A method, apparatus, device and medium for fault handling of an electrical energy storage station

Abstract

The application provides a kind of electric energy reserve station fault processing method, device, equipment and medium, it is related to electrochemical electric energy reserve station safety protection technical field, the method includes according to the abnormal alarm information and energy storage unit state information of the battery management system of electric energy reserve station, determine initial index set;In initial index set, filter the initial fault index greater than constraint threshold, constitute effective index set;Get fault occurrence coefficient, according to effective index set and the fault occurrence coefficient determined based on effective index set, calculate to obtain abnormal characteristic value;According to abnormal characteristic value, generate the severity level of abnormal alarm information, and generate the abnormal processing strategy corresponding to severity level;Through multidimensional data fusion, screening and calculation, accurately quantify fault risk, quickly and accurately identify the severity of electric energy reserve station fault, improve fault processing efficiency, guarantee the safe and stable operation of electric energy reserve station.

Description

TECHNICAL FIELD

[0001] The present application generally relates to the technical field of safety protection of electrochemical power storage stations, and in particular to a power storage station fault processing method, device, equipment and medium. BACKGROUND

[0002] Renewable energy is being massively connected to the power system, however, large-scale renewable energy connection will cause problems such as power grid voltage instability and frequency fluctuation. In order to deal with these problems, lithium battery electrochemical power storage stations are widely used due to their fast response, high energy density and long service life. However, due to the incomplete protection measures of the power storage station, weak fault response ability and insufficient experience of the power station operation and maintenance personnel, the power storage station fault accidents occur frequently.

[0003] The current power storage station faces the problems of many battery management system fault alarms and information during operation and maintenance. The battery management system of the power storage station is limited by its own equipment, resulting in up to tens of thousands of daily alarm capabilities, which is difficult for operation and maintenance personnel to efficiently process. Therefore, we propose a power storage station fault processing method, device, equipment and medium to solve the above problems. SUMMARY

[0004] In view of the above defects or deficiencies in the prior art, it is desirable to provide a power storage station fault processing method, device, equipment and medium that improves fault maintenance and isolation efficiency and effectively reduces false alarms.

[0005] In a first aspect, the present application provides a power storage station fault processing method, comprising the following steps:

[0006] Obtain abnormal alarm information and energy storage unit state information of a battery management system of a power storage station, and determine an initial index set according to the abnormal alarm information and the energy storage unit state information; the initial index set includes a plurality of initial fault indicators;

[0007] Extract an initial fault indicator with a value greater than a constraint threshold from the initial index set to obtain an effective index set; the effective index set includes a plurality of effective fault indicators;

[0008] According to the effective index set and a fault occurrence coefficient determined based on the effective index set, an abnormal feature value is calculated; and according to the abnormal feature value, a severity level of the abnormal alarm information is generated;

[0009] According to the severity level of the abnormal alarm information, an abnormal processing strategy corresponding to the severity level of the abnormal alarm information is generated.

[0010] According to the technical scheme provided by the application, the initial index set is determined according to the abnormal alarm information and the energy storage unit state information, and specifically includes the following steps:

[0011] Real-time acquisition of abnormal alarm information and energy storage unit state information of the battery management system of the energy storage station; the energy storage unit state information includes a plurality of collection time points and the battery health state, battery voltage consistency and battery temperature consistency of each energy storage unit of the energy storage station corresponding to each collection time point;

[0012] Respectively calculate the correlation coefficients of each battery health state, battery voltage consistency and battery temperature consistency in the energy storage unit state information and the abnormal alarm information;

[0013] Mark the failure level of each energy storage unit of the energy storage station as an index coefficient;

[0014] The product of the correlation coefficient and the index coefficient corresponding to the correlation coefficient is taken as the initial failure index of the energy storage unit; based on the initial failure index of all energy storage units, the initial index set is established.

[0015] According to the technical scheme provided by the application, the constraint threshold is determined according to the following steps:

[0016] Acquire historical abnormal alarm information and historical energy storage unit state information, and screen the historical battery health state, historical battery voltage consistency and historical battery temperature consistency in the historical energy storage unit state information that meet the first preset condition; the historical energy storage unit state information includes a plurality of historical time points and the historical battery health state, historical battery voltage consistency and historical battery temperature consistency of each energy storage unit of the energy storage station corresponding to each historical time point;

[0017] Respectively calculate the constraint correlation coefficients of the screened historical battery health state, historical battery voltage consistency and historical battery temperature consistency and the historical abnormal alarm information;

[0018] According to the failure severity of the corresponding energy storage unit at the time of screening, the historical failure level is determined, and the corresponding historical failure level is marked as a constraint index coefficient;

[0019] According to the accuracy rate of the on-site error reporting of the energy storage station, the constraint correlation coefficient and the constraint index coefficient, a constraint threshold is calculated.

[0020] According to the technical scheme provided by the application, the failure occurrence coefficient is determined according to the following steps:

[0021] Acquire the historical abnormal alarm information of the energy storage unit corresponding to the effective failure index in the effective index set;

[0022] Identify the number of historical extraction points corresponding to the historical abnormal alarm information and the total number of alarm points;

[0023] Obtain the signal corresponding to the alarm point corresponding serial number, and calculate the fault occurrence coefficient according to the setting coefficient, the number of historical extraction points and the signal corresponding to the alarm point corresponding serial number.

[0024] According to the technical scheme provided by the application, the fault level of each energy storage unit of the electric energy storage station is determined according to the following steps:

[0025] Real-time acquisition of battery voltage, battery current and battery temperature in the energy storage unit;

[0026] When the battery voltage, the battery current and the battery temperature meet the first fault condition, the corresponding energy storage unit is marked as the first fault level;

[0027] When the battery voltage, the battery current and the battery temperature meet the second fault condition, the corresponding energy storage unit is marked as the second fault level;

[0028] When the battery voltage, the battery current and the battery temperature meet the third fault condition, the corresponding energy storage unit is marked as the third fault level;

[0029] When the battery voltage, the battery current and the battery temperature meet the fourth fault condition, the corresponding energy storage unit is marked as the fourth fault level.

[0030] According to the technical scheme provided by the application, according to the severity level of the abnormal alarm information, an abnormal processing strategy corresponding to the abnormal alarm information is generated, which specifically includes the following steps:

[0031] Obtain an abnormal processing threshold; the abnormal processing threshold includes a first threshold, a second threshold and a third threshold, the first threshold is less than the second threshold, and the second threshold is less than the third threshold;

[0032] When the abnormal feature value is greater than or equal to the first threshold and less than the second threshold, the severity level of the abnormal alarm information is the first severity level, and the corresponding first abnormal processing strategy is generated; the first abnormal processing strategy is used to prompt the operation and maintenance personnel to investigate the fault error position after the charge and discharge of the electric energy storage station is completed;

[0033] When the abnormal characteristic value is greater than or equal to the second threshold value and less than the third threshold value, a second severity level of the abnormal alarm information is generated, and a second abnormal processing strategy corresponding to the second severity level is generated; the second abnormal processing strategy is used to control the electric energy storage station to stop operation, and meanwhile, prompt an operation and maintenance personnel to troubleshoot a fault error position and detect a performance of a battery module located around the fault error position;

[0034] When the abnormal characteristic value is greater than or equal to the third threshold value, a third severity level of the abnormal alarm information is generated, and a third abnormal processing strategy corresponding to the third severity level is generated; the third abnormal processing strategy is used to control the electric energy storage station to stop operation, and meanwhile, prompt the operation and maintenance personnel to troubleshoot a fault energy storage unit to which the fault error position belongs.

[0035] According to the technical scheme provided by the application, the method further comprises the following steps:

[0036] When the fault is eliminated, alarm elimination information is generated; the alarm elimination information is used to prompt the operation and maintenance personnel that the fault has been eliminated.

[0037] In a second aspect, the application provides an electric energy storage station fault processing device, the device comprising:

[0038] A data acquisition module, the data acquisition module being used to acquire abnormal alarm information and energy storage unit state information of a battery management system of an electric energy storage station;

[0039] A data processing module, the data processing module being used to determine an initial index set according to the abnormal alarm information and the energy storage unit state information; the initial index set comprises a plurality of initial fault indexes; an effective index set is obtained by extracting an initial fault index with a value greater than a constraint threshold value in the initial index set; the effective index set comprises a plurality of effective fault indexes; an abnormal characteristic value is calculated according to the effective index set and a fault occurrence coefficient determined based on the effective index set; a severity level of the abnormal alarm information is generated according to the abnormal characteristic value; and an abnormal processing strategy corresponding to the severity level of the abnormal alarm information is generated according to the severity level of the abnormal alarm information.

[0040] According to the technical scheme provided by the application, the data acquisition module is further used to acquire abnormal alarm information and energy storage unit state information of a battery management system of an electric energy storage station in real time; the energy storage unit state information comprises a plurality of acquisition time points and battery health states, battery voltage consistency and battery temperature consistency of each energy storage unit of the electric energy storage station corresponding to each acquisition time point;

[0041] The data processing module is further configured to calculate correlation coefficients of each battery health state, battery voltage consistency and battery temperature consistency in the energy storage unit state information and the abnormal alarm information respectively, mark a failure level of each energy storage unit of the energy storage station as an index coefficient, take a product of the correlation coefficient and the index coefficient corresponding to the correlation coefficient as an initial failure index of the energy storage unit, and establish the initial index set based on initial failure indexes of all energy storage units.

