Method, device and equipment for identifying fault parameters of energy storage system under extreme weather and medium

Abstract

The invention belongs to the technical field of fault parameter identification, and particularly relates to an energy storage system fault parameter identification method, device, equipment and medium under extreme weather, and the method comprises the steps: constructing an identification model of an energy storage system, and determining to-be-identified parameters in the identification model; performing the same low-voltage fault condition test on the 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; constructing a target function by taking the minimum power difference value output by the actual controller and the identification model as a target; solving the objective function by adopting an improved particle swarm algorithm, and obtaining an initial value of the to-be-identified parameter; and iterating the initial value of the identification parameter by adopting an improved grey wolf algorithm to obtain a fault parameter. According to the method, the hardware-in-the-loop simulation system controller parameters can be effectively identified, and the transient simulation requirement of a power system is met.

Description

Technical Field

[0001] The present invention belongs to the technical field of fault parameter identification, and specifically relates to a method, device, equipment and medium for identifying fault parameters of an energy storage system under extreme weather conditions. Background Art

[0002] Extreme weather events can easily cause grid fluctuations and power system failures. Energy storage systems must respond quickly to maintain a stable power supply, making parameter identification of energy storage systems during fault conditions particularly important. The regulation parameters of energy storage controllers play a decisive role in the operating characteristics of energy storage systems. However, due to manufacturers' intellectual property protection, precise controller parameters cannot be obtained, resulting in deviations between simulation analysis and actual operation.

[0003] The Chinese invention with publication number CN117691633A provides a method for identifying PI control parameters of an energy storage system based on an improved gray wolf algorithm. A measured model of the energy storage system is established, and the control method adopted by the grid-side inverter of the measured model of the energy storage system is dual closed-loop control. The measured model of the energy storage system is run to obtain the d-axis and q-axis current curves during steady-state operation; an identification model of the energy storage system is established, and the parameters to be identified are determined to be the grid-side inverter fault parameters based on the measured model of the energy storage system; random values ​​are assigned to the fault parameters of the energy storage system identification model, and the d-axis and q-axis response curves output by the energy storage system identification model are obtained, and the root mean square error between the two is calculated; the root mean square error of the d-axis and q-axis response curves output by the energy storage system identification model and the measured model of the energy storage system is used as the objective function, and the improved gray wolf algorithm is used to solve the optimal value of the objective function. The obtained solution set is the result of the parameters to be identified. However, this method has poor calculation accuracy and slow speed, and it is difficult to meet the needs of timely simulation. Summary of the Invention

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

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for identifying fault parameters of an energy storage system under extreme weather conditions, comprising: Construct an identification model of the energy storage system, and determine that the parameters to be identified in the identification model are the voltage loop PI adjustment parameters, the current loop PI adjustment parameters in the discharge mode, and the current loop PI adjustment parameters in the charging mode; Performing the same low-voltage fault condition test on the actual controller and the identification model, outputting response data of the actual controller, and simultaneously collecting response data of the identification model under different control parameters; the response data includes power data; An objective function is constructed with the goal of minimizing the power difference between the actual controller and the identification model outputs; 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 algorithm is used to solve the objective function and obtain the initial values ​​of the parameters to be identified; the improved particle swarm algorithm improves the inertia weight and the learning factor; An improved grey wolf algorithm is used to iteratively identify the initial value of the parameter, and when the fault characteristic response error is satisfied, the fault parameter is obtained; in the improved grey wolf algorithm, different formulas are used to iterate the wolf group position during charging and discharging.

[0006] Preferably, the step of solving the objective function using the improved particle swarm algorithm and obtaining the initial value of the parameter to be identified includes: Initialize particle swarm parameters; Based on each particle in the particle swarm parameters, calculate the objective function and obtain the objective function value of 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.

[0007] Preferably, the step of updating the particle speed, position, individual optimal value and global optimal value based on the fitness value includes: The update formulas for particle velocity, position, individual optimal value and global optimal value are as follows: ; ; Where: v i For the i The speed of a particle; x i For the i The position of each particle; oh is the inertia weight; c 1. c 2 is the learning factor; r 1. r 2 is a random number between 0 and 1; P best is the individual optimal value after iteration; G best is the global optimal value; in: ; ; ; Where: ohmax 、 oh min are the maximum and minimum values ​​of inertia weight respectively; T 1 is the maximum number of iterations, t 1 is the minimum number of iterations.

