Fault monitoring and diagnosing system for energy storage battery pack based on artificial intelligence

Abstract

The invention relates to the technical field of energy storage battery monitoring, and discloses an energy storage battery pack fault monitoring and diagnosis system based on artificial intelligence, and the system collects the working parameters of each working energy storage battery pack in the working process in real time, and collects the environment parameters of the environment where each working energy storage battery pack is located in real time. Calculating a fault diagnosis coefficient of each working energy storage battery pack according to the working parameters and the environmental parameters of each working energy storage battery pack, and performing fault diagnosis on each working energy storage battery pack according to the fault diagnosis coefficient of each working energy storage battery pack; and the standby energy storage battery pack is selected for replacement for the working energy storage battery pack with the fault based on the rated capacity, the charging efficiency and the discharging efficiency of the energy storage battery pack, so that real-time monitoring and fault diagnosis of the energy storage battery pack are realized, and the reliability and the safety of the energy storage system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage battery monitoring, and in particular to an energy storage battery group fault monitoring and diagnosis system based on artificial intelligence. Background Art

[0002] Energy storage battery packs can balance the peak and valley power of the power system, improve the stability of the power grid, reduce power grid losses, and reduce energy costs. When the power demand is peak, the energy storage battery pack can release the stored power to supplement the power supply shortage of the power grid; when the power demand is low, it can absorb excess power for storage. In addition, the energy storage battery pack can also be used as a backup power source to provide emergency power support for cities and industrial areas in the event of sudden power failures or power outages, ensuring the normal operation of the power system.

[0003] Most existing energy storage battery group fault monitoring and diagnosis systems often rely on manual inspections and professional knowledge, which is not only inefficient, but also difficult to achieve real-time early warning of faults. At the same time, it is impossible to switch to a suitable backup battery pack in time, which leads to the collapse of the power system and causes huge losses. Therefore, the present invention provides an energy storage battery group fault monitoring and diagnosis system based on artificial intelligence. Summary of the invention

[0004] The purpose of the present invention is to provide an energy storage battery pack fault monitoring and diagnosis system based on artificial intelligence to solve the above technical problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An energy storage battery pack fault monitoring and diagnosis system based on artificial intelligence, the system comprising: a data acquisition module, a data processing module, a data analysis module, a fault diagnosis module and a backup battery selection module;

[0007] The data acquisition module is used to collect physical parameters of the energy storage battery pack during operation and environmental parameters of the energy storage battery pack, and send them to the data processing module;

[0008] The data processing module is used to receive the data transmitted by the data acquisition module, perform preprocessing to ensure the accuracy and consistency of the data, and send it to the data analysis module;

[0009] The data analysis module is used to receive the processed data transmitted by the data processing module, perform feature analysis on the data, and send the analysis results to the fault diagnosis module;

[0010] The fault diagnosis module is used to receive the analysis results of the data analysis module, perform fault diagnosis on the working energy storage battery pack according to the analysis results, and issue a real-time warning according to the diagnosis results;

[0011] The backup battery selection module is used to automatically select the most suitable backup energy storage battery pack based on artificial intelligence and switch it according to the real-time warning issued by the fault diagnosis module.

[0012] As a further description of the solution of the present invention, the working process of the data acquisition module includes:

[0013] All working energy storage battery groups are numbered in the following order: 1, 2, ..., n;

[0014] Based on the sensors installed on the energy storage battery pack, the temperature, voltage and current change data of the working process of the working energy storage battery pack are collected in real time;

[0015] Based on environmental monitoring sensors, the environmental impact parameters of the working energy storage battery pack are collected in real time.

[0016] As a further description of the solution of the present invention, the working process of the data analysis module includes:

[0017] Acquire a first impact indicator of each working energy storage battery group based on the temperature, voltage, and current variation over time data of each working energy storage battery group during its working process;

[0018] Acquire a second impact index of each working energy storage battery group based on the environmental impact parameter of each energy storage battery group;

[0019] A fault diagnosis coefficient of each working energy storage battery group is obtained based on the first influencing index and the second influencing index of each working energy storage battery group, and fault diagnosis is performed on each working energy storage battery group according to the fault diagnosis coefficient of each working energy storage battery group.

