Distribution-network-device state simulation method and system based on fault coupling analysis

Abstract

The present invention relates to the technical field of intelligent supervision. Disclosed are a distribution-network-device state simulation method and system based on fault coupling analysis. The method comprises: collecting state data of a distribution network device, and pre-processing the collected data; performing analysis and mining on the state data of the distribution network device, and extracting a device fault mode, a coupling relationship and an anomaly; providing a visualized analysis result, and providing real-time monitoring and alarming functions, such that the anomaly of the distribution network device is discovered in a timely manner; and formulating an intelligent scheduling algorithm to search for the specific location of the abnormal distribution network device. The present invention can implement decision-making and planning of anomalies, thereby facilitating the timely discovery of anomalies in distribution networks.

Description

Distribution network equipment state simulation method and system based on fault coupling analysis Technical Field

[0001] The present invention relates to the field of intelligent supervision technology, and in particular to a method and system for simulating the state of distribution network equipment based on fault coupling analysis. Background Art

[0002] The distribution network equipment status simulation system with fault coupling analysis can help power operators and engineers understand and evaluate the impact of equipment failures in the distribution network and formulate corresponding countermeasures. It can simulate and analyze distribution network equipment, help users quickly discover and handle possible faults, and thus improve the safety and stability of the distribution network.

[0003] The existing system can only simulate some basic fault types and cannot meet the grid requirements of complex faults. This will limit users' ability to analyze and evaluate various fault scenarios in the distribution network, and limit users' ability to conduct in-depth research and decision support for complex fault scenarios. It lacks good scalability and flexibility, and has shortcomings such as low data accuracy, simplified models, functional limitations, and poor user experience. These have become major issues to be solved in the construction of urban distribution network informatization. Summary of the Invention

[0004] In view of the problems existing in the above-mentioned existing systems, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to provide a method and system that can discover potential problems, issue early warnings, analyze large amounts of regulatory data, discover abnormal data therein, and provide data-driven decision support for regulatory authorities.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides a distribution network equipment status simulation method based on fault coupling analysis, which includes collecting distribution network equipment status data and preprocessing the collected data; analyzing and mining the distribution network equipment status data to extract equipment failure modes, coupling relationships and abnormal conditions; providing visual analysis results, and providing real-time monitoring and alarm functions to promptly detect abnormal conditions of distribution network equipment; formulating an intelligent scheduling algorithm to find the specific location of abnormal distribution network equipment.

[0008] As a preferred solution of the distribution network equipment status simulation method based on fault coupling analysis described in the present invention, the distribution network equipment status data includes equipment fault information and equipment operating parameters.

[0009] As a preferred solution of the distribution network equipment status simulation method based on fault coupling analysis described in the present invention, the process of collecting distribution network equipment status data is as follows: deploying sensor equipment, including current sensors, voltage sensors, temperature sensors, vibration sensors and GPS positioning devices; collecting data related to distribution network equipment by obtaining relevant data from sensors; the GPS positioning device continuously obtains the location information of distribution network equipment, sends the operation status and location information of distribution network equipment to the monitoring system for processing, cleans and organizes the collected data, and uses the box plot method to calculate the upper and lower quartiles and inner limits to determine the range of outliers. Data outside the range is identified as outliers. The specific calculation steps are as follows: S = Q3-K1*IQR X = Q1-K1*IQR

[0010] Among them, S represents the upper limit of the outlier, X represents the lower limit of the outlier, Q1 represents the lower quartile, Q3 represents the upper quartile, IQR represents the interquartile range, and K1 is a constant used to determine the range of outliers. Outliers outside the range are deleted from the data.

[0011] As a preferred solution of the distribution network equipment status simulation method based on fault coupling analysis described in the present invention, the process of analyzing and mining the distribution network equipment status data is as follows: the distribution network abnormal data prediction model obtains relevant data on the operation status of the distribution network equipment from the processed data, divides the relevant data on the atmospheric pollution situation into a training set and a test set, establishes a prediction model for equipment failure through a linear regression algorithm, and fits the model by minimizing the residual sum of squares. The specific calculation formula is as follows: Y = w0 + w1x1 + w2x2 + ... + wn*xn

[0012] Among them, x1, x2, ..., xn are the input data related to the operation of distribution network equipment, Y represents the output result, w0, w1, ..., wn are the parameters of the model, the trained model is verified through the validation set, and the model is tuned according to the verification results. The model parameters are adjusted by calculating the evaluation index to improve the model performance. The specific calculation method of the evaluation index is as follows:

[0013] Among them, yi is the actual value, Represents the predicted value, n represents the number of samples, and the least squares method is used to minimize the sum of squares of the residuals between the actual output value and the predicted value. The residual is defined and expressed as: Among them, yi represents the actual output value of the i-th sample, represents the predicted value of the sample, and the model parameters w0, w1,...,wn are solved by minimizing the residual sum of squares.

