Energy storage converter performance evaluation method and evaluation device

Abstract

The invention provides an energy storage converter performance evaluation method and device, and the method comprises the steps: obtaining a plurality of criterion layer indexes for evaluating the performance of an energy storage converter, and determining a plurality of factor layer indexes according to the plurality of criterion layer indexes, the criterion layer indexes being first-level indexes affecting the performance, and the factor layer indexes being second-level indexes affecting the performance; the factor layer index is a secondary index influencing the criterion layer index; determining the relative importance of each factor layer index in performance evaluation, and obtaining a comprehensive weight matrix; establishing a scoring function of the factor layer indexes, wherein the scoring function is used for scoring the index data of each factor layer index; according to the scoring function and the comprehensive weight matrix, calculating to obtain a final score of each factor layer index; and determining the performance of the energy storage converter according to the final score. According to the method, the problem that the energy storage converter cannot be maintained in time due to inaccurate performance evaluation of the energy storage converter in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent evaluation, and in particular to a method and device for evaluating the performance of an energy storage converter. Background Art

[0002] In the power system, the development of grid-type energy storage inverters is a key technology to promote the integration of renewable energy and grid modernization. At present, the systematic evaluation technology for energy storage inverters is still in an exploratory stage, and a set of systematic energy storage inverter test and evaluation methods has not yet been proposed. Grid-type energy storage inverters can stabilize the voltage and frequency of the power grid and significantly improve the ability of the power grid to respond to emergencies. The popularization of grid-type energy storage inverters will significantly increase the demand for evaluation methods.

[0003] At present, the evaluation methods for the operating status of new energy units are becoming increasingly diversified, mainly divided into two categories: model-based evaluation and weight calculation-based evaluation. In terms of model evaluation, the physical element analysis theory or neural network model is usually used to evaluate the operating status. This type of method requires a large amount of data to train the model. Although it can provide accurate evaluation results, it faces the problem of difficulty in obtaining data. On the other hand, the evaluation method based on weight calculation focuses more on the systematic construction and weight quantification of evaluation indicators. The hierarchical analysis method relies too much on subjective judgment, which may lead to consistency problems. The CRITIC method ignores the internal logical connection of the evaluation indicators and has a high dependence on data. If the data is incomplete or of low quality, it may affect the accurate calculation of the weight and the reliability of the evaluation results. That is, when evaluating the performance of the energy storage converter, there are problems of inaccurate and unreliable data, which also leads to the inability to timely maintain the energy storage converter with performance problems. Summary of the invention

[0004] The main purpose of the present application is to provide a method and device for evaluating the performance of an energy storage converter, so as to at least solve the problem in the prior art that the energy storage converter cannot be maintained in time due to inaccurate performance evaluation of the energy storage converter.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for evaluating the performance of an energy storage inverter is provided, comprising: obtaining multiple criterion layer indicators for evaluating the performance of the energy storage inverter, and determining multiple factor layer indicators based on the multiple criterion layer indicators, the criterion layer indicators are primary indicators affecting the performance, and the factor layer indicators are secondary indicators affecting the criterion layer indicators; determining the relative importance of each of the factor layer indicators in the performance evaluation to obtain a comprehensive weight matrix; establishing a scoring function for the factor layer indicators, the scoring function is used to score the indicator data of each of the factor layer indicators; calculating the final score of each of the factor layer indicators based on the scoring function and the comprehensive weight matrix; and determining the performance of the energy storage inverter based on the final score.

[0006] Optionally, the relative importance of each factor layer indicator in the performance evaluation is determined to obtain a comprehensive weight matrix, including: calculating a first weight matrix based on a judgment matrix, the judgment matrix is ​​used to describe the relative importance between each factor layer indicator, and the first weight matrix is ​​a normalized representation of the eigenvector of each element in the judgment matrix; using the CRITIC method to calculate a second weight matrix based on the contrast strength and conflict of each factor layer indicator, the contrast strength is used to describe the difference of each factor layer indicator, the conflict is used to describe the independence of each factor layer indicator, and the second weight matrix is ​​the relative importance of the contrast strength and the conflict of each factor layer indicator.

[0007] Optionally, calculating the first weight matrix according to the judgment matrix includes: establishing the judgment matrix Among them, c ij For x i Relative to x j The importance of x i is the ith indicator in each of the factor layer indicators, and n is the total number of the factor layer indicators; the first intermediate matrix B is calculated according to the judgment matrix = [b 1 ,b 2 ,…,b n ],in, The first intermediate matrix is ​​used to describe the preliminary relative importance of each factor layer indicator; the first weight matrix W is calculated according to the intermediate matrix = [ω 1 ,ω 2 ,…,ω n ],in

[0008] Optionally, the second weight matrix is ​​calculated based on the comparison strength and conflict of each factor layer indicator using the CRITIC method, including: establishing an evaluation matrix Wherein, m is the number of groups of statistical energy storage converter index data, n is the number of factor layer indexes in each group of energy storage converter index data, and x is ij is the score value of the jth factor layer indicator in the i-th group of energy storage converter indicator data after standardized processing; the standard deviation of each factor layer indicator is calculated according to the evaluation matrix in, is the mean, S j is the standard deviation, and the standard deviation is determined as the comparison strength of each factor layer indicator; the correlation between each factor layer indicator is calculated according to the Pearson correlation coefficient The conflict is obtained by calculating the correlation The second intermediate matrix C is calculated based on the comparison strength and the conflict. 1 ,c 2 ,…,c n ], where C j =S j f j The second intermediate matrix is ​​used to describe the preliminary relative correlation between the indicators of each factor layer; the second weight matrix K is calculated according to the second intermediate matrix = [k 1 ,k 2 ,…,k n ],in,

[0009] Optionally, the comprehensive weight matrix is ​​calculated based on the first weight matrix and the second weight matrix, including: calculating an intermediate weight matrix T=αW+(1-α)K based on the first weight matrix and the second weight matrix, where α is a preference coefficient; calculating the comprehensive weight matrix based on the intermediate weight matrix

[0010] Optionally, establishing a scoring function for the factor layer indicators comprises: when the number of indicators of each factor layer indicator obtained does not reach a predetermined value, establishing an initial scoring function according to the indicator characteristics of each factor layer indicator, the initial scoring function comprising a first initial scoring function and a second initial scoring function, the first initial scoring function being used to calculate the score of the smaller the better indicator, the smaller the better indicator being an indicator with a smaller value and a higher performance among each factor layer indicator, and the second initial scoring function being used to calculate the score of the larger the better indicator, the larger the better indicator being an indicator with a larger value and a higher performance among each factor layer indicator; when the number of indicators of each factor layer indicator obtained reaches a predetermined value, establishing a later scoring function according to the normal distribution principle, the later scoring function comprising a first later scoring function and a second later scoring function, the first later scoring function being used to calculate the score of the smaller the better indicator, and the second later scoring function being used to calculate the score of the larger the better indicator; substituting the indicator data of each factor layer indicator into the corresponding scoring function to calculate the score of each factor layer indicator.

