Intelligent management system and method for energy storage cabinet

Abstract

The invention discloses an intelligent management system and method for an energy storage cabinet, and relates to the technical field of energy storage systems, and the method comprises the steps: employing a local controller to distribute a unique equipment ID for an energy storage converter, and carrying out the grouping of the energy storage converter according to the number of DC side buses; setting a running state monitoring strategy of the energy storage converter, and judging the running state of the energy storage converter; obtaining a second main energy storage converter and a first main energy storage converter by setting a second master-slave identification strategy and a first master-slave identification strategy; constructing a particle swarm discharge adjustment algorithm through an energy storage converter operation state monitoring strategy in combination with the first main energy storage converter and the second main energy storage converter, obtaining control parameters according to the particle swarm discharge adjustment algorithm, and adjusting an energy storage cabinet discharge strategy; according to the method, the discharge time and the discharge power can be automatically optimized according to the actual operation state of the energy storage converter, the optimal overall performance of the system is ensured, and the flexibility of energy management and scheduling is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage systems, and in particular to an intelligent management system and method for energy storage cabinets. Background Art

[0002] With the continuous development of the global economy and the advancement of science and technology, the demand for energy is growing, especially the demand for clean and sustainable energy is becoming increasingly urgent. In this context, energy storage technology has received widespread attention as an important means to balance energy supply and demand and improve energy utilization efficiency. As an important part of the energy storage system, the intelligent management level of the energy storage cabinet directly affects the overall performance and economic benefits of the energy storage system. However, with the rapid development and widespread application of energy storage technology, the management of energy storage cabinets faces many challenges: energy storage cabinets may face various potential safety risks during operation, such as battery thermal runaway, electrical failure, etc. At the same time, since energy storage cabinets play an important role in the power system, their operating reliability directly affects the overall stability of the power system. Therefore, how to ensure the safe and reliable operation of energy storage cabinets is an important issue in the development of energy storage technology.

[0003] For example, the existing Chinese patent with publication number CN111355252A discloses a distributed energy storage system and its charging and discharging method, including: a programmable logic controller, a battery management system, an energy storage bidirectional converter, a temperature control unit and an electric meter, wherein the programmable logic controller is connected to the battery management system, the energy storage bidirectional converter, the temperature control unit and the electric meter, and is used to control the operation of the energy storage cabinet; the battery management system is used to obtain battery information; the energy storage bidirectional converter controls the charging and discharging of the battery of the energy storage cabinet according to the control command sent by the programmable logic controller; the temperature control unit is used to control the temperature of the energy storage cabinet; the electric meter is used to obtain the electric energy data of the energy storage cabinet and the load information of the power grid where the energy storage cabinet is located. This invention can solve the problem that the charging and discharging strategy of the existing distributed energy storage system is unreasonable, which easily causes the battery life to be reduced, and improve the battery life.

[0004] However, although the programmable logic controller as a control center can coordinate the work of various components, the preset control strategy may not be able to fully adapt to all operating scenarios and emergencies, resulting in insufficient optimization of the charging and discharging strategy under certain conditions, affecting battery life and energy storage efficiency; the adjustment and optimization of the control strategy may require professional technicians to program and debug, increasing the maintenance cost and difficulty of the system. It also reflects that the traditional energy storage cabinet management system often has the following problems in the control of discharge strategy: Single strategy: Most traditional systems use a fixed discharge strategy and cannot be flexibly adjusted according to factors such as real-time grid load, electricity price fluctuations, and the state of the energy storage cabinet itself, resulting in low energy utilization efficiency; Lack of intelligence: Lack of support for intelligent decision-making and optimization algorithms makes it difficult to achieve precise control and optimized management of the energy storage cabinet discharge process; Insufficient system security: Traditional systems have deficiencies in fault monitoring, early warning, and response, which may increase the operating risks of energy storage cabinets and affect the overall stability of the system.