[0014] As a preferred solution of the distribution network equipment state simulation method based on fault coupling analysis of the present invention, the specific calculation steps for solving the model parameters are as follows: for each model parameter w j (j=0,1,…,n) calculates the partial derivative of the residual sum of squares. The specific calculation method is as follows:

[0015] Among them, S represents the residual sum of squares, which represents the sum of squares of the errors between the model prediction value and the actual value, and w j represents the jth parameter in the linear regression model, Represents the partial derivative of the model's output value jth parameter for the i-th data sample; let all partial derivatives be 0 to obtain a set of linear equations; solve the linear equations to obtain the estimated values ​​of the model parameters w0, w1, ..., w n , for new input data, use the trained model to make predictions.

[0016] As a preferred solution of the distribution network equipment state simulation method based on fault coupling analysis of the present invention, the abnormal condition of the distribution network equipment is determined as follows: The abnormal index of the distribution network equipment is specifically calculated as follows: P_j=dy+yz+wz+zy / 4

[0017] Where dy represents the current abnormality index, yz represents the voltage abnormality index, wz represents the temperature abnormality index, and zy represents the vibration abnormality index. The specific calculation method of the fault index is as follows: D = W1*C1+W2*C2+W3*C3+W4*C4

[0018] Among them, W1, W2, W3, and W4 represent the weight coefficients of each anomaly, C1, C2, C3, and C4 represent the degree of different anomalies, and a heat map is used to display the degree of anomaly in the current area.

[0019] As a preferred solution of the distribution network equipment state simulation method based on fault coupling analysis described in the present invention, the process of formulating the intelligent scheduling algorithm includes: converting the distribution network into a power grid topology map and dividing the routes into different areas, using a search algorithm to find the area where the abnormal distribution network equipment is located, and calculating the heuristic function, cost function and evaluation function; the specific calculation method of the heuristic function is as follows: h(n) = |N_Z-M_Z| + |N_H-M_H|

[0020] Among them, N_Z represents the vertical coordinate of a, M_Z represents the vertical coordinate of the target node, N_H represents the horizontal coordinate of a, and M_H represents the horizontal coordinate of the target node;

[0021] The cost function is used to represent the actual cost from the starting node to the current node a. For each node a, the cost function is calculated by accumulating the weights of the routes passed. Let the parent node of node a be p and the weight of the road segment (a, p) be w. Then the cost function is defined as: g(a) = g(p) + w

[0022] Among them, g(a) represents the actual cost from the starting node to the current node a, g(p) represents the actual cost from the starting node to the parent node P, and w represents the weight of the road segment (a, P), which represents the distance from the parent node P to the current node a.

[0023] The evaluation function comprehensively considers the heuristic function and the cost function to evaluate the priority of node a. The evaluation function is defined as: f(a) = g(a) + h(a)

[0024] The evaluation function represents the cost estimate from the starting node to the target node through node a.

[0025] Secondly, in order to further solve the problems existing in the existing system, the embodiment of the present invention provides a distribution network equipment status simulation system based on fault coupling analysis, which includes: a data acquisition module, which is used to collect distribution network equipment status data and pre-process the collected data; a data analysis module, which is used to analyze and mine the distribution network equipment status data, extract equipment failure modes, coupling relationships and abnormal conditions; a visualization module, which is used to provide visual analysis results, and provide real-time monitoring and alarm functions to promptly detect abnormal conditions of distribution network equipment; an intelligent scheduling module, which is used to formulate an intelligent scheduling algorithm and find the specific location of abnormal distribution network equipment.

[0026] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the distribution network equipment status simulation method based on fault coupling analysis as described in the first aspect of the present invention is implemented.

[0027] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the distribution network equipment status simulation method based on fault coupling analysis as described in the first aspect of the present invention.

