Energy storage power station cluster security prevention system and method

Abstract

The application relates to a kind of energy storage power station cluster safety prevention and control system and method. Including: operation monitoring module, for collecting the statistical data and real-time operation data of multiple levels of energy storage power station, send to the risk monitoring module;The multiple levels include power station level, stack level, cluster level and module level;The risk monitoring module is used for spatial mapping to the real-time operation data of each level, and the mapping relationship between the real-time operation data of each level is established;In each level, according to the statistical data of the level, the real-time operation data of the corresponding level is risk evaluated, and the initial risk evaluation result is obtained;According to the mapping relationship, the initial risk evaluation result of each level is comprehensively processed, and the target risk evaluation result is obtained;Safety alarm is carried out based on the target risk evaluation result. By using the method, full-dimensional control from local hidden danger to system risk can be realized.

Description

Technical Field

[0001] The present application relates to the field of power grid security technology, and in particular to a system, method, computer equipment, computer-readable storage medium, and computer program product for energy storage power station cluster security prevention and control. Background Art

[0002] With the rapid development of the new energy industry, energy storage power stations, as core facilities for smoothing grid fluctuations and improving renewable energy absorption capacity, have become a focus of industry attention for their safe and stable operation. Energy storage power stations consist of battery systems (including multi-layered structures such as modules, clusters, and stacks), power conversion systems (PCS), cooling systems, and fire protection systems. These components are highly interconnected, and anomalies in any link can trigger cascading failures, even leading to safety accidents such as fires and explosions.

[0003] Currently, risk assessments for energy storage power plants primarily rely on pre-set thresholds or simple algorithms to generate risk warnings. However, this approach lacks dynamic adaptability and produces inaccurate warnings. Summary of the Invention

[0004] Based on this, it is necessary to provide a safety control system, method, computer equipment, computer-readable storage medium and computer program product for energy storage power station clusters to address the above technical issues.

[0005] In a first aspect, the present application provides a safety control system for energy storage power station clusters, including: an operation monitoring module and a risk monitoring module; wherein,

[0006] The operation monitoring module is used to collect statistical data and real-time operation data of multiple levels of the energy storage power station and send them to the risk monitoring module; the multiple levels include power station level, stack level, cluster level and module level;

[0007] The risk monitoring module is used to spatially map the real-time operation data of each level and establish a mapping relationship between the real-time operation data of each level; at each level, the real-time operation data of the corresponding level is subjected to risk assessment based on the statistical data of the level to obtain an initial risk assessment result; the initial risk assessment result of each level is comprehensively processed based on the mapping relationship to obtain a target risk assessment result; and a safety alarm is issued based on the target risk assessment result.

[0008] In one embodiment, the real-time operating data of each level includes data of multiple operating indicators, and the operating indicators corresponding to different levels are different;

[0009] The risk monitoring module is further used to determine the correlation between the operating indicators corresponding to each level; based on the correlation between the operating indicators, a mapping relationship between the real-time operating data of each level is established.

[0010] In one embodiment, the risk monitoring module is further configured to obtain a sampling frequency of real-time operating data at each level; synchronize the real-time operating data at each level according to the sampling frequency to obtain synchronized data; and establish a mapping relationship between the synchronized data at each level based on the correlation between the operating indicators;

[0011] The sampling frequencies of the synchronized data at each level are the same.

[0012] In one embodiment, the risk monitoring module is further configured to determine a weight for each level; based on the mapping relationship and the weight for each level, the initial risk assessment results for each level are comprehensively processed to obtain a target risk assessment result;

[0013] The weight of each level is determined according to the data volatility of the real-time operation data of each level and the correlation coefficient between the operation index corresponding to the real-time operation data of each level and the power station risk.

[0014] In one embodiment, the operation monitoring module is further used to collect environmental monitoring data and power conversion system monitoring data;

[0015] The risk monitoring module is further used to assist in determining the initial risk assessment results of each level based on the environmental monitoring data and the power conversion system monitoring data.

[0016] In one embodiment, the system further includes a safety quantification analysis module, which includes a power station portrait submodule for constructing a portrait of each energy storage power station; and determining the energy storage power station to be dispatched to the target scenario based on the scenario application demand information of the target scenario and the portrait of each energy storage power station.

[0017] Secondly, this application also provides a method for safety control of energy storage power station clusters, including:

[0018] Collecting statistical data and real-time operating data of multiple levels of the energy storage power station; the multiple levels include the power station level, the stack level, the cluster level and the module level;

[0019] Perform spatial mapping on the real-time operation data of each level and establish a mapping relationship between the real-time operation data of each level;

[0020] At each level, a risk assessment is conducted on the real-time operating data of the corresponding level based on the statistical data of that level to obtain the initial risk assessment results;

[0021] The initial risk assessment results of each level are comprehensively processed according to the mapping relationship to obtain a target risk assessment result, and a security alarm is issued based on the target risk assessment result.