[0005] To this end, the present invention provides an intelligent management system and method for an energy storage cabinet. Summary of the invention

[0006] The object of the present invention is to provide an intelligent management system and method for an energy storage cabinet to solve the problems existing in the prior art mentioned in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: an intelligent management system for an energy storage cabinet, comprising: The device ID allocation and grouping module uses the local controller to allocate a unique device ID to the energy storage converter and groups the energy storage converter according to the number of DC-side buses; Intelligent monitoring and data analysis module, setting the energy storage converter operation status monitoring strategy and judging the operation status of the energy storage converter; A master-slave identification module, which obtains the second master energy storage converter and the first master energy storage converter by setting the second master-slave identification strategy and the first master-slave identification strategy; The intelligent control module constructs a particle swarm discharge adjustment algorithm by combining the first main energy storage converter and the second main energy storage converter through the energy storage converter operation status monitoring strategy, obtains control parameters according to the particle swarm discharge adjustment algorithm, and adjusts the energy storage cabinet discharge strategy.

[0008] The present invention is further improved in that the energy storage converter operation status monitoring strategy includes a data acquisition sub-strategy and a fault diagnosis sub-strategy. The data acquisition sub-strategy monitors the temperature and pressure of the energy storage converter by evenly installing temperature sensors and pressure sensors in each energy storage converter, and monitors the vibration threat, current load condition and conversion efficiency of the energy storage converter; the fault diagnosis sub-strategy is used to calculate the state value of the energy storage converter in combination with the data in the data acquisition sub-strategy. , and set the energy storage converter state threshold. When the energy storage converter state value is less than or equal to the energy storage converter state threshold, it is sent to the master-slave identification module. When the energy storage converter state value is greater than the energy storage converter state threshold, the energy storage converter that meets the conditions is uploaded to the fault center.

[0009] The present invention is further improved in that the data acquisition sub-strategy specifically includes: measuring the current load data sequence in the energy storage converter ,in Indicates Current load, Indicates the amount of current load collected; evenly set in each energy storage converter Install temperature sensors at each location, extract the measured values ​​of all temperature sensors in the energy storage converter and include them in the temperature data set , Indicates The temperature measurement value of each position is obtained, and the pressure sensor is installed around the temperature sensor to obtain the pressure data set. , Indicates Pressure data at each position; collect surface amplitude data of energy storage converter and vibration speed , get the vibration threat value of the energy storage converter , and calculate the average temperature data of the energy storage converter ; The current load condition includes extracting the maximum value of the current load data sequence in the energy storage converter , by measuring the input and output power, calculate the conversion efficiency .

[0010] A further improvement of the present invention is that the fault diagnosis sub-strategy includes calculating the state value of the energy storage converter, and the calculation formula is: ; in, represents the vibration threat value weight of the energy storage converter, represents the mean weight of the temperature data of the energy storage converter, Indicates the current load condition weight, represents the conversion efficiency weight, .

[0011] A further improvement of the present invention is that the master-slave identification module includes, when any energy storage inverter triggers the second master-slave identification strategy, broadcasting a second identification signal to the group to which it belongs, competing with other energy storage inverters in the same group to become the second master energy storage inverter; when any second master energy storage inverter triggers the first master-slave identification strategy, sending a first identification signal to other second master energy storage inverters, competing with the first master energy storage inverter, and outputting it to the intelligent control module.

[0012] A further improvement of the present invention is that the second master-slave identification strategy includes setting a state condition demarcation value, extracting an energy storage inverter whose state value is greater than the state condition demarcation value as a second candidate, and the second candidate broadcasts a second identification signal to its group. Other energy storage inverters in the group will also receive this signal and check whether they also meet the conditions for becoming the second master energy storage inverter. If there are more than one energy storage inverters in the same group that meet the conditions and send signals, they will compete with each other based on the response time, and the energy storage inverter in the group that meets the conditions of the second master energy storage inverter and has the shortest response time will be included in the second master energy storage inverter sequence.

