A fault detection method and system for power distribution cabinet

Abstract

This invention proposes a fault detection method and system for distribution cabinets. The method comprises: combining internal cabinet sensor data with its spatial distribution to generate a dynamic topology map as a global state representation; extracting fault-related spatiotemporal features based on the dynamic topology map, compressing complexity, and forming a feature sequence; constructing a multi-branch temporal model framework based on the feature sequence to predict distribution cabinet faults and perform risk assessment; determining whether to trigger an alarm based on the prediction results, and updating the dynamic topology map based on maintenance results, forming a closed loop. This invention combines multidimensional data from the distribution cabinet's internal sensor network through dynamic topology analysis and time series modeling to achieve real-time fault detection and trend prediction.

Description

Technical Field

[0001] The present invention belongs to the field of fault detection, and in particular relates to a fault detection method and system for a power distribution cabinet. Background Art

[0002] With the continuous development of modern power systems, distribution cabinets, as a crucial component of power equipment, have a direct impact on the stability and security of the power grid. However, due to long-term load fluctuations, environmental influences (such as humidity and temperature fluctuations), and component aging, distribution cabinets are prone to internal faults such as overheating, short circuits, and poor contact. These faults often manifest as hidden changes in the early stages, making them difficult to detect using traditional detection methods. This can lead to the accumulation of faults and potentially serious consequences.

[0003] The current mainstream fault detection methods have the following technical bottlenecks:

[0004] Lack of real-time performance: Single-point sensor monitoring technology relies on the monitoring of a single specific data (such as temperature or current), and requires manual analysis to determine the fault. This is not only inefficient, but also difficult to meet the demand for rapid response in the operation of the distribution cabinet.

[0005] Lack of predictive capabilities: Existing methods primarily focus on static fault detection and fail to capture the temporal evolution of faults. Due to a lack of modeling of the temporal characteristics of multi-sensor data, existing systems struggle to provide reliable early warning of faults.

[0006] Insufficient global correlation: Components within a power distribution cabinet interact with each other in complex ways. For example, overheating can simultaneously affect busbars, cables, and circuit breakers. However, current detection methods fail to establish dynamic correlation analysis between these components, resulting in low accuracy in fault location and cause analysis.

[0007] To address these issues, how to develop a system that can detect faults in real time and predict their future trends, especially a technology that can combine the dynamic correlation between various components, has become a difficult problem that needs to be solved urgently in the field of distribution cabinet fault detection. Summary of the Invention

[0008] The purpose of the present invention is to propose a fault detection method and system for distribution cabinets. By modeling spatiotemporal sequences and combining dynamic topology analysis with time series modeling, the present invention not only solves the real-time problem of traditional methods but also realizes early fault prediction and global correlation analysis, which has important application value and technical advantages.

[0009] In order to achieve the above object, a first aspect of the present invention provides a fault detection method for a power distribution cabinet, the method comprising:

[0010] S1. Generate a dynamic topology diagram by combining the internal sensor data of the distribution cabinet with its spatial distribution as a global state expression; wherein the internal sensor data of the distribution cabinet includes temperature T i , current I i and voltage V i , i is the sensor number, i=1,2,…,N;

[0011] S2. Extract fault-related spatiotemporal features based on the dynamic topology graph, compress the complexity and form a feature sequence;

[0012] S3. Construct a multi-branch time model framework based on the feature sequence to predict distribution cabinet failures and perform risk assessment;

[0013] S4. Determine whether to trigger an alarm based on the prediction results, and update the dynamic topology map based on the maintenance results to form a closed loop;

[0014] Said S1 specifically includes:

[0015] The sensor nodes are grouped into a set V = {1, 2, ..., N} and the initial topology graph G = 9V, E ) , where E is the connection edge between sensor nodes, and the weight w of each edge (i, j)∈E ij Initialized as:

[0016]

[0017] Where d(i,j) is the physical distance between sensors i and j; the initial topological graph is used to capture the spatial relationship of nodes;

[0018] Dynamically adjust the edge weight w based on the real-time sensor data collected ij , so that it can reflect the dynamic coupling relationship between components:

[0019] w ij,t =α·w ij +β·|ΔP i,t -ΔP j,t | -1

[0020] Among them, w ij,t is the dynamic weight of edge (i, j) at time t; w ij is the static edge weight at initialization; ΔP i,t is the parameter change of sensor i at time t, α and β are edge weight adjustment factors, satisfying α + β = 1; |ΔP i,t -ΔP j,t | is the difference in state changes between sensors i and j;

[0021] The sparse processing is used to reduce noise and computational complexity, and only the k edges with the largest weight are retained, and k≤N, N is the total number of sensors, then the sparse graph is G t =(V,E t ),in Each edge (i,j)∈E t The weight w ij,t After updating, it is used to reflect the real-time dynamic association between sensors;

[0022] The final generated dynamic topology graph G t =(V,E t )include:

[0023] The node set V represents the sensor location;

[0024] Edge set E t Describe the dynamic relationship between sensors, edge weight w ij,t Reflects the coupling characteristics of component states.

