Mutual inductor deviation identification method and system based on pigeon inspired collaborative optimization algorithm

Abstract

The invention provides a mutual inductor deviation identification method and system based on a pigeon inspired collaborative optimization algorithm, and relates to the technical field of a novel electric power system.The method comprises the steps that under a distributed mutual inductor network, measurement data of mutual inductor nodes are obtained through a synchronous vector measurement unit; based on the measurement data, solving a pre-established system parameter model containing a mutual inductor ratio correction coefficient to obtain a local optimal solution of the deviation of the mutual inductor ratio correction coefficient; and according to the local optimal solution, performing deep search in the parameter search space by using a pigeon inspired collaborative optimization algorithm to obtain a search result, and determining a mutual inductor deviation identification result according to the search result. Through the mode, a more accurate mutual inductor deviation identification result is obtained, and the accuracy of mutual inductor deviation identification is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new power system technology, and particularly relates to a mutual inductor deviation identification method and system based on a pigeon swarm collaborative optimization algorithm. BACKGROUND

[0002] With the continuous growth of power demand, the safety and stability of new power systems are increasingly valued. In order to effectively monitor the flow in the new power system, the measurement of voltage and current is crucial. The power transformer is the basis of modern measurement technology and an essential device in the new power system. The transformer includes a current transformer and a voltage transformer, which can provide key measurement data for relay protection, measurement, metering, and flow control devices. The measurement accuracy directly affects the data quality obtained by the secondary equipment and the reliability of related applications. However, due to changes in the working environment or other external factors, the operating characteristics of the transformer may deviate, and the deviation caused by this deviation will cause greater errors in the measured data, making the device unable to accurately reflect the voltage and current characteristics of the primary side. In order to ensure the normal operation of the new power system, it is necessary to calibrate the transformer deviation. In practice, to achieve accurate calibration of the transformer deviation, it is necessary to identify which transformers have deviations.

[0003] Existing transformer deviation identification methods are mainly based on hardware calibration and software algorithms. The hardware calibration-based method can reduce the deviation to a certain extent, but the hardware itself is affected by environmental factors such as temperature, humidity, and electromagnetic interference, which can cause the calibration accuracy to decrease. The software algorithm-based method is highly dependent on measurement data, and when the measurement data contains noise, missing values, or outliers, the accuracy of the algorithm will be affected. In addition, software algorithms rely on certain assumptions, such as the linearity of the system model, while actual power systems have high nonlinearity and time-varying characteristics, making it difficult for the algorithm to accurately adapt to actual situations, and thus leading to inaccurate transformer deviation identification results.

[0004] Therefore, the existing transformer deviation identification methods based on hardware calibration and software algorithms face problems such as complex implementation, insufficient real-time performance, limited application scenarios, and poor accuracy in identifying the deviation of running transformers within the entire system, which cannot provide accurate reference transformers or effective calibration ranges for subsequent calibration work. SUMMARY

[0005] To overcome the shortcomings of the prior art, the present application provides a transformer deviation identification method based on a pigeon swarm collaborative optimization algorithm, comprising:

[0006] Under a decentralized transformer network, the measurement data of the transformer nodes is obtained through a synchronous vector measurement unit;

[0007] Based on the measurement data, a system parameter model containing the mutual inductor ratio correction coefficient is solved to obtain a local optimal solution of the mutual inductor ratio correction coefficient deviation;

[0008] According to the local optimal solution, a depth search is performed in the parameter search space by using a pigeon swarm cooperative optimization algorithm to obtain a search result, and a mutual inductor deviation identification result is determined according to the search result.

[0009] Preferably, the network structure of the distributed mutual inductor network comprises: in the two-node system equivalent circuit model, the bus s end and the bus r end are connected through a π type equivalent model; the bus s end and the bus r end are provided with a synchronous vector measurement unit.

[0010] Preferably, the establishment process of the system parameter model containing the mutual inductor ratio correction coefficient comprises:

[0011] Based on the bus s end ratio initial correction coefficient, an s end voltage target relationship expression between the s end voltage real value and the s end voltage measurement value, and an s end current target relationship expression between the s end current real value and the s end current measurement value are obtained;

[0012] Based on the bus r end ratio initial correction coefficient, an r end voltage target relationship expression between the r end voltage real value and the r end voltage measurement value, and an r end current target relationship expression between the r end current real value and the r end current measurement value are obtained;

[0013] Based on the impedance real value initial dynamic correction factor, an s end impedance real value target relationship expression between the s end impedance real value, the s end voltage real value and the s end current real value, and an r end impedance real value target relationship expression between the r end impedance real value, the r end voltage real value and the r end current real value are obtained;

[0014] Based on the s end voltage target relationship expression, the s end current target relationship expression, the r end voltage target relationship expression, the r end current target relationship expression, the s end impedance real value target relationship expression and the r end impedance real value target relationship expression, a system parameter model containing the mutual inductor ratio correction coefficient is established.

[0015] Preferably, based on the bus s end ratio initial correction coefficient, an s end voltage target relationship expression between the s end voltage real value and the s end voltage measurement value, and an s end current target relationship expression between the s end current real value and the s end current measurement value are obtained, comprising:

[0016] Based on the bus s end ratio initial correction coefficient, an s end voltage initial relationship expression between the s end voltage real value and the s end voltage measurement value is obtained;

[0017] The s-terminal voltage target relationship expression between the s-terminal voltage measurement value and the s-terminal voltage real value is obtained by reverse derivation of the s-terminal voltage initial relationship expression.

[0018] The s-terminal current initial relationship expression between the s-terminal current real value and the s-terminal current measurement value is obtained based on the bus s-terminal ratio initial correction coefficient;

[0019] The s-terminal current target relationship expression between the s-terminal current measurement value and the s-terminal current real value is obtained by reverse derivation of the s-terminal current initial relationship expression;

[0020] The s-terminal target voltage relationship expression is:

[0021]

[0022] The s-terminal current target relationship expression is:

[0023]

[0024] The bus s-terminal ratio initial correction coefficient is V s true The s-terminal voltage real value is V s meas The s-terminal voltage measurement value is θ, the temperature drift factor is ΔT, and the time window is The s-terminal current real value is The s-terminal current measurement value is The time attenuation coefficient is t, and the running time after the start of each new cycle is t.

[0025] Preferably, the r-terminal voltage target relationship expression between the r-terminal voltage real value and the r-terminal voltage measurement value and the r-terminal current target relationship expression between the r-terminal current real value and the r-terminal current measurement value are obtained based on the bus r-terminal ratio initial correction coefficient, and include:

[0026] The r-terminal voltage initial relationship expression between the r-terminal voltage real value and the r-terminal voltage measurement value is obtained based on the bus r-terminal ratio initial correction coefficient;

[0027] The r-terminal voltage target relationship expression between the r-terminal voltage measurement value and the r-terminal voltage real value is obtained by reverse derivation of the r-terminal voltage initial relationship expression;

[0028] The r-terminal current initial relationship expression between the r-terminal current real value and the r-terminal current measurement value is obtained based on the bus r-terminal ratio initial correction coefficient;

[0029] The r-terminal current target relationship expression between the r-terminal current measurement value and the r-terminal current real value is obtained by reverse derivation of the r-terminal current initial relationship expression;

[0030] wherein the r-terminal voltage target relationship expression is:

[0031]

[0032] the r-terminal current target relationship expression is:

[0033]

[0034] wherein, is an initial correction coefficient of the bus r-terminal ratio, is an actual value of the r-terminal voltage, is a measured value of the r-terminal voltage, θ is a temperature drift factor, and ΔT is a time window, is an actual value of the r-terminal current, is a measured value of the r-terminal current, is a time decay coefficient, and t is an elapsed time after the start of each new cycle.

[0035] Preferably, based on the initial dynamic correction factor of the impedance actual value, an s-terminal impedance actual value target relationship expression between the s-terminal impedance actual value and the s-terminal voltage actual value and the s-terminal current actual value, and an r-terminal impedance actual value target relationship expression between the r-terminal impedance actual value and the r-terminal voltage actual value and the r-terminal current actual value are obtained, including:

[0036] determining an initial relationship expression of the s-terminal impedance actual value based on the initial dynamic correction factor of the impedance actual value and the s-terminal voltage actual value and the s-terminal current actual value;

[0037] substituting the s-terminal voltage target relationship expression and the s-terminal current target relationship expression into the initial relationship expression of the s-terminal impedance actual value to obtain the s-terminal impedance actual value target relationship expression;

[0038] determining an initial relationship expression of the r-terminal impedance actual value based on the initial dynamic correction factor of the impedance actual value and the r-terminal voltage actual value and the r-terminal current actual value;

[0039] substituting the r-terminal voltage target relationship expression and the r-terminal current target relationship expression into the initial relationship expression of the r-terminal impedance actual value to obtain the r-terminal impedance actual value target relationship expression;

[0040] wherein the s-terminal impedance actual value target relationship expression is:

[0041]

[0042] the r-terminal impedance actual value target relationship expression is:

[0043]

[0044] is the real value of the impedance at the s terminal, is the real value of the impedance at the r terminal, ΔT is a time window, and ξ is an initial dynamic correction factor of the real value of the impedance, is an initial correction coefficient of the ratio at the s terminal, V s true is the real value of the voltage at the s terminal, V s meas is a measured value of the voltage at the s terminal, θ is a temperature drift factor, and ΔT is a time window, is the real value of the current at the s terminal, is a measured value of the current at the s terminal, is a time decay coefficient, and t is the running time after the start of each new cycle; is an initial correction coefficient of the ratio at the r terminal, is the real value of the voltage at the r terminal, is a measured value of the voltage at the r terminal, is the real value of the current at the r terminal, is a measured value of the current at the r terminal.

[0045] Preferably, based on the measurement data, a system parameter model previously established and containing a mutual inductor ratio correction coefficient is solved to obtain a local optimal solution of the mutual inductor ratio correction coefficient deviation, including:

[0046] Partial derivatives of the mutual inductor ratio correction coefficient in the system parameter model are calculated to establish an objective function for measuring system characteristics;

[0047] The objective function for measuring system characteristics is converted into a matrix form objective function;

[0048] Based on the measurement data, the matrix form objective function is solved to obtain a local optimal solution of the mutual inductor ratio correction coefficient deviation.

