Energy storage equipment fault monitoring platform under remote identification

Abstract

The invention discloses an energy storage equipment fault monitoring platform under remote identification, and relates to the technical field of fault monitoring, and the platform comprises a path analysis unit which is used for obtaining a fault transmission analysis tree; the node configuration unit is used for obtaining a fault monitoring architecture; the activation permission configuration unit is used for configuring hierarchical activation permissions; the fault analysis unit is used for obtaining a real-time energy storage fault; and the load distribution unit is used for carrying out load distribution on the energy storage equipment. The technical problem that a fault transmission path cannot be accurately identified and timely response cannot be performed in the fault monitoring process of the energy storage equipment is solved, an edge monitoring architecture is constructed through a fault transmission analysis tree, and fault positioning and analysis are performed according to hierarchical activation permissions. Therefore, the technical effects of fast and accurate fault monitoring of the energy storage equipment and intelligent load distribution are realized.

Description

Technical Field

[0001] The present application relates to the technical field of fault monitoring, and in particular to a fault monitoring platform for energy storage equipment under remote identification. Background Art

[0002] With the rapid development of new energy technologies, energy storage equipment plays an important role in the power system. Energy storage equipment can stabilize the grid frequency, alleviate power fluctuations, and improve the overall efficiency and stability of the power system by storing and releasing energy when the grid demand peaks and valleys are unbalanced. However, energy storage equipment is composed of a large number of battery modules, which are prone to failure during long-term operation, resulting in equipment performance degradation and even safety hazards. Existing energy storage equipment fault monitoring systems usually rely on a centralized monitoring mode, that is, the remote center monitors and handles the equipment in a unified manner. However, this centralized fault monitoring method has significant shortcomings: when a system failure occurs, the remote center is difficult to respond and isolate the fault in time due to data transmission delays and resource allocation bottlenecks. This lag may cause the scope of the fault to expand, affecting the reliability and safety of the energy storage equipment. In addition, the fault transmission path inside the energy storage device is complex, and there are multiple transmission relationships between different battery modules that affect each other, such as electrical and thermal transmission. Traditional monitoring methods lack a comprehensive analysis of the fault transmission path, and cannot accurately judge the fault propagation situation, resulting in inaccurate fault location, thereby increasing the difficulty and cost of repair and maintenance. Summary of the invention

[0003] This application solves the technical problem of being unable to accurately identify fault transmission paths and respond in a timely manner during energy storage equipment fault monitoring by providing an energy storage equipment fault monitoring platform under remote identification. It realizes the construction of an edge monitoring architecture through a fault transmission analysis tree, and performs fault location and analysis based on hierarchical activation permissions, thereby achieving the technical effect of fast and accurate energy storage equipment fault monitoring and intelligent load distribution.

[0004] The present application provides a fault monitoring platform for energy storage equipment under remote identification, and the platform includes: a path analysis unit, which is used to perform fault transmission path analysis on the energy storage equipment to obtain a fault transmission analysis tree; a node configuration unit, which is used to configure edge detection nodes of the energy storage equipment according to the fault transmission analysis tree to obtain a fault monitoring architecture; an activation authority configuration unit, which is used to locate K local edge nodes with the fault monitoring architecture as a secondary node activation constraint when a primary edge detection node identifies a battery module fault, and configure hierarchical activation authorities for the K local edge nodes according to the fault transmission analysis tree; a fault analysis unit, which is used for the K local edge nodes to perform in-depth fault analysis of K battery modules based on the hierarchical activation authorities to obtain a real-time energy storage fault; a load distribution unit, which is used for the edge management coordination node to send the real-time energy storage fault to a remote operation and maintenance center after receiving the real-time energy storage fault, and distribute the load of the energy storage device according to the real-time energy storage fault, wherein the edge management coordination node is the root node of the fault monitoring architecture.

[0005] The present application proposes to analyze the fault transmission path of the energy storage device to obtain a fault transmission analysis tree; configure the edge detection node of the energy storage device according to the fault transmission analysis tree to obtain a fault monitoring architecture; when the first-level edge detection node identifies a battery module fault, the fault monitoring architecture is used as the secondary node activation constraint to locate K local edge nodes, and hierarchical activation permissions are configured for the K local edge nodes according to the fault transmission analysis tree; the K local edge nodes perform in-depth fault analysis of K battery modules based on the hierarchical activation permissions to obtain real-time energy storage faults; after receiving the real-time energy storage fault, the edge management coordination node sends the real-time energy storage fault to the remote operation and maintenance center, and distributes the load of the energy storage device according to the real-time energy storage fault, wherein the edge management coordination node is the root node of the fault monitoring architecture. The technical problem of being unable to accurately identify the fault transmission path and respond in a timely manner during the energy storage device fault monitoring process is solved, and the edge monitoring architecture is constructed through the fault transmission analysis tree, and fault location and analysis are performed based on hierarchical activation permissions, thereby achieving the technical effect of fast and accurate energy storage device fault monitoring and intelligent load distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure are briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the platform according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0007] Figure 1 A schematic diagram of the structure of an energy storage device fault monitoring platform under remote identification provided in an embodiment of the present application; Figure 2 A schematic diagram of the execution process of obtaining a fault propagation analysis tree in an energy storage device fault monitoring platform under remote identification provided in an embodiment of the present application.

