Power grid fault diagnosis method

Abstract

The invention discloses a power grid fault diagnosis method, which belongs to the field of power grid diagnosis and combines a topological structure and equipment configuration of a power grid, constructs a dynamic fault tree model and dynamically adjusts node states and logic relations in the model so as to realize rapid detection and accurate positioning of faults. The method mainly comprises the following steps: data acquisition: collecting real-time operation data from each monitoring point of a power grid; data preprocessing: cleaning and standardizing the collected data; constructing a static fault tree: constructing a static fault tree model according to the topological structure and equipment configuration of the power grid; dynamically updating the fault tree: dynamically adjusting a fault tree model in combination with real-time operation data; fault mode identification: identifying a potential fault mode through logical operation by using the updated fault tree model; fault positioning and diagnosis: based on the identified fault mode, determining the position and the type of the fault; and fault response: according to the diagnosis result. The method is mainly used for power grid fault diagnosis.

Description

Technical Field

[0001] The present invention relates to the field of power grid diagnosis, and in particular to a power grid fault diagnosis method. Background Art

[0002] As an important infrastructure in modern society, the stability and reliability of the power grid directly affect all aspects of the national economy and social life. With the increasing complexity and large-scale of power systems, the frequency and scope of power grid failures are also increasing. Therefore, efficient power grid fault detection and diagnosis technology has become particularly important. Traditional fault detection methods mainly rely on manual inspections and simple automation systems, which have problems such as low detection accuracy, slow response speed, and high false alarm rate, and are difficult to meet the high requirements of modern power grids for fault detection. Traditional fault detection methods rely on manpower, are inefficient, and are prone to omissions; statistical-based methods have high requirements on data quality and are not effective when processing nonlinear and non-Gaussian distributed data. Summary of the invention

[0003] The purpose of the present invention is to provide a power grid fault diagnosis method, which combines the topological structure and equipment configuration of the power grid to construct a dynamic fault tree model, dynamically adjust the node status and logical relationship in the model, so as to achieve rapid detection and accurate positioning of faults.

[0004] The present invention is achieved through the following technical solutions:

[0005] A method for diagnosing power grid faults comprises the following steps:

[0006] a) Data collection: collect real-time operation data from various monitoring points of the power grid;

[0007] b) Data preprocessing: cleaning and standardizing the collected data;

[0008] c) Construct a static fault tree: Construct a static fault tree model based on the topology of the power grid and the equipment configuration;

[0009] d) Dynamically update the fault tree: dynamically adjust the fault tree model based on real-time operation data;

[0010] e) Fault mode identification: using the updated fault tree model, potential fault modes are identified through logical operations;

[0011] f) Fault location and diagnosis: Based on the identified fault mode, determine the location and type of fault;

[0012] g) Fault response: Take appropriate measures to troubleshoot or isolate the fault based on the diagnosis results.

[0013] Furthermore, the data collection also includes obtaining the voltage, current and temperature physical parameters of the power grid through sensors and remote terminal units (RTUs).

[0014] Furthermore, the data cleaning includes filling missing data with interpolation, using statistical methods or isolation forests to detect and remove outliers; standardization includes converting the data into a standard normal distribution with a mean of 0 and a variance of 1.

[0015] Furthermore, the construction of the static fault tree includes collecting the topological structure of the power grid: obtaining the electrical connection diagram of the power grid, including the connection relationship between the substation, transmission line, and transformer equipment; defining fault nodes: determining the key nodes in the power grid where faults may occur; and constructing logical relationships: using logic gates to represent the logical relationship between different fault nodes to construct the basic structure of the fault tree.

[0016] Furthermore, the dynamic update of the fault tree includes real-time data acquisition: continuously collecting real-time operation data from various monitoring points of the power grid, including voltage, current, and frequency parameters; data synchronization: synchronizing the real-time data with the node status in the fault tree model and updating the node status information; dynamically adjusting the logical relationship: dynamically adjusting the relationship between the logic gates in the fault tree according to the changes in the real-time data. If a circuit breaker is already in a fault state, the corresponding logic gate state is set to "fault".

[0017] Furthermore, the fault mode identification includes using Boolean algebra to analyze the state changes of each node in the fault tree model; extracting the mode that may cause the fault based on the result of the logical operation; recording the occurrence conditions and impact range of each fault mode; matching the extracted fault mode with a known fault mode library to confirm whether there is a known fault mode.

[0018] Furthermore, the fault mode identification includes determining the specific location of the fault according to the characteristics of the fault mode and the topological structure of the power grid; analyzing the characteristics of the fault mode and judging the type of the fault; and generating a detailed fault diagnosis report, such as the fault location, fault type, occurrence time, and impact range information.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] 1. Dynamic fault tree can analyze the operating status of the power grid from multiple dimensions, identify potential fault modes, and improve the accuracy of fault detection.

[0021] 2. Update the fault tree model through real-time data to detect faults in time and avoid false alarms and missed alarms.

[0022] 3. Fault pattern recognition based on dynamic fault tree can quickly determine the location and type of fault and shorten the fault response time.

[0023] 4. Reduce operating costs through high-precision fault detection, reduce false alarms and unnecessary maintenance work. DETAILED DESCRIPTION

[0024] The present invention is further described below.