[0049] Preferably, the expression of the objective function for measuring system characteristics is as follows:

[0050]

[0051] wherein Φ is the objective function for measuring system characteristics, is an initial correction coefficient of the ratio at the s terminal of the bus, is an initial correction coefficient of the ratio at the r terminal of the bus, ξ is an initial dynamic correction factor of the real value of the impedance, and ω ij is a connection weight between node i and node j, k is a noise sensitivity coefficient, Q is the total number of node scales, and N i is the starting number of node j, ΔV j is a voltage variation of node j, is a variation of the ratio correction coefficient at the s terminal, is a bus r terminal ratio correction coefficient variation, Δξ is an impedance real value dynamic correction factor variation, is a node j voltage variation sensitivity to variation, is a node j voltage variation sensitivity to variation, is a node j voltage variation sensitivity to ξ variation, is a noise impact on partial derivative, is a noise impact on partial derivative, is a noise impact on ξ partial derivative;

[0052] The expression of the objective function in matrix form is:

[0053]

[0054] The expression of the local optimal solution of the mutual inductor ratio correction coefficient deviation is:

[0055]

[0056] Wherein, J is the Jacobian matrix, Ω is the node credibility weight matrix, J T is the transpose of the Jacobian matrix, is the deviation matrix of the mutual inductor ratio correction coefficient, is the local optimal solution of the bus s terminal ratio correction coefficient deviation, is the local optimal solution of the bus r terminal ratio correction coefficient deviation, Δξ is the local optimal solution of the impedance real value dynamic correction factor deviation, ΔZ = [ΔZ1, ΔZ2, ΔZ3, …, ΔZ i ] T is the deviation vector of the equivalent impedance of each node in the system, ΔZ i is the deviation vector of the equivalent impedance of node i.

[0057] Preferably, according to the local optimal solution, a pigeon swarm collaborative optimization algorithm is used to perform a deep search in the parameter search space, and a search result is obtained, and according to the search result, a mutual inductor deviation identification result is determined, including:

[0058] According to the local optimal solution, the line parameter vector is initialized to obtain an initialized line parameter vector;

[0059] An elliptical constraint is used to determine the parameter search space according to the value range of the initialized line parameter vector, and a pigeon swarm collaborative optimization algorithm is used to obtain the node target position in the parameter search space;

[0060] The fitness value of the node is calculated according to the fitness function of the node, the node target position is iteratively updated based on a node position updating strategy, and the node target position at this time is determined as the deviation identification result of the mutual inductor until the iteration number reaches a preset iteration number.

[0061] Preferably, the expression of the ellipse constraint is as follows:

[0062]

[0063] Wherein, D is the actual dynamic loss, D0 is the dynamic loss reference value, Y is the time-varying admittance component, Y0 is the time-varying admittance component reference value, a is the dynamic loss correction coefficient range, and b is the time-varying admittance component correction coefficient range.

[0064] The expression of the fitness function is as follows:

[0065]

[0066] Wherein, F i is the fitness value of the ith node, Z k is the equivalent impedance value of node i calculated on the kth line, is the true value of the equivalent impedance of node i on the kth line, k is the actual line number, N is the total number of actual lines, mu is the collaborative error penalty coefficient, P i is the line parameter vector of node i, P j is the line parameter vector of node j, ||P i -P j || is the dot product of node i and node j, d ij is the direction vector from node i to node j, Q is the total number of node scales, N i is the starting number of node j;

[0067] The node position updating strategy is as follows:

[0068] P i t+1 = P i t + p·M·(P best -P i t )+ b·S·(P rand -P i t )+ g·AP local

[0069] Wherein, P i t is the position of node i at the tth iteration, P i t+1 is the position of node i at the t+1th iteration, is the initialized line parameter vector, is the bus s terminal ratio target correction coefficient, is the bus r terminal ratio target correction coefficient, ξ' is the impedance true value target dynamic correction factor, D is the actual dynamic loss, Y is the time-varying admittance component, G is the active gain in the flow transmission process, C is the coupling compensation between nodes, Z s is the slight correction value of the equivalent impedance measurement value of the s terminal, Z r is the slight correction value of the equivalent impedance measurement value of the r terminal, θ is the temperature drift factor; P best is the global optimal solution, P rand is the randomly selected individual position, ΔP local is the local search step length vector, ρ is the geomagnetic information direction control parameter, β is the solar information direction control parameter, γ is the step length control parameter, M is the direction vector of the vector simulation of the geomagnetic information, and S is the direction vector of the vector simulation of the solar information.

[0070] Based on the same inventive concept, the application also provides a mutual inductor deviation identification system based on a pigeon swarm collaborative optimization algorithm, characterized by comprising:

[0071] a measurement data obtaining module, configured to obtain measurement data of a mutual inductor node by a synchronous vector measurement unit under a decentralized mutual inductor network;

[0072] a correction coefficient deviation obtaining module, configured to solve a system parameter model containing mutual inductor ratio correction coefficients pre-established to obtain a local optimal solution of mutual inductor ratio correction coefficient deviation based on the measurement data;

[0073] a deviation identification result determining module, configured to perform deep search in a parameter search space by using the pigeon swarm collaborative optimization algorithm according to the local optimal solution to obtain a search result, and determine a mutual inductor deviation identification result according to the search result.

[0074] Preferably, the network structure of the decentralized mutual inductor network comprises: connecting a bus s terminal and a bus r terminal by a π-type equivalent model in a two-node system equivalent circuit model; and the bus s terminal and the bus r terminal are provided with the synchronous vector measurement unit.

[0075] Preferably, the system further comprises a system parameter model establishing module.

[0076] The system parameter model establishing module is configured to:

[0077] obtain an s terminal voltage target relationship expression between an s terminal voltage true value and an s terminal voltage measurement value, and an s terminal current target relationship expression between an s terminal current true value and an s terminal current measurement value based on a bus s terminal ratio initial correction coefficient;

[0078] obtain an r-terminal voltage target relationship expression between the r-terminal voltage real value and the r-terminal voltage measurement value, and an r-terminal current target relationship expression between the r-terminal current real value and the r-terminal current measurement value based on the bus r-terminal ratio initial correction coefficient;

[0079] obtain an s-terminal impedance real value target relationship expression between the s-terminal impedance real value and the s-terminal voltage real value and the s-terminal current real value, and an r-terminal impedance real value target relationship expression between the r-terminal impedance real value and the r-terminal voltage real value and the r-terminal current real value based on the impedance real value initial dynamic correction factor;

[0080] establish a system parameter model containing the mutual inductor ratio correction coefficient based on the s-terminal voltage target relationship expression, the s-terminal current target relationship expression, the r-terminal voltage target relationship expression, the r-terminal current target relationship expression, the s-terminal impedance real value target relationship expression, and the r-terminal impedance real value target relationship expression.

[0081] Preferably, the system parameter model establishing module is specifically configured to:

[0082] obtain an s-terminal voltage initial relationship expression between the s-terminal voltage real value and the s-terminal voltage measurement value based on the bus s-terminal ratio initial correction coefficient;

[0083] obtain an s-terminal voltage target relationship expression between the s-terminal voltage measurement value and the s-terminal voltage real value by backstepping the s-terminal voltage initial relationship expression;

[0084] obtain an s-terminal current initial relationship expression between the s-terminal current real value and the s-terminal current measurement value based on the bus s-terminal ratio initial correction coefficient;

[0085] obtain an s-terminal current target relationship expression between the s-terminal current measurement value and the s-terminal current real value by backstepping the s-terminal current initial relationship expression;

[0086] The s-terminal target voltage relationship expression is:

[0087]

[0088] The s-terminal current target relationship expression is:

[0089]

[0090] the bus s-terminal ratio initial correction coefficient is the s-terminal voltage real value is V s meas the s-terminal voltage measurement value is θ, the temperature drift factor is ΔT, and the time window is the s-terminal current real value is is a s-terminal current measurement value, is a time decay coefficient, and t is an elapsed time after the start of each new cycle.

[0091] Preferably, the system parameter model establishing module is further configured to:

[0092] obtain an initial relationship expression between the r-terminal voltage real value and the r-terminal voltage measurement value based on the initial correction coefficient of the bus r-terminal ratio;

[0093] obtain a target relationship expression between the r-terminal voltage measurement value and the r-terminal voltage real value by backstepping the r-terminal voltage initial relationship expression;

[0094] obtain an initial relationship expression between the r-terminal current real value and the r-terminal current measurement value based on the initial correction coefficient of the bus r-terminal ratio;

[0095] obtain a target relationship expression between the r-terminal current measurement value and the r-terminal current real value by backstepping the r-terminal current initial relationship expression;

[0096] The target relationship expression of the r-terminal voltage is:

[0097]

[0098] The target relationship expression of the r-terminal current is:

[0099]

[0100] wherein, is an initial correction coefficient of a bus r-terminal ratio, is an r-terminal voltage real value, is an r-terminal voltage measurement value, and θ is a temperature drift factor, and ΔT is a time window, is an r-terminal current real value, is an r-terminal current measurement value, is a time decay coefficient, and t is an elapsed time after the start of each new cycle.

[0101] Preferably, the system parameter model establishing module is further configured to:

[0102] determine an initial relationship expression of the s-terminal impedance real value based on the initial dynamic correction factor of the impedance real value, the s-terminal voltage real value, and the s-terminal current real value;

[0103] obtain a target relationship expression of the s-terminal impedance real value by substituting the target relationship expression of the s-terminal voltage and the target relationship expression of the s-terminal current into the initial relationship expression of the s-terminal impedance real value;

[0104] The r-terminal impedance real value initial relationship expression is determined based on the impedance real value initial dynamic correction factor, the r-terminal voltage real value and the r-terminal current real value;

[0105] The r-terminal impedance real value target relationship expression is obtained by substituting the r-terminal voltage target relationship expression and the r-terminal current target relationship expression into the r-terminal impedance real value initial relationship expression;

[0106] The s-terminal impedance real value target relationship expression is as follows:

[0107]

[0108] The r-terminal impedance real value target relationship expression is as follows:

[0109]

[0110] The s-terminal impedance real value is The r-terminal impedance real value is, ΔT is a time window, and ξ is an impedance real value initial dynamic correction factor. The s-terminal voltage real value is V s true The s-terminal voltage real value is V s meas The s-terminal voltage real value is V The s-terminal current real value is The s-terminal current real value is The time attenuation coefficient is t, and t is an operating time after the start of each new period. The r-terminal ratio initial correction coefficient is The r-terminal voltage real value is V The r-terminal voltage real value is V The r-terminal current real value is The r-terminal current real value is

[0111] Preferably, the correction coefficient deviation obtaining module is specifically used for:

[0112] The partial derivative of the mutual inductor ratio correction coefficient in the system parameter model is calculated, and a target function for measuring system characteristics is established.

[0113] The target function for measuring system characteristics is converted into a matrix form target function.

