基于BP神经网络的水轮机调速器故障诊断

ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501) Pub Date : 2001-08-18 DOI:10.1109/ICEMS.2001.970680

Yu Xiaohui, Liao Ruijin, Yao Chenguo

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引用次数: 0

摘要

本文利用先进的bp神经网络(backpropagation neural network, BPNN)建立了一种新的水轮机控制系统故障诊断模型，该模型由三层组成:输入层(17个神经元)、隐藏层(13个神经元)和输出层(13个神经元)。实践证明，该系统能够正确识别葛洲坝水电站的故障，并能对调节系统进行故障检测和修复。因此，该诊断系统应在实际中得到广泛应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Fault diagnosis in hydraulic turbine governor based on BP neural network

This paper describes a new fault diagnosis model of the hydraulic turbine governing system with the advanced BPNN (backpropagation neural network), which consists of three layers: i.e. input layer (17 neurons), hidden layer, output layer (13 neurons). It is proved that the system can rind the faults correctly in GeZhouBa hydroelectric power station, and it can conduct the faults examination and repair of governing systems. So this diagnosis system should be applied widely in practice.

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来源期刊

ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)

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