An intelligent management system and method for an energy storage cabinet

Abstract

The present invention discloses an intelligent management system and method for an energy storage cabinet, relating to the technical field of energy storage systems. The steps of the method include: using a local controller to assign a unique device ID to an energy storage converter and grouping the energy storage converters according to the number of DC side busbars; setting an operation state monitoring strategy for the energy storage converter to judge the operation 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 regulation algorithm through the operation state monitoring strategy of the energy storage converter in combination with the first main energy storage converter and the second main energy storage converter, and obtaining control parameters according to the particle swarm discharge regulation algorithm to adjust the discharge strategy of the energy storage cabinet. The present invention can automatically optimize the discharge time and discharge power according to the actual operation state of the energy storage converter, ensure the optimal overall performance of the system, and further improve the flexibility of energy management and scheduling.

Description

Technical Field

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

[0002] With the continuous development of the global economy and the progress of technology, the demand for energy is increasing, especially the demand for clean and sustainable energy is becoming increasingly urgent. In this context, energy storage technology, as an important means to balance energy supply and demand and improve energy utilization efficiency, has received extensive attention. 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 wide application of energy storage technology, the management of energy storage cabinets faces many challenges: during operation, energy storage cabinets may face various potential safety risks, such as battery thermal runaway, electrical failures, 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 the publication number CN111355252A discloses a distributed energy storage system and its charge and discharge method, including: a programmable logic controller, a battery management system, an energy storage bidirectional converter, a temperature control unit, and an electric meter. 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 charge and discharge of the battery in 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 electrical 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 charge and discharge strategy of the existing distributed energy storage system is unreasonable and easily causes the reduction of battery life, and improve the battery life.

[0004] However, although the programmable logic controller, as the control center, can coordinate the work of each component, the preset control strategy may not be able to fully adapt to all operating scenarios and emergencies, resulting in insufficient optimization of the charge and discharge strategy under certain specific 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 discharge strategy control:

[0005] Single strategy: Most traditional systems adopt 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;

[0006] Lack of intelligence: Without the support of intelligent decision-making and optimization algorithms, it is difficult to achieve precise control and optimized management of the discharge process of the energy storage cabinet.

[0007] Insufficient system security: Traditional systems have deficiencies in fault monitoring, early warning, and response, which may increase the operating risks of the energy storage cabinet and affect the overall stability of the system.

[0008] Therefore, the present invention provides an intelligent management system and method for an energy storage cabinet. Summary of the Invention

[0009] The purpose 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 as described in the above background art.

[0010] To achieve the above object, the present invention provides the following technical solution: An intelligent management system for an energy storage cabinet, comprising:

[0011] An equipment ID allocation and grouping module that uses a local controller to assign a unique equipment ID to the energy storage converter and groups the energy storage converters according to the number of DC-side busbars;

[0012] An intelligent monitoring and data analysis module that sets a monitoring strategy for the operating state of the energy storage converter to judge the operating state of the energy storage converter;

[0013] A master-slave identification module that obtains 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;

[0014] An intelligent control module that constructs a particle swarm discharge adjustment algorithm by combining the monitoring strategy of the operating state of the energy storage converter with the first master energy storage converter and the second master energy storage converter, obtains control parameters according to the particle swarm discharge adjustment algorithm, and adjusts the discharge strategy of the energy storage cabinet.

[0015] The present invention is further improved in that the monitoring strategy for the operating state of the energy storage converter 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 by combining the data in the data acquisition sub-strategy , and sets a threshold for the energy storage converter state. When the state value of the energy storage converter is less than or equal to the threshold for the energy storage converter state, it is sent to the master-slave identification module. When the state value of the energy storage converter is greater than the threshold for the energy storage converter state, the energy storage converter that meets the conditions is uploaded to the fault center.

