Knowledge graph-based energy storage power plant fault recognition method and device

Abstract

This application provides a knowledge graph-based energy storage power plant fault recognition method and apparatus for the power industry. The knowledge graph-based energy storage power plant fault recognition method includes: acquiring status data of the energy storage power plant; inputting the status data into a pre-built energy storage power plant fault recognition model based on a thermal runaway knowledge graph to characterize the correlation between the status data of the energy storage power plant and the thermal runaway process; predicting the fault status and transition path of the energy storage power plant; and determining the safety risk level of the energy storage power plant based on the fault status and transition path. This application enables comprehensive and accurate evaluation of the operating status of the energy storage power plant and realizing long-term fault warning.

Description

[Technical Field]

[0001] The present application relates to the field of electric power, and in particular to a knowledge graph-based method and apparatus for recognizing faults in an energy storage power plant. [Background technology]

[0002] The application of energy storage power plants is becoming increasingly widespread with the rapid development of renewable energy. However, the problems of failure and safety risks of energy storage power plants are also becoming increasingly prominent. In recent years, there have been frequent fire incidents in energy storage power plants. To ensure the safe and stable operation of energy storage power plants, it is necessary to monitor and analyze their operating status in real time and detect potential failure risks.

[0003] In the prior art, energy storage power plants are monitored mainly by sensors and data collection systems, and the fault conditions and safety risks of the energy storage power plants are assessed through data analysis and processing. However, such methods often only consider individual health characteristic data such as cycle time and number of cycles, and limited electrical parameters such as real-time current and voltage, making it difficult to comprehensively and accurately assess the overall operating conditions and safety risks of the energy storage power plants, and the early prediction time is limited, making it impossible to achieve long-term early warning. Summary of the Invention [Problem to be solved by the invention]

[0004] This application provides a knowledge graph-based fault recognition method and device for energy storage power plants, so as to comprehensively and accurately evaluate the operating status of the energy storage power plants and realize long-term fault early warning.

[0005] In a first aspect, the present application provides a method for producing a method of a method for manufacturing a semiconductor device comprising: Obtaining status data of the energy storage power plant; Inputting the state data into a pre-constructed energy storage power plant fault recognition model based on a thermal runaway knowledge graph for characterizing the association relationship between the state data of the energy storage power plant and the thermal runaway process, and predicting the fault status and transition path of the energy storage power plant; Determining a safety risk level of the energy storage power plant based on the fault conditions and transition paths; A knowledge graph-based fault recognition method for an energy storage power plant is provided.

[0006] The above method uses the real-time monitored state data of the energy storage power plant and the thermal runaway knowledge graph that characterizes the relationship between the state data and the thermal runaway process to predict the fault status and transition path of the energy storage power plant, and comprehensively predict the thermal runaway process that the energy storage power plant may encounter. Furthermore, the safety risk level of the energy storage power plant is determined based on the fault status and transition path, ensuring the safe and stable operation of the energy storage power plant. The state data related to the thermal runaway process is used to predict the fault and transition path of the energy storage power plant, comprehensively covering various data in the thermal runaway process of the energy storage power plant and considering various possible factors related to the thermal runaway process, thereby achieving accurate advance judgment of the safety risk of the energy storage power plant, timely detection of potential faults and safety risks, realizing long-term early warning of faults, providing reference and decision-making support for maintenance personnel, and preventing the occurrence of fire accidents in the energy storage power plant.

[0007] In a preferred embodiment, the step of building an energy storage plant fault recognition model comprises: Obtaining a thermal runaway knowledge graph of the energy storage power plant, including historical state data of the energy storage power plant, historical fault conditions corresponding to the historical state data, and historical transition paths; The historical state data is used as the input of an initial energy storage power plant fault recognition model, the historical fault status and the historical transition path are used as the output of the initial energy storage power plant fault recognition model, and the initial energy storage power plant fault recognition model is trained to obtain an energy storage power plant fault recognition model.

[0008] According to the above embodiment, the thermal runaway knowledge graph includes the association relationship between the state data of the energy storage power plant and the thermal runaway process, and the initial energy storage power plant fault recognition model is trained according to the historical state data, the historical fault conditions corresponding to the historical state data, and the historical transition path, thereby obtaining the energy storage power plant fault recognition model, and based on the state data related to the thermal runaway process of the energy storage power plant, various factors can be comprehensively considered to improve the prediction accuracy of the energy storage power plant fault recognition model.