[0114] Based on the measurement data, the matrix form target function is solved to obtain a local optimal solution of the mutual inductor ratio correction coefficient deviation.

[0115] Preferably, the expression of the target function for measuring system characteristics is as follows:

[0116]

[0117] wherein Φ is a target function measuring system characteristics, is an initial correction coefficient of the bus s-side ratio, is an initial correction coefficient of the bus r-side ratio, ξ is an initial dynamic correction factor of the impedance true value, ω ij is a connection weight between node i and node j, k is a noise sensitivity coefficient, Q is the total number of node scales, N i is the initial number of node j, ΔV j is the voltage variation of node j, is the variation of the s-side ratio correction coefficient, is the variation of the r-side ratio correction coefficient, Δξ is the variation of the dynamic correction factor of the impedance true value, is the sensitivity of the voltage variation of node j to , is the sensitivity of the voltage variation of node j to , is the sensitivity of the voltage variation of node j to ξ, is the influence of noise on partial derivative, is the influence of noise on partial derivative, is the influence of noise on ξ partial derivative;

[0118] The expression of the target function in matrix form is:

[0119]

[0120] The expression of the local optimal solution of the mutual inductor ratio correction coefficient deviation is:

[0121]

[0122] wherein J is a Jacobian matrix, Ω is a node credibility weight matrix, J T is the transpose of the Jacobian matrix, is the deviation matrix of the mutual inductor ratio correction coefficient, is the local optimal solution of the bus s-side ratio correction coefficient deviation, is the local optimal solution of the bus r-side ratio correction coefficient deviation, Δξ is the local optimal solution of the dynamic correction factor of the impedance true value deviation, ΔZ=[ΔZ1,ΔZ2,ΔZ3.,.....,ΔZ i ] T is the deviation vector of the equivalent impedance of each node in the system, ΔZ i is the deviation vector of the equivalent impedance of node i.

[0123] Preferably, the deviation identification result determination module is specifically configured to:

[0124] The line parameter vector is initialized according to the local optimal solution, and an initialized line parameter vector is obtained;

[0125] The parameter search space is determined according to the value range of the initialized line parameter vector by using the elliptical constraint, and the node target position in the parameter search space is obtained by using the pigeon swarm cooperative optimization algorithm.

[0126] The fitness value of the node is calculated according to the fitness function of the node, and the node target position is iteratively updated based on the node position update strategy until the iteration number reaches the preset iteration number, and the node target position at this time is determined as the deviation identification result of the mutual inductor.

[0127] Preferably, the expression of the elliptical constraint is as follows:

[0128]

[0129] Wherein, D is the actual dynamic loss, D0 is the dynamic loss reference value, Y is the time-varying admittance component, Y0 is the time-varying admittance component reference value, a is the dynamic loss correction coefficient range, and β is the time-varying admittance component correction coefficient range.

[0130] The expression of the fitness function is:

[0131]

[0132] Wherein, F i is the fitness value of the i-th node, Z k is the equivalent impedance value of node i calculated on the k-th line, is the true value of the equivalent impedance of node i on the k-th line, k is the actual line number, N is the total number of actual lines, μ is the cooperative error penalty coefficient, P i is the line parameter vector of node i, P j is the line parameter vector of node j, ||P i -P j || is the dot product of node i and node j, d ij is the direction vector from node i to node j, Q is the total number of node scales, N i is the starting number of node j.

[0133] The node position update strategy is:

[0134] P i t+1 =P i t +ρ·M·(P best -P i t)+ beta * S * (P rand -P i t )+ gamma * delta P local

[0135] wherein P i t is the position of node i at iteration t, P i t+1 is the position of node i at iteration t+1, is the initialized line parameter vector, is the bus s-side ratio target correction coefficient, is the bus r-side ratio target correction coefficient, xi is the impedance real value target dynamic correction factor, D is the actual dynamic loss, Y is the time-varying admittance component, G is the active gain in the flow transmission process, C is the coupling compensation between nodes, Z s is the slight correction value of the equivalent impedance measurement value of the s-side, Z r is the slight correction value of the equivalent impedance measurement value of the r-side, theta is the temperature drift factor; P best is the global optimal solution, P rand is the randomly selected individual position, delta P local is the local search step length vector, rho is the geomagnetic information direction control parameter, beta is the solar information direction control parameter, gamma is the step length control parameter, M is the direction vector of the vector simulation geomagnetic information, and S is the direction vector of the vector simulation solar information.

[0136] Based on the same inventive concept, the present application also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected through a bus;

[0137] the memory is used for storing one or more programs;

[0138] when the one or more programs are executed by the at least one processor, a mutual inductor deviation identification method based on pigeon swarm collaborative optimization algorithm is realized.

[0139] Based on the same inventive concept, the present application also provides a readable storage medium, which has an execution program stored thereon, and when the execution program is executed, a mutual inductor deviation identification method based on pigeon swarm collaborative optimization algorithm is realized.

[0140] Compared with the closest prior art, the present application has the beneficial effects as follows:

[0141] The application provides a mutual inductor deviation identification method based on a pigeon swarm cooperative optimization algorithm, and the method comprises the following steps: under a decentralized mutual inductor network, measurement data of a mutual inductor node is obtained through a synchronous vector measurement unit; based on the measurement data, a system parameter model containing mutual inductor ratio correction coefficients is solved to obtain a local optimal solution of mutual inductor ratio correction coefficient deviation; according to the local optimal solution, a depth search is performed in a parameter search space by using the pigeon swarm cooperative optimization algorithm to obtain a search result, and a mutual inductor deviation identification result is determined according to the search result. Under the decentralized mutual inductor network, the system parameter model containing the mutual inductor deviation is established, then the measurement data of the synchronous vector measurement unit is processed by the mutual inductor node, the local optimal solution of the mutual inductor ratio correction coefficient deviation is determined, and then the line parameter space is searched and analyzed according to the pigeon swarm cooperative optimization algorithm, so that a more accurate mutual inductor deviation identification result is obtained, and the accuracy of mutual inductor deviation identification is improved. BRIEF DESCRIPTION OF DRAWINGS

[0142] Figure 1 A flowchart of the mutual inductor deviation identification method based on the pigeon swarm cooperative optimization algorithm is provided for the application.

[0143] Figure 2 A network structure diagram of the decentralized mutual inductor network is provided for the application.

[0144] Figure 3 A structure diagram of the mutual inductor deviation identification system based on the pigeon swarm cooperative optimization algorithm is provided for the application.

[0145] Figure 4 A schematic diagram of the electronic device is provided for the application. DETAILED DESCRIPTION

[0146] Embodiments of the application are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are intended to explain the application, and cannot be understood as a limitation of the application.

[0147] Embodiment 1:

[0148] The application provides a mutual inductor deviation identification method based on a pigeon swarm cooperative optimization algorithm, specifically, Figure 1 A flowchart of the mutual inductor deviation identification method based on the pigeon swarm cooperative optimization algorithm is provided for the application, as shown in the figure, comprising the following steps:

[0149] S1: under a decentralized mutual inductor network, measurement data of a mutual inductor node is obtained through a synchronous vector measurement unit;

[0150] S2: Based on the measurement data, solve the pre-established system parameter model containing the transformer ratio correction coefficient to obtain the local optimal solution of the transformer ratio correction coefficient deviation;

[0151] S3: Based on the local optimum, use the pigeon flock cooperative optimization algorithm to perform a depth search in the parameter search space to obtain the search results, and determine the mutual inductor deviation identification result based on the search results.

[0152] The present invention obtains more accurate current transformer deviation identification results through the above method, improves the accuracy of current transformer deviation identification, and thus provides an accurate reference current transformer or an effective calibration range for subsequent calibration work.

[0153] like Figure 2 The diagram shown is a schematic representation of the network structure of the distributed instrument transformer network provided by this invention. The distributed instrument transformer network can be considered an equivalent circuit for a two-node system. In the two-node equivalent system, the lines connecting the two buses s and r are represented using a π-type (i.e., PI-type) equivalent model, and the relevant line parameters are... Figure 2 The symbol indicates that... r Let V be the current at terminal r. r I is the voltage at terminal r; s V is the current at terminal s. s This refers to the voltage at the S-terminal. PMUs (Phasor Measurement Units) are installed at both the S-terminal and R-terminal of the busbar, allowing direct measurement of the busbar voltage phasor and branch current phasor.

[0154] PMUs play a crucial role in the wide-area protection and stability control systems of modern power systems. They can measure the state variables of various hub points in the power grid under high-precision clock synchronization, package time-stamped phasor data, and transmit it to the data analysis center through a high-speed communication network for real-time monitoring, protection, and control.

[0155] In a distributed instrument transformer network, measurement data of the instrument transformer nodes are obtained through a synchronous vector measurement unit. The measurement data includes the current and voltage measurements at the source (S) and receiver (R) terminals.

[0156] In some alternative implementations, based on Kirchhoff's current law and voltage law, by introducing a time-varying coupling coefficient μ(t) and a dynamic loss adjustment factor ω, the following equation is obtained:

[0157] I s (t)=μ(t)·(G·V s -ω·D·V r )+ε(t)

[0158]

[0159] Among them, Is (t) represents the theoretical value of the current at bus s at time t, I r (t) represents the theoretical value of the current at bus r at time t, μ(t) represents the time-varying coupling coefficient, ω represents the dynamic loss adjustment factor, G represents the active gain in the flow transmission process, D represents the actual dynamic loss, ε(t) represents the influence of environmental noise interference on the current at bus s, Y represents the time-varying admittance component, C represents the coupling compensation between nodes, ΔT represents the time window, λ is the delay weight, V s represents the voltage value at bus s, V r represents the voltage value at bus r.

[0160] μ(t) is used to reflect the change of the coupling relationship between nodes at different times in the system, and ω is used to adjust the dynamic loss of bus s and bus r. μ(t)·(G·V s -ω·D·V r represents the theoretical value of the current at bus s based on the node voltage, active gain, dynamic loss and time-varying coupling coefficient, Y·V r +C·(V s -V r represents the influence of bus r voltage, time-varying admittance component and coupling compensation between nodes on the current, and 1+λ·ΔT represents the influence of the time window and the delay weight on the current calculation.

[0161] Further, according to Kirchhoff's voltage and current law I is the current, V is the voltage, and Z is the impedance. Using the voltage and current measurement values of bus s and r, the following formula can be obtained:

[0162]

[0163] wherein Z s represents a slight correction value of the equivalent impedance measurement value of s, Z r represents a slight correction value of the equivalent impedance measurement value of r, represents the s-end voltage measurement value, represents the s-end current measurement value, represents the r-end voltage measurement value, represents the r-end current measurement value, and ξ1 represents the measurement dynamic impedance correction factor, and ΔT represents the time window. respectively represent the preliminary impedance values of s and r based on the voltage and current measurement values, and (1+ξ1·ΔT) and (1-ξ1·ΔT) respectively represent the correction of the preliminary impedance values of s and r according to the dynamic impedance correction factor and the time window.