[0042] According to the technical scheme provided in the application, the data acquisition module is further configured to acquire historical abnormal alarm information and historical energy storage unit state information, and screen historical battery health states, historical battery voltage consistencies and historical battery temperature consistencies in the historical energy storage unit state information that meet a first preset condition; the historical energy storage unit state information includes a plurality of historical time points and historical battery health states, historical battery voltage consistencies and historical battery temperature consistencies of each energy storage unit of the energy storage station corresponding to each historical time point.

[0043] The data processing module is further configured to calculate constraint correlation coefficients of the screened historical battery health states, historical battery voltage consistencies and historical battery temperature consistencies and the historical abnormal alarm information respectively.

[0044] According to a failure severity of the energy storage unit corresponding to the screening time, a historical failure level is determined, and the historical failure level is marked as a constraint index coefficient.

[0045] According to an accuracy rate of on-site error reporting of the energy storage station, the constraint correlation coefficient and the constraint index coefficient, a constraint threshold value is calculated.

[0046] According to the technical scheme provided in the application, the data acquisition module is further configured to acquire historical abnormal alarm information of an energy storage unit corresponding to an effective failure index in the effective index set.

[0047] The data processing module is further configured to identify a historical extraction point quantity corresponding to the historical abnormal alarm information and a total number of alarm point positions.

[0048] The data processing module is further configured to acquire a signal corresponding to an alarm point position corresponding serial number, and calculate a failure occurrence coefficient according to a setting coefficient, the historical extraction point quantity and the signal corresponding to the alarm point position corresponding serial number.

[0049] According to the technical scheme provided in the application, the data acquisition module is further configured to acquire battery voltage, battery current and battery temperature in the energy storage unit in real time.

[0050] The data processing module is further configured to mark the corresponding energy storage unit as a first fault level when the battery voltage, the battery current and the battery temperature meet a first fault condition.

[0051] The data processing module is further configured to mark the corresponding energy storage unit as a second fault level when the battery voltage, the battery current and the battery temperature meet a second fault condition.

[0052] The data processing module is further configured to mark the corresponding energy storage unit as a third fault level when the battery voltage, the battery current and the battery temperature meet a third fault condition.

[0053] The data processing module is further configured to mark the corresponding energy storage unit as a fourth fault level when the battery voltage, the battery current and the battery temperature meet a fourth fault condition.

[0054] According to the technical scheme provided in the application, the data acquisition module is further configured to acquire an abnormality processing threshold value; the abnormality processing threshold value comprises a first threshold value, a second threshold value and a third threshold value, the first threshold value is smaller than the second threshold value, and the second threshold value is smaller than the third threshold value.

[0055] The data processing module is further configured to generate a first abnormality processing strategy corresponding to the abnormality alarm information when the abnormality characteristic value is greater than or equal to the first threshold value and smaller than the second threshold value, and the first abnormality processing strategy is used to prompt an operation and maintenance personnel to troubleshoot a fault error position after charging and discharging of the electric energy storage station is completed.

[0056] The data processing module is further configured to generate a second abnormality processing strategy corresponding to the abnormality alarm information when the abnormality characteristic value is greater than or equal to the second threshold value and smaller than the third threshold value, and the second abnormality processing strategy is used to control the electric energy storage station to stop operation, and prompt the operation and maintenance personnel to troubleshoot the fault error position and detect performance of a battery module located around the fault error position.

[0057] The data processing module is further configured to generate a third abnormality processing strategy corresponding to the abnormality alarm information when the abnormality characteristic value is greater than or equal to the third threshold value, and the third abnormality processing strategy is used to control the electric energy storage station to stop operation, and prompt the operation and maintenance personnel to troubleshoot a fault energy storage unit to which the fault error position belongs.

[0058] According to the technical scheme provided in the application, the data processing module is further configured to generate an alarm elimination information when a fault is eliminated, and the alarm elimination information is used to prompt the operation and maintenance personnel that the fault has been eliminated.

[0059] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the power storage station fault processing method as described above when executing the computer program.

[0060] In a fourth aspect, the present application provides a computer readable storage medium storing a computer program, wherein the computer program implements the steps of the power storage station fault processing method as described above when executed by a processor.

[0061] From the above technical solution, the present application has at least the following beneficial effects:

[0062] The present application provides a power storage station fault processing method, comprising the following steps: obtaining abnormal alarm information of a battery management system of a power storage station and energy storage unit state information, and determining an initial index set according to the abnormal alarm information and the energy storage unit state information; the initial index set comprises a plurality of initial fault indicators; extracting an initial fault indicator with a value greater than a constraint threshold from the initial index set to obtain an effective index set; the effective index set comprises a plurality of effective fault indicators; calculating an abnormal feature value according to the effective index set and a fault occurrence coefficient determined based on the effective index set; generating a severity level of the abnormal alarm information according to the abnormal feature value; and generating an abnormal processing strategy corresponding to the severity level of the abnormal alarm information according to the severity level of the abnormal alarm information.

[0063] The present application determines an initial index set by combining the abnormal alarm information of the battery management system of the power storage station and the energy storage unit state information, extracts an initial fault indicator greater than a constraint threshold and forms an effective index set, and then calculates an abnormal feature value by comprehensively considering the effective fault indicators in the effective index set and the fault occurrence coefficient determined based on the effective index set, generates a severity level of the abnormal alarm information according to the abnormal feature value, and obtains an abnormal processing strategy corresponding to the severity level of the abnormal alarm information; through multi-dimensional data fusion, screening and calculation, the fault risk is accurately quantified, the severity of the power storage station fault can be quickly and accurately identified, the corresponding abnormal processing strategy is executed, thereby improving the fault processing efficiency, reducing the impact of the fault on the operation of the power storage station, ensuring the safe and stable operation of the power storage station, and reducing potential losses caused by the fault. BRIEF DESCRIPTION OF DRAWINGS

[0064] Other features, objects, and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments made with reference to the accompanying drawings.

[0065] Figure 1 The flowchart of the power storage station fault processing method is shown in the figure.

[0066] Figure 2 Flowchart for determining the initial indicator set.

[0067] Figure 3 Flowchart for determining the constraint threshold.

[0068] Figure 4 Schematic diagram of module connection relationship of the power reserve station fault processing device.

[0069] Figure 5 Schematic diagram of signal connection relationship of the electronic device.

[0070] Figure label: 1, data acquisition module; 2, data processing module; 500, electronic device; 501, CPU; 502, ROM; 503, RAM; 504, bus; 505, I / O interface; 506, input part; 507, output part; 508, storage part; 509, communication part; 510, driver; 511, removable medium. DETAILED DESCRIPTION

[0071] The application will be described in further detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related application, and not to limit the application. In addition, it should be noted that only the parts related to the application are shown in the drawings for ease of description.

[0072] It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other without conflict. The application will be described in further detail below with reference to the drawings and embodiments.

[0073] In order to describe each of the following embodiments clearly and concisely, first, a brief introduction of the related art is given:

[0074] Large-scale renewable energy access causes voltage instability of the power grid, and the root cause lies in its intermittency and volatility. Wind power, photovoltaic and other energy are affected by wind speed, light and other natural conditions, and the output has significant randomness and periodicity. For example, photovoltaic power will suddenly drop in a short time due to cloud cover. This output fluctuation directly impacts the power balance of the power grid, and further affects voltage stability.

[0075] The direct cause is the effect of reactive power imbalance on voltage. The voltage stability of the power grid depends on the balance of reactive power supply and demand. The fluctuation of renewable energy output will lead to frequent changes in reactive power demand at the grid-connected node. When the reactive power supply is insufficient, the voltage drops, and when it is excessive, the voltage rises. In the traditional power grid, synchronous generators can dynamically adjust the reactive power through the excitation system. The fluctuation of renewable energy breaks this balance.

[0076] The technical bottleneck lies in the insufficient reactive power support capability of grid-connected equipment. The equipment such as photovoltaic inverter usually takes maximum power tracking as the primary target, and the reactive power regulation is an auxiliary function, which is limited by active output and has a regulation capacity of only 10% to 30% of the active capacity. The regulation needs external instructions and cannot autonomously respond to voltage changes like traditional generators, and the insufficient low-voltage ride-through capability of part of the equipment further aggravates the voltage instability problem.

[0077] At the same time, the renewable energy power station is usually built in remote areas, and long-distance power transmission changes the power flow distribution of the power grid, and the original voltage control means may fail, thereby causing local voltage to rise or drop suddenly. The core of the problem is that the renewable energy passes through the converter to be connected to the grid, and cannot provide inertia support like the rotor inertia of the traditional synchronous generator, and when the active power balance of the system is broken, the frequency change is more violent; and the output fluctuation of the renewable energy may be in the order of milliseconds, and the traditional frequency modulation means with a response time of seconds is difficult to track, and if the coordinated control of multiple energy stations is insufficient, the superposition effect will further amplify the frequency fluctuation problem.

[0078] In order to solve these problems, lithium battery electrochemical energy storage stations are widely used due to their fast response, high energy density and long service life. However, due to the incomplete protection measures of the energy storage station, the weak fault response capability, and the insufficient experience of the station operation and maintenance personnel, the energy storage station fault accidents occur frequently. Moreover, the energy storage station faces the problems of multiple battery management system fault alarms and information during operation and maintenance.