[0011] Optionally, an initial scoring function is established according to the indicator characteristics of each factor layer indicator, wherein the initial scoring function includes a first initial scoring function and a second initial scoring function, including: establishing the first initial scoring function Among them, x 1 is the indicator data of the smaller the better indicator, a is the preset maximum value of the smaller the better indicator, y 1 The smaller the better the score of the indicator; establish the second initial scoring function Among them, x 2 is the indicator data of the larger the better indicator, b is the preset minimum value of the larger the better indicator, y 2 The larger the better the score of the indicator.

[0012] Optionally, a post-scoring function is established according to the normal distribution principle, including: obtaining indicator data of multiple target indicators to obtain a target data set, wherein the target indicator is any one of the factor layer indicators; arranging the indicator data in the target data set in ascending order to obtain a sorted data set; dividing the sorted data set into five intervals, and establishing a first post-scoring function Among them, d is the maximum value of the limit, x is the indicator data of the factor layer indicator, D min is the minimum value in the sorted data set, D 5％ 5% means less than or equal to the D 5％ The number of the indicator data accounts for 5% of the total number of the indicator data in the sorted data set, D 25％ 25% means less than or equal to the D 25％ The number of the indicator data accounts for 25% of the total number of the indicator data in the sorted data set, D 75％ 75% is less than or equal to the D 75％ The number of the indicator data accounts for 75% of the total number of the indicator data in the sorted data set, D 95％ 95% is less than or equal to the D 95％ The number of the indicator data accounts for 95% of the total number of the indicator data in the sorted data set, and y is the score of the target indicator; establish a second post-scoring function Among them, c is the minimum value of the limit, D max is the maximum value in the sorted data set.

[0013] Optionally, the final score of each of the factor layer indicators is calculated according to the scoring function and the comprehensive weight matrix, including: substituting the indicator data of each of the factor layer indicators into the corresponding scoring function to obtain the initial score of each of the factor layer indicators; multiplying each of the initial scores with the corresponding weight of each of the factor layer indicators in the comprehensive weight matrix to obtain the weighted score of each of the factor layer indicators; and summing up the weighted scores of each of the factor layer indicators to obtain the final score of the energy storage inverter.

[0014] According to another aspect of the present application, a performance evaluation device for an energy storage inverter is provided, comprising: a first acquisition unit, used to acquire multiple criterion layer indicators for evaluating the performance of the energy storage inverter, and determine multiple factor layer indicators based on the multiple criterion layer indicators, the criterion layer indicators are primary indicators affecting the performance, and the factor layer indicators are secondary indicators affecting the criterion layer indicators; a first determination unit, used to determine the relative importance of each of the factor layer indicators in the performance evaluation to obtain a comprehensive weight matrix; a first establishment unit, used to establish a scoring function for the factor layer indicators, and the scoring function is used to score the indicator data of each of the factor layer indicators; a first calculation unit, used to calculate the final score of each of the factor layer indicators based on the scoring function and the comprehensive weight matrix; and a second determination unit, used to determine the performance of the energy storage inverter based on the final score.

[0015] Applying the technical solution of the present application, in the above-mentioned method for evaluating the performance of an energy storage inverter, the method includes: obtaining multiple criterion-level indicators for evaluating the performance of the energy storage inverter, and determining multiple factor-level indicators based on the multiple criterion-level indicators, the above-mentioned criterion-level indicators are primary indicators that affect the above-mentioned performance, and the above-mentioned factor-level indicators are secondary indicators that affect the above-mentioned criterion-level indicators; determining the relative importance of each of the above-mentioned factor-level indicators in the performance evaluation to obtain a comprehensive weight matrix; establishing a scoring function for the above-mentioned factor-level indicators, the above-mentioned scoring function is used to score the indicator data of each of the above-mentioned factor-level indicators; calculating the final score of each of the above-mentioned factor-level indicators based on the above-mentioned scoring function and the above-mentioned comprehensive weight matrix; and determining the performance of the above-mentioned energy storage inverter based on the above-mentioned final score. The present application determines the factor layer indicators that affect the performance of the energy storage inverter, and calculates the comprehensive weight matrix to perform weight calculation on the relative importance of each factor layer indicator in the performance evaluation, and at the same time establishes a scoring function to score each factor layer indicator. Finally, the final score of the energy storage inverter is obtained through the score of each factor layer indicator and the weight matrix calculation. The performance of the energy storage inverter is judged by the final score, so as to optimize and adjust the energy storage inverter and achieve the expected scoring effect, thereby solving the problem in the prior art that the energy storage inverter cannot be maintained in time due to inaccurate performance evaluation of the energy storage inverter. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A hardware structure block diagram of a mobile terminal according to a method for evaluating performance of an energy storage converter provided in an embodiment of the present application is shown;

[0017] Figure 2 A schematic flow chart of a method for evaluating performance of an energy storage converter provided according to an embodiment of the present application is shown;

[0018] Figure 3An evaluation system diagram of an energy storage converter performance evaluation method provided according to an embodiment of the present application is shown;

[0019] Figure 4 shows a scoring function image of a phase angle change response time index provided according to an embodiment of the present application;

[0020] Figure 5 A structural block diagram of a device for evaluating performance of an energy storage converter provided according to an embodiment of the present application is shown;

[0021] The above drawings include the following reference numerals:

[0022] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0023] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] As introduced in the background technology, in the prior art, when evaluating the performance of an energy storage inverter, there are problems of inaccurate and unreliable data. To solve this technical problem, an embodiment of the present application provides an energy storage inverter performance evaluation method and evaluation device.

