Power distribution network fault recovery and energy storage control method based on big data analysis

Abstract

The invention discloses a power distribution network fault recovery and energy storage control method based on big data analysis, and belongs to the technical field of power distribution network operation. The method comprises the steps of constructing a power distribution network operation big data set, identifying a fault type and a fault position in a power distribution network, generating a power distribution network fault recovery strategy, formulating an energy storage control scheme, executing power distribution network fault recovery operation based on the fault recovery strategy and controlling an energy storage system, and completely capturing an operation state from a power supply end to a user side. High-precision data is preferentially selected based on credibility labels, low-quality data interference is reduced, recognition results are corrected by combining adjacent node data verification and historical case comparison, easily-confused fault types are accurately distinguished, faults are quickly responded, recognition time is shortened, and recovery operation and energy storage parameters are adjusted through collaborative linkage. The strategy is continuously optimized by combining data recording analysis, so that the strategy continuously adapts to the change of the power distribution network, the efficient fault recovery capability and the energy storage control level are always kept, and stable operation after the power distribution network fails is realized.

Description

Technical Field

[0001] This invention relates to the field of power distribution network operation technology, and in particular to a method for power distribution network fault recovery and energy storage control based on big data analysis. Background Technology

[0002] The power distribution network covers a wide area and has many types of equipment. The traditional fault handling mode that relies on manual inspection and experience judgment is difficult to quickly capture various faults such as line short circuits, equipment overloads, and grounding faults, resulting in delayed fault identification and low location accuracy. Currently, distribution network fault recovery and energy storage control are mostly independent operations. When formulating fault recovery strategies, the power support potential of energy storage systems is often overlooked, resulting in large voltage fluctuations and high difficulty in power balancing during the recovery process. Traditional power distribution network data collection is limited to core power supply equipment, with serious data gaps on the user side. Furthermore, data processing lacks standardized procedures, making it difficult to integrate heterogeneous data. This makes it impossible to provide comprehensive and reliable data support for fault identification and strategy formulation. After fault recovery, the lack of systematic analysis of process data and strategy optimization mechanisms leads to the continued use of old solutions when similar faults recur, making it difficult to continuously improve the fault handling capabilities of the power distribution network and the utilization efficiency of energy storage systems. Summary of the Invention

[0003] The purpose of this invention is to provide a method for power distribution network fault recovery and energy storage control based on big data analysis, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for distribution network fault recovery and energy storage control based on big data analysis, comprising the following steps: Acquire multi-dimensional operational data during the operation of the power distribution network and construct a large dataset of power distribution network operation. The large dataset of the power distribution network operation is analyzed to identify the fault types and locations in the power distribution network. Based on the fault type and fault location, a power distribution network fault recovery strategy is generated; Obtain the operating status data of the energy storage system associated with the distribution network, and formulate an energy storage control scheme based on the fault recovery strategy and the load demand of the distribution network. The fault recovery strategy is used to perform power distribution network fault recovery operations, and the energy storage system is controlled according to the energy storage control scheme to achieve stable operation after a power distribution network fault.

[0005] Furthermore, the process of acquiring multi-dimensional operational data during the operation of the distribution network and constructing a large dataset of distribution network operations includes: Identify the data acquisition nodes for the power distribution network operation, wherein the acquisition nodes cover the power distribution network's transmission lines, substations, distribution transformers, and user-side equipment; The acquisition nodes collect real-time current, voltage, power, frequency, and equipment temperature data of the power distribution network, and simultaneously acquire historical fault data, equipment parameter data, and meteorological environmental data of the power distribution network. The collected data are processed to unify the format, and data association mapping is established to integrate data from different sources and of different types into a structured big data set for power distribution network operation. Add a timestamp to each data record according to the time sequence of data collection; Based on the device number and installation location information of the data acquisition node, a unique data source identifier is generated to mark the source of each data acquisition. Based on the accuracy level of the data acquisition equipment, the historical data error rate, and the integrity verification results during data transmission, the fuzzy comprehensive evaluation method is used to calculate the credibility of each data point, and different levels of credibility labels are assigned according to the credibility level. Establish a data traceability index that associates timestamps, data source identifiers, and data storage addresses to enable rapid data location based on time or source.

[0006] Furthermore, the process of analyzing the large dataset of power distribution network operations to identify fault types and locations within the power distribution network includes: A fault analysis model for a distribution network is constructed. The model is based on a deep learning algorithm and uses historical fault data and corresponding feature parameters from a large dataset of distribution network operation data as the training set for model training and optimization. The real-time collected power distribution network operation data is input into the trained fault analysis model to extract fault feature vectors from the data; The similarity is calculated by matching the fault feature vector with the preset fault feature template in the model. When the matching similarity is greater than a preset threshold, the corresponding fault type is determined, and the specific location of the fault is located by combining the location information of the data acquisition node and the distribution network topology. Select multiple data acquisition nodes adjacent to the suspected fault location, extract the operating data of these data acquisition nodes during the fault occurrence period, and analyze whether the data change trend is consistent with the fault characteristics. If the data change trend of adjacent acquisition nodes is consistent with the fault characteristics, the credibility of the fault type and location identification results is enhanced; if there are data contradictions, the data is re-entered into the fault analysis model for secondary identification, and the feature weights of the model are adjusted. Search the historical fault case database to find historical cases that are similar to the current fault characteristics and distribution network operation status, and compare the fault types, locations and handling results of the historical cases; If the fault conditions in historical cases closely match the current identification results, the accuracy of the identification results will be further verified; if there are discrepancies, the reasons for the discrepancies will be analyzed, and the identification results will be corrected by combining factors such as the current equipment status and load conditions of the distribution network.

[0007] Furthermore, the process of generating a distribution network fault recovery strategy based on the fault type and fault location includes: Establish a distribution network fault recovery strategy library, and retrieve the corresponding basic recovery scheme from the strategy library based on the identified fault type and fault location; Analyze the current load distribution of the power distribution network, and calculate the total load, proportion of important loads, and load priority in the area affected by the fault; Based on the load distribution and real-time operating parameters of the distribution network, and according to the scheme adjustment rules in the strategy library, the fault isolation range, power supply restoration sequence, and line switching path in the basic restoration scheme are optimized and adjusted. The simulation process of the optimized fault recovery scheme is used to predict the voltage stability, power balance and load recovery rate of the distribution network after the scheme is implemented. When the prediction results meet the preset operating standards, the scheme is determined as the final fault recovery strategy.

