Extreme weather energy storage system failure parameter identification method, device, equipment and medium

Abstract

The application belongs to the technical field of fault parameter identification, and particularly relates to a fault parameter identification method, device, equipment and medium for an energy storage system under extreme weather, wherein the method comprises the following steps: constructing an identification model of the energy storage system, and determining to-be-identified parameters in the identification model; performing the same low-voltage fault working condition test on an actual controller and the identification model, outputting response data of the actual controller, and collecting response data of the identification model under the same control parameter; taking the minimum power difference value output by the actual controller and the identification model as a target, and constructing a target function; solving the target function by using an improved particle swarm algorithm, and obtaining initial values of the to-be-identified parameters; and iteratively identifying the initial values of the to-be-identified parameters by using an improved grey wolf algorithm, and obtaining fault parameters. The application can effectively identify the controller parameters of a hardware-in-the-loop simulation system, and meets the transient simulation demand of a power system.

Description

Technical Field

[0001] This invention belongs to the field of fault parameter identification technology, specifically relating to methods, devices, equipment, and media for identifying fault parameters in energy storage systems under extreme weather conditions. Background Technology

[0002] Extreme weather conditions can easily cause power grid fluctuations and power system failures. Energy storage systems need to respond quickly to stabilize power supply, making parameter identification under fault conditions particularly important. The adjustment parameters of the energy storage controller play a decisive role in the operating characteristics of the energy storage system. However, due to reasons such as manufacturers' intellectual property protection, it is impossible to obtain accurate controller parameters, leading to deviations between simulation analysis and actual operating conditions.

[0003] Chinese invention patent CN117691633A discloses a method for identifying PI control parameters of energy storage systems based on an improved gray wolf algorithm. The method involves establishing a measured model of the energy storage system, where the grid-side inverter uses a dual-closed-loop control mechanism. The measured model is run to obtain the d-axis and q-axis current curves during steady-state operation. An identification model is then established, and the parameters to be identified are determined as grid-side inverter fault parameters based on the measured model. Random values ​​are assigned to the fault parameters of the identification model, and the d-axis and q-axis response curves output by the identification model are obtained. The root mean square error (RMSE) of both curves is calculated. Using the RMS error of the d-axis and q-axis response curves from both the identification and measured models as the objective function, the improved gray wolf algorithm is used to solve for the optimal value of the objective function. The resulting solution set represents the parameters to be identified. However, this method suffers from poor computational accuracy and slow speed, making it difficult to meet the requirements for timely simulation. Summary of the Invention

[0004] The purpose of this invention is to provide a method, device, equipment and medium for identifying fault parameters of energy storage systems under extreme weather conditions, and to solve the problems of slow calculation speed and poor accuracy of fault parameters in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a method for identifying fault parameters of an energy storage system under extreme weather conditions, comprising:

[0007] An identification model of the energy storage system is constructed, and the parameters to be identified in the identification model are the voltage loop PI regulation parameters, the current loop PI regulation parameters in the discharge mode, and the current loop PI regulation parameters in the charging mode.

[0008] The actual controller and the identification model are subjected to the same low-voltage fault condition test. The response data of the actual controller is output, and the response data of the identification model under different control parameters is collected at the same time. The response data includes power data.

[0009] An objective function is constructed with the goal of minimizing the power difference between the actual controller output and the identification model output; the decision variables of the objective function are the parameters to be identified, and the constraints of the objective function are that the power data and frequency are within a preset range.

[0010] An improved particle swarm optimization algorithm is used to solve the objective function and obtain the initial values ​​of the parameters to be identified; the improved particle swarm optimization algorithm improves the inertia weight and learning factor;

[0011] The initial values ​​of the identification parameters are iteratively identified using an improved gray wolf algorithm. When the fault characteristic response error is satisfied, the fault parameters are obtained. In the improved gray wolf algorithm, different formulas are used to iterate the wolf pack position during charging and discharging.

[0012] Preferably, the step of solving the objective function using an improved particle swarm optimization algorithm to obtain the initial values ​​of the parameters to be identified includes:

[0013] Initialize particle swarm parameters;

[0014] Calculate the objective function for each particle in the particle swarm parameters and obtain the objective function value for each particle in the particle swarm parameters;

[0015] The particle velocity, position, individual optimal value, and global optimal value are updated based on the objective function value. When the error accuracy meets the initial value error requirement of the parameter to be identified, the initial value of the parameter to be identified is output.