[0020] As a further description of the solution of the present invention, the working process of obtaining the first impact indicator of each working energy storage battery group includes:

[0021] According to the actual working conditions of the power system, the working time period of the working energy storage battery group within the monitoring period is divided into peak period and valley period;

[0022] Obtain the temperature, voltage, and current variation data of the i-th working energy storage battery group during the peak time period and the valley time period respectively;

[0023] The state parameters of the i-th working energy storage battery group during the peak period are calculated by the following formula:

[0024]

[0025] The state parameters of the i-th working energy storage battery group during the valley period are calculated by the following formula:

[0026]

[0027] In the formula, t1-t2 is the peak time period, t2-t3 is the valley time period, S i1 is the state parameter of the i-th working energy storage battery group during the peak period, S i2 is the state parameter of the i-th working energy storage battery group in the valley period; U i (t) is the voltage variation data of the i-th working energy storage battery group during the monitoring period. i (t) is the current variation data of the i-th working energy storage battery group during the monitoring period, T i (t) is the temperature variation data of the i-th working energy storage battery group during the monitoring period; k1, k2 and k3 are the voltage, current and corresponding weight coefficients respectively, i belongs to n;

[0028] Based on the state parameter S of the i-th working energy storage battery group during the peak period i1 and the state parameter S of the i-th working energy storage battery group in the valley period i2 Construct the mathematical model of the first influencing index of the i-th working energy storage battery group:

[0029]

[0030] In the formula, α and β are the weight coefficients corresponding to the peak time period and the trough time period respectively, and α>β>0, R i1 It is the first influencing indicator of the i-th working energy storage battery group.

[0031] As a further description of the solution of the present invention, the working process of obtaining the second impact indicator of each working energy storage battery group includes:

[0032] The environmental parameters of the working environment of the i-th working energy storage battery group are obtained, and a second influencing indicator mathematical model of the i-th working energy storage battery group is constructed based on the environmental parameters:

[0033]

[0034] Where m is the total number of environmental parameters, j belongs to [1, m], E j is the physical quantity of the jth environmental parameter, E j0 is the jth environmental parameter standard value set by the system, ΔE kth is the reference value of the difference of the jth environmental parameter, ε j is the weight coefficient corresponding to the jth environmental parameter, R i2 It is the second impact indicator of the i-th working energy storage battery group.

[0035] As a further description of the solution of the present invention, the working process of performing fault diagnosis on each working energy storage battery pack includes:

[0036] Construct the mathematical model of the fault diagnosis coefficient of the i-th working energy storage battery group, and the expression is:

[0037]

[0038] In the formula, δ and ρ are the weight coefficients corresponding to the first influencing index and the second influencing index, respectively, μ i is the fault diagnosis coefficient of the i-th working energy storage battery group;

[0039] The fault diagnosis coefficient μ of the i-th working energy storage battery group is i The fault diagnosis coefficient threshold μ of the i-th working energy storage battery set by the system thi For comparison, if μ i Greater than or equal to μ thi , it means that the i-th working energy storage battery pack has a fault, and the backup energy storage battery pack needs to be activated immediately and an early warning is issued immediately. Otherwise, the i-th working energy storage battery pack has not a fault;

[0040] The fault diagnosis coefficient of each working energy storage battery group is calculated in turn, and fault diagnosis is performed on the corresponding working energy storage battery group according to the fault diagnosis coefficient of each working energy storage battery group.

[0041] As a further description of the solution of the present invention, the working process of the backup battery selection module includes:

[0042] When the i-th working energy storage battery group fails, obtain the rated capacity Q of the i-th working energy storage battery group i , Charging efficiency P ci And the discharge efficiency P di ;

[0043] Get the rated capacity Q of the xth backup energy storage battery pack x , Charging efficiency P cx And the discharge efficiency P dx ;

[0044] The replacement factor of the xth backup energy storage battery group relative to the ith working energy storage battery group is calculated by the following formula:

[0045]

[0046] In the formula, k Q , and are the weight coefficients corresponding to the rated capacity, charging efficiency and discharging efficiency, σ x-i is the replacement factor of the xth backup energy storage battery group relative to the ith working energy storage battery group.

[0047] As a further description of the solution of the present invention, the working process of the backup battery selection module also includes:

[0048] Calculate the replacement coefficient of y backup energy storage battery groups relative to the i-th working energy storage battery group in sequence, where y is the number of backup energy storage battery groups, and x belongs to y;

[0049] Arrange the replacement coefficients of the y backup energy storage battery groups relative to the i-th working energy storage battery group from small to large, and select the backup energy storage battery group with the smallest replacement coefficient relative to the i-th working energy storage battery group for replacement.