[0025] Preferably, the parameter change ΔP of the sensor i at time t is i,t ΔP i,t =[ΔT i,t ,ΔI i,t ,ΔV i,t ], where ΔT i,t is the temperature change in degrees Celsius; ΔI i,t is the current change in amperes; ΔV i,t is the voltage change in volts.

[0026] Preferably, the S2 specifically includes:

[0027] According to the generated dynamic topology graph G t =(V,E t ), extract the spatiotemporal feature embedding vector h of each sensor node i,t ,capture the dynamic relationship between nodes, while ensuring that the generated embedding vector can express the potential information of the fault;

[0028] Embed the spatiotemporal features of each sensor node into vector h i,t Aggregate into the global topological feature vector H at time t t , indicating the operating status of the entire distribution cabinet at the current moment;

[0029] The generated global eigenvector H t Perform regularization processing to obtain the global eigenvector H′ after regularization t , ensuring that the feature sequences at different time points have smooth temporal continuity;

[0030] The regularized global eigenvector H′ of all time points t Combined into feature sequences

[0031] Preferably, the dynamic topology graph G generated according to t =(V,E t ), extract the spatiotemporal feature embedding vector h of each sensor node i,t , using dynamic feature embedding, expressed as:

[0032]

[0033] Among them, h i,t is the embedding vector of sensor i at time t, which is used to characterize the fault status of the node; σ(·) is a nonlinear activation function, which is used to limit the value range of the embedding and enhance the nonlinear expression ability of the model; is the neighbor set of node i; w ij,t is the edge weight in the dynamic topology graph, indicating the association strength between sensors i and j; f(x j ,x i ) is the node state difference function; λ· It is a dynamic change regularization term used to constrain the sensitivity of the embedding vector to drastic changes in weights and is suitable for capturing fast-changing dynamic faults.

[0034] The spatiotemporal features of each sensor node are embedded into the vector h i,t Aggregate into the global topological feature vector H at time t t , expressed as:

[0035]

[0036] Among them, H t is the global eigenvector at time t, which represents the overall operating status of the distribution cabinet; γ i The node importance coefficient is assigned a weight based on the physical location or device type of the sensor;

[0037] The regularization process is expressed as:

[0038]

[0039] Among them, H′ t is the regularized global eigenvector, which eliminates unnecessary fluctuations in the feature sequence; is the gradient of the feature vector at time t, which is used to capture the drastic changes of features in time; η is the smoothing factor, which controls the strength of feature regularization;

[0040] The characteristic sequence Expressed as

[0041] Preferably, the node state difference function is defined as:

[0042] f(x j ,x i )=[|T j -T i |,|I j -I i |,|V j -V i |]

[0043] Among them, T j ,T i is the temperature value of nodes j and i; I j ,I i is the current value; V j ,V i is the voltage value.

[0044] Preferably, the S3 specifically includes:

[0045] According to the global feature {H′ after regularization t}Calculate dynamic residual features {R t}, combined into the final combined feature X t =[H′ t ,R t ], where X t is the input feature vector at time t, which contains basic features and dynamic residuals, and fully describes the state and changes;

[0046] The final combined feature is X t =[H′ t ,R t ] form the input sequence Then, a multi-branch time model framework based on distribution cabinet fault detection is constructed for processing and the feature prediction sequence is output;

[0047] Multi-task fault risk assessment is performed based on feature prediction sequences, including prediction of potential fault location, fault type, and occurrence time.

[0048] Preferably, the multi-branch time model framework is constructed as follows:

[0049] Constructing a long-term trend branch and a short-term fluctuation branch; wherein the long-term trend branch is used to capture the slowly changing pattern of the operating state of the power distribution cabinet; the short-term fluctuation branch is used to capture the pattern of rapid changes and sudden failures;

[0050] The advancement relationship of the multi-branch time model framework is constructed as follows:

[0051]

[0052] in, Predict the feature sequence for the next k time steps; F trend (X t ) is the long-term trend branch output, used to describe the slow fault trend; F short (X t ) is the short-term fluctuation branch output, focusing on fast fault detection;

[0053] The multi-task failure risk assessment specifically includes:

[0054] Define the risk scoring function and perform risk analysis for each time step:

[0055]

[0056] Among them, Risk t+k is the risk prediction value at time t+k, including the probability distribution of risk level; W risk is the weight matrix for risk assessment; is a regularization term used to measure the deviation between the future predicted value and the current state and to strengthen the detection of abnormal changes.