[0016] Preferably, the step of updating particle velocity, position, individual optimal value, and global optimal value based on fitness value includes:

[0017] The formulas for updating particle velocity, position, individual optimal value, and global optimal value are as follows:

[0018] ;

[0019] ;

[0020] In the formula: v i For the first i The velocity of each particle; x i For the first i The position of each particle; oh Inertial weights; c 1. c 2 represents the learning factor; r 1.r 2 is a random number between 0 and 1; P best The optimal value for each individual after iteration; G best This is the globally optimal value;

[0021] in:

[0022] ;

[0023] ;

[0024] ;

[0025] In the formula: oh max , oh min These are the maximum and minimum values ​​of the inertia weight, respectively. T 1 represents the maximum number of iterations. t 1 represents the minimum number of iterations.

[0026] Preferably, the step of iterating the initial values ​​of the identification parameters using the improved gray wolf algorithm includes:

[0027] Initialize the parameters of the improved gray wolf algorithm, using the initial values ​​of the parameters to be identified as the initial population of the improved gray wolf algorithm;

[0028] Based on the initial population, the wolf pack position is updated by iterating through the wolf pack positions and using different formulas during charging and discharging.

[0029] Based on the updated wolf pack positions, calculate the objective function value for each gray wolf;

[0030] Based on the objective function value of each gray wolf, the top three gray wolves with the highest objective function values ​​are selected as... α Wolf, β Wolf, d Wolves, the rest of the wolves are gamma wolves;

[0031] Update the current position and objective function value of each gray wolf;

[0032] Based on the updated fitness values ​​of the Grey Wolves, select the new... α Wolf, β Wolf, d Wolves, the rest of the wolves are gamma wolves;

[0033] Continuously update the wolf pack's location; when the identification parameter error requirement is met, output... α Wolf, β Wolf, d The wolf's position is considered the optimal solution.

[0034] Preferably, the step of obtaining the updated wolf pack position by iterating through the wolf pack positions and using different formulas during charging and discharging includes:

[0035] ;

[0036] In the formula, Indicates the current location of the prey. Indicates the current location of the wolf pack. r 1. r Both 2 are random numbers between 0 and 1. a The convergence factor;

[0037] The nonlinear convergence factor expression and position update formula during the discharge of the energy storage system are shown below:

[0038] ;

[0039] ;

[0040] In the formula, This represents the current iteration number. The maximum number of iterations, X 1. X 2. X 3 are respectively α Wolf, β Wolf, d The wolf's position m 1. m 2. m 3 represents the weighting coefficient, which can be taken as follows: α Wolf, β Wolf, d Wolf fitness value is used as a weighting coefficient;

[0041] The nonlinear convergence factor expression and position update formula for the energy storage system during charging are shown below:

[0042] ;

[0043] .

[0044] Preferably, the continuous updating of the wolf pack location outputs an error when the identification parameter error requirement is met. α Wolf, β Wolf, d In the step of determining the optimal solution, the wolf's position is updated using the following formula:

[0045] ;

[0046] ;

[0047] ;

[0048] In the formula, , , They represent α Wolf, β Wolf, d The wolf's location; for α The updated vector coefficients of the wolf. for β The updated vector coefficients of the wolf. for d The updated vector coefficients of the wolf; for α The perturbation coefficient updated by the wolf. for β The perturbation coefficient updated by the wolf. for d The perturbation coefficient updated by the wolf.

[0049] Preferably, in the step of constructing the objective function with the goal of minimizing the power difference between the actual controller and the identification model output, the objective function is:

[0050] ;

[0051] in, k=1,2…N , N This represents the total number of iterations. l 1 represents the weighting coefficient for the active power difference. l 2 represents the weighting coefficient for the reactive power difference, which is the actual controller output of active power and reactive power. P , Q The identification model outputs active and reactive power under different control parameters. P* , Q* The constraints are that the power and frequency are within a limited range.

[0052] A second aspect of the present invention provides a fault parameter identification device for an energy storage system under extreme weather conditions, comprising:

[0053] The module is used to build an identification model of the energy storage system and determine the parameters to be identified in the identification model as voltage loop PI regulation parameters, current loop PI regulation parameters in discharge mode, and current loop PI regulation parameters in charging mode.

[0054] The test module is used to perform the same low-voltage fault condition test on the actual controller and the identification model, output the response data of the actual controller, and at the same time collect the response data of the identification model under the same control parameters.