[0022] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0023] Collecting statistical data and real-time operating data of multiple levels of the energy storage power station; the multiple levels include the power station level, the stack level, the cluster level and the module level;

[0024] Perform spatial mapping on the real-time operation data of each level and establish a mapping relationship between the real-time operation data of each level;

[0025] At each level, a risk assessment is conducted on the real-time operating data of the corresponding level based on the statistical data of that level to obtain the initial risk assessment results;

[0026] The initial risk assessment results of each level are comprehensively processed according to the mapping relationship to obtain a target risk assessment result, and a security alarm is issued based on the target risk assessment result.

[0027] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0028] Collecting statistical data and real-time operating data of multiple levels of the energy storage power station; the multiple levels include the power station level, the stack level, the cluster level and the module level;

[0029] Perform spatial mapping on the real-time operation data of each level and establish a mapping relationship between the real-time operation data of each level;

[0030] At each level, a risk assessment is conducted on the real-time operating data of the corresponding level based on the statistical data of that level to obtain the initial risk assessment results;

[0031] The initial risk assessment results of each level are comprehensively processed according to the mapping relationship to obtain a target risk assessment result, and a security alarm is issued based on the target risk assessment result.

[0032] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0033] Collecting statistical data and real-time operating data of multiple levels of the energy storage power station; the multiple levels include the power station level, the stack level, the cluster level and the module level;

[0034] Perform spatial mapping on the real-time operation data of each level and establish a mapping relationship between the real-time operation data of each level;

[0035] At each level, a risk assessment is conducted on the real-time operating data of the corresponding level based on the statistical data of that level to obtain the initial risk assessment results;

[0036] The initial risk assessment results of each level are comprehensively processed according to the mapping relationship to obtain a target risk assessment result, and a security alarm is issued based on the target risk assessment result.

[0037] The above-mentioned energy storage power station cluster safety control system, method, computer equipment, computer-readable storage medium and computer program product, the operation monitoring module collects statistical data and real-time operation data of multiple levels of the energy storage power station and sends it to the risk monitoring module; the risk monitoring module establishes a mapping relationship between the real-time operation data of each level, first conducts risk assessment on each level, and then comprehensively processes the initial risk assessment results of each level. Therefore, through the refined collection of multi-level data, cross-level correlation analysis and dynamic risk assessment, full-dimensional control from local hidden dangers to system risks can be achieved. Furthermore, based on the spatial mapping relationship, the system can quickly trace the risk source and improve the efficiency of abnormal location. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 A schematic diagram of the structure of a safety control system for an energy storage power station cluster in one embodiment;

[0040] Figure 2 This is a schematic structural diagram of a safety control system for an energy storage power station cluster in another embodiment;

[0041] Figure 3 A schematic flow chart of a method for safety control of an energy storage power station cluster in one embodiment;

[0042] Figure 4 This is a structural block diagram of a safety control device for an energy storage power station cluster in one embodiment;

[0043] Figure 5 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0045] It should be noted that the terms "including" and "having" and any variations thereof used in this application are intended to cover non-exclusive inclusions. The term "plurality" used in this application refers to two or more.

[0046] refer to Figure 1 , is a structural diagram of a safety control system for energy storage power station clusters shown in an embodiment, such as Figure 1 As shown, the system includes an operation monitoring module 110 and a risk monitoring module 120; wherein,

[0047] The operation monitoring module 110 is used to collect statistical data and real-time operation data of the energy storage power station at multiple levels and send it to the risk monitoring module 120; the multiple levels include the power station level, the stack level, the cluster level, and the module level;

[0048] The risk monitoring module 120 is used to perform spatial mapping on the real-time operation data of each level and establish a mapping relationship between the real-time operation data of each level; at each level, perform risk assessment on the real-time operation data of the corresponding level based on the statistical data of the level to obtain an initial risk assessment result; perform comprehensive processing on the initial risk assessment result of each level based on the mapping relationship to obtain a target risk assessment result; and issue a safety alarm based on the target risk assessment result.

[0049] The power station level, stack level, cluster level, and module level are the multiple levels that divide energy storage power stations from high to low, and each level corresponds to different monitoring objects and functions. The power station level covers all equipment, systems, and overall operating status within the power station. The stack level is a larger functional unit composed of multiple battery clusters connected electrically, and is a secondary management unit under the power station level. The cluster level is an independent functional unit composed of multiple battery modules connected in series or parallel, and is a subdivided unit under the stack level. The module level is the smallest independent management unit composed of multiple single cells connected in series / parallel. Between each level, data is aggregated from the bottom layer upwards: real-time data (voltage, temperature, etc.) from the module level is transmitted to the cluster level, which is then aggregated and transmitted to the stack level, and finally integrated into the overall status of the power station at the station level.

[0050] Among them, statistical data represents generally unchanging data with reference function, for example, cluster operation status including charging, discharging, maintenance, standby, shutdown, and failure; the total number of cluster modules, the lowest busbar temperature of modules in the cluster, etc.