[0057] Preferably, the long-term trend branch extracts the slowly changing pattern of the input sequence based on a temporal convolutional network, which is expressed as:

[0058] F trend (X t) =ReLU(W trend *X t )

[0059] Among them, W trend is the temporal convolution kernel weight, used to extract the temporal pattern of a fixed window; ReLU is a nonlinear activation function, used to enhance the sparsity of features;

[0060] The short-term fluctuation branch adopts a dynamic residual network structure to strengthen the modeling of residual features, which can be expressed as:

[0061] F short (X t )=λ·R t +g(W short ·R t )

[0062] Among them, λ is the dynamic amplification factor, which is used to enhance the sensitivity to residual errors; g(W short ·R t ) is a nonlinear activation used to capture complex fluctuation patterns.

[0063] Preferably, the S4 specifically includes:

[0064] Design a comprehensive alarm index A based on the risk prediction value t , used to measure the overall failure risk level of the distribution cabinet in the future period; wherein, the comprehensive alarm index A t , expressed as:

[0065]

[0066] Among them, W alarm is the risk weight vector, which assigns weights to risk levels; Dynamic change term, used to amplify outliers of future feature changes; Indicates the difference between the future feature prediction and the current state; λ is the amplification factor, which increases the sensitivity to significant changes;

[0067] If A t When the set threshold τ is exceeded, an alarm signal is triggered:

[0068]

[0069] Where Alarm is the alarm signal, 1 indicates an alarm is triggered, and 0 indicates no warning; τ is the alarm threshold, which is dynamically adjusted according to the environment and operation strategy; when the alarm is triggered, an alarm report is automatically generated; the content includes fault type prediction, fault location prediction and risk time range;

[0070] When an alarm is triggered, the system's dynamic topology and the parameters of the multi-branch time model framework are updated in real time to ensure that they adapt to the latest status changes.

[0071] In a second aspect of the present invention, a fault detection system for a power distribution cabinet is provided, the system comprising:

[0072] A dynamic topology construction module is used to generate a dynamic topology map by combining the internal sensor data of the distribution cabinet with its spatial distribution as a global state expression; wherein the internal sensor data of the distribution cabinet includes temperature T i Current I i and voltage V i , i is the sensor number, i=1,2,…,N;

[0073] The spatiotemporal feature construction module is used to extract fault-related spatiotemporal features based on the dynamic topology graph, compress the complexity and form a feature sequence;

[0074] The risk prediction module is used to predict distribution cabinet failures and perform risk assessment based on a multi-branch time model framework constructed based on feature sequences;

[0075] The detection, early warning, and optimization module is used to determine whether to trigger an alarm based on the prediction results, and to update the dynamic topology map through maintenance results to form a closed loop;

[0076] The dynamic topology map construction module specifically includes:

[0077] The sensor nodes are organized into a set V = {1, 2, ..., N} and the initial topology graph G = 9V, E) is defined, where E is the connection edge between the sensor nodes and the weight w of each edge (i, j) ∈ E is ij Initialized as:

[0078]

[0079] Where d(i,j) is the physical distance between sensors i and j; the initial topological graph is used to capture the spatial relationship of nodes;

[0080] Dynamically adjust the edge weight w based on the real-time sensor data collected ij , so that it can reflect the dynamic coupling relationship between components:

[0081]

[0082] Among them, w ij,t is the dynamic weight of edge (i, j) at time t; w ij is the static edge weight at initialization; ΔP i,t is the parameter change of sensor i at time t, α and β are edge weight adjustment factors, satisfying α + β = 1; |ΔP i,t -ΔP j,t | is the difference in state changes between sensors i and j;

[0083] The sparse processing is used to reduce noise and computational complexity, and only the k edges with the largest weight are retained, and k≤N, N is the total number of sensors, then the sparse graph is G t =(V,E t ),in Each edge (i,j)∈E t The weight w ij,t After updating, it is used to reflect the real-time dynamic association between sensors;

[0084] The final generated dynamic topology graph G t =(V,E t )include:

[0085] The node set V represents the sensor location;

[0086] Edge set E t Describe the dynamic relationship between sensors, edge weight w ij,t Reflects the coupling characteristics of component states.

[0087] The beneficial technical effects of the present invention are at least as follows:

[0088] This invention captures the spatial relationships between components by constructing a dynamic topology map of the sensor network within the distribution cabinet. During operation, if a component experiences an anomaly (such as temperature rise or increased electromagnetic interference), dynamic topology modeling can analyze the propagation path and impact of the anomaly on the entire system in real time, supporting rapid fault location.

[0089] This invention uses a particle swarm optimization (PSO) algorithm combined with real-time monitoring data to perform multi-objective optimization of screening equipment operating parameters (such as vibration frequency, mesh size, and inclination angle). By introducing a comprehensive evaluation function for screening efficiency and energy consumption, it enables dynamic adjustment of equipment operating parameters, resolving the problem of existing equipment being unable to adapt to changes in mineral flow characteristics.

[0090] Based on dynamic topology, the present invention uses a temporal convolutional network (TCN) to model the time evolution characteristics of multidimensional sensor data, extract potential abnormal trend signals, and thus predict the evolution direction and possible occurrence time of faults.

