Control parameter identification method and system of energy storage grid-connected converter

Abstract

The invention discloses a control parameter identification method for an energy storage grid-connected converter. The method comprises the following steps: acquiring data information of a target power system and data information of a target energy storage grid-connected converter; modeling the control process of the target energy storage grid-connected converter and determining parameters to be identified; identifying the to-be-identified parameters based on a differential evolution particle swarm algorithm and a fuzzy control algorithm; and completing the control parameter identification of the energy storage grid-connected converter according to the obtained identification result. The invention also discloses a system for realizing the control parameter identification method of the energy storage grid-connected converter. Real-time acquisition and model construction are performed on data of the target power system and the energy storage grid-connected converter, and parameter identification and correction are performed on the basis of the differential evolution particle swarm optimization and the fuzzy control algorithm, so that control parameter identification of the energy storage grid-connected converter can be realized, the reliability is higher, the accuracy is better, and the method is suitable for popularization and application. And the efficiency is high.

Description

Technical Field

[0001] The present invention belongs to the field of electrical automation, and in particular relates to a control parameter identification method and system for an energy storage grid-connected converter. Background Art

[0002] With the development of economy and technology and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and life, bringing endless convenience to people's production and life. Therefore, ensuring the stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] As environmental problems become more and more serious, more and more new energy power generation systems are being integrated into the power system to generate electricity. However, the randomness and volatility of the output of new energy power generation systems have brought great operating pressure to the power system. For this reason, the power system has begun to deploy energy storage devices to improve the operating reliability of the power system.

[0004] The energy storage system is connected to the power system through the energy storage grid-connected converter. In order to ensure the stable and reliable operation of the energy storage system, it is particularly important to identify the control parameters of the energy storage grid-connected converter. However, the control parameter identification scheme of the energy storage grid-connected converter currently used is easily limited by the initial value sensitivity and local optimal solution, resulting in inaccurate identification results; at the same time, the existing algorithm converges slowly in complex parameter space, affecting the real-time performance and efficiency of the system. Summary of the invention

[0005] One of the purposes of the present invention is to provide a control parameter identification method for an energy storage grid-connected converter with high reliability, good accuracy and high efficiency.

[0006] A second object of the present invention is to provide a system for implementing the control parameter identification method of the energy storage grid-connected converter.

[0007] The control parameter identification method of the energy storage grid-connected converter provided by the present invention comprises the following steps:

[0008] S1. Obtain data information of the target power system and data information of the target energy storage grid-connected converter;

[0009] S2. Modeling the control process of the target energy storage grid-connected converter according to the data information obtained in step S1 to determine the parameters to be identified;

[0010] S3. Based on differential evolution particle swarm algorithm and fuzzy control algorithm, the parameters to be identified determined in step S2 are identified;

[0011] S4. According to the identification result obtained in step S3, the control parameter identification of the energy storage grid-connected converter is completed.

[0012] The control process of the target energy storage grid-connected converter is modeled according to the data information obtained in step S1 in step S2, so as to determine the parameters to be identified, including the following steps:

[0013] The target energy storage grid-connected converter adopts PQ control mode;

[0014] According to the PQ control method, construct the transfer function of the current loop control and the expression of the current reference value;

[0015] Perform dq decoupling on the transfer function of the current loop control to obtain a single-input single-output model;

[0016] Finally, the parameters to be identified are obtained.

[0017] The step S2 specifically includes the following steps:

[0018] The target energy storage grid-connected converter adopts PQ control mode;

[0019] According to the PQ control method, the transfer function of the current loop control is expressed as

[0020]

[0021] Where V sd (s) is the d-axis output voltage of the controlled voltage source; K pi is the current loop proportional coefficient; K ii is the current loop integral coefficient; i dref is the d-axis component of the current reference value; i d (s) is the d-axis component of the current; w is the current system frequency output by the phase-locked loop; L is the filter inductance value; i q (s) is the q-axis component of the current; V d (s) is the d-axis component of the grid-connected voltage; V sq (s) is the q-axis output voltage of the controlled voltage source; i qref is the q-axis component of the current reference value; V q (s) is the q-axis component of the grid-connected voltage;

[0022] The d-axis component i of the current reference value dref and the q-axis component i of the current reference value qref , expressed as

[0023]

[0024] Where P ref is the active power reference value; Q ref is the reactive power reference value;

[0025] Perform dq decoupling on the transfer function of the current loop control:

[0026] The transfer function of the DC side of the energy storage grid-connected converter is expressed as Substituting into the transfer function of the current loop control, we get Arranged and Thus achieving decoupling;

[0027] Finally, the parameter to be identified is determined as K pi and K ii .

[0028] The step S3 described in which the parameters to be identified determined in step S2 are identified based on the differential evolution particle swarm algorithm and the fuzzy control algorithm includes the following steps:

[0029] Measure the experimental data of the energy storage system under two working conditions: active reference value jump and reactive reference value jump;

[0030] Based on the control process model of the target energy storage grid-connected converter constructed in step S2, a differential evolution particle swarm algorithm is used to perform preliminary parameter identification;

[0031] The obtained preliminary identification results of parameters are imported into the energy storage system model, simulation data is obtained through simulation, and the simulation data is compared with the actual data to obtain the error variable;

[0032] The error variable is fuzzily controlled through a fuzzy controller to obtain the correction amount of the identification parameter;

[0033] The final parameter identification result is obtained according to the correction amount of the identification parameter and the obtained preliminary parameter identification result.