[0055] The objective module is used to construct an objective function with the goal of minimizing the power difference between the actual controller and the identification model output; the decision variables of the objective function are the parameters to be identified, and the constraints of the objective function are that the power data and frequency are within a preset range;

[0056] The first calculation module is used to solve the objective function using an improved particle swarm optimization algorithm and obtain the initial values ​​of the identification parameters;

[0057] The second calculation module is used to iteratively identify the initial values ​​of the parameters using the improved gray wolf algorithm, and obtain the fault parameters when the fault characteristic response error is satisfied.

[0058] In a third aspect, the present invention provides an electronic device, characterized in that it includes a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the method for identifying fault parameters of an energy storage system under extreme weather conditions as described in any one of claims 1 to 7.

[0059] In a fourth aspect, the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the method for identifying fault parameters of an energy storage system under extreme weather conditions as described in any one of claims 1 to 7.

[0060] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0061] This invention employs the aforementioned method for testing and identifying fault parameters of energy storage systems under extreme weather conditions. It constructs a control block diagram of the energy storage system based on hardware-in-the-loop simulation, establishes an identification model of the energy storage system, and determines the control parameters to be identified in the model. The same low-voltage fault condition test is then performed on both the actual controller and the identification model, outputting the active and reactive power fault characteristic response data of the actual controller. P , Q Simultaneously, active and reactive power fault characteristic response data of the identification model are collected under different control parameters. P* , Q* This invention defines an objective function and uses an improved particle swarm optimization (PSO) algorithm to obtain initial values ​​for the control parameters to be identified, thus narrowing the identification range. An improved gray wolf algorithm is then used to accurately identify the control parameters, with different improvement strategies employed for energy storage charging and discharging. When the fault characteristic response error is satisfied, the controller parameters are output, and the PI control parameters obtained by combining the improved PSO and gray wolf algorithms are verified for errors. This invention can effectively identify controller parameters in hardware-in-the-loop simulation systems, meeting the requirements of power system transient simulation. Attached Figure Description

[0062] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0063] Figure 1 This is a schematic diagram of the fault parameter identification method for energy storage systems under extreme weather conditions, as described in Embodiment 1 of the present invention.

[0064] Figure 2 This is a hardware-in-the-loop system schematic diagram of the fault parameter identification method for energy storage systems under extreme weather conditions in Embodiment 1 of the present invention.

[0065] Figure 3 This is a control block diagram of an energy storage system based on hardware-in-the-loop simulation in the method for identifying fault parameters of an energy storage system under extreme weather conditions in Embodiment 1 of the present invention.

[0066] Figure 4 This is a comparison chart of the actual controller output power and the identification model output power in Embodiment 1 of the present invention;

[0067] Figure 5 This is a comparison chart of the actual controller output power and the identification model output power in Embodiment 1 of the present invention;

[0068] Figure 6 This is a structural block diagram of the fault parameter identification device for an energy storage system under extreme weather conditions, as shown in Embodiment 2 of the present invention.

[0069] Figure 7 This is a structural block diagram of the electronic device in Embodiment 3 of the present invention. Detailed Implementation

[0070] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0071] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0072] Example 1

[0073] like Figure 1 As shown, the method for identifying fault parameters of energy storage systems under extreme weather conditions includes:

[0074] S1. Construct an identification model for the energy storage system, and determine the parameters to be identified in the identification model as voltage loop PI regulation parameters, current loop PI regulation parameters in discharge mode, and current loop PI regulation parameters in charging mode.

[0075] S11、 Figure 2 This is a hardware-in-the-loop system schematic. The battery outputs DC current, which is converted into AC current by a DC / AC converter, and then connected to the power grid via an LC filter and a grid-connected switch. The AC side voltage and current are collected and generated into a PWM wave through a voltage-current dual closed-loop control system, which is then input to the inverter for control.

[0076] Figure 3 The control block diagram of the energy storage system based on hardware-in-the-loop simulation is divided into voltage outer loop and current inner loop control. The voltage outer loop adopts constant DC bus voltage control. When the DC bus voltage Udc is less than the reference value Udc*, that is, when the current inner loop reference value is greater than 0, the energy storage system outputs power. When the DC bus voltage Udc is greater than the reference value Udc*, that is, when the current inner loop reference value is less than 0, the energy storage system absorbs power. After voltage and current dual closed-loop control, a PWM wave is generated and input to the DC-AC converter for control.

