FOA-RBF based high-voltage circuit breaker fault diagnosis method

Abstract

The invention discloses an FOA-RBF based high-voltage circuit breaker fault diagnosis method, which comprises the steps of according to the waveform, which varies along with the time, of closing current of a high-voltage circuit breaker, extracting feature variables, and selecting 24 groups of typical data to act as a feature variable sample; performing pretreatment and normalization on the feature variable sample, carrying out binary coding on six groups of diagnosis types, performing iterative optimization on a spreading parameter spread of an RBF network in the global scope, reserving an optimal spread value, and building an FOA-RBF based fault diagnosis model; selecting sample feature variables to act as input of an FOA-RBF network, enabling the six groups of diagnosis types to act as output of the FOA-RBF neural network, and performing high-voltage circuit breaker fault diagnosis. According to the FOA-RBF based high-voltage circuit breaker fault diagnosis method, an artificial intelligence method of FOA-RBF is applied to fault diagnosis for the high-voltage circuit breaker, and quick and effective detection can be performed on a fault.

Description

technical field [0001] The invention relates to a high-voltage circuit breaker fault diagnosis technology, in particular to a high-voltage circuit breaker fault diagnosis method based on FOA-RBF. Background technique [0002] The high-voltage circuit breaker is a switch device that can turn on or off the high-voltage circuit under normal or fault conditions. It plays a dual role in control and protection in the distribution network system. The quality of its operation directly determines whether the entire power system can normal operation. Therefore, it is of great significance to carry out fault diagnosis on high voltage circuit breakers. [0003] In the past, the maintenance of circuit breakers relied on the regular outage maintenance system. This method is blind, not targeted, time-consuming and labor-intensive, and causes large-scale power outages. The fault types of high-voltage circuit breakers are varied, and there are complex and diverse nonlinear relationships be...

AI Technical Summary

Problems solved by technology

[0018] The extended parameter spread is an important factor affecting the performance of the RBF neural network. The larger the spread, the smoother the function fitting, but the approximation error will become larger; the smaller the spread, the more accurate the function approximation will be, but the approximation process will not be smooth and the network performance will be poor. , there will be adaptation

In the existing RBF neural network-based circuit breaker fault diagnosis method, different spread preset values ​​are usually selected for trial, which is time-consuming and laborious, and sometimes the desired effect cannot be achieved

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