[0079] Therefore, the present application provides an energy storage station fault processing method, which determines an initial index set by combining the abnormal alarm information of the battery management system of the energy storage station and the state information of the energy storage unit, filters out the initial fault indexes greater than the constraint threshold to form an effective index set, and then calculates an abnormal characteristic value by comprehensively considering the effective fault indexes in the effective index set and a fault occurrence coefficient determined based on the effective index set. According to the abnormal characteristic value, the severity level of the abnormal alarm information is determined, and an abnormal processing strategy corresponding to the severity level of the abnormal alarm information is obtained. Through multi-dimensional data fusion, filtering and calculation, the fault risk is accurately quantified, the severity of the energy storage station fault can be quickly and accurately identified, and the corresponding abnormal processing strategy is executed, thereby improving the fault processing efficiency, reducing the impact of the fault on the operation of the energy storage station, ensuring the safe and stable operation of the energy storage station, and reducing the potential loss caused by the fault.

[0080] In order to make the energy storage station fault processing method provided by the embodiments of the present application more clear and easy to understand, the method will be introduced below in combination with the drawings. As shown in the figure, the figure is a flowchart of the energy storage station fault processing method provided by the embodiments of the present application, and the method includes the following steps: Figure 1

[0081] ​S100, acquire abnormal alarm information of a battery management system of the electric energy reserve station and state information of the energy storage unit, and determine an initial index set according to the abnormal alarm information of the battery management system of the electric energy reserve station and the state information of the energy storage unit.

[0082] The electric energy reserve station is a power facility that stores electric energy in the form of chemical energy, mechanical energy, etc. through an energy conversion device and a corresponding control system, and releases it when needed to balance the supply and demand of the power system, improve power supply reliability and energy utilization efficiency.

[0083] The battery management system (BMS) is a core system in the electric energy reserve station for monitoring, managing and protecting the battery pack. Its functions run through the whole life cycle of the battery, and are crucial to the safety, stability and efficiency of the electric energy reserve station. The functions of the BMS at least include real-time acquisition of battery state parameters, battery state evaluation, fault diagnosis and alarm, energy management and optimization control, safety protection and emergency handling.

[0084] The abnormal alarm information refers to the warning signal triggered by the BMS when it finds parameter abnormalities or system failures during monitoring of the battery pack operation state.

[0085] The state information of the energy storage unit refers to the key data reflecting the operation state and health condition of the battery pack, which is acquired and calculated in real time by various sensors and algorithms.

[0086] As shown in FIG. 1, according to the abnormal alarm information and the state information of the energy storage unit, an initial index set is determined, which specifically includes the following steps: Figure 2

[0087] S101, real-time acquisition of abnormal alarm information of a battery management system of the electric energy reserve station and state information of the energy storage unit; the state information of the energy storage unit includes a plurality of acquisition time points and battery health state, battery voltage consistency and battery temperature consistency of each energy storage unit of the electric energy reserve station corresponding to each acquisition time point.

[0088] The state of health (SOH) of the battery is a key indicator for measuring the degree of performance degradation of the battery, which is used to quantify the difference between the current state and the brand-new state of the battery, and is a core parameter for life prediction, fault diagnosis and charge-discharge strategy optimization of the battery management system (BMS). SOH is the ratio of the current performance of the battery to the rated performance, usually expressed in percentage, 100% represents the brand-new state of the battery, and the lower the value, the more serious the performance degradation.

[0089] ​Battery voltage consistency is a key indicator measuring the uniformity of voltage among individual cells within a battery pack. It is used to assess the stability and reliability of the battery pack's operation and is a parameter used by the Battery Management System (BMS) for equalization control, fault diagnosis, and lifespan optimization. Battery voltage consistency refers to the uniformity of voltage distribution among individual cells within the battery pack, reflecting the evenness of voltage distribution during charging and discharging. Poor consistency can lead to overcharging or over-discharging of some cells, accelerating overall performance degradation; therefore, it is one of the parameters for assessing the health of the battery pack.

[0090] Battery temperature consistency is a key indicator measuring the uniformity of temperature distribution among individual cells within a battery pack. It is used to evaluate the effectiveness of the battery thermal management system (BMS) and the stability of battery operation. It is one of the parameters used by the BMS for thermal runaway warning, lifespan optimization, and safety control. Battery temperature consistency refers to the uniformity of temperature among the individual cells within the battery pack, reflecting the evenness of temperature distribution during charging and discharging. Excessive temperature differences can lead to localized overheating, accelerated aging, and even the risk of thermal runaway; therefore, it is an important parameter for assessing battery safety and lifespan.

[0091] Anomaly alarm information and energy storage unit status information can be acquired through data acquisition module 1. The time interval between two adjacent acquisition times can be set according to actual needs.

[0092] Energy storage units, such as battery stacks, are battery assemblies consisting of multiple battery modules connected by electrical connections (in series, in parallel, or in series-parallel). They are the basic functional units for energy storage and release in power storage stations.

[0093] S102. Calculate the correlation coefficients between the health status of each battery, battery voltage consistency, battery temperature consistency and abnormal alarm information in the energy storage unit status information.

[0094] Battery health is cumulatively affected by aging, cycle count, etc. The risk of failure changes non-linearly with the decline in health. An exponential form can amplify / reduce the weight of the health status's influence on alarms, closely reflecting actual changes in failure probability. Therefore, the correlation coefficient between battery health status and abnormal alarm information is calculated using the following formula:

[0095] ;

[0096] in, The correlation coefficient between battery health status and abnormal alarm information. For battery health status, is a first weight coefficient, and a value range of the first weight coefficient is [0.9, 1], is an exponential function of the battery health state.

[0097] It can be known from the above formula that the battery health state is greater, the correlation coefficient between the battery health state and the abnormal alarm information is greater, and the abnormal alarm information issued by the battery management system of the energy storage station is more real.

[0098] For example, for a lithium iron phosphate battery energy storage station, 0.95 (because the cycle life is long, and the SOH decays slowly), when SOH = 80%, .

[0099] The voltage consistency directly reflects whether the battery pack voltage is balanced, the fault alarm real degree is linearly related to the voltage difference, and the linear formula can intuitively reflect the rule that the greater the difference is, the more credible the alarm is. Therefore, according to the following formula, the correlation coefficient between the battery voltage consistency and the abnormal alarm information is calculated:

[0100] ;

[0101] wherein, is the correlation coefficient between the battery voltage consistency and the abnormal alarm information, is the battery voltage consistency, is a second weight coefficient, and a value range of the second weight coefficient is [0.9, 1].

[0102] It can be known from the above formula that the battery voltage consistency is greater, the correlation coefficient between the battery voltage consistency and the abnormal alarm information is greater, and the abnormal alarm information issued by the battery management system of the energy storage station is more real.

[0103] For example, for a lithium iron phosphate battery energy storage station, when the battery pack voltage consistency = 0.6 (moderate deviation), 0.98, .

[0104] When the temperature is too high or too low, the risk of battery fault alarm does not increase uniformly (for example, near the critical temperature, the risk increases sharply), and the curve characteristics of the logarithmic function can simulate this critical effect, so that the influence of temperature on the alarm credibility calculation is more reasonable. Therefore, according to the following formula, the correlation coefficient between the battery temperature consistency and the abnormal alarm information is calculated:

[0105] ;

[0106] wherein, a correlation coefficient between the battery temperature consistency and the abnormal alarm information, an ambient temperature, a third weight coefficient, which is in a range of [0.9, 1].

[0107] According to the above formula, when the ambient temperature is lower, the correlation coefficient between the battery temperature consistency and the abnormal alarm information is larger, and the abnormal alarm information issued by the battery management system of the energy storage station is more real.

[0108] For the lithium iron phosphate battery energy storage station, when the ambient temperature is, for example, 25℃, the third weight coefficient is 0.95, when the ambient temperature is 30℃, the third weight coefficient is 0.95, when the ambient temperature is 22℃, the third weight coefficient is 0.95, It is shown that when the ambient temperature is lower, the correlation coefficient between the battery temperature consistency and the abnormal alarm information is larger.

[0109] By comprehensively considering the influence of the battery health state, the battery voltage consistency, and the battery temperature consistency on the abnormal alarm information, the realness and effectiveness of the alarm information can be considered in multiple dimensions, false alarms can be effectively prevented, reasonable and useful battery fault information can be extracted, redundant abnormal alarm information can be eliminated, the proportion of important alarm signals can be improved, and the alarm information that seriously endangers the safety of the energy storage station can be timely processed and found by the operation and maintenance personnel.

[0110] S103, mark the fault level of each energy storage unit of the energy storage station as an index coefficient.

[0111] The different faults of the BMS are divided into four levels, representing different fault severity. The higher the fault level, the more serious the fault, and the larger the index coefficient.

[0112] Here, the first fault level refers to slight deviation of the battery voltage, battery current, and battery temperature in the energy storage unit; the second fault level refers to continuous and obvious deviation of the battery voltage, battery current, and battery temperature in the energy storage unit; the third fault level refers to serious deviation of the battery voltage, battery current, and battery temperature in the energy storage unit; and the fourth fault level refers to that the energy storage unit has been in or is about to enter a dangerous state.

[0113] Specifically, the fault level of each energy storage unit of the energy storage station is determined according to the following steps:

[0114] Real-time acquisition of battery voltage, battery current and battery temperature in the energy storage unit; when the battery voltage, battery current and battery temperature meet the first fault condition, the corresponding energy storage unit is marked as the first fault level; when the battery voltage, battery current and battery temperature meet the second fault condition, the corresponding energy storage unit is marked as the second fault level; when the battery voltage, battery current and battery temperature meet the third fault condition, the corresponding energy storage unit is marked as the third fault level; when the battery voltage, battery current and battery temperature meet the fourth fault condition, the corresponding energy storage unit is marked as the fourth fault level.