[0008] Description of reference numerals: path analysis unit 1 , node configuration unit 2 , activation authority configuration unit 3 , fault analysis unit 4 , load distribution unit 5 . DETAILED DESCRIPTION

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0010] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0011] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, platform, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0012] The present application embodiment provides a remote identification energy storage device fault monitoring platform, such as Figure 1 As shown, the platform includes: The path analysis unit is used to perform fault transmission path analysis on the energy storage device to obtain a fault transmission analysis tree.

[0013] In one embodiment, in the path analysis unit, in order to fully understand the propagation mode and impact range of the fault within the device, the system terminal identifies the connection relationship between the various components in the energy storage device (such as battery modules, lines, interfaces, etc.), and constructs one or more potential fault propagation paths to form an initial fault transmission tree. These paths can show how the fault is transmitted from a component to other connected components after a component fails, thereby affecting the entire device. Subsequently, the initial fault transmission tree is updated through protection delay and thermal runaway simulation to form a fault transmission analysis tree. Through this tree structure, it is possible to clearly understand the possible spread direction of the fault, the affected area, and the priority of fault propagation. This analysis tree provides a clear path reference for subsequent fault detection and processing, which helps to achieve accurate fault monitoring and rapid response. Further, if Figure 2 As shown, the present application provides a method for performing fault transmission path analysis on an energy storage device to obtain a fault transmission analysis tree, including: The fault transfer path is fitted according to the electrical connection relationship of the energy storage device to generate an initial fault transfer tree; a fault diffusion analysis is performed based on the protection delay, and the initial fault transfer tree is updated according to the analysis result to obtain a fault diffusion transfer tree; a thermal runaway simulation is performed according to the distribution of battery modules in the energy storage device, and nodes of the fault diffusion transfer tree are merged according to the simulation result to obtain the fault transfer analysis tree.

[0014] Preferably, the system terminal analyzes the electrical connection relationship of each battery module in the energy storage device. The electrical connection relationship of the energy storage device generally includes series or parallel connections between modules, which determine the distribution path of current and voltage. Based on the identified electrical connection relationship, the fault propagation path between the modules is analyzed. For each module, the module to which it is directly connected is determined as a first-level propagation node, that is, if the module fails, the fault may be directly transmitted to these first-level propagation nodes, and then the second-level propagation nodes, the third-level propagation nodes are analyzed in turn, and so on, to form a complete fault transmission path. This step can help identify how the fault spreads from one module to other modules under the electrical connection relationship. Subsequently, according to the identified fault transmission path, each battery module and their fault propagation relationship are drawn into a tree structure to obtain an initial fault transmission tree. In the tree structure, each node represents a battery module, and the edge represents the fault transmission relationship between modules. The root node of the tree can be the first module to fail, and the first-level, second-level and deeper nodes represent other modules affected by the faulty module, which are arranged in the order of propagation.

[0015] Preferably, after determining the initial fault transmission tree, the influence of the delay in the protection mechanism on the propagation of the fault is analyzed. In the energy storage device, the response time of the protection mechanism will directly affect the degree of propagation of the fault. For example, a protection mechanism with a large delay may cause the fault to be transmitted to the adjacent module before it is detected. The system terminal collects the time from the occurrence of the fault to the detection and response of other identical modules according to the protection system delay of each battery module in the energy storage device. For example, the overcurrent protection delay of a module may be several milliseconds, while the tripping time of the circuit breaker isolating the fault may be longer. These delay data are combined with the initial fault transmission tree as the initial condition for the next step of analysis. In the initial fault transmission tree, how the protection delay of each module may affect the propagation of the fault is analyzed step by step. For each node (module), the rear nodes to which the fault can be spread within the delay time are counted if the fault is not completely isolated within the delay of its protection system, and the transmission path between these rear nodes and the faulty node is used to update the initial fault transmission tree. For example, if the protection system of node A has a long delay, and during this delay period, the statistical fault spreads to nodes B and C, then these two transmission paths will be used to supplement the initial fault transmission tree. By performing similar analysis on each node, the propagation path that may be caused by the delay is identified, thereby gradually improving the initial fault transmission tree and obtaining the fault diffusion transmission tree.