[0051] For example, power station-level statistical data may include power station name, power station address, power station status, communication status, charge / discharge times, daily charge / discharge amount, historical cumulative charge / discharge amount, current operating mode, equivalent cycle number, cumulative charge / discharge hours, rated power, rated capacity, SOH (State of Health), operating time, alarm status, operating coefficient, conversion rate, availability factor, etc. Real-time operating data may include: power station active power, power station SOC (State of Charge), sampling timestamp, chargeable / dischargeable amount, chargeable / dischargeable power, etc.

[0052] Stack-level statistical data may include stack switch status (open, closed, and display of busbar switch status), stack operating status (charging, discharging, maintenance, standby, shutdown, and fault), stack maximum cell voltage, the sequence number of the stack maximum cell voltage, stack minimum cell voltage, the sequence number of the stack minimum cell voltage, stack maximum cell temperature, the sequence number of the stack maximum cell temperature, stack minimum cell temperature, the sequence number of the stack minimum cell temperature, stack average cell voltage, and stack average cell temperature. Real-time operating data may include: stack SOC, stack power, stack voltage (displaying the maximum value of the available battery cell cluster voltage), stack current (displaying the sum of the available battery cell cluster currents), stack maximum charge and discharge power, and stack maximum charge and discharge capacity.

[0053] Cluster-level statistical data may include cluster operating status (charging, discharging, maintenance, standby, shutdown, fault), cluster balancing status, cluster maximum cell voltage, the cluster maximum cell voltage sequence number, cluster minimum cell voltage, the cluster minimum cell voltage sequence number, cluster maximum cell temperature, the cluster maximum cell temperature sequence number, cluster minimum cell temperature, the cluster minimum cell temperature sequence number, intra-cluster module busbar maximum temperature, intra-cluster module busbar maximum temperature number, intra-cluster module busbar minimum temperature number, cluster average cell voltage, cluster average cell temperature, cluster cumulative charge and discharge Ah, cluster cumulative charge and discharge capacity, cluster SOH, total number of cluster battery cells, and total number of cluster modules. Real-time operating data may include cluster SOC, cluster voltage, cluster current, cluster power, cluster charge and discharge capacity, and cluster charge and discharge power.

[0054] Module-level statistics may include maximum voltage, maximum voltage node number, minimum voltage, minimum voltage node number, maximum temperature, maximum temperature node number, minimum temperature, minimum temperature node number, balancing state, balancing current, total module voltage, and module charge and discharge cycles. Real-time operating data may include voltage distribution graphs, temperature distribution graphs, SOC distribution graphs, positive and negative busbar temperatures, cell internal resistance distribution, and sampling timestamps (used for the horizontal axis of the distribution graph).

[0055] In a specific implementation, the statistical data is relatively fixed data, and therefore the operation monitoring module can pre-acquire the statistical data of each level of the energy storage power station. For real-time operation data, the operation monitoring module can sample the real-time operation data of each level according to the corresponding sampling frequency of each level, in which the lower the level, the higher the corresponding sampling frequency. After the data acquisition is completed, the acquired data is sent to the risk monitoring module for processing. The risk monitoring module constructs a spatial mapping matrix based on the physical topology structure of the energy storage power station (a containing relationship of power station → stack → cluster → module), for example, module A belongs to cluster 1, cluster 1 belongs to stack 2, and stack 2 belongs to power station X, and the data association between each level is established through a code or number. Through the mapping relationship, cross-level data tracing can be achieved, such as quickly locating the current abnormal of a specific stack, cluster, or module when the total current of the power station suddenly increases. Further, for each level, risk assessment is performed on the real-time data according to the statistical data. The statistical data can be used as a reference benchmark, for example, the temperature in the real-time operation data is compared with the highest temperature in the statistical data, and if it exceeds the highest temperature, it indicates that there is an abnormality. Thus, the initial risk assessment result of each level is obtained. Further, the initial risk assessment results of each level are comprehensively processed based on the mapping relationship to eliminate isolated risk misjudgment and identify systematic risk. For example, module A (first-level risk) → cluster 1 (second-level risk) → stack 2 (normal) → power station (normal), which is judged as a “local risk”, and the target risk level is the highest value (first level). The target risk assessment result can include multiple levels, and different levels use different alarm methods, for example, a first-level risk triggers an audible and light alarm; a second-level risk pushes a warning message to an operation and maintenance terminal and suggests adjusting the strategy; and a normal state only records the evaluation result for optimizing the subsequent threshold.

[0056] The above-mentioned energy storage power station cluster safety prevention and control system, the operation monitoring module acquires the statistical data and real-time operation data of multiple levels of the energy storage power station and sends them to the risk monitoring module; the risk monitoring module establishes a mapping relationship between the real-time operation data of each level, first performs risk assessment on each level, and then comprehensively processes the initial risk assessment results of each level. Thus, through fine acquisition, cross-level correlation analysis, and dynamic risk assessment of multi-level data, full-dimensional management and control from local hidden dangers to systematic risks can be achieved. Further, based on the spatial mapping relationship, the system can quickly achieve risk tracing and improve the abnormal positioning efficiency.