[0008] Preferably, the step of using the improved grey wolf algorithm to iterate the initial value of the parameter to be identified includes: Initialize the parameters of the improved gray wolf algorithm and use 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 updated wolf pack position is obtained by iterating the wolf pack position and using different formulas during charging and discharging; Based on the updated wolf pack position, calculate the objective function value of each gray wolf; Based on the objective function value of each gray wolf, the top three gray wolves with the highest objective function value are selected as α Wolf, β Wolf, d wolf, the rest are γ wolves; Update the current position of each gray wolf and its objective function value; According to the updated fitness value of the gray wolf, select the new α Wolf, β Wolf, d wolf, the rest are γ wolves; Continuously update the wolf pack position, and when the identification parameter error requirements are met, output α Wolf, β Wolf, d The wolf's position is taken as the optimal solution.

[0009] Preferably, the step of obtaining the updated wolf pack position by iterating the wolf pack position and using different formulas during charging and discharging includes: ; Where, Indicates the current prey location, Indicates the current location of the wolf pack. r 1. r 2 are all random numbers between 0 and 1. a is the convergence factor; Among them, the nonlinear convergence factor expression and position update formula when the energy storage system is discharged are as follows: ; ; Where, is the current iteration number, is the maximum number of iterations, X 1. X 2.X 3 are α Wolf, β Wolf, d The wolf's position m 1. m 2. m 3 is the weight coefficient, which can be taken as α Wolf, β Wolf, d The wolf fitness value is used as the weight coefficient; The nonlinear convergence factor expression and position update formula of the energy storage system during charging are as follows: ; .

[0010] Preferably, the wolf pack position is continuously updated, and when the identification parameter error requirement is met, the output is α Wolf, β Wolf, d The wolf's position is the optimal solution step, and the update formula is: ; ; ; Where, 、 、 Respectively α Wolf, β Wolf, d The location of the wolf; for α The vector coefficients of the wolf update, for β The vector coefficients of the wolf update, for d Vector coefficients of wolf updates; for α The perturbation coefficient for wolf updates, for β The perturbation coefficient for wolf updates, for d Perturbation coefficient for wolf updates.

[0011] Preferably, the objective is to minimize the power difference between the actual controller and the identification model outputs, and in the step of constructing the objective function, the objective function is: ; in, k=1,2…N , N is the total number of iterations, l 1 is the weight coefficient of active power difference, l2 is the weight coefficient of reactive power difference. The actual controller outputs active power and reactive power. P 、 Q ; Identification model output active power and reactive power under different control parameters P* 、 Q* ; The constraints are that the power and frequency are within the specified range.

[0012] In a second aspect, the present invention provides a device for identifying fault parameters of an energy storage system under extreme weather conditions, comprising: A construction module is used to construct an identification model of the energy storage system, and determine that the parameters to be identified in the identification model are voltage loop PI adjustment parameters, current loop PI adjustment parameters in discharge mode, and current loop PI adjustment 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 simultaneously collect the response data of the identification model under the same control parameters; The target 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 the improved particle swarm algorithm to obtain the initial value of the identification parameter; The second calculation module is used to iteratively identify the initial value of the parameter using the improved grey wolf algorithm, and obtain the fault parameter when the fault characteristic response error is satisfied.

[0013] In a third aspect of the present invention, an electronic device is provided, characterized in that it includes a processor and a memory, and the processor is 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 7.

[0014] In a fourth aspect of the present invention, a computer-readable storage medium is provided, characterized in that the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, it 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.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention adopts the above-mentioned energy storage system fault parameter test and identification method under extreme weather conditions, constructs an energy storage system control block diagram 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; respectively, the actual controller and the identification model are tested under the same low-voltage fault condition, and the active power and reactive power fault characteristic response data of the actual controller are output. P 、Q , and simultaneously collect the fault characteristic response data of active power and reactive power output by the identification model under different control parameters P* 、 Q* The objective function is defined, and the improved particle swarm algorithm is used to obtain the initial value of the control parameter to be identified, thereby narrowing the identification range of the control parameter. The improved gray wolf algorithm is used to accurately identify the control parameters, and different improved gray wolf algorithm improvement strategies are used during energy storage charging and discharging. When the fault characteristic response error is met, the controller parameters are output, and the PI control parameters obtained by combining the improved particle swarm algorithm and the improved gray wolf algorithm are error-verified. The present invention can effectively identify the controller parameters of the hardware-in-the-loop simulation system and meet the transient simulation requirements of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings: Figure 1 This is a schematic diagram of a method for identifying fault parameters of an energy storage system under extreme weather conditions according to Example 1 of the present invention; Figure 2 This is a schematic diagram of the hardware-in-the-loop system in the method for identifying fault parameters of an energy storage system under extreme weather conditions in Example 1 of the present invention; 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 Example 1 of the present invention; Figure 4 This is a comparison diagram of the actual controller output power and the identification model output power in Example 1 of the present invention; Figure 5 This is a comparison diagram of the actual controller output power and the identification model output power in Example 1 of the present invention; Figure 6 This is a structural block diagram of a device for identifying fault parameters of an energy storage system under extreme weather conditions according to embodiment 2 of the present invention; Figure 7 This is a structural block diagram of an electronic device according to embodiment 3 of the present invention. DETAILED DESCRIPTION