[0016] Preferably, after obtaining the fault diffusion transmission tree, the system terminal further analyzes the fault diffusion characteristics under thermal runaway. When the battery module is in thermal runaway, the heat diffuses to the module with a closer physical distance through the thermal coupling path between the modules, causing the temperature of the adjacent module to rise, causing it to also fail. In order to accurately understand the nodes that may be involved in thermal runaway, the system terminal will determine the physical position of each battery module in the energy storage device and the distance relationship between the adjacent modules. Since the battery modules in the energy storage device are closely arranged, the distance relationship between the modules determines how the heat is transferred between the modules. This step is to clearly identify the adjacent modules that may be affected by heat for each module. Subsequently, the determined distance relationship is input into the simulation software, the distance between each simulated battery in the simulation software is adjusted, and a predetermined number of thermal runaway simulations are performed, and the nodes that each node can diffuse to through the thermal coupling path are counted, and the number of nodes diffused to each node is counted. Afterwards, the nodes that do not meet the minimum number of times are eliminated from the number of these statistical diffusion nodes, and the remaining nodes are merged. For example, if the nodes that node A can diffuse to without electrical connection are node D, node E, node F and node G, the diffusion times are 3, 5, 2 and 6 respectively. Since the diffusion times of node D and node F do not meet the minimum requirement of 3 times, node D and node F are eliminated and the remaining nodes are merged. The purpose of this merging process is to reflect the unique impact of thermal runaway on fault transmission and further improve the fault transmission tree. Through the above steps, the final fault transmission analysis tree is constructed. The fault transmission analysis tree combines all potential fault propagation paths, including electrical connection paths, protection delay impact paths, and thermal runaway transmission paths. The analysis tree presents a branching structure, in which the secondary nodes of each node contain other modules that may be affected by the fault of the node, showing a complete fault diffusion relationship network. This tree structure can help accurately identify fault propagation paths and provide a clear decision-making basis for fault detection and monitoring.

[0017] A node configuration unit is used to configure edge detection nodes of the energy storage device according to the fault transfer analysis tree to obtain a fault monitoring architecture.

[0018] In one embodiment, in the node configuration unit, the system terminal configures multiple local edge nodes on multiple battery modules of the energy storage device to perform real-time monitoring on the key fault transmission path to ensure that each potential fault path is within the monitoring coverage. Subsequently, the speed of the fault transmission path is analyzed, and the fault transmission analysis tree is disassembled into several local delay topologies. For paths with faster transmission speeds, high-level edge nodes are assigned, while for slower paths, lower-level edge nodes are assigned to achieve optimized resource allocation. Afterwards, according to the local delay topology, edge detection nodes of different levels are configured to form a hierarchical monitoring network, and they are communicatively connected to the edge management coordination node as the core of the fault monitoring architecture, which is used to receive and integrate the fault data of each edge node, and report to the remote operation and maintenance center when necessary, which provides a guarantee for fault location and rapid response of energy storage equipment.

[0019] Furthermore, the present application provides configuring edge detection nodes of the energy storage device according to the fault transfer analysis tree to obtain a fault monitoring architecture, including: Configure multiple local edge nodes for multiple battery modules in the energy storage device; decompose the fault transmission analysis tree into M local delay topologies according to the fault transmission delay; configure M first-level edge detection nodes according to the M local delay topologies; divide the multiple local edge nodes into M groups of local edge nodes according to the M local delay topologies; pre-build an edge management coordination node, and complete the construction of the fault monitoring architecture by hierarchically communicating the edge management coordination node, the M first-level edge detection nodes and the M groups of local edge nodes.

