NDLI: Online local learning soft-sensing method for ball mill load of power plant

Content Provider	IEEE Xplore Digital Library
Author	Wang Wei Chang Hao Zhang Hang Luo Dayong
Copyright Year	2012
Description	Author affiliation: School of Information Science and Engineering, Central South University, Changsha 410083 (Zhang Hang; Luo Dayong) \|\| Huadian Electric Power Research Institute, Hangzhou 310030 (Wang Wei; Chang Hao)
Abstract	Based on the fact that the ball mill load in power plant is hard to detect effectively, an online local learning improved weighted least square support vector machine (WLSSVM) soft-sensing method is proposed form improving the prediction accuracy and adaptive ability of the soft-sensing model. Firstly, a similarity measurement criterion of data samples based on the samples distance and trend information is designed, and the modeling neighborhood dataset for local model is obtained by using two-step search strategy. Secondly, for the purpose of improving the prediction performance of the local model, an improved WLSSVM local model which can reflect the similarity and the error information of the modeling data is proposed, and the parameters of the local model is optimized by using improved differential evolution algorithm with mutation operation. Thirdly, in order to control the data size of the historical database, the active update strategy is adopted to realize the selective preservation of the new data. Finally, the simulation experiments are carried out based on the actual operation data of the coal pulverizing system. Simulation results show that compared with the standard LSSVM model, the proposed soft-sensing model has better prediction accurate and can predict the power plant ball mill load effectively.
Starting Page	6994
Ending Page	6999
File Size	297509
Page Count	6
File Format	PDF
ISBN	9781467325813
ISSN	21612927
e-ISBN	9789881563811
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	Adaptation models Online Local Learning Predictive models Samples Distance and Trend Information Support vector machines Improved Weighted Least Square Support Vector Machine Active Update Strategy Data models Silicon Ball Mill Load Improved Differential Evolution Algorithm Power generation Load modeling
Content Type	Text
Resource Type	Article

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