[0026] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0027] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a method for evaluating performance of an energy storage converter according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is for illustration only and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0028] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as a computer program corresponding to a method for evaluating the performance of an energy storage converter in an embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific network examples may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] In this embodiment, a method for evaluating the performance of an energy storage converter that runs on a mobile terminal, a computer terminal, or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0030] Figure 2 FIG. 1 is a flow chart of a method for evaluating the performance of an energy storage converter according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0031] Step S201, obtaining a plurality of criterion layer indicators for evaluating the performance of the energy storage converter, and determining a plurality of factor layer indicators according to the plurality of criterion layer indicators, wherein the criterion layer indicators are primary indicators affecting the performance, and the factor layer indicators are secondary indicators affecting the criterion layer indicators;

[0032] Specifically, obtaining multiple criterion-level indicators for evaluating the performance of energy storage inverters and determining multiple factor-level indicators based on these criterion-level indicators is to comprehensively evaluate the performance of energy storage inverters. Criterion-level indicators are usually the main primary indicators that affect the performance of energy storage inverters, while factor-level indicators are secondary indicators that affect criterion-level indicators. By evaluating these criterion-level indicators and factor-level indicators, it can help analyze the performance characteristics, advantages and disadvantages of energy storage inverters, and provide a reference for subsequent performance optimization and improvement.

[0033] It should be noted that the above-mentioned criterion layer indicators include overload capacity, phase angle jump adaptability, active power control capability, reactive power control capability, inertia support capability, primary frequency regulation, damping control, voltage regulation, voltage adaptability, frequency adaptability, power quality, grid-connected and off-grid switching time and black start evaluation indicators.

[0034] The above-mentioned factor layer indicators include overload continuous operation time, phase angle change response time, power control charge and discharge response time, power control charge and discharge adjustment time, power control charge and discharge conversion time, active power deviation, time to reach maximum charge and discharge power, power factor adjustable range, reactive power control deviation, inertia response time, inertia time constant, primary frequency modulation response time, primary frequency modulation active power deviation, damping suppression of grid oscillation capability, voltage step response time, voltage adjustable range, frequency range under charge and discharge, frequency deviation, voltage deviation, three-phase imbalance, voltage fluctuation, voltage flicker, harmonic content, off-grid to grid-connected switching time, grid-connected to off-grid switching time, black start capability and black start time. Among them, the second weight cannot be calculated for some factor layer indicators. The factor layer indicators for which the second weight cannot be calculated are power factor adjustable range, grid oscillation control capability, charge and discharge frequency range and black start capability.

[0035] Step S202, determining the relative importance of each of the above factor layer indicators in performance evaluation to obtain a comprehensive weight matrix;

[0036] Specifically, determining the relative importance of factor layer indicators in performance evaluation and obtaining a comprehensive weight matrix is ​​to quantitatively analyze the degree of influence of each factor layer indicator on the performance of the energy storage converter and to help determine the weight ratio of each factor layer indicator in performance evaluation. By determining the relative importance of each factor layer indicator, a more objective and scientific basis can be provided for performance evaluation, thereby improving the accuracy of performance evaluation.

[0037] Step S203, establishing a scoring function for the above factor layer indicators, wherein the above scoring function is used to score the indicator data of each of the above factor layer indicators;

[0038] Specifically, the scoring function of the factor layer indicators is established to score the indicator data of each factor layer indicator in order to quantitatively analyze the influence of each factor layer indicator on the performance of the energy storage converter. The scoring function can convert the indicator data of each factor layer indicator into a specific scoring value in order to compare and analyze the differences in the performance impact of each factor layer indicator, thereby helping system operators to better understand the contribution and importance of each factor layer indicator.

[0039] Step S204, calculating the final score of each of the above factor layer indicators according to the above scoring function and the above comprehensive weight matrix;

[0040] Specifically, the final score of each factor layer indicator is calculated using the scoring function and the comprehensive weight matrix in order to comprehensively consider the impact of each factor layer indicator on the performance of the energy storage converter and quantitatively evaluate the importance and contribution of each factor layer indicator. Through this weighted calculation method, the score of the factor layer indicator can be obtained more scientifically.

[0041] Step S205, determining the performance of the energy storage converter according to the final score.

[0042] Specifically, determining the performance of the energy storage inverter based on the final score is to comprehensively evaluate the performance of the energy storage inverter in various aspects, so as to judge its overall performance level and determine the performance of the energy storage inverter. This can help system operators formulate corresponding improvement plans and measures to improve the performance and reliability of the energy storage inverter.

[0043] Through this embodiment, in the above-mentioned energy storage inverter performance evaluation method, multiple criterion layer indicators for evaluating the performance of the energy storage inverter are obtained, and multiple factor layer indicators are determined based on the multiple criterion layer indicators, the above-mentioned criterion layer indicators are primary indicators affecting the above-mentioned performance, and the above-mentioned factor layer indicators are secondary indicators affecting the above-mentioned criterion layer indicators; the relative importance of each of the above-mentioned factor layer indicators in the performance evaluation is determined to obtain a comprehensive weight matrix; a scoring function for the above-mentioned factor layer indicators is established, and the above-mentioned scoring function is used to score the indicator data of each of the above-mentioned factor layer indicators; the final score of each of the above-mentioned factor layer indicators is calculated according to the above-mentioned scoring function and the above-mentioned comprehensive weight matrix; the performance of the above-mentioned energy storage inverter is determined according to the above-mentioned final score. The present application determines the factor layer indicators that affect the performance of the energy storage inverter, and calculates the comprehensive weight matrix to perform weight calculation on the relative importance of each factor layer indicator in the performance evaluation, and at the same time establishes a scoring function to score each factor layer indicator. Finally, the final score of the energy storage inverter is obtained through the score of each factor layer indicator and the weight matrix calculation. The performance of the energy storage inverter is judged by the final score, so as to optimize and adjust the energy storage inverter and achieve the expected scoring effect, thereby solving the problem in the prior art that the energy storage inverter cannot be maintained in time due to inaccurate performance evaluation of the energy storage inverter.

[0044] In order to obtain a comprehensive weight matrix, in an optional implementation, the relative importance of each of the above factor layer indicators in the performance evaluation is determined to obtain a comprehensive weight matrix. The above step S202 includes:

[0045] Step S2021, calculating a first weight matrix according to a judgment matrix, wherein the judgment matrix is ​​used to describe the relative importance between the indicators of each factor layer, and the first weight matrix is ​​a normalized representation of the eigenvector of each element in the judgment matrix;

[0046] Specifically, by calculating the judgment matrix and eigenvector, the importance ratio of each factor layer indicator can be determined. The first weight matrix provides the weight distribution of each factor layer indicator based on the judgment matrix, which helps system operators to have a more comprehensive understanding of the contribution of each factor layer indicator.

