Load model parameter identification method and device, equipment and storage medium

Abstract

The invention relates to a load model parameter identification method, apparatus and device, and a storage medium. The method comprises the steps of obtaining static load parameters and dynamic load parameters of a target load point and actual measurement data of power, voltage and a phase angle; the static load parameters and the dynamic load parameters are substituted into a pre-established comprehensive load model, a statistical synthesis method is adopted to solve a first independent parameter in the comprehensive load model, and the comprehensive load model comprises a static load model and a dynamic load model; on the basis of an improved grey wolf algorithm, substituting the first independent parameter and measured data of power, voltage and phase angle into a target function, and solving a second independent parameter in the comprehensive load model; and solving non-independent parameters in the comprehensive load model based on the first independent parameters and the second independent parameters. According to the method, the parameters in the comprehensive load model are identified through the statistical synthesis method and the improved grey wolf algorithm, the requirement for the data size is reduced, and meanwhile the modeling precision of the comprehensive load model is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of load model parameters, and in particular to a load model parameter identification method, device, equipment and storage medium. Background Art

[0002] Power load is the core link of energy consumption in power system. The accuracy of power load model can directly affect the accuracy of simulation analysis such as power system flow calculation, transient stability calculation, small disturbance stability calculation, voltage stability calculation, etc. Existing load model parameter identification methods mainly include statistical synthesis method and comprehensive measurement and identification method. Among them, statistical synthesis method estimates the parameters of load model by investigating and statistically analyzing the load operation status, which has a large workload. The comprehensive measurement and identification method mainly monitors the response of load in the system and identifies the load model parameters through optimization algorithm. However, most of the faults in the actual power grid are small disturbances, which are difficult to reflect the dynamic characteristics of the load, thus affecting the accuracy of the power load model. Summary of the invention

[0003] In order to solve the above technical problems, the present disclosure provides a load model parameter identification method, device, equipment and storage medium.

[0004] A first aspect of an embodiment of the present disclosure provides a load model parameter identification method, the method comprising:

[0005] Obtain the static load parameters, dynamic load parameters, and measured data of power, voltage, and phase angle of the target load point;

[0006] Substituting the static load parameter and the dynamic load parameter into a pre-established comprehensive load model, and solving a first independent parameter in the comprehensive load model using a statistical synthesis method, wherein the comprehensive load model includes a static load model and a dynamic load model;

[0007] Based on the improved Grey Wolf algorithm, the first independent parameter and the measured data of the power, voltage and phase angle are substituted into the objective function to solve the second independent parameter in the comprehensive load model;

[0008] A dependent parameter in the comprehensive load model is solved based on the first independent parameter and the second independent parameter.

[0009] A second aspect of an embodiment of the present disclosure provides a load model parameter identification device, the device comprising:

[0010] The first acquisition module is used to obtain the static load parameters, dynamic load parameters and measured data of power, voltage and phase angle of the target load point;

[0011] a first parameter identification module, used for substituting the static load parameter and the dynamic load parameter into a pre-established comprehensive load model, and solving a first independent parameter in the comprehensive load model by using a statistical synthesis method, wherein the comprehensive load model includes a static load model and a dynamic load model;

[0012] A second parameter identification module is used to substitute the first independent parameter and the measured data of the power, voltage and phase angle into the objective function based on the improved grey wolf algorithm to solve the second independent parameter in the comprehensive load model;

[0013] The third parameter identification module is used to solve the dependent parameters in the comprehensive load model based on the first independent parameter and the second independent parameter.

[0014] A third aspect of an embodiment of the present disclosure provides a computer device, including a memory and a processor, and a computer program, wherein the computer program is stored in the memory, and when the computer program is executed by the processor, the load model parameter identification method as described in the first aspect above is implemented.

[0015] A fourth aspect of an embodiment of the present disclosure provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the load model parameter identification method as described in the first aspect above is implemented.

[0016] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:

[0017] In the load model parameter identification method, device, equipment and storage medium provided in the embodiments of the present disclosure, by obtaining the static load parameters, dynamic load parameters and measured data of power, voltage and phase angle of the target load point, the static load parameters and dynamic load parameters are substituted into the pre-established comprehensive load model, and the first independent parameter in the comprehensive load model is solved by the statistical synthesis method. The comprehensive load model includes a static load model and a dynamic load model. Based on the improved gray wolf algorithm, the first independent parameter and the measured data of power, voltage and phase angle are substituted into the objective function, and the second independent parameter in the comprehensive load model is solved. Based on the first independent parameter and the second independent parameter, the non-independent parameters in the comprehensive load model are solved. The statistical synthesis method and the improved gray wolf algorithm can be combined to identify the parameters in the comprehensive load model. Compared with the method of using the statistical synthesis method to identify all parameters, the demand for data volume is reduced. At the same time, the improved gray wolf algorithm has good global optimization capability, improves the accuracy of parameter identification, and thus improves the overall modeling accuracy of the comprehensive load model. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 is a flow chart of a load model parameter identification method provided by an embodiment of the present disclosure;

[0021] Figure 2 is a flow chart of a method for clustering load points provided by an embodiment of the present disclosure;

[0022] Figure 3 is a flow chart of a method for identifying a second independent parameter provided by an embodiment of the present disclosure;

[0023] Figure 4 is a flow chart of a method for identifying impedance parameters of a distribution network provided by an embodiment of the present disclosure;