[0028] The beneficial effects of the present invention are that the present invention monitors the distribution network equipment data, discovers potential problems, issues early warnings, analyzes a large amount of supervision data, can discover abnormal data therein, provide data-driven decision support for the supervision department, can make decisions and plans for abnormal situations, and helps to timely discover the occurrence of abnormal situations in the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0030] FIG1 is a flow chart of a method for simulating the state of distribution network equipment based on fault coupling analysis in Example 1. DETAILED DESCRIPTION

[0031] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0032] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0033] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0034] Example 1

[0035] 1 , which is a first embodiment of the present invention, provides a method for simulating the state of distribution network equipment based on fault coupling analysis, comprising the following steps:

[0036] S1: Collect the status data of distribution network equipment and preprocess the collected data.

[0037] Preferably, the distribution network equipment status data includes equipment fault information and equipment operating parameters.

[0038] Furthermore, the process of collecting the status data of the distribution network equipment is as follows: deploying sensor equipment, including current sensors, voltage sensors, temperature sensors, vibration sensors, and GPS devices;

[0039] Collect data related to distribution network equipment by acquiring relevant data from sensors, install sensors and GPS positioning equipment in the distribution network lines, monitor the current, voltage, temperature and vibration of distribution network equipment, and monitor and evaluate the operation of distribution network equipment;

[0040] The GPS positioning device continuously obtains the location information of the distribution network equipment, sends the operating status and location information of the distribution network equipment to the monitoring system for processing, and cleans and organizes the collected data.

[0041] Preferably, the box plot method is used to calculate the upper and lower quartiles and the inner limit to determine the range of outliers. Data outside the range are identified as outliers. The specific calculation steps are as follows: S = Q3-K1*IQR X = Q1-K1*IQR

[0042] Among them, S represents the upper limit of the outlier, X represents the lower limit of the outlier, Q1 represents the lower quartile, Q3 represents the upper quartile, IQR represents the interquartile range, and K1 is a constant used to determine the range of outliers. Outliers outside the range are deleted from the data.

[0043] S2: Analyze and mine the distribution network equipment status data to extract equipment failure modes, coupling relationships, and abnormal conditions.

[0044] Preferably, the distribution network abnormal data prediction model obtains the relevant data of the distribution network equipment operation from the processed data, divides the relevant data of the atmospheric pollution into a training set and a test set, establishes a prediction model of equipment failure through a linear regression algorithm, and fits the model by minimizing the residual sum of squares. The specific calculation formula is as follows: Y = w0 + w1x1 + w2x2 + ... + wb*xn

[0045] Among them, x1, x2, ..., xn are the input data related to the operation of distribution network equipment, Y represents the output result, w0, w1, ..., wn are the parameters of the model, the trained model is verified through the validation set, and the model is tuned according to the verification results. The model parameters are adjusted by calculating the evaluation index to improve the model performance. The specific calculation method of the evaluation index is as follows:

[0046] Among them, yi is the actual value, Represents the predicted value, n represents the number of samples, and the least squares method is used to minimize the sum of squares of the residuals between the actual output value and the predicted value. The residual is defined and expressed as: Among them, yi represents the actual output value of the i-th sample, represents the predicted value of the sample, and the model parameters w0, w1, ..., wn are solved by minimizing the residual sum of squares. The specific calculation steps for solving the model parameters are as follows:

[0047] S2.1: For each model parameter w j The partial derivative of the residual sum of squares (j=0, 1, ..., n) is calculated as follows:

[0048] Among them, S represents the residual sum of squares, which represents the sum of squares of the errors between the model prediction value and the actual value, and w j represents the jth parameter in the linear regression model, Represents the partial derivative of the model's output value j-th parameter for the i-th data sample.

[0049] S2.2: Setting all partial derivatives to 0, we obtain a set of linear equations:

[0050] S2.3: Solve the linear equations to obtain the estimated values ​​of the model parameters w0,w1,…,w n , for new input data, use the trained model to make predictions.

[0051] S3: Provides visual analysis results, real-time monitoring and alarm functions to promptly detect abnormal conditions of distribution network equipment.