[0047] Step S2022, using the CRITIC method to calculate the second weight matrix according to the contrast strength and conflict of each of the above factor layer indicators, the contrast strength is used to describe the difference of each of the above factor layer indicators, the conflict is used to describe the independence of each of the above factor layer indicators, and the second weight matrix is ​​the relative importance of the contrast strength and conflict of each of the above factor layer indicators;

[0048] Specifically, the second weight matrix is ​​calculated by the CRITIC method in order to comprehensively consider the contrast intensity and conflict of indicators at each factor layer, so as to quantitatively determine the relative importance of indicators at each factor layer. The contrast intensity describes the degree of difference between indicators at each factor layer, while the conflict describes the independence between indicators at each factor layer. The second weight matrix comprehensively considers these two factors to provide a more comprehensive evaluation basis for system operators.

[0049] Step S2023, calculating the comprehensive weight matrix based on the first weight matrix and the second weight matrix.

[0050] Specifically, the purpose of calculating the comprehensive weight matrix using the first weight matrix and the second weight matrix is ​​to comprehensively consider the weight ratio of the indicators of each factor layer, so as to determine the relative importance of each factor in the performance of the energy storage converter.

[0051] In order to obtain the first weight matrix, in an optional implementation, the first weight matrix is ​​calculated according to the judgment matrix, and the above step S2021 includes:

[0052] Step S20211, establish the above judgment matrix Among them, c ij For x i Relative to x j The importance of x i is the i-th indicator in each of the above factor layer indicators, and n is the total number of the above factor layer indicators;

[0053] Specifically, the judgment matrix is ​​created to compare the relative importance of indicators at each factor level and to evaluate their contribution to the performance of the energy storage converter. Through the judgment matrix, the importance relationship between indicators at each factor level can be established, providing a basis for subsequent weight calculation and performance evaluation.

[0054] Step S20212: Calculate the first intermediate matrix B based on the above judgment matrix = [b 1 ,b 2 ,…,b n ],in, The first intermediate matrix is ​​used to describe the preliminary relative importance of each of the above factor layer indicators;

[0055] Specifically, the first intermediate matrix is ​​calculated based on the judgment matrix to describe the preliminary relative importance of indicators at each factor layer. This preliminary evaluation helps provide a basis for the subsequent calculation of the first weight matrix.

[0056] Step S20213, calculate the first weight matrix W=[ω 1 ,ω 2 ,…,ω n],in

[0057] Specifically, the first weight matrix is ​​obtained through the intermediate matrix to describe the relative importance of each factor layer indicator. The first weight matrix reflects the weight ratio of each factor layer indicator in the performance evaluation of the energy storage converter, helping system operators to more accurately understand the contribution of each factor layer indicator.

[0058] In order to obtain the second weight matrix, in an optional implementation, the second weight matrix is ​​calculated according to the comparison strength and conflict of each of the above factor layer indicators using the CRITIC method, and the above step S2022 includes:

[0059] Step S20221, establish an evaluation matrix Among them, m is the number of groups of statistical energy storage converter index data, n is the number of factor layer indicators in each group of the above energy storage converter index data, x ij is the score value of the j-th factor layer index in the i-th group of energy storage converter index data after standardized processing;

[0060] Specifically, the evaluation matrix is ​​established to count and evaluate the index data of the energy storage inverter, and the factor layer indicators in each group of data are standardized and converted into a matrix form. Through the evaluation matrix, the index data of different groups of energy storage inverters can be compared more intuitively, and the differences and patterns between the data can be discovered.

[0061] Step S20222: Calculate the standard deviation of each of the above factor layer indicators based on the above evaluation matrix in, is the mean, S j is the standard deviation, and the standard deviation is determined as the comparison intensity of the indicators of each factor layer;

[0062] Specifically, the evaluation matrix is ​​used to calculate the mean of the indicators at each factor layer, and then the standard deviation is calculated through the mean to determine the differences between the indicators at each factor layer, that is, the contrast intensity. The standard deviation is a statistical indicator that measures the degree of dispersion of the values ​​in the data set. The calculated standard deviation can reflect the degree of difference between the indicators at each factor layer.

[0063] Step S20223, calculate the correlation between the above factor layer indicators based on the Pearson correlation coefficient

[0064]

[0065] Specifically, by using the Pearson correlation coefficient, the method of the present invention can quantify the degree of correlation between the indicators of each factor layer of the energy storage converter, realize the accurate evaluation of the internal connection of the indicator system, and ensure the rationality of the weight distribution.

[0066] Step S20224, calculate the conflict according to the above correlation

[0067] Specifically, conflict calculation is a key step in this method. It is based on the correlation analysis of factor-level indicators and aims to identify and quantify the conflicts or contradictions that may arise from different indicators during the evaluation process. In the performance evaluation of energy storage inverters, different performance indicators may compete with each other to some extent. Through this method, the weights can be adjusted more finely to avoid single or one-sided emphasis on a certain performance indicator during the evaluation, while ignoring the possible negative impact on the overall system performance.

[0068] Step S20225: Calculate the second intermediate matrix C = [c 1 ,c 2 ,…,c n ], where C j =S j f j , the above second intermediate matrix is ​​used to describe the preliminary relative correlation between the indicators of each of the above factor layers;

[0069] Specifically, by integrating the comparative intensity and conflict analysis of factor-level indicators, a second intermediate matrix was constructed to intuitively display the preliminary relative importance and mutual influence of various performance indicators of the energy storage converter, thus achieving an in-depth analysis of the relationship between indicators within the evaluation system.

[0070] Step S20226, calculate the second weight matrix K=[k 1 ,k 2 ,…,k n ],in,

[0071] Specifically, based on the second intermediate matrix that integrates contrast intensity and conflict analysis, the second weight matrix is ​​further refined. This matrix accurately reflects the relative importance of various indicators of the energy storage converter after considering their interactions.

[0072] In order to obtain the comprehensive weight matrix, in an optional implementation, the comprehensive weight matrix is ​​calculated according to the first weight matrix and the second weight matrix, and the step S2023 includes:

[0073] Step S20231, calculating an intermediate weight matrix T=αW+(1-α)K according to the first weight matrix and the second weight matrix, where α is a preference coefficient;

[0074] Specifically, the generation of the comprehensive weight matrix is ​​a key step in evaluating the performance of the energy storage converter. It balances the difference between the first weight matrix and the second weight matrix by adjusting the preference coefficient, ensuring that the evaluation results can better reflect the performance of the energy storage converter in a specific application environment. The value range of α is [0,1].

[0075] Step S20232, calculate the above comprehensive weight matrix based on the above intermediate weight matrix

[0076] Specifically, since the objective weights of some indicators cannot be calculated, the sum of all elements in the calculation result T is not 1, so the intermediate weight matrix T needs to be normalized to obtain the comprehensive weight matrix H.

