Testing method and device of network-forming type energy storage converter, computer equipment, readable storage medium and program product

Abstract

The invention relates to a testing method and device for a network-forming type energy storage converter, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a plurality of control instructions of a network-forming type energy storage converter, respective parameter value ranges of a plurality of first control parameters of power grid simulation equipment and respective parameter value ranges of a plurality of second control parameters of environment simulation equipment; generating a plurality of control data sets; based on the plurality of control data sets, controlling the operation of the network-forming energy storage converter, the power grid simulation equipment and the environment simulation equipment, and based on the data acquisition equipment, obtaining a test data set; and determining a test result corresponding to the test data set based on a pre-constructed analytic hierarchy process model. By adopting the method, the performance of the network-forming type energy storage converter under different power grid working conditions can be comprehensively and accurately tested.

Description

Technical Field

[0001] This application relates to the field of energy storage converter technology, and in particular to a test method, apparatus, computer equipment, computer-readable storage medium and computer program product for a grid-type energy storage converter. Background Technology

[0002] As the global energy transition deepens, the proportion of new energy sources, such as wind power and photovoltaics, in the power system continues to increase. Power Conversion Systems (PCS), as core equipment connecting energy storage systems and the power grid, directly affect the absorption of new energy and the safe and stable operation of the power system. Grid-forming (GFM) energy storage converters, because they can simulate the inertia and voltage source characteristics of synchronous generators, can provide active voltage and frequency support to the power grid, playing a crucial role in building a new power system dominated by new energy sources.

[0003] However, grid-connected energy storage converters exhibit complex dynamic responses and frequent control mode switching under different grid operating conditions, and their stable operation in both grid-connected and off-grid modes must be considered, making testing quite difficult. Therefore, there is an urgent need for a comprehensive and accurate testing method to determine the performance of grid-connected energy storage converters under different grid operating conditions, providing a basis for the optimized design, operation and maintenance, and rational application of grid-connected energy storage converters in power systems. Summary of the Invention

[0004] Therefore, it is necessary to provide a testing method, apparatus, computer equipment, computer-readable storage medium, and computer program product for grid-type energy storage converters that can comprehensively and accurately test the performance of grid-type energy storage converters under different power grid operating conditions, in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a test method for a grid-type energy storage converter.

[0006] A main control device is applied to a testing system, which further includes multiple controlled devices, including a grid-type energy storage converter, a power grid simulation device, an environmental simulation device, and a data acquisition device. The method includes:

[0007] In response to a test request from a grid-connected energy storage converter, the system acquires multiple control commands for the grid-connected energy storage converter, parameter value ranges for multiple first control parameters of the grid simulation device, and parameter value ranges for multiple second control parameters of the environmental simulation device. The multiple control commands include at least one of grid-connected commands, off-grid switching commands, and power step commands. The multiple first control parameters include voltage parameters and frequency parameters. The voltage parameter's value range is within ±20% of AC 380 volts, and the frequency parameter's value range is within ±0.1 Hz of a target frequency value. The multiple second control parameters include temperature parameters and humidity parameters. The temperature parameter's value range is between -40°C and 85°C, and the frequency parameter's value range is within ±3% of a target relative humidity value.

[0008] The plurality of first control parameters are taken multiple times within their respective parameter value ranges to obtain multiple parameter values ​​for each of the plurality of first control parameters; and the plurality of second control parameters are taken multiple times within their respective parameter value ranges to obtain multiple parameter values ​​for each of the plurality of second control parameters.

[0009] Multiple control data sets are generated, each of the multiple control data sets including one control instruction from the multiple control instructions, one parameter value from each first control parameter, and one parameter value from each second control parameter; wherein, different control data sets include different control instructions, different parameter values ​​from at least one first control parameter, or different parameter values ​​from at least one second control parameter;

[0010] Based on the multiple control data sets, the grid-type energy storage converter, the power grid simulation device, and the environmental simulation device are controlled to operate. Based on the data acquisition device, test data sets corresponding to each of the multiple control data sets are acquired. Each test data set includes test data in multiple dimensions, including voltage and current. The sampling frequency of the data acquisition device is greater than or equal to 10 kHz, and the error in acquiring voltage and current by the data acquisition device is less than or equal to 1%.

[0011] Based on a pre-built hierarchical analysis model, weight coefficients corresponding to the test data of the multiple dimensions are determined, and based on the weight coefficients, test results corresponding to the test data set are determined.

[0012] The generation of multiple control data sets includes:

[0013] When the plurality of first control parameters and the total dimension of the plurality of first control parameters are less than or equal to 3, multiple control data sets are generated based on the full permutation combination function;

[0014] When the plurality of first control parameters and the total dimension of the plurality of first control parameters are greater than 3, a plurality of control data sets are generated based on the orthogonal design table.

[0015] In one embodiment, the plurality of first control parameters further includes load parameters, and the plurality of test data dimensions include at least one of voltage transient recovery time, power factor, and total harmonic distortion of current, wherein generating the plurality of control data sets includes:

[0016] Multiple first control data sets are generated. Each first control data set includes a target control instruction from the multiple control instructions, a parameter value of the load, a target parameter value of the first control parameter other than the load from the multiple first control parameters, and a target parameter value of each second control parameter. The different first control data sets include different load parameter values.

[0017] Multiple second control data sets are generated. Each second control data set includes one control instruction from the multiple control instructions, a target parameter value for each first control parameter, and a target parameter value for each second control parameter. Different second control data sets include different control instructions.

[0018] In one embodiment, the plurality of second control parameters further include at least one of a pH parameter and a dust concentration parameter, and the generation of the plurality of control data sets by the second control parameters further includes:

[0019] Multiple sets of third control data sets are generated. Each third control data set in each set includes a target control instruction from the multiple control instructions, a target parameter value for each first control parameter, a parameter value for a target second control parameter, and target parameter values ​​for the second control parameters other than the target second control parameter from the multiple second control parameters. Different sets of third control data sets correspond to different target second control parameters, and the parameter values ​​of the target second control parameters included in different sets of third control data sets within each set are different.

[0020] In one embodiment, the plurality of controlled devices further includes a heat dissipation device with a heat dissipation power greater than or equal to 5 kilowatts. The test data in multiple dimensions includes the temperature rise rate of the insulated gate bipolar transistor module and the performance degradation rate after 1000 thermal cycles. Based on the plurality of control data sets, the operation of the grid-type energy storage converter, the grid simulation device, and the environmental simulation device is controlled. Based on the data acquisition device, test data sets corresponding to the plurality of control data sets are acquired, including:

[0021] Based on multiple third control data sets including a target temperature value, the grid-type energy storage converter, the power grid simulation device, and the environmental simulation device are controlled to operate, and the heat dissipation device is activated; wherein, the target temperature value includes the maximum value among multiple parameter values ​​of the temperature parameter;

[0022] Based on the data acquisition device, acquire test data sets corresponding to the plurality of third control data sets respectively.

[0023] In one embodiment, the plurality of controlled devices further includes a fault simulation device, the fault response time of which is less than or equal to 10 milliseconds, and the test data of the plurality of dimensions includes at least one of mean fault-free operating time and fault type distribution data; the step of controlling the operation of the grid-type energy storage converter, the grid simulation device, and the environmental simulation device based on the plurality of control data sets, and acquiring test data sets corresponding to the plurality of control data sets based on the data acquisition device, further includes:

[0024] Based on the target control data set among the multiple control data sets, the grid-type energy storage converter, the power grid simulation device, and the environmental simulation device are controlled to operate continuously for a preset time, and the fault type simulated by the fault simulation device is switched.

[0025] Based on the data acquisition device, a set of test data corresponding to the fault type is obtained.

[0026] In one embodiment, determining weight coefficients corresponding to the test data of the multiple dimensions based on a pre-built hierarchical analysis model, and determining test results corresponding to the test data set based on the weight coefficients, includes:

[0027] Determine the set of statistical values ​​corresponding to each of the test data sets; each set of statistical values ​​includes statistical values ​​of test data from multiple dimensions;

[0028] Based on a pre-built hierarchical analysis model, a judgment matrix corresponding to the set of statistical values ​​is determined; the judgment matrix includes multiple matrix elements, each of which is used to characterize the importance of a statistical value in one dimension relative to a statistical value in another dimension.

[0029] The largest eigenvalue of the judgment matrix and the eigenvector corresponding to the largest eigenvalue are determined, and a weight matrix corresponding to the set of statistical values ​​is determined based on the largest eigenvalue and the eigenvector; the weight matrix includes multiple weight coefficients that correspond one-to-one with the statistical values ​​of multiple dimensions, and each weight coefficient is used to characterize the importance of the corresponding statistical value in the statistical values ​​of the multiple dimensions;

[0030] Based on the weight matrix, the test results corresponding to the set of statistical values ​​are determined.