[0091] The present invention combines the multi-dimensional data of the sensor network inside the power distribution cabinet through dynamic topology analysis and time series modeling to achieve real-time detection and trend prediction of faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0093] Figure 1 This is a flow chart of a fault detection method for a power distribution cabinet according to the present invention.

[0094] Figure 2 This is a framework diagram of a fault detection system for a power distribution cabinet according to the present invention. DETAILED DESCRIPTION

[0095] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0096] like Figure 1 As shown, an embodiment of the present invention provides a fault detection method for a power distribution cabinet, the method comprising the following steps:

[0097] S1. Generate a dynamic topology diagram by combining the internal sensor data of the distribution cabinet with its spatial distribution as a global state expression; wherein the internal sensor data of the distribution cabinet includes temperature T i Current I i and voltage V i , i is the sensor number, i=1,2,…,N.

[0098] The specific implementation method of this step includes:

[0099] The sensor network collects key physical parameters inside the distribution cabinet, including temperature T i Current I i and voltage V i , where i is the sensor number, i = 1, 2, ..., N. These sensor nodes form a set V = {1, 2, ..., N}, and define the initial topology graph G = (V, E), where E is the connection edge between sensor nodes.

[0100] Among them, the weight w of each edge (i, j)∈E ij Initialized as:

[0101]

[0102] Where d(i,j) is the physical distance between sensors i and j (in meters). The initial topology graph is used to capture the spatial relationship of nodes.

[0103] Furthermore, the edge weight w is dynamically adjusted based on the real-time sensor data collected. ij , so that it can reflect the dynamic coupling relationship between components:

[0104] w ij,t =α·w ij +β·|ΔP i,t -ΔP j,t | -1

[0105] Among them, w ij,t is the dynamic weight of edge (i, j) at time t. ij is the static edge weight at initialization (no time change). ΔP i,t =[ΔT i,t ,ΔI i,t ,ΔV i,t ] is the parameter change of sensor i at time t, where ΔT i,t is the temperature change in degrees Celsius; ΔI i,t is the current change in amperes; ΔV i,t is the voltage change in volts. α and β are edge weight adjustment factors, satisfying α + β = 1. |ΔP i,t-ΔP j,t | is the difference in state change between sensors i and j (vector norm).

[0106] Furthermore, in order to reduce noise and computational complexity, a sparse process is used to retain only the k edges with the largest weights (k≤N). The graph after sparse processing is G t =(V,E t ),in Each edge (i,j)∈E t The weight w ij,t After updating, it is used to reflect the real-time dynamic associations between sensors. This thinning step selects the edges with the largest weights by sorting, preserving important topological relationships.

[0107] Furthermore, the generated dynamic topology graph G t =(V,E t )include:

[0108] The node set V represents the sensor location;

[0109] Edge set E t Describe the dynamic relationship between sensors, edge weight w ij,t Reflects the coupling characteristics of component states.

[0110] The dynamic topology graph G obtained by the above steps t It will serve as the input of step 2 and serve the extraction of topological features.

[0111] S2. Extract fault-related spatiotemporal features based on the dynamic topology graph, compress the complexity and form a feature sequence.

[0112] The specific implementation method of this step includes:

[0113] The dynamic topology graph G generated in step 1 t =(V,E t ), extract the spatiotemporal feature embedding vector h of each sensor node i,t The purpose of this step is to use the topology and sensor state changes to capture the dynamic relationship between nodes, while ensuring that the generated embedding vector can express the potential information of the fault. The dynamic feature embedding formula is as follows:

[0114]

[0115] Among them, h i,t is the embedding vector of sensor i at time t, which is used to characterize the fault status of the node. σ(·) is a nonlinear activation function used to limit the embedding range and enhance the nonlinear expression capability of the model. is the neighbor set of node i. ij,tis the edge weight in the dynamic topology graph of step 1, indicating the association strength between sensors i and j. j ,x i ) is the node state difference function, defined as:

[0116] f(x j ,x i )=[|T j -T i |,|I j -I i |,|V j -V i |]

[0117] Among them, T j ,T i is the temperature value of nodes j and i; I j ,I i is the current value; V j ,V i is the voltage value. It is a dynamic change regularization term used to constrain the sensitivity of the embedding vector to drastic changes in weights and is suitable for capturing fast-changing dynamic faults.

[0118] Furthermore, the formula dynamically changes the regularization term λ·|w ij,t -w ij,t-1 |Capture the changes in weights between sensors, which is particularly suitable for sudden fault scenarios in distribution cabinets (such as short circuit or local overheating). Embedding vector h i,t is the dynamic weight w ij,t and node state change f(x j ,x i ) directly reflects the local and global characteristics of the distribution cabinet's operating status.