[0034] The use of differential evolution particle swarm algorithm for preliminary parameter identification specifically includes the following steps:

[0035] In the differential evolution particle swarm algorithm adopted, the following steps are used to set parameters:

[0036] The following formula is used as the fitness function:

[0037]

[0038] Where J is the fitness function value; w1 is the active weight value; w2 is the reactive weight value; n is the length of the data; i d_Psim is the simulation value of the d-axis current component of the grid-connected current measured under active disturbance; i d_Pmeas is the measured value of the d-axis current component of the grid-connected current under active disturbance; i q_Qsim is the simulation value of the q-axis current component of the grid-connected current measured under reactive disturbance; i q_Qmeas is the measured value of the q-axis current component of the grid-connected current under reactive disturbance;

[0039] The inertia weight is set using the following formula:

[0040]

[0041] Where w k is the inertia weight value at the kth iteration; w max is the maximum value of the inertia weight value w; min is the minimum value of the inertia weight w; M is the maximum number of iterations;

[0042] The learning factor is set using the following formula:

[0043]

[0044]

[0045] In the formula is the first learning factor c1 in the kth iteration; c 1max is the maximum value of the first learning factor; c 1min is the minimum value of the first learning factor; is the second learning factor c2 at the kth iteration; c 2max is the maximum value of the second learning factor; c 2min is the minimum value of the second learning factor;

[0046] The variation factor is set using the following formula:

[0047]

[0048] Where F k is the mutation factor in the kth iteration; F max is the maximum value of the variation factor; F min is the minimum value of the variation factor;

[0049] The crossover factor is set using the following formula:

[0050]

[0051] Where CR k is the crossover factor at the kth iteration; CR max is the maximum value of the crossover factor; CR min is the minimum value of the crossover factor; J i is the fitness value of the i-th individual in the current population; J max is the maximum fitness value in the current population; J min is the minimum fitness value in the current population.

[0052] The method of performing fuzzy control on the error variable through a fuzzy controller to obtain the correction amount of the identification parameter specifically includes the following steps:

[0053] The error variable e is fuzzified as the fuzzy input, and the correction amount of the identification parameter is taken as the fuzzy output:

[0054] The error variable e is fuzzified as fuzzy input;

[0055] The identification parameters are expressed as Where K p is the corrected parameter K to be identified pi , K i is the corrected parameter K to be identified ii , ΔK p K pi Correction value, ΔK i K ii The correction amount;

[0056] ΔK p and ΔK i As fuzzy output;

[0057] Construct fuzzy control rules and establish the relationship between fuzzy input and fuzzy output:

[0058] Taking the error variable e and the error variable change rate ec as input, ΔK p and ΔK i As output;

[0059] The value ranges of the error variable e and the error variable change rate ec are: negative large NB, negative medium NM, negative small NS, zero O, positive small PS, positive medium PM and positive large PB;

[0060] ΔK p The fuzzy control rules are:

[0061] If e is NB and ec is NB, then ΔK p The value is PB;

[0062] If e is NB and ec is NM, then ΔK p The value is PB;

[0063] If e is NB and ec is NS, then ΔK p The value is PM;

[0064] If e is NB and ec is O, then ΔK p The value is PM;

[0065] If e is NB and ec is PS, then ΔK p The value is PS;

[0066] If e is NB and ec is PM, then ΔK p The value is 0;

[0067] If e is NB and ec is PB, then ΔK p The value is 0;

[0068] If e is NM and ec is NB, then ΔK p The value is PB;

[0069] If e is NM and ec is NM, then ΔK p The value is PB;

[0070] If e is NM and ec is NS, then ΔK p The value is PM;

[0071] If e is NM and ec is O, then ΔK p The value is PS;

[0072] If e is NM and ec is PS, then ΔK p The value is PS;

[0073] If e is NM and ec is PM, then ΔK p The value is 0;

[0074] If e is NM and ec is PB, then ΔK p The value is NS;

[0075] If e is NS and ec is NB, then ΔK p The value is PM;

[0076] If e is NS and ec is NM, then ΔK p The value is PM;

[0077] If e is NS and ec is NS, then ΔK p The value is PM;

[0078] If e is NS and ec is O, then ΔK p The value is PS;

[0079] If e is NS and ec is PS, then ΔK p The value is 0;

[0080] If e is NS and ec is PM, then ΔK p The value is NS;

[0081] If e is NS and ec is PB, then ΔK p The value is NS;

[0082] If e is O and ec is NB, then ΔK p The value is PM;

[0083] If e is O and ec is NM, then ΔK p The value is PM;

[0084] If e is O and ec is NS, then ΔK p The value is PS;

[0085] If e is O and ec is O, then ΔK p The value is 0;

[0086] If e is O and ec is PS, then ΔK p The value is NS;

[0087] If e is O and ec is PM, then ΔK p The value is NM;

[0088] If e is O and ec is PB, then ΔK p The value is NM;

[0089] If e is PS and ec is NB, then ΔK p The value is PS;

[0090] If e is PS and ec is NM, then ΔK p The value is PS;

[0091] If e is PS and ec is NS, then ΔK p The value is 0;

[0092] If e is PS and ec is O, then ΔK p The value is NS;

[0093] If e is PS and ec is PS, then ΔK p The value is NS;

[0094] If e is PS and ec is PM, then ΔK p The value is NM;

[0095] If e is PS and ec is PB, then ΔK p The value is NM;

[0096] If e is PM and ec is NB, then ΔK p The value is PS;

[0097] If e is PM and ec is NM, then ΔK p The value is 0;

[0098] If e is PM and ec is NS, then ΔK p The value is NS; e is PM and ec is O, then ΔK p The value is NM; e is PM and ec is PS, then ΔK p The value is NM; e is PM and ec is PM, then ΔK pThe value is NM; e is PM and ec is PB, then ΔK p The value is NB; e is PB and ec is NB, then ΔK p Take the value as O; e is PB and ec is NM, then ΔK p The value is O; e is PB and ec is NS, then ΔK p The value is NM; e is PB and ec is O, then ΔK p The value is NM; e is PB and ec is PS, then ΔK p The value is NM; e is PB and ec is PM, then ΔK p The value is NB; e is PB and ec is PB, then ΔK p The value is NB; ΔK i The fuzzy control rule is: e is NB and ec is NB, then ΔK i The value is NB; e is NB and ec is NM, then ΔK i The value is NB; e is NB and ec is NS, then ΔK i The value is NM; e is NB and ec is O, then ΔK i The value is NM; e is NB and ec is PS, then ΔK i The value is NS; e is NB and ec is PM, then ΔK i The value is O; e is NB and ec is PB, then ΔK i The value is 0;

[0099] If e is NM and ec is NB, then ΔK i The value is NB; e is NM and ec is NM, then ΔK i The value is NB; e is NM and ec is NS, then ΔK i The value is NM;

[0100] If e is NM and ec is O, then ΔK i The value is NS;

[0101] If e is NM and ec is PS, then ΔK i The value is NS;

[0102] If e is NM and ec is PM, then ΔK i The value is 0;

[0103] If e is NM and ec is PB, then ΔK i The value is NS;

[0104] If e is NS and ec is NB, then ΔK i The value is NB;

[0105] If e is NS and ec is NM, then ΔK i The value is NM;