[0031] Secondly, this application also provides a testing device for a grid-type energy storage converter, applied to the main control equipment of a testing system. The testing system further includes multiple controlled devices, including a grid-type energy storage converter, a power grid simulation device, an environmental simulation device, and a data acquisition device. The device includes:

[0032] The acquisition module is used to respond to a test request from a grid-connected energy storage converter by acquiring multiple control commands of the grid-connected energy storage converter, parameter value ranges of multiple first control parameters of the grid simulation device, and parameter value ranges of multiple second control parameters of the environmental simulation device. The multiple control commands include at least one of grid-connected commands, off-grid switching commands, and power step commands. The multiple first control parameters include voltage parameters and frequency parameters. The voltage parameter's value range is within ±20% of AC 380 volts, and the frequency parameter's value range is within ±0.1 Hz of a target frequency value. The multiple second control parameters include temperature parameters and humidity parameters. The temperature parameter's value range is within -40 degrees Celsius to 85 degrees Celsius, and the frequency parameter's value range is within ±3% of a target relative humidity value.

[0033] The value acquisition module is used to acquire multiple values ​​of the plurality of first control parameters within their respective parameter value ranges to obtain multiple parameter values ​​for each of the plurality of first control parameters, and to acquire multiple values ​​of the plurality of second control parameters within their respective parameter value ranges to obtain multiple parameter values ​​for each of the plurality of second control parameters.

[0034] A generation module is used to generate multiple control data sets, each of which includes one control instruction from the multiple control instructions, a parameter value from each first control parameter, and a parameter value from each second control parameter; wherein different control data sets include different control instructions, different parameter values ​​from at least one first control parameter, or different parameter values ​​from at least one second control parameter.

[0035] The control module is used to control the operation of the grid-type energy storage converter, the power grid simulation device, and the environmental simulation device based on the plurality of control data sets, and to acquire test data sets corresponding to the plurality of control data sets based on the data acquisition device; each test data set includes test data in multiple dimensions; the test data in multiple dimensions includes voltage and current, the sampling frequency of the data acquisition device is greater than or equal to 10 kHz, and the error of the data acquisition device in acquiring voltage and current is less than or equal to 1%;

[0036] The determination module is used to determine the weight coefficients corresponding to the test data of the multiple dimensions based on a pre-built hierarchical analysis model, and to determine the test results corresponding to the test data set based on the weight coefficients.

[0037] The generation module is further configured to generate multiple control data sets based on a full permutation combination function when the multiple first control parameters and the total dimension of the multiple first control parameters are less than or equal to 3.

[0038] The generation module is further configured to generate multiple control data sets based on an orthogonal design table when the multiple first control parameters and the total dimension of the multiple first control parameters are greater than 3.

[0039] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the above-mentioned embodiments.

[0040] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the preceding claims.

[0041] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the above claims.

[0042] The aforementioned test method, apparatus, computer equipment, computer-readable storage medium, and computer program product for grid-connected energy storage converters, through the following process, involves the main control equipment responding to the test request during the test initiation phase, obtaining the control commands of the grid-connected energy storage converter and the value ranges of the control parameters of the grid simulation equipment and the environmental simulation equipment. Subsequently, these parameters are repeatedly measured and combined within their respective ranges to generate a diverse set of control data covering different commands and parameters. This process can simulate multiple operating conditions such as electrical performance, thermal performance, and environmental adaptability, avoiding the limitations of single-condition testing. During testing, the main control equipment drives the operation of each device based on the control data set, and simultaneously acquires multi-dimensional test data with the help of data acquisition equipment. Then, using a pre-built hierarchical analysis model, the weight coefficients corresponding to the test data in each dimension are determined. This allows for quantitative analysis of the correlation between various performance indicators, enabling precise identification of key factors affecting the performance of grid-type energy storage converters. This provides a clear direction for optimized design and offers comprehensive and accurate test results for power system operators. As a result, they can rationally configure grid-type energy storage converters according to different application scenarios, fully leverage the performance advantages of grid-type energy storage converters, effectively improve product performance and reliability, ensure the overall operating efficiency and stability of the power system, and help the power system operate efficiently and stably under complex and ever-changing operating conditions. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart illustrating a testing method for a grid-type energy storage converter in one embodiment;

[0045] Figure 2 This is a flowchart illustrating step S03 in one embodiment;

[0046] Figure 3 This is a flowchart illustrating step S03 in another embodiment;

[0047] Figure 4 This is a flowchart illustrating step S04 in one embodiment;

[0048] Figure 5 This is a flowchart illustrating step S05 in one embodiment;

[0049] Figure 6 This is a structural block diagram of a test device for a grid-type energy storage converter in one embodiment.

[0050] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0052] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0053] Current converter characteristic testing methods often focus on single or a small number of performance parameters, making it difficult to comprehensively evaluate the converter's overall performance under complex operating conditions. Furthermore, insufficient research on the evolution of converter characteristics under different operating conditions leads to inaccurate assessments of their actual reliability and stability. Moreover, the dynamic impact of environmental factors is not fully considered, resulting in significant deviations between test data and actual application scenarios. Grid-based energy storage converters may be subject to combined voltage and frequency disturbances, extreme load switching, or operate in complex environments.

[0054] Based on this, in one embodiment, such as Figure 1 As shown, a testing method for a grid-type energy storage converter is provided. This embodiment illustrates the application of this method to the main control equipment in a testing system. The testing system also includes multiple controlled devices, including the grid-type energy storage converter, a power grid simulation device, an environmental simulation device, and a data acquisition device. In this embodiment, the method includes:

[0055] Step S01: In response to the test request of the grid-connected energy storage converter, acquire multiple control commands of the grid-connected energy storage converter, parameter value ranges of multiple first control parameters of the grid simulation device, and parameter value ranges of multiple second control parameters of the environmental simulation device. The multiple control commands include at least one of grid-connected commands, off-grid switching commands, and power step commands; the multiple first control parameters include voltage parameters and frequency parameters, with the voltage parameter value range within ±20% of AC 380 volts, and the frequency parameter value range within ±0.1 Hz of the target frequency value; the multiple second control parameters include temperature parameters and humidity parameters, with the temperature parameter value range between -40°C and 85°C, and the frequency parameter value range within ±3% of the target humidity value.

[0056] The control commands can include grid connection commands (triggered when grid voltage / frequency is within ±5% of rated value), off-grid switching commands (triggered when grid fault lasts ≥50ms), power step commands (e.g., 0→100% rated power, step time ≤100ms), and voltage / frequency support commands (triggered when grid voltage deviation ≥10% or frequency deviation ≥0.5Hz). The command format can be digital signals (RS485 protocol).

[0057] For example, the request type (such as electrical performance, thermal performance, etc.) can be parsed first to extract the core test dimensions; then, the corresponding control instruction set (such as grid connection instruction, fault injection instruction) can be matched from the predefined instruction library. The parameter range of the power grid simulation equipment is dynamically adjusted according to the test type (such as basic range, extreme range), and the parameters of the environmental simulation equipment cover temperature, humidity, dust concentration, etc. Flexible parameter management is achieved through configuration files, and the system can automatically filter and combine parameter ranges to ensure that the test parameters are accurately matched with the target. Before connecting the power grid simulation equipment to the converter, the insulation resistance to ground can be checked to ensure that it is greater than or equal to 100 megohms to ensure safety. When the environmental simulation equipment is tested at high temperature (e.g., greater than or equal to 60°C) or high humidity (e.g., greater than or equal to 90%RH), the explosion-proof rating of the equipment must be greater than or equal to IP65.

[0058] In one possible implementation, a representative grid-type energy storage converter prototype can be selected, ensuring that its power rating, voltage rating, and other control parameters meet the requirements of actual application scenarios. A test platform should be built, equipped with high-precision electrical measurement equipment (such as power analyzers and harmonic analyzers), thermal measurement equipment (thermocouples and infrared thermal imagers), an environmental test chamber, fault simulation equipment, and a data acquisition system. Installation and commissioning should be carried out according to the equipment operating procedures to ensure normal operation. Various control parameters for the tests can be set based on the technical specifications and actual operating requirements of the grid-type energy storage converter. This includes the operating conditions for different test items, such as load type and power in electrical performance tests, ambient temperature and heat dissipation conditions in thermal performance tests, and changes in control commands in control performance tests. Simultaneously, the frequency and time interval of data acquisition should be determined to ensure accurate acquisition of key data. For example, in electrical performance testing, the light load is set to 30% of the rated load, the full load to 100% of the rated load, and the overload to 120% of the rated load; in thermal performance testing, the initial ambient temperature is set to 25℃, and the cooling fan speed is set to 1500 rpm.

[0059] Step S02: Take multiple values ​​for the multiple first control parameters within their respective parameter value ranges to obtain multiple parameter values ​​for each of the multiple first control parameters; and take multiple values ​​for the multiple second control parameters within their respective parameter value ranges to obtain multiple parameter values ​​for each of the multiple second control parameters.

[0060] For example, multiple random numbers can be generated for multiple first control parameters within their respective parameter value ranges, and multiple random numbers can be generated for multiple second control parameters within their respective parameter value ranges.

[0061] Step S03: Generate multiple control data sets. Each control data set includes one control instruction from multiple control instructions, one parameter value for each first control parameter, and one parameter value for each second control parameter. Different control data sets may include different control instructions, different parameter values ​​for at least one first control parameter, or different parameter values ​​for at least one second control parameter.