[0077] In order to score the indicator data of the factor layer indicator, in an optional implementation, a scoring function of the factor layer indicator is established. The step S203 includes:

[0078] Step S2031, when the number of indicators of each of the above-mentioned factor layer indicators obtained does not reach a predetermined value, an initial scoring function is established according to the indicator characteristics of each of the above-mentioned factor layer indicators, the above-mentioned initial scoring function includes a first initial scoring function and a second initial scoring function, the above-mentioned first initial scoring function is used to calculate the score of the smaller the better indicator, the above-mentioned smaller the better indicator is an indicator with a smaller value and a higher performance among the above-mentioned factor layer indicators, and the above-mentioned second initial scoring function is used to calculate the score of the larger the better indicator is an indicator with a larger value and a higher performance among the above-mentioned factor layer indicators;

[0079] Specifically, when the collected performance data of the energy storage inverter was insufficient, an initial scoring function was set, including a first initial scoring function for indicators where smaller is better and a second initial scoring function for indicators where larger is better. This achieved the preliminary standard setting for scoring indicators of different properties, ensuring that even in the case of limited data, a preliminary but effective evaluation of the supporting capacity of the energy storage inverter could be made, laying the foundation for subsequent more comprehensive performance testing and evaluation.

[0080] Step S2032, when the number of indicators of each of the above-mentioned factor layer indicators reaches a predetermined value, a later scoring function is established according to the normal distribution principle, and the later scoring function includes a first later scoring function and a second later scoring function, the first later scoring function is used to calculate the score of the smaller the better indicator, and the second later scoring function is used to calculate the score of the larger the better indicator;

[0081] Specifically, when enough performance data of the energy storage inverter is collected, a post-scoring function is constructed using normal distribution theory, which is divided into a first post-scoring function for smaller but better indicators and a second post-scoring function for larger but better indicators. This implements a dynamic scoring mechanism based on actual test data to ensure that the scoring more accurately reflects the true performance of the energy storage inverter.

[0082] Step S2033: Substitute the indicator data of each of the above factor layer indicators into the corresponding scoring function to calculate and obtain the score of each of the above factor layer indicators.

[0083] Specifically, by inputting the index data of the indicators of different factor layers of the energy storage converter into the corresponding scoring function, the score of each indicator can be accurately calculated, thereby achieving the goal of quantitatively evaluating the performance of the energy storage converter.

[0084] In order to obtain an initial scoring function, in an optional implementation, an initial scoring function is established according to the indicator characteristics of each of the above factor layer indicators, the above initial scoring function includes a first initial scoring function and a second initial scoring function, and the above step S2031 includes:

[0085] Step S20311, establishing the first initial scoring function Among them, x 1 is the indicator data of the above-mentioned smaller the better indicator, a is the preset maximum value of the above-mentioned smaller the better indicator, y 1 The score for the above indicators is as small as possible;

[0086] Specifically, for those characteristics where the smaller the indicator value, the better the performance, the first initial scoring function is defined. It calculates the indicator data and the preset maximum value and converts it into an intuitive scoring value, thereby achieving rapid evaluation and quantification of the performance of the energy storage inverter in the initial stage of data accumulation.

[0087] Step S20312, establishing the second initial scoring function Among them, x 2 is the indicator data of the above-mentioned larger the better indicator, b is the preset minimum value of the above-mentioned larger the better indicator, y 2 The larger the better is the score for the above indicators.

[0088] Specifically, for those characteristics whose performance is better as the indicator value is larger, a second initial scoring function is defined. It calculates the indicator data and the preset minimum value and converts it into an intuitive scoring value, thereby realizing rapid evaluation and quantification of the performance of the energy storage inverter in the initial stage of data accumulation.

[0089] In order to obtain the later scoring function, in an optional implementation, the later scoring function is established according to the normal distribution principle, and the above step S2032 includes:

[0090] Step S20321, obtaining indicator data of multiple target indicators to obtain a target data set, wherein the target indicator is any one of the factor layer indicators;

[0091] Specifically, obtaining indicator data of multiple target indicators and forming a target data set is a basic step in establishing a later scoring function.

[0092] Step S20322, arranging the indicator data in the target data set in ascending order to obtain a sorted data set;

[0093] Specifically, each indicator in the acquired target data set is arranged in ascending order to construct an ordered sorted data set, which provides preparation for the subsequent establishment of the subsequent scoring function using the normal distribution principle, ensuring that the scoring function can accurately reflect the actual distribution of the indicator data.

[0094] Step S20323, divide the sorted data set into five intervals, and establish a first post-scoring function Among them, d is the maximum value of the limit, x is the indicator data of the above factor layer indicator, D min is the minimum value in the above sorted data set, D 5％ 5% means less than or equal to the above D 5％ The number of the above indicator data accounts for 5% of the total number of the above indicator data in the above sorted data set, D 25％ 25% means less than or equal to the above D 25％ The number of the above indicator data accounts for 25% of the total number of the above indicator data in the above sorted data set, D 75％ 75% means less than or equal to the above D 75％ The number of the above indicator data accounts for 75% of the total number of the above indicator data in the above sorted data set, D 95％ 95% is less than or equal to the above D 95％ The number of the above indicator data accounts for 95% of the total number of the above indicator data in the above sorted data set, and y is the score of the above target indicator;

[0095] Specifically, the sorted data set is divided into five statistically significant intervals, and the first post-scoring function is constructed based on these intervals. This function can dynamically evaluate the performance of the device under smaller and better indicators by comparing the indicator data with preset statistical thresholds, such as the maximum limit, the minimum value of the data set, and the values ​​of each percentage position, thereby realizing adaptive adjustment of the score. That is, as the indicator data increases, the elements in the corresponding sorted data set change, thereby realizing self-updating of the function, making the scoring function continuously approach reality and rationality. During calculation, if the calculation result is not an integer, it is rounded up.

[0096] Step S20324, establishing a second post-scoring function Among them, c is the minimum value of the limit, D max is the maximum value in the above sorted data set.

[0097] Specifically, in view of the characteristic that the larger the indicator value, the better the performance of the energy storage inverter, a second late scoring function is established. This function uses the preset minimum limit and the maximum value in the actual sorted data set as boundaries, and can accurately convert the indicator data into a performance score, thereby achieving a precise evaluation of the energy storage inverter on the "larger the better" indicator. As the indicator data increases, the elements in the corresponding sorted data set change, that is, the self-update of the function is realized, making the scoring function continuously approach reality and rationality.