[0052] Preferably, the relevant data of the distribution network equipment operation status is analyzed, the fault and abnormality index of the distribution network equipment in the area is calculated, and the location data of the index calculation results are mapped to the visual map. Specifically, the abnormality index of the distribution network equipment is calculated as follows: P_j=dy+yz+wz+zy / 4

[0053] Among them, dy represents the current abnormality index, yz represents the voltage abnormality index, wz represents the temperature abnormality index, and zy represents the vibration abnormality index.

[0054] The specific calculation method of the fault index is as follows: D=W1*C1+W2*C2+W3*C3+W4*C4

[0055] Among them, W1, W2, W3, and W4 represent the weight coefficients of each abnormality, reflecting the abnormal conditions and influencing factors of the distribution network equipment. C1, C2, C3, and C4 represent the degree of different abnormalities. A heat map is used to display the degree of abnormality in the current area. By updating data in real time, when the fault index D of a certain device exceeds the preset threshold, an early warning of the abnormal situation is issued.

[0056] S4: Develop an intelligent dispatching algorithm to find the specific location of abnormal distribution network equipment.

[0057] Specifically, the distribution network is converted into a power grid topology map, and the routes are divided into different areas. The search algorithm is used to find the area where the abnormal distribution network equipment is located. The heuristic function, cost function and evaluation function are calculated. The specific calculation method of the heuristic function is as follows: h(n)=|N_Z-M_Z|+|N_H-M_H|

[0058] Among them, N_Z represents the vertical coordinate of a, M_Z represents the vertical coordinate of the target node, N_H represents the horizontal coordinate of a, and M_H represents the horizontal coordinate of the target node.

[0059] The cost function is used to represent the actual cost from the starting node to the current node a. For each node a, the cost function is calculated by accumulating the weights of the routes passed. Let the parent node of node a be p and the weight of the road segment (a, p) be w. Then the cost function is defined as: g(a) = g(p) + w

[0060] Among them, g(a) represents the actual cost from the starting node to the current node a, g(p) represents the actual cost from the starting node to the parent node P, and w represents the weight of the road segment (a, P), which represents the distance from the parent node P to the current node a.

[0061] Furthermore, the evaluation function comprehensively considers the heuristic function and the cost function to evaluate the priority of node a. The evaluation function is defined as: f(a) = g(a) + h(a)

[0062] Furthermore, the evaluation function represents the cost estimate from the start node to the target node through node a. The optimal path is determined by creating an open list and a closed list, adding the start node to the open list, setting the g value of the start node to 0, and executing the following loop while the open list is not empty:

[0063] Select the node a with the smallest f value from the open list as the current node; if the current node is the target node, it means that the shortest path has been found and the search ends; remove the current node from the open list and add it to the closed list; for each neighbor node m of the current node, calculate its cost function and evaluation function. If m is already in the closed list, ignore the node; when m is not in the open list, add it to the open list and set the parent node of m to the current node a, and update the q value and f value of m; when m is already in the open list, compare the new g value of m with the current g value. If the new g value is smaller, update the parent node of m to the current node a, and update m's g value and f value; if the current node a is not the target node, remove the current node a from the open list and add it to the closed list. If each neighbor node m of the current node a is already in the closed list, ignore the node. If node m is not in the open list, add node m to the open list and set the current node a as the parent node of node m. If node m is in the open list, compare the calculated new g value with the current g value. If the new g value is smaller, update the parent node of node m to the current node a, and update the g value and f value of node m. Continue to perform the above operations until the target node is found.

[0064] This embodiment also provides a distribution network equipment status simulation system based on fault coupling analysis, including: a data acquisition module, which is used to collect distribution network equipment status data and pre-process the collected data; a data analysis module, which is used to analyze and mine the distribution network equipment status data, extract equipment failure modes, coupling relationships and abnormal conditions; a visualization module, which is used to provide visual analysis results and real-time monitoring and alarm functions to promptly detect abnormal conditions of distribution network equipment; and an intelligent scheduling module, which is used to formulate an intelligent scheduling algorithm and find the specific location of abnormal distribution network equipment.

[0065] This embodiment also provides a computer device, which is suitable for the distribution network equipment status simulation method based on fault coupling analysis, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the distribution network equipment status simulation method based on fault coupling analysis proposed in the above embodiment.