[0062] For example, applicable control commands, first control parameters, and second control parameters can be selected first based on the test request type (electrical / thermal / control / environmental / reliability). Then, orthogonal experimental design or a full permutation combination algorithm is used to cross-combine each parameter within its value range. For instance, for electrical performance testing, the system combines grid connection commands with different voltage / frequency / load parameter values; for environmental adaptability testing, constant temperature operation commands are paired with different temperature and humidity conditions. Boundary value analysis and equivalence class partitioning are introduced during the generation process, prioritizing coverage of extreme operating conditions and typical scenarios. The final control data set is categorized and stored through a data management system, ensuring that each set differs in commands or parameters, achieving comprehensiveness and orthogonality of test conditions.

[0063] In one possible implementation, multiple control data sets can correspond to test items across multiple dimensions. For example, various influencing factors of grid-connected energy storage converters in actual operation can be comprehensively considered to design test items covering multiple dimensions such as electrical performance, thermal performance, control performance, environmental adaptability, and reliability. For example, electrical performance testing can include the measurement and analysis of parameters such as voltage, current, power, and harmonics under different operating conditions. This includes testing the stability and power quality of the converter output under different load conditions (light load, full load, overload), grid voltage fluctuations (±10% of rated voltage), and frequency changes (±5% of rated frequency). Thermal performance testing can measure the temperature distribution and trends of key components (such as power semiconductor devices and magnetic components) during long-term operation of the converter, evaluating the effectiveness of the heat dissipation system. Control performance testing can test the converter's response speed, control accuracy, and collaborative control capabilities under complex operating conditions for various control commands, such as power control, frequency control, and voltage control. Environmental adaptability testing can set different environmental conditions, such as high temperature (e.g., 60℃), low temperature (e.g., -20℃), humidity (e.g., 90%RH), salt spray, and dust, to verify the converter's performance changes and tolerance under harsh environments. Reliability testing can evaluate the converter's failure rate, fault recovery capability, and other reliability indicators through simulated long-term operation and fault injection.

[0064] Step S03 includes:

[0065] Step A1: When the total dimension of the multiple first control parameters is less than or equal to 3, generate multiple control data sets based on the full permutation combination function.

[0066] Step A1: When the number of first control parameters and the total dimension of the number of first control parameters are greater than 3, generate multiple control data sets based on the orthogonal design table.

[0067] Specifically, when the total dimension of the first and second control parameters is ≤3, a full permutation combination is used; when the dimension is ≥4, an L9(3^4) orthogonal experimental design is used. The parameter value selection rules may include: adding boundary values ​​(such as voltage parameters taking ±10% of the rated value) and typical values ​​(such as the rated value, 80% of the rated value), with a sampling interval of 10% of the parameter range.

[0068] Step S04: Based on multiple control data sets, control the operation of the grid-type energy storage converter, grid simulation equipment, and environmental simulation equipment, and acquire test data sets corresponding to each of the multiple control data sets using data acquisition equipment; each test data set includes test data in multiple dimensions. The multi-dimensional test data includes voltage and current, the sampling frequency of the data acquisition equipment is greater than or equal to 10 kHz, and the error of the voltage and current acquired by the data acquisition equipment is less than or equal to 1%.

[0069] For example, the operation of the grid-type energy storage converter, grid simulation equipment, and environmental simulation equipment can be repeatedly controlled based on multiple control data sets. Test data sets corresponding to each of the multiple control data sets can be acquired using data acquisition equipment to achieve repeatable testing. Optionally, reproducibility verification can be performed based on the obtained multiple test data sets. For instance, if the same grid-type energy storage converter is repeatedly tested three times by the same operator within 24 hours, the deviation of the test data in each dimension must be ≤±2%; if different devices of the same model are tested by different operators within 72 hours, the data deviation must be ≤±5%. If the deviation exceeds the standard, the test system needs to be recalibrated.

[0070] In one possible implementation, detailed and scientific testing methods can be developed for each dimension of the test items. High-precision sensors and specialized data acquisition equipment are used to acquire various data during the testing process in real time and accurately. For example, in electrical performance testing, a high-precision power analyzer can be used to measure voltage, current, and power, and a harmonic analyzer can be used for precise harmonic analysis; in thermal performance testing, thermocouples or infrared temperature sensors can be placed on key heat-generating components to record temperature changes; control performance testing can simulate various actual operating conditions, collect control signals and feedback data, and evaluate the performance of the control algorithm; environmental adaptability testing can be conducted in a specialized environmental test chamber, using sensors to monitor environmental parameters and converter performance parameters; reliability testing uses fault simulation equipment to inject faults and record the converter's fault response and recovery time. Optionally, a corresponding data management system can be established to classify, store, and back up the collected data, ensuring data integrity and traceability.

[0071] Step S05: Based on the pre-built hierarchical analysis model, determine the weight coefficients corresponding to the test data of multiple dimensions, and based on the weight coefficients, determine the test results corresponding to the test data set.

[0072] Networked energy storage converters typically exhibit multi-dimensional coupling, strong dynamism, and environmental sensitivity. Therefore, a preliminary approach is to use statistical analysis to study the distribution patterns and trends of various performance indicators, such as calculating the mean and standard deviation of electrical parameters under different operating conditions and plotting thermal performance curves. Then, correlation analysis and principal component analysis are used to uncover the intrinsic relationships between performance indicators across different dimensions, identifying key factors affecting the overall performance of the converter. Next, a converter characteristic evaluation model based on multi-dimensional data is constructed. For example, a pre-built hierarchical analysis model is used to determine the weights of each performance indicator, and fuzzy comprehensive evaluation is combined to quantitatively assess the overall performance of the converter. The pre-built hierarchical analysis model calculates the relative weights of each indicator by constructing a judgment matrix, and the rationality of the weights is ensured through consistency checks. The fuzzy comprehensive evaluation method determines the membership degree of the converter at different evaluation levels based on the membership function, thereby deriving the comprehensive evaluation result.

[0073] The hierarchical structure of the analytic hierarchy process (AHP) model can be: target layer (comprehensive performance evaluation) - criterion layer (electrical performance, thermal performance, control performance, environmental adaptability, reliability) - indicator layer (specific test data for each dimension). The judgment matrix adopts a 1-9 scale (1 = equally important, 3 = slightly important, 5 = significantly important, 7 = strongly important, 9 = extremely important). The weights are obtained by calculating the largest eigenvalue and the corresponding eigenvector, and must meet the consistency test (CR = CI / RI < 0.1, where RI = 1.12 when n = 5).

[0074] In one possible implementation, statistical analysis software can be used to process the collected data and calculate the statistical characteristics of each performance indicator, such as mean, standard deviation, maximum, and minimum values. Correlation analysis can be used to study the degree of correlation between performance indicators of different dimensions, such as analyzing the correlation between changes in electrical parameters and temperature changes. Dimensionality reduction methods such as principal component analysis can be used to identify the main factors affecting converter performance, simplifying the multi-dimensional data structure for easier in-depth understanding and analysis. Then, the Analytic Hierarchy Process (AHP) is used to determine the weights of each performance indicator and construct a judgment matrix. For example, for the five dimensions of electrical performance, thermal performance, control performance, environmental adaptability, and reliability, the relative importance of each dimension is compared pairwise to obtain the judgment matrix. The maximum eigenvalue and eigenvector of the judgment matrix are then calculated, and the eigenvectors are normalized to obtain the weight vectors of each indicator. A random consistency index (RI) is introduced for consistency testing. When the consistency ratio (CR) is less than 0.1, the judgment matrix is ​​considered to have satisfactory consistency, and the weight vectors are considered valid. Finally, fuzzy comprehensive evaluation method is used to quantitatively assess the overall performance of the converter. The evaluation levels are determined, such as five levels: Excellent, Good, Average, Poor, and Very Poor. Based on the actual data of each performance indicator and the membership function, the membership degree of the converter under different evaluation levels is determined, thus deriving the comprehensive evaluation result. Assume the weight vectors for electrical performance, thermal performance, control performance, environmental adaptability, and reliability are as follows: The membership matrix of each indicator is as follows: Then the comprehensive evaluation vector The overall performance evaluation level of the converter is determined by this vector.