[0077] in U dc *Reference DC voltage U dc DC voltage I L * represents the inner loop reference current. I L For inductor current, when the DC bus voltage U dc Less than the reference value U dc * At that time, the energy storage system outputs power when the DC bus voltage... U dc Greater than the reference value U dc * At that time, the energy storage system absorbs power.

[0078] S12. Establish an identification model of the energy storage system in Simulink. In the identification model, all control parameters except the parameters to be identified should be consistent with the control parameters of the actual hardware-in-the-loop controller. The three PI regulation parameters in the voltage and current dual closed-loop control loop are the parameters to be identified, including those in the voltage loop PI regulator. K p1 , K i1 The current loop PI regulator in the energy storage system when it outputs power. K p2 , Ki2 The current loop PI regulator in the energy storage system when absorbing power K p3 , K i3 .

[0079] S2. Perform the same low-voltage fault condition test on the actual controller and the identification model, output the response data of the actual controller, and at the same time collect the response data of the identification model under different identification parameters; the response data includes power data.

[0080] S21. Set a three-phase short-circuit fault for the actual controller and the identification model respectively.

[0081] S22. Collect the actual active power and reactive power output from the controller respectively. P , Q and the active and reactive power outputs of the identification model under different parameters to be identified. P* , Q* ,Will P* and P , Q* and Q Perform an error comparison.

[0082] S3. Construct an objective function with the goal of minimizing the power difference between the actual controller and the identification model output; the decision variables of the objective function are the parameters to be identified, which are achieved by changing the parameters to be identified. P* , Q* The objective function is constrained by the fact that the power data and frequency are within a preset range.

[0083] S31. With the goal of minimizing the power difference between the actual controller and the identification model output, the control parameters in the identification model are continuously updated to minimize the power difference, i.e., the objective function takes the minimum value. The objective function is defined as shown in equation (1).

[0084] (1)

[0085] in, k=1,2…N , N This represents the total number of iterations. l 1 represents the weighting coefficient for the active power difference. l 2 represents the weighting coefficient for the reactive power difference, which is the actual controller output of active power and reactive power. P , Q The identification model outputs active and reactive power under different control parameters. P* , Q* ;

[0086] The constraints are: ;

[0087] In the formula, P max , P min These represent the upper and lower limits of active power, respectively. Q max , Q min These represent the upper and lower limits of reactive power, respectively. f max , f min These represent the upper and lower limits of the frequency, respectively.

[0088] The power generated by the energy storage system is mainly active power. Therefore, in order to balance the error between active and reactive power, a setting is required. l 1 and l 2 satisfies the following relationship:

[0089] (2)

[0090] S4. Use an improved particle swarm optimization algorithm to solve the objective function and obtain the initial values ​​of the parameters to be identified;

[0091] Specifically, including:

[0092] Initialize particle swarm parameters;

[0093] Calculate the objective function for each particle in the particle swarm parameters and obtain the objective function value for each particle in the particle swarm parameters;

[0094] The particle velocity, position, individual optimal value, and global optimal value are updated using the following formula:

[0095] (3)

[0096] (4)

[0097] In the formula: v i For the first i The velocity of each particle; x i For the first i The position of each particle; oh Inertial weights; c 1. c 2 represents the learning factor; r 1. r 2 is a random number between 0 and 1; P best The optimal value for each individual after iteration; G best The global optimum; superscript k +1 is the firstk +1 update, superscript k For the first k This is the second update.

[0098] Based on the traditional particle swarm optimization algorithm, the inertia weight is added. oh Learning factors c 1. c 2. Improvements were made to increase the algorithm's convergence speed and parameter identification accuracy. The improved algorithm... oh , c 1. c 2. As shown in the following formula:

[0099] (5)

[0100] (6)

[0101] (7)

[0102] In the formula: oh max , oh min These are the maximum and minimum values ​​of the inertia weight, respectively. T 1 represents the maximum number of iterations. t 1 represents the minimum number of iterations.

[0103] Continuously update particle velocity, position, individual optimal value, and global optimal value. When the error accuracy meets the initial value error requirement of the parameter to be identified, output the initial value of the parameter to be identified. K p1(0) , K i1(0) , K p2(0) , K i2(0) , K p3(0) , K i3(0) .