[0164] After obtaining the measurement data of the mutual inductor node, a system parameter model containing the mutual inductor ratio correction coefficient is solved based on the measurement data to obtain a local optimal solution of the mutual inductor ratio correction coefficient deviation.

[0165] In some optional embodiments, the application further includes establishment of a system parameter model containing a mutual inductor ratio correction coefficient. In view of the existence of the mutual inductor deviation, the establishment process of the system parameter model containing the mutual inductor ratio correction coefficient is as follows:

[0166] Based on the bus s-end ratio initial correction coefficient, an s-end voltage target relationship expression between the s-end voltage real value and the s-end voltage measurement value is obtained, and an s-end current target relationship expression between the s-end current real value and the s-end current measurement value is obtained.

[0167] Based on the bus r-end ratio initial correction coefficient, an r-end voltage target relationship expression between the r-end voltage real value and the r-end voltage measurement value is obtained, and an r-end current target relationship expression between the r-end current real value and the r-end current measurement value is obtained.

[0168] Based on the impedance real value initial dynamic correction factor, an s-end impedance real value target relationship expression between the s-end impedance real value and the s-end voltage real value and the s-end current real value is obtained, and an r-end impedance real value target relationship expression between the r-end impedance real value and the r-end voltage real value and the r-end current real value is obtained.

[0169] Based on the s-end voltage target relationship expression, the s-end current target relationship expression, the r-end voltage target relationship expression, the r-end current target relationship expression, the s-end impedance real value target relationship expression, and the r-end impedance real value target relationship expression, a system parameter model containing the mutual inductor ratio correction coefficient is established.

[0170] Specifically, based on the bus s-end ratio initial correction coefficient, an s-end voltage target relationship expression between the s-end voltage real value and the s-end voltage measurement value is obtained, and an s-end current target relationship expression between the s-end current real value and the s-end current measurement value is obtained, including:

[0171] Based on the bus s-end ratio initial correction coefficient, an s-end voltage initial relationship expression between the s-end voltage real value and the s-end voltage measurement value is obtained.

[0172] The s-end voltage initial relationship expression is backstepped to obtain an s-end voltage target relationship expression between the s-end voltage measurement value and the s-end voltage real value.

[0173] Based on the bus s-end ratio initial correction coefficient, an s-end current initial relationship expression between the s-end current real value and the s-end current measurement value is obtained.

[0174] The s-terminal current target relationship expression between the s-terminal current measurement value and the s-terminal current true value is obtained by inversely deducing the s-terminal current initial relationship expression.

[0175] The s-terminal voltage initial relationship expression is:

[0176]

[0177] The s-terminal voltage target relationship expression is:

[0178]

[0179] The s-terminal current initial relationship expression is:

[0180]

[0181] The s-terminal current target relationship expression is:

[0182]

[0183] The s-terminal voltage true value is: The s-terminal voltage initial correction coefficient of the bus is V s true The s-terminal voltage true value is V s meas The s-terminal voltage measurement value is θ, the temperature drift factor is ΔT, and the time window is The s-terminal current true value is: The s-terminal current measurement value is: The time attenuation coefficient is t, and t is the running time after the start of each new period.

[0184] The s-terminal voltage initial relationship expression is the s-terminal voltage measurement value expression, and the s-terminal voltage target relationship expression is obtained by inversely deducing the voltage true value from the s-terminal voltage measurement value expression and then subtracting the influence caused by the temperature drift and the time window; the s-terminal current initial relationship expression is the s-terminal current measurement value expression, and the s-terminal current target relationship expression is obtained by inversely deducing the s-terminal current true value from the s-terminal current measurement value expression, using the coefficient adjustment and the time attenuation correction on the s-terminal current measurement value.

[0185] The r-terminal voltage target relationship expression between the r-terminal voltage true value and the r-terminal voltage measurement value and the r-terminal current target relationship expression between the r-terminal current true value and the r-terminal current measurement value are obtained based on the bus r-terminal ratio initial correction coefficient, and the r-terminal voltage target relationship expression and the r-terminal current target relationship expression include:

[0186] The r-terminal voltage initial relationship expression between the r-terminal voltage true value and the r-terminal voltage measurement value is obtained based on the bus r-terminal ratio initial correction coefficient.

[0187] An initial relationship expression of the r-terminal voltage is reversely deduced to obtain a target relationship expression between the r-terminal voltage measured value and the r-terminal voltage real value;

[0188] An initial relationship expression of the r-terminal current is obtained based on the initial correction coefficient of the bus r-terminal ratio to obtain an initial relationship expression between the r-terminal current real value and the r-terminal current measured value;

[0189] The initial relationship expression of the r-terminal current is reversely deduced to obtain a target relationship expression between the r-terminal current measured value and the r-terminal current real value;

[0190] The initial relationship expression of the r-terminal voltage is:

[0191]

[0192] The target relationship expression of the r-terminal voltage is:

[0193]

[0194] The initial relationship expression of the r-terminal current is:

[0195]

[0196] The target relationship expression of the r-terminal current is:

[0197]

[0198] Wherein, The initial correction coefficient of the bus r-terminal ratio, The r-terminal voltage real value, The r-terminal voltage measured value, θ is a temperature drift factor, and ΔT is a time window, The r-terminal current real value, The r-terminal current measured value, The time attenuation coefficient, t is the running time after the start of each new period.

[0199] The initial relationship expression of the r-terminal voltage is the r-terminal voltage measured value expression, and the target relationship expression of the r-terminal voltage is the real value of the voltage reversely deduced from the r-terminal voltage measured value expression plus the influence caused by the temperature drift and the time window; the initial relationship expression of the r-terminal current is the r-terminal current measured value expression, and the target relationship expression of the r-terminal current is obtained by reversely deducing the r-terminal current real value from the r-terminal current measured value, adjusting the coefficient of the r-terminal current measured value, and correcting the time attenuation.

[0200] Based on the impedance real value initial dynamic correction factor, the s-terminal impedance real value target relationship expression between the s-terminal impedance real value and the s-terminal voltage real value and the s-terminal current real value, and the r-terminal impedance real value target relationship expression between the r-terminal impedance real value and the r-terminal voltage real value and the r-terminal current real value are obtained, comprising:

[0201] Based on the impedance real value initial dynamic correction factor, and the s-terminal voltage real value and the s-terminal current real value, the s-terminal impedance real value initial relationship expression is determined;

[0202] The s-terminal voltage target relationship expression and the s-terminal current target relationship expression are substituted into the s-terminal impedance real value initial relationship expression to obtain the s-terminal impedance real value target relationship expression;

[0203] Based on the impedance real value initial dynamic correction factor, and the r-terminal voltage real value and the r-terminal current real value, the r-terminal impedance real value initial relationship expression is determined;

[0204] The r-terminal voltage target relationship expression and the r-terminal current target relationship expression are substituted into the r-terminal impedance real value initial relationship expression to obtain the r-terminal impedance real value target relationship expression;

[0205] Wherein, the s-terminal impedance real value initial relationship expression is:

[0206]

[0207] The s-terminal impedance real value target relationship expression is:

[0208]

[0209] The r-terminal impedance real value initial relationship expression is:

[0210]

[0211] The r-terminal impedance real value target relationship expression is:

[0212]

[0213] The s-terminal impedance real value is: The r-terminal impedance real value is ΔT, and ξ is the impedance real value initial dynamic correction factor, The s-terminal ratio initial correction coefficient is: The s-terminal voltage real value is: The s-terminal voltage measurement value is θ, and ΔT is the time window, The s-terminal current real value is: The s-terminal current measurement value is: is a time attenuation coefficient, t is the running time after the start of each new cycle; is an initial correction coefficient of the r-side ratio, is a true value of the r-side voltage, V r meas is a measured value of the r-side voltage, is a true value of the r-side current, is a measured value of the r-side current.

[0214] After obtaining the s-side voltage target relationship expression, the s-side current target relationship expression, the r-side voltage target relationship expression, the r-side current target relationship expression, the s-side impedance true value target relationship expression, and the r-side impedance true value target relationship expression in the above manner, based on the above target relationship expressions, a system parameter model containing a mutual inductor ratio correction coefficient can be obtained.

[0215] To further study the sensitivity of line parameters to the mutual inductor deviation and then find a local optimal solution of the mutual inductor ratio correction coefficient deviation, the partial derivative of the mutual inductor ratio correction coefficient in the system parameter model is solved, because the partial derivative can reflect the change rate of the system parameter in a certain direction, and by analyzing the relationship between these change rates and the system characteristics, the mutual inductor ratio correction coefficient deviation value that minimizes the system error, i.e. the local optimal solution, can be determined.

[0216] Specifically, the partial derivative of the mutual inductor ratio correction coefficient in the system parameter model is solved, a target function for measuring system characteristics is established; the target function for measuring system characteristics is converted into a matrix form target function; based on the measurement data, the matrix form target function is solved to obtain the local optimal solution of the mutual inductor ratio correction coefficient deviation.

[0217] PMU measurement data is collected under N different load conditions, and the local optimal solution of the mutual inductor ratio correction coefficient deviation is determined according to the expression of the local optimal solution of the mutual inductor ratio correction coefficient deviation.

[0218] The expression of the target function for measuring system characteristics is as follows:

[0219]

[0220] The target function for measuring system characteristics is as follows: is an initial correction coefficient of the s-side ratio of the bus, is an initial correction coefficient of the r-side ratio of the bus, ξ is an initial dynamic correction factor of the impedance true value, ω ij is a connection weight between node i and node j, reflecting the close degree of association between nodes, k is a noise sensitivity coefficient, Q is the total number of node scales, N i is the starting number of node j, ΔV jΔVj is the voltage variation of node j, Δξs is the variation of the s-side ratio correction coefficient, Δξr is the variation of the r-side ratio correction coefficient, and Δξ is the variation of the impedance real value dynamic correction factor, is the sensitivity of the voltage variation of node j to , is the sensitivity of the voltage variation of node j to , is the sensitivity of the voltage variation of node j to the variation of ξ, is the influence of noise on , is the influence of noise on , is the influence of noise on the partial derivative of ξ.

