Converter characteristic parameter identification method and device, storage medium and equipment

Abstract

The invention discloses an identification method and device for characteristic parameters of a converter, a storage medium and equipment, relates to the technical field of electric energy storage, and mainly aims to solve the problem of low characteristic parameter identification accuracy caused by test deviation in the prior art, and the identification method comprises the following steps: determining a plurality of characteristic parameters to be identified which influence low voltage ride through performance; dividing the low voltage ride through of the energy storage system into three time periods before a fault, during the fault and after the fault, respectively taking active power and reactive power output by a converter as observed quantities, and determining sensitivity corresponding to each characteristic parameter to be identified in each time period; calculating an active power average absolute value deviation and a reactive power average absolute value deviation in each time period; the weight coefficient of each deviation in the corresponding time period is determined based on the sensitivity; determining a target function based on the active power average absolute value deviation and the reactive power average absolute value deviation in each time period and the corresponding weight coefficients; and carrying out optimization solution on the target function to obtain an identification result.

Description

Technical Field

[0001] The present invention relates to the field of electric energy storage technology, and in particular to a method and device for identifying characteristic parameters of a converter, a storage medium, and equipment. Background Art

[0002] In order to cope with the inevitable randomness of renewable energy generation such as photovoltaic and wind power, a certain capacity of renewable energy generation system is currently equipped with energy storage of corresponding capacity as support. With the large-scale grid connection of renewable energy, the energy storage capacity equipped is also increasing, and the problems caused by the access of energy storage system to the grid through power electronic converters are becoming increasingly prominent. When the renewable energy generation is strong, the energy storage system absorbs the energy that cannot be absorbed by the load side. When the renewable energy generation is weak or even no generation, the energy storage system serves as a power source to supply power to the load side. Therefore, the energy storage system needs to have good grid connection performance. In order to evaluate the performance of energy storage system connected to a high proportion of renewable energy power system, it is necessary to test the energy storage converter and the corresponding control strategy, and require it to have low voltage ride through (LVRT) capability under short-term disturbances and faults. In order to obtain an accurate mathematical model of low voltage ride through of energy storage converter to meet the needs of grid simulation and calculation, the accuracy of its model characteristic parameters is crucial.

[0003] At present, there are many research results on the identification of characteristic parameters of energy storage systems. Most of the research has improved the identification strategy and the algorithm used. The main factors considered are the working state and control mode of the converter. The proportional and integral parameters of the converter control link are identified, and the processing of the deviation between the test data and the simulation data is ignored. However, due to the inaccurate parameter values ​​of the simulation model itself or the test deviation caused by the lack of professionalism of the testers, the identification accuracy of the characteristic parameters is often not high, which affects the stable operation of the high-proportion new energy power system. Summary of the invention

[0004] In view of this, the present invention provides a method and apparatus, a storage medium, and a device for identifying characteristic parameters of a converter, the main purpose of which is to solve the problem of low characteristic parameter identification accuracy caused by existing test deviations.

[0005] According to one aspect of the present invention, a method for identifying characteristic parameters of a converter is provided, comprising:

[0006] Determine multiple characteristic parameters to be identified that affect low voltage ride-through performance based on the control principle of low voltage ride-through of the converter;

[0007] The low voltage ride-through of the energy storage system is divided into three periods: before the fault, during the fault and after the fault, and the active power and reactive power output by the converter are respectively used as observation quantities to determine the sensitivity corresponding to each characteristic parameter to be identified in each period;

[0008] Calculating the average absolute value deviation of active power and the average absolute value deviation of reactive power output by the converter in each time period; and determining the weight coefficient of the average absolute value deviation of active power and the average absolute value deviation of reactive power in the corresponding time period based on the sensitivity corresponding to each characteristic parameter to be identified in each time period;

[0009] The objective function is determined based on the average absolute value deviation of the active power and the average absolute value deviation of the reactive power output by the converter in each time period, and the corresponding weight coefficient; and the objective function is optimized and solved to obtain an identification result that meets the parameter identification accuracy.

[0010] Furthermore, the characteristic parameters to be identified include active power-related characteristic parameters and reactive power-related characteristic parameters;

[0011] The active power and reactive power output by the converter are respectively used as observation quantities to determine the sensitivity corresponding to the characteristic parameter to be identified in each time period, including:

[0012] Obtaining changes in the active power during multiple low voltage ride-through processes, and determining active parameter sensitivities of each active power-related characteristic parameter corresponding to each time period based on the changes in the active power;

[0013] The change of the reactive power during multiple low voltage ride-through processes is obtained, and the reactive parameter sensitivity of each reactive power-related characteristic parameter corresponding to each time period is determined based on the change of the reactive power.

[0014] Further, the obtaining of the change of the active power during multiple low voltage ride-through processes, and determining the active parameter sensitivity of each active power-related characteristic parameter corresponding to each time period based on the change of the active power, includes:

[0015] Respectively obtain the active power change amount and the active power steady-state value of the active power in three time periods before the fault, during the fault and after the fault; and obtain the initial value and parameter change amount of the active power related characteristic parameter in three time periods before the fault, during the fault and after the fault;

[0016] Determining the sensitivity value of the active power-related characteristic parameter in each time period based on the active power change, the active power steady-state value, the initial value of the active power-related characteristic parameter and the parameter change;

[0017] The average value of the sensitivity value of the active power related characteristic parameter in each time period during multiple low voltage ride-throughs is calculated to obtain the active parameter sensitivity of the active power related characteristic parameter corresponding to the three time periods before the fault, during the fault and after the fault.

[0018] Furthermore, the obtaining of the change of the reactive power during multiple low voltage ride-through processes, and determining the reactive parameter sensitivity of each reactive power-related characteristic parameter corresponding to each time period based on the change of the reactive power, includes:

[0019] Respectively obtain the reactive power change and the reactive power steady-state value of the reactive power in three time periods before the fault, during the fault and after the fault; and obtain the initial value and parameter change of the reactive power related characteristic parameter in three time periods before the fault, during the fault and after the fault;

[0020] Determine the sensitivity value of the reactive power related characteristic parameter in each time period based on the reactive power change, the reactive power steady-state value, the initial value of the reactive power related characteristic parameter and the parameter change;

[0021] The average value of the sensitivity value of the reactive power related characteristic parameter in each time period during multiple low voltage ride-throughs is calculated to obtain the reactive parameter sensitivity of the reactive power related characteristic parameter corresponding to the three time periods before the fault, during the fault and after the fault.