[0104] S5. The initial values ​​of the parameters are identified iteratively using the improved gray wolf algorithm. When the fault characteristic response error is satisfied, the fault parameters are obtained.

[0105] Specifically, including:

[0106] Initialize the improved gray wolf algorithm parameters, obtained using S4. K p1(0) , K i1(0) , K p2(0) , K i2(0) , Kp3(0) , K i3(0) As the initial population for improving the gray wolf algorithm;

[0107] Based on the initial population, the wolf pack location is iterated using equation (8):

[0108] (8)

[0109] In the formula, Indicates the current location of the prey. This indicates the current position of the wolf pack, obtained in S4. K p1(0) , K i1(0) , K p2(0) , K i2(0) , K p3(0) , K i3(0) , r 1. r Both 2 are random numbers between 0 and 1. a The convergence factor;

[0110] For convergence factor a And Grey Wolf position update formula Improvements were made to enhance convergence speed and identification accuracy. Since the bidirectional DC-AC converter operates in different modes during energy storage charging and discharging, adjustments are needed based on different control parameters to improve flexibility. The nonlinear convergence factor expression and position update formula for the energy storage system during discharge are shown below:

[0111] (9)

[0112] (10)

[0113] In the formula, This represents the current iteration number. The maximum number of iterations, X 1. X 2. X 3 are respectively α Wolf, β Wolf, d The wolf's position m 1. m 2. m 3 represents the weighting coefficient, which can be taken as follows: α Wolf, β Wolf, d Wolf fitness value is used as a weighting coefficient;

[0114] The nonlinear convergence factor expression and position update formula for the energy storage system during charging are shown below:

[0115] (11)

[0116] (12)

[0117] Based on the updated wolf pack positions, calculate the objective function value for each gray wolf;

[0118] Based on the objective function value of each gray wolf, the top three gray wolves with the highest objective function values ​​are selected as... α Wolf, β Wolf, d Wolves, the rest of the wolves are gamma wolves:

[0119] Update by pressing α Wolf, β Wolf, d The wolf's current location:

[0120] (13)

[0121] (14)

[0122] (15)

[0123] In the formula, , , They represent α Wolf, β Wolf, d The wolf's location; for α The updated vector coefficients of the wolf. for β The updated vector coefficients of the wolf. for d The vector coefficients updated by the wolf are all calculated using the corresponding random numbers according to formula (14); for α The perturbation coefficient updated by the wolf. for β The perturbation coefficient updated by the wolf. for d The perturbation coefficients updated by the wolf are all calculated using the corresponding random numbers according to Formula 15.

[0124] Based on the updated objective function value of the gray wolf, select the new... α Wolf, β Wolf, d Wolves, the rest of the wolves are c Wolf;

[0125] Continuously update the wolf pack's location, and output the current location when the error requirement for the parameters to be identified is met. α Wolf, β Wolf, d The wolf's position is considered the optimal solution.

[0126] Verify the error of the fault parameters:

[0127] Multiple identifications were performed using the energy storage system, resulting in multiple identification results. The average of these results was taken as the final identification value, as shown in the table below. The control parameters identified using the method combining the improved particle swarm optimization algorithm and the improved gray wolf algorithm proposed in this invention have a maximum error of 4.215% and a minimum error of 0.857%, meeting the error requirements and effectively verifying the effectiveness of the identification method proposed in this invention.

[0128] Table 1 Parameter Identification Results

[0129]

[0130] The three-phase voltage was set to drop to 20% of the rated value at 2.5s, the fault duration was 625ms, and it returned to normal at 3.125s. The actual controller output power was compared with the output power of the identified model. Figure 4 , Figure 5 As shown, the fault characteristic response curve obtained by the identification method proposed in this invention has a high degree of fitting, which effectively verifies the effectiveness of the identification method proposed in this invention.

[0131] Example 2

[0132] like Figure 6 As shown, based on the same inventive concept as the above embodiments, the present invention also provides a fault parameter identification device for energy storage systems under extreme weather conditions, comprising:

[0133] The module is used to build an identification model of the energy storage system and determine the parameters to be identified in the identification model as voltage loop PI regulation parameters, current loop PI regulation parameters in discharge mode, and current loop PI regulation parameters in charging mode.

[0134] The test module is used to perform the same low-voltage fault condition test on the actual controller and the identification model, output the response data of the actual controller, and at the same time collect the response data of the identification model under the same control parameters.

