Transmission line short-circuit fault classification and location method based on summation wavelet-extreme learning machine SW-ELM

Abstract

A transmission line short-circuit fault classification and location method based on a summation wavelet-extreme learning machine SW-ELM includes the following steps: training and test sets are established to train a fault detection and fault diagnosis algorithm; the instantaneous three-phase current difference of a three-phase current difference is collected and input; an input weight and deviation are randomly assigned to the nodes of a sigmoid activation function; the output weight is determined by Moore-Penrose pseudo recurrence and is expressed as y1; if a fault is found, the discrete waveform within a period of the three-phase current difference showing the fault is input in the fault diagnosis step; the initial weight and deviation are input to the nodes of an activated wavelet summation function through a Nguyen-Widrow method; and the output weight is determined by Moore-Penrose pseudo recurrence and is expressed as y2. The method of the invention can be applied to multiple systems, and can complete fault classification and location through only one step. Therefore, the problem that the traditional fault diagnosis method is slow in training and has a tedious training processis solved.

Description

technical field [0001] The invention relates to the technical field of transmission line fault diagnosis, in particular to a short-circuit fault classification and location method of a transmission line based on a wavelet summation extreme learning machine SW-ELM. Background technique [0002] The increasing complexity of power system topology and operation presents challenges for fault diagnosis. More than 80% of power system failures occur on overhead transmission lines. This provides an impetus for more specialized fault diagnosis methods on a single transmission line. The main purpose of the fault diagnosis method is twofold: [0003] 1) Fault classification: the ability to identify short-circuit fault types; [0004] 2) Fault location: the ability to accurately locate the fault point. [0005] Fault detection and diagnosis methods based on artificial intelligence usually need to extract fault features in advance. In the process of diagnosis and evaluation, valuable...

AI Technical Summary

Problems solved by technology

To solve the problems of slow training speed and cumbersome training process of traditional fault diagnosis methods

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