A safety early warning assessment method and device for a large-scale energy storage power station

Abstract

The present invention provides a large-scale energy storage power station safety early warning assessment method and device, which relate to the field of energy storage monitoring technology. The present invention divides a large-scale energy storage power station into blocks of equal-sized areas, analyzes the energy exchange situation, system response situation and battery health situation of each block to generate a corresponding energy assessment coefficient, system response coefficient and battery health assessment coefficient, analyzes the energy assessment coefficient, system response coefficient and battery health assessment coefficient to generate a power station safety assessment coefficient for each block, determines the early warning situation of each block by the size of the power station safety assessment coefficient; generates a power station risk assessment coefficient according to the safety assessment coefficient and battery capacity of each block, and performs a power station safety early warning through the risk assessment coefficient. The present invention can not only perform safety early warnings on large-scale energy storage power stations, but also accurately monitor and timely warn individual areas, effectively preventing local problems from spreading to the entire system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage monitoring, and in particular to a safety early warning assessment method and device for a large-scale energy storage power station. Background Art

[0002] In the context of global energy transformation, with the rapid development of renewable energy (such as wind power and solar energy), large-scale energy storage power stations play an increasingly important role in balancing power supply and demand and improving the flexibility and stability of power systems. However, these energy storage systems face many safety challenges in actual operation. As the core component of energy storage power stations, the safety performance of batteries is directly related to the overall safety and reliability of the power station.

[0003] At present, many energy storage power stations rely on traditional monitoring methods to monitor the working status of batteries, such as battery voltage, temperature, and number of charge and discharge cycles. However, these single parameter monitoring methods often cannot fully reflect the health of the battery and the overall safety performance of the system. Due to the lack of effective comprehensive evaluation methods, many potential safety hazards are difficult to be discovered in time, which may lead to serious accidents such as battery overheating, expansion, short circuit and even fire. In addition, the existing technology is relatively insufficient in monitoring battery aging, capacity decay and internal resistance changes, and cannot provide effective early warning for the safe operation of the battery.

[0004] In the prior art, the publication number CN112861826A discloses a fire safety assessment method and system based on electrochemical energy storage, including: collecting battery safety environment data and historical battery comprehensive data of n batteries in the electrochemical energy storage group; constructing a battery health status model that outputs the battery environmental health status value based on the battery safety environment data; processing all output battery environmental health status values ​​to obtain the internal short circuit probability of the electrochemical energy storage group; constructing an electrochemical energy storage heat conduction model for predicting the battery thermal runaway probability based on the historical battery comprehensive data; synthesizing the internal short circuit probability of the electrochemical energy storage group and the predicted battery thermal runaway probability to obtain the safety hazard probability of the electrochemical energy storage group, and presetting the safety threshold; if the safety hazard probability of the electrochemical energy storage group is greater than or equal to the safety threshold; then sending an alarm to the management terminal that controls the electrochemical energy storage group; which will greatly improve the safety and stability of the electrochemical energy storage. However, the prior art still has defects. The prior art can only provide safety warnings for small-area systems. When the energy storage area expands to a certain scale, the accuracy and effectiveness of these technologies are significantly reduced, and they cannot provide a comprehensive and detailed safety assessment. This limitation means that in large-scale energy storage applications, safety measures still need to be further improved and optimized to ensure the overall safety and stability of the system.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a large-scale energy storage power station safety early warning assessment method and device to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A large-scale energy storage power station safety early warning assessment method, the specific steps include:

[0009] Step 1: Divide large-scale energy storage power stations into The energy data of each area is obtained by analyzing the energy data to obtain energy characteristic data, and the energy characteristic data is mathematically analyzed to obtain energy evaluation coefficients;

[0010] Step 2: Obtain system response data, perform mathematical analysis on the system response data to obtain the system response coefficient; generate the system state evaluation coefficient based on the system response coefficient and the energy evaluation coefficient;

[0011] Step 3: Obtain the historical number of charge and discharge cycles, battery usage time, and actual battery capacity. Use the historical number of charge and discharge cycles and battery usage time as data sets and the actual battery capacity as labels to build a battery capacity prediction model. Use the trained battery capacity prediction model to predict the actual battery capacity of each area.