[0022] Further, the method of determining the weight coefficients of the average absolute value deviation of active power and the average absolute value deviation of reactive power in the corresponding time period based on the sensitivity corresponding to each characteristic parameter to be identified in each time period includes:

[0023] Calculating the sum of the active parameter sensitivities of the active power-related characteristic parameters in each time period; and determining the active sensitivity proportional coefficient corresponding to each time period based on the sum of the active parameter sensitivities in each time period, and determining the active sensitivity proportional coefficient as the first weight coefficient of the active power average absolute value deviation in the corresponding time period;

[0024] Calculate the sum of the reactive parameter sensitivities of the reactive power-related characteristic parameters in each time period; and determine the reactive sensitivity proportional coefficient corresponding to each time period based on the sum of the reactive parameter sensitivities in each time period, and determine the reactive sensitivity proportional coefficient as the second weight coefficient of the reactive power average absolute value deviation in the corresponding time period.

[0025] Further, the objective function is determined based on the average absolute value deviation of the active power and the average absolute value deviation of the reactive power output by the converter in each time period, and the corresponding weight coefficient, including:

[0026] Using the first weight coefficient to perform weighted summation processing on the average absolute value deviation of the active power in each time period to obtain a total active power deviation;

[0027] The second weight coefficient is used to perform weighted sum processing on the reactive power average absolute value deviation in each time period to obtain a total reactive power deviation;

[0028] The total active power deviation and the total reactive power deviation are summed to obtain a total deviation; and a minimum value of the total deviation is determined as the objective function.

[0029] Furthermore, the objective function is optimized and solved to obtain an identification result that satisfies the parameter identification accuracy, including:

[0030] Using the least square method to perform fitting processing on each of the characteristic parameters to be identified, and determining the optimization range of each of the characteristic parameters to be identified based on the obtained fitting results;

[0031] Based on the optimization range, the objective function is optimized and solved using the Black Kite algorithm to obtain the identification result that meets the parameter identification accuracy.

[0032] According to another aspect of the present invention, there is provided a device for identifying characteristic parameters of a converter, comprising:

[0033] A parameter determination module, used to determine a plurality of characteristic parameters to be identified that affect the low voltage ride-through performance based on the control principle of the low voltage ride-through of the converter;

[0034] A sensitivity determination module is used to divide the low voltage ride through of the energy storage system into three time periods: before the fault, during the fault and after the fault, and respectively use the active power and reactive power output by the converter as observation quantities to determine the sensitivity corresponding to each characteristic parameter to be identified in each time period;

[0035] A weight calculation module, used to calculate the average absolute value deviation of active power and the average absolute value deviation of reactive power output by the converter in each time period; and determine the weight coefficient of the average absolute value deviation of active power and the average absolute value deviation of reactive power in the corresponding time period based on the sensitivity corresponding to each characteristic parameter to be identified in each time period;

[0036] The target determination and solution module is used to determine the objective function based on the average absolute value deviation of the active power and the average absolute value deviation of the reactive power output by the converter in each time period, and the corresponding weight coefficient; and optimize and solve the objective function to obtain an identification result that meets the parameter identification accuracy.

[0037] Further, the characteristic parameters to be identified include active power related characteristic parameters and reactive power related characteristic parameters; the sensitivity determination module includes an active power determination unit and a reactive power determination unit;

[0038] The active power determination unit is used to obtain the change of the active power during multiple low voltage ride-through processes, and determine the active power parameter sensitivity of each active power-related characteristic parameter corresponding to each time period based on the change of the active power;

[0039] The reactive power determination unit is used to obtain the change of the reactive power during multiple low voltage ride-through processes, and determine the reactive parameter sensitivity of each reactive power-related characteristic parameter corresponding to each time period based on the change of the reactive power.

[0040] Furthermore, the active power determination unit is further used for:

[0041] Respectively obtain the active power change amount and the active power steady-state value of the active power in three time periods before the fault, during the fault and after the fault; and obtain the initial value and parameter change amount of the active power related characteristic parameter in three time periods before the fault, during the fault and after the fault;

[0042] Determining the sensitivity value of the active power-related characteristic parameter in each time period based on the active power change, the active power steady-state value, the initial value of the active power-related characteristic parameter and the parameter change;

[0043] The average value of the sensitivity value of the active power related characteristic parameter in each time period during multiple low voltage ride-throughs is calculated to obtain the active parameter sensitivity of the active power related characteristic parameter corresponding to the three time periods before the fault, during the fault and after the fault.

[0044] Furthermore, the reactive power determination unit is further used for:

[0045] Respectively obtain the reactive power change and the reactive power steady-state value of the reactive power in three time periods before the fault, during the fault and after the fault; and obtain the initial value and parameter change of the reactive power related characteristic parameter in three time periods before the fault, during the fault and after the fault;

[0046] Determine the sensitivity value of the reactive power related characteristic parameter in each time period based on the reactive power change, the reactive power steady-state value, the initial value of the reactive power related characteristic parameter and the parameter change;

[0047] The average value of the sensitivity value of the reactive power related characteristic parameter in each time period during multiple low voltage ride-throughs is calculated to obtain the reactive parameter sensitivity of the reactive power related characteristic parameter corresponding to the three time periods before the fault, during the fault and after the fault.

[0048] Furthermore, the weight calculation module is also used for:

[0049] Calculating the sum of the active parameter sensitivities of the active power-related characteristic parameters in each time period; and determining the active sensitivity proportional coefficient corresponding to each time period based on the sum of the active parameter sensitivities in each time period, and determining the active sensitivity proportional coefficient as the first weight coefficient of the active power average absolute value deviation in the corresponding time period;

[0050] Calculate the sum of the reactive parameter sensitivities of the reactive power-related characteristic parameters in each time period; and determine the reactive sensitivity proportional coefficient corresponding to each time period based on the sum of the reactive parameter sensitivities in each time period, and determine the reactive sensitivity proportional coefficient as the second weight coefficient of the reactive power average absolute value deviation in the corresponding time period.

[0051] Further, the target determination and solution module includes a target determination unit and a solution unit;

[0052] The target determination unit is used for:

[0053] Using the first weight coefficient to perform weighted summation processing on the average absolute value deviation of the active power in each time period to obtain a total active power deviation;

[0054] The second weight coefficient is used to perform weighted sum processing on the reactive power average absolute value deviation in each time period to obtain a total reactive power deviation;

[0055] The total active power deviation and the total reactive power deviation are summed to obtain a total deviation; and a minimum value of the total deviation is determined as the objective function.

