Trend prediction for generator DCS signal using mRMR  based neural network

YANG Jiarong, LI Hui, GUO Shuangquan, LV Wei

（Center Academe, Shanghai Electric, Shanghai 200072, China）

Abstract: Aiming at the problem that how to select the network input node reasonably in the process of the trend prediction of the generator signals, an input signal selection criteria method based on maxrelevance & minredundancy(mRMR) was proposed. The characteristic of generator distributed control system(DCS) monitoring data was researched, and the feature subset which had the maximum correlation with the described object and minimun redundancy between feature elements from original feature set was selected as the network input through mRMR, and then the fitting precision of the nonlinear function between input and output of network model could be improved effectively. The results indicate that compares with the accuracy of using neural network to trend prediction directly, the proposed method has high accuracy and good generalization ability, therefore has good engineering applicability.

Key words: distributed control system(DCS); maxrelevance & minredundancy; feature selection; trend prediction; generator