[0009] In a preferred embodiment, obtaining a thermal runaway knowledge graph of an energy storage power plant comprises: Determining a state data type of the energy storage power plant based on a battery thermal runaway transition mechanism; Obtaining historical state data of the energy storage power plant based on the state data type; and obtaining, based on the historical state data, a historical failure situation and a historical transition path corresponding to the historical state data.

[0010] In a preferred embodiment, determining a status data type of the energy storage power plant based on a battery thermal runaway transition mechanism comprises: Determining the thermal runaway process of the energy storage power plant based on the battery thermal runaway transition mechanism; and determining a status data type based on the thermal runaway process.

[0011] According to the above embodiment, the status data type of the energy storage power plant is determined based on the battery thermal runaway transition mechanism, which contributes to better analyzing the transition process of battery thermal runaway based on the battery thermal runaway transition mechanism, and by determining the relevant data in the transition process of battery thermal runaway as status data, the fault status and transition path of the energy storage power plant can be more accurately monitored and predicted.

[0012] In a preferred embodiment, the thermal runaway process includes an external process, a battery internal change process, and a thermal runaway occurrence process, and determining the status data type based on the thermal runaway process includes: Determining external parameters of the energy storage power plant based on an external process; Determining battery internal parameters of the energy storage power plant based on the battery internal change process; Determining thermal diffusion parameters of the energy storage power plant based on the thermal runaway generation process; and determining a status data type based on the external parameters, the battery internal parameters, and the heat diffusion parameters.

[0013] The above embodiment comprehensively analyzes the thermal runaway process, divides the thermal runaway process into an external process, a battery internal change process, and a thermal runaway occurrence process, determines external parameters based on the external process, determines battery internal parameters based on the battery internal change process, and determines thermal diffusion parameters based on the thermal runaway occurrence process, and determines corresponding parameters for different thermal runaway processes, thereby comprehensively grasping the operating status and fault status of the energy storage power plant, thereby contributing to improving the prediction accuracy of the energy storage power plant fault recognition model.

[0014] In a preferred embodiment, the knowledge graph-based energy storage power plant fault recognition method comprises: Further comprising transmitting preventative warning information to the energy storage power plant based on the safety risk level.

[0015] According to the above embodiment, based on the safety risk level, preventive warning information is sent to the energy storage power plant in a timely manner, so that maintenance personnel can be alerted to potential safety risks and take appropriate preventive and control measures, thereby improving the efficiency of maintenance work, avoiding accidents, and reducing losses caused by fires in the energy storage power plant.

[0016] In a second aspect, the present application provides a method for producing a method of manufacturing a semiconductor device comprising: an acquisition module for acquiring status data of the energy storage power plant; a prediction module for inputting the state data into a pre-constructed energy storage plant fault recognition model based on a thermal runaway knowledge graph for characterizing the association relationship between the state data of the energy storage plant and the thermal runaway process, and predicting the fault status and transition path of the energy storage plant; A determination module for determining a safety risk level of the energy storage power plant according to the fault condition and the transition path; The present invention further provides an energy storage power plant fault recognition device based on a knowledge graph, including:

[0017] The above device uses the real-time monitored status data of the energy storage power plant and an energy storage power plant fault recognition model constructed based on a thermal runaway knowledge graph that characterizes the correlation between the status data and the thermal runaway process to predict the fault status and transition path of the energy storage power plant, comprehensively predict the thermal runaway process that the energy storage power plant may occur, and further determine the safety risk level of the energy storage power plant based on the fault status and transition path, thereby ensuring the safe and stable operation of the energy storage power plant. The status data related to the thermal runaway process is used to predict the fault and transition path of the energy storage power plant, comprehensively covering various data in the thermal runaway process of the energy storage power plant and considering various possible factors related to the thermal runaway process, thereby achieving accurate advance judgment of the safety risk of the energy storage power plant, timely detection of potential faults and safety risks, realizing long-term early fault warning, providing reference and decision-making support for maintenance personnel, and preventing the occurrence of fire accidents in the energy storage power plant.

[0018] In a preferred embodiment, the prediction module comprises: an acquisition submodule for acquiring a thermal runaway knowledge graph of the energy storage power plant, the thermal runaway knowledge graph including historical state data of the energy storage power plant, historical fault conditions corresponding to the historical state data, and historical transition paths; a training sub-module for taking the historical state data as the input of an initial energy storage power plant fault recognition model, taking the historical fault status and historical transition path as the output of the initial energy storage power plant fault recognition model, and training the initial energy storage power plant fault recognition model to obtain an energy storage power plant fault recognition model.