[0025] Embodiment: a method for diagnosing a power grid fault comprises the following steps:

[0026] a) Data collection: collect real-time operation data from various monitoring points of the power grid;

[0027] b) Data preprocessing: cleaning and standardizing the collected data;

[0028] c) Construct a static fault tree: Construct a static fault tree model based on the topology of the power grid and the equipment configuration;

[0029] d) Dynamically update the fault tree: dynamically adjust the fault tree model based on real-time operation data;

[0030] e) Fault mode identification: using the updated fault tree model, potential fault modes are identified through logical operations;

[0031] f) Fault location and diagnosis: Based on the identified fault mode, determine the location and type of fault;

[0032] g) Fault response: Take appropriate measures to troubleshoot or isolate the fault based on the diagnosis results.

[0033] The data acquisition also includes obtaining the voltage, current and temperature physical parameters of the power grid through sensors and remote terminal units (RTUs);

[0034] The data cleaning includes filling missing data with interpolation, using statistical methods or isolation forests to detect and remove outliers; standardization includes converting the data into a standard normal distribution with a mean of 0 and a variance of 1;

[0035] The construction of the static fault tree includes collecting the topological structure of the power grid: obtaining the electrical connection diagram of the power grid, including the connection relationship between the substation, the transmission line, and the transformer equipment; defining the fault nodes: determining the key nodes in the power grid where faults may occur; constructing the logical relationship: using logic gates to represent the logical relationship between different fault nodes, and constructing the basic structure of the fault tree;

[0036] The dynamic updating fault tree includes real-time data acquisition: continuously collecting real-time operation data from various monitoring points of the power grid, including voltage, current, and frequency parameters; data synchronization: synchronizing the real-time data with the node status in the fault tree model and updating the node status information; dynamically adjusting the logical relationship: dynamically adjusting the relationship between the logic gates in the fault tree according to the changes in real-time data. If a circuit breaker is already in a fault state, the corresponding logic gate state is set to "fault";

[0037] The fault mode identification includes using Boolean algebra to analyze the state changes of each node in the fault tree model; extracting the mode that may cause the fault according to the result of the logical operation; recording the occurrence conditions and impact range of each fault mode; matching the extracted fault mode with the known fault mode library to confirm whether there is a known fault mode;

[0038] The fault mode identification includes determining the specific location of the fault according to the characteristics of the fault mode and the topological structure of the power grid; analyzing the characteristics of the fault mode and judging the type of the fault; and generating a detailed fault diagnosis report, such as the fault location, fault type, occurrence time, and impact range information.

Claims

1. A power grid fault diagnosis method, characterized in that: The steps include: a) Data collection: collect real-time operation data from various monitoring points of the power grid; b) Data preprocessing: cleaning and standardizing the collected data; c) Construct a static fault tree: Construct a static fault tree model based on the topology of the power grid and the equipment configuration; d) Dynamically update the fault tree: dynamically adjust the fault tree model based on real-time operation data; e) Fault mode identification: using the updated fault tree model, potential fault modes are identified through logical operations; f) Fault location and diagnosis: Based on the identified fault mode, determine the location and type of fault; g) Fault response: Take appropriate measures to troubleshoot or isolate the fault based on the diagnosis results.

2. The power grid fault diagnosis method according to claim 1, characterized in that: The data acquisition also includes obtaining the voltage, current and temperature physical parameters of the power grid through sensors and remote terminal units (RTUs).

3. The power grid fault diagnosis method according to claim 1, characterized in that: The cleaning of the collected data includes filling the missing data with interpolation method, using statistical methods or isolation forest to detect and remove outliers; the standardization processing includes converting the data into a standard normal distribution with a mean of 0 and a variance of 1.

4. The power grid fault diagnosis method according to claim 1, characterized in that: The construction of the static fault tree includes collecting the topological structure of the power grid: obtaining the electrical connection diagram of the power grid, including the connection relationship between the substation, the transmission line, and the transformer equipment; Define fault nodes: determine the key nodes in the power grid where faults may occur; build logical relationships: use logic gates to represent the logical relationships between different fault nodes and build the basic structure of the fault tree.

5. The power grid fault diagnosis method according to claim 1, characterized in that: The dynamic update fault tree includes real-time data acquisition: continuously collecting real-time operation data from various monitoring points of the power grid, including voltage, current, and frequency parameters; data synchronization: synchronizing the real-time data with the node status in the fault tree model and updating the node status information; dynamically adjusting the logical relationship: dynamically adjusting the relationship between the logic gates in the fault tree according to the changes in real-time data. If a circuit breaker is already in a fault state, the corresponding logic gate state is set to "fault".

6. The power grid fault diagnosis method according to claim 1, characterized in that: The fault mode identification includes using Boolean algebra to analyze the state changes of each node in the fault tree model; extracting the mode that may cause the fault based on the result of logical operation; recording the occurrence conditions and impact range of each fault mode; matching the extracted fault mode with the known fault mode library to confirm whether there is a known fault mode.

7. The power grid fault diagnosis method according to claim 1, characterized in that: The fault mode identification includes determining the specific location of the fault according to the characteristics of the fault mode and the topological structure of the power grid; analyzing the characteristics of the fault mode and determining the type of the fault; Generate detailed fault diagnosis reports, such as fault location, fault type, occurrence time, and impact range information.