[0106] If e is NS and ec is NS, then ΔK i The value is NS;

[0107] If e is NS and ec is O, then ΔK i The value is NS;

[0108] If e is NS and ec is PS, then ΔK i The value is 0;

[0109] If e is NS and ec is PM, then ΔK i The value is PS;

[0110] If e is NS and ec is PB, then ΔK i The value is PS;

[0111] If e is O and ec is NB, then ΔK i The value is NM;

[0112] If e is O and ec is NM, then ΔK i The value is NM;

[0113] If e is O and ec is NS, then ΔK i The value is NS;

[0114] If e is O and ec is O, then ΔK i The value is 0;

[0115] If e is O and ec is PS, then ΔK i The value is PS;

[0116] If e is O and ec is PM, then ΔK i The value is PM;

[0117] If e is O and ec is PB, then ΔK i The value is PM;

[0118] If e is PS and ec is NB, then ΔK i The value is NM;

[0119] If e is PS and ec is NM, then ΔK i The value is NS;

[0120] If e is PS and ec is NS, then ΔK i The value is 0;

[0121] If e is PS and ec is O, then ΔK i The value is PS;

[0122] If e is PS and ec is PS, then ΔK i The value is PS;

[0123] If e is PS and ec is PM, then ΔK i The value is PM;

[0124] If e is PS and ec is PB, then ΔK i The value is PB;

[0125] If e is PM and ec is NB, then ΔK i The value is 0;

[0126] If e is PM and ec is NM, then ΔK i The value is 0;

[0127] If e is PM and ec is NS, then ΔK i The value is PS;

[0128] If e is PM and ec is O, then ΔK i The value is PS;

[0129] If e is PM and ec is PS, then ΔK i The value is PM;

[0130] If e is PM and ec is PM, then ΔK i The value is PB;

[0131] If e is PM and ec is PB, then ΔK i The value is PB;

[0132] If e is PB and ec is NB, then ΔK i The value is 0;

[0133] If e is PB and ec is NM, then ΔK i The value is 0;

[0134] If e is PB and ec is NS, then ΔK i The value is PS;

[0135] If e is PB and ec is O, then ΔK i The value is PM;

[0136] If e is PB and ec is PS, then ΔK i The value is PM;

[0137] If e is PB and ec is PM, then ΔK i The value is PB;

[0138] If e is PB and ec is PB, then ΔK i The value is PB;

[0139] Trigonometric function is used as membership function;

[0140] Finally, the fuzzy correction result is defuzzified to obtain the correction amount of the identification parameter.

[0141] The method of obtaining the final parameter identification result based on the correction amount of the identification parameter and the obtained preliminary parameter identification result specifically includes the following steps:

[0142] The correction amount of the identification parameter is summed with the obtained preliminary parameter identification result to obtain the final parameter identification result.

[0143] The present invention also provides a system for implementing the control parameter identification method of the energy storage grid-connected converter, comprising a data acquisition module, a model construction module, a preliminary identification module and a parameter identification module; the data acquisition module, the model construction module, the preliminary identification module and the parameter identification module are connected in series in sequence; the data acquisition module is used to acquire data information of the target power system and data information of the target energy storage grid-connected converter, and upload the data information to the model construction module; the model construction module is used to model the control process of the target energy storage grid-connected converter according to the received data information and the acquired data information, so as to determine the parameters to be identified, and upload the data information to the preliminary identification module; the preliminary identification module is used to identify the determined parameters to be identified according to the received data information, based on the differential evolution particle swarm algorithm and the fuzzy control algorithm, and upload the data information to the parameter identification module; the parameter identification module is used to complete the control parameter identification of the energy storage grid-connected converter according to the received data information and the obtained identification results.

[0144] The control parameter identification method and system of the energy storage grid-connected converter provided by the present invention acquires and builds models of the target power system and the energy storage grid-connected converter in real time, and identifies and corrects the parameters based on the differential evolution particle swarm algorithm and the fuzzy control algorithm. Therefore, the present invention can not only realize the control parameter identification of the energy storage grid-connected converter, but also has higher reliability, better accuracy and higher efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0145] Figure 1 The figure is a schematic diagram of the method flow of the present invention.

[0146] Figure 2 It is a schematic diagram of simulation and measurement curves of d-axis and q-axis components of an embodiment of the method of the present invention.

[0147] Figure 3 Schematic diagram of the change process of the minimum value of the objective function of the method embodiment of the present invention.

[0148] Figure 4 ΔK under the d-axis of the method embodiment of the present invention p and ΔK i Schematic diagram of the change curve.

[0149] Figure 5 It is a schematic diagram of the curves before and after optimization of the d-axis component and q-axis component of the current in the embodiment of the method of the present invention.

[0150] Figure 6 Schematic diagram of the functional modules of the system of the present invention. DETAILED DESCRIPTION

[0151] like Figure 1 The method flow chart of the method of the present invention is shown as follows: The control parameter identification method of the energy storage grid-connected converter disclosed in the present invention comprises the following steps:

[0152] S1. Obtain data information of the target power system and data information of the target energy storage grid-connected converter;

[0153] S2. Modeling the control process of the target energy storage grid-connected converter according to the data information obtained in step S1, thereby determining the parameters to be identified; comprising the following steps:

[0154] The target energy storage grid-connected converter adopts the PQ control method; first, the current reference signal of the current inner loop is calculated based on the power control target and the grid-connected voltage, and then the power control of the energy storage grid-connected converter is achieved by adjusting the grid-connected current;

[0155] According to the PQ control method, construct the transfer function of the current loop control and the expression of the current reference value;

[0156] Perform dq decoupling on the transfer function of the current loop control to obtain a single-input single-output model;

[0157] Finally, the parameters to be identified are obtained;

[0158] The specific implementation includes the following steps:

[0159] The target energy storage grid-connected converter adopts PQ control mode;

[0160] According to the PQ control method, the transfer function of the current loop control is expressed as

[0161]

[0162] Where V sd (s) is the d-axis output voltage of the controlled voltage source; K pi is the current loop proportional coefficient; K ii is the current loop integral coefficient; i dref is the d-axis component of the current reference value; i d (s) is the d-axis component of the current; w is the current system frequency output by the phase-locked loop; L is the filter inductance value; i q (s) is the q-axis component of the current; V d(s) is the d-axis component of the grid-connected voltage; V sq (s) is the q-axis output voltage of the controlled voltage source; i qref is the q-axis component of the current reference value; V q (s) is the q-axis component of the grid-connected voltage;