[0135] The objective module is used to construct an objective function with the goal of minimizing the power difference between the actual controller and the identification model output; the decision variables of the objective function are active power and reactive power fault characteristic response data, and the constraints of the objective function are that the power data and frequency are within a preset range.

[0136] The first calculation module is used to solve the objective function using an improved particle swarm optimization algorithm and obtain the initial values ​​of the identification parameters;

[0137] The second calculation module is used to iteratively identify the initial values ​​of the parameters using the improved gray wolf algorithm, and obtain the fault parameters when the fault characteristic response error is satisfied.

[0138] Example 3

[0139] like Figure 7 As shown, the present invention also provides an electronic device 100 for implementing a method for identifying fault parameters of an energy storage system under extreme weather conditions;

[0140] The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0141] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the method for identifying fault parameters of energy storage system under extreme weather conditions in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0142] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0143] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0144] The memory 101 in the electronic device 100 stores multiple instructions to implement a fault parameter identification method for the energy storage system under extreme weather conditions, and the processor 102 can execute multiple instructions to achieve the following:

[0145] An identification model of the energy storage system is constructed, and the parameters to be identified in the identification model are the voltage loop PI regulation parameters, the current loop PI regulation parameters in the discharge mode, and the current loop PI regulation parameters in the charging mode.

[0146] The actual controller and the identification model are subjected to the same low-voltage fault condition test. The response data of the actual controller is output, and the response data of the identification model under the same control parameters is collected at the same time. The response data includes power data.

[0147] An objective function is constructed with the goal of minimizing the power difference between the actual controller and the identification model output; the decision variables of the objective function are active power and reactive power fault characteristic response data, and the constraints of the objective function are that the power data and frequency are within a preset range.

[0148] An improved particle swarm optimization algorithm is used to solve the objective function and obtain the initial values ​​of the parameters to be identified.

[0149] The initial values ​​of the parameters are identified iteratively using the improved gray wolf algorithm. When the fault characteristic response error is satisfied, the fault parameters are obtained.

[0150] Example 4

[0151] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0152] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0153] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure one One or more processes and / or boxes Figure one A device that provides the functions specified in one or more boxes.

[0154] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure one One or more processes and / or boxes Figure one The function specified in one or more boxes.

[0155] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure one One or more processes and / or boxes Figure one The steps of the function specified in one or more boxes.

[0156] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for identifying fault parameters of an energy storage system under extreme weather conditions, characterized in that, include: An identification model of the energy storage system is constructed, and the parameters to be identified in the identification model are the voltage loop PI regulation parameters, the current loop PI regulation parameters in the discharge mode, and the current loop PI regulation parameters in the charging mode. The actual controller and the identification model are subjected to the same low-voltage fault condition test. The response data of the actual controller is output, and the response data of the identification model under different control parameters is collected at the same time. The response data includes power data. An objective function is constructed with the goal of minimizing the power difference between the actual controller output and the identification model output; the decision variables of the objective function are the parameters to be identified, and the constraints of the objective function are that the power data and frequency are within a preset range. An improved particle swarm optimization algorithm is used to solve the objective function and obtain the initial values ​​of the parameters to be identified; the improved particle swarm optimization algorithm improves the inertia weight and learning factor; The initial values ​​of the identification parameters are iteratively identified using an improved gray wolf algorithm. When the fault characteristic response error is satisfied, the fault parameters are obtained. In the improved gray wolf algorithm, different formulas are used to iterate the wolf pack position during charging and discharging. The objective function is: ； ； in, k=1,2…N , N This represents the total number of iterations. λ 1 is the weighting coefficient for the difference in active power. λ 2 represents the weighting coefficient for the reactive power difference, which is the actual controller output of active power and reactive power. P , Q The identification model outputs active and reactive power under different control parameters. P* , Q* .

2. The method for identifying fault parameters of an energy storage system under extreme weather conditions as described in claim 1, characterized in that, The step of solving the objective function using an improved particle swarm optimization algorithm to obtain the initial values ​​of the parameters to be identified includes: Initialize particle swarm parameters; Calculate the objective function for each particle in the particle swarm parameters and obtain the objective function value for each particle in the particle swarm parameters; The particle velocity, position, individual optimal value, and global optimal value are updated based on the objective function value. When the error accuracy meets the initial value error requirement of the parameter to be identified, the initial value of the parameter to be identified is output.

