Novel fuzzy modeling and energy-saving predictive control of coordinated control system in 1000 MW ultra-supercritical unit. (March 2019)

• Record Type: Journal Article
• Authors: Hou, Guolian; Gong, Linjuan; Huang, Congzhi; Zhang, Jianhua
• Is Part Of: ISA transactions. Volume 86(2019)
• Page Start: 48
• Page End: 61
• Publication Date: 2019-03
• Subjects: Coordinated control system -- Fuzzy modeling -- Quantum artificial bee colony -- Predictive control -- Energy-saving; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2018.10.042
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: In order to satisfy the growing demands of control performance and energy conservation in power generation process, a novel T–S fuzzy modeling method combined with the quantum artificial bee colony (QABC) algorithm is proposed and applied to the coordinated control system (CCS) of ultra-supercritical unit in 1000MW power plant. The T–S fuzzy modeling consists of the identifications of premise part and consequence part. In the premise part identification, the cluster number and initial cluster centers are obtained at first by using entropy-based clustering method. Secondly, the initial cluster centers are modified through QABC algorithm to guarantee the integral of data and avoid possible marginalization. Then, the consequence part is identified through exponentially-weighted least squares. Furthermore, on account of the obtained fuzzy model, an energy-saving predictive control (ESPC) algorithm based on the generalized predictive control is introduced. In the rolling optimization process of ESPC, the values of manipulated variables taken as energy consumption indicator are introduced into objective function to decrease the consumption of energy and improve the performance of control process. Meanwhile, the addition of manipulated variables constraints can obtain further improvements of energy-saving efficiency and control performance. The simulation results demonstrate the high precision of identified model and ideal performance along with energy-saving ability of ESPC. Highlights: A novel T–S fuzzy modeling method combined with the quantum artificial bee colony (QABC) algorithm is proposed to model the coordinated control system (CCS) in ultra-supercritical unit. An energy-saving predictive control (ESPC) algorithm can be directly used based on the identified CCS model. The proposed approach is easy to put into practice and achieve higher identification accuracy. The high performance and strong energy-saving ability of proposed ESPC are verified through simulation results.