基于六氟化硫气体分解物的高压设备专家诊断系统

Electrical equipment fault diagnosis system based on the gas decomposition

本文采用小波神经网络的专家诊断方法,提出了一种基于六氟化硫气体分解物的高压设备专家诊断系统的思路和方法,并且将良好的软件和硬件协同应用于高压设备专家诊断中,用于分析气体分解物的含量并做出高压设备的故障判断与对故障的预测。介绍专家诊断系统的硬件组成和软件实现方式,并通过与其他业内常用方法的比较进而突出本方法的优势。最后,通过分析一些列实验数据证明专家诊断系统的可靠性和实用性。

A good synergy of hardware and software is used intheelectrical equipment fault diagnosis, and analysis of gas content of decomposition products to judge if the electrical equipment fault happens and to make a fault prediction. In this paper we wil introduce the hardware design method and the detailed design of the software just because of strong electromagnetic interference environment and it is very important to the system design, finaly by giving the experimental data to prove system reliability and practicality.This paper presents electrical equipment fault diagnosis system based on thegasdecomposition, using advanced wavelet neural network fault diagnosis method,and makes an introduction of a method that electrical equipment fault diagnosis system of hardware and software implementation. To prove the superiority of this algorithm, we make the simulation and comparison with others.