[0016] A further improvement of the present invention lies in that the data acquisition sub-strategy specifically includes: measuring the current load data sequence in the energy storage converter , where represents the th current load, represents the number of collected current loads; evenly set positions in each energy storage converter, install temperature sensors, and extract the measured values of all temperature sensors in the energy storage converter and list them in the temperature data set , represents the measured value of the temperature at the th position. At the same time, install pressure sensors around the temperature sensors to obtain the pressure data set , represents the pressure data at the th position; collect the surface amplitude data and vibration speed of the energy storage converter to obtain the vibration threat value of the energy storage converter, and calculate the average value of the temperature data of the energy storage converter; the current load situation includes extracting the maximum value of the current load data sequence in the energy storage converter, and calculating the conversion efficiency by measuring the input and output powers.

[0017] A further improvement of the present invention lies in that the fault diagnosis sub-strategy includes calculating the state value of the energy storage converter, and the calculation formula is:

[0018] ;

[0019] where represents the weight of the vibration threat value of the energy storage converter, represents the weight of the average value of the temperature data of the energy storage converter, represents the weight of the current load situation, represents the weight of the conversion efficiency, .

[0020] A further improvement of the present invention lies in that the master-slave identification module includes that when any energy storage converter triggers the second master-slave identification strategy, it broadcasts a second identification signal to the group where it is located, competes with other energy storage converters in the same group to select the second main energy storage converter; when any second main energy storage converter triggers the first master-slave identification strategy, it sends a first identification signal to other second main energy storage converters, competes to select the first main energy storage converter, and outputs it to the intelligent control module.

[0021] A further improvement of the present invention lies in that the second master-slave identification strategy includes setting a state condition boundary value, extracting the energy storage converters whose state values are less than the state condition boundary value as the second candidates, and the second candidates broadcast a second identification signal to the group they belong to. Other energy storage converters in the group will also receive this signal and check whether they also meet the conditions to become the second main energy storage converter. If there are more than one energy storage converters in the same group that meet the conditions and send signals, they will compete according to the response time, and the energy storage converter in the group that meets the conditions of the second main energy storage converter and has the shortest response time will be included in the second main energy storage converter sequence.

[0022] A further improvement of the present invention lies in that the first master-slave identification strategy includes collecting the fault records of each energy storage converter in the second main energy storage converter sequence within the same time period, including the occurrence time and duration of the faults. The occurrence time is used to record the number of faults N of each energy storage converter; collecting the maintenance records of each energy storage converter, including the maintenance cycle and the performance comparison before and after maintenance. The performance comparison before and after maintenance is used to obtain the performance change value by calculating the difference between the state values of the energy storage converter before and after maintenance, and the repair score is obtained by calculating the ratio of the performance change value to the state value of the energy storage converter before maintenance; calculating the average value of the duration of each fault of the energy storage converter, and obtaining the recovery score of each energy storage converter by the ratio of the repair score to the average value of the duration; setting a recovery score threshold, and including the energy storage converters in the second main energy storage converter sequence whose recovery scores are greater than the recovery score threshold in the first main energy storage converter candidate sequence, and selecting the second main energy storage converter with the lowest number of faults in the first main energy storage converter candidate sequence as the first main energy storage converter and inputting it into the intelligent control module.

[0023] A further improvement of the present invention lies in that the specific steps of the particle swarm discharge regulation algorithm include:

[0024] Step 1: Define the initialized particle swarm , and the initialized particle swarm is composed of individuals, and each individual includes 2 discharge control operations, including adjusting the discharge time and adjusting the discharge power;

[0025] 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;

[0026] Step 3: Sort the fitness of the randomly generated multiple sets of discharge control operation data from high to low, obtain the fitness sequence, and obtain the particle swarm as the first parental population;

[0027] Step 4: Select the discharge control operation with the lowest fitness in the fitness sequence and directly copy it to the next-generation offspring. Perform individual crossover operations on the remaining discharge control operations to form a new population. Include the new population and the discharge control operation with the lowest fitness in the second-generation offspring population. , and replace it with to become the second parental population;

[0028] Step 5: Set a fitness change threshold . When the fitness change is less than , stop the particle swarm discharge regulation algorithm and proceed to Step 6. If not satisfied, return to Step 3 to generate a third-generation offspring population to replace as the third parental population until the iteration condition is met and stop the iteration;

[0029] Step 6: Take the iteration result in Step 5 as the control parameter and output it. Send the control parameter to the control system of the energy storage cabinet through the second main energy storage converter. The control system adjusts the discharge strategy of the energy storage cabinet according to the received control parameter.