[0012] Step 4: Obtain battery internal resistance data, analyze the battery internal resistance data and battery capacity data to generate a battery health assessment coefficient, and generate a power station safety assessment coefficient according to the system state assessment coefficient and the battery health assessment coefficient; the battery internal resistance data includes the actual internal resistance and initial internal resistance of the battery, and the battery capacity data includes the actual battery capacity and the rated battery capacity;

[0013] Step 5: Determine the warning status of each area by comparing the power plant safety assessment coefficient with the preset power plant safety assessment threshold;

[0014] Step 6: Generate a power plant risk assessment coefficient based on the safety assessment coefficient and battery capacity of each area, and issue a power plant safety warning through the risk assessment coefficient.

[0015] Further, the energy data includes total battery input power, total battery output power, inverter input power, inverter output power, line input power and line output power;

[0016] The specific logic for obtaining the energy evaluation coefficient is: analyzing the energy data to obtain energy characteristic data, and performing mathematical analysis on the energy characteristic data to obtain the energy evaluation coefficient; the energy characteristic data includes energy conversion coefficient, inverter loss and line loss;

[0017] The specific logic for obtaining the energy conversion coefficient is:

[0018] ;

[0019] in, is the energy conversion coefficient, is the total battery input power, is the total output power of the battery;

[0020] The specific logic for obtaining inverter loss and line loss is:

[0021] ;

[0022] in, is the inverter loss, is the line loss, Inverter input power, Inverter output power, Line input power, Line output power;

[0023] The specific formula for obtaining the energy assessment coefficient is:

[0024] ;

[0025] in, is the energy assessment coefficient.

[0026] Furthermore, the system response data includes the maximum response time allowed by the system, the system discharge time, the system full load time, and the voltage change rate, current change rate, and power change rate of the system from discharge to full load;

[0027] The specific logic for generating the system state evaluation coefficient is as follows: obtaining the system response data, performing mathematical analysis on the system response data to obtain the system response coefficient; generating the system state evaluation coefficient based on the system response coefficient and the energy evaluation coefficient; the specific formula for generating the system response coefficient is as follows:

[0028] ;

[0029] in, is the system response coefficient, The maximum response time allowed for the system, is the system discharge time, is the system full load time, is the voltage change rate from discharge to full load, is the current change rate from discharge to full load, is the power change rate from discharge to full load;

[0030] The specific formula for generating the system status assessment coefficient is:

[0031] ;

[0032] in, is the system state assessment coefficient, is the energy assessment coefficient.

[0033] Furthermore, the specific logic for generating the power station safety assessment coefficient is as follows: obtaining battery internal resistance data, analyzing the battery internal resistance data and battery capacity data to generate a battery health assessment coefficient, and generating a power station safety assessment coefficient based on the system state assessment coefficient and the battery health assessment coefficient; the specific formula for generating the battery health assessment coefficient is as follows:

[0034] ;

[0035] in, is the battery health assessment coefficient, For the region The actual internal resistance of a battery, For the region The initial internal resistance of the battery, is the actual total battery capacity, For the region The rated battery capacity of the battery, is the number of battery blocks in the area, is the index of the battery block in the corresponding area, and ;

[0036] The specific formula for obtaining the total battery capacity is:

[0037] ;

[0038] in, For the The actual battery capacity of the battery;

[0039] The specific formula for generating the power station safety assessment coefficient is:

[0040] ;

[0041] in, is the power station safety assessment factor, is the system status assessment coefficient.

[0042] Furthermore, the specific logic for determining the warning situation of each area is as follows: a preset power plant safety assessment threshold is set. If the power plant safety assessment coefficient is greater than the power plant safety assessment threshold, the corresponding area is judged to be dangerous and a safety warning is required; if the power plant safety assessment coefficient is less than or equal to the power plant safety assessment threshold, it is judged that no danger has occurred in the corresponding area and no safety warning needs to be issued.