[0019] In a third aspect, the present application further provides a computer apparatus including a memory having computer instructions stored therein, and a processor communicatively coupled to the memory and configured to execute the computer instructions to perform the steps of the knowledge graph based energy storage plant fault recognition method of the first aspect, or any embodiment of the first aspect.

[0020] In a fourth aspect, the present application further provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the knowledge graph based energy storage plant fault recognition method of the first aspect or any embodiment of the first aspect.

[0021] In order to more clearly describe the specific embodiments of the present application or the technical solutions in the prior art, the drawings that need to be used in describing the specific embodiments or the prior art will be briefly described below. The drawings in the following description are some embodiments of the present application, and it is obvious to those skilled in the art that other drawings can also be obtained based on these drawings without any creative work. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a flowchart of a knowledge graph-based energy storage plant fault recognition method according to an exemplary embodiment. [Figure 2] 1 is a structural schematic diagram of an energy storage power plant fault recognition device based on knowledge graph according to an exemplary embodiment; [Figure 3] FIG. 2 is a schematic diagram of a hardware structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0023] The technical solution of the present application will be clearly and completely described below with reference to the drawings, and it is clear that the described embodiments are not all embodiments but only some embodiments of the present application. All other embodiments that a person skilled in the art can obtain without creative work according to the embodiments of the present application are included in the scope of the present invention.

[0024] It should be noted that the technical features according to different embodiments of the present application described below can be combined with each other as long as they are not inconsistent with each other.

[0025] This application provides a knowledge graph-based fault recognition method and device for energy storage power plants, so as to comprehensively and accurately evaluate the operating status of the energy storage power plants and realize long-term fault early warning.

[0026] 1 is a flowchart of a knowledge graph-based energy storage power plant fault recognition method according to an exemplary embodiment. As shown in FIG. 1, the knowledge graph-based energy storage power plant fault recognition method includes the following steps S101 to S103.

[0027] Step S101: Obtain the status data of the energy storage power plant.

[0028] In a preferred embodiment, the energy storage power plant is an equipment system that stores, converts, and releases cyclable electrical energy using electrochemical batteries or electromagnetic energy storage media. It can effectively solve problems such as peak modulation, output stabilization, electrical energy quality improvement, and peak shaving in the power system, which is of great significance for balancing load fluctuations. At the same time, the energy storage power plant has a scalability effect, can stabilize voltage during heavy loads during peak periods, suppress power grid fluctuations, and ensure power supply quality. Furthermore, the energy storage power plant can be charged and discharged using an energy storage system, storing electrical energy during valley times and releasing electrical energy during peak times, thereby reducing electricity usage costs and improving energy utilization efficiency.

[0029] In a preferred embodiment, the energy storage power plant includes devices such as a battery pack, an energy storage converter, and an isolation transformer. Here, the battery pack is the core device of the energy storage power plant and is used to store electrical energy. The battery pack usually consists of multiple battery cells, which may be lithium-ion batteries, lead batteries, sodium-sulfur batteries, etc. The energy storage converter is used to realize energy conversion between the battery pack and the power grid, including charging and discharging. The isolation transformer isolates the energy storage power plant from the power grid, ensuring the independent operation and safety of the energy storage power plant.

[0030] In a preferred embodiment, the state data of the energy storage power plant may include external parameters, battery internal parameters, thermal diffusion parameters, etc., based on relevant factors in the thermal runaway process of the energy storage power plant.

[0031] Step S102: The state data is input into an energy storage power plant fault recognition model pre-constructed based on a thermal runaway knowledge graph to characterize the association relationship between the state data of the energy storage power plant and the thermal runaway process, and predict the fault status and transition path of the energy storage power plant.

[0032] In a preferred embodiment, the energy storage power plant fault recognition model may be constructed by an algorithm such as machine learning, which is not specifically limited herein.

[0033] In a preferred embodiment, the failure situation predicted based on the energy storage power plant failure recognition model may be the failure situation of each device in the energy storage power plant, may be the failure situation of the battery, or may be the failure situation of other devices in the energy storage power plant, and is not specifically limited herein.

[0034] In a preferred embodiment, the transition path predicted based on the energy storage plant fault recognition model is the transition path of each device in the energy storage plant under the state data.

[0035] Step S103: Determine the safety risk level of the energy storage power plant according to the fault situation and transition path.

[0036] In a preferred embodiment, the safety risk level of the energy storage power plant is determined based on the degree of loss caused by the fault conditions and transition paths, and the safety risk levels corresponding to different fault conditions and different transition paths are different.