[0017] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0018] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise indicated, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0019] Example 1 like Figure 1 As shown in FIG, the method for identifying fault parameters of an energy storage system under extreme weather conditions includes: S1. Construct an identification model of the energy storage system, and determine that the parameters to be identified in the identification model are the voltage loop PI adjustment parameters, the current loop PI adjustment parameters in the discharge mode, and the current loop PI adjustment parameters in the charging mode; S11, Figure 2 This is the schematic diagram of a hardware-in-the-loop system. The battery outputs DC current, which is converted to AC current by a DC / AC converter. The current is then connected to the grid via an LC filter and a grid-connected switch. The AC side voltage and current are collected and controlled through a dual closed-loop voltage and current control loop to generate a PWM waveform, which is then input into the inverter for control.

[0020] Figure 3 This is a control block diagram of an energy storage system based on hardware-in-the-loop simulation. It is divided into voltage outer loop and current inner loop control. The voltage outer loop adopts fixed DC bus voltage control. When the DC bus voltage Udc is less than the reference value Udc*, that is, the current inner loop reference value is greater than 0, the energy storage system emits power; when the DC bus voltage Udc is greater than the reference value Udc*, that is, the current inner loop reference value is less than 0, the energy storage system absorbs power. After the voltage and current dual closed-loop control, a PWM wave is generated and input into the DC-AC converter for control.

[0021] in U dc * is the reference DC voltage, U dc is the DC voltage, I L * is the inner loop reference current, I L is the inductor current, when the DC bus voltage U dc Less than the reference value U dc * When the DC bus voltage U dc Greater than the reference value U dc * When , the energy storage system absorbs power.

[0022] S12. Establish an identification model of the energy storage system in Simulink. In the identification model, the control parameters other than the parameters to be identified are consistent with the control parameters of the actual hardware-in-the-loop controller; the three PI adjustment parameters in the voltage and current double closed-loop control link are the parameters to be identified, including the voltage loop PI regulator K p1 、 K i1 ; When the energy storage system emits power, the current loop PI regulator K p2 、 K i2 ; When the energy storage system absorbs power, the current loop PI regulator K p3 、 K i3 .

[0023] S2. Performing the same low-voltage fault condition test on the actual controller and the identification model, outputting response data of the actual controller, and simultaneously collecting response data of the identification model under different parameters to be identified; the response data includes power data; S21. Set a three-phase short-circuit fault for the actual controller and the identification model respectively.

[0024] S22, respectively collect the actual controller output active power and reactive power P 、 Q And the identification model output active power and reactive power under different parameters to be identified P* 、 Q* ,Will P* and P 、 Q* and Q Perform error comparison.

[0025] S3, with the goal of minimizing the power difference between the actual controller and the identification model output, construct an objective function; the decision variable of the objective function is the parameter to be identified, and the decision variable is achieved by changing the parameter to be identified. P* 、 Q* The constraint condition of the objective function is that the power data and frequency are within a preset range; S31. With the goal of minimizing the power difference between the actual controller and the output of the identification model, the control parameters in the identification model are continuously updated to minimize the power difference, that is, the objective function takes the minimum value. The objective function is defined as shown in formula (1).

[0026] ; (1) in, k=1,2…N , N is the total number of iterations, l1 is the weight coefficient of active power difference, l 2 is the weight coefficient of reactive power difference. The actual controller outputs active power and reactive power. P 、 Q ; Identification model output active power and reactive power under different control parameters P* 、 Q* ; The constraints are: ; Where, P max 、 P min Respectively represent the upper and lower limits of active power, Q max 、 Q min Respectively represent the upper and lower limits of reactive power, f max 、 f min Represent the upper and lower limits of the frequency respectively.