[0043] Furthermore, the specific logic for early warning of power station safety is as follows: the safety assessment coefficient of each area is processed, and the power station risk assessment coefficient is generated according to the processed safety assessment coefficient and battery capacity, and the power station risk assessment threshold is preset. If the power station risk assessment coefficient is less than or equal to the preset power station risk assessment threshold, the large-scale energy storage power station can operate normally or the danger is controllable, and there is no need to issue a serious safety warning; if the power station risk assessment coefficient is greater than the preset power station risk assessment threshold, the large-scale energy storage power station has uncontrollable safety hazards and a safety warning is required;

[0044] The specific formula for processing the safety assessment coefficient of each area is:

[0045] ;

[0046] in, is the power station safety assessment coefficient after processing, is the power station safety assessment factor, assessing thresholds for power station safety;

[0047] The specific formula for generating the power plant risk assessment coefficient is:

[0048] ;

[0049] in, is the power plant risk assessment coefficient, For the The power station safety assessment coefficient after the area is processed is: For the The actual total battery capacity of each area, The number of regions divided for large-scale energy storage power stations.

[0050] The present invention further provides a large-scale energy storage power station safety early warning assessment device, which is used to implement the above-mentioned large-scale energy storage power station safety early warning assessment method, specifically comprising:

[0051] Energy analysis module, used to divide large-scale energy storage power stations into The energy data of each area is obtained by analyzing the energy data to obtain energy characteristic data, and the energy characteristic data is mathematically analyzed to obtain energy evaluation coefficients;

[0052] The response analysis module is used to obtain system response data, perform mathematical analysis on the system response data to obtain the system response coefficient; generate the system state evaluation coefficient according to the system response coefficient and the energy evaluation coefficient;

[0053] The capacity acquisition module is used to obtain the historical number of charge and discharge cycles, battery usage time and actual battery capacity. The battery capacity prediction model is constructed with the historical number of charge and discharge cycles and battery usage time as data sets and the actual battery capacity as labels. The actual battery capacity of each area is predicted using the trained battery capacity prediction model.

[0054] A single block overall evaluation module is used to obtain battery internal resistance data, analyze the battery internal resistance data and battery capacity data to generate a battery health assessment coefficient, and generate a power station safety assessment coefficient based on the system status assessment coefficient and the battery health assessment coefficient; the battery internal resistance data includes the actual internal resistance and initial internal resistance of the battery, and the battery capacity data includes the actual battery capacity and the rated battery capacity;

[0055] A single block early warning module is used to determine the early warning status of each area by comparing the power plant safety assessment coefficient with the preset power plant safety assessment threshold;

[0056] The overall early warning module is used to generate a power plant risk assessment coefficient based on the safety assessment coefficient and battery capacity of each area, and to provide a power plant safety early warning through the risk assessment coefficient.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The present invention divides a large-scale energy storage power station into N equal-sized areas, analyzes the energy exchange, system response and battery health of each area to generate a power station safety assessment coefficient, and can complete the safety warning of each area through the power station safety assessment coefficient; this regionalized safety warning mechanism can achieve accurate monitoring and timely warning of each individual area, thereby effectively preventing local problems from spreading to the entire system. Through zoning management and analysis, potential safety hazards can be quickly identified and located, improving the overall reliability and response speed of the power station.

[0059] The present invention also generates a power station risk assessment coefficient that can reflect the overall situation of a large-scale energy storage power station by analyzing the safety assessment coefficient and battery capacity of each area. The power station risk assessment coefficient is used to provide safety warnings for large-scale energy storage power stations. The risk assessment coefficient provides a scientific basis for comprehensive safety warnings for power stations, allowing managers to have overall control over the operating status of the entire power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a schematic diagram of the overall method flow of the present invention.

[0061] Figure 2 It is a schematic diagram of the overall structural flow of the present invention. DETAILED DESCRIPTION

[0062] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0063] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0064] Example:

[0065] See also Figure 1 , the present invention provides a technical solution:

[0066] A large-scale energy storage power station safety early warning assessment method, the specific steps include:

[0067] Step 1: Divide large-scale energy storage power stations into The energy data of each area is obtained by analyzing the energy data to obtain energy characteristic data, and the energy characteristic data is mathematically analyzed to obtain energy evaluation coefficients;

[0068] The energy data includes total battery input power, total battery output power, inverter input power, inverter output power, line input power and line output power;

[0069] The specific logic for obtaining the energy evaluation coefficient is: analyzing the energy data to obtain energy characteristic data, and performing mathematical analysis on the energy characteristic data to obtain the energy evaluation coefficient; the energy characteristic data includes energy conversion coefficient, inverter loss and line loss;

[0070] The specific logic for obtaining the energy conversion coefficient is:

[0071] ;

[0072] in, is the energy conversion coefficient, is the total battery input power, is the total output power of the battery; energy conversion coefficient Reflects the energy exchange between the system and the outside world. The larger the value, the more frequent the energy exchange between the system and the outside world.

