0139] Calculate the state error covariance weight matrix and the measurement error covariance weight matrix ;

[0140] Calculate the weighted state error covariance and the weighted measurement error covariance matrix ;

[0141] Compute the estimator local gain ;

[0142] Compute estimator neighbor gain ;

[0143] The posterior state estimate backup, let , and calculate the posterior state estimate value;

[0144] Calculation cycle stop judgment condition: ;

[0145] Iteration time update: .

[0146] Through the fixed point iteration algorithm, the posterior state estimation can be solved and In the calculation process, there are coupling problems and set the iteration accuracy parameters. and timeout parameters To reduce the amount of iterative calculations.

[0147] By designing a reasonable Cauchy kernel maximum relative entropy condition to replace the minimum mean square error condition, the state estimation in a non-Gaussian noise environment is improved. Compared with the traditional Kalman filter based on the least squares condition, the method of this patent has a better estimation effect on the power system state estimation in a non-Gaussian noise environment.

[0148] Optionally, the step of calculating a target a posteriori state estimation value equation, an upper bound of a state error covariance matrix and a neighbor gain matrix of the power system to be measured at the next moment according to the local measurement data, the neighbor measurement signal and the neighbor state estimation value signal comprises:

[0149] The target a posteriori state estimation value equation of the power system to be measured at the next moment is calculated according to the local measurement data, the neighbor measurement signal and the neighbor state estimation value signal through a preset a posteriori state estimation value equation, wherein the preset a posteriori state estimation value equation is:

[0150]

[0151] in, and Indicated in The prior state estimate at time t is the prior state estimate, Indicated in The posterior state estimate at time , and For the state estimator The local gain matrix and neighbors in the local measurement data The neighbor gain matrix in the neighbor measurement signal, and Represents the power system The measurement matrix in the local measurement data and the measurement matrix in the neighbor measurement signal, and The local data measurement value in the local measurement data and the neighbor data measurement value in the neighbor measurement signal.

[0152] The target posterior state estimation equation is:

[0153]

[0154] in, and Indicated in The prior state estimation error and the posterior state estimation error calculated at all times based on all the collected data, is the identity matrix of the corresponding dimension, and Represents the power system exist The process noise and measurement noise at each moment, .

[0155] The neighbor gain matrix is ​​obtained according to the target posterior state estimation value equation, wherein the neighbor gain matrix is:

[0156]

[0157] Obtaining an estimated error covariance matrix according to the neighbor gain matrix, and simplifying the estimated error covariance matrix by an approximation method of Young's inequality to obtain a simplified matrix;

[0158] The upper bound of the state error covariance matrix of the power system to be measured at the next moment is obtained according to the simplified matrix, wherein the upper bound of the state error covariance matrix is:

[0159]

[0160] in, For the proposed power system exist The upper bound of the estimation error covariance at time , Indicates the preset power system area The number of neighbors of is a positive real number, , is the identity matrix of the corresponding dimension, Power system area calculated for the local area The estimator gain matrix, For local power system area The output matrix of For local power system area The system matrix, ,in Electricity system area for neighbors The output matrix of Electricity system area for neighbors and Region The coupling matrix is ​​used to characterize the physical connection characteristics. , is the system noise covariance matrix, is the output noise covariance matrix.

[0161] Optionally, the step of obtaining a state error covariance decomposition matrix and a measurement error covariance decomposition matrix by Cholesky decomposition includes:

[0162] The state error covariance matrix is ​​determined according to the upper bound of the state error covariance matrix, and the state error covariance matrix and the measurement error covariance matrix are decomposed by Cholesky to obtain a state error covariance decomposition matrix and a measurement error covariance decomposition matrix.

[0163] Optionally, the step of calculating the state error covariance weight matrix and the measurement error covariance weight matrix of the power system to be measured at the next moment by a preset Cauchy kernel function, and calculating the weighted state error covariance matrix and the weighted measurement error covariance matrix based on the state error covariance weight matrix, the measurement error covariance weight matrix, the state error covariance decomposition matrix and the measurement error covariance decomposition matrix includes:

[0164] Calculating a posteriori estimate of the power system to be measured at the next moment by using the target posteriori state estimate equation according to the local measurement data, the neighbor measurement signal and the neighbor state estimate signal;

[0165] Calculate a target a priori state estimation error equation of the power system to be tested at the next moment through a preset a priori state estimation error equation according to the local measurement data, the neighbor measurement signal and the neighbor state estimation value signal, and calculate a priori estimation value of the power system to be tested at the next moment through the target a priori state estimation error equation;

[0166] Calculate a performance indicator vector of a preset Cauchy kernel function according to the posterior estimation value and the prior estimation value;

[0167] Calculating a state error covariance weight matrix and a measurement error covariance weight matrix according to the performance indicator vector;

[0168] A weighted state error covariance matrix and a weighted measurement error covariance matrix are calculated based on the state error covariance weight matrix, the measurement error covariance weight matrix, the state error covariance decomposition matrix, and the measurement error covariance decomposition matrix.