[0037] The above method uses the real-time monitored state data of the energy storage power plant and the thermal runaway knowledge graph that characterizes the relationship between the state data and the thermal runaway process to predict the fault status and transition path of the energy storage power plant, and comprehensively predict the thermal runaway process that the energy storage power plant may encounter. Furthermore, the safety risk level of the energy storage power plant is determined based on the fault status and transition path, ensuring the safe and stable operation of the energy storage power plant. The state data related to the thermal runaway process is used to predict the fault and transition path of the energy storage power plant, comprehensively covering various data in the thermal runaway process of the energy storage power plant and considering various possible factors related to the thermal runaway process, thereby achieving accurate advance judgment of the safety risk of the energy storage power plant, timely detection of potential faults and safety risks, realizing long-term early warning of faults, providing reference and decision-making support for maintenance personnel, and preventing the occurrence of fire accidents in the energy storage power plant.

[0038] In one example, in the above step S102, the energy storage power plant fault recognition model is constructed by the following steps:

[0039] Step a1: Obtain a thermal runaway knowledge graph of the energy storage power plant, including the historical state data of the energy storage power plant, the historical fault conditions corresponding to the historical state data, and the historical transition paths.

[0040] Step a2: Take the historical state data as the input of the initial energy storage power plant fault recognition model, take the historical fault status and historical transition path as the output of the initial energy storage power plant fault recognition model, and train the initial energy storage power plant fault recognition model to obtain the energy storage power plant fault recognition model.

[0041] In the embodiment of the present application, the thermal runaway knowledge graph includes the association relationship between the state data of the energy storage power plant and the thermal runaway process, and the initial energy storage power plant fault recognition model is trained according to the historical state data, the historical fault conditions corresponding to the historical state data, and the historical transition path to obtain the energy storage power plant fault recognition model, and based on the state data related to the thermal runaway process of the energy storage power plant, various factors are comprehensively considered to improve the prediction accuracy of the energy storage power plant fault recognition model.

[0042] In one example, in the above step a1, the thermal runaway knowledge graph of the energy storage power plant is obtained by the following steps:

[0043] Step b1: Determine the status data type of the energy storage power plant based on the battery thermal runaway transition mechanism.

[0044] In a preferred embodiment, the battery thermal runaway transition mechanism includes trigger factors of battery thermal runaway, chemical reactions of battery thermal runaway, heat transfer and heat accumulation, physical manifestations of thermal runaway, and chain reactions of thermal runaway. When determining the thermal runaway process of an energy storage power plant, the above factors must be comprehensively considered and analyzed according to the actual operating conditions of the energy storage power plant. A deeper understanding of the battery thermal runaway transition mechanism can more accurately predict and prevent thermal runaway events in energy storage power plants, thereby improving the safety and stability of energy storage power plants.

[0045] In a preferred embodiment, battery thermal runaway can be triggered by a variety of factors, such as overcharging, over-discharging, high temperature, and short circuits, which can cause accelerated chemical reactions inside the battery and generate large amounts of heat.

[0046] In a preferred embodiment, the chemical reactions that cause thermal runaway in a battery typically include oxidation-reduction reactions between the positive and negative electrode materials, decomposition of the electrolyte, etc. These reactions generate large amounts of heat and gas, causing the temperature and pressure inside the battery to rise.

[0047] In a preferred embodiment, heat transfer refers to the transfer of heat through the battery housing to the external environment as the temperature inside the battery rises. Without proper thermal control, the battery temperature continues to rise, causing heat buildup.

[0048] In a preferred embodiment, as the temperature of the battery rises, the physical properties of the battery also change, such as the expansion of the housing, leakage of electrolyte, etc. These physical manifestations can be important grounds for determining whether the battery has experienced thermal runaway.

[0049] In a preferred embodiment, the thermal runaway chain reaction refers to the fact that once a battery experiences thermal runaway, it may cause a chain reaction of adjacent batteries, such as temperature rise, electrolyte leakage, etc. These chain reactions may affect the safety of the entire energy storage power plant.

[0050] Step b2: Obtain historical state data of the energy storage power plant according to the state data type.

[0051] Step b3: Based on the historical state data, a historical failure state and a historical transition path corresponding to the historical state data are obtained.

[0052] In one example, in the above step b1, the status data type of the energy storage power plant is determined as follows:

[0053] First, the thermal runaway process of the energy storage power plant is determined based on the battery thermal runaway transition mechanism. Exemplarily, the thermal runaway process includes external processes (e.g., external usage conditions), internal battery change processes (including, but not limited to, SEI film decomposition, dendrite growth, positive electrode oxygen release, negative electrode lithium deposition, internal short circuit, liquid leakage, etc.), and thermal runaway occurrence processes.