3. The method for identifying fault parameters of an energy storage system under extreme weather conditions as described in claim 2, characterized in that, The steps of updating particle velocity, position, individual optimal value, and global optimal value based on fitness value include: The formulas for updating particle velocity, position, individual optimal value, and global optimal value are as follows: ； ； In the formula: v i For the first i The velocity of each particle; x i For the first i The position of each particle; ω Inertial weights; c 1. c 2 represents the learning factor; r 1. r 2 is a random number between 0 and 1; P best The optimal value for each individual after iteration; G best This is the globally optimal value; in: ； ； ； In the formula: ω max , ω min These are the maximum and minimum values ​​of the inertia weight, respectively. T 1 represents the maximum number of iterations. t 1 represents the minimum number of iterations.

4. The method for identifying fault parameters of an energy storage system under extreme weather conditions as described in claim 1, characterized in that, The step of iterating the initial values ​​of the parameters to be identified using the improved gray wolf algorithm includes: Initialize the parameters of the improved gray wolf algorithm, using the initial values ​​of the parameters to be identified as the initial population of the improved gray wolf algorithm; Based on the initial population, the wolf pack position is updated by iterating through the wolf pack positions and using different formulas during charging and discharging. Based on the updated wolf pack positions, calculate the objective function value for each gray wolf; Based on the objective function value of each gray wolf, the top three gray wolves with the highest objective function values ​​are selected as... α Wolf, β Wolf, δ Wolves, the rest of the wolves are gamma wolves; Update the current position and objective function value of each gray wolf; Based on the updated fitness values ​​of the Grey Wolves, select the new... α Wolf, β Wolf, δ Wolves, the rest of the wolves are gamma wolves; Continuously update the wolf pack's location; when the identification parameter error requirement is met, output the current location. α Wolf, β Wolf, δ The wolf's position is considered the optimal solution.

5. The method for identifying fault parameters of an energy storage system under extreme weather conditions as described in claim 4, characterized in that, The step of obtaining the updated wolf pack position by iterating through the wolf pack positions and using different formulas during charging and discharging includes: ； In the formula, Indicates the current location of the prey. Indicates the current location of the wolf pack. ρ 1. ρ Both 2 are random numbers between 0 and 1. a The convergence factor; The nonlinear convergence factor expression and position update formula during the discharge of the energy storage system are shown below: ； ； In the formula, This represents the current iteration number. The maximum number of iterations, X 1. X 2. X 3 are respectively α Wolf, β Wolf, δ The wolf's position m 1. m 2. m 3 represents the weighting coefficient, which is taken as follows: α Wolf, β Wolf, δ Wolf fitness value is used as a weighting coefficient; The nonlinear convergence factor expression and position update formula for the energy storage system during charging are shown below: ； 。 6. The method for identifying fault parameters of an energy storage system under extreme weather conditions as described in claim 4, characterized in that, In the step of updating the current position and objective function value of each gray wolf, the update formula is: ； ； ； In the formula, , , They represent α Wolf, β Wolf, δ The wolf's location; for α The updated vector coefficients of the wolf. for β The updated vector coefficients of the wolf. for δ The updated vector coefficients of the wolf; for α The perturbation coefficient updated by the wolf. for β The perturbation coefficient updated by the wolf. for δ The perturbation coefficient updated by the wolf.

7. A fault parameter identification device for an energy storage system under extreme weather conditions, characterized in that, include: The module is used to build an identification model of the energy storage system and determine the parameters to be identified in the identification model as voltage loop PI regulation parameters, current loop PI regulation parameters in discharge mode, and current loop PI regulation parameters in charging mode. The test module is used to perform the same low-voltage fault condition test on the actual controller and the identification model, output the response data of the actual controller, and at the same time collect the response data of the identification model under the same control parameters. The objective module is used to construct an objective function with the goal of minimizing the power difference between the actual controller and the identification model output; the decision variables of the objective function are the parameters to be identified, and the constraints of the objective function are that the power data and frequency are within a preset range; The first calculation module is used to solve the objective function using an improved particle swarm optimization algorithm and obtain the initial values ​​of the identification parameters; The second calculation module is used to iteratively identify the initial values ​​of the parameters using the improved gray wolf algorithm, and obtain the fault parameters when the fault characteristic response error is satisfied.

8. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the method for identifying fault parameters of an energy storage system under extreme weather conditions as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the method for identifying fault parameters of an energy storage system under extreme weather conditions as described in any one of claims 1 to 6.

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