[0073] The specific logic for obtaining inverter loss and line loss is:

[0074] ;

[0075] in, is the inverter loss, is the line loss, Inverter input power, Inverter output power, Line input power, Line output power; inverter losses Reflects the energy loss generated by the inverter during the energy conversion process. The larger the value, the greater the loss, which means that the inverter loses more energy in the process of converting DC power to AC power, and the lower the efficiency;

[0076] The specific formula for obtaining the energy assessment coefficient is:

[0077] ;

[0078] in, Energy evaluation coefficient. It reflects the impact of energy exchange on the normal operation of the system. The larger the value, the smaller the impact of energy exchange on the normal operation of the system, and the less likely the system is to fail. The generation of this coefficient can provide an important basis for the safety warning of the system.

[0079] Step 2: Obtain system response data, perform mathematical analysis on the system response data to obtain the system response coefficient; generate the system state evaluation coefficient based on the system response coefficient and the energy evaluation coefficient;

[0080] The system response data includes the maximum allowable response time of the system, the system discharge time, the system full load time, and the voltage change rate, current change rate and power change rate of the system from discharge to full load;

[0081] The specific logic for generating the system state evaluation coefficient is as follows: obtaining the system response data, performing mathematical analysis on the system response data to obtain the system response coefficient; generating the system state evaluation coefficient based on the system response coefficient and the energy evaluation coefficient; the specific formula for generating the system response coefficient is as follows:

[0082] ;

[0083] in, is the system response coefficient, The maximum response time allowed for the system, is the system discharge time, is the system full load time, is the voltage change rate from discharge to full load, is the current change rate from discharge to full load, is the power change rate from discharge to full load; system response coefficient It reflects the response performance of the system from issuing instructions to stabilizing the load state. The larger the value, the faster and more stable the system response. The generation of this coefficient can provide an important basis for the system's safety warning.

[0084] The specific formula for generating the system status assessment coefficient is:

[0085] ;

[0086] in, is the system state assessment coefficient, is the energy assessment coefficient. System state assessment coefficient It reflects the energy exchange and system response status of the battery energy storage system. The larger the value, the worse the energy exchange and system response status of the battery energy storage system. The generation of this coefficient can provide an important basis for the safety warning of the system.

[0087] Step 3: Obtain the historical number of charge and discharge cycles, battery usage time, and actual battery capacity. Use the historical number of charge and discharge cycles and battery usage time as data sets and the actual battery capacity as labels to build a battery capacity prediction model. Use the trained battery capacity prediction model to predict the actual battery capacity of each area.

[0088] A feedforward neural network is adopted, and the actual capacity of the battery is used as a label. The battery capacity prediction model is trained and optimized using a data set. It should be noted that the existing technology can be used to train and optimize the battery capacity prediction model with the actual capacity of the battery as a label, specifically including: input layer, hidden layer, output layer and activation function, and a root mean square error loss function is adopted; the input data is calculated once through the network to obtain the output result, the loss function is calculated according to the predicted value and the true value, the gradient of the loss function for each weight and bias is calculated by the chain rule, and the weights and biases of the network are updated using the gradient descent algorithm to minimize the loss function.

[0089] Step 4: Obtain battery internal resistance data, analyze the battery internal resistance data and battery capacity data to generate a battery health assessment coefficient, and generate a power station safety assessment coefficient according to the system state assessment coefficient and the battery health assessment coefficient; the battery internal resistance data includes the actual internal resistance and initial internal resistance of the battery, and the battery capacity data includes the actual battery capacity and the rated battery capacity;

[0090] The specific logic for generating the power station safety assessment coefficient is as follows: obtain the battery internal resistance data, analyze the battery internal resistance data and battery capacity data to generate the battery health assessment coefficient, and generate the power station safety assessment coefficient based on the system status assessment coefficient and the battery health assessment coefficient; the specific formula for generating the battery health assessment coefficient is as follows:

[0091] ;

[0092] in, is the battery health assessment coefficient, For the region The actual internal resistance of a battery, For the region The initial internal resistance of the battery, is the actual total battery capacity, For the region The rated battery capacity of the battery, is the number of battery blocks in the area, is the index of the battery block in the corresponding area, and ; Generate battery health assessment coefficient It reflects the health of the battery, taking into account the difference between the actual internal resistance of the battery and the initial internal resistance, as well as the relationship between the actual value of the battery capacity and the rated value. The larger the value, the better the battery health. The generation of this coefficient can provide an important basis for the system's safety warning.

[0093] The specific formula for obtaining the total battery capacity is:

[0094] ;

[0095] in, For the The actual battery capacity of the battery;

[0096] The specific formula for generating the power station safety assessment coefficient is:

[0097] ;

[0098] in, is the power station safety assessment factor, is the system status assessment coefficient. Power station safety assessment coefficient It reflects the safety status of the battery system in the area. The larger the value, the worse the safety status of the battery system in the area, and the more likely it is to have a safety accident. This coefficient quantifies the relationship between the system status assessment coefficient and the battery health assessment coefficient to judge the overall safety of the power station. This coefficient can be used directly to issue a safety warning for the area.

[0099] Step 5: Determine the warning status of each area by comparing the power plant safety assessment coefficient with the preset power plant safety assessment threshold;

[0100] The specific logic for determining the warning situation of each area is as follows: a preset power plant safety assessment threshold is set. If the power plant safety assessment coefficient is greater than the power plant safety assessment threshold, the corresponding area is judged to be dangerous and a safety warning is required; if the power plant safety assessment coefficient is less than or equal to the power plant safety assessment threshold, it is judged that no danger has occurred in the corresponding area and no safety warning needs to be issued.

[0101] Step 6: Generate a power plant risk assessment coefficient based on the safety assessment coefficient and battery capacity of each area, and issue a power plant safety warning through the risk assessment coefficient.

[0102] The specific logic for early warning of power station safety is as follows: the safety assessment coefficient of each area is processed, and the power station risk assessment coefficient is generated according to the processed safety assessment coefficient and battery capacity, and the power station risk assessment threshold is preset. If the power station risk assessment coefficient is less than or equal to the preset power station risk assessment threshold, the large-scale energy storage power station can operate normally or the danger is controllable, and there is no need to issue a serious safety warning; if the power station risk assessment coefficient is greater than the preset power station risk assessment threshold, the large-scale energy storage power station has uncontrollable safety hazards and a safety warning is required;

[0103] The specific formula for processing the safety assessment coefficient of each area is:

[0104] ;

[0105] in, is the power station safety assessment coefficient after processing, is the power station safety assessment factor, is the power station safety assessment threshold; the power station safety assessment coefficient after processing Reflects the influence of the power station safety assessment coefficient under different safety conditions in the corresponding area on the overall safety of the power station. The safety assessment coefficient of the power station after processing It can provide important basis for early warning of subsequent large-scale energy storage power stations.

[0106] The specific formula for generating the power plant risk assessment coefficient is:

[0107] ;

[0108] in, is the power plant risk assessment coefficient, For the The power station safety assessment coefficient after the area is processed is: For the The actual total battery capacity of each area, The number of regions for large-scale energy storage power stations. Power station risk assessment coefficient It reflects the safety situation of large-scale energy storage power stations. The larger the value, the worse the safety of large-scale energy storage power stations and the more likely accidents will occur.

[0109] See also Figure 2 The present invention further provides a large-scale energy storage power station safety early warning assessment device, which is used to implement the above-mentioned large-scale energy storage power station safety early warning assessment method, specifically comprising:

[0110] Energy analysis module, used to divide large-scale energy storage power stations into The energy data of each area is obtained by analyzing the energy data to obtain energy characteristic data, and the energy characteristic data is mathematically analyzed to obtain energy evaluation coefficients;

[0111] The response analysis module is used to obtain system response data, perform mathematical analysis on the system response data to obtain the system response coefficient; generate the system state evaluation coefficient according to the system response coefficient and the energy evaluation coefficient;

[0112] The capacity acquisition module is used to obtain the historical number of charge and discharge cycles, battery usage time and actual battery capacity. The battery capacity prediction model is constructed with the historical number of charge and discharge cycles and battery usage time as data sets and the actual battery capacity as labels. The actual battery capacity of each area is predicted using the trained battery capacity prediction model.