[0075] The test results can correspond to optimization strategies. For example, if a grid-connected energy storage converter experiences a 10% voltage drop in the grid, resulting in an output voltage deviation rate of ±6% (exceeding the standard ±5%), a recovery time of 120ms (exceeding the standard 50ms), and a total current harmonic distortion rate of 8% (exceeding the standard 5%), the corresponding optimization strategies could include, but are not limited to: optimizing the voltage outer loop control parameters for control commands, adding feedforward compensation links, improving dynamic response speed (e.g., shortening the power control response time from 80ms to within ms), introducing harmonic suppression algorithms (e.g., adaptive notch filters), and reducing current harmonic content; and increasing the DC-side capacitor capacity to enhance voltage support capabilities and reduce the impact of voltage fluctuations. If the temperature of the IGBT (Insulated Gate Bipolar Transistor) module in a grid-type energy storage converter rises to 110℃ (close to the standard upper limit of 125℃), with a temperature rise rate of 15℃ / h (exceeding the standard of 10℃ / h), and the noise level of the cooling fan exceeds the standard when running at full speed, the corresponding optimization strategies may include, but are not limited to: for the cooling system, replacing the thermal grease with a high thermal conductivity thermal grease and optimizing the contact thermal resistance between the IGBT module and the heat sink; increasing the surface area of ​​the heat sink or using phase change materials (such as graphene coating) to improve heat conduction efficiency; for thermal management strategies, introducing intelligent temperature-controlled fans that dynamically adjust the speed according to the real-time temperature (e.g., the fan runs at low speed when the temperature is <90℃ to reduce noise). If the time for the grid-type energy storage converter to reach the target power is 100ms (exceeding the standard of 50ms), the power fluctuation range is ±8% (exceeding the standard of ±3%), and oscillations occur when working in conjunction with voltage control commands, the corresponding optimization strategies may include, but are not limited to: for the control algorithm, adopting predictive control or sliding mode control algorithms to shorten the power response time (e.g., reducing the adjustment time to within 40ms); adding multi-control loop decoupling design to avoid mutual interference between power and voltage control (e.g., introducing cross-feedback compensation); performing parameter tuning, retuning the PI (Proportional-Integral) controller parameters through particle swarm optimization algorithm to reduce overshoot and oscillation amplitude.

[0076] For example, the test results can be used to: if the voltage deviation rate is ≥6% at high temperature (60℃), it is recommended to optimize the heat dissipation structure (such as using graphene-coated heat sinks) or the dynamic derating algorithm (power is reduced to 80% of the rated value when the temperature is ≥60℃); if the fault recovery time is ≥500ms, it is recommended to shorten the fault detection delay (from 50ms to 20ms) and optimize the protection logic.

[0077] Optionally, a complete and scientific evaluation standard system for the characteristics of grid-type energy storage converters can be established based on multi-dimensional test data and analysis results, combined with industry standards and actual engineering needs. For example, the test conditions, test procedures, performance index requirements, and pass / fail criteria for each test item should be clearly defined. For electrical performance indicators, the voltage deviation range under different operating conditions (e.g., ±5% of rated voltage), the upper limit of current harmonic distortion rate (e.g., THD < 5%), and the power factor requirement (e.g., > 0.95) should be specified; for thermal performance indicators, the upper limit of temperature for key components (e.g., IGBT module temperature < 125℃) and the allowable temperature rise range (e.g., temperature rise < 10℃ per hour) should be set; for control performance indicators, response time standards (e.g., power control response time < 50ms) and control accuracy requirements (e.g., frequency control accuracy ± 0.1Hz) should be established; for environmental adaptability indicators, the performance retention rate under different environmental conditions should be clearly defined (e.g., performance retention rate > 90% under high temperature conditions); and for reliability indicators, the upper limit of failure rate (e.g., number of failures per year < 2 times) and the mean time to recovery (MTBF) (e.g., < 2 hours) should be determined. By establishing a unified evaluation standard system, clear basis and norms will be provided for the research, development, production, testing and application of grid-type energy storage converters.

[0078] Optionally, multiple grid-type energy storage converters of different models can be tested and verified based on the established evaluation standard system. The test results are compared with the performance requirements in the standard to check the rationality and feasibility of the standard. If it is found that some indicators are difficult to meet in actual testing or cannot effectively distinguish product performance differences, the standard is adjusted accordingly. For example, when testing a certain model of converter, it is found that most products cannot meet a certain harmonic content indicator specified in the standard under current technological conditions. After analysis and research, combined with the actual technical level, the requirement for this indicator is appropriately relaxed, and verification is carried out again. The evaluation standard system can be improved based on the problems found during the verification process, combined with new research results and industry development needs. The test items, test conditions, performance requirements, and pass / fail criteria can be supplemented or revised to ensure that the standard system can continuously adapt to the needs of technological development and practical applications, and always provide a scientific and accurate basis for the performance evaluation of grid-type energy storage converters. For example, with the emergence of new heat dissipation materials and technologies, relevant standards for thermal performance testing are updated, and evaluation indicators and testing methods for new heat dissipation technologies are added; in accordance with the market's higher requirements for converter reliability, reliability testing standards are improved.

[0079] In the aforementioned test method for grid-connected energy storage converters, during the test initiation phase, the main control equipment responds to the test request, obtains the control commands of the grid-connected energy storage converter and the value ranges of the control parameters of the grid simulation equipment and the environmental simulation equipment, and then takes and combines these parameters multiple times within their respective ranges to generate a diverse set of control data covering different commands and parameters. This process can simulate multiple operating conditions such as electrical performance, thermal performance, and environmental adaptability, avoiding the limitations of single-condition testing. During testing, the main control equipment drives the operation of each device based on the control data set, and simultaneously acquires multi-dimensional test data with the help of data acquisition equipment. Then, using a pre-built hierarchical analysis model, the weight coefficients corresponding to the test data in each dimension are determined. This allows for quantitative analysis of the correlation between various performance indicators, enabling precise identification of key factors affecting the performance of grid-type energy storage converters. This provides a clear direction for optimized design and offers comprehensive and accurate test results for power system operators. As a result, they can rationally configure grid-type energy storage converters according to different application scenarios, fully leverage the performance advantages of grid-type energy storage converters, effectively improve product performance and reliability, ensure the overall operating efficiency and stability of the power system, and help the power system operate efficiently and stably under complex and ever-changing operating conditions.

[0080] In one exemplary embodiment, the multiple first control parameters also include load parameters, and the multi-dimensional test data also includes at least one of voltage transient recovery time, power factor, and total harmonic distortion of current, such as... Figure 2 As shown, step S03 may include steps S031 to S032. Wherein:

[0081] Step S031: Generate multiple first control data sets. Each first control data set includes a target control command from multiple control commands, a parameter value of the load, a target parameter value of the first control parameter excluding the load from multiple first control parameters, and a target parameter value of each second control parameter. Different first control data sets include different load parameter values. In one possible implementation, electrical performance tests under different load conditions can be performed sequentially, recording parameters such as output voltage, current, power, and harmonic content of the grid-type energy storage converter. Under light load, full load, and overload conditions, the stability of voltage, waveform distortion of current, and output accuracy of power are measured respectively to obtain corresponding test data sets. Simulate grid voltage fluctuations (e.g., ±10% of rated voltage) and frequency changes (e.g., ±5% of rated frequency) to test the adaptability and regulation capability of the grid-type energy storage converter, recording the dynamic response process and regulation time of voltage and current. For example, when the grid voltage suddenly drops by 10%, monitor the change in the output voltage of the grid-connected energy storage converter within 50 milliseconds, record the time required for it to adjust to a stable value, and the voltage deviation after stabilization. The formula for calculating the voltage deviation rate includes:

[0082] (1)

[0083] Where U is the actual output voltage, U N This is the rated voltage value.

[0084] The formula for calculating the total harmonic distortion rate of current can include:

[0085] (2)

[0086] Among them, I n In is the effective value of the nth harmonic current, and I1 is the effective value of the fundamental current. Voltage deviation rate and total harmonic distortion rate can be used to evaluate the performance of grid-type energy storage converters under different electrical conditions.

[0087] Specifically, in electrical performance testing, multiple test data may include voltage transient recovery time (the time it takes for the grid voltage to recover to ±5% of the rated value after a sudden drop of 30%), power factor (lagging 0.9 to leading 0.9) and total harmonic distortion of current (THD≤5%).

[0088] Step S032: Generate multiple second control data sets. Each second control data set includes one control instruction from multiple control instructions, a target parameter value for each first control parameter, and a target parameter value for each second control parameter. Different second control data sets include different control instructions.

[0089] In one possible implementation, various real-world operating conditions can be simulated to test the control performance of the grid-connected energy storage converter. In power control tests, different power commands are given, such as a step change from 0 to rated power. The response speed and control accuracy of the grid-connected energy storage converter are observed, and the time to reach the target power and the power fluctuation range are recorded. In frequency control tests, grid frequency fluctuations are simulated to test the frequency regulation capability of the grid-connected energy storage converter. For example, under frequency fluctuations of ±2Hz, the adjustment of the output frequency of the grid-connected energy storage converter is detected. In voltage control tests, the grid voltage is changed to check the grid-connected energy storage converter's ability to regulate the output voltage. The adjustment time and stability accuracy of the output voltage are recorded. Simultaneously, the collaborative working capability between different control strategies is tested. For example, when power control and voltage control are applied simultaneously, the overall performance of the grid-connected energy storage converter is observed. Indicators such as power control response time, frequency control accuracy, and voltage adjustment time are calculated to evaluate the control performance of the grid-connected energy storage converter.