[0056] The solving unit is used to:

[0057] Using the least square method to perform fitting processing on each of the characteristic parameters to be identified, and determining the optimization range of each of the characteristic parameters to be identified based on the obtained fitting results;

[0058] Based on the optimization range, the objective function is optimized and solved using the Black Kite algorithm to obtain the identification result that meets the parameter identification accuracy.

[0059] According to another aspect of the present invention, a storage medium is provided, wherein the storage medium stores at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the above-mentioned method for identifying characteristic parameters of a converter.

[0060] According to another aspect of the present invention, there is provided a device, comprising a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0061] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned method for identifying characteristic parameters of the converter.

[0062] By means of the above technical solution, the technical solution provided by the embodiment of the present invention has at least the following advantages:

[0063] The present invention provides a method and device, storage medium and equipment for identifying characteristic parameters of a converter. Compared with the prior art, the present invention divides the low voltage crossing of an energy storage system into three time periods: before the fault, during the fault and after the fault, and respectively uses the active power and reactive power output by the converter as observation quantities to determine the sensitivity corresponding to each characteristic parameter to be identified in each time period, and fully considers the influence of the characteristic parameters to be identified on the active power and reactive power in different time periods; by weighting the average absolute deviation between the measured power and the simulated power in each time period of the converter low voltage crossing, the influence of each characteristic parameter to be identified on the output characteristics of the converter can be weighed, thereby further improving the identification accuracy of the characteristic parameters.

[0064] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0066] Figure 1 A schematic flow chart of a method for identifying characteristic parameters of a converter provided in an embodiment of the present invention is shown;

[0067] Figure 2 A schematic flow chart of another method for identifying characteristic parameters of a converter provided in an embodiment of the present invention is shown;

[0068] Figure 3 A schematic flow chart of another method for identifying characteristic parameters of a converter provided in an embodiment of the present invention is shown;

[0069] Figure 4 A schematic flow chart of another method for identifying characteristic parameters of a converter provided in an embodiment of the present invention is shown;

[0070] Figure 5 The optimization iteration curve of the comparison algorithm provided by the embodiment of the present invention is shown;

[0071] Figure 6 shows the identification curve of the comparison algorithm provided by the embodiment of the present invention;

[0072] Figure 7 A schematic diagram showing the structure of a device for identifying characteristic parameters of a converter provided by an embodiment of the present invention is shown;

[0073] Figure 8 A schematic structural diagram of a device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0074] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0075] The embodiment of the present invention provides a method for identifying characteristic parameters of a converter, such as Figure 1 As shown, the method includes:

[0076] 101. Determine multiple characteristic parameters to be identified that affect low voltage ride-through performance based on the control principle of low voltage ride-through of the converter;

[0077] In the embodiment of the present invention, the current execution end analyzes the characteristic parameters to be identified based on the low voltage ride-through (hereinafter referred to as LVRT) control principle of the energy storage converter. The LVRT of the energy storage converter mainly includes a fault stage and a fault recovery stage. The specific modeling process is as follows:

[0078] (1) During the fault phase, when the grid connection point voltage meets the LVRT condition, the converter needs to cut off the voltage outer loop to improve the response speed and provide dynamic reactive power support to achieve low voltage ride through. During this period, the active current command value I″pc for:

[0079]

[0080] Among them, I max It represents the upper limit of the total current of the converter during a fault, usually 1.1 times the rated current; I n is the rated current.

[0081] Reactive current command value I″ qc for:

[0082]

[0083] Among them, K Q Indicates the reactive current support factor during a fault; U PCC* is the per unit voltage of the grid-connected point of the converter.

[0084] (2) Fault recovery stage: To meet the power recovery rate requirement, the low voltage ride-through mathematical model of the energy storage converter in the embodiment of the present invention adopts a slope recovery characteristic. The specific control strategy is: the active current is set at a fixed slope k p Gradually increase to the rated value, and then switch to the normal working condition control strategy. At this stage, the active current command value I″ pc As shown below:

[0085] I″ pc =k p (t-t1)+I p0

[0086] Among them, k p is the active power recovery rate; t1 is the fault removal time; I p0 is the initial value of the active current of the converter during steady state.

[0087] Reactive current command value I" qc for:

[0088] I″ qc =k q (t-t1)+I q0

[0089] Among them, k q is the reactive current recovery rate; I q0 is the initial value of the reactive current of the converter during steady state.

[0090] Through the analysis of various parameters in the above fault stage and fault recovery stage, the relevant parameters during the fault period, reactive current support factor K Q As a key parameter during LVRT; the initial value of the active current of the converter during steady state I p0, active power recovery rate k during the climbing process p ; Initial value of reactive current of the converter during steady state I q0 , and the reactive current recovery rate k q Both can be used as key parameters during LVRT. Parameter I p0 , k p ,I q0 and k q The dynamic characteristics of the output power after the low voltage ride-through are jointly determined and are the key parameters after the fault. Based on the above analysis, the characteristic parameters to be identified may include: K Q ,I p0 , k p ,I q0 and k q The embodiments of the present invention are not specifically limited.

[0091] 102. Divide the low voltage ride through of the energy storage system into three time periods: before the fault, during the fault, and after the fault, and use the active power and reactive power output by the converter as observation quantities to determine the sensitivity corresponding to each characteristic parameter to be identified in each time period;

[0092] In the embodiment of the present invention, the current execution end divides the low voltage ride-through of the energy storage system into three time periods: before the fault, during the fault, and after the fault, and the three time periods are recorded as A, B, and C respectively. In addition, the current execution end also uses the active power and reactive power output by the converter as observation quantities to determine the sensitivity corresponding to each of the characteristic parameters to be identified in each time period; wherein the sensitivity represents the influence of each characteristic parameter to be identified in each time period, that is, the higher the sensitivity value, the greater the influence, and the lower the sensitivity, the smaller the influence, which is not specifically limited in the embodiment of the present invention.