[0119] Furthermore, the embedding vector h of each node is i,t Aggregate into the global topological feature vector H at time t t , indicating the operating status of the entire distribution cabinet at that moment.

[0120]

[0121] Among them, H t is the global eigenvector at time t, which represents the overall operating status of the distribution cabinet. i The node importance coefficient is assigned a weight based on the physical location or device type of the sensor (e.g., bus duct sensors have a greater weight than corner sensors).

[0122] Furthermore, the polymerization process is carried out by γ iThe introduction of H reflects the importance of different components in the distribution cabinet, for example, focusing on equipment with higher temperature rise (such as circuit breakers). t is for all embedding vectors h i,t The weighted average of , while retaining the global information of the dynamic topology.

[0123] Furthermore, the generated global feature vector H t Regularization is performed to ensure that the feature sequences at different time points have smooth temporal continuity, thus facilitating subsequent time series modeling:

[0124]

[0125] Among them, H′ t is the regularized global eigenvector, which eliminates unnecessary fluctuations in the feature sequence. is the gradient of the feature vector at time t, which is used to capture the drastic changes in the features over time. η is the smoothing factor that controls the strength of feature regularization.

[0126] Furthermore, the time gradient regularization term The introduction of solves the problem of characteristic fluctuation of sensor data in the distribution cabinet caused by noise and improves the stability of the characteristic sequence.

[0127] Furthermore, the regularized global feature vector H′ of all time points t Combined into feature sequences As input for time series modeling:

[0128]

[0129] The output feature sequence fully characterizes the dynamic evolution process of the distribution cabinet operating status, can capture time correlation and potential trends, and provide a basis for the next step of time modeling.

[0130] This step generates From the dynamic topology graph G t The extracted feature sequence directly inherits the output of step 1. Through innovative designs such as dynamic regularization and feature smoothing, the feature sequence is ensured to have good temporal and spatial correlation and stability, making it particularly suitable for fault detection and prediction in the complex and changing environment of power distribution cabinets.

[0131] S3. Based on the feature sequence, a multi-branch time model framework is constructed to predict distribution cabinet failures and perform risk assessment.

[0132] The specific implementation method of this step includes:

[0133] According to the regularized feature sequence output in step 2 Constructing input data suitable for time series modeling. Considering the complexity of the distribution cabinet operating environment and possible abnormal trends, it is necessary to construct more representative and robust input features.

[0134] The input features are divided into two parts:

[0135] Basic global features {H′ t}, indicating the overall operating status of the distribution cabinet at each time point;

[0136] Dynamic residual features {R t}, and capture abnormal trends by comparing state changes at adjacent time points:

[0137] R t =H′ t -H′ t-1

[0138] Among them, R t is the residual eigenvector at time t, which represents the change between the current state and the state at the previous moment and is suitable for capturing the mutation mode caused by the fault. t ,H′ t-1 are the regularized global feature vectors at time t and t-1, respectively.

[0139] The final combined features are:

[0140] X t =[H′ t ,R t ]

[0141] Among them, X t It is the input feature vector at time t, which contains basic features and dynamic residuals, and fully describes the state and changes.

[0142] Furthermore, the input sequence This is handled through a multi-branch temporal modeling framework designed specifically for distribution cabinet fault detection. The framework consists of two parts:

[0143] Long-term trend branch: Captures slowly changing patterns in the operating status of the distribution cabinet and is suitable for gradually evolving faults (such as overtemperature or equipment aging).

[0144] Short-term fluctuation branch: focuses on fast changes and sudden faults (such as short circuits or arcing phenomena).

[0145] The core recursive relationship of the combination model is as follows:

[0146]

[0147] in, Predict the feature sequence for the next k time steps. trend (Xt ) is the long-term trend branch output, used to describe the slow fault trend. short (X t ) is the short-term fluctuation branch output, focusing on fast fault detection.

[0148] Further, detailed calculation:

[0149] Long-term trend branches:

[0150] The long-term trend branch is based on the Temporal Convolutional Network (TCN), which extracts slowly changing patterns of the input sequence:

[0151] F trend (X t )=ReLU(W trend *X t )

[0152] Among them, W trend is the temporal convolution kernel weight, used to extract the temporal pattern of a fixed window. ReLU is a nonlinear activation function used to enhance the sparsity of features.

[0153] Short-term volatility branch:

[0154] The short-term fluctuation branch adopts the Dynamic Residual Network (DRN) structure to strengthen the modeling of residual features:

[0155] F short (X t )=λ·R t +g(W short ·R t )

[0156] Among them, λ is the dynamic amplification factor, which is used to enhance the sensitivity to residual errors; g(W short ·R t ) is a nonlinear activation used to capture complex fluctuation patterns.