[0020] Optionally, the system terminal configures multiple local edge nodes on each battery module in the energy storage device in a distributed manner. These nodes all include a fault identification model, which is responsible for real-time status monitoring of a single battery module, so as to capture the fault signal in the early stage of the fault and provide a basis for subsequent multi-level monitoring. Subsequently, the fault transmission analysis tree is disassembled into multiple local delay topologies based on the fault transmission delay and fault detection delay of the energy storage device. Each topological area represents the fault propagation delay range between a group of battery modules, so that the transmission path of the fault in the area is clearer, which is convenient for the subsequent setting of edge detection nodes. After that, according to the disassembled M local delay topologies, a first-level edge detection node is configured in each topological area. These nodes contain a macro fault identification model with a high response priority, which can quickly detect faults in the area and ensure that faults on important propagation paths can be responded to quickly. Then, according to each local delay topology, the local edge nodes are divided into M groups. These nodes are responsible for module status monitoring in each topological area, and report fault information to the first-level edge detection nodes in their respective areas step by step, so as to build a hierarchical fault monitoring network. Finally, an edge management coordination node is set up, which is connected to the hierarchical fault monitoring network constructed by M first-level edge detection nodes and M groups of local edge nodes through hierarchical communication to complete the construction of the fault monitoring architecture. As a core node, the edge management coordination node undertakes multiple key tasks, including sending fault information to the remote operation and maintenance center, coordinating the load distribution of the remaining battery modules, isolating and managing the faulty modules, etc., to ensure the stable operation and fault response efficiency of the entire energy storage device. This fault monitoring architecture ensures that faults can be quickly and accurately located and responded to when a fault occurs through multi-level node configuration and hierarchical management, thereby improving the reliability and safety of energy storage equipment.

[0021] For the fault identification model, the system terminal collects historical data including key operating parameters such as current, voltage, and temperature, and marks them as faulty or normal. Subsequently, the collected data is standardized to ensure that different parameters are within the same numerical range, thereby improving the training efficiency and stability of the model. The model adopts a multi-layer perceptron structure. The input layer receives these parameters, and after processing through several hidden layers, it outputs a prediction result (0 for normal, 1 for fault). The model uses binary cross entropy as the loss function and uses the Adam optimizer to achieve fast convergence. During the training process, the system terminal inputs the training data into the model, calculates the error between the predicted output and the true label according to the loss function, and adjusts the model weight layer by layer through the back propagation algorithm. This training process is iterated multiple times to gradually optimize the fault identification ability of the model. After each round of training, the model performance is evaluated using the validation set, and the model hyperparameters are adjusted through indicators such as accuracy and F1 score to improve the generalization ability. After the training is completed, the prediction effect of the model is evaluated on the test set. If the expected accuracy is achieved, the model weight is saved so that the device status can be monitored in real time and faults can be identified in actual applications.

[0022] Furthermore, the present application provides a method for decomposing the fault transfer analysis tree into M local delay topologies according to the fault transfer delay, including: The fault transmission analysis tree is traversed according to the fault transmission type to calculate the fault transmission delay and obtain the fault delay topology; the remote operation and maintenance center is interacted to obtain the fault detection delay; the fault delay topology is topologically aggregated using the fault detection delay to obtain the M local delay topologies.

[0023] Optionally, the system terminal traverses each node (battery module) in the tree according to the fault transmission analysis tree of the energy storage device, and analyzes the propagation time of the fault on different types of transmission paths. Fault transmission types include electrical connection, thermal conduction, etc. In each traversal process, the fault transmission type of the current transmission path is identified, and the same simulation process as above is used to perform a preset number of transmission simulations, and the simulated fault delay of each transmission simulation is recorded, and then the average of all recorded simulated fault delays is calculated to obtain the fault delay of the starting node corresponding to the transmission path. After that, according to the position relationship of each node, the topological connection is combined with the corresponding fault delay to form a preliminary topological structure containing the fault propagation delay between each node, that is, the fault delay topology. After completing the construction of the fault delay topology, interact with the remote operation and maintenance center to obtain the fault detection delay. The fault detection delay refers to the total time from detecting the fault signal to feeding back the information to the remote center. This data helps to understand the speed and efficiency of the system response during the fault propagation process. Using the obtained fault detection delay, the system terminal performs topological aggregation processing on the fault delay topology. Specifically, an agglomerative hierarchical clustering algorithm is used to take the fault detection delay of each node as the characteristic value of the cluster, and nodes with similar detection delays are gradually aggregated together to form several local delay topology areas. Each area represents a group of nodes within a similar delay range, which is convenient for the subsequent setting and response optimization of edge detection nodes. Through the shortest distance aggregation method of the hierarchical clustering algorithm, the system terminal first finds the node or node group with the shortest distance and merges them. After each aggregation, the distance between the newly generated cluster and other clusters is recalculated, and the merging continues until the set clustering level is reached. For example, a maximum delay difference threshold (such as 10ms) can be set. When the detection delay difference of the nodes in the cluster exceeds the threshold, the merging stops, and finally several clusters are formed, namely M local delay topology areas. These local delay topology areas all contain nodes within a specific delay range, which is convenient for configuring corresponding edge detection nodes for each area to achieve hierarchical monitoring and more efficient fault response.