[0024] Figure 5 is a structural schematic diagram of a load model parameter identification device provided by an embodiment of the present disclosure;

[0025] Figure 6 It is a structural diagram of a computer device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0026] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0027] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0028] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0029] Figure 1This is a flow chart of a load model parameter identification method provided by an embodiment of the present disclosure. The method can be executed by a load model parameter identification device. The load model parameter identification device can be implemented in software and / or hardware. The load model parameter identification device can be configured in an electronic device, such as a server or a terminal, wherein the terminal specifically includes a mobile phone, a computer or a tablet computer, etc. Figure 1 As shown, the load model parameter identification method provided in this embodiment includes the following steps:

[0030] S101. Obtaining static load parameters, dynamic load parameters, and measured data of power, voltage, and phase angle of a target load point.

[0031] The target load point in the disclosed embodiment can be understood as the load point whose load condition needs to be modeled, the static load parameters can be understood as the parameters related to static load equipment such as resistance heaters, lighting, and arc furnaces, and the dynamic load parameters can be understood as the parameters related to dynamic load equipment such as induction motors.

[0032] In the disclosed embodiment, the load model parameter identification device can obtain static load parameters and dynamic load parameters by searching the equipment parameter table or technical specification sheet, or can obtain static load parameters and dynamic load parameters by measuring with various sensors. When obtaining the measured data of rate, voltage and phase angle, the measured data of active power, reactive power, voltage and phase angle of the target load point at each moment can be specifically obtained.

[0033] In an exemplary implementation of the disclosed embodiment, the load model parameter identification device can count the composition of various types of electrical equipment at the target load point at the time of modeling, including the equipment type and the power share at the time of modeling, and then investigate the negative static load parameters and dynamic load parameters of various types of electrical equipment. Finally, the power share of each device at the time of modeling is used as a weighting factor, and the static load parameters and dynamic load parameters of the target load point are obtained by weighted synthesis.

[0034] Optionally, since the measured power data of the target load point uses the system capacity as a reference value, it is necessary to transform it into measured power data with the rated capacity of the equivalent generator model as a reference value, and use the transformed measured power data for subsequent calculations.

[0035] S102, substituting the static load parameters and the dynamic load parameters into a pre-established comprehensive load model, and solving the first independent parameter in the comprehensive load model by using a statistical synthesis method, wherein the comprehensive load model includes a static load model and a dynamic load model.

[0036] In the disclosed embodiment, the comprehensive load model includes a static load model and a dynamic load model. The static load parameters are parameters that can be directly or indirectly obtained (for example, by table lookup, measurement, and simple calculation) in the static load model, and the dynamic load parameters are parameters that can be directly or indirectly obtained in the dynamic load model. In addition to the static load parameters and the dynamic load parameters, there are also some parameters to be solved in the comprehensive load model, including the first independent parameters.

[0037] In the disclosed embodiment, the load model parameter identification device may substitute the static load parameters and the dynamic load parameters into the static load model and the dynamic load model respectively, and use the statistical synthesis method to solve the first independent parameters in the static load model and the dynamic load model.

[0038] S103. Based on the improved Grey Wolf algorithm, the first independent parameter and the measured data of power, voltage and phase angle are substituted into the objective function to solve the second independent parameter in the comprehensive load model.

[0039] The objective function in the embodiment of the present disclosure may be understood as a function for calculating the fitness value of the gray wolf population.

[0040] The second independent parameter in the embodiment of the present disclosure can be understood as an independent parameter with higher identification sensitivity or greater influence on transient calculation among the partial parameters to be solved of the comprehensive load model.

[0041] In the disclosed embodiment, the load model parameter identification device can use an improved grey wolf algorithm after the first independent parameter is determined, substitute the first independent parameter and the measured data of power, voltage and phase angle into the objective function, and iteratively optimize the second independent parameter in the comprehensive load model to obtain the optimal solution.

[0042] S104. Solve the dependent parameters in the comprehensive load model based on the first independent parameter and the second independent parameter.

[0043] The non-independent parameters in the embodiments of the present disclosure may be understood as non-independent parameters in some parameters of the comprehensive load model to be solved, and may be solved through model constraints based on the known independent parameters.

[0044] In the disclosed embodiment, the load model parameter identification device can solve the non-independent parameters based on the first independent parameter and the second independent parameter and preset model constraints after the first independent parameter and the second independent parameter are determined.

[0045] In some embodiments, the static load model may be:

[0046]

[0047] Where P represents the active power consumed by the static load, Q represents the reactive power consumed by the static load, U represents the voltage at the target load point, P0 represents the active load at the initial operating point, Q0 represents the reactive load at the initial operating point, U0 represents the voltage at the initial operating point, and p z It represents the ratio of the active power consumed by the constant impedance load in the steady state to the total static active power, p i It represents the ratio of active power consumed by constant current load in steady state to total static active power, p p It represents the ratio of active power consumed by constant power load in steady state to total static active power, q z It represents the proportion of the reactive power consumed by the constant impedance load in the steady state to the total static reactive power, q i It indicates the proportion of reactive power consumed by constant current load in steady state to total static reactive power, q p It indicates the proportion of reactive power consumed by constant power load in steady state to total static reactive power, Δf indicates the frequency change, f0 indicates the frequency of the initial operating point, L dp Indicates the frequency variation coefficient corresponding to active power, L dq , represents the frequency change coefficient corresponding to the reactive power, and the static load parameters include P, Q, U, P0, Q0, U0, Δf, f0, L dp , L dq At least one of the first independent parameters includes p z 、p i 、p p ,q z ,q i ,q p At least one of the non-independent parameters includes p p ,q p At least one of .