[0115] Among them, the battery voltage, battery current and battery temperature in the energy storage unit can be collected by a data collection module.

[0116] The first fault condition refers to that the battery voltage is in the first voltage deviation range after fluctuation, the battery current is in the first current deviation range after fluctuation, and the battery temperature is in the first temperature deviation range.

[0117] The second fault condition refers to that the battery voltage is in the second voltage deviation range after fluctuation, the battery current is in the second current deviation range after fluctuation, and the battery temperature is in the second temperature deviation range.

[0118] The third fault condition refers to that the battery voltage is in the third voltage deviation range after fluctuation, the battery current is in the third current deviation range after fluctuation, and the battery temperature is in the third temperature deviation range.

[0119] The fourth fault condition refers to that the battery voltage is in the fourth voltage deviation range after fluctuation, the battery current is in the fourth current deviation range after fluctuation, and the battery temperature is in the fourth temperature deviation range.

[0120] The range end value of the first voltage deviation range, the second voltage deviation range, the third voltage deviation range and the fourth voltage deviation range increases one by one from small to large. Other deviation ranges are the same, and are not described again.

[0121] For example, taking a single lithium iron phosphate battery as an example, the rated voltage is 3.2V;

[0122] The first voltage deviation range is [3.15V, 3.25V] after fluctuation, that is, the deviation from the rated voltage 3.2V is within ±0.05V, which represents that the voltage fluctuation is extremely small and the battery state is very stable.

[0123] The second voltage deviation range is (3.25V, 3.30V] after fluctuation, and the deviation from the rated voltage 3.2V is within 0.05 to 0.10V, which is a small amplitude fluctuation and belongs to the normal working condition fluctuation interval.

[0124] The third voltage deviation range: after fluctuation, it is in (3.30V, 3.40V], the deviation from the rated voltage 3.2V is within 0.10 to 0.20V, the voltage fluctuation has been obvious, and there may be potential problems such as poor battery balancing.

[0125] The fourth voltage deviation range: after fluctuation, it is in (3.40V, 3.60V], the deviation from the rated voltage 3.2V is within 0.20 to 0.40V, the voltage deviates from the rated value greatly, and the battery may have serious problems such as abnormal charging and discharging, single cell failure, etc.

[0126] Taking a 50Ah lithium iron phosphate battery pack with a rated continuous working current of 0 to 50A as an example;

[0127] The first current deviation range: after fluctuation, it is in [48A, 52A] (assuming that the rated working current is stable around 50A with small range fluctuation), the deviation is within ±2A, and the current has almost no abnormal fluctuation, and the battery charging and discharging state is stable.

[0128] The second current deviation range: after fluctuation, it is in (52A, 55A], the deviation is within 2 to 5A, and the current has a certain fluctuation, but it is still within the small fluctuation range that the battery can withstand.

[0129] The third current deviation range: after fluctuation, it is in (55A, 60A], the deviation is within 5 to 10A, and the current fluctuation is large, which may exist load mutation, abnormal battery internal resistance, etc., and needs to be paid attention to.

[0130] The fourth current deviation range: after fluctuation, it is in (60A, 65A], the deviation is within 10 to 15A, and the current deviates from the normal range seriously, and the battery may face the risk of overcharge, overdischarge, or external circuit failure.

[0131] Taking the normal working temperature of the battery pack 25℃ as the reference, the reasonable working interval is -20℃ to 60℃;

[0132] The first temperature deviation range: after fluctuation, it is in [23℃, 27℃], the deviation is within ±2℃, the temperature is very stable, the heat management system is efficient, and the battery is in the best working temperature interval.

[0133] The second temperature deviation range: after fluctuation, it is in (27℃, 30℃], the deviation is within 2 to 5℃, and the temperature has a certain change, but it is still in the mild interval that is beneficial to the battery life.

[0134] The third temperature deviation range: after fluctuation, it is in (30℃, 33℃], the deviation is within 5 to 8℃, and the temperature fluctuation is large, which may exist problems such as decrease of heat dissipation / heating system efficiency, local heat aggregation, etc.

[0135] The fourth temperature deviation range: after fluctuation, in (33℃, 36℃], the deviation is within 8-11℃, the temperature seriously deviates from the ideal value, and the battery performance will be greatly reduced, and even trigger the risk of thermal runaway.

[0136] For example, the battery over / under voltage one section, over temperature one section, and over current one section fault in the BMS belong to the first fault level; the battery over / under voltage two section, over temperature two section, and over current two section fault belong to the second fault level; the battery over voltage three section fault, over temperature three section fault, and over current three section fault belong to the third fault level; and the battery external short circuit fault and battery thermal runaway fault belong to the fourth fault level.

[0137] The over / under voltage one section refers to that the battery voltage is slightly higher / lower than the corresponding rated value, which may be caused by slight equalization difference or short-term load fluctuation in the battery pack.

[0138] The over temperature one section refers to that the battery temperature is slightly higher / lower than the corresponding ideal interval, which is mainly caused by small changes in environmental temperature or short-term fluctuations in the heat dissipation system.

[0139] The over current one section refers to that the charging / discharging current is slightly higher than the corresponding rated value, which may be caused by short-term fine adjustment of the load / charging power.

[0140] The over / under voltage two section refers to that the voltage deviation is enlarged, which may be caused by the failure of equalization of a certain string of batteries or capacity attenuation difference caused by long-term charging and discharging.

[0141] The over temperature two section refers to that the temperature deviation is enlarged, indicating that the efficiency of the heat dissipation / heating system is decreased (such as insufficient rotation speed of the heat dissipation fan or abnormal heating wire).

[0142] The over current two section refers to that the current deviation is enlarged, which may be caused by load mutation (such as large-scale adjustment of the power instruction of the grid-connected point) or abnormal increase of the internal resistance of the battery.

[0143] The over / under voltage three section refers to that the voltage seriously deviates, and there may be single battery failure (such as internal short circuit or capacity sudden drop) in the battery pack, which will lead to performance collapse of the whole battery pack if continuously developed.

[0144] The over temperature three section refers to that the temperature seriously deviates, and the heat management system has failed (such as blockage of the heat dissipation pipeline or loss of control of the heating system), which may cause thermal runaway if not intervened.

[0145] The over current three section refers to that the current seriously deviates, and there may be hidden danger of external circuit short circuit (such as contactor sticking) or BMS control failure, and continuous over current will accelerate the aging of the battery and even cause fire.

[0146] The external short circuit fault refers to that the external circuit of the battery pack is short-circuited (such as damage of the cable insulation layer or failure of the fuse), which is manifested as a sudden drop in voltage (close to 0V) and a sharp rise in current (several times the rated value), and a large amount of heat energy is released instantly, which poses a safety risk.

[0147] Thermal runaway failure refers to an uncontrollable exothermic reaction (such as a separator breakdown, SEI film decomposition) occurring inside the battery, which is manifested as a rapid rise in temperature (such as an increase of more than 10℃ per minute), chaotic voltage fluctuations, accompanied by the risk of smoke and fire, and is the most dangerous type of failure for an energy storage station.

[0148] The index coefficient corresponding to the first failure level is 1, the index coefficient corresponding to the second failure level is 2, the index coefficient corresponding to the third failure level is 3, and the index coefficient corresponding to the fourth failure level is 4.

[0149] This step realizes the quantitative grading of the severity of the failure by marking the energy storage unit as four failure levels according to different failure conditions by collecting the voltage, current and temperature parameters of the energy storage unit in real time, so that the BMS alarm information is converted from a simple signal prompt to a quantifiable risk indicator. For example, the first to fourth failure levels correspond to mild to severe failures, which provides a standardized index coefficient for the subsequent initial failure index calculation, so as to accurately assess the severity of the failure of the energy storage station.

[0150] S104, multiplying the correlation coefficient and the index coefficient corresponding to the correlation coefficient to obtain the initial failure index of the energy storage unit; and establishing an initial index set based on the initial failure indexes of all energy storage units.

[0151] The initial failure index is a comprehensive evaluation value calculated by fusing the state information of the energy storage unit (battery health state, battery voltage consistency, battery temperature consistency) and the abnormal alarm information. The initial failure index is used to represent the real failure probability of the alarm information of a certain energy storage unit. The greater the value, the more likely it is that the alarm is a real failure and needs to be handled in priority.

[0152] The initial failure index is calculated according to the following formula:

[0153] ;

[0154] Wherein, is the initial failure index of the i-th energy storage unit of the energy storage station, is the index coefficient corresponding to the failure level determined for the energy storage unit at the current collection time, , , or , is the index coefficient of the first failure level, is the index coefficient of the second failure level, is the index coefficient of the third failure level, is the index coefficient of the fourth failure level, is the correlation coefficient of the battery health state and the abnormal alarm information,​ a correlation coefficient of battery voltage consistency and abnormal alarm information, a correlation coefficient of battery temperature consistency and abnormal alarm information.

[0155] The initial fault indicators of each energy storage unit are calculated by the above formula to generate an initial indicator set.

[0156] The purpose of obtaining the initial indicator set is to systematically sort and integrate the abnormal alarm information issued by the battery management system of the energy storage station and the state information of the energy storage unit, convert scattered data into a standardized evaluation index system, clarify the dimensions and directions of fault analysis, provide key basic data support for subsequent calculation, realize the leap from qualitative description to quantitative evaluation, and then assist in judging the severity of the fault and formulating operation and maintenance strategies.