[0024] Furthermore, the present application provides configuring M primary edge detection nodes according to the M local delay topologies, including: Interactively obtain multiple sample fault fusion images; construct a fault dimension reduction recognition layer based on a convolutional neural network, and use the multiple sample fault fusion images to optimize the recognition performance of the fault dimension reduction recognition layer; call a timing chart plug-in, and obtain multiple image conversion plug-ins by copying the timing chart plug-in; connect the multiple image conversion plug-ins in parallel to complete the construction of the image conversion layer; after introducing the image fusion layer, complete the construction of a macro fault recognition model by cascading the image conversion layer, the image fusion layer and the fault dimension reduction recognition layer; copy the macro fault recognition model to the M first-level edge detection nodes.

[0025] Optionally, the system terminal obtains fused images of multiple sample faults through interaction. These images contain comprehensive feature information under different fault states, providing rich sample data for subsequent identification. Subsequently, a fault dimension reduction recognition layer is constructed through the convolutional neural network (CNN) architecture. This layer is designed to extract the most representative fault features from the fault fusion image for use in subsequent analysis. Specifically, the CNN hierarchical structure includes multiple convolutional layers and pooling layers. The convolutional layer is used to extract low-level features (such as edges and textures) from the input image, while the pooling layer is used to reduce the data size, reduce the computational complexity, and achieve dimensionality reduction processing. The system terminal inputs multiple sample fault fusion images into the fault dimension reduction recognition layer. Each fused image contains comprehensive features of different fault states, which can provide the model with diverse fault data. Through the convolution kernel in the CNN, the feature patterns in the image are scanned and learned layer by layer, and the size of the feature map is reduced through the pooling layer. In this process, the most important feature information is gradually compressed and retained to complete the dimensionality reduction of the image data. In order to improve the recognition performance of the fault dimension reduction recognition layer, the back propagation algorithm is used to optimize the recognition layer, that is, the recognition result is compared with the actual label, the loss function is calculated, and the convolution kernel parameters are adjusted by gradient descent, so that the fault dimension reduction recognition layer can continuously improve its feature extraction ability for the fault fusion image. This optimization process will be repeated until the recognition accuracy reaches the expected requirements. The fault dimension reduction recognition layer constructed by this process can effectively reduce the dimension and extract features of the input fusion image, and realize accurate recognition of different fault states. This dimension reduction recognition layer provides a basis for the construction of subsequent macro fault recognition models, ensuring that the system terminal can perform fault detection efficiently and accurately. In order to process the time series characteristics of the fault image, the system terminal calls the time series chart plug-in, which is used to capture and process the dynamic characteristics of the fault image over time. In the fault identification process, the fault state may change gradually or suddenly over time, and a single image is difficult to reflect the complete fault process. By analyzing the time series characteristics of the image, the time series chart plug-in can extract the image change pattern at different time points to ensure sensitivity to changes in the time dimension. To achieve parallel processing, the system terminal copies the timing chart plug-in to generate multiple image conversion plug-ins (such as Pillow, OpenCV) to form a parallel image conversion layer. Each image conversion plug-in is responsible for image processing in a time period and independently processes the changes in fault images in a specific time series. For example, one plug-in can focus on capturing the features of the initial fault, while another plug-in focuses on changes during the fault expansion process. This parallel architecture ensures that the model does not miss any key time series information, thereby enhancing sensitivity to time changes. After building the image conversion layer, an image fusion layer (such as Pyramid Fusion, Wavelet Transform Fusion, etc.) is introduced to fuse the image features processed by each plug-in.Afterwards, a macro fault identification model is constructed layer by layer by cascading the image conversion layer, image fusion layer, and fault dimension reduction identification layer. This model has the ability to identify and judge faults from a holistic perspective and can quickly respond to the complex fault characteristics of energy storage equipment. Finally, the constructed macro fault identification model is copied to M first-level edge detection nodes to achieve real-time monitoring and rapid response of macro faults in various regions. With this model, each first-level edge detection node can independently complete the macro identification of fault characteristics and improve monitoring accuracy and efficiency.

[0026] The activation authority configuration unit is used to locate K local edge nodes based on the fault monitoring architecture as a secondary node activation constraint when the primary edge detection node identifies a battery module fault, and configure hierarchical activation authorities for the K local edge nodes according to the fault transfer analysis tree.