[0048] Specifically, the parameter p z 、p i 、p p Satisfy p z +p i +p p =1, parameter q z ,q i ,q p Satisfy q z +q i +q p =1. Since the frequency changes slightly during actual operation, the parameter L dp , L dq Can be ignored.

[0049] In some embodiments, the dynamic load model may include:

[0050]

[0051] X′=X s +X r X m / (X s +X m )

[0052]

[0053] ω r =1-s

[0054]

[0055] ω0=1-s0

[0056]

[0057] Among them, U d Indicates the stator direct-axis voltage of the induction motor, U q represents the stator quadrature axis voltage of the induction motor, R s Represents the resistance of the stator winding, I d Indicates the stator direct axis current of the induction motor, I q represents the stator quadrature axis current of the induction motor, X′ represents the rotor blocked equivalent reactance, E′ d Indicates the transient voltage of the direct axis of the induction motor, E′ q Indicates the quadrature axis transient potential of the induction motor, X s represents the reactance of the stator winding, X r represents the reactance of the rotor winding, X m represents the magnetizing reactance, T′ d0 represents the stator open circuit time constant, X represents the rotor open circuit reactance, ω r Indicates the slip rate, R r The resistance of the rotor winding, T j represents the induction motor rotor inertia time constant, T m Represents the mechanical torque of the induction motor, T e represents the electromagnetic torque of the induction motor, T0 is the steady-state mechanical torque, A, B, C are the mechanical torque coefficients, s represents the rotor slip, ω0 represents the initial slip rate, s0 represents the initial rotor slip, s0 is related to the load rate L f Correspondingly, p per Indicates the initial active power ratio of the motor. The dynamic load parameters include X', E' d , E′ q , T′ d0 ,X,T m 、T e , s, the first independent parameter includes Rs , R r , X r , X m 、T j , A, B, the second independent parameter includes X s , L f 、p per At least one of the non-independent parameters includes at least one of T0 and C.

[0058] Specifically, in order to ensure the accuracy of the model and avoid complex equation calculations, the dynamic load model can use a third-order electromechanical transient model to describe the dynamic process of the induction motor. It represents the rate of change of the direct axis (d-axis) voltage. Indicates the rate of change of the quadrature-axis (q-axis) voltage.

[0059] Optionally, R s , R r You can check directly in the motor's technical specification, X s , X r It can be obtained by testing the AC impedance of the machine equivalent circuit, X m It can be obtained by measuring reactive power, and s can be calculated by measuring the difference between the actual speed of the motor and the synchronous speed.

[0060] In some embodiments, the load model parameter identification device identifies the dependent parameter p based on the first independent parameter and the second independent parameter and the preset model constraint condition. p ,q p When solving T0 and C, the model constraints can be as follows:

[0061]

[0062] C=1-BA

[0063]

[0064] Thus, the parameter identification of the comprehensive load model is completed.

[0065] The disclosed embodiment obtains the static load parameters, dynamic load parameters and measured data of power, voltage and phase angle of the target load point, substitutes the static load parameters and dynamic load parameters into a pre-established comprehensive load model, and uses a statistical synthesis method to solve the first independent parameter in the comprehensive load model. The comprehensive load model includes a static load model and a dynamic load model. Based on the improved gray wolf algorithm, the first independent parameter and the measured data of power, voltage and phase angle are substituted into the objective function to solve the second independent parameter in the comprehensive load model. Based on the first independent parameter and the second independent parameter, the non-independent parameters in the comprehensive load model are solved. The statistical synthesis method and the improved gray wolf algorithm can be combined to identify the parameters in the comprehensive load model. Compared with the method of using the statistical synthesis method to identify all parameters, the demand for data volume is reduced. At the same time, the improved gray wolf algorithm has good global optimization capability, improves the accuracy of parameter identification, and thus improves the overall modeling accuracy of the comprehensive load model.

[0066] Figure 2 is a flow chart of a method for clustering load points provided by an embodiment of the present disclosure, such as Figure 2 As shown, based on the above embodiment, load points can be clustered by the following method.

[0067] S201. Obtaining collected data of each load point at different times.

[0068] In the disclosed embodiment, the load points may be set at different locations such as the user-side bus, key equipment ports, both sides of the transformer, and load equipment.

[0069] The collected data in the embodiments of the present disclosure can be understood as the corresponding data collected by data collection devices such as voltage sensors, current transformers, power meters, and frequency meters. The collected data will be serialized by time and location, and the collected data of each load point can constitute a time series sequence.

[0070] In the disclosed embodiment, the load model parameter identification device can obtain the collected data collected by each data collection device set at each load point at different times.

[0071] S202: For each load point, construct a feature vector of the load point based on the difference between the collected data of the load point at adjacent moments.

[0072] In the embodiment of the present disclosure, the load model parameter identification device can take the collected data corresponding to each load point as a cluster, initialize the cluster, and calculate the difference between the collected data at adjacent times within a cluster, and then calculate the slope between the collected data at adjacent times to form a feature vector. For the load point j containing the collected data at time t, a t-1-dimensional feature vector v can be obtained. j .