[0098] In a specific embodiment, if the evaluation of the energy storage inverter is based on the evaluation results of the k groups with the best scores among the evaluated energy storage inverters, the scores of the k groups of energy storage inverters are arranged from small to large. If the score of the energy storage inverter evaluated later is greater than the minimum value of these k scores, the data of the various indicators of the group of energy storage inverters are replaced with the data of the various indicators of the energy storage inverter corresponding to the minimum value, and the various indicator data of the k energy storage inverters obtained after the replacement are arranged from small to large and divided into regions according to the normal distribution, and the piecewise linear function is recalculated.

[0099] In order to obtain the final score of the factor layer indicator, in an optional implementation, the final score of each of the above factor layer indicators is calculated according to the above scoring function and the above comprehensive weight matrix, and the above step S204 includes:

[0100] Step S2041, substituting the indicator data of each of the above factor layer indicators into the corresponding scoring function to obtain an initial score of each of the above factor layer indicators;

[0101] Specifically, the indicator data of each factor-level indicator of the energy storage inverter are respectively input into the previously constructed scoring function to generate an initial performance score for each indicator. This process realizes the direct conversion of factor-level indicators to scores, ensuring that the scoring results directly reflect the actual operating conditions of the equipment.

[0102] Step S2042, multiplying each of the above initial scores by the weight of each of the above factor layer indicators corresponding to the above comprehensive weight matrix, and calculating the weighted score of each of the above factor layer indicators;

[0103] Specifically, a weighted score for each factor-layer indicator is generated by multiplying the preliminary score of each factor-layer indicator by the corresponding weight in the pre-calculated comprehensive weight matrix. This method achieves a comprehensive consideration of the score and importance, ensuring that the evaluation results can fully reflect the true performance of the energy storage inverter in key areas.

[0104] Step S2043, calculating and summing the weighted scores of the factor layer indicators to obtain the final score of the energy storage converter.

[0105] Specifically, the comprehensive performance score of the energy storage inverter is calculated by summing up the weighted scores of all factor layer indicators. This calculation method realizes the accurate quantification of the overall performance of the energy storage inverter and ensures that the evaluation results can fully reflect the comprehensive performance of the equipment under different factor layer indicators.

[0106] In a specific embodiment, the above-mentioned energy storage converter performance evaluation method is described in detail.

[0107] Assuming that the test data of 101 energy storage converters are obtained, the following evaluation process is performed on the 101st energy storage converter based on the test data of the previous 100 energy storage converters:

[0108] like Figure 3 As shown in Figure 1, the energy storage converter evaluation problem is modeled as a hierarchical structure of a primary indicator layer and a secondary indicator layer.

[0109] Furthermore, when calculating the comprehensive weight matrix, T = αW + (1-α)K, α is the preference coefficient, and its value is 0.5.

[0110] Assuming that 100 sets of various index data of energy storage converters have been measured, the comprehensive weights are calculated based on these 100 sets of index data of energy storage converters as shown in Table 1.

[0111] Table 1

[0112]

[0113]

[0114]

[0115] Furthermore, after a large number of energy storage converter tests, based on a large amount of stored index data, a scoring function based on normal distribution was used to evaluate the 101st group of energy storage converters using a scoring function calculated under the normal distribution of the first 100 groups of data. Based on the principle of normal distribution, the index data were arranged in order from small to large, and then divided into 5 data segments of 5%, 20%, 50%, 20%, and 5% for linear scoring function calculation.

[0116] Taking the phase angle change response time indicator as an example, the scoring function of this indicator based on 100 sets of test data is: The corresponding scoring function image is as follows Figure 4 shown.

[0117] Furthermore, the scoring functions of other factor layer indicators are established based on 100 sets of test data.

[0118] The measured index data of the 101st group of energy storage converters are shown in Table 2.

[0119] Table 2

[0120]

[0121]

[0122]

[0123] Substitute the indicator data of each factor layer indicator into the scoring functions obtained by the above calculations to obtain the scores of each factor layer indicator, as shown in Table 3.

[0124] Table 3

[0125]

[0126]

[0127] Furthermore, by multiplying the score of each factor layer indicator by the corresponding weight in Table 1 and summing the scores, the comprehensive score of the energy storage inverter is 84.605 points, which can objectively and directly observe the performance of the energy storage inverter.

[0128] The embodiment of the present application also provides a device for evaluating the performance of an energy storage converter. It should be noted that the device for evaluating the performance of an energy storage converter in the embodiment of the present application can be used to execute a method for evaluating the performance of an energy storage converter provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0129] The following introduces a performance evaluation device for an energy storage converter provided in an embodiment of the present application.

[0130] Figure 5 is a structural block diagram of a device for evaluating the performance of an energy storage converter according to an embodiment of the present application. Figure 5 As shown, the device comprises:

[0131] The first acquisition unit 10 is used to acquire multiple criterion layer indicators for evaluating the performance of the energy storage inverter, and determine multiple factor layer indicators based on the multiple criterion layer indicators, the criterion layer indicators are primary indicators affecting the above performance, and the factor layer indicators are secondary indicators affecting the above criterion layer indicators; the first determination unit 20 is used to determine the relative importance of each of the above factor layer indicators in the performance evaluation and obtain a comprehensive weight matrix; the first establishment unit 30 is used to establish a scoring function for the above factor layer indicators, and the above scoring function is used to score the indicator data of each of the above factor layer indicators; the first calculation unit 40 is used to calculate the final score of each of the above factor layer indicators based on the above scoring function and the above comprehensive weight matrix; the second determination unit 50 is used to determine the performance of the above energy storage inverter based on the above final score.

[0132] Through this embodiment, the first acquisition unit is used to acquire multiple criterion layer indicators for evaluating the performance of the energy storage inverter, and determine multiple factor layer indicators based on the multiple criterion layer indicators, the above-mentioned criterion layer indicators are primary indicators affecting the above-mentioned performance, and the above-mentioned factor layer indicators are secondary indicators affecting the above-mentioned criterion layer indicators; the first determination unit is used to determine the relative importance of each of the above-mentioned factor layer indicators in the performance evaluation to obtain a comprehensive weight matrix; the first establishment unit is used to establish a scoring function for the above-mentioned factor layer indicators, and the above-mentioned scoring function is used to score the indicator data of each of the above-mentioned factor layer indicators; the first calculation unit is used to calculate the final score of each of the above-mentioned factor layer indicators according to the above-mentioned scoring function and the above-mentioned comprehensive weight matrix; the second determination unit is used to determine the performance of the above-mentioned energy storage inverter according to the above-mentioned final score. The present application determines the factor layer indicators that affect the performance of the energy storage inverter, and calculates the comprehensive weight matrix to perform weight calculation on the relative importance of each factor layer indicator in the performance evaluation, and at the same time establishes a scoring function to score each factor layer indicator. Finally, the final score of the energy storage inverter is obtained through the score of each factor layer indicator and the weight matrix calculation. The performance of the energy storage inverter is judged by the final score, so as to optimize and adjust the energy storage inverter and achieve the expected scoring effect, thereby solving the problem in the prior art that the energy storage inverter cannot be maintained in time due to inaccurate performance evaluation of the energy storage inverter.