[0027] In one embodiment, in the activation authority configuration unit, when the first-level edge detection node detects that a battery module has failed, the system terminal activates the secondary nodes related to the fault based on the fault monitoring architecture to form an activation chain that expands layer by layer. First, according to the fault monitoring architecture, the K local edge nodes associated with the faulty node are quickly located through the configuration relationship between the local edge node and the battery module to ensure that these nodes can centrally monitor the affected area. Subsequently, through the positive node traversal and negative node traversal of the fault transmission analysis tree, the fault propagation relationship and position of these local nodes are evaluated, and hierarchical activation permissions are set for the K local edge nodes one by one, so that nodes with high response priority can obtain activation permissions before the fault impact expands, thereby effectively controlling the spread of faults at different levels and ensuring timely status feedback.

[0028] Furthermore, the present application provides that when a primary edge detection node identifies a battery module fault, the fault monitoring architecture is used as a secondary node activation constraint, K local edge nodes are located, and hierarchical activation permissions are configured for the K local edge nodes according to the fault transfer analysis tree, including: According to the configuration relationship between the local edge nodes and the battery modules, the K associated batteries of the K local edge nodes are located in the fault transfer analysis tree; a positive node traversal order and a negative node traversal order are constructed according to the K associated batteries; and the hierarchical activation authority is constructed according to the parallel relationship between the positive node traversal order and the negative node traversal order.

[0029] Optionally, the system terminal locates K local edge nodes and their K associated battery modules associated with the node where the fault occurs in the fault transmission analysis tree according to the configuration relationship between the local edge node and the battery module. Through these associated battery modules, the fault detection is divided into two traversal paths in the positive and negative directions. On the positive path, the system terminal starts from the fault node and traverses to each associated battery module step by step along the direction of fault transmission to build a positive node traversal order. On the negative path, the fault source is traced back from the associated battery module to form a negative node traversal order. This two-way traversal can not only cover the fault diffusion path, but also ensure that each node is cross-validated during detection. According to the parallel relationship between the positive node traversal order and the negative node traversal order, the system terminal constructs hierarchical activation permissions, preferentially activates the core nodes on the fault transmission path, and gradually activates the peripheral nodes. Through parallel fault detection in both positive and negative directions, the system terminal effectively narrows the detection range, realizes efficient cross-validation and parallel detection, thereby speeding up the positioning speed of the faulty battery and improving the fault response and processing efficiency.

[0030] A fault analysis unit, configured for the K local edge nodes to perform in-depth fault analysis of the K battery modules based on the hierarchical activation authority to obtain real-time energy storage faults; In one embodiment, in the fault analysis unit, after obtaining the hierarchical activation authority, K local edge nodes conduct in-depth fault analysis on their associated battery modules according to different priorities. Each node collects and analyzes the detailed operating data of the battery module in turn according to the authority level, such as key parameters such as current, voltage, and temperature, and identifies potential fault characteristics through the internal fault identification model. Through this hierarchical and step-by-step in-depth analysis, it is possible to quickly collect real-time fault information, comprehensively evaluate the status of the battery module, and generate a real-time fault report of the battery module, that is, real-time energy storage fault, to ensure timely and accurate identification of the fault location.

[0031] A load distribution unit is used for sending the real-time energy storage fault to a remote operation and maintenance center after the edge management coordination node receives the real-time energy storage fault, and distributing the load of the energy storage device according to the real-time energy storage fault, wherein the edge management coordination node is the root node of the fault monitoring architecture.

[0032] In one embodiment, in the load distribution unit, when the edge management coordination node receives real-time energy storage fault information, it first transmits the fault data to the remote operation and maintenance center in a timely manner so that the remote monitoring team can obtain the real-time status of the equipment and prepare for fault handling. At the same time, the edge management coordination node will adjust the load distribution of the energy storage device according to the received fault information, and give priority to reducing or isolating the load of the battery module affected by the fault, so as to ensure the overall stable operation of the energy storage device. As the root node of the fault monitoring architecture, the edge management coordination node is responsible for coordinating fault handling and load adjustment to ensure that the entire energy storage device can effectively mitigate the impact of the fault and maintain core functions in a fault state.

[0033] Furthermore, the present application also provides that after receiving the real-time energy storage fault, the edge management coordination node sends the real-time energy storage fault to the remote operation and maintenance center, and performs load distribution of the energy storage device according to the real-time energy storage fault, wherein the edge management coordination node is the root node of the fault monitoring architecture, including: After receiving the real-time energy storage fault, the edge management coordination node locates and isolates the faulty battery module according to the real-time energy storage fault; obtains the fault delay parameters of the faulty battery module and the remaining battery modules according to the fault delay topology to perform load weight distribution and obtain a load distribution strategy; and uses the load distribution strategy to update the load distribution of the energy storage device.