[0008] Furthermore, the process of acquiring operational status data of the energy storage system associated with the distribution network, and formulating an energy storage control scheme in conjunction with the aforementioned fault recovery strategy and distribution network load demand, includes: Collect real-time state of charge, charge and discharge power, battery temperature, charge and discharge cycle count and fault alarm information of the energy storage system to construct an energy storage system operation status dataset; Based on the fault recovery strategy, the power support requirements of the distribution network for the energy storage system during the fault recovery process are determined. The power support requirements include power compensation amount, support duration and response speed requirements. Analyze the load change trend after a distribution network fault, predict the load demand in different time periods, and calculate the adjustable capacity and maximum charging and discharging power of the energy storage system by combining the operating status data of the energy storage system. Based on the power support requirements, load demand forecast results, and the adjustability of the energy storage system, a charging and discharging control strategy for the energy storage system is formulated, and the charging and discharging power thresholds and state of charge maintenance ranges for different time periods are defined. The energy storage control scheme is configured with safety constraints, including upper limit of battery temperature, limit of charge and discharge current, and upper and lower limits of state of charge.

[0009] Furthermore, based on the power support requirements, load demand forecasts, and the adjustability of the energy storage system, a charging and discharging control strategy for the energy storage system is formulated, defining the charging and discharging power thresholds and state of charge maintenance range for different time periods. This process includes: The fault recovery cycle is divided into multiple time intervals; Based on the load demand forecast and power deficit of the distribution network for each time interval, the target charging and discharging power of the energy storage system is determined for each time interval. By combining the current state of charge and charging / discharging efficiency of the energy storage system, the change in the state of charge of the energy storage system in each time interval is calculated to ensure that the state of charge of the energy storage system is always maintained within the preset safe range throughout the entire fault recovery cycle. When it is predicted that the state of charge of the energy storage system will exceed the safe range within a certain time interval, the charging and discharging power thresholds of that time interval and adjacent time intervals are adjusted by power transfer or charging and discharging duration.

[0010] Furthermore, based on the fault recovery strategy, distribution network fault recovery operations are performed, and simultaneously, the energy storage system is controlled according to the energy storage control scheme, including: Send a fault recovery command to the distribution network control equipment. The command includes the action sequence of fault isolation switches, power supply line switching command and load recovery priority information, and controls the control equipment to perform operations according to the fault recovery strategy. Real-time monitoring of the distribution network's operating parameters during fault recovery, including node voltage, line current, and power flow direction; when parameters are found to exceed the normal operating range, the recovery operation steps are adjusted. Send charging and discharging control commands to the energy storage system. The charging and discharging control commands include the charging and discharging power and the target value of the state of charge for each time interval, and control the energy storage system to output or absorb power according to the energy storage control scheme. Establish a coordinated mechanism for fault recovery and energy storage control. When the operating parameters of the distribution network change suddenly, adjust the fault recovery operation progress and energy storage control parameters simultaneously to maintain the power balance and voltage stability of the distribution network. Once the power distribution network fault has been restored and the operating parameters have returned to a normal and stable state, the energy storage control scheme is gradually adjusted to switch the energy storage system from fault support mode to normal operation mode, and the data during the fault restoration and energy storage control process are recorded.

[0011] Furthermore, record the data from this fault recovery and energy storage control process, including: Collect key data during the fault recovery and energy storage control process. The key data includes fault occurrence time, fault type, fault location, recovery operation steps, recovery duration, changes in energy storage charging and discharging power, distribution network operating parameter change curves, and final recovery effect data. The collected key data were categorized and organized, and a database was established according to the classification dimensions of fault type, recovery strategy, and energy storage control scheme. Based on historical data, the implementation effects of different fault recovery strategies and energy storage control schemes are analyzed, and evaluation indicators such as recovery efficiency, energy loss and energy storage system loss are calculated. Based on the evaluation metrics, identify the shortcomings in the existing strategies and adjust the strategy parameters and model algorithms.

[0012] Furthermore, a distribution network fault recovery strategy library will be established, including: Data from the power distribution network is acquired and standardized to obtain standardized basic data; the standardized basic data includes fault data, load data, and real-time parameters. Feature extraction is performed on the standardized basic data to obtain fault-load-parameter combination features, which are used as target feature combinations. A preset distribution network fault recovery rule base is obtained, and candidate rule sets corresponding to the target feature combinations are selected from the distribution network fault recovery rule base by feature keyword matching. A preset rule contribution event base is obtained, and the historical application events corresponding to each candidate rule in the candidate rule set are determined. The contribution of the historical application events is analyzed to obtain the rule contribution value, and the rule contribution value is assigned a preset first weight to obtain a first value. A feature-rule evaluation node set is constructed. The set includes multiple evaluation nodes. Each evaluation node scores the suitability of the target feature combination with the candidate rules. The average score of each node is calculated, and a preset second weight is assigned to the average score to obtain a second value. The first value and the second value are summed to obtain the feature-rule joint value. The target feature combination, candidate rules whose feature-rule joint value is greater than or equal to a preset value rule threshold, and their corresponding feature-rule joint values ​​are combined and paired to obtain pairing items. All pairing items are integrated into a database and stored in a blockchain to obtain the feature-rule-value three-dimensional association database. A standard basic scheme is generated based on the combination of target features; an optimal adaptation rule is generated based on the feature-rule-value three-dimensional association library; the optimal adaptation rule is embedded into the standard basic scheme to obtain an initial optimized scheme; the initial optimized scheme is subjected to hierarchical threshold verification to obtain a fault recovery scheme to be executed; the fault recovery scheme to be executed is corrected and merged based on feedback, and the strategy library is updated to complete the construction of the distribution network fault recovery strategy library.