[0027] The power generated by the energy storage system is mainly active power. Therefore, in order to balance the error between active power and reactive power, the l 1 and l 2 satisfies the following relationship: ;(2) S4, using the improved particle swarm algorithm to solve the objective function and obtain the initial values ​​of the parameters to be identified; Specifically, they include: Initialize particle swarm parameters; Based on each particle in the particle swarm parameters, calculate the objective function and obtain the objective function value of each particle in the particle swarm parameters; Use the following formula to update particle velocity, position, individual optimal value and global optimal value: ;(3) ;(4) Where: v i For the i The speed of a particle; x i For the i The position of each particle; oh is the inertia weight; c 1. c 2 is the learning factor; r 1. r 2 is a random number between 0 and 1; P best is the individual optimal value after iteration;G best is the global optimal value; k +1 for the k +1 update, superscript k For the k Updated.

[0028] Based on the traditional particle swarm optimization algorithm, the inertia weight oh , learning factor c 1. c 2 to improve the algorithm convergence speed and parameter identification accuracy. oh 、 c 1. c 2 is shown in the following formula: ;(5) ;(6) ;(7) Where: oh max 、 oh min are the maximum and minimum values ​​of inertia weight respectively; T 1 is the maximum number of iterations, t 1 is the minimum number of iterations.

[0029] Continuously update the particle speed, 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) .

[0030] S5. Use the improved grey wolf algorithm to iteratively identify the initial value of the parameter, and obtain the fault parameter when the fault characteristic response error is satisfied.

[0031] Specifically, they include: Initialize the improved gray wolf algorithm parameters with S4 K p1(0) 、 K i1(0) 、 K p2(0) 、 K i2(0) 、 K p3(0) 、K i3(0) As the initial population of the improved grey wolf algorithm; Based on the initial population, the wolf pack position is iterated using formula (8): ; (8) Where, Indicates the current prey location, Indicates the current wolf pack position, which is obtained by S4 K p1(0) 、 K i1(0) 、 K p2(0) 、 K i2(0) 、 K p3(0) 、 K i3(0) , r 1. r 2 are all random numbers between 0 and 1. a is the convergence factor; Convergence Factor a And the gray wolf position update formula Improvements are made to increase convergence speed and identification accuracy. Since the bidirectional DC-AC converter operates in different modes during energy storage charging and discharging, adjustments need to be made based on different control parameters to improve flexibility. The nonlinear convergence factor expression and position update formula during energy storage system discharge are shown below: ; (9) ; (10) Where, is the current iteration number, is the maximum number of iterations, X 1. X 2. X 3 are α Wolf, β Wolf, d The wolf's position m 1. m 2. m 3 is the weight coefficient, which can be taken as α Wolf, β Wolf, d The wolf fitness value is used as the weight coefficient; The nonlinear convergence factor expression and position update formula of the energy storage system during charging are as follows: ; (11) ; (12) Based on the updated wolf pack position, calculate the objective function value of each gray wolf; Based on the objective function value of each gray wolf, the top three gray wolves with the highest objective function value are selected as α Wolf, β Wolf, d Wolf, the rest of the wolves are γ wolves: Press to update α Wolf, β Wolf, d Wolf's current location: ; (13) ; (14) ; (15) Where, 、 、 Respectively α Wolf, β Wolf, d The location of the wolf; for α The vector coefficients of the wolf update, for β The vector coefficients of the wolf update, for d The vector coefficients of the wolf update are all calculated using the corresponding random numbers using formula (14); for α The perturbation coefficient for wolf updates, for β The perturbation coefficient for wolf updates, for d The disturbance coefficients of wolf updates are calculated using the corresponding random numbers using formula 15.

[0032] According to the updated objective function value of the gray wolf, select the new α Wolf, β Wolf, d Wolf, the rest of the wolves are c Wolf; Continuously update the wolf pack position, and when the error requirements of the parameters to be identified are met, output the current α Wolf, β Wolf, d The wolf's position is taken as the optimal solution.

[0033] Error verification of fault parameters: Multiple identification results were obtained using the energy storage system, and the average of these results was taken as the final identification value. The identification results are shown in the table below. The control parameters identified using the proposed method combining the improved particle swarm optimization algorithm and the improved gray wolf algorithm 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 proposed identification method.