[0093] 103. Calculate the average absolute value deviation of active power and the average absolute value deviation of reactive power output by the converter in each time period; and determine the weight coefficient of the average absolute value deviation of active power and the average absolute value deviation of reactive power in the corresponding time period based on the sensitivity corresponding to each characteristic parameter to be identified in each time period;

[0094] In the embodiment of the present invention, the current execution end calculates the average absolute value deviation of the active power and the average absolute value deviation of the reactive power output by the converter in each time period by comparing the simulation data of the low voltage ride-through mathematical model of the energy storage converter with the test data of the active power and the reactive power during the low voltage ride-through of the actual energy storage converter. The specific calculation formula of the average absolute value deviation E is as follows:

[0095]

[0096] Wherein, S1 and S2 are the starting and ending sequence numbers of the data sequence respectively, and in the embodiment of the present invention, they are the starting and ending sequence numbers of the three time periods before the fault, during the fault, and after the fault; X M , X S are the active power or reactive power output by test and simulation respectively. The average absolute value deviation of active power before the fault is finally obtained and recorded as E AP , the average absolute value deviation of active power during the fault period is recorded as E BP The deviation between the average absolute value of active power after the fault is recorded as E CP ; The average absolute value deviation of reactive power before the fault is recorded as E AQ , the average absolute value deviation of reactive power during the fault period is recorded as E BQ The deviation from the average absolute value of reactive power after the fault is recorded as E CQ .

[0097] In addition, in the embodiment of the present invention, the current execution end further determines the weight coefficient of the average absolute value deviation of active power and the average absolute value deviation of reactive power in the corresponding time period based on the sensitivity corresponding to each characteristic parameter to be identified in each time period obtained in step 102; that is, the above E is determined based on the sensitivity. AP 、E BP 、E CP 、E AQ 、E BQ and E CQ The weight coefficients can be recorded as a P , b P 、c P 、a Q , b Q and c Q , the embodiments of the present invention are not specifically limited.

[0098] 104. Determine an objective function based on the average absolute value deviation of the active power and the average absolute value deviation of the reactive power output by the converter in each time period and the corresponding weight coefficient; and optimize and solve the objective function to obtain an identification result that meets the parameter identification accuracy.

[0099] In the embodiment of the present invention, the current execution end calculates the average absolute value deviation E of the active power output by the converter in each time period based on step 103. AP 、E BP and E CP , and the corresponding weight coefficient a P , b P and c P ; The average absolute value deviation E of the reactive power output by the converter in each period AQ 、E BQ and E CQ, and the corresponding weight coefficient a Q , b Q and c Q The objective function is determined so that the method of the present invention can effectively deal with the identification accuracy problem caused by the test deviation. Then, the determined objective function is optimized and solved to obtain an identification result that satisfies the parameter identification accuracy.

[0100] Furthermore, as a refinement and extension of the specific implementation of the above embodiment, in order to further quantify the influence of each characteristic parameter to be identified in each time period, another method for identifying the characteristic parameters of the converter is provided, such as Figure 2 As shown, the characteristic parameters to be identified in the method include active power related characteristic parameters and reactive power related characteristic parameters; the steps of taking the active power and reactive power output by the converter as observation quantities, respectively, and determining the sensitivity corresponding to the characteristic parameters to be identified in each time period, include:

[0101] 201. Obtain changes in the active power during multiple low voltage ride-through processes, and determine active parameter sensitivities of each active power-related characteristic parameter corresponding to each time period based on the changes in the active power;

[0102] In the embodiment of the present invention, the current execution end obtains the change of active power during multiple low voltage ride-through processes, and determines the active parameter sensitivity of each active power related characteristic parameter corresponding to each time period based on the change of active power. The active power related characteristic parameter may be the active power recovery rate k recorded in step 101. p and the initial value of the active current of the converter during steady state I p0 The specific method for determining the active parameter sensitivity includes the following steps:

[0103] (1) respectively obtaining the active power change amount and the active power steady-state value of the active power in three time periods before the fault, during the fault, and after the fault; and obtaining the initial value and parameter change amount of the active power-related characteristic parameter in three time periods before the fault, during the fault, and after the fault;

[0104] (2) Determine the sensitivity value of the active power related characteristic parameter in each time period based on the active power change, the active power steady-state value, the initial value of the active power related characteristic parameter and the parameter change. The specific formula is as follows:

[0105]

[0106] Among them, S θi is the i-th characteristic parameter to be identified at time t θ iThe kth sensitivity of i Specifically, it is active power related characteristic parameters, such as I p0 , k p etc. k represents the kth active power observation value; Δθ i is the active power related characteristic parameter θ i The change in θ i0 is the active power related characteristic parameter θ i The initial value of y k0 is the steady-state value of the active power observable.

[0107] (3) Calculate the average value of the sensitivity value of the active power-related characteristic parameter in each time period during multiple low voltage ride-throughs. The specific formula is as follows:

[0108]

[0109] Among them, A Sθ is the average sensitivity value; K is the total number of sensitivities. In the embodiment of the present invention, the active parameter sensitivity of the active power-related characteristic parameter corresponding to the three time periods before the fault, during the fault, and after the fault is calculated by the above formula. For example, the active parameter sensitivity of the active power-related characteristic parameter corresponding to the three time periods before the fault, during the fault, and after the fault is obtained. p0 , k p The active parameter sensitivity is not specifically limited in this embodiment of the present invention.

[0110] 202. Obtain changes in the reactive power during multiple low voltage ride-through processes, and determine reactive parameter sensitivities of each reactive power-related characteristic parameter corresponding to each time period based on the changes in the reactive power.

[0111] In the embodiment of the present invention, the current execution end obtains the change of the reactive power during multiple low voltage ride-through processes, and determines the reactive parameter sensitivity of each reactive power-related characteristic parameter corresponding to each time period based on the change of the reactive power. The reactive power-related characteristic parameter may be the reactive current support coefficient K during the fault period recorded in step 101. Q , the initial value of the reactive current of the converter during steady state I q0 , reactive current recovery rate k q The specific method for determining the reactive parameter sensitivity includes the following steps:

[0112] (1) respectively obtaining the reactive power variation and the reactive power steady-state value of the reactive power in three time periods before the fault, during the fault, and after the fault; and obtaining the initial value and parameter variation of the reactive power-related characteristic parameter in three time periods before the fault, during the fault, and after the fault;

[0113] (2) Determine the sensitivity value of the reactive power-related characteristic parameter in each time period based on the reactive power change, the reactive power steady-state value, the initial value of the reactive power-related characteristic parameter and the parameter change. The specific formula is as follows:

[0114]

[0115] in, is the i-th characteristic parameter to be identified at time t θ i The kth sensitivity of i Specifically, it is the reactive power related characteristic parameters, such as K Q ,I q0 , k q etc. k represents the kth reactive power observation value; Δθ i is the reactive power related characteristic parameter θ i The change in θ i0 is the reactive power related characteristic parameter θ i The initial value of y k0 is the steady-state value of the reactive power observable.