[0090] In this embodiment, during the electrical performance testing phase, the first control data set locks the target control command, non-load-type first control parameters, and environmental parameters, and only dynamically adjusts the load parameter values. This allows for precise quantification of the impact of load changes on the electrical performance of the converter, such as voltage stability, frequency response characteristics, and power output accuracy. During the control performance testing phase, the second control data set fixes various hardware parameters and switches between different control commands. This enables specific verification of the converter's command execution efficiency, logic switching reliability, and dynamic response capability under different control modes, such as constant power and constant voltage / frequency.

[0091] In one exemplary embodiment, the plurality of second control parameters further include at least one of a pH parameter and a dust concentration parameter, such as Figure 3 As shown, step S03 may further include step S033. Wherein:

[0092] Step S033: Generate multiple sets of third control data sets. Each third control data set in each set includes a target control instruction from multiple control instructions, a target parameter value for each first control parameter, a parameter value for a target second control parameter, and target parameter values ​​for the second control parameters other than the target second control parameter from multiple second control parameters. Different sets of third control data sets correspond to different target second control parameters, and the parameter values ​​of the target second control parameters included in different sets of third control data sets in each set are different.

[0093] In one possible implementation, the grid-type energy storage converter can be placed in an environmental test chamber to undergo environmental tests, including high temperature, low temperature, humidity, salt spray, and dust storms. In the high-temperature test, the temperature is set to 60℃ and maintained for 48 hours to test the performance changes of the grid-type energy storage converter under high-temperature conditions, monitoring whether electrical parameters and control performance are normal. In the low-temperature test, the temperature is lowered to -20℃ and maintained for 48 hours before performance testing. The humidity test sets the relative humidity to 90% and continues for a certain period (e.g., 24 hours) to observe changes in the equipment's insulation and electrical performance. The salt spray test is conducted according to standard salt spray testing methods and lasts for 24 hours to evaluate the equipment's corrosion resistance. The dust storm test simulates a dusty environment and lasts for 8 hours to check the equipment's dustproof capability and its impact on performance. The performance parameter changes of the grid-type energy storage converter under different environmental conditions are recorded to evaluate its environmental adaptability.

[0094] Furthermore, the controlled devices also include heat dissipation equipment with a heat dissipation power of 5 kilowatts or more. Multiple test data points include the temperature rise rate of the insulated-gate bipolar transistor module and the performance degradation rate after 1000 thermal cycles, such as... Figure 4 As shown, step S04 may include steps S041 and S042. Wherein:

[0095] Step S041: Based on multiple third control data sets including the target temperature value, control the operation of the grid-type energy storage converter, the grid simulation equipment, and the environmental simulation equipment, and start the heat dissipation equipment; wherein, the target temperature value includes the maximum value among multiple parameter values ​​of the temperature parameter.

[0096] Step S042: Based on the data acquisition device, acquire the test data sets corresponding to the multiple third control data sets respectively.

[0097] In one possible implementation, the grid-type energy storage converter can be placed under a set ambient temperature and operated continuously at full load. Thermocouples and infrared thermal imagers are used to measure the temperature distribution and changes of key components (such as IGBT modules and reactors), and temperature-time curves are plotted. By changing heat dissipation conditions (such as adjusting fan speed or increasing heat sink area), the temperature change trend is observed to evaluate the performance of the heat dissipation system. For example, at a fan speed of 1500 rpm, the temperature change of the IGBT module is recorded over one hour; then the fan speed is increased to 2000 rpm, and the temperature change is recorded again to compare the heat dissipation effect under different fan speeds. The formula for calculating component temperature rise can include:

[0098] (3)

[0099] Where T represents the actual temperature of the component during operation, and T0 represents the ambient temperature. Component temperature rise can be used to evaluate the ability of the heat dissipation system to control the temperature of critical components.

[0100] Specifically, the test data corresponding to qualified test results can include IGBT module temperature rise rate ≤10℃ / min and performance degradation rate ≤5% after 1000 thermal cycles of -40℃ to 85℃.

[0101] Furthermore, the controlled devices also include fault simulation devices. The fault response time of the fault simulation devices is less than or equal to 10 milliseconds. The test data includes at least one of the following dimensions: mean time between failures (MTBF) and fault type distribution data. Based on multiple control data sets, the grid-type energy storage converter, grid simulation device, and environmental simulation device are controlled to operate. Test data sets corresponding to each of the multiple control data sets are acquired using data acquisition equipment. Please refer to [link / reference]. Figure 4 Step S04 may further include steps S043 and S044, wherein:

[0102] Step S043: Based on the target control data set among multiple control data sets, control the grid-type energy storage converter, grid simulation equipment and environmental simulation equipment to run continuously for a preset time, and switch the fault type simulated by the fault simulation equipment.

[0103] Step S044: Based on the data acquisition device, obtain the test data set corresponding to the fault type.

[0104] The specific fault types can include: three-phase short circuit (duration 100ms), single-phase ground fault (duration 500ms), voltage drop (depth 30% / 50%, duration 100ms / 200ms), frequency mutation (±2Hz / 100ms), and communication interruption (duration 5s). Fault injection is performed 30 minutes after the equipment has been running at full load. When operating the fault simulation equipment, personnel must maintain a safe distance of at least 3m from the test area, and the main circuit power supply must be disconnected first.

[0105] In one possible implementation, long-term operation can be simulated, with a continuous operating time set at 1000 hours, to monitor the occurrence of faults in the grid-type energy storage converter during operation. Different types of faults, such as short-circuit faults and open-circuit faults, are injected using fault simulation equipment, and the fault response and recovery times of the grid-type energy storage converter are recorded. The fault incidence rate is calculated using the formula: Fault Incidence Rate = Number of Faults / Total Operating Time. This evaluates the reliability and fault recovery capability of the grid-type energy storage converter.

[0106] Specifically, the test data corresponding to a qualified test result can include multiple dimensions such as mean time between failures (MTBF) ≥ 10,000 h and fault type distribution data, such as power device fault rate ≤ 30%.

[0107] In this embodiment, the third control data set fixes the control commands, electrical parameters, and other environmental parameters, and only adjusts single target environmental parameters such as temperature and humidity. This allows for precise quantification of the independent impact of each environmental factor on the performance of the grid-type energy storage converter. Furthermore, by locking the temperature parameter to its maximum value in the third control data set and simultaneously activating the heat dissipation equipment, a test environment is created that counteracts high-temperature stress and active heat dissipation. This allows for verification of the collaborative working efficiency of the heat dissipation system and the grid-type energy storage converter under extreme temperature conditions. Furthermore, by dynamically switching the fault type of the fault simulation equipment during the continuous operation of the target control data set-driven equipment for a preset duration, various abnormal operating conditions that the grid-type energy storage converter may encounter in actual operation can be simulated. This allows for a systematic evaluation of the robustness of the grid-type energy storage converter under multiple stresses such as electrical faults and sudden environmental changes.

[0108] In one exemplary embodiment, such as Figure 5 As shown, step S05 may include steps S051 to S054. Wherein:

[0109] Step S051: Determine the set of statistical values ​​corresponding to each test data set; each set of statistical values ​​includes statistical values ​​of test data from multiple dimensions.

[0110] Step S052: Based on the pre-built hierarchical analysis model, determine the judgment matrix corresponding to the set of statistical values; the judgment matrix includes multiple matrix elements, each matrix element is used to characterize the importance of the statistical value of one dimension relative to the statistical value of another dimension.

[0111] Step S053: Determine the largest eigenvalue of the judgment matrix and the eigenvector corresponding to the largest eigenvalue, and determine the weight matrix corresponding to the set of statistical values ​​based on the largest eigenvalue and the eigenvector; the weight matrix includes multiple weight coefficients that correspond one-to-one with the statistical values ​​of multiple dimensions, and each weight coefficient is used to characterize the importance of the corresponding statistical value in the statistical values ​​of multiple dimensions.

[0112] Step S054: Based on the weight matrix, determine the test results corresponding to the set of statistical values.

[0113] In one possible implementation, the importance of five dimensions—electrical performance, thermal performance, control performance, environmental adaptability, and reliability—can be compared pairwise by expert scoring to obtain a judgment matrix A. For example, if electrical performance is "slightly more important" than thermal performance, it is assigned the value 'a'. 12 =3、a 21 =1 / 3. The largest eigenvalue can be calculated. And the eigenvectors, and calculate the weight vectors after normalization of the eigenvectors. ,and Furthermore, consistency checks can be performed on the results, and consistency indices, random consistency indices, and consistency ratios can be calculated. The formula for calculating the consistency indices may include:

[0114] (4)

[0115] Where n is the number of indicators, n=5. The random consistency index, obtained from a table, is 1.12. The formula for calculating the consistency ratio can include:

[0116] (5)

[0117] The weights are effective when CR < 0.1.

[0118] For example, experts might assess the importance of the five dimensions as follows:

[0119] (6)

[0120] at this time, CI=0.025, CR=0.022<0.1, weight vector is Electrical performance has the highest weight (because it directly affects the grid's support capacity). If data analysis reveals a high failure rate for "reliability," its weight can be increased from 0.2 to 0.3 using the analytic hierarchy process (AHP), thus amplifying the membership component corresponding to reliability in the final comprehensive evaluation vector.