[0034] Table 1 Parameter identification results

[0035] The three-phase voltage drops to 20% of the rated value at 2.5s, the fault duration is 625ms, and it returns to normal at 3.125s. The actual controller output power is compared with the identification model output power. Figure 4 、 Figure 5 As shown, it can be seen that the fault characteristic response curve obtained by using the identification method proposed in the present invention has a high degree of fitting, which effectively verifies the effectiveness of the identification method proposed in the present invention.

[0036] Example 2 like Figure 6 As shown, based on the same inventive concept as the above embodiment, the present invention also provides a device for identifying fault parameters of an energy storage system under extreme weather conditions, comprising: A construction module is used to construct an identification model of the energy storage system, and determine that the parameters to be identified in the identification model are voltage loop PI adjustment parameters, current loop PI adjustment parameters in discharge mode, and current loop PI adjustment 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 simultaneously collect the response data of the identification model under the same control parameters; The target 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 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; The first calculation module is used to solve the objective function using the improved particle swarm algorithm to obtain the initial value of the identification parameter; The second calculation module is used to iteratively identify the initial value of the parameter using the improved grey wolf algorithm, and obtain the fault parameter when the fault characteristic response error is satisfied.

[0037] Example 3 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; 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 the at least one processor 102 , and at least one communication bus 104 .

[0038] The memory 101 can be used to store a computer program 103. The processor 102 implements the steps of the method for identifying fault parameters of an energy storage system under extreme weather conditions in Example 1 by running or executing the computer program stored in the memory 101 and calling data stored in the memory 101.

[0039] The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data (such as audio data) created according to the use of the electronic device 100. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.

[0040] The at least one processor 102 may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100 and connects various parts of the entire electronic device 100 using various interfaces and lines.

[0041] The memory 101 in the electronic device 100 stores multiple instructions to implement a method for identifying fault parameters of an energy storage system under extreme weather conditions. The processor 102 can execute the multiple instructions to implement: Construct an identification model of the energy storage system, and determine that the parameters to be identified in the identification model are the voltage loop PI adjustment parameters, the current loop PI adjustment parameters in the discharge mode, and the current loop PI adjustment parameters in the charging mode; Performing the same low-voltage fault condition test on the actual controller and the identification model, outputting response data of the actual controller, and simultaneously collecting response data of the identification model under the same control parameters; the response data includes power data; An objective function is constructed with the goal of minimizing the power difference between the actual controller and the identification model outputs; the decision variables of the objective function are the 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; The improved particle swarm algorithm is used to solve the objective function and obtain the initial values ​​of the parameters to be identified; The improved grey wolf algorithm is used to iteratively identify the initial values ​​of the parameters. When the fault characteristic response error is satisfied, the fault parameters are obtained.

[0042] Example 4 If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. Computer-readable media may include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0043] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0044] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure one a process or multiple processes and / or boxes Figure one A device that provides the functions specified in a block or multiple blocks.

[0045] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure one a process or multiple processes and / or boxes Figure one The function specified in one or more boxes.

[0046] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure one a process or multiple processes and / or boxes Figure one A step that specifies a function in one or more boxes.

[0047] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these 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 any one or more embodiments or examples.

[0048] 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, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by 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: Construct an identification model of the energy storage system, and determine that the parameters to be identified in the identification model are the voltage loop PI adjustment parameters, the current loop PI adjustment parameters in the discharge mode, and the current loop PI adjustment parameters in the charging mode; Performing the same low-voltage fault condition test on the actual controller and the identification model, outputting response data of the actual controller, and simultaneously collecting response data of the identification model under different control parameters; the response data includes power data; An objective function is constructed with the goal of minimizing the power difference between the actual controller and the identification model outputs; 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 algorithm is used to solve the objective function and obtain the initial values ​​of the parameters to be identified; the improved particle swarm algorithm improves the inertia weight and the learning factor; The improved grey wolf algorithm is used to iteratively identify the initial value of the parameter, and the fault parameter is obtained when the fault characteristic response error is satisfied. In the improved grey wolf algorithm, different formulas are used to iterate the wolf group position during charging and discharging.