[0133] In order to obtain a comprehensive weight matrix, in an optional implementation, the relative importance of each of the above factor layer indicators in the performance evaluation is determined to obtain a comprehensive weight matrix, and the above first determination unit includes:

[0134] A first determining subunit, configured to execute the above step S2021;

[0135] A second determining subunit, configured to execute the above step S2022;

[0136] The third determining subunit is used to execute the above step S2023.

[0137] In order to obtain the first weight matrix, in an optional implementation manner, the first weight matrix is ​​calculated according to the judgment matrix, and the first determination subunit includes:

[0138] A first determining module, used to execute the above step S20211;

[0139] A second determining module, used to execute the above step S20212;

[0140] The third determination module is used to execute the above step S20213.

[0141] In order to obtain the second weight matrix, in an optional implementation, the second weight matrix is ​​calculated according to the comparison strength and conflict of each of the above factor layer indicators using the CRITIC method, and the above second determination subunit includes:

[0142] A fourth determination module, used to execute the above step S20221;

[0143] A fifth determining module, used to execute the above step S20222;

[0144] A sixth determining module, used to execute the above step S20223;

[0145] A seventh determination module, used to execute the above step S20224;

[0146] An eighth determination module, used to execute the above step S20225;

[0147] The ninth determination module is used to execute the above step S20226.

[0148] In order to obtain the comprehensive weight matrix, in an optional implementation manner, the comprehensive weight matrix is ​​calculated according to the first weight matrix and the second weight matrix, and the third determining subunit includes:

[0149] A tenth determining module, used to execute the above step S20231;

[0150] The eleventh determination module is used to execute the above step S20232.

[0151] In order to score the indicator data of the factor layer indicator, in an optional implementation manner, a scoring function of the factor layer indicator is established, and the first establishing unit includes:

[0152] A first establishing subunit, used to execute the above step S2031;

[0153] A second establishing subunit, used to execute the above step S2032;

[0154] The third establishing subunit is used to execute the above step S2033.

[0155] In order to obtain an initial scoring function, in an optional implementation, an initial scoring function is established according to the indicator characteristics of each of the above factor layer indicators, the above initial scoring function includes a first initial scoring function and a second initial scoring function, and the above first establishing subunit includes:

[0156] A first establishing module, used to execute the above step S20311;

[0157] The second establishing module is used to execute the above step S20312.

[0158] In order to obtain the later scoring function, in an optional implementation, the later scoring function is established according to the normal distribution principle, and the second establishing subunit includes:

[0159] A third establishing module, used to execute the above step S20321;

[0160] A fourth establishing module, used to execute the above step S20322;

[0161] A fifth establishing module, used to execute the above step S20323;

[0162] The sixth establishing module is used to execute the above step S20324.

[0163] In order to obtain the final score of the factor layer indicator, in an optional implementation manner, the final score of each of the above factor layer indicators is calculated according to the above scoring function and the above comprehensive weight matrix, and the above first calculation unit includes:

[0164] A first computing subunit, configured to execute the above step S2041;

[0165] A second computing subunit, used to execute the above step S2042;

[0166] The third computing subunit is used to execute the above step S2043.

[0167] The above-mentioned energy storage converter performance evaluation device includes a processor and a memory. The above-mentioned first acquisition unit, the first determination unit, the first establishment unit, the first calculation unit and the second determination unit are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are located in different processors in the form of any combination. The processor includes a kernel, and the kernel retrieves the corresponding program unit from the memory. One or more kernels can be set, and the accuracy of the performance evaluation of the energy storage converter can be improved by adjusting the kernel parameters. The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one storage chip.

[0168] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes a method for evaluating the performance of an energy storage converter when running.

[0169] 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, 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.

[0170] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0171] 1) A performance evaluation method for an energy storage inverter of the present application determines the factor layer indicators that affect the performance of the energy storage inverter, and calculates the comprehensive weight matrix to perform weight calculation on the relative importance of each factor layer indicator in the performance evaluation, and at the same time establishes a scoring function to score each factor layer indicator. Finally, the final score of the energy storage inverter is obtained by calculating the score of each factor layer indicator and the weight matrix. The performance of the energy storage inverter is judged by the final score, so as to optimize and adjust the energy storage inverter and achieve the expected scoring effect, thereby solving the problem in the prior art that the energy storage inverter cannot be maintained in time due to inaccurate performance evaluation of the energy storage inverter.

[0172] 2) A performance evaluation device for an energy storage inverter of the present application determines factor-layer indicators that affect the performance of the energy storage inverter, and calculates a comprehensive weight matrix to perform weight calculation on the relative importance of each factor-layer indicator in the performance evaluation, and at the same time establishes a scoring function to score each factor-layer indicator. Finally, the final score of the energy storage inverter is obtained through the score of each factor-layer indicator and the weight matrix calculation. The performance of the energy storage inverter is judged by the final score, so as to optimize and adjust the energy storage inverter and achieve the expected scoring effect, thereby solving the problem in the prior art that the energy storage inverter cannot be maintained in time due to inaccurate performance evaluation of the energy storage inverter.

[0173] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for evaluating the performance of an energy storage converter, characterized in that: include: Acquire multiple criterion layer indicators for evaluating the performance of the energy storage converter, and determine multiple factor layer indicators based on the multiple criterion layer indicators, wherein the criterion layer indicators are primary indicators that affect the performance, and the factor layer indicators are secondary indicators that affect the criterion layer indicators; Determine the relative importance of each factor layer indicator in the performance evaluation and obtain a comprehensive weight matrix; Establishing a scoring function for the factor layer indicators, wherein the scoring function is used to score the indicator data of each factor layer indicator; Calculating the final score of each factor layer indicator according to the scoring function and the comprehensive weight matrix; The performance of the energy storage converter is determined according to the final score.