[0073] S203 , clustering the load points based on the similarity between the feature vectors of the load points, and determining a preset number of target load points according to the clustering results.

[0074] In the disclosed embodiment, the load model parameter identification device can, after determining the characteristic vector of each load point, calculate the similarity between the characteristic vectors corresponding to any two load points, and cluster the load points based on the similarity between the characteristic vectors to obtain a preset number of target load points.

[0075] In an exemplary implementation of the present disclosure, the load model parameter identification device can calculate the Euclidean distance between the feature vectors of any two clusters and construct a distance matrix D, wherein in the distance matrix D, the Euclidean distance d between the feature vectors of the two clusters corresponding to the load points i and j is ij It can be expressed as follows:

[0076]

[0077] v i1 Represents the eigenvector v corresponding to load point i i Features in the first dimension, v j1 The eigenvector v corresponding to load point j is represented by j Features in the first dimension, v i2 The eigenvector v corresponding to load point i is represented by i Features in the second dimension, and so on.

[0078] After the distance matrix D is constructed, the load model parameter identification device can merge the two clusters corresponding to the minimum element in the distance matrix D into a new cluster, take the mean of the corresponding distances between the characteristic vectors of each of the two clusters and the characteristic vectors of the other clusters as the corresponding distances between the new cluster and the other clusters, and judge whether the number of remaining clusters reaches the preset number. If so, exit the calculation and determine the load point corresponding to the remaining cluster as the target load point. Otherwise, continue to execute the steps of constructing the distance matrix D, merging new clusters, and judging whether the number of remaining clusters reaches the preset number until the number of remaining clusters reaches the preset number.

[0079] The disclosed embodiment acquires the collected data of each load point at different times, constructs a feature vector of each load point based on the difference between the collected data of the load point at adjacent times, clusters the load points based on the similarity between the feature vectors of each load point, determines a preset number of target load points based on the clustering results, and can cluster the load points according to the dynamic characteristics of the collected data corresponding to each load point in time series, thereby reducing the scale of the comprehensive load model and constructing a representative comprehensive load model.

[0080] Figure 3 is a flow chart of a method for identifying a second independent parameter provided by an embodiment of the present disclosure, such as Figure 3 As shown, based on the above embodiment, the second independent parameter can be identified by the following method.

[0081] S301. Calculate a model response of a comprehensive load model based on a first independent parameter.

[0082] In the disclosed embodiment, the identification of the second independent parameter is an iterative process. In the first iteration, the initial value corresponding to the second independent parameter can be used in combination with the first independent parameter, and then the first independent parameter, the static load parameter and the dynamic load parameter are substituted into the comprehensive load model to obtain the model response. In the non-first iteration, the optimal solution of the second independent parameter determined in the last iteration is substituted into the comprehensive load model to obtain the model response. Optionally, the model response can be the active and reactive power output by the static load model and the dynamic load model.

[0083] S302, substituting the model response and the measured data of power, voltage and phase angle into the objective function, and calculating the first fitness value of the gray wolf population.

[0084] In the disclosed embodiment, after determining the model response of the comprehensive load model, the load model parameter identification device can use the measured data of power, voltage and phase angle as the measured response, substitute them into the objective function together with the model response, and calculate the first fitness value of the gray wolf population.

[0085] In an exemplary implementation of the present disclosure, the load model parameter identification device can set the gray wolf population size N, the maximum number of iterations Max_iter, the parameter dimension to be optimized dim, the gray wolf algorithm convergence factor m, the attack coefficient vector A, the synergy coefficient vector C, the differential evolution algorithm scaling factor F r and the crossover probability C r , and select the second independent parameter p per , L f and X s As the gray wolf population X(i), the first fitness value of the gray wolf population is calculated according to the set objective function, and it is iterated to minimize the first fitness value. The objective function expression is as follows:

[0086]

[0087] Where J represents the first fitness value, n is the number of sample points, k is the sampling time, P and Q are the measured data of bus active and reactive power, P m , Q m is the active and reactive power output by the dynamic load model in the model response, P s , Qs It is the active and reactive power output by the static load model in the model response.

[0088] S303: Based on the improved grey wolf algorithm, the second independent parameter is optimized and solved according to the first fitness value until the number of iterations reaches a preset number of iterations.

[0089] In the disclosed embodiment, the load model parameter identification device can optimize and solve the second independent parameter according to the first fitness value based on the improved gray wolf algorithm after calculating the first fitness value of the gray wolf population, until the number of iterations reaches a preset number of iterations, and record the current optimal solution of the second independent parameter.

[0090] Optionally, the load model parameter identification device can adopt a dynamic weight update strategy, use the learning rate of ω wolf to α wolf, β wolf, and δ wolf as the weight coefficient, update the gray wolf position, and use the differential evolution algorithm to randomly mutate the updated gray wolf position, and calculate the second fitness value of the mutated gray wolf population, compare the first fitness value with the second fitness value, and determine the current optimal solution of the second independent parameter based on the comparison result and the number of iterations.