[0221] The expression of the objective function in matrix form is:

[0222]

[0223] The expression of the local optimal solution of the mutual inductor ratio correction coefficient deviation is:

[0224]

[0225] where J is the Jacobian matrix, Ω is the node credibility weight matrix, J T is the transpose of the Jacobian matrix, is the deviation matrix of the mutual inductor ratio correction coefficient, is the local optimal solution of the bus s-side ratio correction coefficient deviation, is the local optimal solution of the bus r-side ratio correction coefficient deviation, Δξ is the local optimal solution of the impedance real value dynamic correction factor deviation, and ΔZ = [ΔZ1, ΔZ2, ΔZ3, …, ΔZ i ] T is the deviation vector of the equivalent impedance of each node in the system, ΔZ i is the deviation vector of the equivalent impedance of node i.

[0226] The Jacobian matrix J contains the partial derivative information of the system parameters with respect to the variables, which is used to describe the local linearization relationship of the system, and Ω is used to measure the credibility of the data of different nodes.

[0227] By taking the partial derivative of the objective function Φ that measures the characteristics of the system with respect to the mutual inductor ratio correction coefficient The partial derivative of ξ is obtained, and the expression related to the variation of node voltage, connection weight, noise sensitivity coefficient, etc. is obtained. These expressions reflect the influence degree of the variation of each parameter on the system characteristics. By adjusting these parameters, the minimum value of the objective function Φ is obtained, and the corresponding mutual inductor ratio correction coefficient deviation is the local optimal solution, i.e. the local minimum value.

[0228] For example, there are 3 nodes (i=3) in the system, and the deviation matrix of the mutual inductor ratio correction coefficient to be estimated is The deviation vector of the equivalent impedance ΔZ=[ΔZ1, ΔZ2, ΔZ3] T J is a 3-row i-column matrix, and the elements thereof are composed of the partial derivatives of the objective function for measuring the system characteristics. After calculation, the following can be obtained:

[0229]

[0230] Wherein, a ij represents the value of the i-th partial derivative (for example: ) with respect to the j-th equivalent impedance deviation ΔZ j , for example, (Here, for simplicity, a represents the partial derivative of the corresponding partial derivative with respect to ΔZ1, ΔV j is the voltage variation of node j, is the partial derivative with respect to ΔZ1.

[0231] Suppose that the credibility weights of the 3 nodes are w1, w2, and w3, respectively. Then, the node credibility weight matrix Ω is a 3×3 diagonal matrix:

[0232]

[0233] Calculate J T ·Ω·J:

[0234]

[0235] Calculate J T ·Ω·ΔZ:

[0236]

[0237] The expression of the objective function in matrix form is

[0238]

[0239] The expression of the local optimal solution of the mutual inductor ratio correction coefficient deviation is:

[0240]

[0241] In the system, J, Ω, ΔZ are obtained through testing and calculation, these values are substituted into the expression of the partial optimal solution of the transformer ratio correction coefficient deviation, and the value of is obtained through matrix operation. In the above example, the calculation result is Δξ=0.03. This set of values is the partial optimal solution of the transformer ratio correction coefficient deviation in the current situation, and through this partial optimal solution, the approximate situation of the transformer deviation can be preliminarily understood, providing a basis for subsequent more accurate analysis.

[0242] Through the specific example, the specific process of obtaining the partial optimal solution of the transformer ratio correction coefficient deviation is described in detail. In actual application, the number of nodes n may be larger, but the calculation principle is the same.

[0243] After obtaining the partial optimal solution of the transformer ratio correction coefficient deviation, according to the partial optimal solution, the pigeon swarm collaborative optimization algorithm is used to perform deep search in the parameter search space, obtain the search result, and determine the transformer deviation identification result according to the search result. Specifically, the line parameter vector is initialized according to the partial optimal solution, and the initialized line parameter vector is obtained; the parameter search space is determined according to the value range of the initialized line parameter vector by using the elliptical constraint, and the node target position in the parameter search space is obtained by using the pigeon swarm collaborative optimization algorithm; the fitness value of the node is calculated according to the fitness function of the node, and the node target position is updated iteratively based on the node position update strategy until the iteration number reaches the preset iteration number, and the node target position at this time is determined as the deviation identification result of the transformer.

[0244] The transformer ratio correction coefficient deviation matrix obtained by the above method provides an important basis for subsequent search using the pigeon swarm collaborative optimization algorithm.

[0245] In this step, the line parameter vector is initialized according to the partial optimal solution, and the initialized line parameter vector is obtained. The value range of the line parameter vector P used to determine the initial population, wherein, is the initialized line parameter vector, is the bus s end ratio target correction coefficient, is the bus r end ratio target correction coefficient, and ξ' is the impedance real value target dynamic correction factor, ξ' are all reasonable initialization settings of these parameters according to the value of Δφ. In addition, D is the actual dynamic loss, Y is the time-varying admittance component, G is the active gain in the flow transmission process, C is the coupling compensation between nodes, Z s is a slight correction value of the equivalent impedance measurement value of the s end, and Z rθ is a slight correction to the equivalent impedance measurement at terminal r, and θ is the temperature drift factor.

[0246] After obtaining the initialized line parameter vector, the pigeon flock cooperative optimization algorithm is used to perform a deep search in a wider parameter search space to accurately determine the mutual inductor deviation.

[0247] By limiting the range of line parameters and system correction coefficients, the following expression can be obtained:

[0248]

[0249] Where D is the actual dynamic loss, D0 is the dynamic loss reference value, Y is the time-varying admittance component, Y0 is the time-varying admittance component reference value, α is the dynamic loss correction coefficient range, and β is the time-varying admittance component correction coefficient range.

[0250] This expression uses elliptical constraints to determine the range of values ​​for the actual line parameters within an elliptical region centered at (D0,Y0) with α and β as the major and minor axes, respectively (α>β>0), thus constraining the search space.

[0251] In the pigeon flocking cooperative algorithm, the flock adjusts its flight direction based on factors such as its distance from the target during flight. Using this algorithm, the optimal solution for mutual inductor deviation identification in this scenario is considered the target position, and the fitness of each individual is calculated using the following fitness function formula:

[0252]

[0253] Among them, F i Z represents the fitness value of the i-th node. k Let i be the equivalent impedance value calculated for node i on the k-th line. Let P be the true equivalent impedance of node i on the k-th line, k be the actual number of lines, N be the total number of actual lines, μ be the cooperative error penalty coefficient, and P be the actual impedance of node i on the k-th line. i Let P be the line parameter vector of node i. j Let ||P| be the line parameter vector of node j. i -P j || is the dot product of node i and node j, d ij Let N be the direction vector from node i to node j, Q be the total number of nodes, and N be the total number of nodes. i Let be the starting number of node j;

[0254] Z represents the equivalent impedance of the i-th node calculated on N lines. k With the true value of equivalent impedance The sum of relative errors, μ, is used to balance the influence of the node's own error and the differences with other nodes. represents the influence of the relative position relationship between node i and other nodes j on fitness, d ij is the direction vector from node i to node j, the dot product of the direction vector from node i to node j is calculated and normalized, and the contribution of the relative position difference of the individual to the fitness is measured.

[0255] In the present application, the node is a pigeon individual, which can also be referred to as an individual.

[0256] In the pigeon swarm cooperative algorithm, the pigeon individual updates its own position by perceiving information such as geomagnetic and sun, and in the actual scene, these information can be analogized as the position updating strategy adopted in the algorithm. The pigeon swarm cooperative algorithm is used in the present application, and the following strategy is used for individual position updating, and the position of the individual is updated by the following strategy:

[0257] P i t+1 = P i t + ρ·M·(P best -P i t )+ β·S·(P rand -P i t )+ γ·ΔP local

[0258] Wherein, P i t is the position of node i at the tth iteration, P i t+1 is the position of node i at the t+1th iteration, is the line parameter vector after initialization, is the bus s end ratio target correction coefficient, is the bus r end ratio target correction coefficient, ξ' is the impedance real value target dynamic correction factor, D is the actual dynamic loss, Y is the time-varying admittance component, G is the active gain in the flow transmission process, C is the coupling compensation between nodes, Z s is the slight correction value of the equivalent impedance measurement value at the s end, Z r is the slight correction value of the equivalent impedance measurement value at the r end, θ is the temperature drift factor; P best is the global optimal solution, P rand is a randomly selected individual position, ΔP local is the local search step length vector, ρ is the geomagnetic information direction control parameter, β is the sun information direction control parameter, γ is the step length control parameter, M is the vector simulating the direction vector of the geomagnetic information, and S is the vector simulating the direction vector of the sun information.

[0259] P best The line parameter vector corresponding to the individual with the minimum fitness, that is, the individual that finds the solution that minimizes the transformer deviation; ΔP local For fine search in the local range of the individual, that is, by calculating the average position difference of the current individual and several nearest neighbor individuals (determined according to the distance of the line parameter vector) around the current individual; and ρ, β and γ represent control parameters, respectively used for adjusting the step length of moving in the direction of the global optimal solution, a random individual and a local optimal solution, and balancing the global search and local search capabilities of the algorithm;

[0260] M is obtained by calculating the direction of the global optimal solution and the center of mass of the population (the average of the line parameter vectors of all individuals in the population), the M vector can guide the individual to search in the direction between the historical optimal solution and the average state of the population, and is helpful for the algorithm to further optimize on the basis of the existing good solution; S is a direction vector for simulating the sun information, and is set as a vector that randomly changes in a range related to the iteration number, so as to increase the diversity of the search and avoid falling into a local optimum; represents the part of the individual moving in the direction of the global optimal solution; The randomness of the search is increased, and the algorithm is prevented from falling into a local optimum; γ·ΔP local The three parts are used for more detailed search in the local region of the individual, and the comprehensive action of the three parts makes the individual gradually move to a better position and approach the global optimal solution, and when the iteration number reaches the set iteration number, the optimal solution is output.

[0261] The present application aims at the difficulty and challenge of transformer deviation identification of a new power system, establishes a system parameter model containing the transformer deviation, the transformer node calculates the transformer deviation by using the PMU measurement data, and combines the pigeon swarm collaborative optimization algorithm to design a transformer deviation identification method and system based on the pigeon swarm collaborative optimization algorithm, so that the power equipment state data processing can be completed nearby, and the accurate identification of the transformer deviation can be completed.

[0262] The method provided by the present application comprises:

[0263] (1) According to the characteristics that the system parameters and the measurement data need to be used for transformer deviation identification, a transformer ratio correction coefficient is introduced, and a system model containing the transformer ratio correction coefficient is established under a decentralized transformer network;

[0264] (2) On the basis of the established system parameter model, the transformer node solves the local optimal value of the transformer ratio correction coefficient deviation of the PMU measurement data;

[0265] (3) The pigeon swarm collaborative optimization algorithm is used to perform deep search in a wider parameter search space, and the deviation of the transformer is accurately determined.