2. The method according to claim 1, characterized in that Determine the relative importance of each factor layer indicator in the performance evaluation and obtain a comprehensive weight matrix, including: A first weight matrix is ​​calculated according to a judgment matrix, wherein the judgment matrix is ​​used to describe the relative importance between the indicators of each factor layer, and the first weight matrix is ​​a normalized representation of the eigenvector of each element in the judgment matrix; The second weight matrix is ​​calculated according to the contrast strength and conflict of each factor layer indicator using the CRITIC method, wherein the contrast strength is used to describe the difference of each factor layer indicator, and the conflict is used to describe the independence of each factor layer indicator. The second weight matrix is ​​the relative importance of the contrast strength and conflict of each factor layer indicator. The comprehensive weight matrix is ​​calculated based on the first weight matrix and the second weight matrix.

3. The method according to claim 2, characterized in that The first weight matrix is ​​calculated according to the judgment matrix, including: Establish the judgment matrix Among them, c ij For x i Relative to x j The importance of x i is the ith indicator in each of the factor layer indicators, and n is the total number of the factor layer indicators; The first intermediate matrix B=[b1, b2, ..., b n ],in, The first intermediate matrix is ​​used to describe the preliminary relative importance of each factor layer indicator; The first weight matrix W=[ω1,ω2,…,ω n ],in 4. The method according to claim 3, characterized in that The second weight matrix is ​​calculated based on the comparison strength and conflict of the indicators of each factor layer using the CRITIC method, including: Create an evaluation matrix Wherein, m is the number of groups of statistical energy storage converter index data, n is the number of factor layer indexes in each group of energy storage converter index data, and x is ij is the score value of the j-th factor layer index in the i-th group of energy storage converter index data after standardized processing; The standard deviation of each factor layer index is calculated according to the evaluation matrix in, is the mean, S j is the standard deviation, and the standard deviation is determined as the comparison strength of each factor layer indicator; The correlation between the indicators of each factor layer is calculated according to the Pearson correlation coefficient The conflict is obtained by calculating the correlation The second intermediate matrix C=[c1, c2, ..., c n ], where C j =S j f j , the second intermediate matrix is ​​used to describe the preliminary relative correlation between the indicators of each factor layer; The second weight matrix K=[k1, k2, ..., k n ],in, 5. The method according to claim 4, characterized in that The comprehensive weight matrix is ​​calculated according to the first weight matrix and the second weight matrix, including: An intermediate weight matrix T=αW+(1-α)K is calculated according to the first weight matrix and the second weight matrix, wherein α is a preference coefficient; The comprehensive weight matrix is ​​obtained by calculating the intermediate weight matrix 6. The method according to claim 1, characterized in that Establishing a scoring function for the factor layer indicator includes: In the case that the number of indicators of each factor layer indicator obtained does not reach a predetermined value, an initial scoring function is established according to the indicator characteristics of each factor layer indicator, the initial scoring function includes a first initial scoring function and a second initial scoring function, the first initial scoring function is used to calculate the score of the smaller the better indicator, the smaller the better indicator is, the smaller the value of each factor layer indicator is, the higher the performance is, the second initial scoring function is used to calculate the score of the larger the better indicator is, the larger the value of each factor layer indicator is, the higher the performance is; When the number of indicators of each factor layer indicator obtained reaches a predetermined value, a later scoring function is established according to the normal distribution principle, wherein the later scoring function includes a first later scoring function and a second later scoring function, wherein the first later scoring function is used to calculate the score of the smaller the better indicator, and the second later scoring function is used to calculate the score of the larger the better indicator; Substitute the indicator data of each factor layer indicator into the corresponding scoring function to calculate and obtain the score of each factor layer indicator.

7. The method according to claim 6, characterized in that An initial scoring function is established according to the indicator characteristics of each factor layer indicator, wherein the initial scoring function includes a first initial scoring function and a second initial scoring function, including: Establish the first initial scoring function Wherein, x1 is the indicator data of the smaller the better indicator, a is the preset maximum value of the smaller the better indicator, and y1 is the score of the smaller the better indicator; Establish the second initial scoring function Among them, x2 is the indicator data of the larger the better indicator, b is the preset minimum value of the larger the better indicator, and y2 is the score of the larger the better indicator.

8. The method according to claim 7, characterized in that The later scoring function is established according to the normal distribution principle, including: Acquire indicator data of multiple target indicators to obtain a target data set, wherein the target indicator is any one of the factor layer indicators; Arrange the indicator data in the target data set in ascending order to obtain a sorted data set; Divide the sorted data set into five intervals and establish the first post-scoring function Among them, d is the maximum value of the limit, x is the indicator data of the factor layer indicator, D min is the minimum value in the sorted data set, D 5％ 5% means less than or equal to the D 5％ The number of the indicator data accounts for 5% of the total number of the indicator data in the sorted data set, D 25％ 25% means less than or equal to the D 25％ The number of the indicator data accounts for 25% of the total number of the indicator data in the sorted data set, D 75％ 75% is less than or equal to the D 75％ The number of the indicator data accounts for 75% of the total number of the indicator data in the sorted data set, D 95％ 95% is less than or equal to the D 95％ The number of the indicator data accounts for 95% of the total number of the indicator data in the sorted data set, and y is the score of the target indicator; Establishing the second post-scoring function Among them, c is the minimum value of the limit, D max is the maximum value in the sorted data set.

9. The method according to claim 8, characterized in that The final score of each factor layer indicator is calculated according to the scoring function and the comprehensive weight matrix, including: Substituting the indicator data of each factor layer indicator into the corresponding scoring function to obtain an initial score of each factor layer indicator; Multiplying each of the initial scores by the weight of each of the corresponding factor-layer indicators in the comprehensive weight matrix to calculate a weighted score for each of the factor-layer indicators; The weighted scores of the factor layer indicators are summed up to obtain the final score of the energy storage converter.

10. A device for evaluating the performance of an energy storage converter, characterized in that: include: A first acquisition unit is used to acquire a plurality of criterion layer indicators for evaluating the performance of the energy storage converter, and determine a plurality of factor layer indicators according to the plurality of criterion layer indicators, wherein the criterion layer indicators are primary indicators affecting the performance, and the factor layer indicators are secondary indicators affecting the criterion layer indicators; A first determination unit is used to determine the relative importance of each factor layer indicator in the performance evaluation to obtain a comprehensive weight matrix; A first establishing unit is used to establish a scoring function for the factor layer indicators, wherein the scoring function is used to score the indicator data of each factor layer indicator; A first calculation unit, used for calculating the final score of each factor layer indicator according to the scoring function and the comprehensive weight matrix; The second determining unit is used to determine the performance of the energy storage converter according to the final score.

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