[0163] The d-axis component i of the current reference value dref and the q-axis component i of the current reference value qref , expressed as

[0164]

[0165] Where P ref is the active power reference value; Q ref is the reactive power reference value;

[0166] It can be seen that the above model to be identified is a multi-input single-output model, which is obviously not conducive to parameter identification; therefore, the following measures are taken to perform dq decoupling on the transfer function of the current loop control, thereby simplifying the model to be identified into a single-input single-output model;

[0167] Perform dq decoupling on the transfer function of the current loop control:

[0168] The transfer function of the DC side of the energy storage grid-connected converter is expressed as Substituting into the transfer function of the current loop control, we get Arranged and Thus achieving decoupling;

[0169] Finally, the parameter to be identified is determined as K pi and K ii ;

[0170] Through the to-be-identified model of the decoupled current loop d-axis and q-axis transfer functions, it can be seen that the model realizes the dq-axis decoupling of the converter control equation;

[0171] S3. Based on the differential evolution particle swarm algorithm and the fuzzy control algorithm, the parameters to be identified determined in step S2 are identified; comprising the following steps:

[0172] The basic idea of ​​energy storage grid-connected converter control parameter identification is to treat the converter as a whole, apply artificial disturbances on site, collect dynamic data, and identify the converter control parameters using disturbance data under the premise of known model structure; since the control parameter identification of the converter model belongs to post-processing, the identification parameter optimization process can be closed-loop, so that the identification parameters can be fine-tuned according to the output feedback, thereby achieving the purpose of further reducing the output deviation and enhancing the robustness of the identification parameters;

[0173] Measure the experimental data of the energy storage system under two working conditions: active reference value jump and reactive reference value jump;

[0174] According to the control process model of the target energy storage grid-connected converter constructed in step S2, a differential evolution particle swarm algorithm is used to perform preliminary parameter identification; specifically, the following steps are included:

[0175] In the differential evolution particle swarm algorithm adopted, the following steps are used to set parameters:

[0176] In order to enhance the robustness of the identification parameters and improve the accuracy of parameter identification, the d-axis and q-axis models to be identified are combined with each other; therefore, the following formula is used as the fitness function:

[0177]

[0178] Where J is the fitness function value; w1 is the active weight value, preferably 0.5; w2 is the reactive weight value, preferably 0.5; n is the length of the data; i d_Psim is the simulation value of the d-axis current component of the grid-connected current measured under active disturbance; i d_Pmeas is the measured value of the d-axis current component of the grid-connected current under active disturbance; i q_Qsim is the simulation value of the q-axis current component of the grid-connected current measured under reactive disturbance; i q_Qmeas is the measured value of the q-axis current component of the grid-connected current under reactive disturbance;

[0179] Inertia weight is an important parameter that controls global and local search capabilities. The idea of ​​focusing on global search in the early stage and local search in the later stage is adopted. The inertia weight adopts a linear decreasing rule; therefore, the inertia weight is set using the following formula:

[0180]

[0181] Where w k is the inertia weight value at the kth iteration; w max is the maximum value of the inertia weight value w; min is the minimum value of the inertia weight w; M is the maximum number of iterations;

[0182] The learning factor is a random acceleration term that controls each particle to move toward the local and global optimum. It focuses on the particle's own cognition in the early stage and the group's social cognition in the later stage. Therefore, the learning factor is set using the following formula:

[0183]

[0184] In the formula is the first learning factor c1 in the kth iteration; c 1max is the maximum value of the first learning factor; c1min is the minimum value of the first learning factor; is the second learning factor c2 at the kth iteration; c 2max is the maximum value of the second learning factor; c 2min is the minimum value of the second learning factor;

[0185] The mutation factor is used to balance population diversity and convergence. A smaller mutation factor is conducive to population convergence, but the population difference is too small, which is not conducive to the evolution process jumping out of the local optimal solution; a larger mutation factor is conducive to global search, but the convergence speed will slow down; therefore, the mutation factor is set using the following formula:

[0186]

[0187] Where F k is the mutation factor in the kth iteration; F max is the maximum value of the variation factor; F min is the minimum value of the variation factor;

[0188] The crossover factor can balance the local and global search capabilities and control the degree of individual crossover participation. When the crossover factor CR is too small, the population diversity decreases and it is easy to fall into the local optimal solution. When CR is too large, the perturbation is greater than the group difference, which may lead to slower convergence. Therefore, the idea of ​​retaining the elite is adopted, and the crossover factor is set using the following formula:

[0189]

[0190] Where CR k is the crossover factor at the kth iteration; CR max is the maximum value of the crossover factor; CR min is the minimum value of the crossover factor; J i is the fitness value of the i-th individual in the current population; J max is the maximum fitness value in the current population; J min is the minimum fitness value in the current population;

[0191] The obtained preliminary identification results of parameters are imported into the energy storage system model, simulation data is obtained through simulation, and the simulation data is compared with the actual data to obtain the error variable;

[0192] The error variable is fuzzily controlled by a fuzzy controller to obtain the correction amount of the identification parameter; specifically, the following steps are included:

[0193] The error variable e is fuzzified as the fuzzy input, and the correction amount of the identification parameter is taken as the fuzzy output:

[0194] The error variable e is fuzzified as fuzzy input;

[0195] The identification parameters are expressed as Where K p is the corrected parameter K to be identified pi , K i is the corrected parameter K to be identified ii , ΔK p K pi Correction value, ΔK i K ii The correction amount;

[0196] ΔK p and ΔK i As fuzzy output;

[0197] Construct fuzzy control rules and establish the relationship between fuzzy input and fuzzy output:

[0198] Taking the error variable e and the error variable change rate ec as input, ΔK p and ΔK i As output;

[0199] The value ranges of the error variable e and the error variable change rate ec are: negative large NB, negative medium NM, negative small NS, zero O, positive small PS, positive medium PM and positive large PB;

[0200] ΔK p The fuzzy control rules are:

[0201] If e is NB and ec is NB, then ΔK p The value is PB;

[0202] If e is NB and ec is NM, then ΔK p The value is PB;

[0203] If e is NB and ec is NS, then ΔK p The value is PM;

[0204] If e is NB and ec is O, then ΔK p The value is PM;

[0205] If e is NB and ec is PS, then ΔK p The value is PS;