[0057] It can be understood that the core operation indicators of different levels are significantly different due to the physical properties and functional positioning of the monitoring objects, and therefore, the real-time operation data of each level includes data of multiple operation indicators, and the operation indicators corresponding to different levels can be different. In an exemplary embodiment, the risk monitoring module is further configured to determine the correlation between the operation indicators corresponding to each level; and establish a mapping relationship between the real-time operation data of each level based on the correlation between the operation indicators.

[0058] In specific implementations, the correlation between the operation indicators corresponding to each level can be determined based on the working principle of the energy storage system, for example, the "cell temperature" of the module level corresponds to the "average temperature" of the cluster level, and for another example, the "cluster current" of the cluster level corresponds to the "stack current" of the stack level. In some embodiments, the non-intuitive correlation between the levels can also be mined through a machine learning algorithm to compensate for the limitations of physical rules. For example, the correlation between the operation indicators of different levels is established by calculating the correlation coefficient between different level indicators. After determining the correlation between the operation indicators corresponding to each level, a mapping relationship between the real-time operation data of each level can be established based on the correlation between the operation indicators. For example, "Module A→Cluster 1→Stack 2→Power Station X", the corresponding mapping relationship is bound for each indicator to ensure that the indicator attribution is traceable.

[0059] In this embodiment, the correlation between the indicators of each level is determined and a dynamic mapping is established, thereby realizing the deep fusion of multi-level data, and thus the accuracy of risk assessment can be improved and false positives and false negatives can be reduced.

[0060] In an exemplary embodiment, the risk monitoring module is further configured to obtain a sampling frequency of the real-time operation data of each level; perform data synchronization processing on the real-time operation data of each level according to the sampling frequency to obtain synchronized data; establish a mapping relationship between the synchronized data of each level based on the correlation between the operation indicators; and wherein the sampling frequencies of the synchronized data of each level are the same.

[0061] Specifically, to eliminate the data misplacement caused by the difference in sampling frequencies between levels, a hybrid strategy of interpolation completion + frequency reduction alignment can be used to synchronize all level data to the same target frequency. Specifically, for levels with a higher target frequency, the sliding window mean frequency reduction method can be used to reduce the frequency to the target frequency. For low-frequency levels, interpolation methods such as linear interpolation and polynomial interpolation can be used to increase the frequency. In this way, the collection timestamps of all indicators are synchronized and aligned, and the time misplacement caused by the difference in sampling frequencies between different levels (e.g., module data 100ms / second, power station data 30s / second) is solved.

[0062] In this embodiment, based on the correlation between the operating indicators of each level, the sampling frequency of each level is unified, and a "time-space consistent" cross-level mapping relationship is constructed, which can improve the accuracy of cross-level correlation analysis and eliminate the interference of the time dimension.

[0063] In an exemplary embodiment, the risk monitoring module is further used to determine the weight of each level; based on the mapping relationship and the weight of each level, the initial risk assessment results of each level are comprehensively processed to obtain the target risk assessment results.

[0064] The weight of each level is determined based on the data volatility of the real-time operation data of each level and the correlation coefficient between the operation indicators corresponding to the real-time operation data of each level and the power plant risk.

[0065] Specifically, data volatility reflects the stability of hierarchical indicators. The greater the volatility, the more sensitive the risk warning is, and the weight should be appropriately increased. For example, for each level of synchronized data, the coefficient of variation can be used to quantify volatility: coefficient of variation V = standard deviation of the indicator data / mean of the indicator data.

[0066] The correlation coefficient reflects the degree of influence of a hierarchical indicator on the overall system risk; the higher the correlation, the higher the weight. For example, using the target risk level of the power plant as the dependent variable and the initial risk assessment results of each level as the independent variable, a Pearson correlation analysis was used to calculate the correlation coefficient between each level and the power plant risk.

[0067] After determining the data volatility and correlation coefficient, the final weights of the indicators at each level can be calculated by weighting them together. In some embodiments, the weights of each level can also be determined based on data volatility or correlation coefficients. Furthermore, according to the weight of each level, the initial risk assessment results of each level with a mapping relationship are weighted and fused to obtain the target risk assessment result.

[0068] In this embodiment, the weights of each level are quantified by data volatility and correlation coefficients, which can improve the accuracy of risk assessment results. Moreover, the weights of each level are dynamically updated with data volatility and correlation, so that the assessment model can always adapt to each operating stage of the system.

[0069] In an exemplary embodiment, the operation monitoring module is also used to collect environmental monitoring data and power conversion system monitoring data; the risk monitoring module is also used to assist in determining the initial risk assessment results of each level based on the environmental monitoring data and power conversion system monitoring data.

