Title: Parameter optimisation research for hydraulic turbine regulating system based on CGABC algorithm

Authors: Xiaoxia Tan; Jing Chen; Gonggui Chen

Addresses: Chongqing Key Laboratory of Complex Systems and Bionic Control, Chongqing University of Posts and Telecommunications, Ministry of Education, Chongqing 400065, China ' Chongqing Key Laboratory of Complex Systems and Bionic Control, Chongqing University of Posts and Telecommunications, Ministry of Education, Chongqing 400065, China ' Key Laboratory of Network control & Intelligent Instrument, Chongqing Key Laboratory of Complex Systems and Bionic Control, Chongqing University of Posts and Telecommunications, Ministry of Education, Chongqing 400065, China

Abstract: Artificial bee colony (ABC) algorithm is regularly taken in the parameter optimisation of the hydraulic turbine regulating system (HTRS). The chaotic and global artificial bee colony (CGABC) algorithm is proposed to overcome the slow convergence speed and low convergence precision that existed in standard ABC algorithm. Among them, global optimal is used to enhance the global exploration ability, and chaos optimisation is adopted to increase the diversity of bee colony. The simulation results show that compared with fuzzy strategy, ZN, DE and ABC algorithm, the proposed CGABC algorithm significantly improves the dynamic transition process of HTRS. Simultaneously, the simulation experiment is carried out on HTRS with different governor parameters and controlled parameters when it is under the frequency and load disturbance condition. The results show that the dynamic characteristics and robustness is determined by the parameter selection, and the appropriate combination of parameters allows HTRS to achieve optimal dynamic performance and robustness.

Keywords: HTRS; hydraulic turbine regulating system; CGABC algorithm; parameter optimisation; frequency and load disturbance condition; dynamic performance; robustness.

DOI: 10.1504/IJSCIP.2020.114253

International Journal of System Control and Information Processing, 2020 Vol.3 No.2, pp.93 - 113

Received: 05 Dec 2019
Accepted: 05 Dec 2019

Published online: 28 Mar 2021 *

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