[0113] A single block overall evaluation module is used to obtain battery internal resistance data, analyze the battery internal resistance data and battery capacity data to generate a battery health assessment coefficient, and generate a power station safety assessment coefficient based on the system status assessment coefficient and the battery health assessment coefficient; the battery internal resistance data includes the actual internal resistance and initial internal resistance of the battery, and the battery capacity data includes the actual battery capacity and the rated battery capacity;

[0114] A single block early warning module is used to determine the early warning status of each area by comparing the power plant safety assessment coefficient with the preset power plant safety assessment threshold;

[0115] The overall early warning module is used to generate a power plant risk assessment coefficient based on the safety assessment coefficient and battery capacity of each area, and to provide a power plant safety early warning through the risk assessment coefficient.

[0116] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0117] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0118] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0119] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A safety early warning assessment method for a large-scale energy storage power station, characterized in that: The specific steps include: Step 1: Divide the large-scale energy storage power station into N areas of equal area, obtain energy data of each area, analyze the energy data to obtain energy characteristic data, and perform mathematical analysis on the energy characteristic data to obtain energy evaluation coefficients; Step 2: Obtain system response data, perform mathematical analysis on the system response data to obtain the system response coefficient; generate the system state evaluation coefficient based on the system response coefficient and the energy evaluation coefficient; Step 3: Obtain the historical number of charge and discharge cycles, battery usage time, and actual battery capacity. Use the historical number of charge and discharge cycles and battery usage time as data sets and the actual battery capacity as labels to build a battery capacity prediction model. Use the trained battery capacity prediction model to predict the actual battery capacity of each area. Step 4: Obtain battery internal resistance data, analyze the battery internal resistance data and battery capacity data to generate a battery health assessment coefficient, and generate a power station safety assessment coefficient according to the system state assessment coefficient and the battery health assessment coefficient; the battery internal resistance data includes the actual internal resistance and initial internal resistance of the battery, and the battery capacity data includes the actual battery capacity and the rated battery capacity; Step 5: Determine the warning status of each area by comparing the power plant safety assessment coefficient with the preset power plant safety assessment threshold; Step 6: Generate a power plant risk assessment coefficient based on the safety assessment coefficient and battery capacity of each area, and issue a power plant safety warning through the risk assessment coefficient; The energy data includes total battery input power, total battery output power, inverter input power, inverter output power, line input power and line output power; The specific logic for obtaining the energy evaluation coefficient is: analyzing the energy data to obtain energy characteristic data, and performing mathematical analysis on the energy characteristic data to obtain the energy evaluation coefficient; the energy characteristic data includes energy conversion coefficient, inverter loss and line loss; The specific logic for obtaining the energy conversion coefficient is: Where Ee is the energy conversion coefficient, P Ap is the total input power of the battery, P Au is the total output power of the battery; The specific logic for obtaining inverter loss and line loss is: Where Ne is the inverter loss, Xl is the line loss, P np Inverter input power, P nu Inverter output power, P xp Line input power, P xU Line output power; The specific formula for obtaining the energy assessment coefficient is: Wherein, Ve is the energy evaluation coefficient; The system response data includes the maximum allowable response time of the system, the system discharge time, the system full load time, and the voltage change rate, current change rate and power change rate of the system from discharge to full load; The specific logic for generating the system state evaluation coefficient is as follows: obtaining the system response data, performing mathematical analysis on the system response data to obtain the system response coefficient; generating the system state evaluation coefficient based on the system response coefficient and the energy evaluation coefficient; the specific formula for generating the system response coefficient is as follows: Where De is the system response coefficient, T max T is the maximum response time allowed by the system. sta is the system discharge time, T FULL is the system full load time, VCR is the voltage change rate from discharge to full load, ICR is the current change rate from discharge to full load, and PCR is the power change rate from discharge to full load; The specific formula for generating the system status assessment coefficient is: Among them, Vs is the system state evaluation coefficient, Ve is the energy evaluation coefficient; The specific logic for generating the power station safety assessment coefficient is as follows: obtain the battery internal resistance data, analyze the battery internal resistance data and battery capacity data to generate the battery health assessment coefficient, and generate the power station safety assessment coefficient based on the system status assessment coefficient and the battery health assessment coefficient; the specific formula for generating the battery health assessment coefficient is as follows: Among them, He is the battery health assessment coefficient, R Rj is the actual internal resistance of the jth battery in the area, R Sj is the initial internal resistance of the jth battery in the area, AM is the actual total battery capacity, Ah Bj is the rated battery capacity of the jth battery in the area, M is the number of battery blocks in the area, j is the index of the battery block in the corresponding area, and j∈[1,M]; The specific formula for obtaining the total battery capacity is: Among them, Ah Rj is the actual battery capacity of the jth battery; The specific formula for generating the power station safety assessment coefficient is: Among them, SY is the power station safety assessment coefficient, Vs is the system status assessment coefficient; The specific logic for early warning of power station safety is as follows: the safety assessment coefficient of each area is processed, and the power station risk assessment coefficient is generated according to the processed safety assessment coefficient and battery capacity, and the power station risk assessment threshold is preset. If the power station risk assessment coefficient is less than or equal to the preset power station risk assessment threshold, the large-scale energy storage power station can operate normally or the danger is controllable, and there is no need to issue a serious safety warning; if the power station risk assessment coefficient is greater than the preset power station risk assessment threshold, the large-scale energy storage power station has uncontrollable safety hazards and a safety warning is required; The specific formula for processing the safety assessment coefficient of each area is: Among them, SY′ is the power station safety assessment coefficient after processing, SY is the power station safety assessment coefficient, and SY0 is the power station safety assessment threshold; The specific formula for generating the power plant risk assessment coefficient is: Among them, SYA is the power plant risk assessment coefficient, SY i ′ is the power station safety assessment coefficient after processing in the i-th area, AM i is the actual total battery capacity of the ith region, and N is the number of regions where the large-scale energy storage power station is divided.