[0157] Furthermore, by predicting the future feature sequence Perform multi-task failure risk assessment, including prediction of failure risk level, type, and location: define a risk scoring function and perform risk analysis for each time step:

[0158]

[0159] Among them, Risk t+k is the risk prediction value at time t+k, including the probability distribution of risk level. risk is the weight matrix for risk assessment. is a regularization term used to measure the deviation between the future predicted value and the current state and to strengthen the detection of abnormal changes.

[0160] According to the risk scoring function, the following information is output:

[0161] Potential fault location: Determine specific sensors or devices by combining node characteristics and high-risk scores;

[0162] Fault type: Based on the change pattern of the predicted characteristics, it is classified into overheating, short circuit, arc, etc.

[0163] Occurrence time: Outputs the predicted time range through the high risk score time point.

[0164] Furthermore, the prediction results are output, which include the following two parts:

[0165] Feature prediction sequence for the future time period t+1 to t+k Used for system status trend analysis;

[0166] Multi-task risk assessment results Risk t+1:t+k , providing a basis for the alarm and feedback optimization in step 4.

[0167] S4. Determine whether to trigger an alarm based on the prediction results, and update the dynamic topology map through the maintenance results to form a closed loop.

[0168] The specific implementation method of this step includes:

[0169] Based on the future risk assessment results output in step 3, t+1:t+k , design comprehensive alarm index A t , used to measure the overall failure risk level of the distribution cabinet in the future period. To meet the needs of complex environments, special items are introduced to enhance the sensitivity to potential anomalies. The alarm indicator formula is:

[0170]

[0171] Among them, A t Risk is the comprehensive alarm index at time t. t+k The risk prediction result output in step 3 represents the risk value at the next k moments. alarm is the risk weight vector, which assigns weights to risk levels. It is a dynamic change term used to amplify abnormal values ​​of future feature changes. represents the difference between the future feature prediction and the current state. λ is the amplification factor, which increases the sensitivity to significant changes.

[0172] If A t When the set threshold τ is exceeded, an alarm signal is triggered:

[0173]

[0174] Where Alarm is the alarm signal, 1 indicates an alarm is triggered, and 0 indicates no warning. τ is the alarm threshold, which is dynamically adjusted according to the environment and operation strategy.

[0175] It is understandable that by introducing dynamic changes Strengthen the ability to respond to drastic changes in future conditions. alarm The risk weight design enables the system to pay more attention to high-risk level failures.

[0176] Furthermore, when an alarm is triggered, the system automatically generates an alarm report, which includes:

[0177] Failure type prediction: By analyzing Risk t+k The class probability is used to determine the most likely fault type (such as overheating, short circuit, or arc).

[0178] Fault location prediction: Based on For nodes with higher medium risk scores, locate possible faulty sensors and associated components.

[0179] Risk time range: Provides the time range of high risk in the future [t+k min ,t+k max ] to facilitate maintenance decisions.

[0180] Report format example: Alarm trigger time: tFault type: short circuitFault location: node 5 (bus duct)Predicted risk time range: t+3 ​​to t+6

[0181] Furthermore, after the alarm is triggered, the system's dynamic topology map G is updated in real time. t and model parameters to ensure they adapt to the latest state changes. The specific update steps are as follows:

[0182] Sensor data update: Add new sensor data after collecting alarms {T i,t ,I i,t ,V i,t}, recalculate the edge weight w ij,t , generate the updated topology graph G′ t .

[0183] Risk weight optimization: Risk assessment weight W risk Adjustments have been made to add feedback learning items for recent alarms:

[0184] W′ risk =W risk +η·Δ feedback

[0185] Among them, Δfeedback : The feedback increment calculated based on the deviation between the actual alarm and the predicted result. η: The learning rate, which controls the amplitude of weight adjustment.

[0186] It is understandable that the real-time feedback mechanism improves the system's ability to adapt to environmental changes. feedback , so that the model can be gradually optimized after multiple rounds of running, reducing false positives and missed negatives.

[0187] Furthermore, the updated topology graph is finally outputted and the updated topology graph G′ is t Passed back to step 1 as the basic input for the next round of fault detection, forming a complete closed-loop process.

[0188] Furthermore, the alarm mechanism combines characteristic changes with risk levels to achieve rapid response. Dynamic feedback optimizes and improves the system's long-term adaptability. This closed-loop process ensures the system can adapt to the operating status of the distribution cabinet in real time, achieving the real-time and predictive requirements of the patent.