[0266] The application utilizes a distributed mutual inductor network and a swarm intelligence optimization algorithm (pigeon swarm cooperative optimization algorithm) to improve the identification accuracy of mutual inductors. From the theoretical model aspect, starting from the Kirchhoff current law and the voltage law, a comprehensive system parameter model is constructed, considering various factors such as time-varying coupling coefficients, dynamic loss adjustment factors, and more, to more accurately describe the actual operating state of the power system. In the mutual inductor deviation processing, multiple correction coefficients such as aging coefficients, temperature drift factors, and time decay coefficients are introduced, comprehensively considering the deviation influence of mutual inductors under different operating conditions, and improving the accuracy of deviation identification. In the algorithm application, the pigeon swarm cooperative algorithm is used for global search and optimization. This algorithm can dynamically adjust the search strategy according to the operating state of the system and the historical optimal solution, effectively avoiding falling into local optimization, and has stronger adaptability and optimization ability compared with traditional algorithms, which can more accurately determine the mutual inductor deviation.

[0267] Embodiment 2:

[0268] Based on the same inventive concept, the application also provides a mutual inductor deviation identification system 300 based on the pigeon swarm cooperative optimization algorithm, specifically, Figure 3 The structure diagram of the mutual inductor deviation identification system based on the pigeon swarm cooperative optimization algorithm provided by the application is shown in the figure, which includes:

[0269] The measurement data obtaining module 301 is used to obtain the measurement data of the mutual inductor node through the synchronous vector measurement unit under the distributed mutual inductor network.

[0270] The correction coefficient deviation obtaining module 302 is used to solve the system parameter model containing the mutual inductor ratio correction coefficient based on the measurement data, and obtain the local optimal solution of the mutual inductor ratio correction coefficient deviation.

[0271] The deviation identification result determining module 303 is used to perform deep search in the parameter search space by using the pigeon swarm cooperative optimization algorithm according to the local optimal solution, obtain the search result, and determine the mutual inductor deviation identification result according to the search result.

[0272] Preferably, the network structure of the distributed mutual inductor network includes: in a two-node system equivalent circuit model, the bus s end and the bus r end are connected through a π type equivalent model; the bus s end and the bus r end are provided with the synchronous vector measurement unit.

[0273] Preferably, the system further includes a system parameter model establishing module.

[0274] The system parameter model establishing module is used to:

[0275] obtain a s-terminal voltage target relationship expression between the s-terminal voltage real value and the s-terminal voltage measured value, and a s-terminal current target relationship expression between the s-terminal current real value and the s-terminal current measured value based on the bus s-terminal ratio initial correction coefficient;

[0276] obtain a r-terminal voltage target relationship expression between the r-terminal voltage real value and the r-terminal voltage measured value, and a r-terminal current target relationship expression between the r-terminal current real value and the r-terminal current measured value based on the bus r-terminal ratio initial correction coefficient;

[0277] obtain a s-terminal impedance real value target relationship expression between the s-terminal impedance real value and the s-terminal voltage real value and the s-terminal current real value, and a r-terminal impedance real value target relationship expression between the r-terminal impedance real value and the r-terminal voltage real value and the r-terminal current real value based on the impedance real value initial dynamic correction factor;

[0278] establish a system parameter model containing the mutual inductor ratio correction coefficient based on the s-terminal voltage target relationship expression, the s-terminal current target relationship expression, the r-terminal voltage target relationship expression, the r-terminal current target relationship expression, the s-terminal impedance real value target relationship expression and the r-terminal impedance real value target relationship expression.

[0279] Preferably, the system parameter model establishing module is specifically used for:

[0280] obtain a s-terminal voltage initial relationship expression between the s-terminal voltage real value and the s-terminal voltage measured value based on the bus s-terminal ratio initial correction coefficient;

[0281] obtain a s-terminal voltage target relationship expression between the s-terminal voltage measured value and the s-terminal voltage real value by backstepping the s-terminal voltage initial relationship expression;

[0282] obtain a s-terminal current initial relationship expression between the s-terminal current real value and the s-terminal current measured value based on the bus s-terminal ratio initial correction coefficient;

[0283] obtain a s-terminal current target relationship expression between the s-terminal current measured value and the s-terminal current real value by backstepping the s-terminal current initial relationship expression;

[0284] The s-terminal target voltage relationship expression is:

[0285]

[0286] The s-terminal current target relationship expression is:

[0287]

[0288] is the bus s-terminal ratio initial correction coefficient, Vs true V is the s-terminal voltage true value, θ is a temperature drift factor, ΔT is a time window, s meas V is the s-terminal voltage true value, θ is a temperature drift factor, ΔT is a time window, I is the s-terminal current true value, I is the s-terminal current true value, τ is a time decay coefficient, t is the running time after the start of each new cycle.

[0289] Preferably, the system parameter model establishing module is further used for:

[0290] based on the bus r-terminal ratio initial correction coefficient, obtaining an r-terminal voltage initial relationship expression between the r-terminal voltage true value and the r-terminal voltage measured value;

[0291] based on the bus r-terminal ratio initial correction coefficient, obtaining an r-terminal voltage initial relationship expression between the r-terminal voltage true value and the r-terminal voltage measured value;

[0292] based on the bus r-terminal ratio initial correction coefficient, obtaining an r-terminal current initial relationship expression between the r-terminal current true value and the r-terminal current measured value;

[0293] based on the bus r-terminal ratio initial correction coefficient, obtaining an r-terminal current initial relationship expression between the r-terminal current true value and the r-terminal current measured value;

[0294] wherein, the r-terminal voltage target relationship expression is:

[0295]

[0296] the r-terminal current target relationship expression is:

[0297]

[0298] wherein, is the bus r-terminal ratio initial correction coefficient, is the r-terminal voltage true value, V is the s-terminal voltage true value, θ is a temperature drift factor, ΔT is a time window, I is the s-terminal current true value, I is the s-terminal current true value, τ is a time decay coefficient, t is the running time after the start of each new cycle.

[0299] Preferably, the system parameter model establishing module is further used for:

[0300] based on the impedance true value initial dynamic correction factor, and the s-terminal voltage true value and the s-terminal current true value, determining an s-terminal impedance true value initial relationship expression;

[0301] The s-terminal voltage target relationship expression and the s-terminal current target relationship expression are substituted into the s-terminal impedance real value initial relationship expression to obtain an s-terminal impedance real value target relationship expression;

[0302] Based on the impedance real value initial dynamic correction factor, and the r-terminal voltage real value and the r-terminal current real value, an r-terminal impedance real value initial relationship expression is determined;

[0303] The r-terminal voltage target relationship expression and the r-terminal current target relationship expression are substituted into the r-terminal impedance real value initial relationship expression to obtain an r-terminal impedance real value target relationship expression;

[0304] The s-terminal impedance real value target relationship expression is:

[0305]

[0306] The r-terminal impedance real value target relationship expression is:

[0307]

[0308] The s-terminal impedance real value is: The r-terminal impedance real value is ΔT is a time window, and ξ is an impedance real value initial dynamic correction factor, The s-terminal ratio initial correction coefficient is V s true The s-terminal voltage real value is V s meas The s-terminal voltage measurement value is θ is a temperature drift factor, and ΔT is a time window, The s-terminal current real value is: The s-terminal current measurement value is: The time attenuation coefficient is t is an elapsed time after the start of each new period; The r-terminal ratio initial correction coefficient is: The r-terminal voltage real value is: The r-terminal voltage measurement value is: The r-terminal current real value is: The r-terminal current measurement value is.

[0309] Preferably, the correction coefficient deviation obtaining module is specifically used for:

[0310] Partial derivatives of the mutual inductor ratio correction coefficient in the system parameter model are calculated to establish a target function for measuring system characteristics;

[0311] The target function for measuring system characteristics is converted into a matrix form target function;

[0312] Based on the measured data, the target function in matrix form is solved to obtain the local optimal solution of the mutual inductor ratio correction coefficient deviation.

[0313] Preferably, the expression of the target function for measuring system characteristics is as follows:

[0314]

[0315] Wherein, Φ is the target function for measuring system characteristics, is the initial correction coefficient of the bus s end ratio, is the initial correction coefficient of the bus r end ratio, ξ is the initial dynamic correction factor of the impedance real value, ω ij is the connection weight between node i and node j, k is the noise sensitivity coefficient, Q is the total number of node scales, N i is the initial quantity of the node, ΔV j is the voltage variation of node j, is the variation of the s end ratio correction coefficient, is the variation of the r end ratio correction coefficient, Δξ is the variation of the dynamic correction factor of the impedance real value, is the sensitivity of the voltage variation of node j to variation, is the sensitivity of the voltage variation of node j to variation, is the sensitivity of the voltage variation of node j to ξ variation, is the influence of noise on partial derivative, is the influence of noise on partial derivative, is the influence of noise on ξ partial derivative;

[0316] The expression of the target function in matrix form is as follows:

[0317]

[0318] The expression of the local optimal solution of the mutual inductor ratio correction coefficient deviation is as follows:

[0319]

[0320] Wherein, J is the Jacobian matrix, Ω is the node credibility weight matrix, J T is the transpose of the Jacobian matrix, is the deviation matrix of the mutual inductor ratio correction coefficient, is the local optimal solution of the bus s end ratio correction coefficient deviation, The partial optimal solution of the bus r end ratio correction coefficient deviation, the partial optimal solution of the impedance true value dynamic correction factor deviation, ΔZ = [ΔZ1, ΔZ2, ΔZ3,..., ΔZ i ] T The partial optimal solution of the bus r end ratio correction coefficient deviation, the partial optimal solution of the impedance true value dynamic correction factor deviation, ΔZ = [ΔZ1, ΔZ2, ΔZ3,..., ΔZ i The partial optimal solution of the bus r end ratio correction coefficient deviation, the partial optimal solution of the impedance true value dynamic correction factor deviation, ΔZ = [ΔZ1, ΔZ2, ΔZ3,..., ΔZ

[0321] Preferably, the deviation identification result determination module is specifically configured to:

[0322] According to the partial optimal solution, the line parameter vector is initialized to obtain an initialized line parameter vector;

[0323] An ellipse constraint is used to determine a parameter search space according to a value range of the initialized line parameter vector, and a pigeon swarm cooperative optimization algorithm is used to obtain a node target position in the parameter search space.

[0324] The fitness value of the node is calculated according to the fitness function of the node, and the node target position is updated iteratively based on a node position update strategy until the number of iterations reaches a preset iteration number, and the node target position at this time is determined as the deviation identification result of the mutual inductor.