[0206] If e is NB and ec is PM, then ΔK p The value is 0;

[0207] If e is NB and ec is PB, then ΔK p The value is 0;

[0208] If e is NM and ec is NB, then ΔK p The value is PB;

[0209] If e is NM and ec is NM, then ΔK p The value is PB;

[0210] If e is NM and ec is NS, then ΔK p The value is PM;

[0211] If e is NM and ec is O, then ΔK p The value is PS;

[0212] If e is NM and ec is PS, then ΔK p The value is PS;

[0213] If e is NM and ec is PM, then ΔK p The value is 0;

[0214] If e is NM and ec is PB, then ΔK p The value is NS;

[0215] If e is NS and ec is NB, then ΔK p The value is PM;

[0216] If e is NS and ec is NM, then ΔK p The value is PM;

[0217] If e is NS and ec is NS, then ΔK p The value is PM;

[0218] If e is NS and ec is O, then ΔK p The value is PS;

[0219] If e is NS and ec is PS, then ΔK p The value is 0;

[0220] If e is NS and ec is PM, then ΔK p The value is NS;

[0221] If e is NS and ec is PB, then ΔK p The value is NS;

[0222] If e is O and ec is NB, then ΔK p The value is PM;

[0223] If e is O and ec is NM, then ΔK p The value is PM;

[0224] If e is O and ec is NS, then ΔK p The value is PS;

[0225] If e is O and ec is O, then ΔK p The value is 0;

[0226] If e is O and ec is PS, then ΔK p The value is NS;

[0227] If e is O and ec is PM, then ΔK p The value is NM;

[0228] If e is O and ec is PB, then ΔK p The value is NM;

[0229] If e is PS and ec is NB, then ΔK p The value is PS;

[0230] If e is PS and ec is NM, then ΔK p The value is PS;

[0231] If e is PS and ec is NS, then ΔK p The value is 0;

[0232] If e is PS and ec is O, then ΔK p The value is NS;

[0233] If e is PS and ec is PS, then ΔK p The value is NS;

[0234] If e is PS and ec is PM, then ΔK p The value is NM;

[0235] If e is PS and ec is PB, then ΔK p The value is NM;

[0236] If e is PM and ec is NB, then ΔK p The value is PS;

[0237] If e is PM and ec is NM, then ΔK p The value is 0;

[0238] If e is PM and ec is NS, then ΔK p The value is NS;

[0239] If e is PM and ec is O, then ΔK p The value is NM;

[0240] If e is PM and ec is PS, then ΔK p The value is NM;

[0241] If e is PM and ec is PM, then ΔK p The value is NM;

[0242] If e is PM and ec is PB, then ΔK p The value is NB;

[0243] If e is PB and ec is NB, then ΔK p The value is 0;

[0244] If e is PB and ec is NM, then ΔK p The value is O; e is PB and ec is NS, then ΔK p The value is NM; e is PB and ec is O, then ΔK p The value is NM; e is PB and ec is PS, then ΔK p The value is NM; e is PB and ec is PM, then ΔK p The value is NB; e is PB and ec is PB, then ΔK p The value is NB; ΔK i The fuzzy control rule is: e is NB and ec is NB, then ΔK i The value is NB; e is NB and ec is NM, then ΔK i The value is NB; e is NB and ec is NS, then ΔK i The value is NM; e is NB and ec is O, then ΔK i The value is NM; e is NB and ec is PS, then ΔK i The value is NS; e is NB and ec is PM, then ΔK i The value is O; e is NB and ec is PB, then ΔK i The value is O; e is NM and ec is NB, then ΔK i The value is NB; e is NM and ec is NM, then ΔK i The value is NB; e is NM and ec is NS, then ΔK i The value is NM; e is NM and ec is O, then ΔK i The value is NS; e is NM and ec is PS, then ΔK i The value is NS; e is NM and ec is PM, then ΔK i The value is O; e is NM and ec is PB, then ΔK i The value is NS; e is NS and ec is NB, then ΔK i The value is NB; e is NS and ec is NM, then ΔK i The value is NM;

[0245] If e is NS and ec is NS, then ΔK i The value is NS;

[0246] If e is NS and ec is O, then ΔK i The value is NS;

[0247] If e is NS and ec is PS, then ΔK i The value is 0;

[0248] If e is NS and ec is PM, then ΔK i The value is PS;

[0249] If e is NS and ec is PB, then ΔK i The value is PS;

[0250] If e is O and ec is NB, then ΔK i The value is NM;

[0251] If e is O and ec is NM, then ΔK i The value is NM;

[0252] If e is O and ec is NS, then ΔK i The value is NS;

[0253] If e is O and ec is O, then ΔK i The value is 0;

[0254] If e is O and ec is PS, then ΔK i The value is PS;

[0255] If e is O and ec is PM, then ΔK i The value is PM;

[0256] If e is O and ec is PB, then ΔK i The value is PM;

[0257] If e is PS and ec is NB, then ΔK i The value is NM;

[0258] If e is PS and ec is NM, then ΔK i The value is NS;

[0259] If e is PS and ec is NS, then ΔK i The value is 0;

[0260] If e is PS and ec is O, then ΔK i The value is PS;

[0261] If e is PS and ec is PS, then ΔK i The value is PS;

[0262] If e is PS and ec is PM, then ΔK i The value is PM;

[0263] If e is PS and ec is PB, then ΔK i The value is PB;

[0264] If e is PM and ec is NB, then ΔK i The value is 0;

[0265] If e is PM and ec is NM, then ΔK i The value is 0;

[0266] If e is PM and ec is NS, then ΔK i The value is PS;

[0267] If e is PM and ec is O, then ΔK i The value is PS;

[0268] If e is PM and ec is PS, then ΔK i The value is PM;

[0269] If e is PM and ec is PM, then ΔK i The value is PB;

[0270] If e is PM and ec is PB, then ΔK i The value is PB;

[0271] If e is PB and ec is NB, then ΔK i The value is 0;

[0272] If e is PB and ec is NM, then ΔK i The value is 0;

[0273] If e is PB and ec is NS, then ΔK i The value is PS;

[0274] If e is PB and ec is O, then ΔK i The value is PM;

[0275] If e is PB and ec is PS, then ΔK i The value is PM;

[0276] If e is PB and ec is PM, then ΔK i The value is PB;

[0277] If e is PB and ec is PB, then ΔK i The value is PB;

[0278] Trigonometric function is used as membership function;

[0279] Finally, the fuzzy correction result is defuzzified to obtain the correction amount of the identification parameter;

[0280] According to the correction amount of the identification parameter and the obtained preliminary parameter identification result, the final parameter identification result is obtained; specifically, the following steps are included:

[0281] The correction amount of the identification parameter is summed with the obtained preliminary parameter identification result to obtain the final parameter identification result;

[0282] S4. According to the identification result obtained in step S3, the control parameter identification of the energy storage grid-connected converter is completed.