[0121] Experts may also assess the importance of the five dimensions as follows:

[0122] (7)

[0123] at this time, CI=0.05, CR=0.045<0.1, weight vector is .

[0124] Optionally, the membership degree of the grid-type converter under different evaluation levels can be determined based on the membership function to obtain a comprehensive evaluation result. The membership function can adopt a gradient distribution, and the evaluation index can be set as follows: Its qualified range is The excellent range is ( The non-compliant range is or Then the membership function is:

[0125] (8)

[0126] in, These are the upper and lower limits of the acceptable range for the indicator (e.g., ±5% of the voltage deviation rate). This represents the upper limit of the excellent range (e.g., ±3% of the voltage deviation rate).

[0127] For example, the "good" rating range for "voltage deviation rate" is [±3%, ±5%), and the membership function can include:

[0128] (9)

[0129] In this embodiment, the test data set is transformed into a statistical value set, and a judgment matrix is ​​constructed using a pre-built hierarchical analysis model to quantify the relative importance of statistical values ​​in each dimension. Then, by solving the maximum eigenvalue and eigenvector of the judgment matrix, a weight matrix containing the weight coefficients of each dimension is generated, and finally, the objective weighted calculation of the test results is realized, which can accurately reflect the differences in the importance of each performance indicator under different application scenarios.

[0130] In an exemplary embodiment, the testing method for the above-described grid-type energy storage converter may include:

[0131] Step S01: In response to the test request of the grid-type energy storage converter, obtain multiple control commands of the grid-type energy storage converter, the parameter value ranges of multiple first control parameters of the grid simulation device, and the parameter value ranges of multiple second control parameters of the environmental simulation device.

[0132] Step S02: Take multiple values ​​for the multiple first control parameters within their respective parameter value ranges to obtain multiple parameter values ​​for each of the multiple first control parameters; and take multiple values ​​for the multiple second control parameters within their respective parameter value ranges to obtain multiple parameter values ​​for each of the multiple second control parameters.

[0133] In one possible implementation, identical test platforms can be built for the three converter models to ensure consistency in the test environment and equipment. In the electrical performance test, the light load is set to 30% of the rated load, the full load to 100% of the rated load, and the overload to 120% of the rated load; grid voltage fluctuations are set to ±10% of the rated voltage, and frequency fluctuations are set to ±5% of the rated frequency. The initial ambient temperature for the thermal performance test is set to 25℃, and the initial cooling fan speed is set to 1500 rpm. In the control performance test, the power control command is set to a step change from 0 to the rated power, the frequency control simulates grid frequency fluctuations of ±2Hz, and the voltage control is set to allow grid voltage fluctuations of ±10% of the rated value. Environmental adaptability tests are strictly prepared according to the set conditions: high temperature (60℃, 48 hours), low temperature (-20℃, 48 hours), humidity (90%, 24 hours), salt spray (standard method, 24 hours), and dust (simulated environment, 8 hours). The reliability test is set to a continuous operating time of 1000 hours.

[0134] Step S03: Generate multiple control data sets. Each control data set includes one control instruction from multiple control commands, one parameter value for each first control parameter, and one parameter value for each second control parameter. Different control data sets may include different control instructions, at least one different parameter value for a first control parameter, or at least one different parameter value for a second control parameter. Step S04: Based on the multiple control data sets, control the operation of the grid-connected energy storage converter, the grid simulation equipment, and the environmental simulation equipment. Using data acquisition equipment, acquire test data sets corresponding to each of the multiple control data sets. Each test data set includes test data from multiple dimensions.

[0135] In one possible implementation, three types of converters, A, B, and C, can be tested under different load and grid fluctuation conditions. The results show that converter A, under full load and a 10% drop in grid voltage, can recover its output voltage within 80 milliseconds, with a voltage deviation rate of ±3% and a total current harmonic distortion rate of 4%; converter B recovers its stability in 120 milliseconds, with a voltage deviation rate of ±4% and a total current harmonic distortion rate of 6%; and converter C recovers its stability in 100 milliseconds, with a voltage deviation rate of ±3.5% and a total current harmonic distortion rate of 5%, thus achieving electrical performance testing.

[0136] The converter can operate continuously under full load conditions and the temperature of key components can be monitored. After one hour of operation, the IGBT module of converter A reached a temperature of 75℃. After increasing the heat sink area, the temperature stabilized at 70℃. After one hour of operation, the IGBT module of converter B reached a temperature of 80℃. After adjusting the fan speed to 2000 rpm, the temperature dropped to 75℃. After one hour of operation, the IGBT module of converter C reached a temperature of 78℃. After optimizing the heat dissipation airflow, the temperature stabilized at 73℃, thus achieving thermal performance testing.

[0137] In the power control test, converter A reaches the target power in 60 milliseconds with a power fluctuation range of ±3%; converter B reaches the target power in 80 milliseconds with a power fluctuation range of ±5%; and converter C reaches the target power in 70 milliseconds with a power fluctuation range of ±4%. In the frequency control test, converter A adjusts its output frequency to the target frequency within 40 milliseconds with an adjustment accuracy of ±0.08Hz when the grid frequency fluctuates by ±2Hz; converter B adjusts in 60 milliseconds with an adjustment accuracy of ±0.12Hz; and converter C adjusts in 50 milliseconds with an adjustment accuracy of ±0.1Hz. In the voltage control test, converter A adjusts its output voltage in 100 milliseconds with a stability accuracy of ±1.2%; converter B adjusts in 130 milliseconds with a stability accuracy of ±1.5%; and converter C adjusts in 120 milliseconds with a stability accuracy of ±1.3%. In the test of different control strategies working together, converter A responded quickly and the various control functions worked well together; converter B responded slightly slower and had some control delay; converter C's coordination effect was between that of A and B, so as to achieve the control performance test.

[0138] After the high-temperature test, the electrical performance parameters of converter A changed little, and its control performance remained normal; the parameters of some electronic components in converter B drifted, resulting in a slight decrease in control accuracy; the performance of converter C remained basically stable. In the low-temperature test, converter A started normally, and its performance was not significantly affected; converter B exhibited a start-up delay, and the performance of some capacitors decreased; converter C started and operated normally. After the humidity test, converter A showed good insulation performance; converter B showed slight leakage; and the insulation performance of converter C remained unchanged. After the salt spray test, converter A showed a small amount of corrosion on its surface, but this did not affect its performance; converter B showed more severe corrosion on its surface, and some metal parts rusted; converter C showed only slight corrosion. After the dust test, a small amount of dust entered converter A, but this did not affect its normal operation; the cooling ducts of converter B were blocked by dust, resulting in a decrease in heat dissipation; the dust prevention measures of converter C were effective, and virtually no dust entered its interior, thus achieving environmental adaptability testing.

[0139] During a simulated long-term operation of 1000 hours, converter A experienced two failures with a mean time to recovery (MTR) of 1.5 hours; converter B experienced three failures with an MTR of 2 hours; and converter C experienced one failure with an MTR of 1 hour. After a short-circuit fault was injected using the fault simulation equipment, converter A responded within 500 milliseconds and returned to normal operation within 10 minutes; converter B responded within 800 milliseconds and recovered within 15 minutes; and converter C responded within 600 milliseconds and recovered within 12 minutes, thus achieving reliability testing.

[0140] Step S044: Based on the data acquisition device, obtain the test data set corresponding to the fault type.

[0141] In one possible implementation, a data acquisition system can be used to collect experimental data, and statistical analysis software can be used to calculate the statistical characteristics of each performance index. Correlation analysis revealed that in the electrical performance of converter A, the total harmonic distortion rate of the current is positively correlated with load changes, with a correlation coefficient of 0.8; in terms of thermal performance, the correlation coefficient between the temperature of key components and power loss is 0.9. Analysis of converter B shows that its control performance response speed is correlated with its electrical performance voltage adjustment time, with a correlation coefficient of -0.75. In the mechanical performance of converter C, structural stress is also correlated with environmental adaptability temperature changes, with a correlation coefficient of 0.6.

[0142] Principal component analysis can be used to identify the main factors affecting the performance of each type of converter. For converter A, the combined influence of electrical and thermal performance accounts for 70%, making it the key factor affecting its overall performance; for converter B, the combined influence of control performance and environmental adaptability accounts for approximately 65%; and for converter C, the main influencing factors are electrical and mechanical performance, with a combined share of 75%.

[0143] The weights of each performance indicator can be determined using the analytic hierarchy process (AHP). Assume the weight vector for electrical performance, thermal performance, control performance, environmental adaptability, and reliability is W = (0.3, 0.2, 0.15, 0.15, 0.2). Combining this with the fuzzy comprehensive evaluation method, five evaluation levels are determined: Excellent, Good, Average, Poor, and Very Poor. The membership matrix R of the converter under different evaluation levels is determined based on the actual data and membership functions of each performance indicator. Taking converter A as an example, the comprehensive evaluation vector B = W⋅R is calculated, resulting in a good comprehensive evaluation for converter A; an average comprehensive evaluation for converter B; and a good comprehensive evaluation for converter C.