[0325] Preferably, the expression of the ellipse constraint is as follows:

[0326]

[0327] Wherein, D is the actual dynamic loss, D0 is the dynamic loss reference value, Y is the time-varying admittance component, Y0 is the time-varying admittance component reference value, α is the dynamic loss correction coefficient range, and β is the time-varying admittance component correction coefficient range.

[0328] The expression of the fitness function is as follows:

[0329]

[0330] Wherein, F i is the fitness value of the i th node, Z k is the equivalent impedance value of the i th node calculated on the k th line, is the equivalent impedance true value of the i th node on the k th line, k is the actual line number, N is the total number of actual lines, μ is the cooperative error penalty coefficient, P i is the line parameter vector of the i th node, P j is the line parameter vector of the j th node, ||P i -P j || is the dot product of the i th node and the j th node, d ij is the direction vector from the i th node to the j th node, Q is the total number of node scales, N iThe starting number for node j;

[0331] The node position updating strategy is:

[0332] P i t+1 = P i t + p M (P best - P i t ) + beta S (P rand - P i t ) + gamma Delta P local

[0333] Wherein, P i t is the position of node i at the tth round of iteration, P i t+1 is the position of node i at the t+1th round of iteration, is the initialized line parameter vector, is the bus s end ratio target correction coefficient, is the bus r end ratio target correction coefficient, xi' is the impedance real value target dynamic correction factor, D is the actual dynamic loss, Y is the time-varying admittance component, G is the active gain in the flow transmission process, C is the coupling compensation between nodes, Z s is the slight correction value of the equivalent impedance measurement value of the s end, Z r is the slight correction value of the equivalent impedance measurement value of the r end, theta is the temperature drift factor; P best is the global optimal solution, P rand is the randomly selected individual position, Delta P local is the local search step length vector, p is the geomagnetic information direction control parameter, beta is the solar information direction control parameter, gamma is the step length control parameter, M is the direction vector of vector simulation geomagnetic information, S is the direction vector of vector simulation solar information.

[0334] Embodiment 3:

[0335] Based on the same inventive concept, as shown in Figure 4 The electronic device in the embodiment can include a processor, a memory, a transceiver component, etc. The memory, the processor and the transceiver component are connected through a bus; the memory can be used to store an execution program, and the exemplary execution program can include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be called and / or modified when the instructions are executed.

[0336] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in the readable storage medium to implement a corresponding method flow or a corresponding function, so as to implement the steps of the mutual inductor deviation identification method based on the pigeon swarm cooperative optimization algorithm in the above embodiment.

[0337] Embodiment 4:

[0338] Based on the same inventive concept, the present application also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in the electronic device, and is used for storing programs and data. It can be understood that the readable storage medium here can include the built-in storage medium in the electronic device, and of course can also include the expansion storage medium supported by the electronic device. The storage medium provides a storage space, and the storage space stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory, or a non-volatile memory, for example, at least one disk memory. One or more instructions stored in the storage medium can be loaded and executed by the processor to implement the steps of the mutual inductor deviation identification method based on the pigeon swarm cooperative optimization algorithm in the above embodiment.

[0339] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0340] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps in one or more flow or blocks

[0341] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps in one or more flow or blocks

[0342] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps in one or more flow or blocks

[0343] Finally, it should be noted that the above-described embodiments are merely used to illustrate the technical solutions of the present application, rather than limit the scope of protection of the present application. Although the present application is described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the specification, they can make various modifications or equivalent replacements to the specific embodiments of the application. However, these modifications or equivalent replacements should be considered to fall within the scope of protection of the present application.

Claims

1. A mutual inductor deviation identification method based on pigeon swarm cooperative optimization algorithm, characterized in that, The application relates to a method for identifying mutual inductor deviation. The method comprises the following steps: under a distributed mutual inductor network, obtaining measurement data of a mutual inductor node through a synchronous vector measurement unit; based on the measurement data, solving a system parameter model containing mutual inductor ratio correction factors to obtain a local optimal solution of mutual inductor ratio correction factor deviation; 2. The method of claim 1, wherein, according to the local optimal solution, performing deep search in a parameter search space by using a pigeon swarm collaborative optimization algorithm to obtain a search result, and determining a mutual inductor deviation identification result according to the search result.

3. The method of claim 2, wherein, The network structure of the distributed mutual inductor network comprises: in a two-node system equivalent circuit model, connecting a bus s end and a bus r end through a pi equivalent model; the bus s end and the bus r end are provided with the synchronous vector measurement unit. The establishment process of the system parameter model containing mutual inductor ratio correction factors comprises: based on bus s end ratio initial correction factors, obtaining an s end voltage target relationship expression between s end voltage real values and s end voltage measurement values, and an s end current target relationship expression between s end current real values and s end current measurement values; based on bus r end ratio initial correction factors, obtaining an r end voltage target relationship expression between r end voltage real values and r end voltage measurement values, and an r end current target relationship expression between r end current real values and r end current measurement values; based on impedance real value initial dynamic correction factors, obtaining an s end impedance real value target relationship expression between s end impedance real values, s end voltage real values and s end current real values, and an r end impedance real value target relationship expression between r end impedance real values, r end voltage real values and r end current real values; 4. The method of claim 3, wherein, based on the s end voltage target relationship expression, the s end current target relationship expression, the r end voltage target relationship expression, the r end current target relationship expression, the s end impedance real value target relationship expression and the r end impedance real value target relationship expression, the system parameter model containing mutual inductor ratio correction factors is established. The s end voltage target relationship expression between s end voltage real values and s end voltage measurement values and the s end current target relationship expression between s end current real values and s end current measurement values based on bus s end ratio initial correction factors comprises: based on bus s end ratio initial correction factors, obtaining an s end voltage initial relationship expression between s end voltage real values and s end voltage measurement values; backstepping the s end voltage initial relationship expression to obtain an s end voltage target relationship expression between s end voltage measurement values and s end voltage real values; based on bus s end ratio initial correction factors, obtaining an s end current initial relationship expression between s end current real values and s end current measurement values; backstepping the s end current initial relationship expression to obtain an s end current target relationship expression between s end current measurement values and s end current real values; wherein the s end target voltage relationship expression is: the s end current target relationship expression is: is the initial correction factor for the ratio of bus s end, is the true value of the voltage at s end, V s meas is the measured value of the voltage at s end, θ is the temperature drift factor, ΔT is the time window, is the true value of the current at s end, is the measured value of the current at s end, is the time decay factor, t is the time on stream after the start of each new cycle.

5. The method of claim 4, wherein, The r-terminal voltage target relationship expression between the r-terminal voltage real value and the r-terminal voltage measurement value is obtained based on the bus r-terminal ratio initial correction coefficient, and the r-terminal current target relationship expression between the r-terminal current real value and the r-terminal current measurement value is obtained. The r-terminal voltage real value and the r-terminal voltage measurement value are obtained based on the bus r-terminal ratio initial correction coefficient. The r-terminal voltage target relationship expression between the r-terminal voltage real value and the r-terminal voltage measurement value is obtained based on the bus r-terminal ratio initial correction coefficient, and the r-terminal current target relationship expression between the r-terminal current real value and the r-terminal current measurement value is obtained. The r-terminal current real value and the r-terminal current measurement value are obtained based on the bus r-terminal ratio initial correction coefficient. The r-terminal current target relationship expression between the r-terminal current real value and the r-terminal current measurement value is obtained based on the bus r-terminal ratio initial correction coefficient. The r-terminal voltage target relationship expression is: The r-terminal current target relationship expression is: wherein, is the initial correction factor for the bus r-end ratio, is the true value of the r-end voltage, is the measured value of the r-end voltage, θ is the temperature drift factor, ΔT is the time window, is the true value of the r-end current, is the measured value of the r-end current, is the time decay factor, t is the elapsed time since the beginning of each new cycle.

6. The method according to claim 3 or 5, characterized in that, The s-terminal impedance real value target relationship expression between the s-terminal impedance real value and the s-terminal voltage real value and the s-terminal current real value, and the r-terminal impedance real value target relationship expression between the r-terminal impedance real value and the r-terminal voltage real value and the r-terminal current real value are obtained based on the impedance real value initial dynamic correction factor, including: The s-terminal impedance real value initial relationship expression is determined based on the impedance real value initial dynamic correction factor, and the s-terminal voltage real value and the s-terminal current real value. The s-terminal impedance real value target relationship expression is obtained by substituting the s-terminal voltage target relationship expression and the s-terminal current target relationship expression into the s-terminal impedance real value initial relationship expression. The r-terminal impedance real value initial relationship expression is determined based on the impedance real value initial dynamic correction factor, and the r-terminal voltage real value and the r-terminal current real value. The r-terminal impedance real value target relationship expression is obtained by substituting the r-terminal voltage target relationship expression and the r-terminal current target relationship expression into the r-terminal impedance real value initial relationship expression. The s-terminal impedance real value target relationship expression is: The r-terminal impedance real value target relationship expression is: is the real value of the impedance at the s terminal, is the real value of the impedance at the r terminal, ΔT is the time window, and ξ is the initial dynamic correction factor of the impedance real value, is the initial correction factor of the ratio at the s terminal, is the real value of the voltage at the s terminal, V s meas is the measured value of the voltage at the s terminal, θ is the temperature drift factor, and ΔT is the time window, is the real value of the current at the s terminal, is the measured value of the current at the s terminal, is the time decay factor, and t is the elapsed time after the start of each new cycle; is the initial correction factor of the ratio at the r terminal, is the real value of the voltage at the r terminal, is the measured value of the voltage at the r terminal, is the real value of the current at the r terminal, is the measured value of the current at the r terminal.

7. The method of claim 1, wherein, The local optimal solution of the mutual inductor ratio correction coefficient deviation is obtained by solving the system parameter model containing the mutual inductor ratio correction coefficient based on the measurement data, including: The partial derivative of the mutual inductor ratio correction coefficient in the system parameter model is solved to establish a target function for measuring system characteristics. The target function for measuring system characteristics is converted into a matrix form target function. The local optimal solution of the mutual inductor ratio correction coefficient deviation is obtained by solving the matrix form target function based on the measurement data.

