A combining condition prediction model and its application in power plant
Yu-Liang Dong
Yu-Jiong Gu
Kun Yang
Wen-Kun Zhang
Dept. of Power Eng., North China Electr. Power Univ., Beijing, China;
Abstract
Aiming at the problem that the equipment in power plant is complex and difficult to predict their conditions accurately, a model of combining condition prediction for equipment in power plant based on grey GM (1,1) model and BP neural network is proposed on the basis of characteristic condition parameters extraction. By fully using the operating parameters, condition monitoring parameters and operation statistic parameters, the conditions of equipment are predicted. Applying the model to the fluid coupling subsystem of a feed-water system, the result shows that this model has high efficiency and precision. The predicted results can be used to provide powerful support in realizing condition-based maintenance.
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