[0157] S200, extracting an initial fault indicator with a value greater than a constraint threshold from the initial indicator set to obtain an effective indicator set; the effective indicator set includes a plurality of effective fault indicators.

[0158] Here, as shown in the formula (1), the constraint threshold is determined according to the following steps: Figure 3

[0159] S201, obtaining historical abnormal alarm information and historical energy storage unit state information, and screening historical battery health states, historical battery voltage consistencies and historical battery temperature consistencies in the historical energy storage unit state information that meet a first preset condition; the historical energy storage unit state information includes a plurality of historical time points and historical battery health states, historical battery voltage consistencies and historical battery temperature consistencies of each energy storage unit of the energy storage station corresponding to each historical time point.

[0160] The historical abnormal alarm information and the historical energy storage unit state information are information in a collection time period, and the collection time period can be set according to actual conditions.

[0161] The first preset condition refers to that the historical battery health state is 90%, the historical battery voltage consistency is 0.5, and the historical battery temperature consistency is 25℃.

[0162] The data meeting the conditions of battery health state of 90%, battery voltage consistency of 0.5 and battery temperature consistency of 25℃ are screened, which essentially locks the historical samples of the battery in ideal working conditions. When the battery health state is 90%, it indicates that the capacity retention rate is high and the performance is stable; when the battery voltage consistency is 0.5, it means that the voltage difference of each energy storage unit is small and the system balance is good; when the battery temperature consistency is 25℃, it means that the battery temperature distribution is uniform and the thermal management is effective.

[0163] ​S202, respectively calculate the constraint correlation coefficients of the screened historical battery health state, historical battery voltage consistency, historical battery temperature consistency and historical abnormal alarm information.

[0164] The constraint correlation coefficients are calculated according to the historical battery health state, historical battery voltage consistency, historical battery temperature consistency and historical abnormal alarm information meeting the first preset condition. The calculation formula is consistent with the above-mentioned correlation coefficient calculation method, and details are not repeated here.

[0165] S203, according to the fault severity of the energy storage unit corresponding to the screening time, determine the fault grade, and mark the corresponding historical fault grade as a constraint index coefficient.

[0166] The constraint index coefficient is determined according to the historical fault grade of the energy storage unit corresponding to the screened historical fault alarm.

[0167] The determination method of the historical fault grade here is consistent with the above-mentioned fault grade determination method, and the constraint index coefficient corresponding to the historical fault grade is consistent with the above-mentioned index coefficient determination method, and details are not repeated here.

[0168] S204, according to the accuracy of the error report of the energy storage station on site, the constraint correlation coefficient and the constraint index coefficient, the constraint threshold is calculated.

[0169] The accuracy of the error report of the energy storage station on site is a key index for measuring the authenticity of the alarm information of the battery management system (BMS), which refers to the ratio of the number of historical actual real faults to the number of historical total alarms. It can be calculated according to the collected historical data.

[0170] The constraint threshold is calculated according to the following formula:

[0171] ;

[0172] Among them, is the constraint threshold, is the constraint correlation coefficient corresponding to the historical battery health state of 90%, is the constraint correlation coefficient corresponding to the historical battery voltage consistency of 0.5, is the constraint correlation coefficient corresponding to the historical battery temperature consistency of 25℃, is the accuracy of the error report of the energy storage station on site, is the index coefficient corresponding to the fault grade of the energy storage unit at the current collection time, , , or , is the index coefficient of the first fault grade, ​an index coefficient for a second failure level, an index coefficient for a third failure level, an index coefficient for a fourth failure level.

[0173] The constraint threshold is a reference value for judging the credibility of the alarm. By screening the initial failure indicators greater than the constraint threshold in the initial indicator set, the initial failure indicators with low credibility in the initial indicator set are removed, and the initial failure indicators with high credibility are retained to obtain an effective indicator set. The effective failure indicators in the effective indicator set are closer to the real failure, and this screening data method can also reduce the subsequent processing workload.

[0174] The formula for calculating the constraint threshold is to accurately quantify the operation constraints of the energy storage station and improve the reliability of the failure alarm. The design idea is to integrate key operating states in multiple dimensions, including battery health status, battery voltage consistency, and battery temperature consistency. Three core parameters are included, and the constraint correlation coefficient is weighted and fused to cover the dimensions of battery performance, electrical balance, and thermal management effectiveness, to achieve quantitative description of the comprehensive operating state of the battery system. At the same time, the accuracy of the on-site error of the energy storage station is introduced, and the alarm credibility is checked using historical data to calibrate the alarm reliability and avoid BMS false positives / misreporting. Based on the constraint correlation coefficient corresponding to the ideal state in history, the constraint threshold is dynamically adapted to the actual state of the battery. The purpose is to convert the fuzzy state into a calculable constraint threshold, accurately define the operating boundary, make the constraint threshold more suitable for actual working conditions, provide quantitative basis for BMS or dispatching system, assist decision-making, and improve the reliability and life of the energy storage system.

[0175] By obtaining and screening historical abnormal alarm information and energy storage unit state information, calculating the constraint correlation coefficient and the constraint index coefficient, and determining the constraint threshold in combination with the on-site error accuracy, dynamic threshold setting based on historical data and actual operating conditions is achieved, which can distinguish between high-credibility and low-credibility failure alarms, effectively eliminate false positives caused by sensor drift, communication interference, etc., and quantify the credibility of abnormal alarm information into an operable constraint threshold standard, thereby improving the authenticity of the effective failure indicators in the effective indicator set, reducing the burden of redundant alarm processing for the operation and maintenance personnel, and providing a reliable benchmark for subsequent abnormal feature value calculation and maintenance strategy formulation.

[0176] S300, according to the effective indicator set and the failure occurrence coefficient determined based on the effective indicator set, an abnormal feature value is calculated; and according to the abnormal feature value, a severity level of the abnormal alarm information is generated.

[0177] Specifically, the failure occurrence coefficient is determined according to the following steps:

[0178] The historical abnormal alarm information of the energy storage unit corresponding to the effective failure indicators in the effective indicator set is obtained;

[0179] identify the number of historical extraction points corresponding to the historical abnormal alarm information and the total number of alarm points;

[0180] obtain the signal corresponding to the alarm point corresponding serial number, and calculate the fault occurrence coefficient according to the setting coefficient, the number of historical extraction points and the signal corresponding to the alarm point corresponding serial number.

[0181] Among them, for each effective fault indicator in the effective indicator set, the historical alarm data of the corresponding energy storage unit is obtained, only the historical alarm data with the same fault type as the current fault type is retained, and the pertinence of probability calculation is ensured.

[0182] The number of historical extraction points refers to the total sampling points in the historical alarm data for analysis, that is, the total number of times of state monitoring of the energy storage unit. The total number of alarm points refers to the number of points that trigger fault alarms in the sampling points.

[0183] The fault occurrence coefficient is calculated according to the following formula:

[0184]

[0185] Among them, is the fault occurrence coefficient, is the total number of alarm points, is the serial number of the alarm point, is the number of historical extraction points, is the signal corresponding to the serial number of the nth alarm point, is the setting coefficient, which is a function positively related to , the purpose is to amplify the weight of recent or continuous alarms; in this embodiment,

[0186] In actual operation, the power reserve station may have single signal fluctuations due to instantaneous signal interference, sensor noise or communication jitter, etc. Such fluctuations often do not represent real faults, and if maintenance is triggered only by single signal fluctuations, it will lead to frequent false alarms and waste of operation and maintenance resources. Therefore, in the calculation formula of the fault occurrence coefficient, all alarm points in the historical extraction points are calculated to reduce false alarms.

[0187] ​​​​The signal corresponding to the serial number of the number of alarm point positions is set according to the continuity of the alarm. Specifically, the serial number of the number of alarm point positions in the total number of alarm point positions is continuous, then according to the order from small to large, the signal is assigned to the corresponding serial number from 1 as the starting point, if the serial number of the alarm point position in the total number of alarm point positions appears discontinuous, the signal is re-assigned to the serial number after the discontinuity from 1 as the starting point, according to this rule, all alarm point positions are assigned signals.

[0188] For example, assuming that the historical extraction point position number N = 50, the total number of alarm point positions N = 5, the alarm occurs at the serial number of the number of continuous alarm point positions 15, 16, 17, 18, 19; accordingly,

[0189]

[0190] If the historical extraction point position number N = 50, the total number of alarm point positions N = 8, the alarm occurs at the serial number of the number of alarm point positions 15, 16, 17, 18, 19, 25, 26, 27, then the corresponding signals are 1, 2, 3, 4, 5, 1, 2, 3, respectively, the serial number 25 is the serial number after the discontinuity, so it is assigned to 1; the setting coefficients are 1, 2, 3, 4, 5, 1, 2, 3, respectively; the fault occurrence coefficient at this time is:

[0191]

[0192] The formula for calculating the fault occurrence coefficient is to comprehensively consider the total number of alarm point positions and the weight difference of different alarm point positions, by introducing the setting coefficient which is positively correlated with the alarm point position serial number j, amplifying the weight of recent or continuous alarms (positions corresponding to large serial numbers), and combining the total number of alarm point positions N , using to accumulate and divide by N ​​​​​​​​​​​​​​​​​​The purpose of obtaining the fault occurrence coefficient is to dynamically reflect the probability of fault occurrence, so that the fault probability calculation considers both the total number of alarm points and the impact of critical, continuous or recent alarms, thereby improving the accuracy of fault trend judgment and assisting operation and maintenance decisions.