[0070] Environmental monitoring data may include video surveillance data, temperature and humidity data, and firefighting data. Video surveillance data may include camera number, camera name (image or text), camera installation location (e.g., inside a room, inside a cabinet), and captured video. Temperature and humidity data may include real-time temperature, maximum temperature, average temperature, real-time humidity, maximum humidity, and average humidity. Firefighting data may include system status, air conditioning status, combustible gas detector status, cabinet temperature, cabinet humidity, and more. In some embodiments, when temperature, humidity, or firefighting data are abnormal, alarm entries can be displayed in reverse chronological order, with dynamic scrolling or list display.

[0071] The power conversion system (PCS) is a key component connecting the energy storage battery system (including stacks, clusters, and modules) with the external power grid. Each layer serves as the "target" and "data source" for the PCS's energy conversion and control. Power conversion system monitoring data can include statistical data and real-time operating data. Statistical data includes operating status (charging (PCS total active power is negative), discharging (PCS total active power is positive), maintenance, standby (PCS total active power is zero and can respond to commands to switch to charging or discharging at any time), outage, and fault), cumulative charge capacity, cumulative discharge capacity, operating mode (PQ mode, PV mode), rated power, rated voltage, rated current, rated frequency, and PCS efficiency. Real-time operating data may include: AB line voltage, BC line voltage, CA line voltage, phase A current, phase B current, phase C current, AC frequency, DC side voltage, DC side current, power, active power, reactive power, power factor, and operating module temperature.

[0072] Specifically, given that environmental monitoring data can be influenced by operational data at each level, and that power conversion system monitoring data can interact with operational data at each level, for example, if module temperature rises, the initial risk level is set to Level 3. However, if PCS data reveals a PCS overcurrent (not a battery failure) and the ambient temperature is normal, the risk level can be revised, for example, down to Level 2. Therefore, this embodiment also configures the operational monitoring module to collect environmental monitoring data and power conversion system monitoring data, and transmits these data to the risk monitoring module. The risk monitoring module then analyzes the environmental monitoring data and power conversion system monitoring data, using the analysis results to assist in determining the initial risk assessment results for each level. This further improves the accuracy of the risk assessment results.

[0073] In an exemplary embodiment, the system further includes a safety quantification analysis module, which includes a power station portrait submodule for constructing a portrait of each energy storage power station; and determining the energy storage power station to be dispatched to the target scenario based on the scenario application demand information of the target scenario and the portrait of each energy storage power station.

[0074] As can be understood, the energy storage power station indicator profile can fully reveal the power station's status. Therefore, a power station profile can be created based on the scenario application demand information, thereby scheduling the most suitable power station. In this embodiment, different scenario demand indicators are considered for different scenario demand information. Therefore, different profiles can be constructed for energy storage power stations, and then scheduling can be carried out to ensure that the selected energy storage power station matches the actual scenario requirements.

[0075] In another embodiment, Figure 2 The figure shows the structure of the energy storage power station cluster safety prevention and control system for the application. In addition to the operation monitoring module and the risk monitoring module, this system also includes a panoramic cockpit module, a ledger information management module, a safety quantitative analysis module, a system management module, a data analysis module and a control management module.

[0076] The panoramic cockpit includes submodules such as commissioning overview, operation overview, battery statistics, PCS statistics, fire protection overview, map display, process safety, and equipment overview. The commissioning overview includes information such as installed capacity (such as power plant type, grid connection scope, capacity, power, and number) and installed capacity distribution (such as power plant classification and commissioning time). The operation overview includes information such as power generation and energy efficiency (such as overall efficiency and charge / discharge power), and reliability (such as power plant name, fault type, equipment type, and outage duration). Battery statistics include battery type statistics (such as battery manufacturer, battery type, battery rated power, and battery rated capacity) and battery reliability information (such as battery manufacturer, number of battery faults, number of failures, and fault duration). PCS statistics include PCS manufacturer statistics (such as PCS manufacturer, PCS type, PCS power, PCS capacity, and number) and PCS status statistics (such as PCS status type, number of faults, and fault duration). The fire protection overview includes fire protection distribution statistics (such as fire protection type and number) and fire protection status statistics (such as fire protection type, fire protection status, number, etc.). The map display can comprehensively display power plant images and introductions by region, and click to jump to the power plant operation monitoring interface. Process safety includes alarm statistics (such as the number of alarms and their proportions by power plant, battery, PCS, fire protection, and environmental types; the number of alarms and their proportions by risk level (alarm level)), alarm distribution (trend distribution by power plant, region, and equipment type), alarm heat description (displaying the alarm heat distribution by map area), and alarm list (displaying a scrolling list of first-level alarms). The equipment overview includes statistical information on equipment data such as battery manufacturers and PCS manufacturers, all of which are counted from dimensions such as the number of power plants, installed power, and installed capacity.