[0050] Beneficial effects of the present invention: The present invention collects the working parameters of each working energy storage battery group during the working process in real time, and collects the environmental parameters of the environment in which each working energy storage battery group is located in real time, and calculates the first impact index of each working energy storage battery group according to the working parameters of each working energy storage battery group, and calculates the second impact index of each working energy storage battery group according to the environmental parameters of each working energy storage battery group, and then calculates the fault diagnosis coefficient of each working energy storage battery group according to the first impact index and the second impact index of each working energy storage battery group, and performs fault diagnosis on each working energy storage battery group according to the fault diagnosis coefficient of each working energy storage battery group, and selects a spare energy storage battery group for replacement based on the rated capacity, charging efficiency and discharging efficiency of the energy storage battery group for the faulty working energy storage battery group, thereby realizing real-time monitoring and fault diagnosis of the energy storage battery group and improving the reliability and safety of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The present invention will be further described below in conjunction with the accompanying drawings.

[0052] Figure 1 It is a partial structural diagram of the energy storage battery group fault monitoring and diagnosis system based on artificial intelligence provided by the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] See also Figure 1 , the present invention is an energy storage battery group fault monitoring and diagnosis system based on artificial intelligence, the system comprises: a data acquisition module, a data processing module, a data analysis module, a fault diagnosis module and a backup battery selection module;

[0055] The data acquisition module is used to collect physical parameters of the energy storage battery pack during operation and environmental parameters of the energy storage battery pack, and send them to the data processing module;

[0056] The data processing module is used to receive the data transmitted by the data acquisition module, perform preprocessing to ensure the accuracy and consistency of the data, and send it to the data analysis module;

[0057] The data analysis module is used to receive the processed data transmitted by the data processing module, perform feature analysis on the data, and send the analysis results to the fault diagnosis module;

[0058] The fault diagnosis module is used to receive the analysis results of the data analysis module, perform fault diagnosis on the working energy storage battery pack according to the analysis results, and issue a real-time warning according to the diagnosis results;

[0059] The backup battery selection module is used to automatically select the most suitable backup energy storage battery pack based on artificial intelligence and switch it according to the real-time warning issued by the fault diagnosis module.

[0060] Through the above technical scheme, the present invention collects the working parameters of each working energy storage battery group during the working process in real time, collects the environmental parameters of the environment in which each working energy storage battery group is located in real time, and calculates the first impact index of each working energy storage battery group according to the working parameters of each working energy storage battery group, calculates the second impact index of each working energy storage battery group according to the environmental parameters of each working energy storage battery group, and then calculates the fault diagnosis coefficient of each working energy storage battery group according to the first impact index and the second impact index of each working energy storage battery group, and performs fault diagnosis on each working energy storage battery group according to the fault diagnosis coefficient of each working energy storage battery group, and selects a spare energy storage battery group for replacement of the faulty working energy storage battery group based on the rated capacity, charging efficiency and discharging efficiency of the energy storage battery group.

[0061] As a further description of the solution of the present invention, the working process of the data acquisition module includes:

[0062] All working energy storage battery groups are numbered in the following order: 1, 2, ..., n;

[0063] Based on the sensors installed on the energy storage battery pack, the temperature, voltage and current change data of the working process of the working energy storage battery pack are collected in real time;

[0064] Based on environmental monitoring sensors, the environmental impact parameters of the working energy storage battery pack are collected in real time.

[0065] As a further description of the solution of the present invention, the working process of the data analysis module includes:

[0066] Acquire a first impact indicator of each working energy storage battery group based on the temperature, voltage, and current variation over time data of each working energy storage battery group during its working process;

[0067] Acquire a second impact index of each working energy storage battery group based on the environmental impact parameter of each energy storage battery group;

[0068] A fault diagnosis coefficient of each working energy storage battery group is obtained based on the first influencing index and the second influencing index of each working energy storage battery group, and fault diagnosis is performed on each working energy storage battery group according to the fault diagnosis coefficient of each working energy storage battery group.