8. The method of claim 7, wherein, The expression of the target function for measuring system characteristics is as follows: wherein, Φ is the objective function measuring the system characteristics, is the initial correction coefficient of the bus s ratio, is the initial correction coefficient of the bus r ratio, ξ is the initial dynamic correction factor of the impedance real value, ω ij is the connection weight between node i and node j, k is the noise sensitivity coefficient, Q is the total number of node scales, N i is the initial number of node j, ΔV j is the voltage variation of node j, is the correction coefficient variation of the bus s ratio, is the correction coefficient variation of the bus r ratio, Δξ is the dynamic correction factor variation of the impedance real value, is the sensitivity of the voltage variation of node j to variation, is the sensitivity of the voltage variation of node j to variation, is the sensitivity of the voltage variation of node j to ξ variation, is the influence of noise on partial derivative, is the influence of noise on partial derivative, is the influence of noise on ξ partial derivative; The expression of the matrix form target function is as follows: The expression of the local optimal solution of the mutual inductor ratio correction coefficient deviation is as follows: where J is the Jacobian matrix, Ω is the node credibility weight matrix, J T is the transpose of the Jacobian matrix, is the deviation matrix of the transformer ratio correction coefficient, is the local optimal solution of the bus s end ratio correction coefficient deviation, is the local optimal solution of the bus r end ratio correction coefficient deviation, Δξ is the local optimal solution of the impedance true value dynamic correction factor deviation, ΔZ = [ΔZ1, ΔZ2, ΔZ3.,....., ΔZ i ] T is the deviation vector of the equivalent impedance of each node in the system, ΔZ i is the deviation vector of the equivalent impedance of node i.

9. The method of claim 1, wherein, The search result is obtained by using the pigeon swarm cooperative optimization algorithm to perform deep search in the parameter search space according to the local optimal solution, and the mutual inductor deviation identification result is determined according to the search result, including: Initialize the line parameter vector according to the local optimal solution, and obtain an initialized line parameter vector; Determine a parameter search space according to a value range of the initialized line parameter vector by using an elliptical constraint, and obtain a node target position in the parameter search space by using a pigeon swarm cooperative optimization algorithm; Calculate a fitness value of the node according to the fitness function of the node, and iteratively update the node target position based on a node position update strategy until a preset iteration number is reached, and determine the node target position at this time as the deviation identification result of the mutual inductor.

10. The method of claim 9, wherein, An expression of the elliptical constraint is as follows: wherein D is an actual dynamic loss, D0 is a dynamic loss reference value, Y is a time-varying admittance component, Y0 is a time-varying admittance component reference value, a is a dynamic loss correction coefficient range, and β is a time-varying admittance component correction coefficient range; An expression of the fitness function is as follows: Wherein, F i is the fitness value of the ith node, Z k is the equivalent impedance value calculated by the ith node on the kth line, is the real value of the equivalent impedance of the ith node on the kth line, k is the actual line number, N is the total number of actual lines, μ is the collaborative error penalty coefficient, P i is the line parameter vector of the ith node, P j is the line parameter vector of the jth node, ||P i -P j ||is the dot product of the ith node and the jth node, d ij is the direction vector from the ith node to the jth node, Q is the total number of nodes, N i is the starting number of the jth node; The node position update strategy is as follows: P i t+1 = P i t + p · M · (P best - P i t + β · S · (P rand - P i t + γ · ΔP local where P i t is the position of node i at the tth iteration, P i t+1 is the position of node i at the t+1th iteration, is the initialized line parameter vector, is the bus s-side ratio target correction coefficient, is the bus r-side ratio target correction coefficient, ξ′ is the impedance true value target dynamic correction factor, D is the actual dynamic loss, Y is the time-varying admittance component, G is the active gain in the flow transmission process, C is the coupling compensation between nodes, Z s is the slight correction value of the equivalent impedance measurement value of the s-side, Z r is the slight correction value of the equivalent impedance measurement value of the r-side, θ is the temperature drift factor; P best is the global optimal solution, P rand is the randomly selected individual position, ΔP local is the local search step length vector, ρ is the geomagnetic information direction control parameter, β is the solar information direction control parameter, γ is the step length control parameter, M is the direction vector simulating the geomagnetic information of the vector, and S is the direction vector simulating the solar information of the vector.

11. A mutual inductor deviation identification system based on pigeon swarm cooperative optimization algorithm, characterized in that, comprises: A measurement data obtaining module is configured to obtain measurement data of a mutual inductor node by using a synchronous vector measurement unit in a distributed mutual inductor network; A correction coefficient deviation obtaining module is configured to solve a system parameter model containing mutual inductor ratio correction coefficients based on the measurement data, and obtain a local optimal solution of mutual inductor ratio correction coefficient deviation; A deviation identification result determining module is configured to perform a deep search in a parameter search space by using a pigeon swarm cooperative optimization algorithm according to the local optimal solution, obtain a search result, and determine a mutual inductor deviation identification result according to the search result.

12. The system of claim 11, wherein, A network structure of the distributed mutual inductor network comprises: in a two-node system equivalent circuit model, a bus s end and a bus r end are connected by a π-type equivalent model; and the bus s end and the bus r end are provided with the synchronous vector measurement unit.

13. The system of claim 12, wherein, The system further comprises a system parameter model establishing module; The system parameter model establishing module is configured to: obtain an s-end voltage target relationship expression between an s-end voltage true value and an s-end voltage measurement value, and an s-end current target relationship expression between an s-end current true value and an s-end current measurement value based on an s-end ratio initial correction coefficient; obtain an r-end voltage target relationship expression between an r-end voltage true value and an r-end voltage measurement value, and an r-end current target relationship expression between an r-end current true value and an r-end current measurement value based on an r-end ratio initial correction coefficient; obtain an s-end impedance true value target relationship expression between an s-end impedance true value and the s-end voltage true value and the s-end current true value, and an r-end impedance true value target relationship expression between an r-end impedance true value and the r-end voltage true value and the r-end current true value based on an impedance true value initial dynamic correction factor; establish a system parameter model containing mutual inductor ratio correction coefficients based on the s-end voltage target relationship expression, the s-end current target relationship expression, the r-end voltage target relationship expression, the r-end current target relationship expression, the s-end impedance true value target relationship expression, and the r-end impedance true value target relationship expression.

14. The system of claim 13, wherein, The system parameter model establishing module is specifically configured to: An initial relationship expression between the s-terminal voltage real value and the s-terminal voltage measured value is obtained based on the initial correction coefficient of the s-terminal ratio of the bus; An initial relationship expression between the s-terminal voltage real value and the s-terminal voltage measured value is obtained based on the initial correction coefficient of the s-terminal ratio of the bus; An initial relationship expression between the s-terminal current real value and the s-terminal current measured value is obtained based on the initial correction coefficient of the s-terminal ratio of the bus; An initial relationship expression between the s-terminal current real value and the s-terminal current measured value is obtained based on the initial correction coefficient of the s-terminal ratio of the bus; The s-terminal target voltage relationship expression is: The s-terminal target current relationship expression is: is the initial correction factor for the ratio of the bus s terminal, is the true value of the s terminal voltage, V s meas is the measured value of the s terminal voltage, θ is the temperature drift factor, ΔT is the time window, is the true value of the s terminal current, is the measured value of the s terminal current, is the time decay factor, t is the time already run after the start of each new cycle.

15. The system of claim 14, wherein, The system parameter model establishing module is further specifically used for: An initial relationship expression between the r-terminal voltage real value and the r-terminal voltage measured value is obtained based on the initial correction coefficient of the r-terminal ratio of the bus; An initial relationship expression between the r-terminal voltage real value and the r-terminal voltage measured value is obtained based on the initial correction coefficient of the r-terminal ratio of the bus; An initial relationship expression between the r-terminal current real value and the r-terminal current measured value is obtained based on the initial correction coefficient of the r-terminal ratio of the bus; An initial relationship expression between the r-terminal current real value and the r-terminal current measured value is obtained based on the initial correction coefficient of the r-terminal ratio of the bus; The r-terminal target voltage relationship expression is: The r-terminal target current relationship expression is: wherein, is the initial correction factor for the bus r-end ratio, is the true value of the r-end voltage, is the measured value of the r-end voltage, θ is the temperature drift factor, ΔT is the time window, is the true value of the r-end current, is the measured value of the r-end current, is the time decay factor, t is the elapsed time since the beginning of each new cycle.

16. The system of claim 13 or 15, wherein, The system parameter model establishing module is further specifically used for: An initial relationship expression of the s-terminal impedance real value is determined based on the initial dynamic correction factor of the impedance real value and the s-terminal voltage real value and the s-terminal current real value; The s-terminal target voltage relationship expression, the s-terminal target current relationship expression are substituted into the initial relationship expression of the s-terminal impedance real value to obtain a target relationship expression of the s-terminal impedance real value; An initial relationship expression of the r-terminal impedance real value is determined based on the initial dynamic correction factor of the impedance real value and the r-terminal voltage real value and the r-terminal current real value; The r-terminal target voltage relationship expression, the r-terminal target current relationship expression are substituted into the initial relationship expression of the r-terminal impedance real value to obtain a target relationship expression of the r-terminal impedance real value; The target relationship expression of the s-terminal impedance real value is: The target relationship expression of the r-terminal impedance real value is: is the real value of the impedance at the s terminal, is the real value of the impedance at the r terminal, ΔT is the time window, and ξ is the initial dynamic correction factor of the real value of the impedance, is the initial correction factor of the ratio at the s terminal, is the real value of the voltage at the s terminal, V s meas is the measured value of the voltage at the s terminal, θ is the temperature drift factor, and ΔT is the time window, is the real value of the current at the s terminal, is the measured value of the current at the s terminal, is the time decay factor, and t is the elapsed time after the start of each new cycle; is the initial correction factor of the ratio at the r terminal, is the real value of the voltage at the r terminal, is the measured value of the voltage at the r terminal, is the real value of the current at the r terminal, is the measured value of the current at the r terminal.

17. The system of claim 11, wherein, The deviation identification result determining module is specifically used for: The line parameter vector is initialized according to the local optimal solution to obtain an initialized line parameter vector; An parameter search space is determined according to the value range of the initialized line parameter vector by using an ellipse constraint, and a node target position in the parameter search space is obtained by using a pigeon swarm cooperative optimization algorithm; The fitness value of the node is calculated according to the fitness function of the node, and the node target position is updated iteratively based on a node position updating strategy until the iteration number reaches a preset iteration number, and the node target position at this time is determined as the deviation identification result of the mutual inductor.

18. An electronic device, comprising: comprise: at least one processor and a memory; the memory and the processor are connected through a bus; The memory, configured to store one or more programs; When the one or more programs are executed by the at least one processor, a mutual inductor deviation identification method based on pigeon swarm collaborative optimization algorithm is implemented.

19. A readable storage medium, characterized by, The computer program product, and the computer program product has a computer program stored thereon, and the computer program is executed to implement the mutual inductor deviation identification method based on the pigeon swarm collaborative optimization algorithm.

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