[0116] (4)(3) Calculate the average value of the sensitivity value of the reactive power related characteristic parameter in each time period during multiple low voltage ride-throughs. The specific formula is as follows:

[0117]

[0118] in, is the average sensitivity value; K is the total number of sensitivities. In the embodiment of the present invention, the reactive parameter sensitivity of the reactive power-related characteristic parameter corresponding to the three time periods before the fault, during the fault, and after the fault is calculated by the above formula. For example, the K corresponding to the three time periods before the fault, during the fault, and after the fault is obtained. Q ,I q0 , k q The reactive parameter sensitivity is not specifically limited in this embodiment of the present invention.

[0119] Furthermore, as a refinement and extension of the specific implementation of the above embodiment, in order to further quantify the influence of each characteristic parameter to be identified in each time period, another method for identifying the characteristic parameters of the converter is provided, such as Figure 3As shown, the step of determining the weight coefficient of the average absolute value deviation of active power and the average absolute value deviation of reactive power in the corresponding time period based on the sensitivity corresponding to each characteristic parameter to be identified in each time period includes:

[0120] 301. Calculate the sum of active parameter sensitivities of the active power-related characteristic parameters in each time period; determine the active sensitivity proportional coefficient corresponding to each time period based on the sum of the active parameter sensitivities in each time period, and determine the active sensitivity proportional coefficient as the first weight coefficient of the active power average absolute value deviation in the corresponding time period;

[0121] In the embodiment of the present invention, the current execution end calculates the sum of the active parameter sensitivities of the active power-related characteristic parameters in each time period based on the sensitivities corresponding to the active power-related characteristic parameters in each time period obtained in step 201. p0 and k p The sum of the active parameter sensitivities is denoted as A AP ; Calculate the fault period I p0 and k p The sum of the active parameter sensitivities is denoted as A BP ; Calculate the fault after I p0 and k p The sum of the active parameter sensitivities is denoted as A CP Then, the current execution end determines the active sensitivity proportional coefficient corresponding to each time period based on the sum of the active parameter sensitivities of each time period, and determines the obtained active sensitivity proportional coefficient as the first weight coefficient of the active power average absolute value deviation in the corresponding time period. The specific formula is as follows:

[0122] a P =A AP / (A AP +A BP +A CP )

[0123] b P =A BP / (A AP +A BP +A CP )

[0124] c P =A CP / (A AP +A BP +A CP )

[0125] Among them, a P is the average absolute value deviation of active power before the fault E APThe first weight coefficient of b P is the average absolute value deviation of active power during the fault period E BP The first weight coefficient; is the average absolute value deviation of active power after the fault E CP The first weight coefficient of .

[0126] 302. Calculate the sum of reactive parameter sensitivities of the reactive power-related characteristic parameters in each time period; determine the reactive sensitivity proportional coefficient corresponding to each time period based on the sum of the reactive parameter sensitivities in each time period, and determine the reactive sensitivity proportional coefficient as the second weight coefficient of the reactive power average absolute value deviation in the corresponding time period.

[0127] In the embodiment of the present invention, the current execution end calculates the sum of the reactive parameter sensitivities of the reactive power-related characteristic parameters in each time period based on the sensitivities corresponding to the reactive power-related characteristic parameters in each time period obtained in step 202. For example, calculate the K before the fault Q ,I q0 and k q The sum of the reactive parameter sensitivities is denoted as A AQ ; Calculate the fault period K Q ,I q0 and k q The sum of the reactive parameter sensitivities is denoted as A BQ ; Calculate K after failure Q ,I q0 and k q The sum of the reactive parameter sensitivities is denoted as A CQ The embodiment of the present invention does not make specific limitations. Then the current execution end determines the reactive sensitivity proportional coefficient corresponding to each time period based on the sum of the reactive parameter sensitivities of each time period, and determines the obtained reactive sensitivity proportional coefficient as the second weight coefficient of the reactive power average absolute value deviation in the corresponding time period. The specific formula is as follows:

[0128] a Q =A AQ / (A AQ +A BQ +A CQ )

[0129] b Q =A BQ / (A AQ +A BQ +A CQ )

[0130] c Q =A CQ / (A AQ +A BQ +A CQ )

[0131] Among them, a Q is the average absolute deviation of reactive power before the fault E AQ The second weight coefficient of b Q is the average absolute value deviation of reactive power during the fault period E BQ The second weight coefficient is the average absolute value deviation of reactive power after the fault E CQ The second weight coefficient of .

[0132] Furthermore, as a refinement and extension of the above-mentioned specific implementation methods, in order to further improve the accuracy of parameter identification, another method for identifying characteristic parameters of a converter is provided, such as Figure 4 As shown, the steps are based on the average absolute value deviation of the active power and the average absolute value deviation of the reactive power output by the converter in each time period, and the corresponding weight coefficient to determine the objective function, including:

[0133] 401. Perform weighted sum processing on the average absolute value deviation of the active power in each time period by using the first weight coefficient to obtain a total active power deviation;

[0134] 402. Perform weighted sum processing on the reactive power average absolute value deviation in each time period using the second weight coefficient to obtain a total reactive power deviation.

[0135] In the embodiment of the present invention, the current execution end uses the first weight coefficient to perform weighted summation processing on the average absolute value deviation of the active power in each time period to obtain a total active deviation; and uses the second weight coefficient to perform weighted summation processing on the average absolute value deviation of the reactive power in each time period to obtain a total reactive deviation. The specific formula is as follows:

[0136]

[0137] Among them, E P Indicates the total active power deviation, E Q Indicates the total reactive power deviation.

[0138] 403. Sum the total active power deviation and the total reactive power deviation to obtain a total deviation; and determine a minimum value of the total deviation as the objective function.

[0139] In the embodiment of the present invention, the current execution end calculates the total active power deviation E P and total reactive power deviation E Q Sum them up to get the total deviation E. The specific formula is as follows:

[0140] E=E P +E Q

[0141] In the embodiment of the present invention, the current execution end determines the minimum value of the total deviation E as the objective function. The smaller the value of the objective function, the better the identification effect.