[0091] Specifically, in the gray wolf algorithm, it is divided into four levels: α, β, δ, and ω. The optimal solution, suboptimal solution, and reoptimal solution are sorted by the fitness value of the gray wolf population. The optimal solution is marked as α gray wolf, the suboptimal solution is marked as β gray wolf, and the reoptimal solution is marked as δ gray wolf. The optimal solution α gray wolf, the suboptimal solution β gray wolf, and the reoptimal solution δ gray wolf are used to guide the ω gray wolf to complete the predation behavior, thereby realizing iterative optimization. During the iteration process, the distance vectors between the α, β, and δ gray wolves and the prey can be expressed as follows:

[0092]

[0093] Among them, X(i) represents the position of the gray wolf in the current iteration, D α , D β and D δ represents the distance vector between the α, β, δ gray wolf and the prey, which is given by the Euclidean distance between the data at the corresponding positions in the matrix, X α , X β and X δ They represent the position vectors of the gray wolves α, β, and δ respectively, and C1, C2, and C3 are the coordination coefficient vectors.

[0094] In order to avoid falling into the local optimum during parameter identification, the load model parameter identification device can use a dynamic weight factor to improve the convergence speed of the gray wolf algorithm. The ω gray wolf position update formula of the dynamic weight factor is expressed as follows:

[0095]

[0096] Among them, w1, w2, w3 correspond to the learning rates of the ω gray wolf to the α, β, and δ gray wolf respectively, and A1, A2, and A3 represent the attack coefficient vectors, which can be used to update the position information in the next iteration process:

[0097]

[0098] Among them, X1(i), X2(i), and X3(i) represent the update amounts of the positions of the gray wolves α, β, and δ.

[0099] In order to avoid falling into the local optimum and improve the optimization stability, the load model parameter identification device can adopt a differential evolution algorithm with global optimization capability. After iteratively updating the gray wolf position, the first fitness value corresponding to the updated gray wolf position is calculated, and the updated gray wolf position is randomly mutated. The mutation operation can be expressed as follows:

[0100] v l (i) = x r1 (i)+F r *(x r2 (i)-x r3 (i))

[0101] Among them, v l (i) represents the position vector after the mutation operation, l represents the population sequence number, r1, r2 and r3 are different random integers in the interval [1, N], N represents the size of the population, and F r is a random scaling factor, and its value range is [0,2].

[0102] After the mutation operation is completed, the load model parameter identification device can convert the mutated gray wolf vector v l (i) and the updated gray wolf vector x l (i) Some genes are crossed to obtain the test vector gray wolf u l (i), the calculation formula can be expressed as follows:

[0103]

[0104] Among them, rand is a random number in the interval [0,1], j represents the jth gene of the population individual, C r represents the crossover probability constant, whose value range is [0,1], C r The smaller the size, the stronger the global search capability. r When the size is larger, the local search capability is stronger. n Represents a random integer in the gene dimension, s n The setting ensures that there is at least one gene crossover in the test vector.

[0105] In getting the test vector gray wolf ul (i) After that, the load model parameter identification device can be based on the test vector gray wolf u l (i) Calculate the second fitness value of the mutated gray wolf population, and compare the first fitness value corresponding to the updated gray wolf vector with the second fitness value, and update the gray wolf vector corresponding to the one with the smaller fitness value as the offspring gray wolf vector. This round of iteration is completed. The calculation formula is as follows:

[0106]

[0107] Among them, f(u l (i)) represents the second fitness value corresponding to the test gray wolf vector, f(x l (i)) represents the first fitness value corresponding to the updated gray wolf vector.

[0108] After a round of iteration of the offspring grey wolf vector is completed, the load model parameter identification device can update the number of iterations i=i+1 and the convergence factor m=2-2i / i max , the attack coefficient vector and coordination coefficient vector are And check whether the current number of iterations i meets the set maximum value. If it does, the iteration ends and the global optimal solution (α gray wolf) is output; otherwise, it returns to the next round of iteration, where r1 and r2 are one-dimensional random number vectors between [0,1].

[0109] The disclosed embodiment calculates the model response of the comprehensive load model based on the first independent parameter, substitutes the model response and the measured data of power, voltage and phase angle into the objective function, calculates the first fitness value of the gray wolf population, and optimizes and solves the second independent parameter according to the first fitness value based on the improved gray wolf algorithm until the number of iterations reaches a preset number of iterations. This can balance the global and local search capabilities, avoid falling into the local optimal solution, and further improve the search efficiency and accuracy of the algorithm.

[0110] Figure 4 FIG. 1 is a flow chart of a method for identifying impedance parameters of a distribution network provided by an embodiment of the present disclosure. Figure 4 As shown, based on the above embodiment, the impedance parameters of the distribution network can be identified by the following method.

[0111] In the disclosed embodiment, the load model parameter identification device can not only perform parameter identification on the comprehensive load model including the static load model and the dynamic load model, but also take into account the distribution network equivalent network and capacitor compensation, and perform parameter estimation on the distribution network impedance parameters. Specifically, a virtual load bus can be set between the load bus and the load to separate the distribution network equivalent impedance model from the load characteristics, thereby decoupling the dynamic characteristics of the load part from the influence of the impedance part, and the line impedance between the virtual load bus and the actual load bus is the distribution network equivalent impedance.

[0112] S401, obtaining voltage measurement data on the distribution network side and a current matrix of each line section in the distribution network.