[0054] Then, a status data type is determined based on the thermal runaway process.

[0055] In the embodiment of the present application, the status data type of the energy storage power plant is determined based on the battery thermal runaway transition mechanism. The battery thermal runaway transition mechanism contributes to better analyzing the transition process of battery thermal runaway. By determining the relevant data in the transition process of battery thermal runaway as status data, the fault status and transition path of the energy storage power plant can be monitored and predicted more comprehensively and accurately.

[0056] In one example, the thermal runaway process includes an external process, a battery internal change process, and a thermal runaway occurrence process.

[0057] Here, the external process refers to an external use process, and illustratively includes a process that mechanically triggers battery thermal runaway (for example, a lithium battery pack being hit and deformed, an internal short circuit in the battery pack, or other actions that damage the battery pack may cause battery thermal runaway).

[0058] The internal battery process refers to the electrochemical reactions that occur within the battery due to thermal runaway, such as the decomposition of the positive and negative electrodes, interactions between the positive and negative electrodes, and internal short circuits. At the same time, when the temperature reaches 120-130°C, the protective SEI film is destroyed, the negative electrode reacts with the solvent and binder, the temperature rises, and the separator melts and closes. After the temperature continues to rise above 150°C, the internal electrolyte begins to decompose, releasing heat and further heating the battery.

[0059] The thermal runaway process occurs when the battery temperature reaches 200°C or higher, causing the positive electrode material to decompose, releasing a large amount of heat and gas, and the temperature continues to rise. At 250-350°C, the negative electrode, which has lithium occlusion, begins to react with the electrolyte. In the final reaction process, the electrolyte reacts violently with the oxygen generated by the reaction with the positive electrode, causing the battery to go into thermal runaway.

[0060] In the present embodiment, the status data type is determined by the following:

[0061] First, external parameters of the energy storage power plant are determined based on external processes, which in the present embodiment include external usage parameters and external environmental parameters.

[0062] In a preferred embodiment, the external usage parameters include mechanical abuse data (e.g., needling, impact), thermal abuse data (high and low temperatures), electrical abuse data (e.g., overcharging, short circuits), and battery quality data. External environmental parameters include, but are not limited to, temperature, humidity, wind speed, and illuminance. These external environmental parameters reflect the environmental conditions of the energy storage power plant and can affect the operating status and lifespan of the battery. The battery's charge / discharge performance and lifespan are closely related to temperature. Excessive temperature can lead to risks such as internal short circuits and thermal runaway. Therefore, by monitoring the temperature of an energy storage power plant, potential safety risks can be detected in a timely manner and corresponding countermeasures can be taken. Excessive humidity can cause problems such as internal short circuits and corrosion in the battery. Therefore, monitoring the humidity of an energy storage power plant can ensure the normal operation and lifespan of the battery. Wind speed has a significant impact on the heat dissipation and cooling effect of an energy storage power plant. In hot weather, a slow wind speed can cause the battery temperature to become too high, leading to thermal runaway. Therefore, by monitoring the wind speed of the energy storage power plant, the cooling system can be adjusted in a timely manner to ensure the normal operation of the battery. For solar energy storage power plants, the light intensity is an important factor affecting the amount of power generated by the battery. If the light intensity is too strong, the battery may overheat and affect its lifespan. Therefore, by monitoring the light intensity of the energy storage power plant, the operating status of the battery can be adjusted in a timely manner to ensure the normal operation and lifespan of the battery.

[0063] Then, the battery internal parameters of the energy storage power plant are determined based on the battery internal change process.

[0064] In a preferred embodiment, the battery internal parameters of the energy storage plant include voltage, current, temperature, state of charge, and the like.

[0065] Battery voltage is an important parameter that reflects the state of the electrochemical reaction inside the battery. By monitoring the battery voltage, it is possible to determine the charge / discharge state of the battery and whether or not there is an abnormality such as an internal short circuit in the battery.

[0066] The battery current is a parameter that reflects the battery's charging and discharging capabilities. By monitoring the battery current, it is possible to determine the charging and discharging speed and efficiency of the battery, as well as whether there is an overcharge or overdischarge situation.

[0067] Battery temperature is an important parameter that reflects the thermal equilibrium state inside the battery. By monitoring the battery temperature, it is possible to determine the heat dissipation effect and safety of the battery, as well as the risk of thermal runaway.