[0142] Further, as a refinement and expansion of the specific implementation of the above embodiment, in order to narrow the optimization range, speed up the parameter identification efficiency, and improve the accuracy of parameter identification, another method for identifying characteristic parameters of a converter is provided, wherein the objective function is optimized and solved to obtain an identification result that satisfies the parameter identification accuracy, including:

[0143] Using the least square method to perform fitting processing on each of the characteristic parameters to be identified, and determining the optimization range of each of the characteristic parameters to be identified based on the obtained fitting results;

[0144] Based on the optimization range, the objective function is optimized and solved using the Black Kite algorithm to obtain the identification result that meets the parameter identification accuracy.

[0145] In the embodiment of the present invention, the current execution end uses the least squares method (LS) to perform fitting processing on each of the characteristic parameters to be identified based on multiple groups of test data to obtain fitting results. The fitting results can reflect the fluctuation range of each characteristic parameter to be identified in each time period. Therefore, the current execution end can determine the optimization range of each characteristic parameter to be identified based on the obtained fitting results.

[0146] Next, the current execution end uses the Black-winged kite algorithm to optimize and solve the objective function based on the above-determined optimization range of the characteristic parameters to be identified, and obtains the identification result that meets the parameter identification accuracy. Among them, the Black-winged kite algorithm is a natural heuristic optimization algorithm based on the attack strategy and migration behavior of the Black-winged kite. It solves the optimization problem by simulating the predation behavior and migration behavior of the Black-winged kite, combining the strategies of global search and local search. It should be noted that in addition to the Black-winged kite algorithm used in the embodiment of the present invention to optimize and solve the objective function, other optimization solutions can also be used to solve the objective function, such as particle swarm optimization algorithm, ant colony optimization algorithm, genetic algorithm, simulated annealing algorithm, etc., which are not specifically limited in the embodiment of the present invention.

[0147] In order to demonstrate the advantages of using the black kite algorithm to optimize the objective function in the embodiment of the present invention, the present invention first uses LS to fit the preliminary values ​​of the LVRT characteristic parameters, then sets the optimization range of each parameter, and uses the particle swarm optimization algorithm (PSO) and the black kite algorithm (BKO) to optimize the characteristic parameters of the LVRT. Taking the voltage drop of the grid connection point to 50% of the rated voltage due to a three-phase short circuit fault as an example, the optimization iteration curve of the comparison algorithm is obtained as shown in FIG. Figure 5As shown in Table 2, the parameter identification results are shown. Then, the identification results of each method in Table 2 are substituted into the energy storage model for simulation and compared with the measured data. The root mean square error between the simulation results and the measured data is shown in Table 3. The comparison of the identification curves is shown in Figure 6 shown.

[0148] Depend on Figure 5 It can be seen that the BKO algorithm has better convergence performance than the PSO algorithm. The BKO algorithm has converged when the convergence times are about 80, while the PSO algorithm has not converged when the convergence times exceed 100 times, indicating that the BKO algorithm converges faster than the traditional particle swarm algorithm. At the same time, compared with the convergence value of the PSO algorithm, the convergence value of the BKO algorithm is smaller, indicating that the BKO algorithm has higher convergence accuracy than the traditional algorithm, indicating the superiority of the method used in the present invention.

[0149] Table 2

[0150] parameter KQ Ip0 kp Iq k LS 1.891 0.873 6.871 0.258 -2.335 Optimization range (1,3) (0.5,1) (6,7) (0,0.5) (-3,-1) PSO 2.371 0.859 6.652 0.285 -2.673 BKO 2.127 0.899 6.921 0.301 -2.511

[0151] Table 3

[0152] Identification method Active power error (%) Reactive error (%) LS 7.24 4.01 PSO 5.93 2.98 BKO 4.61 1.57

[0153] Combining Table 2, Table 3 and Figure 6 It can be seen that under this condition, the preliminary parameter results obtained by LS are substituted into the model for simulation. During the low voltage ride-through period, the curve deviates greatly from the original measured active and reactive curves, and the calculated error is large. After determining the optimization range, the PSO and BKO algorithms are used to optimize the parameters. The parameter values ​​obtained by optimization are more accurate, which improves the problem of large curve deviation during the low voltage ride-through period. The simulation results are consistent with the measured data, but the identification effect obtained by using the BKO algorithm is better. The algorithm has better dynamic search capability, which enables it to effectively cope with dynamically changing low voltage ride-through conditions, thereby verifying the effectiveness and superiority of the method used in the present invention.

[0154] An embodiment of the present invention provides a method for identifying characteristic parameters of a converter. Compared with the prior art, the present invention divides the low voltage crossing of an energy storage system into three time periods: before the fault, during the fault, and after the fault, and respectively uses the active power and reactive power output by the converter as observation quantities to determine the sensitivity corresponding to each characteristic parameter to be identified in each time period, and fully considers the degree of influence of the characteristic parameters to be identified on the active power and reactive power in different time periods; by weighting the average absolute deviation between the measured power and the simulated power in each time period of the converter low voltage crossing, the influence of each characteristic parameter to be identified on the output characteristics of the converter can be weighed, thereby further improving the identification accuracy of the characteristic parameters.

[0155] As the above Figure 1 In order to realize the method shown in the figure, the embodiment of the present invention provides a device for identifying characteristic parameters of a converter, such as Figure 7 As shown, the device comprises:

[0156] A parameter determination module 51, used to determine a plurality of characteristic parameters to be identified that affect the low voltage ride-through performance based on the control principle of the low voltage ride-through of the converter;

[0157] The sensitivity determination module 52 is used to divide the low voltage ride through of the energy storage system into three time periods: before the fault, during the fault and after the fault, and respectively use the active power and reactive power output by the converter as observation quantities to determine the sensitivity corresponding to each characteristic parameter to be identified in each time period;

[0158] The weight calculation module 53 is used to calculate the average absolute value deviation of the active power and the average absolute value deviation of the reactive power output by the converter in each time period; and determine the weight coefficient of the average absolute value deviation of the active power and the average absolute value deviation of the reactive power in the corresponding time period based on the sensitivity corresponding to each characteristic parameter to be identified in each time period;

[0159] The target determination and solution module 54 is used to determine the target function based on the average absolute value deviation of the active power and the average absolute value deviation of the reactive power output by the converter in each time period, and the corresponding weight coefficient; and optimize and solve the target function to obtain an identification result that meets the parameter identification accuracy.

[0160] Further, the characteristic parameters to be identified include active power related characteristic parameters and reactive power related characteristic parameters; the sensitivity determination module 52 includes an active power determination unit and a reactive power determination unit;

[0161] The active power determination unit is used to obtain the change of the active power during multiple low voltage ride-through processes, and determine the active power parameter sensitivity of each active power-related characteristic parameter corresponding to each time period based on the change of the active power;

[0162] The reactive power determination unit is used to obtain the change of the reactive power during multiple low voltage ride-through processes, and determine the reactive parameter sensitivity of each reactive power-related characteristic parameter corresponding to each time period based on the change of the reactive power.