[0013] The present invention is further improved in that the first master-slave identification strategy includes collecting fault records of each energy storage converter in the second master energy storage converter sequence within the same time period, including the occurrence time and duration of the fault, wherein the occurrence time is used to record the number of faults N of each energy storage converter; collecting maintenance records of each energy storage converter, including the maintenance cycle and the performance comparison before and after maintenance, wherein the performance comparison before and after maintenance is performed by calculating the difference between the state values ​​of the energy storage converter before and after maintenance to obtain a performance change value, and calculating the ratio of the performance change value to the state value of the energy storage converter before maintenance to obtain a repair score; calculating the average duration of each fault of the energy storage converter, and obtaining a recovery score of each energy storage converter by the ratio of the repair score to the average duration; setting a recovery score threshold, and listing the energy storage converters in the second master energy storage converter sequence whose recovery scores are greater than the recovery score threshold in the first master energy storage converter candidate sequence, and selecting the second master energy storage converter with the lowest number of faults in the first master energy storage converter candidate sequence as the first master energy storage converter, and inputting it into the intelligent control module.

[0014] The present invention is further improved in that the particle swarm discharge adjustment algorithm specifically comprises the following steps: Step 1: Define and initialize the particle swarm , the initialized particle swarm It is composed of individuals, and the individuals include two discharge control operations, including adjusting the discharge time and adjusting the discharge power; Step 2: randomly generate a set of discharge control operation data on the first main energy storage converter, and use the energy storage converter state value calculation formula as the fitness evaluation function FF; Step 3: Sort the fitness of the randomly generated multiple groups of discharge control operation data from high to low, obtain the fitness sequence, and obtain the particle swarm. As the first parent population; Step 4: Select the discharge control operation with the highest fitness in the fitness sequence and directly copy it to the next generation, and form a new population by performing individual crossover operations on the remaining discharge control operations, and include the new population and the discharge control operation with the highest fitness in the second generation population. , replace with the second progeny population Become the second parent population; Step 5: Set the fitness change threshold , when the fitness change satisfies less than When , stop the particle swarm discharge adjustment algorithm and proceed to step 6. If it is not satisfied, return to step 3 to generate the third generation population replace As the third parent population, it stops iterating until the iteration condition is met; Step 6: The iteration result in step 5 is used as a control parameter and outputted, and the control parameter is sent to the control system of the energy storage cabinet through the second main energy storage converter, and the control system adjusts the discharge strategy of the energy storage cabinet according to the received control parameter.

[0015] In another aspect, the present invention provides an intelligent management method for an energy storage cabinet, comprising the following steps: S1. Use the local controller to assign a unique device ID to the energy storage converter, and group the energy storage converter according to the number of DC side buses; S2. Setting the energy storage converter operation status monitoring strategy to determine the operation status of the energy storage converter; S3, obtaining a second master energy storage converter and a first master energy storage converter by setting a second master-slave identification strategy and a first master-slave identification strategy; S4. A particle swarm discharge adjustment algorithm is constructed by combining the first main energy storage converter and the second main energy storage converter through the energy storage converter operation status monitoring strategy. According to the particle swarm discharge adjustment algorithm, control parameters are obtained to adjust the energy storage cabinet discharge strategy.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention firstly enhances the stability and reliability of the system through the master-slave identification mechanism, and ensures that the system can still operate normally even when some components fail by dynamically selecting the main energy storage converter; the second master-slave identification and the first master-slave identification strategies can effectively realize the collaborative work between devices and ensure the efficient operation of the system.