2. A large-scale energy storage power station safety early warning assessment method according to claim 1, characterized in that: The specific logic for determining the early warning situation of each area is as follows: a power plant safety assessment threshold is preset. If the power plant safety assessment coefficient is greater than the power plant safety assessment threshold, the corresponding area is judged to be dangerous and a safety warning is required; If the power plant safety assessment coefficient is less than or equal to the power plant safety assessment threshold, it is judged that no danger has occurred in the corresponding area and there is no need to issue a safety warning.

3. A large-scale energy storage power station safety early warning assessment device, characterized in that: The system is used to implement the large-scale energy storage power station safety early warning assessment method according to any one of claims 1-2, specifically comprising: The energy analysis module is used to divide the large-scale energy storage power station into N areas of equal area, obtain the energy data of each area, analyze the energy data to obtain energy characteristic data, and perform mathematical analysis on the energy characteristic data to obtain the energy evaluation coefficient; The response analysis module is used to obtain system response data, perform mathematical analysis on the system response data to obtain the system response coefficient; generate the system state evaluation coefficient according to the system response coefficient and the energy evaluation coefficient; The capacity acquisition module is used to obtain the historical number of charge and discharge cycles, battery usage time and actual battery capacity. The battery capacity prediction model is constructed with the historical number of charge and discharge cycles and battery usage time as data sets and the actual battery capacity as labels. The actual battery capacity of each area is predicted using the trained battery capacity prediction model. A single block overall evaluation module is used to obtain battery internal resistance data, analyze the battery internal resistance data and battery capacity data to generate a battery health assessment coefficient, and generate a power station safety assessment coefficient based on the system status assessment coefficient and the battery health assessment coefficient; the battery internal resistance data includes the actual internal resistance and initial internal resistance of the battery, and the battery capacity data includes the actual battery capacity and the rated battery capacity; A single block early warning module is used to determine the early warning status of each area by comparing the power plant safety assessment coefficient with the preset power plant safety assessment threshold; The overall early warning module is used to generate a power plant risk assessment coefficient based on the safety assessment coefficient and battery capacity of each area, and to provide a power plant safety early warning through the risk assessment coefficient.

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