[0034] Optionally, when the edge management coordination node receives the real-time energy storage fault information, it identifies the specific faulty battery module according to the location information in the real-time energy storage fault, and immediately takes isolation measures to remove the module from the main load link of the energy storage system to avoid the spread of the fault and further damage. Subsequently, the edge management coordination node uses the fault delay topology to analyze the fault delay parameters between the faulty battery module and other normal battery modules. Through these parameters, the node can determine the degree of delay of each module affected by the fault, and assign corresponding load weights to each module accordingly. For example, a module with lower delay and less impact will obtain a higher load weight, while a module with higher delay will be assigned a lower load weight. After that, based on the calculated load weight, a load distribution strategy is generated to preferentially allocate the load to the battery module with higher weight. In this process, the node will classify the modules according to the fault delay parameters, set the modules with lower delay to high priority, and allocate more power load so that after the faulty module is isolated, these modules can bear the core load requirements, and then set the power upper limit of each module to prevent overload. For modules with lower weights, the load distribution strategy will allocate less power to reduce their operating pressure. This weight-based allocation platform ensures that the energy storage device can continue to operate in an unbalanced state while minimizing the impact of the fault on the overall system. Finally, the system terminal will dynamically update the load of the energy storage device based on this strategy, transferring more load to healthy modules, thereby ensuring the stability and continuous operation of the energy storage device while minimizing the impact of the fault on the entire energy storage device.

[0035] Furthermore, the present application also includes: The load weights of the faulty battery module and the remaining battery modules are optimized according to the distribution of battery modules in the energy storage device to obtain an optimized allocation strategy; a thermal runaway time domain is preset; and when the energy storage device executes the load distribution update and reaches the thermal runaway time domain, the optimized allocation strategy is used to dynamically update the load of the energy storage device.

[0036] Optionally, the system terminal optimizes the load weights of the faulty battery module and the remaining battery modules around it according to the physical distribution of the battery modules in the energy storage device. The key to this step is to use the spatial relationship between the faulty battery module and the remaining battery modules to identify the battery modules with physical distance or heat conduction path within the priority distance, and ensure that the load is preferentially distributed to the adjacent modules around the faulty battery module, so that the modules within the fault impact range can effectively share the load and reduce the impact of the fault on the overall system. Subsequently, the system terminal will generate an optimized load distribution strategy based on these spatial distribution relationships, and allocate higher load weights to the remaining battery modules that are within the priority distance from the faulty battery module and are not affected by the fault. This strategy not only takes into account the spatial distribution of the battery modules, but also reduces the risk of secondary failure of the battery modules under high load by optimizing the load path. At the same time, a thermal runaway time domain is preset, that is, when the temperature and load state of the energy storage device reach the thermal runaway time domain, it indicates that there is a possibility of thermal runaway. When the load distribution update of the system terminal reaches the thermal runaway time domain, it will automatically switch to the previously generated optimized distribution strategy to dynamically update the load in real time. Through real-time adjustment of this optimization strategy, the system terminal can still reasonably distribute the load in space under high load and fault conditions, prevent the spread of thermal runaway, and ensure the stable operation of the energy storage equipment.

[0037] The energy storage device fault monitoring platform under remote identification according to the embodiment of the present invention is used to solve the technical problem that the fault transmission path cannot be accurately identified and the fault response cannot be timely responded to during the energy storage device fault monitoring process, and realizes the construction of an edge monitoring architecture through a fault transmission analysis tree, and performs fault location and analysis based on hierarchical activation permissions, thereby achieving the technical effect of fast and accurate energy storage device fault monitoring and intelligent load distribution. The energy storage device fault monitoring platform under remote identification includes: a path analysis unit 1, a node configuration unit 2, an activation permission configuration unit 3, a fault analysis unit 4, and a load distribution unit 5.

[0038] Although the present application makes various references to certain modules in the platform according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be implemented; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0039] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. The energy storage equipment fault monitoring platform under remote identification is characterized by: The platform includes: A path analysis unit, used to perform fault transmission path analysis on the energy storage device to obtain a fault transmission analysis tree; A node configuration unit, configured to configure edge detection nodes of the energy storage device according to the fault transfer analysis tree to obtain a fault monitoring architecture; an activation authority configuration unit, for locating K local edge nodes using the fault monitoring architecture as a secondary node activation constraint when the primary edge detection node identifies a battery module fault, and configuring hierarchical activation authorities for the K local edge nodes according to the fault transfer analysis tree; A fault analysis unit, configured for the K local edge nodes to perform in-depth fault analysis of the K battery modules based on the hierarchical activation authority to obtain real-time energy storage faults; A load distribution unit is used for sending the real-time energy storage fault to a remote operation and maintenance center after the edge management coordination node receives the real-time energy storage fault, and distributing the load of the energy storage device according to the real-time energy storage fault, wherein the edge management coordination node is the root node of the fault monitoring architecture.