[0077] The risk monitoring module includes submodules such as station-level alarm monitoring, meter-level alarm monitoring, PCS-level alarm monitoring, environmental-level alarm monitoring, stack-level alarm monitoring, cluster-level alarm monitoring, and module-level alarm monitoring, as well as risk policy control and information reporting. These submodules all include alarm statistics and alarm information query capabilities. Alarm statistics include alarm time, alarm type, alarm device, risk level, alarm content, and the number of alarms reported this month, today, and cumulatively. Alarm information query capabilities include alarm time, alarm type, alarm device, risk level, attention level (which serves as a priority for alarm sorting and reminders), and alarm content. It's understandable that different levels have different alarm types. For example, meter-level alarm monitoring may include phase sequence alarms, overvoltage and undervoltage alarms, overvoltage alarms, and undervoltage alarms. Environmental-level alarm monitoring may include flammable gas alarms, fire protection system alarms, and fault alarms. Risk strategy control may include alarm type, alarm device, risk level, attention level, and alarm content. Information reporting may include alarm type, alarm device, risk level, alarm content, reporting personnel, and contact number.

[0078] The ledger information management module includes sub-modules such as power station ledger (such as power station code, power station name, power station type, power station address, application scenario, energy storage rated power / energy, connected substation information, etc.), PCS ledger (such as PCS equipment information, rated active power, DC maximum voltage / current, grid frequency / voltage range, etc.), battery ledger (such as battery equipment number, number of each device, battery manufacturer, battery model, rated voltage / capacity / energy, standard cycle number, etc.), equipment hierarchy structure ledger, other ledgers (such as models / manufacturers of air conditioners, fire protection, and electric meters, etc.), and equipment models (such as PCS / BMS / EMS equipment models, statistics / ledger / operator data models).

[0079] The safety quantitative analysis module includes cluster-level current consistency analysis, cluster-level cell voltage consistency analysis, cluster-level cell voltage operation and maintenance recommendations, cluster-level cell temperature consistency analysis, cluster-level cell temperature operation and maintenance recommendations, cluster-level cell temperature rise rate analysis, data curve characterization, power station characteristics, internal resistance consistency, power consumption indicators, accurate estimation of battery cycle times, battery residual value assessment, automatic report generation, power station quality analysis / power station profiling, operator management, data management and other sub-modules.

[0080] Among them, cluster-level current consistency analysis: compare the consistency of battery cluster current data of the same parallel circuit, compare data differences through means such as mean, range, and variance, and thus compare cluster current circulation differences, persistent cumulative charge and discharge differences, and internal resistance differences.

[0081] Cluster-level single-cell voltage consistency analysis: This compares the consistency of all voltage data for cells within the same battery cluster. Comparable aspects include: calculating discrete cell voltage data at the same time section T (especially at the end of charge and discharge) to compare differences between cells; and comparing cell voltage change rates within the same time period [T, T+m] to compare differences between cells.

[0082] Cluster-level single-cell voltage operation and maintenance recommendations: Based on the difference data results of the two types of cells, the defective cell is located, the capacity of the optimized cell is calculated, and cell handling recommendations are provided.

[0083] Cluster-level cell temperature consistency analysis: This compares the consistency of all temperature data for cells within the same battery cluster. Comparable aspects include: calculating discrete cell temperature data at the same time step, T, and comparing cell differences; and comparing cell temperature change rates within the same time period [T, T+m].

[0084] Cluster-level cell temperature operation and maintenance recommendations: Based on the differential data results of four types of cells, defective cells are located, operational risks are predicted, and cell handling recommendations are provided.

[0085] Cluster-level cell temperature rise rate analysis: Statistics on cell temperature change rate, extreme values, and temperature rise per unit of charge under the same operating conditions are used to evaluate cell operating indicators.

[0086] Internal resistance consistency: Comparison of the calculated internal resistance data consistency of cells in the same battery cluster group. Comparable aspects: at the same time section T, the discrete internal resistance data of each cell is calculated and the differences between each cell are compared; the internal resistance change rate of each cell within the same time period [T, T+m] is compared and the differences between each cell are compared.

[0087] Data curve characterization: Characterize the sampled data, extract feature dimensions, and reduce high-dimensional data to low-dimensional data.

[0088] Power plant characteristics: Comprehensively describe the power plant's operating capacity, operational capacity, risk quantification and other indicators through multiple dimensions such as the power plant's charging and discharging capacity, availability, available hours, effective utilization rate, efficiency, etc.

[0089] Electric power and electricity index: Calculate the electric power and electricity index parameters according to the national standard calculation requirements.

[0090] Estimation of battery cycle times: The battery cycle times are estimated based on comprehensive indicators such as battery life attenuation, cumulative charge and discharge conditions, and charge and discharge depth.

[0091] Battery residual value assessment: Assess the battery's residual value and retirement time based on indicators such as battery charge and discharge capacity, number of cycles, and equipment replacement rate.

[0092] Report automatic generation: generate report templates from multiple angles according to the requirements of owners, operation and maintenance.

[0093] Power station quality analysis / power station image: label the energy storage power station from multiple data indicators by borrowing the concept of user image, so as to analyze the application value of the energy storage power station.

[0094] The system management module includes user management, role management, menu management, department management, log management and other sub-modules.