[0069] As a further description of the solution of the present invention, the working process of obtaining the first impact indicator of each working energy storage battery group includes:

[0070] According to the actual working conditions of the power system, the working time period of the working energy storage battery group within the monitoring period is divided into peak period and valley period;

[0071] Obtain the temperature, voltage, and current variation data of the i-th working energy storage battery group during the peak time period and the valley time period respectively;

[0072] The state parameters of the i-th working energy storage battery group during the peak period are calculated by the following formula:

[0073]

[0074] The state parameters of the i-th working energy storage battery group during the valley period are calculated by the following formula:

[0075]

[0076] In the formula, t1-t2 is the peak time period, t2-t3 is the valley time period, S i1 is the state parameter of the i-th working energy storage battery group during the peak period, S i2 is the state parameter of the i-th working energy storage battery group in the valley period; U i (t) is the voltage variation data of the i-th working energy storage battery group during the monitoring period. i (t) is the current variation data of the i-th working energy storage battery group during the monitoring period, T i (t) is the temperature variation data of the i-th working energy storage battery group during the monitoring period; k1, k2 and k3 are the voltage, current and corresponding weight coefficients respectively, i belongs to n;

[0077] Based on the state parameter S of the i-th working energy storage battery group during the peak period i1 and the state parameter S of the i-th working energy storage battery group in the valley period i2Construct the mathematical model of the first influencing index of the i-th working energy storage battery group:

[0078]

[0079] In the formula, α and β are the weight coefficients corresponding to the peak time period and the trough time period respectively, and α>β>0, R i1 It is the first influencing indicator of the i-th working energy storage battery group.

[0080] Through the above technical solution, this embodiment provides a method for obtaining the first influencing index of the working energy storage battery group based on the working parameters of the working energy storage battery group. First, the working process of the working energy storage battery group is divided into peak time period and valley time period, and the temperature, voltage, and current change data of the peak time period and valley time period in the monitoring period are obtained in turn, and the obtained data are respectively substituted into the formula Calculate the state parameters of the i-th working energy storage battery group during the peak time period and the valley time period, and then substitute the state parameters of the i-th working energy storage battery group during the peak time period and the valley time period into the mathematical model Calculate the mathematical model of the first influencing index of the i-th working energy storage battery group.

[0081] As a further description of the solution of the present invention, the working process of obtaining the second impact indicator of each working energy storage battery group includes:

[0082] The environmental parameters of the working environment of the i-th working energy storage battery group are obtained, and a second influencing indicator mathematical model of the i-th working energy storage battery group is constructed based on the environmental parameters:

[0083]

[0084] Where m is the total number of environmental parameters, j belongs to [1, m], E j is the physical quantity of the jth environmental parameter, E j0 is the jth environmental parameter standard value set by the system, ΔE kth is the reference value of the difference of the jth environmental parameter, ε j is the weight coefficient corresponding to the jth environmental parameter, R i2 It is the second impact indicator of the i-th working energy storage battery group.

[0085] Through the above technical solution, the present invention obtains the environmental parameters of the working environment of each working energy storage battery pack, and based on the environmental parameters, uses the formula Calculate the mathematical model of the second influencing index of the i-th working energy storage battery group.

[0086] As a further description of the solution of the present invention, the working process of performing fault diagnosis on each working energy storage battery pack includes:

[0087] Construct the mathematical model of the fault diagnosis coefficient of the i-th working energy storage battery group, and the expression is:

[0088]

[0089] In the formula, δ and ρ are the weight coefficients corresponding to the first influencing index and the second influencing index, respectively, μ i is the fault diagnosis coefficient of the i-th working energy storage battery group;

[0090] The fault diagnosis coefficient μ of the i-th working energy storage battery group is i The fault diagnosis coefficient threshold μ of the i-th working energy storage battery set by the system thi For comparison, if μ i Greater than or equal to μ thi , it means that the i-th working energy storage battery pack has a fault, and the backup energy storage battery pack needs to be activated immediately and an early warning is issued immediately. Otherwise, the i-th working energy storage battery pack has not a fault;

[0091] The fault diagnosis coefficient of each working energy storage battery group is calculated in turn, and fault diagnosis is performed on the corresponding working energy storage battery group according to the fault diagnosis coefficient of each working energy storage battery group.

[0092] Through the above technical solution, the present invention substitutes the first influencing index and the second influencing index into the formula Calculate the fault diagnosis coefficient of the ith working energy storage battery group, calculate the fault diagnosis coefficient of each working energy storage battery group in turn, compare the fault diagnosis coefficient of each working energy storage battery group with the fault diagnosis coefficient threshold of each working energy storage battery group set by the system, if the fault diagnosis coefficient of each working energy storage battery group is greater than or equal to the fault diagnosis coefficient threshold of each working energy storage battery group set by the system, it means that the current working energy storage battery group has a fault, the backup energy storage battery group needs to be immediately activated, and an early warning is immediately issued.