[0068] The charging rate is a parameter that reflects the remaining capacity of a battery. By monitoring the charging rate, it is possible to determine the remaining capacity of the battery and whether the battery is insufficiently or overcharged.

[0069] Then, the thermal diffusion parameters of the energy storage power plant are determined based on the thermal runaway generation process.

[0070] In a preferred embodiment, the heat diffusion parameters include, but are not limited to, the thermal resistance between the cell and the side plate, the thermal resistance between the cells, the thickness of the outer heat insulating pad of the side plate, the thermal resistance between the cell and the water-cooled plate, etc. The heat diffusion parameters may further include a trigger condition, a diffusion rate, and a diffusion range. Here, the trigger condition is a voltage drop, a temperature rise rate, etc. > 1℃ / s, which means that the temperature exceeds the battery's allowable upper limit.

[0071] Finally, a status data type is determined based on the external parameters, the battery internal parameters, and the thermal diffusion parameters. Exemplarily, the external parameters, the battery internal parameters, and the thermal diffusion parameters are set as status data types, and status data corresponding to the energy storage power plant can be collected based on the status data types.

[0072] In the embodiments of the present application, the thermal runaway process is comprehensively analyzed, and the thermal runaway process is divided into an external process, a battery internal change process, and a thermal runaway occurrence process. External parameters are determined based on the external process, battery internal parameters are determined based on the battery internal change process, and thermal diffusion parameters are determined based on the thermal runaway occurrence process. Corresponding parameters are determined for different thermal runaway processes, and the operating status and fault conditions of the energy storage power plant are comprehensively grasped, thereby contributing to improving the prediction accuracy of the energy storage power plant fault recognition model.

[0073] In one example, the method provided in the examples of the present application comprises: Further comprising transmitting preventative warning information to the energy storage power plant based on the safety risk level.

[0074] In the embodiment of the present application, based on the safety risk level, preventive warning information is sent to the energy storage power plant in a timely manner to alert maintenance personnel to potential safety risks, so that appropriate prevention and control measures can be taken, thereby improving maintenance work efficiency, avoiding accidents, and reducing losses caused by fires in the energy storage power plant.

[0075] According to the same inventive idea, an embodiment of the present application further provides an energy storage power plant fault recognition device based on knowledge graph. As shown in FIG. 2, the device includes:

[0076] The acquisition module 201 is for acquiring the status data of the energy storage power plant. For details, please refer to the description of step S101 in the above embodiment, and the description here will be omitted.

[0077] The prediction module 202 inputs the state data into an energy storage power plant fault recognition model pre-constructed based on a thermal runaway knowledge graph for characterizing the association relationship between the state data of the energy storage power plant and the thermal runaway process, to predict the fault status and transition path of the energy storage power plant. For details, please refer to the description of step S102 in the above embodiment, and the description here will be omitted.

[0078] The determining module 203 is for determining the safety risk level of the energy storage power plant according to the fault condition and transition path, for details, please refer to the description of step S103 in the above embodiment, and the description here will be omitted.

[0079] The above device uses the real-time monitored status data of the energy storage power plant and an energy storage power plant fault recognition model constructed based on a thermal runaway knowledge graph that characterizes the correlation between the status data and the thermal runaway process to predict the fault status and transition path of the energy storage power plant, comprehensively predict the thermal runaway process that the energy storage power plant may occur, and further determine the safety risk level of the energy storage power plant based on the fault status and transition path, thereby ensuring the safe and stable operation of the energy storage power plant. The status data related to the thermal runaway process is used to predict the fault and transition path of the energy storage power plant, comprehensively covering various data in the thermal runaway process of the energy storage power plant and considering various possible factors related to the thermal runaway process, thereby achieving accurate advance judgment of the safety risk of the energy storage power plant, timely detection of potential faults and safety risks, realizing long-term early fault warning, providing reference and decision-making support for maintenance personnel, and preventing the occurrence of fire accidents in the energy storage power plant.

[0080] In one example, the prediction module 202 includes an acquisition sub-module and a training sub-module.

[0081] The acquisition submodule is for acquiring a thermal runaway knowledge graph of the energy storage power plant, which includes the historical state data of the energy storage power plant, the historical fault conditions corresponding to the historical state data, and the historical transition path. For details, please refer to the description in the above embodiment, and the description here will be omitted.

[0082] The training sub-module is for taking the historical state data as the input of the initial energy storage power station fault recognition model, taking the historical fault status and historical transition path as the output of the initial energy storage power station fault recognition model, and training the initial energy storage power station fault recognition model to obtain the energy storage power station fault recognition model. For details, please refer to the description in the above embodiment, and the description here will be omitted.