[0030] On the other hand, the present invention provides an intelligent management method for an energy storage cabinet, including the following steps:

[0031] S1. Use the local controller to assign a unique device ID to the energy storage converter and group the energy storage converters according to the number of DC side busbars;

[0032] S2. Set an energy storage converter operation status monitoring strategy to judge the operation status of the energy storage converter;

[0033] S3. Obtain the second main energy storage converter and the first main energy storage converter by setting the second master-slave identification strategy and the first master-slave identification strategy;

[0034] S4. Construct a particle swarm discharge regulation algorithm through the energy storage converter operation status monitoring strategy in combination with the first main energy storage converter and the second main energy storage converter. According to the particle swarm discharge regulation algorithm, obtain the control parameter and adjust the discharge strategy of the energy storage cabinet.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] 1. The present invention first enhances the stability and reliability of the system through the master-slave identification mechanism. By dynamically selecting the main energy storage converter, it ensures that the system can still operate normally even when some components fail. The second master-slave identification and the first master-slave identification strategies can effectively achieve the collaborative work between devices and ensure the efficient operation of the system.

[0037] 2. Secondly, the particle swarm optimization algorithm is used to dynamically adjust the discharge strategy to maximize the efficiency and lifespan of the energy storage system. It can automatically optimize the discharge time and power according to the actual operating state of the energy storage converter to ensure the optimal overall performance of the system. Through the iterative optimization process, the best combination of control parameters is found, further improving the flexibility of energy management and scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a framework diagram of an intelligent management system for an energy storage cabinet according to the present invention;

[0039] Figure 2 It is a schematic diagram of the working principle of the master-slave identification module in an intelligent management system for an energy storage cabinet according to the present invention;

[0040] Figure 3 It is a flow chart of the particle swarm discharge adjustment algorithm of an intelligent management system for an energy storage cabinet according to the present invention;

[0041] Figure 4 It is a flow chart of an intelligent management method for an energy storage cabinet according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The technical solution of the present invention will be described in detail below with reference to the drawings and specific embodiments. It should be understood that the specific features in the embodiments of the present invention and the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. Without conflict, the technical features in the embodiments of the present invention and the embodiments can be combined with each other.

[0043] The term "and / or" is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " generally represents an "or" relationship between the associated objects before and after.

[0044] Embodiment 1

[0045] Figure 1 It shows a framework diagram of an intelligent management system for an energy storage cabinet disclosed in this embodiment, including:

[0046] The device ID allocation and grouping module uses the local controller to assign a unique device ID to the energy storage converter and group the energy storage converters according to the number of DC side buses;

[0047] The intelligent monitoring and data analysis module sets the monitoring strategy for the operating state of the energy storage converter to judge the operating state of the energy storage converter;

[0048] The monitoring strategy for the operating status of the energy storage converter 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 status value of the energy storage converter by combining the data in the data acquisition sub-strategy , and set the threshold of the energy storage converter status. When the status value of the energy storage converter is less than or equal to the threshold of the energy storage converter status, it is sent to the master-slave identification module. When the status value of the energy storage converter is greater than the threshold of the energy storage converter status, the energy storage converter that meets the conditions is uploaded to the fault center

[0049] The data acquisition sub-strategy specifically includes: measuring the current load data sequence in the energy storage converter , where represents the th current load quantity, represents the collected current load quantity; evenly set positions in each energy storage converter, install temperature sensors, and extract the measured values of all temperature sensors in the energy storage converter into the temperature data set , represents the temperature measurement value at the th position. At the same time, install pressure sensors around the temperature sensors to obtain the pressure data set , represents the pressure data at the th position; collect the surface amplitude data and vibration velocity of the energy storage converter. Since the pressure can reflect the vibration situation 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 value of the 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, and calculating the conversion efficiency by measuring the input and output powers

[0050] The fault diagnosis sub-strategy includes calculating the status value of the energy storage converter, and the calculation formula is:

[0051] ;

[0052] where represents the weight of the vibration threat value of the energy storage converter, represents the weight of the average value of the temperature data of the energy storage converter, Indicates the weight of the current load condition, Indicates the weight of the conversion efficiency, .