[0013] Furthermore, by combining the operating status data of the energy storage system, the adjustable capacity and maximum charging and discharging power of the energy storage system are calculated, including: Based on the real-time state of charge, battery temperature, charge-discharge cycle count and fault alarm information in the energy storage system's operating status data, the adjustable capacity of the energy storage system is calculated. ; in, This indicates the adjustable capacity of the energy storage system; Indicates the energy demand of the load; Indicates the available coefficients for SOC; Indicates the temperature capacity coefficient; This represents the loop count and capacity coefficient. Indicates the fault capacity coefficient; Based on the charging and discharging power, battery temperature, number of charge and discharge cycles, and fault alarm information in the energy storage system's operating status data, the maximum charging and discharging power of the energy storage system is calculated. ; in, This indicates the maximum charging and discharging power of the energy storage system; Indicates the direction of power; This indicates the maximum charge / discharge power of the energy storage system as specified by the manufacturer. Indicates the SOC power factor; Indicates the temperature power coefficient; Indicates the power coefficient based on the number of cycles; Indicates the fault power factor; This represents the load power limitation factor.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention collects multiple types of data by covering all nodes of the power distribution network, and combines timestamps, source identifiers and credibility tags to achieve full lifecycle management of data. It fully captures the operating status from the power supply end to the user side, solves the problem of data heterogeneity, provides high-quality standardized data for subsequent analysis, and prioritizes the selection of high-precision data based on credibility tags to reduce interference from low-quality data, ensure the accuracy of fault identification and strategy formulation, and improve the overall process stability.

[0015] 2. This invention constructs a fault analysis model trained with historical fault data, combines adjacent node data verification and historical case comparison to correct the identification results, accurately distinguishes easily confused fault types, quickly responds to faults to shorten the identification time, eliminates misjudgment of a single node through dual verification, and corrects deviations by combining the current power grid status, avoiding misjudgment and omission of faults, providing accurate fault information for fault recovery, reducing recovery strategy errors, reducing economic losses and power outages caused by faults, and improving the accuracy and efficiency of fault identification.

[0016] 3. This invention adjusts the recovery operation and energy storage parameters in a coordinated manner, and continuously optimizes the strategy by combining data recording and analysis. The coordination mechanism ensures that both are adjusted synchronously when the distribution network parameters change suddenly, maintaining power balance and voltage stability, avoiding secondary faults. The closed-loop optimization evaluates the advantages and disadvantages of the strategy based on historical data, and makes targeted improvements to shortcomings, so that the strategy can continuously adapt to changes in the power grid, always maintaining efficient fault recovery capability and energy storage control level, achieving stable operation after distribution network faults, and extending the life of the energy storage system. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the steps of the power distribution network fault recovery and energy storage control method of the present invention; Figure 2 This is a schematic diagram of the power distribution network fault recovery and energy storage control process of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1-2 The present invention provides the following technical solutions: A method for power distribution network fault recovery and energy storage control based on big data analysis includes the following steps: Acquire multi-dimensional operational data during the operation of the power distribution network and construct a large dataset of power distribution network operation. The large dataset of the power distribution network operation is analyzed to identify the fault types and locations in the power distribution network. Based on the fault type and fault location, a power distribution network fault recovery strategy is generated; Obtain the operating status data of the energy storage system associated with the distribution network, and formulate an energy storage control scheme based on the fault recovery strategy and the load demand of the distribution network. The fault recovery strategy is used to perform power distribution network fault recovery operations, and the energy storage system is controlled according to the energy storage control scheme to achieve stable operation after a power distribution network fault.

[0020] The process of acquiring multi-dimensional operational data during the operation of the power distribution network and constructing a large dataset of power distribution network operations includes: Identify the data acquisition nodes for the power distribution network operation, wherein the acquisition nodes cover the power distribution network's transmission lines, substations, distribution transformers, and user-side equipment; The acquisition nodes collect real-time current, voltage, power, frequency, and equipment temperature data of the power distribution network, and simultaneously acquire historical fault data, equipment parameter data, and meteorological environmental data of the power distribution network. The collected data are processed to unify the format, and data association mapping is established to integrate data from different sources and of different types into a structured big data set for power distribution network operation. Add a timestamp to each data record according to the time sequence of data collection; Based on the device number and installation location information of the data acquisition node, a unique data source identifier is generated to mark the source of each data acquisition. Based on the accuracy level of the data acquisition equipment, the historical data error rate, and the integrity verification results during data transmission, the fuzzy comprehensive evaluation method is used to calculate the credibility of each data point, and different levels of credibility labels are assigned according to the credibility level. Establish a data traceability index, associating timestamps, data source identifiers, and data storage addresses to enable rapid data location based on time or source; In subsequent data usage, high-confidence data is prioritized for analysis based on confidence labels. When low-confidence data has a significant impact on the analysis results, that part of the data is re-collected or data repair algorithms are used to correct it.

[0021] In the above embodiments, by incorporating user-side equipment into the data acquisition node system, the entire operational status of the power distribution network from the power supply end to the power consumption end can be captured completely, avoiding analysis bias caused by missing user-side data. It can accurately identify power distribution network anomalies caused by sudden changes in user-side load, generate structured datasets, provide a standardized data foundation for subsequent big data analysis, and improve data utilization efficiency. The triple labeling of timestamps, data source identifiers, and credibility tags enables full lifecycle traceability of data, which can quickly locate data acquisition nodes and acquisition time, investigate equipment failures or environmental interference factors, reduce the interference of low-quality data on analysis results, and ensure data quality by re-acquiring or data repair if low-credibility data is found to affect the accuracy of analysis.

[0022] The process of analyzing the large dataset of power distribution network operations to identify fault types and locations in the power distribution network includes: A fault analysis model for a distribution network is constructed. The model is based on a deep learning algorithm and uses historical fault data and corresponding feature parameters from a large dataset of distribution network operation data as the training set for model training and optimization. The real-time collected power distribution network operation data is input into the trained fault analysis model to extract fault feature vectors from the data; The similarity is calculated by matching the fault feature vector with the preset fault feature template in the model. When the matching similarity is greater than a preset threshold, the corresponding fault type is determined, and the specific location of the fault is located by combining the location information of the data acquisition node and the distribution network topology. Select multiple data acquisition nodes adjacent to the suspected fault location, extract the operating data of these data acquisition nodes during the fault occurrence period, and analyze whether the data change trend is consistent with the fault characteristics. If the data change trend of adjacent acquisition nodes is consistent with the fault characteristics, the credibility of the fault type and location identification results is enhanced; if there are data contradictions, the data is re-entered into the fault analysis model for secondary identification, and the feature weights of the model are adjusted. Search the historical fault case database to find historical cases that are similar to the current fault characteristics and distribution network operation status, and compare the fault types, locations and handling results of the historical cases; If the fault conditions in historical cases closely match the current identification results, the accuracy of the identification results will be further verified; if there are discrepancies, the reasons for the discrepancies will be analyzed, and the identification results will be corrected by combining factors such as the current equipment status and load conditions of the distribution network.