[0066] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0067] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the distribution network equipment state simulation method based on fault coupling analysis proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0068] In summary, the present invention monitors the data of distribution network equipment, discovers potential problems, issues early warnings, and analyzes a large amount of supervisory data to discover abnormal data. This provides data-driven decision support for supervisory departments, enables decision-making and planning for abnormal situations, and helps to promptly detect the occurrence of abnormal situations in the distribution network.

[0069] Example 2

[0070] 1 , which shows a second embodiment of the present invention, comparative data between a distribution network equipment state simulation method based on fault coupling analysis and the prior art are provided to further verify the advancement of the present invention.

[0071] A certain city's power distribution network is complex, encompassing multiple substations, hundreds of distribution rooms, and tens of thousands of power-consuming nodes. Traditional manual inspections, which rely on blind spots, prevent rapid fault location, posing a serious threat to power supply.

[0072] To address this issue, 500 current, voltage, temperature, and vibration sensors were installed on key equipment in the city's main power distribution lines, along with 50 GPS positioning devices. These sensors monitor the equipment's operating status in real time and transmit the data via a 4G network to a monitoring center. The monitoring center then deployed the intelligent monitoring and fault prediction system proposed in this invention.

[0073] The system's data acquisition module preprocesses and cleans the large amount of collected status data. First, a boxplot method is used to detect outliers and remove erroneous data. For example, the temperature data sequence for a power distribution room is [15, 17, 18, 115, 16, 17]. The value 115 is an abnormally high value, which is marked as an anomaly by the boxplot method and automatically filtered out by the system.

[0074] Linear regression is used to establish a fault prediction model. The system collects current, voltage and other data from 100 faulty devices as a training set, and the label indicates whether there is a fault.

[0075] Through iterative optimization using the least squares method, the model parameters were obtained: w0 = 0.2, w1 = 0.5, w2 = -0.1, etc. This model achieved a prediction accuracy of 88%. The monitoring system's visual interface displays a thermal image of each sensor location, with the color representing the equipment's failure index, providing real-time insights into the risk of failure. If the index exceeds the warning threshold, an alarm is automatically triggered to alert the on-duty personnel.

[0076] Compared with traditional manual inspections, the system achieves all-weather automatic monitoring, early warning of fault risks, and the ability to quickly locate faulty equipment, greatly improving the operation and maintenance capabilities of the distribution network and the reliability of power supply, showing great progress.

[0077] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A distribution network equipment status simulation method based on fault coupling analysis is characterized by: include: Collect distribution network equipment status data and pre-process the collected data; Analyze and mine distribution network equipment status data to extract equipment failure modes, coupling relationships, and abnormal conditions; Provides visual analysis results, real-time monitoring and alarm functions to promptly detect abnormal conditions of distribution network equipment; Develop intelligent dispatching algorithms to find the specific locations of abnormal distribution network equipment.

2. The method for simulating distribution network equipment status based on fault coupling analysis according to claim 1, wherein: The distribution network equipment status data includes equipment fault information and equipment operating parameters.

3. The method for simulating distribution network equipment status based on fault coupling analysis according to claim 2, characterized in that: The process of collecting distribution network equipment status data is as follows: Deploy sensor equipment, including current sensors, voltage sensors, temperature sensors, vibration sensors, and GPS positioning equipment; Collect data related to distribution network equipment by acquiring relevant data from sensors; The GPS positioning device continuously obtains the location information of the distribution network equipment, sends the distribution network equipment operation status and location information to the monitoring system for processing, cleans and organizes the collected data, and uses the box plot method to calculate the upper and lower quartiles and inner limits to determine the range of outliers. Data outside this range is identified as an outlier. The specific calculation steps are as follows: S=Q3+K1*IQR X=Q1-K1*IQR Among them, S represents the upper limit of the outlier, X represents the lower limit of the outlier, Q1 represents the lower quartile, Q3 represents the upper quartile, IQR represents the interquartile range, and K1 is a constant used to determine the range of outliers. Outliers outside the range are deleted from the data.