[0053] Master-slave identification module, Figure 2 Shows a schematic diagram of the working principle of the master-slave identification module of an intelligent management system for an energy storage cabinet according to the present invention. The second main energy storage converter and the first main energy storage converter are obtained by setting the second master-slave identification strategy and the first master-slave identification strategy;

[0054] The master-slave identification module includes that when any energy storage converter triggers the second master-slave identification strategy, it broadcasts a second identification signal to the group where it is located, and competes with other energy storage converters in the same group to select the second main energy storage converter; when any second main energy storage converter triggers the first master-slave identification strategy, it sends a first identification signal to other second main energy storage converters, competes to select the first main energy storage converter, and outputs it to the intelligent control module.

[0055] The second master-slave identification strategy includes setting a state condition boundary value, extracting the energy storage converters whose state values are less than the state condition boundary value as the second candidates. The second candidates broadcast a second identification signal to the group where they are located, and other energy storage converters in the group will also receive this signal and check whether they also meet the conditions for becoming the second main energy storage converter. If there is more than 1 energy storage converter that meets the conditions and sends a signal in the same group, they will compete according to the response time, and the energy storage converter that meets the conditions for the second main energy storage converter and has the shortest response time in the group will be included in the second main energy storage converter sequence.

[0056] The first master-slave identification strategy includes collecting the fault records of each energy storage converter in the second main energy storage converter sequence within the same time period, including the occurrence time and duration of the faults. The occurrence time is used to record the number of faults N of each energy storage converter; collecting the maintenance records of each energy storage converter, including the maintenance period and the performance comparison before and after maintenance. The performance change value is obtained by calculating the difference between the state values of the energy storage converter before and after maintenance;

[0057] Although a greater change in performance indicates a better repair effect, the severity of the initial equipment failure also affects the state assessment of the energy storage converter. Therefore, a repair score is obtained by calculating the ratio of the performance change value to the state value of the energy storage converter before repair to balance the influence of the repair effect and the severity of the initial failure. Calculate the average value of the duration of each failure of the energy storage converter, and obtain the recovery score of each energy storage converter by the ratio of the repair score to the average value of the duration. Set a recovery score threshold, list the energy storage converters with a recovery score greater than the recovery score threshold in the second main energy storage converter sequence into the first main energy storage converter candidate sequence, and select the second main energy storage converter with the lowest number of failures in the first main energy storage converter candidate sequence as the first main energy storage converter and input it to the intelligent control module.

[0058] The intelligent control module constructs a particle swarm discharge regulation algorithm by combining the operation state monitoring strategy of the energy storage converter with the first main energy storage converter and the second main energy storage converter. According to the particle swarm discharge regulation algorithm, control parameters are obtained to adjust the discharge strategy of the energy storage cabinet.

[0059] Figure 3 The flowchart of the particle swarm discharge regulation algorithm of an intelligent management system for an energy storage cabinet according to the present invention is shown. The specific steps of the particle swarm discharge regulation algorithm include:

[0060] Step 1: Define the initial particle swarm , and the initial particle swarm is composed of individuals. Each individual includes 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;

[0061] 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 to quantitatively evaluate the advantages and disadvantages of different discharge control operations, providing a basis for subsequent selection and optimization;

[0062] Step 3: Sort the fitness of the randomly generated multiple sets of discharge control operation data from high to low to obtain a fitness sequence, and obtain the particle swarm as the first parental population;

[0063] Step 4: Select the discharge control operation with the lowest fitness in the fitness sequence and directly copy it to the next-generation offspring. Cross the remaining discharge control operations to form a new population, and include the new population and the discharge control operation with the lowest fitness in the second offspring population , and replace with it to become the second parental population;

[0064] Step 5: Set the fitness change threshold When the fitness change satisfies being less than , stop the particle swarm discharge regulation algorithm and proceed to Step Six. If not satisfied, return to Step Three to generate the third-generation population Replace as the third parental population until the iteration condition is met and stop the iteration;

[0065] Step Six: Use the iteration result in Step Five as the control parameter and output it. Send the control parameter to the control system of the energy storage cabinet through the second main energy storage converter. The control system adjusts the discharge strategy of the energy storage cabinet according to the received control parameter.