[0023] In the above embodiments, by using deep learning algorithms and training on massive amounts of historical fault data, the model can autonomously learn the complex features of different fault types, effectively solving the identification problem caused by the complexity and diversity of fault features in the distribution network. The process of real-time data input and feature vector matching can quickly respond to distribution network faults and shorten the fault identification time. The dual verification mechanism of adjacent node data verification and historical case comparison improves the credibility of the identification results.

[0024] In the above embodiments, adjacent node data verification can eliminate misjudgments caused by abnormal data from a single node. When a fault in a certain acquisition node generates erroneous data, the consistency of adjacent node data trends can be used to promptly detect and correct erroneous identification results. Historical case comparisons, based on past fault handling experience and combined with the analysis of the current actual operating status of the distribution network, optimize the identification results and avoid identification deviations caused by changes in the operating status of the distribution network. When data contradictions or identification differences occur, the model feature weights are adjusted through secondary identification, enabling the model to have self-optimization capabilities, improving identification accuracy, and reducing recovery strategy errors caused by fault misjudgments or omissions.

[0025] The process of determining fault type and location, and generating a distribution network fault recovery strategy, includes: Establish a distribution network fault recovery strategy library. The strategy library stores basic recovery schemes corresponding to different fault types and different fault locations, as well as scheme adjustment rules for different load levels and different power grid operating states. Based on the identified fault type and fault location, the corresponding basic recovery scheme is retrieved from the strategy library. Analyze the current load distribution of the power distribution network, and calculate the total load, proportion of important loads, and load priority in the area affected by the fault; Based on the load distribution and real-time operating parameters of the distribution network, and according to the scheme adjustment rules in the strategy library, the fault isolation range, power supply restoration sequence, and line switching path in the basic restoration scheme are optimized and adjusted. The simulation process of the optimized fault recovery scheme is used to predict the voltage stability, power balance and load recovery rate of the distribution network after the scheme is implemented. When the prediction results meet the preset operating standards, the scheme is determined as the final fault recovery strategy.

[0026] In the above embodiments, a strategy library is established to provide a standardized foundation for fault recovery. Basic solutions corresponding to different fault types and locations ensure that the initial recovery process can be quickly initiated when a fault occurs, avoiding recovery delays caused by the lack of a solution. By calculating the total load, proportion of important loads, and priority of the affected area, a targeted recovery sequence can be formulated to minimize the impact of the fault on social production and life. Combined with real-time voltage, current, and power parameters of the distribution network, the fault isolation range and line switching path are optimized to avoid secondary faults in the distribution network caused by blind isolation or switching. By predicting voltage stability, power balance status, and load recovery rate, potential problems with the optimized solution can be identified in advance, ensuring that the final determined recovery strategy meets the safe operation standards of the distribution network, improving the efficiency of fault recovery, and ensuring the safety and reliability of the recovery process.

[0027] The process of acquiring operational status data of energy storage systems associated with the distribution network, and formulating an energy storage control scheme based on the aforementioned fault recovery strategy and distribution network load demand, includes: Collect real-time state of charge, charge and discharge power, battery temperature, charge and discharge cycle count and fault alarm information of the energy storage system to construct an energy storage system operation status dataset; Based on the fault recovery strategy, the power support requirements of the distribution network for the energy storage system during the fault recovery process are determined. The power support requirements include power compensation amount, support duration and response speed requirements. Analyze the load change trend after a distribution network fault, predict the load demand in different time periods, and calculate the adjustable capacity and maximum charging and discharging power of the energy storage system by combining the operating status data of the energy storage system. Based on the power support requirements, load demand forecast results, and the adjustability of the energy storage system, a charging and discharging control strategy for the energy storage system is formulated, and the charging and discharging power thresholds and state of charge maintenance ranges for different time periods are defined. The fault recovery cycle is divided into multiple time intervals; Based on the load demand forecast and power deficit of the distribution network for each time interval, the target charging and discharging power of the energy storage system is determined for each time interval. By combining the current state of charge and charging / discharging efficiency of the energy storage system, the change in the state of charge of the energy storage system in each time interval is calculated to ensure that the state of charge of the energy storage system is always maintained within the preset safe range throughout the entire fault recovery cycle. When it is predicted that the state of charge of the energy storage system will exceed the safe range within a certain time interval, the charging and discharging power thresholds of that time interval and adjacent time intervals are adjusted by power transfer or charging and discharging duration. The energy storage control scheme is configured with safety constraints, including upper limit of battery temperature, limit of charging and discharging current, and upper and lower limits of state of charge, to ensure the safe and stable operation of the energy storage system during the control process, while meeting the power requirements for power distribution network fault recovery.

[0028] In the above embodiments, by constructing an energy storage system operation status dataset, key parameters such as state of charge, battery temperature, and number of charge-discharge cycles are comprehensively captured, providing complete data support and avoiding the risk of energy storage system damage due to neglecting excessively high battery temperature or too many cycles. Based on the fault recovery strategy, the power support requirements are determined, ensuring that energy storage control closely revolves around the distribution network fault recovery target. This ensures that the power output or absorbed energy of the energy storage system accurately matches the power shortage or redundancy during the fault recovery process. By predicting the load demand at different time periods, the fault recovery cycle is divided into multiple intervals, and the target charge-discharge power for each interval is specifically formulated, achieving refined management of energy storage control. This avoids the waste of energy storage resources or insufficient power support caused by traditional extensive control. Combined with the calculation of changes in the state of charge of the energy storage system, the charge-discharge power threshold is dynamically adjusted to ensure that the energy storage system remains within a safe charge range throughout the entire recovery cycle.