2. The method for identifying fault parameters of an energy storage system under extreme weather conditions according to claim 1, wherein: The step of using the improved particle swarm algorithm to solve the objective function and obtain the initial value of the parameter to be identified includes: Initialize particle swarm parameters; Based on each particle in the particle swarm parameters, calculate the objective function and obtain the objective function value of 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 according to claim 2, wherein: The step of updating the particle speed, position, individual optimal value and global optimal value based on the fitness value includes: The update formulas for particle velocity, position, individual optimal value and global optimal value are as follows: ； ； Where: v i For the i The speed of a particle; x i For the i The position of each particle; ω is the inertia weight; c 1. c 2 is the learning factor; r 1. r 2 is a random number between 0 and 1; P best is the individual optimal value after iteration; G best is the global optimal value; in: ； ； ； Where: ω max 、 ω min are the maximum and minimum values ​​of inertia weight respectively; T 1 is the maximum number of iterations, t 1 is the minimum number of iterations.

4. The method for identifying fault parameters of an energy storage system under extreme weather conditions according to claim 1, wherein: The step of using the improved grey wolf algorithm to iterate the initial value of the parameter to be identified includes: Initialize the parameters of the improved gray wolf algorithm and use 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 updated wolf pack position is obtained by iterating the wolf pack position and using different formulas during charging and discharging; Based on the updated wolf pack position, calculate the objective function value of each gray wolf; Based on the objective function value of each gray wolf, the top three gray wolves with the highest objective function value are selected as α Wolf, β Wolf, δ wolf, the rest are γ wolves; Update the current position of each gray wolf and its objective function value; According to the updated fitness value of the gray wolf, select the new α Wolf, β Wolf, δ wolf, the rest are γ wolves; Continuously update the wolf pack position, and when the identification parameter error requirements are met, output the current α Wolf, β Wolf, δ The wolf's position is taken as the optimal solution.

5. The method for identifying fault parameters of an energy storage system under extreme weather conditions according to claim 4, wherein: The step of iterating the wolf pack positions and using different formulas during charging and discharging to obtain updated wolf pack positions includes: ； Where, Indicates the current prey location, Indicates the current location of the wolf pack. ρ 1. ρ 2 are all random numbers between 0 and 1. a is the convergence factor; Among them, the nonlinear convergence factor expression and position update formula when the energy storage system is discharged are as follows: ； ； Where, is the current iteration number, is the maximum number of iterations, X 1. X 2. X 3 are α Wolf, β Wolf, δ The wolf's position, m 1. m 2. m 3 is the weight coefficient, respectively α Wolf, β Wolf, δ The wolf fitness value is used as the weight coefficient; The nonlinear convergence factor expression and position update formula of the energy storage system during charging are as follows: ； 。 6. The method for identifying fault parameters of an energy storage system under extreme weather conditions according to claim 4, wherein: In the step of updating the current position of each gray wolf and its objective function value, the update formula is: ； ； ； Where, 、 、 Respectively α Wolf, β Wolf, δ The location of the wolf; for α The vector coefficients of the wolf update, for β The vector coefficients of the wolf update, for δ Vector coefficients of wolf updates; for α The perturbation coefficient for wolf updates, for β The perturbation coefficient for wolf updates, for δ Perturbation coefficient for wolf updates.

7. The method for identifying fault parameters of an energy storage system under extreme weather conditions according to claim 1, wherein: The objective is to minimize the power difference between the actual controller and the identification model output. In the step of constructing the objective function, the objective function is: ； in, k=1,2…N , N is the total number of iterations, λ 1 is the weight coefficient of active power difference, λ 2 is the weight coefficient of reactive power difference. The actual controller outputs active power and reactive power. P 、 Q ; Identification model output active power and reactive power under different control parameters P* 、 Q* .

8. A device for identifying fault parameters of an energy storage system under extreme weather conditions, characterized in that: include: A construction module is used to construct an identification model of the energy storage system, and determine that the parameters to be identified in the identification model are voltage loop PI adjustment parameters, current loop PI adjustment parameters in discharge mode, and current loop PI adjustment 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 simultaneously collect the response data of the identification model under the same control parameters; The target 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 the improved particle swarm algorithm to obtain the initial value of the identification parameter; The second calculation module is used to iteratively identify the initial value of the parameter using the improved grey wolf algorithm, and obtain the fault parameter when the fault characteristic response error is satisfied.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is 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 claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, the method for identifying fault parameters of an energy storage system under extreme weather conditions according to any one of claims 1 to 7 is implemented.

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