RBF neural network based fault diagnosis for the thermodynamic system of a thermal power generating unit

被引：0

作者：

Ma, YG ^{[1
]}

Ma, LY ^{[1
]}

Ma, J ^{[1
]}

机构：

[1] N China Elect Power Univ, Sch Control Sci & Engn, Baoding 071003, Peoples R China

来源：

Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9 | 2005年

关键词：

thermodynamic system; fault diagnosis; radial basis function (RBF); neural network; power station;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a new style radial basis function (RBF) neural network is used for fault diagnosis of the thermodynamic cycle system in a thermal power generating unit. The structure of the RBF network and its training algorithm are discussed. Besides, another important factor to realize neural network based diagnosis, fault symptom calculating methods for different fault symptoms, are discussed in detail. At last, the high-pressure feed-water heater system of a 300MW thermal power generating unit is taken as an example of thermodynamic system fault diagnosis. The fault knowledge library of the system is summarized with the fault symptom calculation method, and the fault diagnosis is further realized based on above RBF neural network.

引用

页码：4738 / 4743

页数：6

共 4 条

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[2] LEONARDJA, 1991, IEEE CONTROL SYS APR, P31
[3] MA LY, 2002, 2002 IEEE PES INT C, V3, P1535
[4] Application of a Radial Basis Function (RBF) Neural Network for fault diagnosis in a HVDC system
Narendra, KG
Sood, VK
Khorasani, K
Patel, R
[J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 1998, 13 (01) : 177 - 183

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