[0029] Based on the aforementioned fault recovery strategy, distribution network fault recovery operations are performed, and simultaneously, the energy storage system is controlled according to the aforementioned energy storage control scheme, including: Send a fault recovery command to the distribution network control equipment. The command includes the action sequence of fault isolation switches, power supply line switching command and load recovery priority information, and controls the control equipment to perform operations according to the fault recovery strategy. Real-time monitoring of the distribution network's operating parameters during fault recovery, including node voltage, line current, and power flow direction; when parameters are found to exceed the normal operating range, the recovery operation steps are adjusted. Send charging and discharging control commands to the energy storage system. The charging and discharging control commands include the charging and discharging power and the target value of the state of charge for each time interval, and control the energy storage system to output or absorb power according to the energy storage control scheme. Establish a coordinated mechanism for fault recovery and energy storage control. When the operating parameters of the distribution network change suddenly, adjust the fault recovery operation progress and energy storage control parameters simultaneously to maintain the power balance and voltage stability of the distribution network. Once the power distribution network fault has been restored and the operating parameters have returned to a normal and stable state, the energy storage control scheme is gradually adjusted to switch the energy storage system from fault support mode to normal operation mode, and the data during this fault restoration and energy storage control process are recorded. Collect key data during the fault recovery and energy storage control process. The key data includes fault occurrence time, fault type, fault location, recovery operation steps, recovery duration, changes in energy storage charging and discharging power, distribution network operating parameter change curves, and final recovery effect data. The collected key data were categorized and organized, and a database was established according to the classification dimensions of fault type, recovery strategy, and energy storage control scheme. Based on historical data, the implementation effects of different fault recovery strategies and energy storage control schemes are analyzed, and evaluation indicators such as recovery efficiency, energy loss and energy storage system loss are calculated. Based on the evaluation indicators, identify the shortcomings in existing strategies and solutions, such as excessively long recovery time, excessively high energy consumption of energy storage, and significant impact on the distribution network, and adjust the strategy parameters and model algorithms accordingly. The optimized strategies and models are applied to subsequent fault recovery and energy storage control processes. The optimization effect is verified through actual operation data, continuously improving the performance of distribution network fault recovery and energy storage control. In the above embodiments, efficient execution and continuous improvement of distribution network fault recovery are achieved through collaborative linkage and data closed loop. The accurate transmission of fault recovery commands and energy storage control commands ensures that the control equipment and energy storage system can operate strictly in accordance with the preset strategy. The real-time parameter monitoring and collaborative linkage mechanism builds a dynamic adjustment guarantee system. In the optimization stage after recovery, key data is collected, classified and stored to provide rich practical samples for analysis. By calculating evaluation indicators such as recovery efficiency, energy loss and energy storage loss, the advantages and disadvantages of different strategy schemes are objectively evaluated, and problems such as excessive recovery time and excessive energy storage consumption are accurately identified. The optimization effect is verified through actual operation, so that the distribution network fault recovery and energy storage control strategies can continuously adapt to changes in the operating status of the distribution network.

[0030] Establish a distribution network fault recovery strategy library, including: Data from the power distribution network is acquired and standardized to obtain standardized basic data; the standardized basic data includes fault data, load data, and real-time parameters. Feature extraction is performed on the standardized basic data to obtain fault-load-parameter combination features, which are used as target feature combinations. A preset distribution network fault recovery rule base is obtained, and candidate rule sets corresponding to the target feature combinations are selected from the distribution network fault recovery rule base by feature keyword matching. A preset rule contribution event base is obtained, and the historical application events corresponding to each candidate rule in the candidate rule set are determined. The contribution of the historical application events is analyzed to obtain the rule contribution value, and the rule contribution value is assigned a preset first weight to obtain a first value. A feature-rule evaluation node set is constructed. The set includes multiple evaluation nodes. Each evaluation node scores the suitability of the target feature combination with the candidate rules. The average score of each node is calculated, and a preset second weight is assigned to the average score to obtain a second value. The first value and the second value are summed to obtain the feature-rule joint value. The target feature combination, candidate rules whose feature-rule joint value is greater than or equal to a preset value rule threshold, and their corresponding feature-rule joint values ​​are combined and paired to obtain pairing items. All pairing items are integrated into a database and stored in a blockchain to obtain the feature-rule-value three-dimensional association database. A standard basic scheme is generated based on the combination of target features; an optimal adaptation rule is generated based on the feature-rule-value three-dimensional association library; the optimal adaptation rule is embedded into the standard basic scheme to obtain an initial optimized scheme; the initial optimized scheme is subjected to hierarchical threshold verification to obtain a fault recovery scheme to be executed; the fault recovery scheme to be executed is corrected and merged based on feedback, and the strategy library is updated to complete the construction of the distribution network fault recovery strategy library.

[0031] In this embodiment, a standard basic scheme is generated based on the combination of target features, including: Based on the target feature combination, the corresponding candidate basic scheme set is retrieved from the preset basic scheme template library; technical nodes, timeliness nodes and economic nodes are set as scoring node sets, the historical scoring accuracy and contribution of each scoring node are obtained, and the node weight of each scoring node is calculated. The scoring nodes are assigned scores to each candidate basic scheme in the candidate basic scheme set, and weights are assigned to the corresponding scoring nodes to obtain weighted scores. The weighted scores of each candidate basic scheme are accumulated to obtain the total score of the scheme. Candidate basic schemes with a total score ≥ 7.5 are selected as standard basic schemes.

[0032] In this embodiment, the optimal adaptation rule is generated based on the feature-rule-value three-dimensional association library, including: Valid rules related to the standard basic scheme are extracted from the feature-rule-value three-dimensional association library to obtain a set of rules to be adapted; a preset distribution network digital twin platform is obtained, and the standard basic scheme, the set of rules to be adapted, and the real-time parameters in the standardized basic data are input into the distribution network digital twin platform to simulate execution for a preset duration; voltage stability, power balance, and load recovery rate indicators during the simulation process are recorded, and the effect score of each rule to be adapted is calculated based on the indicators; the rule to be adapted with an effect score ≥ 0.9 is taken as the optimal adaptation rule.

[0033] In this embodiment, the initial optimization scheme is subjected to hierarchical threshold verification to obtain the fault recovery scheme to be executed. This includes: calculating the feature fit degree and rule fit degree of the initial optimization scheme; the feature fit degree is the matching degree between the target feature combination and the initial optimization scheme; the rule fit degree is the matching degree between the optimal fit rule and the real-time parameters; calculating the comprehensive fit value; the comprehensive fit value = feature fit degree × 0.6 + rule fit degree × 0.4; the preset comprehensive fit value threshold is 0.8. If the comprehensive fit value ≥ 0.8, the effect threshold verification is performed; the simulation time is extended in the distribution network digital twin platform to predict the steady-state values ​​of voltage stability, power balance, and load recovery rate; the preset voltage stability steady-state value threshold is ≤ ±3%, the power balance steady-state value threshold is ≤ 3% of rated power, and the load recovery rate steady-state value threshold is ≥ 98%. If all three indicators meet the corresponding thresholds, the initial optimization scheme is verified and marked as the target fault recovery scheme to be executed; if any indicator does not meet the corresponding threshold, the parameters of the optimal fit rule are adjusted, and the simulation is repeated.

