Fuzzy modeling and prediction of combustion layers^′ temperature for power plant

Article type: Research Article

Authors: Yang, Guotian | Wang, Xiaowei^{; *} | Wang, Yingnan

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

Correspondence: [*] Corresponding author. Xiaowei Wang, School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China. E-mail: [email protected].

Abstract: This paper develops a fuzzy modeling strategy to study the temperature of different combustion layers in a power plant. First, a new infrared temperature measurement system is developed to measure three layers (bottom, middle and upper) temperature on both sides of the boiler. Then, a fuzzy clustering modeling algorithm is designed based on entropy to determine the structure of the fuzzy model and the corresponding fuzzy memberships of local models. The effect of modeling mismatches are overcome by the use of online identification of parameters. Simulation results show that the effectiveness of the proposed method can be achieved for a 660 MW power plant.

Keywords: Data-driven modeling, combustion layer temperature, multi-model, fuzzy subtractive clustering

DOI: 10.3233/JIFS-210965

Journal: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7081-7091, 2021