[0017] 2. Secondly, the particle swarm optimization algorithm is used to dynamically adjust the discharge strategy to maximize the efficiency and life of the energy storage system. It can automatically optimize the discharge time and discharge power according to the actual operating status of the energy storage inverter to ensure the overall optimal performance of the system. Through the iterative optimization process, the best control parameter combination is found, further improving the flexibility of energy management and scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a framework diagram of an intelligent management system for an energy storage cabinet according to the present invention; Figure 2 A schematic diagram of the working principle of a master-slave identification module in an intelligent management system for an energy storage cabinet according to the present invention; Figure 3 This is a flow chart of a particle swarm discharge adjustment algorithm for an intelligent management system for an energy storage cabinet of the present invention; Figure 4 This is a flow chart of an intelligent management method for energy storage cabinets according to the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0020] The term "and / or" is only a description of the association relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " generally indicates that the related objects are in an "or" relationship.

[0021] Example 1 Figure 1 The framework diagram of an intelligent management system for an energy storage cabinet disclosed in this embodiment is shown, including: The device ID allocation and grouping module uses the local controller to allocate a unique device ID to the energy storage converter and groups the energy storage converter according to the number of DC-side buses; Intelligent monitoring and data analysis module, setting the energy storage converter operation status monitoring strategy and judging the operation status of the energy storage converter; The energy storage converter operation status monitoring strategy includes a data acquisition sub-strategy and a fault diagnosis sub-strategy. The data acquisition sub-strategy monitors the temperature and pressure of the energy storage converter by evenly installing temperature sensors and pressure sensors in each energy storage converter, and monitors the vibration threat, current load condition and conversion efficiency of the energy storage converter; the fault diagnosis sub-strategy is used to calculate the state value of the energy storage converter in combination with the data in the data acquisition sub-strategy. , and set the energy storage converter state threshold. When the energy storage converter state value is less than or equal to the energy storage converter state threshold, it is sent to the master-slave identification module. When the energy storage converter state value is greater than the energy storage converter state threshold, the energy storage converter that meets the conditions is uploaded to the fault center.

[0022] The data acquisition sub-strategy specifically includes: measuring the current load data sequence in the energy storage converter ,in Indicates The number of current loads, Indicates the amount of current load collected; evenly set in each energy storage converter Install temperature sensors at each location, extract the measured values ​​of all temperature sensors in the energy storage converter and include them in the temperature data set , Indicates The temperature measurement value of each position is obtained, and the pressure sensor is installed around the temperature sensor to obtain the pressure data set. , Indicates Pressure data at each position; collect surface amplitude data of energy storage converter and vibration speed Since pressure can reflect vibration to a certain extent, the average value of the pressure of the energy storage converter is calculated by combining the surface amplitude data and vibration velocity of the energy storage converter to obtain the vibration threat value of the energy storage converter. , and calculate the average temperature data of the energy storage converter ; The current load condition includes extracting the maximum value of the current load data sequence in the energy storage converter , by measuring the input and output power, calculate the conversion efficiency .

[0023] The fault diagnosis sub-strategy includes calculating the state value of the energy storage converter, and the calculation formula is: ; in, represents the vibration threat value weight of the energy storage converter, represents the mean weight of the temperature data of the energy storage converter, Indicates the current load condition weight, represents the conversion efficiency weight, .

[0024] Master-slave identification module, Figure 2 The present invention shows a schematic diagram of the working principle of a master-slave identification module of an intelligent management system for an energy storage cabinet, and the second master-slave identification strategy and the first master-slave identification strategy are set to obtain the second master energy storage converter and the first master energy storage converter; The master-slave identification module includes: when any energy storage inverter triggers the second master-slave identification strategy, broadcasting the second identification signal to the group to which it belongs, competing with other energy storage inverters in the same group to become the second master energy storage inverter; when any second master energy storage inverter triggers the first master-slave identification strategy, sending the first identification signal to other second master energy storage inverters, competing with the first master energy storage inverter, and outputting it to the intelligent control module.

[0025] The second master-slave identification strategy includes setting a state condition delimiting value, extracting an energy storage inverter whose state value is greater than the state condition delimiting value as a second candidate, and the second candidate broadcasts a second identification signal to its group. Other energy storage inverters in the group will also receive this signal and check whether they also meet the conditions for becoming the second master energy storage inverter. If there are more than one energy storage inverters in the same group that meet the conditions and send signals, they will compete with each other based on the response time, and the energy storage inverter in the group that meets the conditions of the second master energy storage inverter and has the shortest response time will be included in the second master energy storage inverter sequence.