[0283] The method of the present invention is further described below in conjunction with an embodiment:

[0284] Set the active power reference value to P ref In 0.2s, it jumps from 20KW to 40KW, and the reactive power reference value is Q ref In 0.3s, it jumps from 0KW to 10KW. Inverter control parameter K pi and K ii Identification can be performed under both the d-axis and q-axis models to be identified. Therefore, based on the method of the present invention, the following three strategies can be used to identify the control parameters of the energy storage grid-connected converter:

[0285] Strategy 1: Under the sudden disturbance of active reference value, measure i dref and i d The data is used to perform parameter identification using the d-axis model to be identified.

[0286] Strategy 2: Under the sudden disturbance of reactive reference value, measure i qref and i q The data is used to perform parameter identification using the q-axis model to be identified.

[0287] Strategy 3: Under the sudden disturbance of active reference value and reactive reference value, measure i dref 、i d 、i qref and i q The d-axis and q-axis models to be identified are used, and the above-mentioned objective function is used to combine the two for parameter identification.

[0288] The identification results of the three identification strategies are shown in Table 1:

[0289] Table 1 Schematic diagram of identification results under different strategies

[0290]

[0291] It can be seen from Table 1 that strategy three has the highest identification accuracy, strategy one has the second highest identification accuracy, and strategy two has the worst identification accuracy, which verifies the effectiveness of the method of the present invention and significantly improves the accuracy of parameter identification.

[0292] The simulation and measurement curves of the current d-axis and q-axis components under strategy three are as follows Figure 2 As shown, Figure 2 (a) corresponds to the d-axis component, Figure 2 (b) corresponds to the q-axis component; Figure 2It can be seen that when the active power reference value and the reactive power reference value suddenly change, the fitting effect between the simulation waveform and the measured waveform will be poor, but the deviation between the simulation value and the measured value in the other stages is very small;

[0293] Under the same experimental conditions, PSO and DE algorithms are used to identify the parameters to be identified. The identification results of the three algorithms are shown in Table 2 below:

[0294] Table 2 Schematic diagram of identification results of different algorithms

[0295]

[0296] As can be seen from Table 2, the three algorithms have different pi The ranking of the identification error rates is: PSO>DE>the present invention; the three algorithms have different ii The identification error rates are ranked as follows: DE>PSO>the present invention; the minimum values ​​of the objective functions of the three algorithms are ranked as follows: PSO=DE>the present invention. The identification results show that the present invention has stronger global search capability and better identification accuracy than the other two algorithms.

[0297] The change process of the objective function J during the identification process is as follows Figure 3 As shown in the figure, it can be seen that the method of the present invention (corresponding to DEPSO in the figure) has the fastest convergence speed and tends to be stable after 5 iterations; the PSO algorithm has the second fastest convergence speed and tends to be stable after 30 iterations; the DE algorithm has the slowest iterative convergence speed and tends to be stable after 82 iterations. Therefore, compared with the PSO and DE algorithms, the method of the present invention has a faster convergence speed.

[0298] From the above simulation analysis, it can be seen that in the process of converter control parameter identification, there is a problem of large deviation between the simulation waveform and the actual measured waveform. In order to enhance the robustness of the identification parameters, an identification parameter adaptive optimization strategy is constructed, that is, the correction strategy based on fuzzy control in the solution of the present invention;

[0299] The range of error e and error change rate ec is defined as the domain (-6,6), (-60000,60000); ΔK p and ΔK i The domain of is defined as (-0.5, 0.5), (-20, 20), and the initial value of the identification parameter to be adjusted is selected as: K pi =0.6006, K ii =500.038;

[0300] During the entire simulation process, the d-axis ΔK p and ΔK i The change curve is as Figure 4 As shown, Figure 4 (a) corresponds to ΔKp changes, Figure 4 (b) corresponds to ΔK i Changes in q-axis ΔK p and ΔK i The change curve is basically consistent with the change curve of d. As the error between the measured data and the simulation data changes, ΔK p and ΔK i The value of ΔK changes accordingly. p The variation range is basically between (-0.3, 0.2), ΔK under the d-axis and q-axis i The variation range is basically between (-8,12).

[0301] The simulation curves of the d-axis and q-axis components of the grid-connected current before and after adaptive optimization are as follows: Figure 5 As shown, Figure 5 (a) corresponds to the d-axis component, Figure 5 (b) corresponds to the q-axis component; it can be seen from the figure that after the adaptive optimization of the identification parameters, the overshoot of the d-axis simulation curve of the current in the initial stage increases significantly, while the overshoot of the q-axis decreases. The minimum value of the objective function increases from the value before optimization to the value before optimization. min =433.8332 is reduced to J min =414.9901, which proves that the output deviation can be further reduced by correcting the identification parameters.

[0302] like Figure 6 The functional module schematic diagram of the system of the present invention is shown as follows: the system for realizing the control parameter identification method of the energy storage grid-connected converter disclosed in the present invention comprises a data acquisition module, a model construction module, a preliminary identification module and a parameter identification module; the data acquisition module, the model construction module, the preliminary identification module and the parameter identification module are connected in series in sequence; the data acquisition module is used to acquire data information of the target power system and data information of the target energy storage grid-connected converter, and upload the data information to the model construction module; the model construction module is used to model the control process of the target energy storage grid-connected converter according to the received data information and the acquired data information, so as to determine the parameters to be identified, and upload the data information to the preliminary identification module; the preliminary identification module is used to identify the determined parameters to be identified according to the received data information, based on the differential evolution particle swarm algorithm and the fuzzy control algorithm, and upload the data information to the parameter identification module; the parameter identification module is used to complete the control parameter identification of the energy storage grid-connected converter according to the received data information and the obtained identification results.