[0144] Optionally, the three types of converters can be tested and verified based on the established evaluation standard system. During the testing process, it was found that the standard's evaluation index for the dustproof capability of converters in dusty environments was not detailed enough, making it difficult to accurately distinguish the differences between different products. Therefore, this index was revised, adding a quantitative indicator of dust ingress and an evaluation standard for the degree of impact on key components. Simultaneously, based on new trends in heat dissipation technology, testing methods and performance requirements for new heat dissipation materials and structures were added. After further testing and verification on multiple converters of different batches and models, it was proven that the revised standard system can more accurately evaluate the performance of grid-type energy storage converters, effectively distinguish the quality differences between different products, and provide a more scientific and reasonable basis for product research and development, production, and quality control.

[0145] Based on the test results of various converter models, converter A performed well in terms of electrical performance and reliability, but there is still room for improvement in thermal performance optimization; converter B has shortcomings in several aspects, and key improvements are needed in control performance, environmental adaptability, and reliability; converter C has relatively balanced overall performance and performs well in all aspects, but the response speed of control performance can be further optimized.

[0146] In summary, the above-mentioned testing method for grid-connected energy storage converters involves the main control equipment responding to the test request during the test initiation phase, obtaining the control commands of the grid-connected energy storage converter and the value ranges of the control parameters of the grid simulation equipment and the environmental simulation equipment. Subsequently, these parameters are repeatedly measured and combined within their respective ranges to generate a diverse set of control data covering different commands and parameters. This process can simulate multiple operating conditions such as electrical performance, thermal performance, and environmental adaptability, avoiding the limitations of single-condition testing. During testing, the main control equipment drives the operation of each device based on the control data set, and simultaneously acquires multi-dimensional test data with the help of data acquisition equipment. Then, using a pre-built hierarchical analysis model, the weight coefficients corresponding to the test data in each dimension are determined. This allows for quantitative analysis of the correlation between various performance indicators, enabling precise identification of key factors affecting the performance of grid-type energy storage converters. This provides a clear direction for optimized design and offers comprehensive and accurate test results for power system operators. As a result, they can rationally configure grid-type energy storage converters according to different application scenarios, fully leverage the performance advantages of grid-type energy storage converters, effectively improve product performance and reliability, ensure the overall operating efficiency and stability of the power system, and help the power system operate efficiently and stably under complex and ever-changing operating conditions.

[0147] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0148] Based on the same inventive concept, this application also provides a testing apparatus for a grid-type energy storage converter to implement the testing method for the grid-type energy storage converter described above. The solution provided by this apparatus is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the testing apparatus for a grid-type energy storage converter provided below can be found in the limitations of the testing method for the grid-type energy storage converter described above, and will not be repeated here.

[0149] In one exemplary embodiment, such as Figure 6As shown, a test device 10 for a grid-type energy storage converter is provided, comprising: an acquisition module 11, a value acquisition module 12, a generation module 13, a control module 14, and a determination module 15, wherein:

[0150] The acquisition module 11 is used to respond to the test request of the grid-connected energy storage converter by acquiring multiple control commands of the grid-connected energy storage converter, the parameter value ranges of multiple first control parameters of the grid simulation device, and the parameter value ranges of multiple second control parameters of the environmental simulation device. The multiple control commands include at least one of grid connection command, off-grid switching command, and power step command. The multiple first control parameters include voltage parameters and frequency parameters. The parameter value range of the voltage parameter is within ±20% of AC 380 volts, and the parameter value range of the frequency parameter is within ±0.1 Hz of the target frequency value. The multiple second control parameters include temperature parameters and humidity parameters. The parameter value range of the temperature parameter is within -40 degrees Celsius to 85 degrees Celsius, and the parameter value range of the frequency parameter is within ±3% of the target humidity value.

[0151] The value acquisition module 12 is used to acquire multiple values ​​of multiple first control parameters within their respective parameter value ranges to obtain multiple parameter values ​​for each of the multiple first control parameters, and to acquire multiple values ​​of multiple second control parameters within their respective parameter value ranges to obtain multiple parameter values ​​for each of the multiple second control parameters.

[0152] The generation module 13 is used to generate multiple control data sets. Each control data set includes one control instruction from multiple control instructions, one parameter value for each first control parameter, and one parameter value for each second control parameter. Different control data sets may include different control instructions, different parameter values ​​for at least one first control parameter, or different parameter values ​​for at least one second control parameter.

[0153] The control module 14 is used to control the operation of the grid-type energy storage converter, the power grid simulation equipment, and the environmental simulation equipment based on multiple control data sets, and to acquire test data sets corresponding to the multiple control data sets based on the data acquisition equipment. Each test data set includes test data in multiple dimensions. The test data in multiple dimensions includes voltage and current. The sampling frequency of the data acquisition equipment is greater than or equal to 10 kHz, and the error of the voltage and current acquired by the data acquisition equipment is less than or equal to 1%.

[0154] Module 15 is used to determine the weight coefficients corresponding to the test data in multiple dimensions based on the pre-built hierarchical analysis model, and to determine the test results corresponding to the test data set based on the weight coefficients.

[0155] The aforementioned generation module 13 is also used to generate multiple control data sets based on a full permutation combination function when the multiple first control parameters and the total dimension of the multiple first control parameters are less than or equal to 3.

[0156] The aforementioned generation module 13 is also used to generate multiple control data sets based on an orthogonal design table when the multiple first control parameters and the total dimension of the multiple first control parameters are greater than 3.

[0157] In one embodiment, the aforementioned plurality of first control parameters further include load parameters, and the multi-dimensional test data includes at least one of voltage transient recovery time, power factor, and total harmonic distortion of current, and the aforementioned generation module 13 includes:

[0158] The first generation submodule is used to generate multiple first control data sets. Each first control data set includes a target control instruction from multiple control instructions, a parameter value of the load, a target parameter value of the first control parameter other than the load from multiple first control parameters, and a target parameter value of each second control parameter. The load parameter values ​​included in different first control data sets are different.

[0159] The second generation submodule is used to generate multiple second control data sets. Each second control data set includes one control instruction from multiple control instructions, the target parameter value of each first control parameter, and the target parameter value of each second control parameter. Different second control data sets include different control instructions.

[0160] In one embodiment, the aforementioned plurality of second control parameters further include at least one of a pH parameter and a dust concentration parameter, and the aforementioned generation module 13 further includes:

[0161] The third generation submodule is used to generate multiple sets of third control data sets. Each third control data set in each set includes a target control instruction from multiple control instructions, a target parameter value for each first control parameter, a parameter value for a target second control parameter, and target parameter values ​​for the second control parameters other than the target second control parameter. Different sets of third control data sets correspond to different target second control parameters, and the parameter values ​​of the target second control parameters included in different sets of third control data sets within each set are different.

[0162] In one embodiment, the aforementioned plurality of controlled devices further include a heat dissipation device, the heat dissipation power of which is greater than or equal to 5 kilowatts, and the test data in multiple dimensions include the temperature rise rate of the insulated gate bipolar transistor module and the performance degradation rate after 1000 thermal cycles. The aforementioned control module 14 includes:

[0163] The operation submodule is used to control the operation of the grid-type energy storage converter, the grid simulation equipment and the environmental simulation equipment based on multiple third control data sets including the target temperature value, and to start the heat dissipation equipment; wherein, the target temperature value includes the maximum value among multiple temperature parameter values.

[0164] The acquisition submodule is used to acquire test data sets corresponding to multiple third control data sets based on the data acquisition device.

[0165] In one embodiment, the plurality of controlled devices further includes a fault simulation device, and the aforementioned operation submodule is further configured to:

[0166] Based on the target control data set among multiple control data sets, the grid-type energy storage converter, power grid simulation equipment, and environmental simulation equipment are controlled to operate continuously for a preset time, and the fault type simulated by the fault simulation equipment is switched.

[0167] The aforementioned acquisition submodule is also used to acquire a set of test data corresponding to the fault type based on the data acquisition device.

[0168] In one embodiment, the determining module 15 is further configured to:

[0169] Determine the set of statistical values ​​corresponding to each test dataset; each set of statistical values ​​includes statistical values ​​of the test data across multiple dimensions;

[0170] Based on a pre-built hierarchical analysis model, a judgment matrix corresponding to the set of statistical values ​​is determined. The judgment matrix includes multiple matrix elements, each of which is used to characterize the importance of a statistical value in one dimension relative to a statistical value in another dimension.

[0171] Determine the largest eigenvalue of the judgment matrix and the eigenvector corresponding to the largest eigenvalue, and based on the largest eigenvalue and the eigenvector, determine the weight matrix corresponding to the set of statistical values; the weight matrix includes multiple weight coefficients that correspond one-to-one with the statistical values ​​of multiple dimensions, and each weight coefficient is used to characterize the importance of the corresponding statistical value among the statistical values ​​of multiple dimensions;

[0172] Based on the weight matrix, the test results corresponding to the set of statistical values ​​are determined.