[0169] Optionally, the fixed point iteration method is used, under preset conditions, to calculate a gain matrix of a local estimator according to the weighted state error covariance matrix and the weighted measurement error covariance matrix, and based on the gain matrix, the posterior state estimate of the power system to be measured at the current moment is updated to complete the step of state estimation for the power system to be measured, including:

[0170] When the preset condition is a value condition, the gain matrix of the local estimator when the partial derivative of the value condition is zero is calculated according to the weighted state error covariance matrix and the weighted measurement error covariance matrix by a fixed point iteration method, wherein the mathematical representation of the value condition is:

[0171]

[0172] in, Represents vector No. Dimensional data, For variables The Cauchy kernel function is for The bandwidth parameter at the time.

[0173] In a possible implementation, Figure 2 As shown, a state estimation system for a distributed multi-regional power system is provided, comprising:

[0174] The data acquisition module 201 is used to obtain local measurement data, neighbor measurement signals and neighbor state estimation value signals of the power system to be measured in the target area of ​​the distributed multi-area power system;

[0175] A first calculation module 202 is used to calculate the target a posteriori state estimation value equation, the state error covariance matrix upper bound and the neighbor gain matrix of the power system to be measured at the next moment according to the local measurement data, the neighbor measurement signal and the neighbor state estimation value signal;

[0176] A decomposition module 203, configured to obtain a state error covariance decomposition matrix and a measurement error covariance decomposition matrix by Cholesky decomposition according to the state error covariance matrix upper bound and the neighbor gain matrix;

[0177] A second calculation module 204 is used to calculate the state error covariance weight matrix and the measurement error covariance weight matrix of the power system to be measured at the next moment by a preset Cauchy kernel function, and calculate a weighted state error covariance matrix and a weighted measurement error covariance matrix based on the state error covariance weight matrix, the measurement error covariance weight matrix, the state error covariance decomposition matrix and the measurement error covariance decomposition matrix, wherein the preset Cauchy kernel function is a Cauchy kernel function that minimizes the maximum relative entropy;

[0178] The updating module 205 is used to calculate the gain matrix of the local estimator according to the weighted state error covariance matrix and the weighted measurement error covariance matrix under preset conditions through a fixed point iteration method, and to update the posterior state estimate of the power system under test at the current moment based on the gain matrix to complete the state estimation of the power system under test.

[0179] In a possible implementation, Figure 3 As shown, a state estimation system for a distributed multi-regional power system is provided, comprising:

[0180] Power system area i and power system area j are connected at the physical layer through a transmission line, and at the information layer through a data transmission unit, and the data transmission unit is used to interact with the estimator for data.

[0181] In one possible implementation, the present application provides an electronic device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to perform: obtaining local measurement data, neighbor measurement signals and neighbor state estimation signals of a power system to be tested in a target area of ​​a distributed multi-area power system; calculating a target a posteriori state estimation equation, an upper bound of a state error covariance matrix and a neighbor gain matrix of the power system to be tested at the next moment based on the local measurement data, the neighbor measurement signals and the neighbor state estimation signals; obtaining a state error covariance decomposition through Cholesky decomposition based on the upper bound of the state error covariance matrix and the neighbor gain matrix. matrix and measurement error covariance decomposition matrix; calculate the state error covariance weight matrix and measurement error covariance weight matrix of the power system to be tested at the next moment by a preset Cauchy kernel function, and calculate the weighted state error covariance matrix and weighted measurement error covariance matrix based on the state error covariance weight matrix, the measurement error covariance weight matrix, the state error covariance decomposition matrix and the measurement error covariance decomposition matrix, wherein the preset Cauchy kernel function is the Cauchy kernel function that minimizes the maximum relative entropy; calculate the gain matrix of the local estimator according to the weighted state error covariance matrix and the weighted measurement error covariance matrix under preset conditions by a fixed point iteration method, and update the posterior state estimate of the power system to be tested at the current moment based on the gain matrix, thereby completing the step of state estimation for the power system to be tested.