[0095] The data analysis module includes submodules such as data query, energy consumption data visualization, stack data visualization, cluster data visualization, module data visualization, PCS data visualization, and custom data visualization. Data query includes raw data query and raw data export functions. Energy consumption data visualization includes visualization of auxiliary energy consumption statistics, DC-side energy consumption statistics, and PCS energy consumption statistics. The stack data visualization submodule displays the stack maximum value curve, the stack cluster current curve, the stack cluster pressure difference curve, and the stack cluster power deviation. The stack maximum value curve includes the maximum and minimum stack cell voltages, and the average stack cell voltage; the maximum and minimum stack cell temperatures, and the average stack cell temperature. The stack cluster current curve includes the cluster-level current curve and the cluster current deviation curve (maximum-minimum). The cluster pressure difference curve includes the cluster-level voltage curve and the cluster voltage deviation curve (maximum-minimum). The stack cluster power deviation calculates the deviation capacity (in Ah) ranking of each cluster based on the average stack current. The cluster data visualization submodule displays cluster maximum value curves, cluster differential pressure curves, cluster cell voltage curves, cluster cell temperature curves, cluster multi-module cell voltage curves, and cluster multi-module cell temperature curves. Cluster maximum value curves include maximum cell voltage, cluster minimum cell voltage, and cluster average cell voltage; cluster maximum cell temperature, cluster minimum cell temperature, and cluster average cell temperature; and cluster busbar maximum cell temperature and cluster busbar minimum cell temperature. The cluster differential pressure curve displays the cell pressure difference curve within the cluster (maximum - minimum). The cluster cell voltage curve displays the cell voltage curve within the cluster (resource-intensive and requires data structure design). The cluster cell temperature curve displays the cell temperature curve within the cluster (resource-intensive and requires data structure design). The cluster multi-module cell voltage curve displays the cell voltage curve within a selected module (resource-intensive and requires data structure design). The cluster multi-module cell temperature curve displays the cell temperature curve within a selected module (resource-intensive and requires data structure design). The module data visualization submodule displays module maximum value curves, module differential pressure curves, module cell voltage curves, and module cell temperature curves. Module maximum value curves include maximum and minimum cell voltages, average cell voltages, maximum and minimum cell temperatures, and average cell temperatures; as well as positive and negative busbar temperatures. The module differential pressure curve represents the cell pressure differential curve within the module (maximum - minimum). The module cell voltage curve represents the cell voltage curve within the module (resource-intensive and requires data structure design). The module cell temperature curve represents the cell temperature curve within the module (resource-intensive and requires data structure design). The PCS data visualization submodule selects and plots PCS data indicators. The custom data visualization submodule selects devices (objects) by level, queries time, and automatically plots corresponding data charts.

[0096] The control management module includes sub-modules such as policy control, information update and remote upgrade.

[0097] The coverage capacity of this system is ≥1GWh: the platform has the ability to access data from distributed energy storage power stations in multiple locations; supports ≥6 million points of second-level measurement data: the platform should have the characteristics of multiple connections and high concurrency; the second-level measurement processing speed reaches 1 million points / s: the platform should have reliable caching capabilities, concurrent processing capabilities, high-parallel database writing capabilities, etc., and the data flow should be robust.

[0098] In an exemplary embodiment, the present application also provides a method for safety control of energy storage power station clusters, such as Figure 3 As shown, the method includes:

[0099] Step S310: Collecting statistical data and real-time operating data of multiple levels of the energy storage power station; the multiple levels include power station level, stack level, cluster level, and module level;

[0100] Step S320: spatially map the real-time operation data at each level and establish a mapping relationship between the real-time operation data at each level;

[0101] Step S330 , at each level, performing a risk assessment on the real-time operating data of the corresponding level based on the statistical data of the level to obtain an initial risk assessment result;

[0102] In step S340 , the initial risk assessment results of each level are comprehensively processed according to the mapping relationship to obtain a target risk assessment result, and a security alarm is issued based on the target risk assessment result.

[0103] This method, through the refined collection of multi-level data, cross-level correlation analysis, and dynamic risk assessment, enables comprehensive risk management, from local hazards to systemic risks. Furthermore, based on spatial mapping relationships, the system can quickly trace risks and improve the efficiency of anomaly location.

[0104] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0105] Based on the same inventive concept, embodiments of the present application also provide a device for implementing the aforementioned method for implementing the safety control method for energy storage power station clusters. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations in the embodiments of one or more energy storage power station cluster safety control devices provided below can be found in the above-mentioned limitations on the energy storage power station cluster safety control method and will not be further elaborated here.

[0106] In an exemplary embodiment, Figure 4 As shown, a safety control device for energy storage power station cluster is provided, including:

[0107] The collection module 410 is used to collect statistical data and real-time operating data of multiple levels of the energy storage power station; the multiple levels include the power station level, the stack level, the cluster level, and the module level;

[0108] Establishing module 420, for performing spatial mapping on the real-time operation data of each level and establishing a mapping relationship between the real-time operation data of each level;

[0109] An assessment module 430 is configured to perform risk assessment on the real-time operation data of the corresponding level at each level based on the statistical data of the level to obtain an initial risk assessment result;

[0110] The alarm module 440 is used to comprehensively process the initial risk assessment results of each level according to the mapping relationship to obtain a target risk assessment result, and issue a security alarm based on the target risk assessment result.