[0113] In the embodiment of the present disclosure, the load model parameter identification device can obtain voltage measurement data on the distribution network side and the current matrix of each line section in the distribution network through voltage and current sensors set in the distribution network.

[0114] S402: Based on the voltage measurement data and the current matrix, the impedance of the distribution network is estimated by using the preset upper and lower bound constraint matrix of the line impedance to obtain the impedance parameters of the distribution network.

[0115] In the embodiment of the present disclosure, the load model parameter identification device can estimate the impedance of the distribution network based on the preset line impedance upper and lower bound constraint matrix after determining the voltage measurement data on the distribution network side and the current matrix of each line segment in the distribution network, and obtain the distribution network impedance parameter, which can be specifically expressed as follows:

[0116]

[0117] Z D =[R1X1…R D X D ] T

[0118] S D,t =[P 1t Q 1t …P Dt Q Dt ] T

[0119] Among them, V m,t is the voltage measurement data of the meter at any time section t, V D,t To evaluate the voltage at any time interval t, I D,t is the current matrix of each line in the distribution network, Z D represents a series of potential impedance combinations, S D,t Represents a series of potential active and reactive expressions for any time section t, where R1 represents the resistance of line 1 in the distribution network, X1 represents the reactance of line 1 in the distribution network, P1 represents the active power of line 1 in the distribution network, and Q1 represents the reactive power of line 1 in the distribution network.

[0120] The embodiment of the present disclosure obtains voltage measurement data on the distribution network side and the current matrix of each line section in the distribution network, and based on the voltage measurement data and the current matrix, uses preset upper and lower bound constraint matrices of line impedance to estimate the impedance of the distribution network, and obtains the impedance parameters of the distribution network. It can comprehensively consider the conditions on the distribution network side and the load side, thereby more comprehensively reflecting the actual operating status of the distribution network.

[0121] Figure 5 Schematic diagram of a load model parameter identification device provided by an embodiment of the present disclosure. Figure 5 As shown, the load model parameter identification device 500 includes: a first acquisition module 510, a first parameter identification module 520, a second parameter identification module 530, and a third parameter identification module 540, wherein the acquisition module 510 is used to obtain the static load parameters, dynamic load parameters, and measured data of power, voltage, and phase angle of the target load point; the first parameter identification module 520 is used to substitute the static load parameters and the dynamic load parameters into a pre-established comprehensive load model, and adopt a statistical synthesis method to solve the first independent parameter in the comprehensive load model, and the comprehensive load model includes a static load model and a dynamic load model; the second parameter identification module 530 is used to substitute the first independent parameter and the measured data of power, voltage, and phase angle into the objective function based on the improved gray wolf algorithm, and solve the second independent parameter in the comprehensive load model; the third parameter identification module 540 is used to solve the non-independent parameter in the comprehensive load model based on the first independent parameter and the second independent parameter.

[0122] Optionally, the load model parameter identification device 500 also includes: a second acquisition module, used to obtain the collected data of each load point at different times; a construction module, used to construct, for each load point, a feature vector of the load point based on the difference between the collected data of the load point at adjacent times; a clustering module, used to cluster the load points based on the similarity between the feature vectors of each load point, and determine a preset number of target load points according to the clustering results.

[0123] Optionally, the second parameter identification module 530 includes: a first calculation unit, used to calculate the model response of the comprehensive load model based on the first independent parameter; a second calculation unit, used to substitute the model response and the measured data of the power, voltage and phase angle into the objective function to calculate the first fitness value of the gray wolf population; a parameter identification unit, used to optimize and solve the second independent parameter according to the first fitness value based on an improved gray wolf algorithm until the number of iterations reaches a preset number of iterations.

[0124] Optionally, the parameter identification unit includes: an updating subunit, used to adopt a dynamic weight updating strategy, using the learning rate of ω wolf to α wolf, β wolf, and δ wolf as the weight coefficient to update the gray wolf position; a mutation subunit, used to adopt a differential evolution algorithm to randomly mutate the updated gray wolf positions, and calculate the second fitness value of the mutated gray wolf population; a determination subunit, used to compare the first fitness value with the second fitness value, and determine the current optimal solution of the second independent parameter based on the comparison result and the number of iterations.

[0125] Optionally, the load model parameter identification device 500 also includes: a third acquisition module, used to obtain voltage measurement data on the distribution network side and the current matrix of each line section in the distribution network; a fourth parameter identification module, used to estimate the impedance of the distribution network based on the voltage measurement data and the current matrix, using a preset line impedance upper and lower bound constraint matrix to obtain the distribution network impedance parameters.

[0126] Optionally, the static load model is:

[0127]

[0128] Where P represents the active power consumed by the static load, Q represents the reactive power consumed by the static load, U represents the voltage at the target load point, P0 represents the active load at the initial operating point, Q0 represents the reactive load at the initial operating point, U0 represents the voltage at the initial operating point, and p z It represents the ratio of the active power consumed by the constant impedance load in the steady state to the total static active power, p i It represents the ratio of active power consumed by constant current load in steady state to total static active power, p p It represents the ratio of active power consumed by constant power load in steady state to total static active power, q z It represents the proportion of the reactive power consumed by the constant impedance load in the steady state to the total static reactive power, q i It indicates the proportion of reactive power consumed by constant current load in steady state to total static reactive power, q p It indicates the proportion of reactive power consumed by constant power load in steady state to total static reactive power, Δf indicates the frequency change, f0 indicates the frequency of the initial operating point, L dp Indicates the frequency variation coefficient corresponding to active power, L dq , represents the frequency change coefficient corresponding to the reactive power, and the static load parameters include P, Q, U, P0, Q0, U0, Δf, f0, L dp , L dq At least one of the first independent parameters includes p z 、p i 、p p ,q z ,q i ,q p At least one of the non-independent parameters includes p p ,q p At least one of .