[0083] In one example, the acquisition sub-module includes a determination unit, a first acquisition unit and a second acquisition unit.

[0084] The determining unit is for determining the status data type of the energy storage power plant according to the battery thermal runaway transition mechanism, for details, please refer to the description of the above embodiment, and the description here is omitted.

[0085] The first acquiring unit is for acquiring the historical status data of the energy storage power plant according to the status data type, for details, please refer to the description in the above embodiment, and the description here is omitted.

[0086] The second acquisition unit is for acquiring the historical fault status and the historical transition path corresponding to the historical status data based on the historical status data. For details, please refer to the description in the above embodiment, and the description here will be omitted.

[0087] In one example, the determination unit includes a first determination subunit and a second determination subunit.

[0088] The first determination sub-unit is for determining the thermal runaway process of the energy storage power plant based on the battery thermal runaway transition mechanism, for details, please refer to the description of the above embodiment, and the description here is omitted.

[0089] The second determining sub-unit is for determining the status data type based on the thermal runaway process, for details, please refer to the description in the above embodiment, and the description here will be omitted.

[0090] In one example, the thermal runaway process includes an external process, a battery internal change process, and a thermal runaway occurrence process, and the second determination subunit determines external parameters of the energy storage power plant based on the external process, determines battery internal parameters of the energy storage power plant based on the battery internal change process, determines thermal diffusion parameters of the energy storage power plant based on the thermal runaway occurrence process, and determines a status data type based on the external parameters, the battery internal parameters, and the thermal diffusion parameters. For details, please refer to the description in the above embodiment, and description here will be omitted.

[0091] In one example, the device comprises: The energy storage power plant further includes an alarm module for sending preventive alarm information to the energy storage power plant based on the safety risk level. For details, please refer to the description in the above embodiment, and the description here will be omitted.

[0092] The specific limitations and beneficial effects of the above device can be referred to the limitations of the above-mentioned knowledge graph-based energy storage power plant fault recognition method, and further description here is omitted. All or part of the above modules may be realized by software, hardware, or a combination thereof. Each module may be embedded in a processor in a computer device as hardware, or may be independent, or may be stored in a memory in a computer device as software, so that the processor can call and execute operations corresponding to each module.

[0093] 3 is a schematic diagram of the hardware structure of a computer device according to an exemplary embodiment. As shown in FIG. 3, the device includes one or more processors 310 and memory 320 including persistent memory, volatile memory, and a hard disk. In FIG. 3, one processor 310 is shown as an example. The device may further include an input device 330 and an output device 340.

[0094] The processor 310, memory 320, input device 330, and output device 340 may be connected by a bus or in other forms, and FIG. 3 shows the connection by a bus as an example.

[0095] Processor 310 may be a central processing unit (CPU). Processor 310 may also be other general-purpose processors, chips such as digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of various of the foregoing chips. The general-purpose processor may be a microprocessor, any conventional processor, or the like.

[0096] The memory 320 is a non-transitory computer-readable storage medium, including persistent memory, volatile memory, and a hard disk, for storing non-transitory software programs, non-transitory computer-executable programs and modules, such as program instructions / modules corresponding to the knowledge graph-based energy storage power plant fault recognition method in the embodiments of the present application. The processor 310 executes the non-transitory software programs, instructions, and modules stored in the memory 320 to perform various functional applications and data processing of the server, i.e., to realize any of the knowledge graph-based energy storage power plant fault recognition methods described above.

[0097] Memory 320 may include a program storage area capable of storing an operating system and application programs required for at least one function, and a data storage area capable of storing data used as needed. Memory 320 may also include high-speed random access memory and non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 320 may include memory located remotely from processor 310, and such remote memory may be connected to the data processing device via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0098] The input device 330 can receive input numeric or textual information and generate signal inputs related to user settings and function control. The output device 340 can include a display device such as a display.

[0099] The one or more modules, when stored in memory 320 and executed by one or more processors 310, perform the method illustrated in FIG.

[0100] The above product can implement the method according to the embodiment of the present application, and has corresponding functional modules and beneficial effects for implementing the method. For technical details not described in detail in this embodiment, please refer to the relevant description in the embodiment shown in FIG.

[0101] An embodiment of the present application further provides a non-transitory computer storage medium having stored thereon computer-executable instructions capable of performing the method of any of the above-described method embodiments, wherein the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), a solid-state drive (SSD), etc., and the storage medium may further include a combination of the above types of memory.