[0026] The first master-slave identification strategy includes collecting fault records of each energy storage converter in the second master energy storage converter sequence within the same time period, including the occurrence time and duration of the fault, wherein the occurrence time is used to record the number of faults N of each energy storage converter; collecting maintenance records of each energy storage converter, including the maintenance cycle and the performance comparison before and after maintenance, wherein the performance comparison before and after maintenance is performed by calculating the difference between the state values ​​of the energy storage converter before and after maintenance to obtain a performance change value; Although the greater the performance change, the better the repair effect, the severity of the initial equipment failure will also affect the status evaluation of the energy storage inverter. Therefore, the repair score is obtained by calculating the ratio of the performance change value to the status value of the energy storage inverter before maintenance to balance the impact of the repair effect and the severity of the initial failure; the average duration of each failure of the energy storage inverter is calculated, and the recovery score of each energy storage inverter is obtained by the ratio of the repair score and the average duration; the recovery score threshold is set, and the energy storage inverters with a recovery score greater than the recovery score threshold in the second main energy storage inverter sequence are included in the first main energy storage inverter candidate sequence, and the second main energy storage inverter with the lowest number of failures in the first main energy storage inverter candidate sequence is selected as the first main energy storage inverter, and input into the intelligent control module.

[0027] The intelligent control module constructs a particle swarm discharge adjustment algorithm by combining the first main energy storage converter and the second main energy storage converter through the energy storage converter operation status monitoring strategy, obtains control parameters according to the particle swarm discharge adjustment algorithm, and adjusts the energy storage cabinet discharge strategy.

[0028] Figure 3 The present invention shows a flow chart of a particle swarm discharge adjustment algorithm for an intelligent management system of an energy storage cabinet. The specific steps of the particle swarm discharge adjustment algorithm include: Step 1: Define and initialize the particle swarm , the initialized particle swarm It is composed of individuals, which include two discharge control operations, including adjusting the discharge time and adjusting the discharge power. The algorithm can adjust the discharge strategy according to actual needs; Step 2: randomly generate a set of discharge control operation data on the first main energy storage converter, use the energy storage converter state value calculation formula as the fitness evaluation function FF, quantitatively evaluate the advantages and disadvantages of different discharge control operations, and provide a basis for subsequent selection and optimization; Step 3: Sort the fitness of the randomly generated multiple groups of discharge control operation data from high to low, obtain the fitness sequence, and obtain the particle swarm. As the first parent population; Step 4: Select the discharge control operation with the highest fitness in the fitness sequence and directly copy it to the next generation, and form a new population by performing individual crossover operations on the remaining discharge control operations, and include the new population and the discharge control operation with the highest fitness in the second generation population. , replace with the second progeny population Become the second parent population; Step 5: Set the fitness change threshold , when the fitness change satisfies less than When , stop the particle swarm discharge adjustment algorithm and proceed to step 6. If it is not satisfied, return to step 3 to generate the third generation population replace As the third parent population, it stops iterating until the iteration condition is met; Step 6: The iteration result in step 5 is used as a control parameter and outputted, and the control parameter is sent to the control system of the energy storage cabinet through the second main energy storage converter, and the control system adjusts the discharge strategy of the energy storage cabinet according to the received control parameter.

[0029] The threshold and weight settings can be set by the operator.