4. The method for simulating distribution network equipment status based on fault coupling analysis according to claim 3, wherein: The process of analyzing and mining the distribution network equipment status data is as follows: The distribution network abnormal data prediction model obtains relevant data on the operation of distribution network equipment from the processed data, divides the relevant data on air pollution into training and test sets, establishes a prediction model for equipment failure through a linear regression algorithm, and fits the model by minimizing the residual sum of squares. The specific calculation formula is as follows: Y=w0+w1x1+w2x2+...+wn*xn Among them, x1, x2, ..., xn are the input data related to the operation of distribution network equipment, Y represents the output result, w0, w1, ..., wn are the parameters of the model, the trained model is verified through the validation set, and the model is tuned according to the verification results. The model parameters are adjusted by calculating the evaluation index to improve the model performance. The specific calculation method of the evaluation index is as follows: Among them, yi is the actual value, Represents the predicted value, n represents the number of samples, and the least squares method is used to minimize the sum of squares of the residuals between the actual output value and the predicted value. The residual is defined and expressed as: Among them, yi represents the actual output value of the i-th sample, represents the predicted value of the sample, and the model parameters w0, w1,...,wn are solved by minimizing the residual sum of squares.

5. The method for simulating distribution network equipment status based on fault coupling analysis according to claim 4, characterized in that: The specific calculation steps for solving the model parameters are as follows: For each model parameter w j The partial derivative of the residual sum of squares (j=0,1,...,n) is calculated as follows: Among them, S represents the residual sum of squares, which represents the sum of squares of the errors between the model prediction value and the actual value, wj represents the jth parameter in the linear regression model, Represents the partial derivative of the model's output value j-th parameter for the i-th data sample; Let all partial derivatives be 0, and we get a set of linear equations; Solve the linear equations to get the estimated values ​​of the model parameters w0,w1,...,w n , for new input data, use the trained model to make predictions.

6. The method for simulating distribution network equipment status based on fault coupling analysis according to claim 5, characterized in that: The abnormal situation of the distribution network equipment is determined as follows: The specific calculation method of the abnormal index of distribution network equipment is as follows: P_j=dy+yz+wz+zy / 4 Among them, dy represents the current abnormality index, yz represents the voltage abnormality index, wz represents the temperature abnormality index, and zy represents the vibration abnormality index; The specific calculation method of the failure index is as follows: DW1*C1+W2*C2+W3*C3+W4*C4 Among them, W1, W2, W3, and W4 represent the weight coefficients of each anomaly, C1, C2, C3, and C4 represent the degree of different anomalies, and a heat map is used to display the degree of anomaly in the current area.

7. The method for simulating distribution network equipment status based on fault coupling analysis according to claim 6, characterized in that: The formulation process of the intelligent scheduling algorithm includes: The distribution network is converted into a power grid topology map and the routes are divided into different areas. The search algorithm is used to find the area where the abnormal distribution network equipment is located, and the heuristic function, cost function and evaluation function are calculated. The specific calculation method of the heuristic function is as follows: h(n)=|N_Z-M_Z|+|N_H-M_H| Among them, N_Z represents the vertical coordinate of a, M_Z represents the vertical coordinate of the target node, N_H represents the horizontal coordinate of a, and M_H represents the horizontal coordinate of the target node; The cost function is used to represent the actual cost from the starting node to the current node a. For each node a, the cost function is calculated by accumulating the weights of the routes passed. Let the parent node of node a be p and the weight of the segment (a, p) be w. Then the cost function is defined as: g(a)=g(p)+w Among them, g(a) represents the actual cost from the starting node to the current node a, g(p) represents the actual cost from the starting node to the parent node P, and w represents the weight of the road segment (a, P), which represents the distance from the parent node P to the current node a. The evaluation function comprehensively considers the heuristic function and the cost function to evaluate the priority of node n. The evaluation function is defined as: f(a)=g(a)+h(a) The evaluation function represents the estimated cost from the starting node to the target node through the node n.

8. A distribution network equipment state simulation system based on fault coupling analysis, based on the distribution network equipment state simulation method based on fault coupling analysis according to any one of claims 1 to 7, characterized in that: include: Data acquisition module, used to collect status data of distribution network equipment and pre-process the collected data; Data analysis module, used to analyze and mine distribution network equipment status data, extract equipment failure modes, coupling relationships and abnormal conditions; The visualization module is used to provide visual analysis results, real-time monitoring and alarm functions, and timely detect abnormal conditions of distribution network equipment; The intelligent dispatching module is used to formulate intelligent dispatching algorithms and find the specific location of abnormal distribution network equipment.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for simulating the state of distribution network equipment based on fault coupling analysis according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for simulating the state of distribution network equipment based on fault coupling analysis according to any one of claims 1 to 7 are implemented.

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