[0102] It should be noted that, in this specification, relational terms such as "first" and "second" are merely used to distinguish one entity or operation from another and do not necessarily require or imply that such an actual relationship or order exists between those entities or operations. Furthermore, the terms "comprise," "include," or any other variation thereof imply a non-exclusive inclusion, such that a process, method, article, or device that includes a set of elements not only includes those elements, but also other elements not expressly listed or inherent in such process, method, article, or device. Unless otherwise specified, an element qualified by the phrase "comprises one of" does not exclude the presence of other identical elements in the process, method, article, or device that includes the element.

[0103] The foregoing are merely specific embodiments of the present application, enabling those skilled in the art to understand or realize the present application. Various modifications to these examples will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other examples without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the examples set forth herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. Obtaining status data of the energy storage power plant; Inputting the state data into an energy storage power plant fault recognition model pre-constructed based on a thermal runaway knowledge graph for characterizing the association relationship between the state data of the energy storage power plant and the thermal runaway process, and predicting the fault status and transition path of the energy storage power plant; Determining a safety risk level of the energy storage power plant based on the fault condition and the transition path; A knowledge graph-based energy storage power plant fault recognition method, comprising:

2. The step of constructing the energy storage power plant fault recognition model includes: Obtaining a thermal runaway knowledge graph of the energy storage power plant, the thermal runaway knowledge graph including historical state data of the energy storage power plant, historical fault conditions corresponding to the historical state data, and historical transition paths; and obtaining the energy storage power plant fault recognition model by training the initial energy storage power plant fault recognition model.

2. The energy storage power plant fault recognition method according to claim 1.

3. The step of obtaining a thermal runaway knowledge graph of the energy storage power plant includes: determining a status data type of the energy storage power plant based on a battery thermal runaway transition mechanism; obtaining historical status data for the energy storage power plant based on the status data type; and acquiring a historical failure state and a historical transition path corresponding to the historical state data based on the historical state data.

3. The energy storage power plant fault recognition method according to claim 2.

4. determining a state data type of the energy storage power plant based on a battery thermal runaway transition mechanism; determining a thermal runaway process of the energy storage power plant based on the battery thermal runaway transition mechanism; determining the status data type based on the thermal runaway process; 4. The energy storage power plant fault recognition method according to claim 3, further comprising:

5. The thermal runaway process includes an external process, a battery internal change process, and a thermal runaway occurrence process, and the step of determining the status data type based on the thermal runaway process includes: determining external parameters of the energy storage power plant based on the external process; Determining battery internal parameters of the energy storage power plant based on the battery internal change process; determining a thermal diffusion parameter of the energy storage power plant based on the thermal runaway generation process; determining the status data type based on the external parameters, the battery internal parameters, and the thermal diffusion parameters; 5. The energy storage power plant fault recognition method according to claim 4, further comprising:

6. The energy storage power plant fault recognition method according to claim 1, further comprising: sending preventive warning information to the energy storage power plant based on the safety risk level.

7. an acquisition module for acquiring status data of the energy storage power plant; a prediction module for inputting the state data into an energy storage plant fault recognition model pre-constructed based on a thermal runaway knowledge graph for characterizing the association relationship between the state data of the energy storage plant and the thermal runaway process, and predicting the fault status and transition path of the energy storage plant; a determining module for determining a safety risk level of the energy storage power plant according to the fault condition and the transition path; A knowledge graph-based energy storage power plant fault recognition device comprising:

8. The prediction module: an acquisition sub-module for acquiring a thermal runaway knowledge graph of the energy storage power plant, the thermal runaway knowledge graph including historical state data of the energy storage power plant, historical fault conditions corresponding to the historical state data, and historical transition paths; a training submodule for taking the history state data as input of an initial energy storage power plant fault recognition model, taking the history fault status and the history transition path as output of the initial energy storage power plant fault recognition model, and training the initial energy storage power plant fault recognition model to obtain the energy storage power plant fault recognition model; 8. The energy storage power plant fault recognition device according to claim 7, further comprising:

9. A computer device comprising: a memory in which computer instructions are stored; and a processor communicatively connected to the memory and configured to execute the computer instructions to perform the steps of the knowledge graph-based energy storage power plant fault recognition method according to any one of claims 1 to 7.

10. A computer-readable storage medium on which a computer program is stored, A computer-readable storage medium, characterized in that when the computer program is executed by a processor, it realizes the steps of the energy storage power plant fault recognition method based on knowledge graphs according to any one of claims 1 to 7.

Images