[0030] Example 2 Figure 4The flow chart of an intelligent management method for an energy storage cabinet of the present invention is shown. Based on the same inventive concept as that of Example 1, the present invention provides an intelligent management method for an energy storage cabinet, comprising the following steps: S1. Use a local controller to assign a unique device ID to the energy storage converter, and group the energy storage converter according to the number of DC-side busbars; S2. Setting the energy storage converter operation status monitoring strategy to determine the operation status of the energy storage converter; S3, obtaining a second master energy storage converter and a first master energy storage converter by setting a second master-slave identification strategy and a first master-slave identification strategy; S4. A particle swarm discharge adjustment algorithm is constructed by combining the first main energy storage converter and the second main energy storage converter through the energy storage converter operation status monitoring strategy. According to the particle swarm discharge adjustment algorithm, control parameters are obtained to adjust the energy storage cabinet discharge strategy.

[0031] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0032] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0033] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0034] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0035] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. An intelligent management system for energy storage cabinets, characterized in that: include: The device ID allocation and grouping module uses the local controller to allocate a unique device ID to the energy storage converter and groups the energy storage converter according to the number of DC-side buses; Intelligent monitoring and data analysis module, setting the energy storage converter operation status monitoring strategy and judging the operation status of the energy storage converter; A master-slave identification module, which obtains the second master energy storage converter and the first master energy storage converter by setting the second master-slave identification strategy and the first master-slave identification strategy; The intelligent control module constructs a particle swarm discharge adjustment algorithm by combining the first main energy storage converter and the second main energy storage converter through the energy storage converter operation status monitoring strategy, obtains control parameters according to the particle swarm discharge adjustment algorithm, and adjusts the energy storage cabinet discharge strategy.

2. The intelligent management system for energy storage cabinet according to claim 1, characterized in that: The energy storage converter operation status monitoring strategy includes a data acquisition sub-strategy and a fault diagnosis sub-strategy. The data acquisition sub-strategy monitors the temperature and pressure of the energy storage converter by evenly installing temperature sensors and pressure sensors in each energy storage converter, and monitors the vibration threat, current load condition and conversion efficiency of the energy storage converter; the fault diagnosis sub-strategy is used to calculate the state value of the energy storage converter in combination with the data in the data acquisition sub-strategy. , and set the energy storage converter state threshold. When the energy storage converter state value is less than or equal to the energy storage converter state threshold, it is sent to the master-slave identification module. When the energy storage converter state value is greater than the energy storage converter state threshold, the energy storage converter that meets the conditions is uploaded to the fault center.

3. The intelligent management system for energy storage cabinet according to claim 2, characterized in that: The data acquisition sub-strategy specifically includes: measuring the current load data sequence in the energy storage converter ,in Indicates Current load, Indicates the amount of current load collected; evenly set in each energy storage converter Install temperature sensors at each location, extract the measured values ​​of all temperature sensors in the energy storage converter and include them in the temperature data set , Indicates The temperature measurement value of each position is obtained, and the pressure sensor is installed around the temperature sensor to obtain the pressure data set. , Indicates Pressure data at each position; collect surface amplitude data of energy storage converter and vibration speed , get the vibration threat value of the energy storage converter , and calculate the average temperature data of the energy storage converter ; The current load condition includes extracting the maximum value of the current load data sequence in the energy storage converter , by measuring the input and output power, calculate the conversion efficiency .

4. The intelligent management system for energy storage cabinet according to claim 3, characterized in that: The fault diagnosis sub-strategy includes calculating the state value of the energy storage converter, and the calculation formula is: ； in, represents the vibration threat value weight of the energy storage converter, represents the mean weight of the temperature data of the energy storage converter, Indicates the current load condition weight, represents the conversion efficiency weight, .

5. The intelligent management system for energy storage cabinet according to claim 4, characterized in that: The master-slave identification module includes: when any energy storage inverter triggers the second master-slave identification strategy, broadcasting the second identification signal to the group to which it belongs, competing with other energy storage inverters in the same group to become the second master energy storage inverter; when any second master energy storage inverter triggers the first master-slave identification strategy, sending the first identification signal to other second master energy storage inverters, competing with the first master energy storage inverter, and outputting it to the intelligent control module.