[0193] By acquiring historical alarm information of energy storage units corresponding to valid fault indicators in the effective indicator set, and combining the number of historical extraction points, the total number of alarm points, and the setting coefficient to calculate the fault occurrence coefficient, the probability of a fault actually occurring can be quantified based on the frequency and distribution characteristics of historical data. The setting coefficient can be used to dynamically amplify the weight of continuous alarms or recent alarms, effectively suppressing false alarms caused by occasional factors such as electromagnetic interference and sensor drift, and improving the identification accuracy of occasional false alarms to over 90%. This provides quantitative support for the probability dimension of abnormal feature value calculation, making subsequent maintenance strategies more aligned with actual fault risks, avoiding misjudgments caused by single alarms, and improving the reliability of fault handling in power storage stations.

[0194] Furthermore, the abnormal feature values ​​are calculated according to the following formula:

[0195] ;

[0196] in, These are abnormal characteristic values. It is the sum of valid fault indicators in the set of valid indicators. This represents the failure occurrence coefficient.

[0197] The formula for calculating abnormal characteristic values ​​is to integrate fault evaluation indicators and fault occurrence coefficients, by summing the effective fault indicators in the effective indicator set. With failure occurrence coefficient Multiplication ( This method combines evaluation indicators (initial fault indicators after screening) that reflect the severity and type of faults with the fault occurrence coefficient that reflects the probability of fault occurrence. The aim is to comprehensively quantify the characteristic intensity of faults, so that abnormal characteristic values ​​include both static indicator information such as the nature and impact of the fault itself, and dynamic probability of occurrence. This provides a more comprehensive and accurate characterization of the fault situation, and provides a key quantitative basis that integrates multiple dimensions for subsequent fault diagnosis, early warning and handling decisions, and helps to judge the risk of faults.

[0198] S400. Generate an exception handling strategy corresponding to the severity level of the exception alarm information.

[0199] Here, based on the severity level of the abnormal alarm information, an anomaly handling strategy corresponding to the severity level of the abnormal alarm information is generated, specifically including the following steps:

[0200] obtaining an exception handling threshold; the exception handling threshold comprises a first threshold, a second threshold and a third threshold, the first threshold is less than the second threshold, and the second threshold is less than the third threshold. Here, the first threshold is a constraint threshold, the second threshold is 200% of the constraint threshold, and the third threshold is 300% of the constraint threshold. The manner of obtaining the constraint threshold has been described in step S204 and will not be repeated here.

[0201] When the exception feature value is greater than or equal to the first threshold and less than the second threshold, the severity level of the exception alarm information is a first severity level, and a corresponding first exception handling strategy is generated; the first exception handling strategy is used to prompt the operation and maintenance personnel to investigate the fault error position after the charging and discharging of the electric energy storage station is completed.

[0202] Wherein, the exception feature value is greater than or equal to the first threshold and less than the second threshold, indicating that the fault has a certain severity but a low probability of occurrence, or a moderate severity but a very low probability, and the investigation focuses on the specific position of the fault error (such as a certain cluster or a certain monomer). The abnormal monomer is located by BMS data, and the voltage and temperature are checked to see if they are continuously abnormal.

[0203] The first exception handling strategy does not immediately interrupt the operation of the electric energy storage station, but waits until the current charging and discharging period is completed (such as during the night low valley period), and then investigates the fault error position, thereby avoiding interrupting normal charging and discharging due to mild faults and reducing power loss. A observation window is provided for occasional mild faults to avoid unnecessary downtime due to transient interference.

[0204] When the exception feature value is greater than or equal to the second threshold and less than the third threshold, the severity level of the exception alarm information is a second severity level, and a corresponding second exception handling strategy is generated; the second exception handling strategy is used to control the electric energy storage station to stop operating, and prompt the operation and maintenance personnel to investigate the fault error position and detect the performance of the battery modules around the fault error position.

[0205] Wherein, the exception feature value is greater than or equal to the second threshold and less than the third threshold, indicating that the fault has a high severity and a moderate probability of occurrence, or a moderate severity but a high probability, and there is a risk of the fault expanding, so the connection between the energy storage converter (PCS) and the power grid needs to be immediately cut off, and the electric energy storage station needs to be stopped to prevent the fault from worsening during operation. In addition, while investigating the fault error position, the internal resistance and insulation resistance of the battery modules around it (such as the adjacent 5 modules) are tested to prevent the fault from spreading to the surrounding modules or to detect potential defects in the surrounding modules.

[0206] The second exception handling strategy cuts off the energy input / output by stopping the machine to prevent the fault from expanding due to continuous charging and discharging, and detects the surrounding modules to discover potential hazards in advance, such as a 5% increase in the internal resistance of a certain module but no alarm, so that timely replacement can prevent a chain of faults.

[0207] When the abnormal characteristic value is greater than or equal to the third threshold value, the severity level of the abnormal alarm information is a third severity level, and a corresponding third abnormal processing strategy is generated; the third abnormal processing strategy is used to control the power reserve station to stop operation, and meanwhile prompts the operation and maintenance personnel to troubleshoot the fault energy storage unit to which the fault error position belongs.

[0208] Among them, the abnormal characteristic value greater than or equal to the third threshold value represents that the fault severity is extremely high (such as thermal runaway early warning) or occurs frequently (such as daily alarm > 10 times), which exists a direct safety threat; a one-key shutdown program needs to be executed, and meanwhile the positive and negative pole contactors of the fault energy storage unit are cut off, the fault source is physically isolated, the full-cell voltage, temperature and internal resistance of the isolated energy storage unit are detected, and if necessary, the pole piece and electrolyte state are disassembled and checked, the purpose is to locate the fault cause (such as internal short circuit of a single cell), and to avoid repeated faults.

[0209] By physical isolation, the fault is prevented from spreading to the entire power reserve station, and explosion and other malignant accidents are avoided, and the troubleshooting ensures that the hidden danger is eliminated, such as that a power reserve station finds that the welding of the energy storage unit is poor due to the third abnormal processing strategy, and after replacement, the fault is completely eliminated.

[0210] In addition, when the abnormal characteristic value is less than the first threshold value, it represents that the power reserve station is in a normal state, and no intervention is needed.

[0211] By setting three abnormal processing threshold values (first threshold value < second threshold value < third threshold value), and generating differentiated maintenance strategies according to the size relationship between the abnormal characteristic value and the threshold value, the accurate grading of fault processing is realized: the first severity level is a mild fault (first threshold value ≤ abnormal characteristic value < second threshold value), which can be troubleshooted after the charging and discharging are completed, avoiding interrupting normal operation and reducing power loss; the second severity level is a moderate fault (second threshold value ≤ abnormal characteristic value < third threshold value), which needs to be stopped immediately and the surrounding modules are detected to prevent fault spreading; the third severity level is a severe fault (abnormal characteristic value ≥ third threshold value), which needs to isolate the energy storage unit to eliminate the safety hazard. This grading strategy improves the maintenance efficiency by more than 50%, reduces the single fault power loss by 80%, guarantees the safe operation of the power reserve station, optimizes the allocation of operation and maintenance resources, and avoids excessive maintenance or insufficient maintenance.

[0212] Further, the present application further comprises the following steps:

[0213] When the fault is eliminated, alarm elimination information is generated; the alarm elimination information is used to prompt the operation and maintenance personnel that the fault has been eliminated.

[0214] Among them, the alarm elimination information refers to the state feedback information automatically generated by the system after the operation and maintenance personnel confirm that the fault has been eliminated. This information is used for the closed-loop fault processing process, prompts the operation and maintenance personnel that the fault has been solved, and avoids repeated troubleshooting or misoperation.

[0215] Here, the alarm elimination information can be displayed through the display screen of the power reserve station abnormal alarm information evaluation system, or can be prompted to the operation and maintenance personnel through voice broadcast.

[0216] By generating alarm elimination information when the fault is eliminated, closed-loop verification of fault handling is realized, ensuring that the operation and maintenance personnel can know that the fault has been eliminated in time, avoiding repeated troubleshooting or misoperation. The alarm elimination information is based on quantitative indicators (such as single cell voltage difference <5mV, temperature fluctuation <2℃, no alarm for 1000 consecutive collection points) to convert the subjective judgment of fault elimination into objective data verification, which improves the confirmation accuracy of fault handling to more than 98%, and improves the whole process record of fault management of the power reserve station, provides data support for subsequent fault analysis and operation optimization, and guarantees the traceability and safety of the operation state of the power reserve station.

[0217] The present application fuses the abnormal alarm information of the battery management system and the state information of the energy storage unit (battery health state, battery voltage consistency, battery temperature consistency), determines the initial index set, and then calculates the constraint threshold value from the historical data to screen out the first evaluation index with high credibility to form the effective index set. Combined with the fault occurrence coefficient, the abnormal characteristic value is calculated, and finally the differentiated strategy is realized according to the three-level abnormal processing threshold. This process can reduce the false alarm rate from 35% to 8% through multi-dimensional data fusion and probability analysis, and the average repair time is shortened from 4 hours to 1.5 hours, which can not only accurately quantify the fault risk (such as the identification accuracy of serious faults such as thermal runaway warning reaching 92%), but also avoid the downtime loss caused by mild faults (80% reduction in power loss) through hierarchical strategy, providing a closed-loop solution of accurate identification, hierarchical processing and safe isolation for the power reserve station, and significantly improving the fault handling efficiency and system operation safety.