[0034] In this embodiment, the target fault recovery plan is modified and merged based on feedback, and relevant data in the strategy library is updated. This includes: obtaining feedback from schedulers, operations and maintenance teams, and users on the target fault recovery plan; analyzing the feedback based on semantic analysis technology to extract modification suggestions; obtaining the decision weights corresponding to the feedback providers; accumulating and calculating the decision weights to obtain the sum of decision weights. If the sum of the decision weights is ≥0.5, the corresponding correction suggestion is adopted; the adopted correction suggestion is merged with the target fault recovery scheme, duplicate steps are removed and correction steps are added to obtain the corrected scheme; the corrected scheme and its execution effect data are used as new training samples and added to the preset strategy training sample library; based on the new training samples, the feature-rule joint value corresponding to the same target feature combination in the feature-rule-value three-dimensional association library is updated, and the rule contribution event library is updated at the same time; the node weights of the basic scheme generation engine and the effect score weights of the optimization rule adaptation engine are adjusted to complete the strategy library update.

[0035] The working principle and beneficial effects of the above technical solution are as follows: Historical application events corresponding to candidate rules are determined through a rule contribution event database, and contribution analysis is performed to obtain rule contribution values. Rule contribution values ​​reflect the actual effect and role of rules in historical fault recovery, providing objective historical data references for rule evaluation. A feature-rule evaluation node set is constructed, allowing multiple evaluation nodes to score the suitability of target feature combinations with candidate rules. This multi-node evaluation method can comprehensively consider different angles and factors, more comprehensively evaluating the suitability of rules and features. The average score of nodes is calculated and assigned a preset second weight to obtain a second value, further improving the rule evaluation system and enhancing the accuracy and suitability of rule selection. The first and second value scores are accumulated to obtain the feature-rule joint value. The joint value comprehensively considers the historical contribution of the rule and its suitability with the current features, enabling a more comprehensive and accurate evaluation of the rule's merits. Candidate rules with a feature-rule joint value greater than or equal to a preset value rule threshold are selected. The process involves combining and pairing rules to ensure high quality and practicality of the rules in the database. A standard basic scheme is generated based on the combination of target features, and an optimal matching rule is generated based on a three-dimensional feature-rule-value association library. This optimal matching rule is then embedded into the standard basic scheme to obtain an initial optimized scheme. The initial optimized scheme undergoes hierarchical threshold verification to ensure it meets certain safety and reliability requirements. Based on feedback, the fault recovery schemes to be executed are revised and merged, and the strategy library is updated. This feedback mechanism enables the strategy library to continuously learn and improve, adapting to changes in the distribution network's operating status and new fault situations. As the strategy library is continuously updated and improved, the accuracy and effectiveness of fault recovery strategies will continuously improve, further ensuring the reliable operation of the distribution network. Through this series of data processing, rule screening, evaluation, and strategy generation processes, effective fault recovery schemes can be quickly and accurately formulated, significantly shortening fault recovery time, reducing the impact of power outages on users, and improving the power supply reliability and service quality of the distribution network.

[0036] Based on the operating status data of the energy storage system, calculate the adjustable capacity and maximum charge / discharge power of the energy storage system, including: Based on the real-time state of charge, battery temperature, charge-discharge cycle count and fault alarm information in the energy storage system's operating status data, the adjustable capacity of the energy storage system is calculated. ; in, This indicates the adjustable capacity of the energy storage system; Indicates the energy demand of the load; Indicates the available coefficients for SOC; Indicates the temperature capacity coefficient; This represents the loop count and capacity coefficient. Indicates the fault capacity coefficient; Based on the charging and discharging power, battery temperature, number of charge and discharge cycles, and fault alarm information in the energy storage system's operating status data, the maximum charging and discharging power of the energy storage system is calculated. ; in, This indicates the maximum charging and discharging power of the energy storage system; Indicates the direction of power; This indicates the maximum charge / discharge power of the energy storage system as specified by the manufacturer. Indicates the SOC power factor; Indicates the temperature power coefficient; Indicates the power coefficient based on the number of cycles; Indicates the fault power factor; This represents the load power limitation factor.

[0037] In this embodiment, , indicating after the fault arrive The sum of the energy difference between the predicted load and the pre-fault base load within a given time period; Indicates the base load of the distribution network before the fault; express The predicted load after the fault at any given time; Indicates the time interval arrive The total energy integral of the load forecast over time is the total energy consumed (or supplied) by the load during the time period following the fault.

[0038] In this embodiment, Indicates the available SOC range; Represents an empirical coefficient; during discharge, The difference between the current SOC and the minimum SOC represents the usable SOC range for discharge; during charging, The difference between the maximum SOC and the current SOC is the range of SOC available for charging. Indicates the maximum permissible state of charge of the energy storage system; This indicates the minimum state of charge that the energy storage system is allowed to have.

[0039] In this embodiment, ; Indicates the temperature decay coefficient; This indicates the real-time temperature of the energy storage battery; Indicates the battery's optimal operating temperature; This indicates the maximum allowable operating temperature of the battery.

[0040] In this embodiment, Indicates the cyclic aging coefficient; This indicates the cumulative number of charge-discharge cycles of the energy storage battery; This indicates the battery's design cycle life.

[0041] In this embodiment, Indicates fault weight; Indicates the fault status, with a value of 0 or 1; This represents the capacity impact weight for the i-th type of fault; Indicates the number of fault types.

[0042] In this embodiment, When the load exceeds the base load =1 discharge; conversely =-1 charging; Indicates the base load of the distribution network before the fault; This represents the predicted load value at time t.

[0043] In this embodiment, This is expressed as the SOC power sensitivity coefficient; Indicates the real-time state of charge of the energy storage; , representing the midpoint of the SOC interval.

[0044] In this embodiment, This represents the power attenuation coefficient due to temperature.

[0045] In this embodiment, This represents the cyclic power aging factor.

[0046] In this embodiment, This indicates the fault power weight.