6. The intelligent management system for energy storage cabinet according to claim 5, characterized in that: The second master-slave identification strategy includes setting a state condition delimiting value, extracting an energy storage inverter whose state value is greater than the state condition delimiting value as a second candidate, and the second candidate broadcasts a second identification signal to its group. Other energy storage inverters in the group will also receive this signal and check whether they also meet the conditions for becoming the second master energy storage inverter. If there are more than one energy storage inverters in the same group that meet the conditions and send signals, they will compete with each other based on the response time, and the energy storage inverter in the group that meets the conditions of the second master energy storage inverter and has the shortest response time will be included in the second master energy storage inverter sequence.

7. The intelligent management system for energy storage cabinet according to claim 6, characterized in that: The first master-slave identification strategy includes collecting fault records of each energy storage converter in the second master energy storage converter sequence within the same time period, including the occurrence time and duration of the fault, wherein the occurrence time is used to record the number of faults N of each energy storage converter; Collect maintenance records for each energy storage converter, including maintenance cycles and performance comparisons before and after maintenance, wherein the performance comparisons before and after maintenance are performed by calculating the difference between the state values ​​of the energy storage converter before and after maintenance to obtain a performance change value, and by calculating the ratio of the performance change value to the state value of the energy storage converter before maintenance to obtain a repair score; calculate the average duration of each fault of the energy storage converter, and obtain a recovery score for each energy storage converter by the ratio of the repair score to the average duration; A recovery score threshold is set, and the energy storage converters with a recovery score greater than the recovery score threshold in the second main energy storage converter sequence are included in the first main energy storage converter selection sequence, and the second main energy storage converter with the lowest number of failures in the first main energy storage converter selection sequence is selected as the first main energy storage converter, and input into the intelligent control module.

8. The intelligent management system for energy storage cabinet according to claim 7, characterized in that: The specific steps of the particle swarm discharge adjustment algorithm include: Step 1: Define and initialize the particle swarm , the initialized particle swarm It is composed of individuals, and the individuals include two discharge control operations, including adjusting the discharge time and adjusting the discharge power; Step 2: randomly generate a set of discharge control operation data on the first main energy storage converter, and use the energy storage converter state value calculation formula as the fitness evaluation function FF; Step 3: Sort the fitness of the randomly generated multiple groups of discharge control operation data from high to low, obtain the fitness sequence, and obtain the particle swarm. As the first parent population; Step 4: Select the discharge control operation with the highest fitness in the fitness sequence and directly copy it to the next generation, and form a new population by performing individual crossover operations on the remaining discharge control operations, and include the new population and the discharge control operation with the highest fitness in the second generation population. , replace with the second progeny population Become the second parent population; Step 5: Set the fitness change threshold , when the fitness change satisfies less than When , stop the particle swarm discharge adjustment algorithm and proceed to step 6. If it is not satisfied, return to step 3 to generate the third generation population replace As the third parent population, it stops iterating until the iteration condition is met; Step 6: The iteration result in step 5 is used as a control parameter and outputted, and the control parameter is sent to the control system of the energy storage cabinet through the second main energy storage converter, and the control system adjusts the discharge strategy of the energy storage cabinet according to the received control parameter.

9. An intelligent management method for an energy storage cabinet, implemented based on an intelligent management system for an energy storage cabinet according to any one of claims 1 to 8, characterized in that: include: S1. Use the local controller to assign a unique device ID to the energy storage converter, and group the energy storage converter according to the number of DC side buses; S2. Setting the energy storage converter operation status monitoring strategy to determine the operation status of the energy storage converter; S3, obtaining a second master energy storage converter and a first master energy storage converter by setting a second master-slave identification strategy and a first master-slave identification strategy; S4. A particle swarm discharge adjustment algorithm is constructed by combining the first main energy storage converter and the second main energy storage converter through the energy storage converter operation status monitoring strategy. According to the particle swarm discharge adjustment algorithm, control parameters are obtained to adjust the energy storage cabinet discharge strategy.

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