[0093] As a further description of the solution of the present invention, the working process of the backup battery selection module includes:

[0094] When the i-th working energy storage battery group fails, obtain the rated capacity Q of the i-th working energy storage battery group i , Charging efficiency P ci And the discharge efficiency P di ;

[0095] Get the rated capacity Q of the xth backup energy storage battery pack x , Charging efficiency P cx And the discharge efficiency Pdx ;

[0096] The replacement factor of the xth backup energy storage battery group relative to the ith working energy storage battery group is calculated by the following formula:

[0097]

[0098] In the formula, k Q , and are the weight coefficients corresponding to the rated capacity, charging efficiency and discharging efficiency, σ x-i is the replacement factor of the xth backup energy storage battery group relative to the ith working energy storage battery group.

[0099] As a further description of the solution of the present invention, the working process of the backup battery selection module also includes:

[0100] Calculate the replacement coefficient of y backup energy storage battery groups relative to the i-th working energy storage battery group in sequence, where y is the number of backup energy storage battery groups, and x belongs to y;

[0101] Arrange the replacement coefficients of the y backup energy storage battery groups relative to the i-th working energy storage battery group from small to large, and select the backup energy storage battery group with the smallest replacement coefficient relative to the i-th working energy storage battery group for replacement.

[0102] Through the above technical solution, when a working energy storage battery group fails, the rated capacity, charging efficiency and discharging efficiency of the current working energy storage battery group are obtained, and the rated capacity, charging efficiency and discharging efficiency of the backup energy storage battery group are obtained in turn, and the replacement coefficient of each backup energy storage battery group relative to the failed working energy storage battery group is calculated according to the physical quantity parameters of the failed working energy storage battery group and all the backup energy storage battery groups, and the replacement coefficients of all the backup energy storage battery groups relative to the failed working energy storage battery group are arranged from small to large, and the backup energy storage battery group with the smallest replacement coefficient relative to the failed working energy storage battery group is selected for replacement.

[0103] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. The energy storage battery fault monitoring and diagnosis system based on artificial intelligence is characterized by: The system comprises: a data acquisition module, a data processing module, a data analysis module, a fault diagnosis module and a backup battery selection module; The data acquisition module is used to collect physical parameters of the energy storage battery pack during operation and environmental parameters of the energy storage battery pack, and send them to the data processing module; The data processing module is used to receive the data transmitted by the data acquisition module, perform preprocessing to ensure the accuracy and consistency of the data, and send it to the data analysis module; The data analysis module is used to receive the processed data transmitted by the data processing module, perform feature analysis on the data, and send the analysis results to the fault diagnosis module; The fault diagnosis module is used to receive the analysis results of the data analysis module, perform fault diagnosis on the working energy storage battery pack according to the analysis results, and issue a real-time warning according to the diagnosis results; The backup battery selection module is used to automatically select the most suitable backup energy storage battery pack based on artificial intelligence and switch it according to the real-time warning issued by the fault diagnosis module.

2. The energy storage battery fault monitoring and diagnosis system based on artificial intelligence according to claim 1 is characterized in that: The working process of the data acquisition module includes: All working energy storage battery groups are numbered in the following order: 1, 2, ..., n; Based on the sensors installed on the energy storage battery pack, the temperature, voltage and current change data of the working process of the working energy storage battery pack are collected in real time; Based on environmental monitoring sensors, the environmental impact parameters of the working energy storage battery pack are collected in real time.

3. The energy storage battery fault monitoring and diagnosis system based on artificial intelligence according to claim 2 is characterized in that: The working process of the data analysis module includes: Acquire a first impact indicator of each working energy storage battery group based on the temperature, voltage, and current variation over time data of each working energy storage battery group during its working process; Acquire a second impact index of each working energy storage battery group based on the environmental impact parameter of each energy storage battery group; A fault diagnosis coefficient of each working energy storage battery group is obtained based on the first influencing index and the second influencing index of each working energy storage battery group, and fault diagnosis is performed on each working energy storage battery group according to the fault diagnosis coefficient of each working energy storage battery group.