[0047] In this embodiment, This represents the predicted load value at time t; Indicates the base load of the distribution network before the fault; This indicates the maximum charging / discharging power of the energy storage system as specified by the manufacturer.

[0048] The working principle and beneficial effects of the above technical solution are as follows: By comprehensively considering multiple factors such as real-time state of charge (SOC), battery temperature, charge-discharge cycle count, and fault alarm information to calculate the adjustable capacity and maximum charge-discharge power, it can more accurately reflect the actual performance of the energy storage system in actual operation; the consideration of charge-discharge cycle count can reflect the impact of battery aging on its performance; as the number of cycles increases, the battery capacity and charge-discharge power will gradually decrease. By introducing cycle count capacity coefficient and cycle count power coefficient, this performance degradation can be accurately assessed, providing a scientific basis for the maintenance and replacement of the energy storage system; accurately calculating the adjustable capacity and maximum charge-discharge power of the energy storage system enables power system dispatchers to better grasp the adjustment capabilities of the energy storage system; when power fluctuations or faults occur in the power system, the energy storage system can respond quickly according to its adjustable capacity and maximum charge-discharge power to perform power compensation and maintain the frequency and voltage stability of the power system.

[0049] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for power distribution network fault recovery and energy storage control based on big data analysis, characterized in that, Includes the following steps: Acquire multi-dimensional operational data during the operation of the distribution network to construct a large dataset of distribution network operation data; analyze the large dataset of distribution network operation data to identify the fault types and fault locations in the distribution network; Based on the fault type and fault location, a power distribution network fault recovery strategy is generated; Obtain the operating status data of the energy storage system associated with the distribution network, and formulate an energy storage control scheme based on the fault recovery strategy and the load demand of the distribution network. The power distribution network fault recovery operation is performed based on the fault recovery strategy, and the energy storage system is controlled according to the energy storage control scheme.

2. The method for distribution network fault recovery and energy storage control based on big data analysis as described in claim 1, characterized in that, The process of acquiring multi-dimensional operational data during the operation of the power distribution network and constructing a large dataset of power distribution network operations includes: Identify the data acquisition nodes for the power distribution network operation, wherein the acquisition nodes cover the power distribution network's transmission lines, substations, distribution transformers, and user-side equipment; The acquisition nodes collect real-time current, voltage, power, frequency, and equipment temperature data of the power distribution network, and simultaneously acquire historical fault data, equipment parameter data, and meteorological environmental data of the power distribution network. The collected data are processed to unify the format, and data association mapping is established to integrate data from different sources and of different types into a structured big data set for power distribution network operation. Add a timestamp to each data record according to the time sequence of data collection; Based on the device number and installation location information of the data acquisition node, a unique data source identifier is generated to mark the source of each data acquisition. Based on the accuracy level of the data acquisition equipment, the historical data error rate, and the integrity verification results during data transmission, the fuzzy comprehensive evaluation method is used to calculate the credibility of each data point, and different levels of credibility labels are assigned according to the credibility level. Establish a data traceability index that associates timestamps, data source identifiers, and data storage addresses to enable rapid data location based on time or source.

3. The method for distribution network fault recovery and energy storage control based on big data analysis as described in claim 1, characterized in that, The process of analyzing the large dataset of power distribution network operations to identify fault types and locations in the power distribution network includes: A fault analysis model for a distribution network is constructed. The model is based on a deep learning algorithm and uses historical fault data and corresponding feature parameters from a large dataset of distribution network operation data as the training set for model training and optimization. The real-time collected power distribution network operation data is input into the trained fault analysis model to extract fault feature vectors from the data; The similarity is calculated by matching the fault feature vector with the preset fault feature template in the model. When the matching similarity is greater than a preset threshold, the corresponding fault type is determined, and the specific location of the fault is located by combining the location information of the data acquisition node and the distribution network topology. Select multiple data acquisition nodes adjacent to the suspected fault location, extract the operating data of these data acquisition nodes during the fault occurrence period, and analyze whether the data change trend is consistent with the fault characteristics. If the data change trend of adjacent acquisition nodes is consistent with the fault characteristics, the credibility of the fault type and location identification results is enhanced; if there are data contradictions, the data is re-entered into the fault analysis model for secondary identification, and the feature weights of the model are adjusted. Search the historical fault case database to find historical cases that are similar to the current fault characteristics and distribution network operation status, and compare the fault types, locations and handling results of the historical cases; If the fault conditions in historical cases closely match the current identification results, the accuracy of the identification results will be further verified; if there are discrepancies, the reasons for the discrepancies will be analyzed, and the identification results will be corrected in conjunction with the current equipment status and load conditions of the distribution network.

4. The method for distribution network fault recovery and energy storage control based on big data analysis as described in claim 1, characterized in that, The process of generating a distribution network fault recovery strategy based on the fault type and fault location includes: Establish a distribution network fault recovery strategy library, and retrieve the corresponding basic recovery scheme from the strategy library based on the identified fault type and fault location; Analyze the current load distribution of the power distribution network, and calculate the total load, proportion of important loads, and load priority in the area affected by the fault; Based on the load distribution and real-time operating parameters of the distribution network, and according to the scheme adjustment rules in the strategy library, the fault isolation range, power supply restoration sequence, and line switching path in the basic restoration scheme are optimized and adjusted. The simulation optimizes the fault recovery scheme execution process, predicts the distribution network voltage stability, power balance status and load recovery rate after the scheme is implemented, and determines the final fault recovery strategy when the prediction results meet the preset operating standards.

5. The method for distribution network fault recovery and energy storage control based on big data analysis as described in claim 1, characterized in that, The process of acquiring operational status data of energy storage systems associated with the distribution network, and formulating an energy storage control scheme based on the aforementioned fault recovery strategy and distribution network load demand, includes: Collect real-time state of charge, charge and discharge power, battery temperature, charge and discharge cycle count and fault alarm information of the energy storage system to construct an energy storage system operation status dataset; Based on the fault recovery strategy, the power support requirements of the distribution network for the energy storage system during the fault recovery process are determined. The power support requirements include power compensation amount, support duration and response speed requirements. Analyze the load change trend after a distribution network fault, predict the load demand in different time periods, and calculate the adjustable capacity and maximum charging and discharging power of the energy storage system by combining the operating status data of the energy storage system. Based on the power support requirements, load demand forecast results, and the adjustability of the energy storage system, a charging and discharging control strategy for the energy storage system is formulated, and the charging and discharging power thresholds and state of charge maintenance ranges for different time periods are defined. The energy storage control scheme is configured with safety constraints, including upper limit of battery temperature, limit of charge and discharge current, and upper and lower limits of state of charge.

