Method for diagnosing short circuit fault of power system power transmission line based on deterministic learning

A short-circuit fault and power system technology, applied in the field of power system transmission line short-circuit fault diagnosis based on deterministic learning, can solve problems such as difficulty in processing high-dimensional data

Inactive Publication Date: 2019-09-10
GUANGDONG UNIV OF TECH
View PDF5 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This makes it difficult for learning methods to handle the lar

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for diagnosing short circuit fault of power system power transmission line based on deterministic learning
  • Method for diagnosing short circuit fault of power system power transmission line based on deterministic learning
  • Method for diagnosing short circuit fault of power system power transmission line based on deterministic learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0077] Such as figure 1 As shown, a power system transmission line short-circuit fault diagnosis method based on deterministic learning includes the following steps:

[0078] S1. Using graph theory to describe the topology of the power system, the graph theory of topology can be expressed as G(V,ε), where is a set of nodes, node i corresponds to the i-th synchronous motor, is a set of edges, (i,j) means that the i-th synchronous motor is connected to the j-th synchronous motor, is a neighboring set, representing the set of synchronous motors connected to the i-th synchronous motor;

[0079] Using the third-order classical dynamic model of the power system, the model of the i-th synchronous motor in the multi-machine power system is as follows:

[0080] Mechanical equation:

[0081] Synchronous motor electrodynamics:

[0082] Electrical equation:

[0083] System input:

[0084] where: δ i is the power angle of the i-th synchronous motor, ω i is the relative ...

Embodiment 2

[0137] In this embodiment, the fault diagnosis is performed on the short circuit of the transmission line of the IEEE16-machine 68-node system,

[0138] S1. Use graph theory to describe the topological structure of IEEE 16-machine 68-node system: G(V,ε) represents the structural diagram of the system, where is a set of nodes, node i corresponds to the i-th synchronous motor, is a set of edges, (i,j) means that the i-th synchronous motor is connected to the j-th synchronous motor. is the neighbor set, which represents the set of synchronous motors connected to the i-th synchronous motor.

[0139] The IEEE 16-machine 68-node system diagram is as follows figure 2 As shown, the third-order classical dynamic model of the power system is adopted, and the model of the i-th synchronous motor in the system is as follows:

[0140]

[0141] Mechanical equation:

[0142] Synchronous motor electrodynamics:

[0143] Electrical equation:

[0144] System input:

[0145] ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a method for diagnosing a short circuit fault of a power system power transmission line based on deterministic learning. The method for diagnosing the short circuit fault of the power system power transmission line based on deterministic learning is specifically carried out according to the following steps that S1, a multi-machine power system model is established; S2, short circuit fault dynamics, fault impact set, and subsystem fault set are defined; S3, the short circuit fault dynamics is learned; S4, a short circuit fault monitor and a residual norm are constructed;S5, a shortcircuit fault rapid diagnostic strategy is designed and used for rapid diagnosis of the shortcircuit fault on the power transmission line. By using the deterministic learning method, it can be ensured that some continuous excitation conditions are met, thus it is ensured that neural network weights converge to the optimal value;by using a local activation operator, only neurons near asystem trajectory can be activated, thus calculated load is greatly reduced,and rapid shortcircuit fault diagnosis can be realizedby using knowledge.

Description

technical field [0001] The invention relates to the field of fault diagnosis of power systems, in particular to a method for diagnosing short-circuit faults of transmission lines in power systems based on definite learning. Background technique [0002] Fast fault diagnosis is of great significance to the safe and reliable operation of power systems. If the faults in the power system are not dealt with in time, it may cause serious consequences, even lead to large-scale power outages. In the past few decades, the problem of power system fault diagnosis has attracted extensive attention. Among them, in order to solve the problem of fault diagnosis of transmission lines, researchers have proposed a variety of technologies, which can be mainly divided into two categories: one method is based on relay protection and circuit breaker action information to identify fault areas and fault components. Diagnosis, such as optimization method, diagnosis method based on Petri net and di...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G01R31/08G01R31/02G06N3/04G06N3/08
CPCG01R31/086G06N3/08G01R31/50G06N3/045Y04S10/50Y04S10/52
Inventor 陈填锐邵俊峰王聪
Owner GUANGDONG UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products