[0189] like Figure 2 As shown, an embodiment of the present invention further provides a fault detection system for a power distribution cabinet, the system comprising:

[0190] The dynamic topology map construction module 101 is used to generate a dynamic topology map by combining the internal sensor data of the distribution cabinet with its spatial distribution as a global state expression; wherein the internal sensor data of the distribution cabinet includes temperature T i Current I i and voltage V i , i is the sensor number, i=1,2,…,N;

[0191] The spatiotemporal feature construction module 102 is used to extract spatiotemporal features related to faults based on the dynamic topology graph, compress the complexity and form a feature sequence;

[0192] The risk prediction module 103 is used to construct a multi-branch time model framework based on the feature sequence to predict the failure of the distribution cabinet and perform risk assessment;

[0193] The detection, warning and optimization module 104 is used to determine whether to trigger an alarm based on the prediction results and update the dynamic topology map through the maintenance results to form a closed loop;

[0194] The dynamic topology map construction module specifically includes:

[0195] The sensor nodes are organized into a set V = {1, 2, ..., N}, and the initial topology graph G = (V, E) is defined, where E is the connection edge between the sensor nodes, and the weight w of each edge (i, j) ∈ E is ij Initialized as:

[0196]

[0197] Where d(i,j) is the physical distance between sensors i and j; the initial topological graph is used to capture the spatial relationship of nodes;

[0198] Dynamically adjust the edge weight w based on the real-time sensor data collected ij , so that it can reflect the dynamic coupling relationship between components:

[0199] w ij,t =α·w ij +β·|ΔP i,t -ΔP j,t | -1

[0200] Among them, w ij,t is the dynamic weight of edge (i, j) at time t; w ij is the static edge weight at initialization; ΔP i,t is the parameter change of sensor i at time t, α and β are edge weight adjustment factors, satisfying α + β = 1; |ΔP i,t -ΔP j,t | is the difference in state changes between sensors i and j;

[0201] The sparse processing is used to reduce noise and computational complexity, and only the k edges with the largest weight are retained, and k≤N, N is the total number of sensors, then the sparse graph is G t =(V,E t ),in Each edge (i,j)∈E t The weight w ij,t After updating, it is used to reflect the real-time dynamic association between sensors;

[0202] The final generated dynamic topology graph G t =(V,E t )include:

[0203] The node set V represents the sensor location;

[0204] Edge set E t Describe the dynamic relationship between sensors, edge weight w ij,t Reflects the coupling characteristics of component states.

[0205] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0206] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a division of logical functions. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0207] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0208] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A fault detection method for a power distribution cabinet, characterized in that: The method comprises: S1, combine the internal sensor data of the distribution cabinet with its spatial distribution to generate a dynamic topology map as a global state expression; wherein, the internal sensor data of the distribution cabinet includes temperature , current and voltage , i is the sensor number, ; S2. Extract fault-related spatiotemporal features based on the dynamic topology graph, compress the complexity and form a feature sequence; S3. Construct a multi-branch time model framework based on the feature sequence to predict distribution cabinet failures and perform risk assessment; S4. Determine whether to trigger an alarm based on the prediction results, and update the dynamic topology map based on the maintenance results to form a closed loop; Said S1 specifically includes: Group sensor nodes into sets , and define the initial topology ,in is the connection edge between sensor nodes, each edge Weight Initialized as: ； in, It is a sensor and The physical distance between nodes; initialize the topological graph to capture the spatial relationship between nodes; Dynamically adjust edge weights based on real-time sensor data , so that it can reflect the dynamic coupling relationship between components: ； in, It's time Time Edge Dynamic weight of is the static edge weight at initialization; It is a sensor In time The parameter changes, and is the edge weight adjustment factor, satisfying ; For sensors and The difference in state changes; Use sparse processing to reduce noise and computational complexity, and only retain the largest weight edge, and , N is the total number of sensors, then the sparse graph is ,in , each edge Weight After updating, it is used to reflect the real-time dynamic association between sensors; The final dynamic topology diagram include: Node Collection Indicates the sensor position; Edge Set Describe the dynamic relationship between sensors, edge weight Reflects the coupling characteristics of component states; Said S2 specifically includes: According to the generated dynamic topology , extract the spatiotemporal feature embedding vector of each sensor node ; Embed the spatiotemporal features of each sensor node into a vector Aggregation into time The global topological eigenvector of ; The generated global feature vector Perform regularization processing to obtain the global eigenvector after regularization ; The normalized global feature vector of all time points Combined into feature sequences ; The dynamic topology map generated according to , extract the spatiotemporal feature embedding vector of each sensor node , expressed as: ； in, For time Time sensor Embedding vector of is a nonlinear activation function; For nodes The set of neighbors of is the edge weight in the dynamic topology graph; is the node state difference function; is a dynamically changing regularization term; The spatiotemporal features of each sensor node are embedded into the vector Aggregation into time The global topological eigenvector of , expressed as: ； in, For time The global eigenvector of ; is the node importance coefficient; The regularization process is expressed as: ； in, is the global eigenvector after regularization; For time The gradient of the eigenvector; is the smoothing factor; The characteristic sequence Expressed as .

2. A fault detection method for a power distribution cabinet according to claim 1, characterized in that: The sensor In time Parameter changes for ,in, is the temperature change in degrees Celsius; is the change in current, in amperes; is the voltage change in volts.

3. A fault detection method for a power distribution cabinet according to claim 1, characterized in that: The node state difference function is defined as: ； in, For nodes and Temperature value; is the current value; is the voltage value.