[0182] In one possible implementation, the present application provides a computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and the computer program is executed by a processor when it is running: obtaining local measurement data, neighbor measurement signals and neighbor state estimation value signals of a power system to be tested in a target area in a distributed multi-area power system; calculating a target a posteriori state estimation value equation, an upper bound of a state error covariance matrix and a neighbor gain matrix of the power system to be tested at the next moment according to the local measurement data, the neighbor measurement signals and the neighbor state estimation value signals; obtaining a state error covariance decomposition matrix and a measurement error covariance decomposition matrix through Cholesky decomposition according to the upper bound of the state error covariance matrix and the neighbor gain matrix; and obtaining the state error covariance decomposition matrix and the measurement error covariance decomposition matrix through a preset Cholesky decomposition. The Cauchy kernel function calculates the state error covariance weight matrix and the measurement error covariance weight matrix of the power system to be tested at the next moment, and calculates the weighted state error covariance matrix and the weighted measurement error covariance matrix based on the state error covariance weight matrix, the measurement error covariance weight matrix, the state error covariance decomposition matrix and the measurement error covariance decomposition matrix, wherein the preset Cauchy kernel function is the Cauchy kernel function that minimizes the maximum relative entropy; through the fixed point iteration method, under preset conditions, the gain matrix of the local estimator is calculated according to the weighted state error covariance matrix and the weighted measurement error covariance matrix, and the posterior state estimate of the power system to be tested at the current moment is updated based on the gain matrix, thereby completing the step of state estimation for the power system to be tested.

[0183] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant descriptions on the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0184] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0185] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0186] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A state estimation method for a distributed multi-regional power system, characterized in that: include: Acquire local measurement data, neighbor measurement signals, and neighbor state estimation value signals of a power system to be measured in a target area of ​​a distributed multi-area power system; Calculate the target a posteriori state estimation value equation, the state error covariance matrix upper bound and the neighbor gain matrix of the power system to be measured at the next moment according to the local measurement data, the neighbor measurement signal and the neighbor state estimation value signal; According to the state error covariance matrix upper bound and the neighbor gain matrix, a state error covariance decomposition matrix and a measurement error covariance decomposition matrix are obtained by Cholesky decomposition; The state error covariance weight matrix and the measurement error covariance weight matrix of the power system to be tested at the next moment are calculated by a preset Cauchy kernel function, and a weighted state error covariance matrix and a weighted measurement error covariance matrix are calculated based on the state error covariance weight matrix, the measurement error covariance weight matrix, the state error covariance decomposition matrix and the measurement error covariance decomposition matrix, wherein the preset Cauchy kernel function is a Cauchy kernel function that minimizes the maximum relative entropy; By means of a fixed point iteration method, under preset conditions, a gain matrix of a local estimator is calculated according to the weighted state error covariance matrix and the weighted measurement error covariance matrix, and a posterior state estimate of the power system to be tested at the current moment is updated based on the gain matrix, so as to complete the state estimation for the power system to be tested; The step of calculating the target a posteriori state estimation value equation, the upper bound of the state error covariance matrix and the neighbor gain matrix of the power system to be measured at the next moment according to the local measurement data, the neighbor measurement signal and the neighbor state estimation value signal comprises: The target a posteriori state estimation value equation of the power system to be measured at the next moment is calculated according to the local measurement data, the neighbor measurement signal and the neighbor state estimation value signal through a preset a posteriori state estimation value equation, wherein the preset a posteriori state estimation value equation is: in, and Indicated in The prior state estimate at time , Indicated in The posterior state estimate at time , and For the state estimator The local gain matrix and neighbors in the local measurement data The neighbor gain matrix in the neighbor measurement signal, and Represents the power system The measurement matrix in the local measurement data and the measurement matrix in the neighbor measurement signal, and The local data measurement value in the local measurement data and the neighbor data measurement value in the neighbor measurement signal; The target posterior state estimation equation is: in, and Indicated in The prior state estimation error and the posterior state estimation error calculated at all times based on all the collected data, is the identity matrix of the corresponding dimension, and Represents the power system exist The process noise and measurement noise at each moment, ; The neighbor gain matrix is ​​obtained according to the target posterior state estimation value equation, wherein the neighbor gain matrix is: Obtaining an estimated error covariance matrix according to the neighbor gain matrix, and simplifying the estimated error covariance matrix by an approximation method of Young's inequality to obtain a simplified matrix; The upper bound of the state error covariance matrix of the power system to be measured at the next moment is obtained according to the simplified matrix, wherein the upper bound of the state error covariance matrix is: in, For the proposed power system exist The upper bound of the estimation error covariance at time , Indicates the preset power system area The number of neighbors of is a positive real number, , is the identity matrix of the corresponding dimension, Power system area calculated for the local area The estimator gain matrix, For local power system area The output matrix of For local power system area The system matrix, ,in Electricity system area for neighbors The output matrix of Electricity system area for neighbors and Region The coupling matrix is ​​used to characterize the physical connection characteristics. , is the system noise covariance matrix, is the output noise covariance matrix.