[0129] Optionally, the dynamic load model includes:

[0130]

[0131] X′=X s +X r X m / (X s +X m )

[0132]

[0133] ω r =1-s

[0134]

[0135] ω0=1-s0

[0136]

[0137] Among them, U d Indicates the stator direct-axis voltage of the induction motor, U q represents the stator quadrature axis voltage of the induction motor, R s Represents the resistance of the stator winding, I d Indicates the stator direct axis current of the induction motor, I q represents the stator quadrature axis current of the induction motor, X′ represents the rotor blocked equivalent reactance, E′ d Indicates the transient voltage of the direct axis of the induction motor, E′ q Indicates the quadrature axis transient potential of the induction motor, X s represents the reactance of the stator winding, X r represents the reactance of the rotor winding, X m represents the magnetizing reactance, T′ d0 represents the stator open circuit time constant, X represents the rotor open circuit reactance, ω r Indicates the slip rate, R r The resistance of the rotor winding, T j represents the induction motor rotor inertia time constant, T m Represents the mechanical torque of the induction motor, T e represents the electromagnetic torque of the induction motor, T0 is the steady-state mechanical torque, A, B, C are the mechanical torque coefficients, s represents the rotor slip, ω0 represents the initial slip rate, s0 represents the initial rotor slip, s0 is related to the load rate L f Correspondingly, p per Indicates the initial active power ratio of the motor. The dynamic load parameters include X', E' d , E′ q , T′ d0 ,X,T m 、T e , s, the first independent parameter includes R s , R r , Xr , X m 、T j , A, B, the second independent parameter includes X s , L f 、p per At least one of the non-independent parameters includes at least one of T0 and C.

[0138] The load model parameter identification device provided in this embodiment can execute the method described in any of the above embodiments. Its execution method and beneficial effects are similar and will not be described in detail here.

[0139] Figure 6 It is a structural diagram of a computer device provided in an embodiment of the present disclosure.

[0140] like Figure 6 As shown, the computer device may include a processor 610 and a memory 620 storing computer program instructions.

[0141] Specifically, the processor 610 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0142] The memory 620 may include a large capacity memory for information or instructions. By way of example and not limitation, the memory 620 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory 620 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 620 may be inside or outside the integrated gateway device. In a particular embodiment, the memory 620 is a non-volatile solid-state memory. In a particular embodiment, the memory 620 includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (Electrically Erasable Programmable ROM, EEPROM), an electrically rewritable ROM (EAROM) or a flash memory, or a combination of two or more of these.

[0143] The processor 610 reads and executes the computer program instructions stored in the memory 620 to perform the steps of the load model parameter identification method provided in the embodiment of the present disclosure.

[0144] In one example, the computer device may further include a transceiver 630 and a bus 640. Figure 6 As shown, the processor 610, the memory 620 and the transceiver 630 are connected via a bus 640 and communicate with each other.

[0145] The bus 640 includes hardware, software, or both. For example, but not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, the bus 640 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0146] The embodiment of the present disclosure further provides a computer-readable storage medium, which may store a computer program. When the computer program is executed by a processor, the processor implements the load model parameter identification method provided by the embodiment of the present disclosure.

[0147] The above-mentioned storage medium may, for example, include a memory 620 of computer program instructions, and the above-mentioned instructions may be executed by the processor 610 of the load model parameter identification device to complete the load model parameter identification method provided by the embodiment of the present disclosure. Optionally, the storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a ROM, a random access memory (Random Access Memory, RAM), a compact disc read-only memory (Compact Disc ROM, CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc. The above-mentioned computer program may be written in any combination of one or more programming languages ​​to perform the program code of the operation of the embodiment of the present disclosure, and the programming language includes an object-oriented programming language, such as Java, C++, etc., and also includes a conventional procedural programming language, such as "C" language or a similar programming language. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device, partially on the remote computing device, or entirely on the remote computing device or server.

[0148] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0149] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying load model parameters, characterized in that: include: Obtain the static load parameters, dynamic load parameters, and measured data of power, voltage, and phase angle of the target load point; Substituting the static load parameter and the dynamic load parameter into a pre-established comprehensive load model, and solving a first independent parameter in the comprehensive load model using a statistical synthesis method, wherein the comprehensive load model includes a static load model and a dynamic load model; Based on the improved Grey Wolf algorithm, the first independent parameter and the measured data of the power, voltage and phase angle are substituted into the objective function to solve the second independent parameter in the comprehensive load model; A dependent parameter in the comprehensive load model is solved based on the first independent parameter and the second independent parameter.

2. The method according to claim 1, characterized in that Before obtaining the static load parameters, dynamic load parameters, and measured data of power, voltage, and phase angle of the target load point, the method further includes: Obtain the collected data of each load point at different times; For each load point, constructing a feature vector of the load point based on the difference between the collected data of the load point at adjacent moments; The load points are clustered based on the similarity between the characteristic vectors of each load point, and a preset number of target load points are determined according to the clustering results.