[0173] Each module in the test device 10 for the aforementioned grid-type energy storage converter can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0174] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a test method for a grid-type energy storage converter. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0175] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0176] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0177] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0178] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0179] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0180] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0181] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A test method for a grid-type energy storage converter, characterized in that, A main control device is applied to a testing system, which further includes multiple controlled devices, including a grid-type energy storage converter, a power grid simulation device, an environmental simulation device, and a data acquisition device. The method includes: In response to a test request from a grid-connected energy storage converter, the system acquires multiple control commands for the grid-connected energy storage converter, parameter value ranges for multiple first control parameters of the grid simulation device, and parameter value ranges for multiple second control parameters of the environmental simulation device. The multiple control commands include at least one of grid-connected commands, off-grid switching commands, and power step commands. The multiple first control parameters include voltage parameters and frequency parameters. The voltage parameter's value range is within ±20% of AC 380 volts, and the frequency parameter's value range is within ±0.1 Hz of a target frequency value. The multiple second control parameters include temperature parameters and humidity parameters. The temperature parameter's value range is between -40°C and 85°C, and the frequency parameter's value range is within ±3% of a target relative humidity value. The plurality of first control parameters are taken multiple times within their respective parameter value ranges to obtain multiple parameter values ​​for each of the plurality of first control parameters; and the plurality of second control parameters are taken multiple times within their respective parameter value ranges to obtain multiple parameter values ​​for each of the plurality of second control parameters. Multiple control data sets are generated, each of the multiple control data sets including one control instruction from the multiple control instructions, one parameter value from each first control parameter, and one parameter value from each second control parameter; wherein, different control data sets include different control instructions, different parameter values ​​from at least one first control parameter, or different parameter values ​​from at least one second control parameter; Based on the multiple control data sets, the grid-type energy storage converter, the power grid simulation device, and the environmental simulation device are controlled to operate. Based on the data acquisition device, test data sets corresponding to each of the multiple control data sets are acquired. Each test data set includes test data in multiple dimensions, including voltage and current. The sampling frequency of the data acquisition device is greater than or equal to 10 kHz, and the error in acquiring voltage and current by the data acquisition device is less than or equal to 1%. Based on a pre-built hierarchical analysis model, weight coefficients corresponding to the test data of the multiple dimensions are determined, and based on the weight coefficients, test results corresponding to the test data set are determined. The generation of multiple control data sets includes: When the plurality of first control parameters and the total dimension of the plurality of first control parameters are less than or equal to 3, multiple control data sets are generated based on the full permutation combination function; When the plurality of first control parameters and the total dimension of the plurality of first control parameters are greater than 3, a plurality of control data sets are generated based on the orthogonal design table.

2. The method according to claim 1, characterized in that, The plurality of first control parameters also include load parameters, and the plurality of test data dimensions include at least one of voltage transient recovery time, power factor, and total harmonic distortion of current, and the generation of a plurality of control data sets includes: Multiple first control data sets are generated. Each first control data set includes a target control instruction from the multiple control instructions, a parameter value of the load, a target parameter value of the first control parameter other than the load from the multiple first control parameters, and a target parameter value of each second control parameter. The different first control data sets include different load parameter values. Multiple second control data sets are generated. Each second control data set includes one control instruction from the multiple control instructions, a target parameter value for each first control parameter, and a target parameter value for each second control parameter. Different second control data sets include different control instructions.

3. The method according to claim 2, characterized in that, The plurality of second control parameters further includes at least one of a pH parameter and a dust concentration parameter, and the generation of the plurality of control data sets by the second control parameters further includes: Multiple sets of third control data sets are generated. Each third control data set in each set includes a target control instruction from the multiple control instructions, a target parameter value for each first control parameter, a parameter value for a target second control parameter, and target parameter values ​​for the second control parameters other than the target second control parameter from the multiple second control parameters. Different sets of third control data sets correspond to different target second control parameters, and the parameter values ​​of the target second control parameters included in different sets of third control data sets within each set are different.

4. The method according to claim 3, characterized in that, The multiple controlled devices also include heat dissipation devices with a heat dissipation power greater than or equal to 5 kilowatts. The multiple dimensions of test data include the temperature rise rate of the insulated gate bipolar transistor module and the performance degradation rate after 1000 thermal cycles. Based on the multiple control data sets, the grid-type energy storage converter, the grid simulation device, and the environmental simulation device are controlled to operate. Based on the data acquisition device, test data sets corresponding to the multiple control data sets are acquired, including: Based on multiple third control data sets including a target temperature value, the grid-type energy storage converter, the power grid simulation device, and the environmental simulation device are controlled to operate, and the heat dissipation device is activated; wherein, the target temperature value includes the maximum value among multiple parameter values ​​of the temperature parameter; Based on the data acquisition device, acquire test data sets corresponding to the plurality of third control data sets respectively.

5. The method according to claim 4, characterized in that, The plurality of controlled devices further includes a fault simulation device, wherein the fault response time of the fault simulation device is less than or equal to 10 milliseconds, and the test data of the plurality of dimensions includes at least one of mean fault-free operating time and fault type distribution data; the step of controlling the operation of the grid-type energy storage converter, the grid simulation device, and the environmental simulation device based on the plurality of control data sets, and acquiring test data sets corresponding to the plurality of control data sets based on the data acquisition device, further includes: Based on the target control data set among the multiple control data sets, the grid-type energy storage converter, the power grid simulation device, and the environmental simulation device are controlled to operate continuously for a preset time, and the fault type simulated by the fault simulation device is switched. Based on the data acquisition device, a set of test data corresponding to the fault type is obtained.

6. The method according to claim 1, characterized in that, The pre-built hierarchical analysis model determines the weight coefficients corresponding to the test data of the multiple dimensions, and based on the weight coefficients, determines the test results corresponding to the test data set, including: Determine the set of statistical values ​​corresponding to each of the test data sets; each set of statistical values ​​includes statistical values ​​of test data from multiple dimensions; Based on a pre-built hierarchical analysis model, a judgment matrix corresponding to the set of statistical values ​​is determined; the judgment matrix includes multiple matrix elements, each of which is used to characterize the importance of a statistical value in one dimension relative to a statistical value in another dimension. The largest eigenvalue of the judgment matrix and the eigenvector corresponding to the largest eigenvalue are determined, and a weight matrix corresponding to the set of statistical values ​​is determined based on the largest eigenvalue and the eigenvector; the weight matrix includes multiple weight coefficients that correspond one-to-one with the statistical values ​​of multiple dimensions, and each weight coefficient is used to characterize the importance of the corresponding statistical value in the statistical values ​​of the multiple dimensions; Based on the weight matrix, the test results corresponding to the set of statistical values ​​are determined.

7. A testing device for a grid-type energy storage converter, characterized in that, A main control device is used in a testing system, which also includes multiple controlled devices, including a grid-type energy storage converter, a power grid simulation device, an environmental simulation device, and a data acquisition device. The device comprises: The acquisition module is used to respond to a test request from a grid-connected energy storage converter by acquiring multiple control commands of the grid-connected energy storage converter, parameter value ranges of multiple first control parameters of the grid simulation device, and parameter value ranges of multiple second control parameters of the environmental simulation device. The multiple control commands include at least one of grid-connected commands, off-grid switching commands, and power step commands. The multiple first control parameters include voltage parameters and frequency parameters. The voltage parameter's value range is within ±20% of AC 380 volts, and the frequency parameter's value range is within ±0.1 Hz of a target frequency value. The multiple second control parameters include temperature parameters and humidity parameters. The temperature parameter's value range is within -40 degrees Celsius to 85 degrees Celsius, and the frequency parameter's value range is within ±3% of a target relative humidity value. The value acquisition module is used to acquire multiple values ​​of the plurality of first control parameters within their respective parameter value ranges to obtain multiple parameter values ​​for each of the plurality of first control parameters, and to acquire multiple values ​​of the plurality of second control parameters within their respective parameter value ranges to obtain multiple parameter values ​​for each of the plurality of second control parameters. A generation module is used to generate multiple control data sets, each of which includes one control instruction from the multiple control instructions, a parameter value from each first control parameter, and a parameter value from each second control parameter; wherein different control data sets include different control instructions, different parameter values ​​from at least one first control parameter, or different parameter values ​​from at least one second control parameter. The control module is used to control the operation of the grid-type energy storage converter, the power grid simulation device, and the environmental simulation device based on the plurality of control data sets, and to acquire test data sets corresponding to the plurality of control data sets based on the data acquisition device; each test data set includes test data in multiple dimensions; the test data in multiple dimensions includes voltage and current, the sampling frequency of the data acquisition device is greater than or equal to 10 kHz, and the error of the data acquisition device in acquiring voltage and current is less than or equal to 1%; The determination module is used to determine the weight coefficients corresponding to the test data of the multiple dimensions based on a pre-built hierarchical analysis model, and to determine the test results corresponding to the test data set based on the weight coefficients. The generation module is further configured to generate multiple control data sets based on a full permutation combination function when the multiple first control parameters and the total dimension of the multiple first control parameters are less than or equal to 3. The generation module is further configured to generate multiple control data sets based on an orthogonal design table when the multiple first control parameters and the total dimension of the multiple first control parameters are greater than 3.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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