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1

School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China

2

State Grid Zhejiang Electric Power Research Institute, Hangzhou 310014, China





*

Author to whom correspondence should be addressed.



Academic Editor: Jihong Wang

Abstract A suitable model of coordinated control system CCS with high accuracy and simple structure is essential for the design of advanced controllers which can improve the efficiency of the ultra-super-critical USC power plant. Therefore, with the demand of plant performance improvement, an improved T-S fuzzy model identification approach is proposed in this paper. Firstly, the improved entropy cluster algorithm is applied to identify the premise parameters which can automatically determine the cluster numbers and initial cluster centers by introducing the concept of a decision-making constant and threshold. Then, the learning algorithm is used to modify the initial cluster center and a new structure of concluding part is discussed, the incremental data around the cluster center is used to identify the local linear model through a weighted recursive least-square algorithm. Finally, the proposed approach is employed to model the CCS of a 1000 MW USC one-through boiler power plant by using on-site measured data. Simulation results show that the T-S fuzzy model built in this paper is accurate enough to reflect the dynamic performance of CCS and can be treated as a foundation model for the overall optimizing control of the USC power plant. View Full-Text

Keywords: modeling; ultra super-critical power plant; coordinated control system; T-S fuzzy model; performance improvement modeling; ultra super-critical power plant; coordinated control system; T-S fuzzy model; performance improvement





Autor: Guolian Hou 1, Yu Yang 1,* , Zhuo Jiang 1, Quan Li 2 and Jianhua Zhang 1

Fuente: http://mdpi.com/



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