2. The energy storage device fault monitoring platform under remote identification as claimed in claim 1, characterized in that: Performing fault transmission path analysis on the energy storage device to obtain a fault transmission analysis tree, the platform includes: Perform fault transmission path fitting according to the electrical connection relationship of the energy storage device to generate an initial fault transmission tree; Performing fault diffusion analysis based on protection delay, and updating the initial fault propagation tree according to the analysis result to obtain a fault diffusion propagation tree; A thermal runaway simulation is performed according to the distribution of battery modules in the energy storage device, and nodes of the fault diffusion propagation tree are merged according to the simulation results to obtain the fault propagation analysis tree.

3. The energy storage device fault monitoring platform under remote identification as claimed in claim 2, characterized in that: The energy storage device is configured with edge detection nodes according to the fault transfer analysis tree to obtain a fault monitoring architecture, wherein the platform includes: Configuring multiple local edge nodes for multiple battery modules in the energy storage device; Decomposing the fault transfer analysis tree into M local delay topologies according to the fault transfer delay; Configure M first-level edge detection nodes according to the M local delay topologies; Dividing the multiple local edge nodes into M groups of local edge nodes according to the M local delay topologies; An edge management coordination node is pre-built, and the construction of the fault monitoring architecture is completed by connecting the edge management coordination node, M first-level edge detection nodes and M groups of local edge nodes in a hierarchical communication manner.

4. The energy storage device fault monitoring platform under remote identification as claimed in claim 3, characterized in that: Decomposing the fault transfer analysis tree into M local delay topologies according to the fault transfer delay, the platform includes: According to the fault transmission type, the fault transmission analysis tree is traversed to calculate the fault transmission delay to obtain the fault delay topology; Interact with the remote operation and maintenance center to obtain a fault detection delay; The fault detection delay is used to perform topology aggregation on the fault delay topology to obtain the M local delay topologies.

5. The energy storage device fault monitoring platform under remote identification as claimed in claim 3, characterized in that: M first-level edge detection nodes are configured according to the M local delay topologies, and the platform includes: Interactively obtain multiple sample fault fusion images; Building a fault dimension reduction recognition layer based on a convolutional neural network, and optimizing the recognition performance of the fault dimension reduction recognition layer by using the multiple sample fault fusion images; Calling a timing chart plug-in, and obtaining a plurality of image conversion plug-ins by copying the timing chart plug-in; Connecting the plurality of image conversion plug-ins in parallel to complete the construction of the image conversion layer; After the image fusion layer is introduced, the construction of the macro fault identification model is completed by cascading the image conversion layer, the image fusion layer and the fault dimension reduction identification layer; The macro fault identification model is copied to the M first-level edge detection nodes.

6. The energy storage device fault monitoring platform under remote identification as claimed in claim 3, characterized in that: When the primary edge detection node identifies a battery module fault, the fault monitoring architecture is used as a secondary node activation constraint, K local edge nodes are located, and hierarchical activation permissions are configured for the K local edge nodes according to the fault transfer analysis tree. The platform includes: According to the configuration relationship between the local edge nodes and the battery modules, locating the K associated batteries of the K local edge nodes in the fault transfer analysis tree; Constructing a positive node traversal order and a negative node traversal order according to the K associated batteries; The hierarchical activation authority is constructed according to the parallel relationship between the positive node traversal order and the negative node traversal order.

7. The energy storage device fault monitoring platform under remote identification as claimed in claim 4, characterized in that: After receiving the real-time energy storage fault, the edge management coordination node sends the real-time energy storage fault to the remote operation and maintenance center, and performs load distribution of the energy storage device according to the real-time energy storage fault, wherein the edge management coordination node is the root node of the fault monitoring architecture, and the platform includes: After receiving the real-time energy storage fault, the edge management coordination node locates and isolates the faulty battery module according to the real-time energy storage fault; Obtaining the fault delay parameters of the faulty battery module and the remaining battery modules according to the fault delay topology to perform load weight distribution and obtain a load distribution strategy; The load distribution strategy is adopted to update the load distribution of the energy storage device.

8. The energy storage device fault monitoring platform under remote identification as claimed in claim 7, characterized in that: The platform includes: Optimizing the load weights of the faulty battery module and the remaining battery modules according to the distribution of the battery modules in the energy storage device to obtain an optimized allocation strategy; Preset thermal runaway time domain; When the load distribution update of the energy storage device reaches the thermal runaway time domain, the optimized distribution strategy is used to dynamically update the load of the energy storage device.

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