[0111] Each module in the energy storage power station cluster safety control device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device's memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0112] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 5As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be realized through WIFI, mobile cellular network, near field communication (Near Field Communication, NFC) or other technologies. The computer program is executed by the processor to realize a method for preventing and controlling the safety of a power storage plant cluster. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0113] Those skilled in the art can understand that, Figure 5 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0114] In one embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to realize the steps in the above method embodiments.

[0115] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to realize the steps in the above method embodiments.

[0116] In one embodiment, a computer program product is provided, including a computer program, and the computer program is executed by a processor to realize the steps in the above method embodiments.

[0117] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0118] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0119] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A safety control system for energy storage power station cluster, characterized in that: The system includes an operation monitoring module and a risk monitoring module; wherein, The operation monitoring module is used to collect statistical data and real-time operation data at multiple levels of the energy storage power station and send them to the risk monitoring module; the multiple levels include the power station level, the stack level, the cluster level, and the module level, and the real-time operation data at each level includes data on multiple operation indicators, and the operation indicators corresponding to different levels are different; The risk monitoring module is used to determine the correlation between the operating indicators corresponding to each level; based on the correlation between the operating indicators, the real-time operating data of each level is spatially mapped to establish a mapping relationship between the real-time operating data of each level; at each level, the real-time operating data of the corresponding level is risk assessed based on the statistical data of the level to obtain an initial risk assessment result; based on the mapping relationship and the weight of each level, the initial risk assessment result of each level is comprehensively processed to obtain a target risk assessment result; a safety alarm is issued based on the target risk assessment result; wherein the weight of each level is determined based on the data volatility of the real-time operating data of each level and the correlation coefficient between the operating indicator corresponding to the real-time operating data of each level and the power plant risk; The risk monitoring module is also used to obtain the sampling frequency of real-time operation data at each level; synchronize the real-time operation data at each level according to the sampling frequency to obtain synchronized data; and establish a mapping relationship between the synchronized data at each level based on the correlation between the operation indicators; wherein the sampling frequency of the synchronized data at each level is the same.

2. The system according to claim 1, wherein: The operation monitoring module is also used to collect environmental monitoring data and power conversion system monitoring data; The risk monitoring module is further used to assist in determining the initial risk assessment results of each level based on the environmental monitoring data and the power conversion system monitoring data.

3. The system according to claim 2, characterized in that The environmental monitoring data includes video surveillance data, temperature and humidity data, and fire protection data.

4. The system according to claim 1, wherein: The system also includes a safety quantitative analysis module, which includes a power station portrait sub-module for constructing a portrait of each energy storage power station; based on the scenario application demand information of the target scenario and the portrait of each energy storage power station, determining the energy storage power station to be dispatched to the target scenario.

5. A method for safety control of energy storage power station clusters, characterized in that: The energy storage power station cluster safety control system according to any one of claims 1 to 4, wherein the method comprises: Collect statistical data and real-time operating data from multiple levels of the energy storage power station; the multiple levels include the power station level, the stack level, the cluster level, and the module level. The real-time operating data of each level includes data on multiple operating indicators, and the operating indicators corresponding to different levels are different; Determine the correlation between the operating indicators corresponding to each level; based on the correlation between the operating indicators, perform spatial mapping on the real-time operating data of each level, and establish a mapping relationship between the real-time operating data of each level; At each level, a risk assessment is conducted on the real-time operating data of the corresponding level based on the statistical data of that level to obtain the initial risk assessment results; Based on the mapping relationship and the weight of each level, the initial risk assessment results of each level are comprehensively processed to obtain a target risk assessment result, and a safety alarm is issued based on the target risk assessment result; wherein the weight of each level is determined based on the data volatility of the real-time operating data of each level and the correlation coefficient between the operating indicators corresponding to the real-time operating data of each level and the power plant risk; The establishing of a mapping relationship between the real-time operation data of each level also includes: obtaining the sampling frequency of the real-time operation data of each level; performing data synchronization processing on the real-time operation data of each level according to the sampling frequency to obtain synchronized data; and establishing a mapping relationship between the synchronized data of each level based on the correlation between the operation indicators; wherein the sampling frequency of the synchronized data of each level is the same.

6. The method according to claim 5, characterized in that The method further comprises: Collect environmental monitoring data and power conversion system monitoring data; Based on the environmental monitoring data and the power conversion system monitoring data, an initial risk assessment result of each level is assisted in determining.

7. The method according to claim 6, characterized in that The environmental monitoring data includes video surveillance data, temperature and humidity data, and fire protection data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 5 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 5 to 7 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 5 to 7 are implemented.

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