[0163] Furthermore, the active power determination unit is further used for:

[0164] Respectively obtain the active power change amount and the active power steady-state value of the active power in three time periods before the fault, during the fault and after the fault; and obtain the initial value and parameter change amount of the active power related characteristic parameter in three time periods before the fault, during the fault and after the fault;

[0165] Determining the sensitivity value of the active power-related characteristic parameter in each time period based on the active power change, the active power steady-state value, the initial value of the active power-related characteristic parameter and the parameter change;

[0166] The average value of the sensitivity value of the active power related characteristic parameter in each time period during multiple low voltage ride-throughs is calculated to obtain the active parameter sensitivity of the active power related characteristic parameter corresponding to the three time periods before the fault, during the fault and after the fault.

[0167] Furthermore, the reactive power determination unit is further used for:

[0168] Respectively obtain the reactive power change and the reactive power steady-state value of the reactive power in three time periods before the fault, during the fault and after the fault; and obtain the initial value and parameter change of the reactive power related characteristic parameter in three time periods before the fault, during the fault and after the fault;

[0169] Determine the sensitivity value of the reactive power related characteristic parameter in each time period based on the reactive power change, the reactive power steady-state value, the initial value of the reactive power related characteristic parameter and the parameter change;

[0170] The average value of the sensitivity value of the reactive power related characteristic parameter in each time period during multiple low voltage ride-throughs is calculated to obtain the reactive parameter sensitivity of the reactive power related characteristic parameter corresponding to the three time periods before the fault, during the fault and after the fault.

[0171] Furthermore, the weight calculation module 53 is also used for:

[0172] Calculating the sum of the active parameter sensitivities of the active power-related characteristic parameters in each time period; and determining the active sensitivity proportional coefficient corresponding to each time period based on the sum of the active parameter sensitivities in each time period, and determining the active sensitivity proportional coefficient as the first weight coefficient of the active power average absolute value deviation in the corresponding time period;

[0173] Calculate the sum of the reactive parameter sensitivities of the reactive power-related characteristic parameters in each time period; and determine the reactive sensitivity proportional coefficient corresponding to each time period based on the sum of the reactive parameter sensitivities in each time period, and determine the reactive sensitivity proportional coefficient as the second weight coefficient of the reactive power average absolute value deviation in the corresponding time period.

[0174] Further, the target determination and solution module 54 includes a target determination unit and a solution unit;

[0175] The target determination unit is used for:

[0176] Using the first weight coefficient to perform weighted summation processing on the average absolute value deviation of the active power in each time period to obtain a total active power deviation;

[0177] The second weight coefficient is used to perform weighted sum processing on the reactive power average absolute value deviation in each time period to obtain a total reactive power deviation;

[0178] The total active power deviation and the total reactive power deviation are summed to obtain a total deviation; and a minimum value of the total deviation is determined as the objective function.

[0179] The solving unit is used to:

[0180] Using the least square method to perform fitting processing on each of the characteristic parameters to be identified, and determining the optimization range of each of the characteristic parameters to be identified based on the obtained fitting results;

[0181] Based on the optimization range, the objective function is optimized and solved using the Black Kite algorithm to obtain the identification result that meets the parameter identification accuracy.

[0182] An embodiment of the present invention provides a device for identifying characteristic parameters of a converter. Compared with the prior art, the present invention divides the low voltage crossing of an energy storage system into three time periods: before the fault, during the fault, and after the fault, and respectively uses the active power and reactive power output by the converter as observation quantities to determine the sensitivity corresponding to each characteristic parameter to be identified in each time period, and fully considers the degree of influence of the characteristic parameters to be identified on the active power and reactive power in different time periods; by weighting the average absolute deviation between the measured power and the simulated power in each time period of the converter low voltage crossing, the influence of each characteristic parameter to be identified on the output characteristics of the converter can be weighed, thereby further improving the identification accuracy of the characteristic parameters.

[0183] According to an embodiment of the present invention, a storage medium is provided, wherein the storage medium stores at least one executable instruction, and the computer executable instruction can execute the method for identifying characteristic parameters of a converter in any of the above method embodiments.

[0184] Figure 8 A schematic diagram of the structure of a device provided according to an embodiment of the present invention is shown, and the specific embodiment of the present invention does not limit the specific implementation of the device.

[0185] like Figure 8 As shown, the device may include: a processor (processor) 602 , a communication interface (Communications Interface) 604 , a memory (memory) 606 , and a communication bus 608 .

[0186] The processor 602 , the communication interface 604 , and the memory 606 communicate with each other via a communication bus 608 .

[0187] The communication interface 604 is used to communicate with other devices such as clients or other servers.

[0188] The processor 602 is used to execute the program 610, and specifically can execute the relevant steps of the above-mentioned method for identifying characteristic parameters of the converter.

[0189] Specifically, the program 610 may include program codes, which include computer operation instructions.

[0190] The processor 602 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0191] The memory 606 is used to store the program 610. The memory 606 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0192] The program 610 may be specifically configured to enable the processor 602 to perform the following operations:

[0193] Determine multiple characteristic parameters to be identified that affect low voltage ride-through performance based on the control principle of low voltage ride-through of the converter;

[0194] The low voltage ride-through of the energy storage system is divided into three periods: before the fault, during the fault and after the fault, and the active power and reactive power output by the converter are respectively used as observation quantities to determine the sensitivity corresponding to each characteristic parameter to be identified in each period;

[0195] Calculating the average absolute value deviation of active power and the average absolute value deviation of reactive power output by the converter in each time period; and determining the weight coefficient of the average absolute value deviation of active power and the average absolute value deviation of reactive power in the corresponding time period based on the sensitivity corresponding to each characteristic parameter to be identified in each time period;

[0196] The objective function is determined based on the average absolute value deviation of the active power and the average absolute value deviation of the reactive power output by the converter in each time period, and the corresponding weight coefficient; and the objective function is optimized and solved to obtain an identification result that meets the parameter identification accuracy.