6. The method for distribution network fault recovery and energy storage control based on big data analysis as described in claim 5, characterized in that, Based on the power support requirements, load demand forecasts, and the adjustability of the energy storage system, a charging and discharging control strategy for the energy storage system is formulated, defining the charging and discharging power thresholds and state of charge maintenance range for different time periods. This process includes: The fault recovery cycle is divided into multiple time intervals; Based on the load demand forecast and power deficit of the distribution network for each time interval, the target charging and discharging power of the energy storage system is determined for each time interval. By combining the current state of charge and charging / discharging efficiency of the energy storage system, the change in the state of charge of the energy storage system in each time interval is calculated to ensure that the state of charge of the energy storage system is always maintained within the preset safe range throughout the entire fault recovery cycle. When it is predicted that the state of charge of the energy storage system will exceed the safe range within a certain time interval, the charging and discharging power thresholds of that time interval and adjacent time intervals are adjusted by power transfer or charging and discharging duration.

7. The method for distribution network fault recovery and energy storage control based on big data analysis as described in claim 1, characterized in that, Based on the aforementioned fault recovery strategy, distribution network fault recovery operations are performed, and simultaneously, the energy storage system is controlled according to the aforementioned energy storage control scheme, including: Send a fault recovery command to the distribution network control equipment. The command includes the action sequence of fault isolation switches, power supply line switching command and load recovery priority information, and controls the control equipment to perform operations according to the fault recovery strategy. Real-time monitoring of the distribution network's operating parameters during fault recovery, including node voltage, line current, and power flow direction; when parameters are found to exceed the normal operating range, the recovery operation steps are adjusted. Send charging and discharging control commands to the energy storage system. The charging and discharging control commands include the charging and discharging power and the target value of the state of charge for each time interval, and control the energy storage system to output or absorb power according to the energy storage control scheme. Establish a coordinated mechanism for fault recovery and energy storage control. When the operating parameters of the distribution network change suddenly, adjust the fault recovery operation progress and energy storage control parameters simultaneously to maintain the power balance and voltage stability of the distribution network. Once the power distribution network fault has been restored and the operating parameters have returned to a normal and stable state, the energy storage control scheme is gradually adjusted to switch the energy storage system from fault support mode to normal operation mode, and the data during the fault restoration and energy storage control process are recorded.

8. The method for distribution network fault recovery and energy storage control based on big data analysis as described in claim 7, characterized in that, Record the data from this fault recovery and energy storage control process, including: Collect key data during the fault recovery and energy storage control process. The key data includes fault occurrence time, fault type, fault location, recovery operation steps, recovery duration, changes in energy storage charging and discharging power, distribution network operating parameter change curves, and final recovery effect data. The collected key data were categorized and organized, and a database was established according to the classification dimensions of fault type, recovery strategy, and energy storage control scheme. Based on historical data, the implementation effects of different fault recovery strategies and energy storage control schemes are analyzed, and evaluation indicators such as recovery efficiency, energy loss and energy storage system loss are calculated. Based on the evaluation metrics, identify the shortcomings in the existing strategies and adjust the strategy parameters and model algorithms.

9. The method for distribution network fault recovery and energy storage control based on big data analysis as described in claim 4, characterized in that, Establish a distribution network fault recovery strategy library, including: Data from the power distribution network is acquired and standardized to obtain standardized basic data; the standardized basic data includes fault data, load data, and real-time parameters. Feature extraction is performed on the standardized basic data to obtain fault-load-parameter combination features, which are used as target feature combinations. A preset distribution network fault recovery rule base is obtained, and candidate rule sets corresponding to the target feature combinations are selected from the distribution network fault recovery rule base by feature keyword matching. A preset rule contribution event base is obtained, and the historical application events corresponding to each candidate rule in the candidate rule set are determined. The contribution of the historical application events is analyzed to obtain the rule contribution value, and the rule contribution value is assigned a preset first weight to obtain a first value. A feature-rule evaluation node set is constructed. The set includes multiple evaluation nodes. Each evaluation node scores the suitability of the target feature combination with the candidate rules. The average score of each node is calculated, and a preset second weight is assigned to the average score to obtain a second value. The first value and the second value are summed to obtain the feature-rule joint value. The target feature combination, candidate rules whose feature-rule joint value is greater than or equal to a preset value rule threshold, and their corresponding feature-rule joint values ​​are combined and paired to obtain pairing items. All pairing items are integrated into a database and stored in a blockchain to obtain the feature-rule-value three-dimensional association database. A standard basic scheme is generated based on the combination of target features; an optimal adaptation rule is generated based on the feature-rule-value three-dimensional association library; the optimal adaptation rule is embedded into the standard basic scheme to obtain an initial optimized scheme; the initial optimized scheme is subjected to hierarchical threshold verification to obtain a fault recovery scheme to be executed; the fault recovery scheme to be executed is corrected and merged based on feedback, and the strategy library is updated to complete the construction of the distribution network fault recovery strategy library.

10. The method for distribution network fault recovery and energy storage control based on big data analysis as described in claim 5, characterized in that, Based on the operating status data of the energy storage system, calculate the adjustable capacity and maximum charge / discharge power of the energy storage system, including: Based on the real-time state of charge, battery temperature, charge-discharge cycle count and fault alarm information in the energy storage system's operating status data, the adjustable capacity of the energy storage system is calculated. ； in, This indicates the adjustable capacity of the energy storage system; Indicates the energy demand of the load; Indicates the available coefficients for SOC; Indicates the temperature capacity coefficient; This represents the loop count and capacity coefficient. Indicates the fault capacity coefficient; Based on the charging and discharging power, battery temperature, number of charge and discharge cycles, and fault alarm information in the energy storage system's operating status data, the maximum charging and discharging power of the energy storage system is calculated. ； in, This indicates the maximum charging and discharging power of the energy storage system; Indicates the direction of power; This indicates the maximum charge / discharge power of the energy storage system as specified by the manufacturer. Indicates the SOC power factor; Indicates the temperature power coefficient; Indicates the power coefficient based on the number of cycles; Indicates the fault power factor; This represents the load power limitation factor.

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