3. The method according to claim 1, characterized in that The improved grey wolf algorithm is based on substituting the first independent parameter and the measured data of the power, voltage and phase angle into the objective function to solve the second independent parameter in the comprehensive load model, including: calculating a model response of the integrated load model based on the first independent parameter; Substituting the model response and the measured data of power, voltage and phase angle into the objective function, and calculating a first fitness value of the gray wolf population; Based on the improved grey wolf algorithm, the second independent parameter is optimized and solved according to the first fitness value until the number of iterations reaches a preset number of iterations.

4. The method according to claim 3, characterized in that The step of optimizing and solving the second independent parameter based on the improved grey wolf algorithm according to the first fitness value includes: Adopting a dynamic weight update strategy, the learning rate of ω wolf to α wolf, β wolf and δ wolf is used as the weight coefficient to update the position of the gray wolf; Using the differential evolution algorithm, the updated gray wolf positions are randomly mutated, and the second fitness value of the mutated gray wolf population is calculated; The first fitness value is compared with the second fitness value, and a current optimal solution of the second independent parameter is determined based on the comparison result and the number of iterations.

5. The method according to claim 1, characterized in that The method further comprises: Obtain voltage measurement data on the distribution network side and the current matrix of each line in the distribution network; Based on the voltage measurement data and the current matrix, the impedance of the distribution network is estimated by using a preset line impedance upper and lower bound constraint matrix to obtain the distribution network impedance parameters.

6. The method according to claim 1, characterized in that The static load model is: Where P represents the active power consumed by the static load, Q represents the reactive power consumed by the static load, U represents the voltage at the target load point, P0 represents the active load at the initial operating point, Q0 represents the reactive load at the initial operating point, U0 represents the voltage at the initial operating point, and p z It represents the ratio of the active power consumed by the constant impedance load in the steady state to the total static active power, p i It represents the ratio of active power consumed by constant current load in steady state to total static active power, p p It represents the ratio of active power consumed by constant power load in steady state to total static active power, q z It represents the proportion of the reactive power consumed by the constant impedance load in the steady state to the total static reactive power, q i It indicates the proportion of reactive power consumed by constant current load in steady state to total static reactive power, q p It indicates the proportion of reactive power consumed by constant power load in steady state to total static reactive power, Δf indicates the frequency change, f0 indicates the frequency of the initial operating point, L dp Indicates the frequency variation coefficient corresponding to active power, L dq , represents the frequency change coefficient corresponding to the reactive power, and the static load parameters include P, Q, U, P0, Q0, U0, Δf, f0, L dp , L dq At least one of the first independent parameters includes p z 、p i 、p p ,q z ,q i ,q p At least one of the non-independent parameters includes p p ,q p At least one of .

7. The method according to claim 1, characterized in that The dynamic load model includes: X ′ =X s +X r X m / (X s +X m ) oh r =1-s ω0=1-s0 Among them, U d Indicates the stator direct-axis voltage of the induction motor, U q represents the stator quadrature axis voltage of the induction motor, R s Represents the resistance of the stator winding, I d Indicates the stator direct axis current of the induction motor, I q represents the stator quadrature axis current of the induction motor, X′ represents the rotor blocked equivalent reactance, E′ d Indicates the transient voltage of the direct axis of the induction motor, E′ q Indicates the quadrature axis transient potential of the induction motor, X s represents the reactance of the stator winding, X r represents the reactance of the rotor winding, X m represents the magnetizing reactance, T′ d0 represents the stator open circuit time constant, X represents the rotor open circuit reactance, ω r Indicates the slip rate, R r The resistance of the rotor winding, T j represents the induction motor rotor inertia time constant, T m Represents the mechanical torque of the induction motor, T e represents the electromagnetic torque of the induction motor, T0 is the steady-state mechanical torque, A, B, C are the mechanical torque coefficients, s represents the rotor slip, ω0 represents the initial slip rate, s0 represents the initial rotor slip, s0 is related to the load rate L f Correspondingly, p per represents the initial active power ratio of the motor. The dynamic load parameters include X ′ 、E ′ d 、E ′ q , T d ′ 0, X, T m , T e , s, the first independent parameter includes R s , R r , X r , X m , T j , A, B, the second independent parameter includes X s , L f 、p per At least one of the non-independent parameters includes at least one of T0 and C.

8. A load model parameter identification device, characterized in that: include: The first acquisition module is used to obtain the static load parameters, dynamic load parameters and measured data of power, voltage and phase angle of the target load point; a first parameter identification module, used for substituting the static load parameter and the dynamic load parameter into a pre-established comprehensive load model, and solving a first independent parameter in the comprehensive load model by using a statistical synthesis method, wherein the comprehensive load model includes a static load model and a dynamic load model; A second parameter identification module is used to substitute the first independent parameter and the measured data of the power, voltage and phase angle into the objective function based on the improved grey wolf algorithm to solve the second independent parameter in the comprehensive load model; The third parameter identification module is used to solve the dependent parameters in the comprehensive load model based on the first independent parameter and the second independent parameter.

9. A computer device, characterized in that: include: Memory; processor; And a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the load model parameter identification method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the load model parameter identification method according to any one of claims 1 to 7 is implemented.

Images