[0197] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0198] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for identifying characteristic parameters of a converter, characterized in that: include: Determine multiple characteristic parameters to be identified that affect low voltage ride-through performance based on the control principle of low voltage ride-through of the converter; The low voltage ride-through of the energy storage system is divided into three periods: before the fault, during the fault and after the fault, and the active power and reactive power output by the converter are respectively used as observation quantities to determine the sensitivity corresponding to each characteristic parameter to be identified in each period; Calculate the average absolute value deviation of active power and the average absolute value deviation of reactive power output by the converter in each time period; and determining the weight coefficients of the average absolute value deviation of the active power and the average absolute value deviation of the reactive power in the corresponding time period based on the sensitivity corresponding to each characteristic parameter to be identified in each time period; The objective function is determined based on the average absolute value deviation of the active power and the average absolute value deviation of the reactive power output by the converter in each time period, and the corresponding weight coefficient; and the objective function is optimized and solved to obtain an identification result that meets the parameter identification accuracy.

2. The method according to claim 1, characterized in that The characteristic parameters to be identified include active power related characteristic parameters and reactive power related characteristic parameters; The active power and reactive power output by the converter are respectively used as observation quantities to determine the sensitivity corresponding to the characteristic parameter to be identified in each time period, including: Obtaining changes in the active power during multiple low voltage ride-through processes, and determining active parameter sensitivities of each active power-related characteristic parameter corresponding to each time period based on the changes in the active power; The change of the reactive power during multiple low voltage ride-through processes is obtained, and the reactive parameter sensitivity of each reactive power-related characteristic parameter corresponding to each time period is determined based on the change of the reactive power.

3. The method according to claim 2, characterized in that The obtaining of the change of the active power during multiple low voltage ride-through processes, and determining the active parameter sensitivity of each active power-related characteristic parameter corresponding to each time period based on the change of the active power, includes: Respectively obtain the active power change amount and the active power steady-state value of the active power in three time periods before the fault, during the fault and after the fault; and obtain the initial value and parameter change amount of the active power related characteristic parameter in three time periods before the fault, during the fault and after the fault; Determining the sensitivity value of the active power-related characteristic parameter in each time period based on the active power change, the active power steady-state value, the initial value of the active power-related characteristic parameter and the parameter change; The average value of the sensitivity value of the active power related characteristic parameter in each time period during multiple low voltage ride-throughs is calculated to obtain the active parameter sensitivity of the active power related characteristic parameter corresponding to the three time periods before the fault, during the fault and after the fault.

4. The method according to claim 1, characterized in that: The obtaining of the change of the reactive power during multiple low voltage ride-through processes, and determining the reactive parameter sensitivity of each reactive power-related characteristic parameter corresponding to each time period based on the change of the reactive power, includes: Respectively obtain the reactive power change and the reactive power steady-state value of the reactive power in three time periods before the fault, during the fault and after the fault; and obtain the initial value and parameter change of the reactive power related characteristic parameter in three time periods before the fault, during the fault and after the fault; Determine the sensitivity value of the reactive power related characteristic parameter in each time period based on the reactive power change, the reactive power steady-state value, the initial value of the reactive power related characteristic parameter and the parameter change; The average value of the sensitivity value of the reactive power related characteristic parameter in each time period during multiple low voltage ride-throughs is calculated to obtain the reactive parameter sensitivity of the reactive power related characteristic parameter corresponding to the three time periods before the fault, during the fault and after the fault.

5. The method according to claim 2, characterized in that: The step of determining the weight coefficients of the average absolute value deviation of active power and the average absolute value deviation of reactive power in the corresponding time period based on the sensitivity corresponding to each characteristic parameter to be identified in each time period includes: Calculating the sum of the active parameter sensitivities of the active power-related characteristic parameters in each time period; and determining the active sensitivity proportional coefficient corresponding to each time period based on the sum of the active parameter sensitivities in each time period, and determining the active sensitivity proportional coefficient as the first weight coefficient of the active power average absolute value deviation in the corresponding time period; Calculate the sum of the reactive parameter sensitivities of the reactive power-related characteristic parameters in each time period; and determine the reactive sensitivity proportional coefficient corresponding to each time period based on the sum of the reactive parameter sensitivities in each time period, and determine the reactive sensitivity proportional coefficient as the second weight coefficient of the reactive power average absolute value deviation in the corresponding time period.

6. The method according to claim 5, characterized in that The objective function is determined based on the average absolute value deviation of the active power and the average absolute value deviation of the reactive power output by the converter in each time period, and the corresponding weight coefficient, including: Using the first weight coefficient to perform weighted summation processing on the average absolute value deviation of the active power in each time period to obtain a total active power deviation; The second weight coefficient is used to perform weighted sum processing on the reactive power average absolute value deviation in each time period to obtain a total reactive power deviation; The total active power deviation and the total reactive power deviation are summed to obtain a total deviation; and a minimum value of the total deviation is determined as the objective function.

7. The method according to any one of claims 1 to 6, characterized in that: The optimizing and solving the objective function to obtain an identification result satisfying the parameter identification accuracy includes: Using the least square method to perform fitting processing on each of the characteristic parameters to be identified, and determining the optimization range of each of the characteristic parameters to be identified based on the obtained fitting results; Based on the optimization range, the objective function is optimized and solved using the Black Kite algorithm to obtain the identification result that meets the parameter identification accuracy.

8. A device for identifying characteristic parameters of a converter, characterized in that: include: A parameter determination module, used to determine a plurality of characteristic parameters to be identified that affect the low voltage ride-through performance based on the control principle of the low voltage ride-through of the converter; A sensitivity determination module is used to divide the low voltage ride through of the energy storage system into three time periods: before the fault, during the fault and after the fault, and respectively use the active power and reactive power output by the converter as observation quantities to determine the sensitivity corresponding to each characteristic parameter to be identified in each time period; A weight calculation module, used to calculate the average absolute value deviation of active power and the average absolute value deviation of reactive power output by the converter in each time period; and determining the weight coefficients of the average absolute value deviation of the active power and the average absolute value deviation of the reactive power in the corresponding time period based on the sensitivity corresponding to each characteristic parameter to be identified in each time period; The target determination and solution module is used to determine the objective function based on the average absolute value deviation of the active power and the average absolute value deviation of the reactive power output by the converter in each time period, and the corresponding weight coefficient; and optimize and solve the objective function to obtain an identification result that meets the parameter identification accuracy.

9. A storage medium, wherein at least one executable instruction is stored in the storage medium, and the executable instruction executes an operation corresponding to the method for identifying characteristic parameters of a converter according to any one of claims 1 to 7.

10. A device comprising a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the method for identifying characteristic parameters of a converter according to any one of claims 1 to 7.

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