4. The energy storage battery fault monitoring and diagnosis system based on artificial intelligence according to claim 3 is characterized in that: The working process of obtaining the first impact indicator of each working energy storage battery group includes: According to the actual working conditions of the power system, the working time period of the working energy storage battery group within the monitoring period is divided into peak period and valley period; Obtain the temperature, voltage, and current variation data of the i-th working energy storage battery group during the peak time period and the valley time period respectively; The state parameters of the i-th working energy storage battery group during the peak period are calculated by the following formula: The state parameters of the i-th working energy storage battery group during the valley period are calculated by the following formula: In the formula, t1-t2 is the peak time period, t2-t3 is the valley time period, S i1 is the state parameter of the i-th working energy storage battery group during the peak period, S i2 is the state parameter of the i-th working energy storage battery group in the valley period; U i (t) is the voltage variation data of the i-th working energy storage battery group during the monitoring period. i (t) is the current variation data of the i-th working energy storage battery group during the monitoring period, T i (t) is the temperature variation data of the i-th working energy storage battery group during the monitoring period; k1, k2 and k3 are the voltage, current and corresponding weight coefficients respectively, i belongs to n; Based on the state parameter S of the i-th working energy storage battery group during the peak period i1 and the state parameter S of the i-th working energy storage battery group in the valley period i2 Construct the mathematical model of the first influencing index of the i-th working energy storage battery group: In the formula, α and β are the weight coefficients corresponding to the peak time period and the trough time period respectively, and α>β>0, R i1 It is the first influencing indicator of the i-th working energy storage battery group.

5. The energy storage battery fault monitoring and diagnosis system based on artificial intelligence according to claim 3 is characterized in that: The working process of obtaining the second impact indicator of each working energy storage battery group includes: The environmental parameters of the working environment of the i-th working energy storage battery group are obtained, and a second influencing indicator mathematical model of the i-th working energy storage battery group is constructed based on the environmental parameters: Where m is the total number of environmental parameters, j belongs to [1, m], E j is the physical quantity of the jth environmental parameter, E j0 is the jth environmental parameter standard value set by the system, ΔE kth is the reference value of the difference of the jth environmental parameter, ε j is the weight coefficient corresponding to the jth environmental parameter, R i2 It is the second impact indicator of the i-th working energy storage battery group.

6. The energy storage battery fault monitoring and diagnosis system based on artificial intelligence according to claim 3 is characterized in that: The working process of performing fault diagnosis on each working energy storage battery pack includes: Construct the mathematical model of the fault diagnosis coefficient of the i-th working energy storage battery group, and the expression is: In the formula, δ and ρ are the weight coefficients corresponding to the first influencing index and the second influencing index, respectively, μ i is the fault diagnosis coefficient of the i-th working energy storage battery group; The fault diagnosis coefficient μ of the i-th working energy storage battery group is i The fault diagnosis coefficient threshold μ of the i-th working energy storage battery set by the system thi For comparison, if μ i Greater than or equal to μ thi , it means that the i-th working energy storage battery pack has a fault, and the backup energy storage battery pack needs to be activated immediately and an early warning is issued immediately. Otherwise, the i-th working energy storage battery pack has not a fault; The fault diagnosis coefficient of each working energy storage battery group is calculated in turn, and fault diagnosis is performed on the corresponding working energy storage battery group according to the fault diagnosis coefficient of each working energy storage battery group.

7. The energy storage battery fault monitoring and diagnosis system based on artificial intelligence according to claim 1 is characterized in that: The working process of the backup battery selection module includes: When the i-th working energy storage battery group fails, obtain the rated capacity Q of the i-th working energy storage battery group i , Charging efficiency P ci And the discharge efficiency P di ; Get the rated capacity Q of the xth backup energy storage battery pack x , Charging efficiency P cx And the discharge efficiency P dx ; The replacement factor of the xth backup energy storage battery group relative to the ith working energy storage battery group is calculated by the following formula: In the formula, k Q , and are the weight coefficients corresponding to the rated capacity, charging efficiency and discharging efficiency, σ x-i is the replacement factor of the xth backup energy storage battery group relative to the ith working energy storage battery group.

8. The energy storage battery fault monitoring and diagnosis system based on artificial intelligence according to claim 7 is characterized in that: The working process of the backup battery selection module also includes: Calculate the replacement coefficient of y backup energy storage battery groups relative to the i-th working energy storage battery group in sequence, where y is the number of backup energy storage battery groups, and x belongs to y; Arrange the replacement coefficients of the y backup energy storage battery groups relative to the i-th working energy storage battery group from small to large, and select the backup energy storage battery group with the smallest replacement coefficient relative to the i-th working energy storage battery group for replacement.

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