4. A fault detection method for a power distribution cabinet according to claim 3, characterized in that: Said S3 specifically includes: According to the global features after regularization Calculate dynamic residual features , combined into the final combined feature For time The input feature vector of , which includes basic features and dynamic residuals, fully describes the state and changes; The final combination feature is Composing input sequence ,Then construct a multi-branch time model framework based on distribution cabinet fault detection for processing,and output the feature prediction sequence; Multi-task fault risk assessment is performed based on feature prediction sequences, including prediction of potential fault location, fault type, and occurrence time.

5. A fault detection method for a power distribution cabinet according to claim 4, characterized in that: The multi-branch time model framework is constructed as follows: Constructing a long-term trend branch and a short-term fluctuation branch; wherein the long-term trend branch is used to capture the slowly changing pattern of the operating state of the power distribution cabinet; the short-term fluctuation branch is used to capture the pattern of rapid changes and sudden failures; The advancement relationship of the multi-branch time model framework is constructed as follows: ； in, For the future feature prediction sequence of time steps; Output of the long-term trend branch, used to describe the slow fault trend; Branch output for short-term fluctuations, focusing on fast fault detection; The multi-task failure risk assessment specifically includes: Define the risk scoring function and perform risk analysis for each time step: ； in, For time The risk prediction value, including the probability distribution of risk levels; is the weight matrix for risk assessment; is a regularization term used to measure the deviation between the future predicted value and the current state and to strengthen the detection of abnormal changes.

6. A fault detection method for a power distribution cabinet according to claim 5, characterized in that: The long-term trend branch extracts the slowly changing pattern of the input sequence based on a temporal convolutional network, which is expressed as: ； in, is the temporal convolution kernel weight, which is used to extract the temporal pattern of a fixed window; It is a nonlinear activation function used to enhance the sparsity of features; The short-term fluctuation branch adopts a dynamic residual network structure to strengthen the modeling of residual features, which can be expressed as: ； in, is the dynamic amplification factor, which is used to enhance the sensitivity to residual errors; It is a nonlinear activation used to capture complex fluctuation patterns.

7. A fault detection method for a power distribution cabinet according to claim 6, characterized in that: Said S4 specifically includes: Design comprehensive alarm indicators based on the risk prediction value , used to measure the overall failure risk level of the distribution cabinet in the future period; wherein the comprehensive alarm index , expressed as: ； in, is the risk weight vector, which assigns weights to risk levels; : Dynamic change term, used to amplify abnormal values ​​of future feature changes; , represents the difference between the future feature prediction and the current state; is the amplification factor, which increases sensitivity to significant changes; like Exceeding the set threshold , triggering the alarm signal: ； in, For alarm signal, Indicates that the alarm is triggered. Indicates no warning; The alarm threshold is dynamically adjusted according to the environment and operation strategy; when the alarm is triggered, an alarm report is automatically generated; wherein, the alarm report includes the fault type prediction, fault location prediction and risk time range; When an alarm is triggered, the system's dynamic topology and the parameters of the multi-branch time model framework are updated in real time to ensure that they adapt to the latest status changes.

8. A system for executing the fault detection method for a power distribution cabinet according to claim 1, characterized in that: The system comprises: Dynamic topology construction module, used to generate dynamic topology by combining the internal sensor data of the distribution cabinet with its spatial distribution as a global state expression; wherein, the internal sensor data of the distribution cabinet includes temperature , current and voltage , i is the sensor number, ; The spatiotemporal feature construction module is used to extract fault-related spatiotemporal features based on the dynamic topology graph, compress the complexity and form a feature sequence; The risk prediction module is used to build a multi-branch time model framework based on the feature sequence to predict the failure of the distribution cabinet and perform risk assessment; The detection, early warning and optimization module is used to determine whether to trigger an alarm based on the prediction results, and to update the dynamic topology map through maintenance results to form a closed loop; The dynamic topology map construction module specifically includes: Group sensor nodes into sets , and define the initial topology ,in is the connection edge between sensor nodes, each edge Weight Initialized as: ； in, It is a sensor and The physical distance between nodes; initialize the topological graph to capture the spatial relationship between nodes; Dynamically adjust edge weights based on real-time sensor data , so that it can reflect the dynamic coupling relationship between components: ； in, It's time Time Edge Dynamic weight of is the static edge weight at initialization; It is a sensor In time The parameter changes, and is the edge weight adjustment factor, satisfying ; For sensors and The difference in state changes; Use sparse processing to reduce noise and computational complexity, and only retain the largest weight edge, and , N is the total number of sensors, then the sparse graph is ,in , each edge Weight After updating, it is used to reflect the real-time dynamic association between sensors; The final dynamic topology diagram include: Node Collection Indicates the sensor position; Edge Set Describe the dynamic relationship between sensors, edge weight Reflects the coupling characteristics of component states.

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