2. The state estimation method of a distributed multi-regional power system according to claim 1, characterized in that: The step of obtaining a state error covariance decomposition matrix and a measurement error covariance decomposition matrix by Cholesky decomposition includes: The state error covariance matrix is ​​determined according to the upper bound of the state error covariance matrix, and the state error covariance matrix and the measurement error covariance matrix are decomposed by Cholesky to obtain a state error covariance decomposition matrix and a measurement error covariance decomposition matrix.

3. The state estimation method of a distributed multi-regional power system according to claim 1, characterized in that: The step of calculating the state error covariance weight matrix and the measurement error covariance weight matrix of the power system to be tested at the next moment by a preset Cauchy kernel function, and calculating the weighted state error covariance matrix and the weighted measurement error covariance matrix based on the state error covariance weight matrix, the measurement error covariance weight matrix, the state error covariance decomposition matrix and the measurement error covariance decomposition matrix includes: Calculating a posteriori estimate of the power system to be measured at the next moment by using the target posteriori state estimate equation according to the local measurement data, the neighbor measurement signal and the neighbor state estimate signal; Calculate a target a priori state estimation error equation of the power system to be tested at the next moment through a preset a priori state estimation error equation according to the local measurement data, the neighbor measurement signal and the neighbor state estimation value signal, and calculate a priori estimation value of the power system to be tested at the next moment through the target a priori state estimation error equation; Calculate a performance indicator vector of a preset Cauchy kernel function according to the posterior estimation value and the prior estimation value; Calculating a state error covariance weight matrix and a measurement error covariance weight matrix according to the performance indicator vector; A weighted state error covariance matrix and a weighted measurement error covariance matrix are calculated based on the state error covariance weight matrix, the measurement error covariance weight matrix, the state error covariance decomposition matrix, and the measurement error covariance decomposition matrix.

4. The state estimation method of a distributed multi-regional power system according to claim 1, characterized in that: The fixed point iteration method is used to calculate the gain matrix of the local estimator according to the weighted state error covariance matrix and the weighted measurement error covariance matrix under preset conditions, and the posterior state estimation value of the power system to be tested at the current moment is updated based on the gain matrix to complete the state estimation step for the power system to be tested, including: When the preset condition is a value condition, the gain matrix of the local estimator when the partial derivative of the value condition is zero is calculated according to the weighted state error covariance matrix and the weighted measurement error covariance matrix by a fixed point iteration method, wherein the mathematical representation of the value condition is: in, Represents vector No. Dimensional data, For variables The Cauchy kernel function is for The bandwidth parameter at the time.

5. A state estimation system for a distributed multi-regional power system, characterized in that: include: A data acquisition module, used for acquiring local measurement data, neighbor measurement signals and neighbor state estimation value signals of the power system to be measured in the target area of ​​the distributed multi-area power system; A first calculation module, used for calculating the target a posteriori state estimation value equation, the upper bound of the state error covariance matrix and the neighbor gain matrix of the power system to be measured at the next moment according to the local measurement data, the neighbor measurement signal and the neighbor state estimation value signal; A decomposition module, used for obtaining a state error covariance decomposition matrix and a measurement error covariance decomposition matrix by Cholesky decomposition according to the upper bound of the state error covariance matrix and the neighbor gain matrix; A second calculation module is used to calculate the state error covariance weight matrix and the measurement error covariance weight matrix of the power system to be measured at the next moment by a preset Cauchy kernel function, and calculate the weighted state error covariance matrix and the weighted measurement error covariance matrix based on the state error covariance weight matrix, the measurement error covariance weight matrix, the state error covariance decomposition matrix and the measurement error covariance decomposition matrix, wherein the preset Cauchy kernel function is the Cauchy kernel function that minimizes the maximum relative entropy; An updating module is used to calculate the gain matrix of the local estimator according to the weighted state error covariance matrix and the weighted measurement error covariance matrix under preset conditions through a fixed point iteration method, and to update the posterior state estimate of the power system to be tested at the current moment based on the gain matrix to complete the state estimation of the power system to be tested.

6. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the steps of the state estimation method of a distributed multi-regional power system as described in any one of claims 1 to 4 are performed.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the state estimation method of a distributed multi-regional power system as claimed in any one of claims 1 to 4 are executed.

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