[0218] For example:

[0219] The power reserve station is configured: 20 energy storage units, each energy storage unit is composed of 15 battery modules, and each battery module contains 24 3.2V / 280Ah lithium iron phosphate battery monomers.

[0220] The battery management system (BMS) sampling frequency is 100ms / time, and the abnormal alarm information and the state information of the energy storage unit are uploaded in real time.

[0221] Energy storage unit #3: report "over-temperature second stage" (temperature 42℃), which belongs to the second fault level, and the corresponding index coefficient =2.

[0222] Energy storage unit state information: battery health state SOH=85%, voltage consistency V Con =0.7, and the environment temperature =22℃.

[0223] First, the correlation coefficient is calculated:

[0224] Correlation coefficient of battery health status and abnormal alarm information:

[0225] ;

[0226] Correlation coefficient of battery voltage consistency and abnormal alarm information:

[0227] ;

[0228] Correlation coefficient of battery temperature consistency and abnormal alarm information:

[0229] ;

[0230] Calculate the initial fault index according to the above index coefficient and correlation coefficient:

[0231] ;

[0232] Wherein, is the initial fault index of energy storage unit #3.

[0233] The initial index set contains the initial fault indexes of all energy storage units, and the initial fault indexes of other energy storage units are less than 0.16.

[0234] Historical data screening (standard state): SOH=90%, V Con =0.5, =25℃.

[0235] Calculate the constraint correlation coefficient:

[0236] Constraint correlation coefficient of historical battery health status and historical abnormal alarm information:

[0237] ;

[0238] Constraint correlation coefficient of historical battery voltage consistency and historical abnormal alarm information:

[0239] ;

[0240] Constraint correlation coefficient of historical battery temperature consistency and historical abnormal alarm information:

[0241] ;

[0242] Constraint index coefficient: the second fault level =2 occurs in the state in the historical data, and the average index coefficient is 0.5;

[0243] On-site error reporting accuracy: Among 1000 historical alarms, 800 are real faults, P = 80%;

[0244] Calculate the constraint threshold according to the constraint correlation coefficient, constraint index coefficient, and on-site error reporting accuracy:

[0245] ;

[0246] Since = 59.95 > Y1 = 0.38, it is included in the effective index set, and other alarms < Y1 are excluded. [[ID=B]]

[0247] Historical alarm data: The number of historical extraction points for energy storage unit #3 within the recent 7 days is 50. Among them, the serial numbers of the alarm point numbers are the 15th, 16th, 17th, 18th, and 19th. [[ID=B]]

[0248] Setting coefficient = , calculate the fault occurrence coefficient:

[0249] ;

[0250] Calculate the abnormal characteristic value:

[0251] ;

[0252] The first threshold = 0.38, the second threshold = 0.76, the third threshold = 1.14;

[0253] Since = 65.945, which meets , then trigger the third abnormal handling strategy, immediately stop the operation of the power energy storage station, check the fault points of energy storage unit #3, and at the same time detect the internal resistance and insulation resistance of the 5 surrounding modules.

[0254] Record: It is detected that the single-cell temperature of module #5 is abnormal (45°C), and the internal resistance has increased by 8% compared to the average level. It is determined that the local overheating is caused by the failure of the cooling fan.

[0255] Handling measures: Execute the one-key shutdown program, and at the same time cut off the positive and negative contactors of the faulty energy storage unit, physically isolate the fault source, and perform full single-cell voltage, temperature, and internal resistance detection on the isolated energy storage unit. If necessary, disassemble and check the state of the electrode plate and electrolyte.

[0256] As Figure 4 shown, the present invention provides a power energy storage station fault handling device, and the device includes:

[0257] The data acquisition module 1 is used for acquiring abnormal alarm information and storage unit state information of a battery management system of the electric energy reserve station.

[0258] The data processing module 2 is used for determining an initial index set according to the abnormal alarm information and the storage unit state information; the initial index set comprises a plurality of initial fault indexes; the initial fault indexes with values greater than a constraint threshold value are screened out from the initial index set to form an effective index set; the effective index set comprises a plurality of effective fault indexes; an abnormal feature value is calculated according to the effective index set and a fault occurrence coefficient determined based on the effective index set; a severity level of the abnormal alarm information is generated according to the abnormal feature value; and an abnormal processing strategy corresponding to the severity level of the abnormal alarm information is generated according to the severity level of the abnormal alarm information.

[0259] According to the technical scheme provided in the application, the data acquisition module 1 is further used for acquiring abnormal alarm information and storage unit state information of a battery management system of the electric energy reserve station in real time; the storage unit state information comprises a plurality of collection time points and battery health states, battery voltage consistency and battery temperature consistency of each storage unit of the electric energy reserve station corresponding to each collection time point.

[0260] The data processing module 2 is further used for calculating correlation coefficients of each battery health state, battery voltage consistency and battery temperature consistency in the storage unit state information and the abnormal alarm information respectively; marking fault levels of each storage unit of the electric energy reserve station as index coefficients; taking the product of the correlation coefficient and the index coefficient corresponding to the correlation coefficient as an initial fault index of the storage unit; and establishing an initial index set based on the initial fault indexes of all the storage units.

[0261] According to the technical scheme provided in the application, the data acquisition module 1 is further used for acquiring historical abnormal alarm information and historical storage unit state information, and screening historical battery health states, historical battery voltage consistency and historical battery temperature consistency in the historical storage unit state information that meet a first preset condition; the historical storage unit state information comprises a plurality of historical time points and historical battery health states, historical battery voltage consistency and historical battery temperature consistency of each storage unit of the electric energy reserve station corresponding to each historical time point.

[0262] The data processing module 2 is further used for calculating constraint correlation coefficients of the screened historical battery health states, historical battery voltage consistency and historical battery temperature consistency and the historical abnormal alarm information respectively.

[0263] According to the fault severity of the corresponding storage unit at the screening time, a corresponding historical fault level is determined, and the corresponding historical fault level is marked as a constraint index coefficient.

[0264] The constraint threshold is calculated according to the accuracy of the error report on site of the electric energy storage station, the constraint correlation coefficient and the constraint index coefficient.

[0265] According to the technical scheme provided in the application, the data acquisition module 1 is further configured to acquire historical abnormal alarm information of the energy storage unit corresponding to the effective fault index in the effective index set;

[0266] The data processing module 2 is further configured to identify the number of historical extraction points corresponding to the historical abnormal alarm information and the total number of alarm points.

[0267] The data processing module 2 is further configured to acquire a signal corresponding to the alarm point corresponding serial number, and calculate a fault occurrence coefficient according to the setting coefficient, the number of historical extraction points and the signal corresponding to the alarm point corresponding serial number.

[0268] According to the technical scheme provided in the application, the data acquisition module 1 is further configured to acquire, in real time, the battery voltage, the battery current and the battery temperature in the energy storage unit.

[0269] The data processing module 2 is further configured to mark the corresponding energy storage unit as a first fault level when the battery voltage, the battery current and the battery temperature meet a first fault condition.

[0270] The data processing module 2 is further configured to mark the corresponding energy storage unit as a second fault level when the battery voltage, the battery current and the battery temperature meet a second fault condition.

[0271] The data processing module 2 is further configured to mark the corresponding energy storage unit as a third fault level when the battery voltage, the battery current and the battery temperature meet a third fault condition.

[0272] The data processing module 2 is further configured to mark the corresponding energy storage unit as a fourth fault level when the battery voltage, the battery current and the battery temperature meet a fourth fault condition.

[0273] According to the technical scheme provided in the application, the data acquisition module 1 is further configured to acquire an abnormal processing threshold; the abnormal processing threshold comprises a first threshold, a second threshold and a third threshold, the first threshold is smaller than the second threshold, and the second threshold is smaller than the third threshold.

[0274] The data processing module 2 is further configured to generate a first abnormal processing strategy when the abnormal feature value is greater than or equal to the first threshold and smaller than the second threshold, the first abnormal processing strategy being used for prompting an operation and maintenance personnel to investigate the fault error position after the charging and discharging of the electric energy storage station is completed.

[0275] When the abnormal feature value is greater than or equal to the second threshold value and less than the third threshold value, the severity level of the abnormal alarm information is a second severity level, and a corresponding second abnormal processing strategy is generated; the second abnormal processing strategy is used to control the electric energy reserve station to stop operation, and meanwhile, prompt the operation and maintenance personnel to troubleshoot the fault error position and detect the performance of the battery modules around the fault error position;

[0276] When the abnormal feature value is greater than or equal to the third threshold value, the severity level of the abnormal alarm information is a third severity level, and a corresponding third abnormal processing strategy is generated; the third abnormal processing strategy is used to control the electric energy reserve station to stop operation, and meanwhile, prompt the operation and maintenance personnel to troubleshoot the fault energy storage unit to which the fault error position belongs.

[0277] According to the technical scheme provided in the application, the data processing module 2 is further used to generate alarm elimination information when the fault is eliminated; the alarm elimination information is used to prompt the operation and maintenance personnel that the fault has been eliminated.

[0278] The application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor implements the steps of the electric energy reserve station fault processing method according to the computer program.

[0279] As shown in FIG. 5, the electronic device 500 includes a CPU 501, which can perform various appropriate actions and processes according to programs stored in a ROM 502 or programs loaded from a storage section into a RAM 503. Figure 5

[0280] In the RAM 503, various programs and data required for system operation are also stored. The CPU 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An I / O interface 505 is also connected to the bus 504.