[0066] The setting of the threshold and weight can be set by the operator himself.

[0067] Embodiment 2

[0068] Figure 4 shows a flowchart of an intelligent management method for an energy storage cabinet according to the present invention. Based on the same inventive concept as Embodiment 1, the present invention provides an intelligent management method for an energy storage cabinet, including the following steps:

[0069] S1: Use the local controller to assign a unique device ID to the energy storage converter and group the energy storage converters according to the number of DC side busbars;

[0070] S2: Set the operation state monitoring strategy of the energy storage converter and judge the operation state of the energy storage converter;

[0071] S3: Obtain the second main energy storage converter and the first main energy storage converter by setting the second master-slave identification strategy and the first master-slave identification strategy;

[0072] S4: Construct a particle swarm discharge regulation algorithm through the energy storage converter operation state monitoring strategy in combination with the first main energy storage converter and the second main energy storage converter. According to the particle swarm discharge regulation algorithm, obtain the control parameter and adjust the discharge strategy of the energy storage cabinet.

[0073] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can adopt the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt 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.

[0074] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0075] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operating steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0077] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit and scope protected by the claims of the present invention. These all fall within the protection scope of the present invention.

Claims

1. An intelligent management system for an energy storage cabinet, characterized in that, Including: An equipment ID allocation and grouping module that uses a local controller to allocate a unique equipment ID to the energy storage converter and groups the energy storage converters according to the number of DC-side busbars; An intelligent monitoring and data analysis module that sets a monitoring strategy for the operating state of the energy storage converter to judge the operating state of the energy storage converter; A master-slave identification module that obtains 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; An intelligent control module that constructs a particle swarm discharge regulation algorithm through the energy storage converter operating state monitoring strategy in combination with the first master energy storage converter and the second master energy storage converter, obtains control parameters according to the particle swarm discharge regulation algorithm, and adjusts the energy storage cabinet discharge strategy; The monitoring strategy for the operating state of the energy storage converter 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 by combining the data in the data acquisition sub-strategy , and set the energy storage converter state threshold. When the state value of the energy storage converter is less than or equal to the energy storage converter state threshold, it is sent to the master-slave identification module. When the state value of the energy storage converter is greater than the energy storage converter state threshold, the energy storage converter that meets the conditions is uploaded to the fault center; The specific steps of the particle swarm discharge regulation algorithm include: Step 1: Define the initialized particle swarm , where the initialized particle swarm is composed of individuals, and each individual includes two discharge control operations, namely adjusting the discharge time and adjusting the discharge power; Step 2: Randomly generate a set of discharge control operation data on the first master energy storage converter, and use the energy storage converter state value calculation formula as the fitness evaluation function FF.

2. The intelligent management system for an energy storage cabinet according to claim 1, wherein: The specific data acquisition sub-strategy includes: measuring the current load data sequence in the energy storage converter , where represents the th current load, represents the number of collected current loads; evenly set positions in each energy storage converter, install temperature sensors, and extract the measured values of all temperature sensors in the energy storage converter into the temperature data set , represents the measured value of the temperature at the th position. At the same time, install pressure sensors around the temperature sensors to obtain the pressure data set , represents the pressure data at the th position; collect the surface amplitude data and vibration speed of the energy storage converter to obtain the vibration threat value of the energy storage converter, and calculate the average value of the temperature data of the energy storage converter; the current load situation includes extracting the maximum value of the current load data sequence in the energy storage converter, and calculating the conversion efficiency by measuring the input and output powers.

3. An intelligent management system for an energy storage cabinet according to claim 2, characterized in that, The fault diagnosis sub-strategy includes calculating the energy storage converter state value, and the calculation formula is: ； Among them, represents the weight of the vibration threat value of the energy storage converter, represents the weight of the average value of the temperature data of the energy storage converter, represents the weight of the current load condition, represents the weight of the conversion efficiency, .