Claims

1. A control parameter identification method for an energy storage grid-connected converter, comprising the following steps: S1. Obtain data information of the target power system and data information of the target energy storage grid-connected converter; S2. Modeling the control process of the target energy storage grid-connected converter according to the data information obtained in step S1 to determine the parameters to be identified; S3. Based on differential evolution particle swarm algorithm and fuzzy control algorithm, the parameters to be identified determined in step S2 are identified; S4. According to the identification result obtained in step S3, the control parameter identification of the energy storage grid-connected converter is completed.

2. The control parameter identification method of the energy storage grid-connected converter according to claim 1 is characterized in that The control process of the target energy storage grid-connected converter is modeled according to the data information obtained in step S1 in step S2, so as to determine the parameters to be identified, including the following steps: The target energy storage grid-connected converter adopts PQ control mode; According to the PQ control method, construct the transfer function of the current loop control and the expression of the current reference value; Perform dq decoupling on the transfer function of the current loop control to obtain a single-input single-output model; Finally, the parameters to be identified are obtained.

3. The control parameter identification method of the energy storage grid-connected converter according to claim 2 is characterized in that The step S2 specifically includes the following steps: The target energy storage grid-connected converter adopts PQ control mode; According to the PQ control method, the transfer function of the current loop control is expressed as Where V sd (s) is the d-axis output voltage of the controlled voltage source; K pi is the current loop proportional coefficient; K ii is the current loop integral coefficient; i dref is the d-axis component of the current reference value; i d (s) is the d-axis component of the current; w is the current system frequency output by the phase-locked loop; L is the filter inductance value; i q (s) is the q-axis component of the current; V d (s) is the d-axis component of the grid-connected voltage; V sq (s) is the q-axis output voltage of the controlled voltage source; i qref is the q-axis component of the current reference value; V q (s) is the q-axis component of the grid-connected voltage; The d-axis component i of the current reference value dref and the q-axis component i of the current reference value qref , expressed as Where P ref is the active power reference value; Q ref is the reactive power reference value; Perform dq decoupling on the transfer function of the current loop control: The transfer function of the DC side of the energy storage grid-connected converter is expressed as Substituting into the transfer function of the current loop control, we get Arranged and Thus achieving decoupling; Finally, the parameter to be identified is determined as K pi and K ii .

4. The control parameter identification method of the energy storage grid-connected converter according to claim 3 is characterized in that The step S3 described in which the parameters to be identified determined in step S2 are identified based on the differential evolution particle swarm algorithm and the fuzzy control algorithm includes the following steps: Measure the experimental data of the energy storage system under two working conditions: active reference value jump and reactive reference value jump; Based on the control process model of the target energy storage grid-connected converter constructed in step S2, a differential evolution particle swarm algorithm is used to perform preliminary parameter identification; The obtained preliminary identification results of parameters are imported into the energy storage system model, simulation data is obtained through simulation, and the simulation data is compared with the actual data to obtain the error variable; The error variable is fuzzily controlled through a fuzzy controller to obtain the correction amount of the identification parameter; The final parameter identification result is obtained according to the correction amount of the identification parameter and the obtained preliminary parameter identification result.

5. The control parameter identification method of the energy storage grid-connected converter according to claim 4 is characterized in that The use of differential evolution particle swarm algorithm for preliminary parameter identification specifically includes the following steps: In the differential evolution particle swarm algorithm adopted, the following steps are used to set parameters: The following formula is used as the fitness function: Where J is the fitness function value; w1 is the active weight value; w2 is the reactive weight value; n is the length of the data; i d_Psim is the simulation value of the d-axis current component of the grid-connected current measured under active disturbance; i d_Pmeas is the measured value of the d-axis current component of the grid-connected current under active disturbance; i q_Qsim is the simulation value of the q-axis current component of the grid-connected current measured under reactive disturbance; i q_Qmeas is the measured value of the q-axis current component of the grid-connected current under reactive disturbance; The inertia weight is set using the following formula: Where w k is the inertia weight value at the kth iteration; w max is the maximum value of the inertia weight value w; min is the minimum value of the inertia weight w; M is the maximum number of iterations; The learning factor is set using the following formula: In the formula is the first learning factor c1 in the kth iteration; c 1max is the maximum value of the first learning factor; c 1min is the minimum value of the first learning factor; is the second learning factor c2 at the kth iteration; c 2max is the maximum value of the second learning factor; c 2min is the minimum value of the second learning factor; The variation factor is set using the following formula: Where F k is the mutation factor in the kth iteration; F max is the maximum value of the variation factor; F min is the minimum value of the variation factor; The crossover factor is set using the following formula: Where CR k is the crossover factor at the kth iteration; CR max is the maximum value of the crossover factor; CR min is the minimum value of the crossover factor; J i is the fitness value of the i-th individual in the current population; J max is the maximum fitness value in the current population; J min is the minimum fitness value in the current population.