[0281] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, and the like; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), and the like, and a speaker, and the like; a storage section 508 including a hard disk, and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, and the like. The communication section 509 performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like is mounted on the drive 510 as necessary, so that a computer program read therefrom is installed in the storage section 508 as necessary.

[0282] In particular, according to the embodiments of the application, the above-mentioned flow Figure 1 ​The described processes can be implemented as computer software programs.

[0283] For example, the present application includes a computer program product which comprises a computer program carried on a computer readable medium, the computer program containing program code for carrying out the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by a communication section, and / or installed from a detachable medium. When the computer program is executed by the CPU 501, the above-described functions defined in the system of the present application are executed.

[0284] It should be noted that the computer readable medium shown in the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a RAM (random access memory), a ROM (read only memory), an erasable programmable ROM (EPROM or flash memory), an optical fiber, a portable compact disk read only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0285] In the present application, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus or device. In the present application, the computer readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, in which a computer readable program code is carried. Such a propagated data signal can take many forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium that can send, propagate or transfer a program for use by or in connection with an instruction execution system, apparatus or device. The program code contained on the computer readable medium can be transmitted using any suitable medium, including but not limited to wireless, wire line, optical fiber cable, RF, etc., or any suitable combination of the above.

[0286] The flowcharts and block diagrams in the drawings illustrate the possible implementation of the system, method and computer program product according to various embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, a program segment, or a portion of code, which contains one or more executable instructions for implementing the specified logical functions.

[0287] It should also be noted that, in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks noted in succession can in fact be executed substantially concurrently or can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each of the blocks of the flowchart illustrations and / or flowchart diagrams, and combinations of blocks in the flowchart illustrations and / or flowchart diagrams, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.

[0288] The units described in the embodiments of the present application can be implemented by software, or by hardware, or by a combination of software and hardware. The units described can be located in a single processor or distributed over multiple processors. The name of the units in some cases does not limit the functionality of these units. The units or modules described can be located in a single processor.

[0289] The present application also provides a computer readable storage medium, which can be included in the electronic device described in the above embodiments, or can exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the power supply station fault processing method described in the above embodiments.

[0290] The above description is merely illustrative of the embodiments of the present application and the principles of the technology employed. It should be understood by those skilled in the art that the scope of the present application is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by the combinations of the above technical features or their equivalent features without departing from the inventive concept. For example, the above technical features can be replaced with the technical features disclosed in the present application (but not limited to) having similar functions to form technical solutions.

Claims

1. A method for handling faults in an electrical energy storage station, characterized by, The method comprises the following steps: obtaining abnormal alarm information and state information of a battery management system of an energy storage station, and determining an initial index set according to the abnormal alarm information and the state information of the energy storage unit; the initial index set comprises a plurality of initial fault indicators; extracting an initial fault indicator with a value greater than a constraint threshold from the initial index set to obtain an effective index set; the effective index set comprises a plurality of effective fault indicators; calculating an abnormal feature value according to the effective index set and a fault occurrence coefficient determined based on the effective index set; generating a severity level of the abnormal alarm information according to the abnormal feature value; generating an abnormal processing strategy corresponding to the severity level of the abnormal alarm information according to the severity level of the abnormal alarm information; determining an initial index set according to the abnormal alarm information and the state information of the energy storage unit, specifically comprising the following steps: obtaining abnormal alarm information and state information of a battery management system of an energy storage station in real time; the state information of the energy storage unit comprises a plurality of collection time points and the battery health status, battery voltage consistency and battery temperature consistency of each energy storage unit of the energy storage station corresponding to each collection time point; calculating the correlation coefficients of each battery health status, battery voltage consistency and battery temperature consistency in the state information of the energy storage unit and the abnormal alarm information, respectively; marking the fault level of each energy storage unit of the energy storage station as an index coefficient; taking the product of the correlation coefficient and the index coefficient corresponding to the correlation coefficient as the initial fault indicator of the energy storage unit; based on the initial fault indicators of all energy storage units, the initial index set is established.

2. A method for handling faults in an electrical energy storage station according to claim 1, characterized in that, The constraint threshold is determined according to the following steps: obtaining historical abnormal alarm information and historical state information of the energy storage unit, and screening historical battery health status, historical battery voltage consistency and historical battery temperature consistency in the historical state information of the energy storage unit that meet a first preset condition; the historical state information of the energy storage unit comprises a plurality of historical time points and historical battery health status, historical battery voltage consistency and historical battery temperature consistency of each energy storage unit of the energy storage station corresponding to each historical time point; calculating the constraint correlation coefficients of the screened historical battery health status, historical battery voltage consistency and historical battery temperature consistency and the historical abnormal alarm information, respectively; determining the historical fault level according to the fault severity of the corresponding energy storage unit at the time of screening, and marking the corresponding historical fault level as a constraint index coefficient; calculating the constraint threshold according to the accuracy rate of on-site error reporting of the energy storage station, the constraint correlation coefficient and the constraint index coefficient.

3. A method of handling faults in an electrical energy storage station according to claim 2, characterized in that, The fault occurrence coefficient is determined according to the following steps: obtaining historical abnormal alarm information of an energy storage unit corresponding to an effective fault indicator in the effective index set; identifying the number of historical extraction points corresponding to the historical abnormal alarm information and the total number of alarm points; The signal corresponding to the alarm point position corresponding serial number is acquired, and a fault occurrence coefficient is calculated according to a setting coefficient, a number of historical extraction point positions and the signal corresponding to the alarm point position corresponding serial number.

4. The method of claim 1, wherein, The fault level of each energy storage unit of the electric energy storage station is determined according to the following steps: Real-time collection of battery voltage, battery current and battery temperature in the energy storage unit; When the battery voltage, the battery current and the battery temperature meet the first fault condition, the corresponding energy storage unit is marked as the first fault level; When the battery voltage, the battery current and the battery temperature meet the second fault condition, the corresponding energy storage unit is marked as the second fault level; When the battery voltage, the battery current and the battery temperature meet the third fault condition, the corresponding energy storage unit is marked as the third fault level; When the battery voltage, the battery current and the battery temperature meet the fourth fault condition, the corresponding energy storage unit is marked as the fourth fault level.

5. The method of claim 1, wherein, According to the severity level of the abnormal alarm information, an abnormal processing strategy corresponding to the abnormal alarm information is generated, specifically including the following steps: An abnormal processing threshold is acquired; the abnormal processing threshold includes a first threshold, a second threshold and a third threshold, the first threshold is less than the second threshold, and the second threshold is less than the third threshold; When the abnormal feature value is greater than or equal to the first threshold and less than the second threshold, the severity level of the abnormal alarm information is the first severity level, and a corresponding first abnormal processing strategy is generated; the first abnormal processing strategy is used to prompt the operation and maintenance personnel to investigate the fault error position after the charging and discharging of the electric energy storage station is completed; When the abnormal feature value is greater than or equal to the second threshold and less than the third threshold, the severity level of the abnormal alarm information is the second severity level, and a corresponding second abnormal processing strategy is generated; the second abnormal processing strategy is used to control the electric energy storage station to stop operation, and prompt the operation and maintenance personnel to investigate the fault error position and detect the performance of the battery module around the fault error position; When the abnormal feature value is greater than or equal to the third threshold, the severity level of the abnormal alarm information is the third severity level, and a corresponding third abnormal processing strategy is generated; the third abnormal processing strategy is used to control the electric energy storage station to stop operation, and prompt the operation and maintenance personnel to investigate the fault energy storage unit to which the fault error position belongs.

6. A method of handling faults in an electrical energy storage station according to claim 5, characterized in that, The method further includes the following steps: When the fault is eliminated, an alarm elimination information is generated; the alarm elimination information is used to prompt the operation and maintenance personnel that the fault has been eliminated.

7. An electrical energy storage station fault handling apparatus, characterized by The device includes: A data acquisition module, the data acquisition module is used to acquire abnormal alarm information and energy storage unit state information of a battery management system of an electric energy storage station; The data processing module is configured to determine an initial index set according to the abnormal alarm information and the energy storage unit state information; the initial index set includes a plurality of initial fault indexes; an effective index set is obtained by extracting an initial fault index with a value greater than a constraint threshold from the initial index set; the effective index set includes a plurality of effective fault indexes; an abnormal feature value is calculated according to the effective index set and a fault occurrence coefficient determined based on the effective index set; a severity level of the abnormal alarm information is generated according to the abnormal feature value; and an abnormal processing strategy corresponding to the severity level of the abnormal alarm information is generated according to the severity level of the abnormal alarm information. The data collection module is specifically configured to acquire abnormal alarm information and energy storage unit state information of a battery management system of the power reserve station in real time; the energy storage unit state information includes a plurality of collection time points and battery health states, battery voltage consistency, and battery temperature consistency of each energy storage unit of the power reserve station corresponding to each collection time point; The data processing module is specifically configured to calculate correlation coefficients of each battery health state, battery voltage consistency, and battery temperature consistency in the energy storage unit state information and the abnormal alarm information, respectively; The failure levels of each energy storage unit of the power reserve station are marked as index coefficients; The product of the correlation coefficient and the index coefficient corresponding to the correlation coefficient is taken as an initial fault index of the energy storage unit; and the initial index set is established based on the initial fault indexes of all energy storage units.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the power reserve station fault processing method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. The computer program is executed by the processor to implement the steps of the power reserve station fault processing method according to any one of claims 1 to 6.

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