4. The intelligent management system for an energy storage cabinet according to claim 3, characterized in that: The master-slave identification module includes that when any energy storage converter triggers the second master-slave identification strategy, it broadcasts a second identification signal to the group it belongs to, competes with other energy storage converters in the same group to select the second master energy storage converter; when any second master energy storage converter triggers the first master-slave identification strategy, it sends a first identification signal to other second master energy storage converters, competes to select the first master energy storage converter, and outputs it to the intelligent control module.

5. The intelligent management system for an energy storage cabinet according to claim 4, characterized in that: The second master-slave identification strategy includes setting a state condition boundary value, extracting the energy storage converters whose energy storage converter state values are less than the state condition boundary value as second candidates, and the second candidates broadcast a second identification signal to the group they belong to. Other energy storage converters in the group will also receive this signal and check whether they also meet the conditions to become the second master energy storage converter. If there are more than 1 energy storage converters that meet the conditions and send signals in the same group, they will compete according to the response time. Then, the energy storage converter that meets the conditions of the second master energy storage converter in the group and has the shortest response time will be included in the second master energy storage converter sequence.

6. The intelligent management system for an energy storage cabinet according to claim 5, wherein: The first master-slave identification strategy includes collecting the 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 faults. The occurrence time is used to record the number of faults N of each energy storage converter; Collect the maintenance records of each energy storage converter, including the maintenance cycle and the performance comparison before and after maintenance. The performance comparison before and after maintenance obtains the performance change value by calculating the difference between the energy storage converter state values before and after maintenance, and obtains the repair score by calculating the ratio of the performance change value to the energy storage converter state value before maintenance; calculate the average value of the duration of each fault of the energy storage converter, and obtain the recovery score of each energy storage converter by calculating the ratio of the repair score to the average value of the duration. Set a recovery score threshold, include the energy storage converters in the second main energy storage converter sequence with a recovery score greater than the recovery score threshold in the first main energy storage converter candidate sequence, and select the second main energy storage converter with the lowest number of faults in the first main energy storage converter candidate sequence as the first main energy storage converter, and input it to the intelligent control module.

7. An intelligent management system for an energy storage cabinet according to claim 6, characterized in that: The specific steps of the particle swarm discharge regulation algorithm further include: 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 parental population; Step 4: Select the discharge control operation with the lowest fitness in the fitness sequence and directly copy it into the next-generation offspring. Perform individual crossover operations on the remaining discharge control operations to form a new population. Include the new population and the discharge control operation with the lowest fitness in the second-generation offspring population , and replace it with the second-generation offspring population to become the second parental population; Step 5. Set the fitness change threshold , when the fitness change satisfies being less than , stop the particle swarm discharge adjustment algorithm and proceed to Step 6. If not satisfied, return to Step 3 to generate the third generation population Replace as the third parental population until the iteration condition is satisfied and stop the iteration; Step 6: Use the iteration result in Step 5 as a control parameter and output it. Send the control parameter 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.

8. An intelligent management method for an energy storage cabinet, implemented based on an intelligent management system for an energy storage cabinet as described in any one of claims 1-7, 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 converters according to the number of DC side busbars; S2: Set an energy storage converter operation status monitoring strategy to judge the operation status of the energy storage converter; S3: Obtain the second main energy storage converter and the first main energy storage converter by setting the second master-slave identification strategy and the first master-slave identification strategy; S4: Construct a particle swarm discharge regulation algorithm through the energy storage converter operation status monitoring strategy in combination with the first main energy storage converter and the second main energy storage converter. According to the particle swarm discharge regulation algorithm, obtain control parameters and adjust the discharge strategy of the energy storage cabinet; The monitoring strategy for the operating state of the energy storage converter 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 by combining the data in the data acquisition sub-strategy , and set the energy storage converter state threshold. When the state value of the energy storage converter is less than or equal to the energy storage converter state threshold, it is sent to the master-slave identification module. When the state value of the energy storage converter is greater than the energy storage converter state threshold, the energy storage converter that meets the conditions is uploaded to the fault center; The specific steps of the particle swarm discharge regulation algorithm include: Step 1: Define the initialized particle swarm , where the initialized particle swarm is composed of individuals, and each individual includes two discharge control operations, namely, 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.

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