6. The control parameter identification method of the energy storage grid-connected converter according to claim 5 is characterized in that The method of performing fuzzy control on the error variable through a fuzzy controller to obtain the correction amount of the identification parameter specifically includes the following steps: The error variable e is fuzzified as the fuzzy input, and the correction amount of the identification parameter is taken as the fuzzy output: The error variable e is fuzzified as fuzzy input; The identification parameters are expressed as Where K p is the corrected parameter to be identified K pi , K i is the corrected parameter to be identified K ii , ΔK p K pi The correction amount, ΔK i K ii The correction amount; ΔK p and ΔK i As fuzzy output; Construct fuzzy control rules and establish the relationship between fuzzy input and fuzzy output: Taking the error variable e and the error variable change rate ec as input, ΔK p and ΔK i As output; The value ranges of the error variable e and the error variable change rate ec are: negative large NB, negative medium NM, negative small NS, zero O, positive small PS, positive medium PM and positive large PB; ΔK p The fuzzy control rules are: If e is NB and ec is NB, then ΔK p The value is PB; If e is NB and ec is NM, then ΔK p The value is PB; If e is NB and ec is NS, then ΔK p The value is PM; If e is NB and ec is O, then ΔK p The value is PM; If e is NB and ec is PS, then ΔK p The value is PS; If e is NB and ec is PM, then ΔK p The value is 0; If e is NB and ec is PB, then ΔK p The value is 0; If e is NM and ec is NB, then ΔK p The value is PB; If e is NM and ec is NM, then ΔK p The value is PB; If e is NM and ec is NS, then ΔK p The value is PM; If e is NM and ec is O, then ΔK p The value is PS; If e is NM and ec is PS, then ΔK p The value is PS; e is NM and ec is PM, then ΔK p Take the value as O; e is NM and ec is PB, then ΔK p The value is NS; e is NS and ec is NB, then ΔK p The value is PM; e is NS and ec is NM, then ΔK p Take the value as PM; e is NS and ec is NS, then ΔK p Take the value as PM; e is NS and ec is O, then ΔK p The value is PS; e is NS and ec is PS, then ΔK p Take the value as O; e is NS and ec is PM, then ΔK p The value is NS; e is NS and ec is PB, then ΔK p The value is NS; e is O and ec is NB, then ΔK p Take the value as PM; e is O and ec is NM, then ΔK p Take the value as PM; e is O and ec is NS, then ΔK p Take the value as PS; e is O and ec is O, then ΔK p The value is 0; If e is O and ec is PS, then ΔK p The value is NS; e is O and ec is PM, then ΔK p The value is NM; e is O and ec is PB, then ΔK p The value is NM; e is PS and ec is NB, then ΔK p The value is PS; e is PS and ec is NM, then ΔK p The value is PS; e is PS and ec is NS, then ΔK p The value is O; e is PS and ec is O, then ΔK p The value is NS; e is PS and ec is PS, then ΔK p The value is NS; e is PS and ec is PM, then ΔK p The value is NM; e is PS and ec is PB, then ΔK p The value is NM; e is PM and ec is NB, then ΔK p The value is PS; e is PM and ec is NM, then ΔK p Take the value as O; e is PM and ec is NS, then ΔK p The value is NS; e is PM and ec is O, then ΔK p The value is NM; e is PM and ec is PS, then ΔK p The value is NM; e is PM and ec is PM, then ΔK p The value is NM; e is PM and ec is PB, then ΔK p The value is NB; e is PB and ec is NB, then ΔK p Take the value as O; e is PB and ec is NM, then ΔK p The value is O; e is PB and ec is NS, then ΔK p The value is NM; e is PB and ec is O, then ΔK p The value is NM; e is PB and ec is PS, then ΔK p The value is NM; e is PB and ec is PM, then ΔK p The value is NB; e is PB and ec is PB, then ΔK p The value is NB; ΔK i The fuzzy control rules are: If e is NB and ec is NB, then ΔK i The value is NB; e is NB and ec is NM, then ΔK i The value is NB; e is NB and ec is NS, then ΔK i The value is NM; e is NB and ec is O, then ΔK i The value is NM; e is NB and ec is PS, then ΔK i The value is NS; e is NB and ec is PM, then ΔK i The value is O; e is NB and ec is PB, then ΔK i The value is O; e is NM and ec is NB, then ΔK i The value is NB; e is NM and ec is NM, then ΔK i The value is NB; e is NM and ec is NS, then ΔK i The value is NM; e is NM and ec is O, then ΔK i The value is NS; e is NM and ec is PS, then ΔK i The value is NS; e is NM and ec is PM, then ΔK i Take the value as O; e is NM and ec is PB, then ΔK i The value is NS; e is NS and ec is NB, then ΔK i The value is NB; e is NS and ec is NM, then ΔK i The value is NM; e is NS and ec is NS, then ΔK i The value is NS; e is NS and ec is O, then ΔK i The value is NS; e is NS and ec is PS, then ΔK i Take the value as O; e is NS and ec is PM, then ΔK i The value is PS; e is NS and ec is PB, then ΔK i The value is PS; e is O and ec is NB, then ΔK i The value is NM; e is O and ec is NM, then ΔK i The value is NM; e is O and ec is NS, then ΔK i The value is NS; e is O and ec is O, then ΔK i The value is 0; If e is O and ec is PS, then ΔK i The value is PS; e is O and ec is PM, then ΔK i Take the value as PM; e is O and ec is PB, then ΔK i The value is PM; e is PS and ec is NB, then ΔK i The value is NM; e is PS and ec is NM, then ΔK i The value is NS; e is PS and ec is NS, then ΔK i The value is O; e is PS and ec is O, then ΔK i The value is PS; e is PS and ec is PS, then ΔK i The value is PS; e is PS and ec is PM, then ΔK i The value is PM; e is PS and ec is PB, then ΔK i The value is PB; e is PM and ec is NB, then ΔK i Take the value as O; e is PM and ec is NM, then ΔK i Take the value as O; e is PM and ec is NS, then ΔK i The value is PS; e is PM and ec is O, then ΔK i The value is PS; e is PM and ec is PS, then ΔK i The value is PM; e is PM and ec is PM, then ΔK i The value is PB; e is PM and ec is PB, then ΔK i The value is PB; e is PB and ec is NB, then ΔK i Take the value as O; e is PB and ec is NM, then ΔK i The value is O; e is PB and ec is NS, then ΔK i The value is PS; e is PB and ec is O, then ΔK i The value is PM; e is PB and ec is PS, then ΔK i The value is PM; e is PB and ec is PM, then ΔK i The value is PB; e is PB and ec is PB, then ΔK i The value is PB; Trigonometric function is used as membership function; Finally, the fuzzy correction result is defuzzified to obtain the correction amount of the identification parameter.

7. The control parameter identification method of the energy storage grid-connected converter according to claim 6 is characterized in that The method of obtaining the final parameter identification result based on the correction amount of the identification parameter and the obtained preliminary parameter identification result specifically includes the following steps: The correction amount of the identification parameter is summed with the obtained preliminary parameter identification result to obtain the final parameter identification result.

8. A system for implementing the control parameter identification method of the energy storage grid-connected converter according to any one of claims 1 to 7, characterized in that It includes a data acquisition module, a model building module, a preliminary identification module and a parameter identification module; the data acquisition module, the model building module, the preliminary identification module and the parameter identification module are connected in series in sequence; the data acquisition module is used to acquire data information of the target power system and data information of the target energy storage grid-connected converter, and upload the data information to the model building module; The model building module is used to model the control process of the target energy storage grid-connected converter according to the received data information and the acquired data information, so as to determine the parameters to be identified, and upload the data information to the preliminary identification module; the preliminary identification module is used to identify the determined parameters to be identified based on the received data information, the differential evolution particle swarm algorithm and the fuzzy control algorithm, and upload the data information to the parameter identification module; The parameter identification module is used to complete the control parameter identification of the energy storage grid-connected converter according to the received data information and the obtained identification results.

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