A Mixed-Strategy-Based Whale Optimization Algorithm for Parameter Identification of Hydraulic Turbine Governing Systems with a Delayed Water Hammer Effect

Tan Ding, Li Chang, Chaoshun Li, Chen Feng and Nan Zhang
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Tan Ding: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Li Chang: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Chaoshun Li: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Chen Feng: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Nan Zhang: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2018, vol. 11, issue 9, 1-29

Abstract: For solving the parameter optimization problem of a hydraulic turbine governing system (HTGS) with a delayed water hammer (DWH) effect, a Mixed-Strategy-based Whale Optimization Algorithm (MSWOA) is proposed in this paper, in which three improved strategies are designed and integrated to promote the optimization ability. Firstly, the movement strategies of WOA have been improved to balance the exploration and exploitation. In the improved movement strategies, a dynamic ratio based on improved JAYA algorithm is applied on the strategy of searching for prey and a chaotic dynamic weight is designed for improving the strategies of bubble-net attacking and encircling prey. Secondly, a guidance of the elite’s memory inspired by Particle swarm optimization (PSO) is proposed to lead the movement of the population to accelerate the convergence speed. Thirdly, the mutation strategy based on the sinusoidal chaotic map is employed to avoid prematurity and local optimum points. The proposed MSWOA are compared with six popular meta-heuristic optimization algorithms on 23 benchmark functions in numerical experiments and the results show that the MSWOA has achieved significantly better performance than others. Finally, the MSWOA is applied on parameter identification problem of HTGS with a DWH effect, and the comparative results confirm the effectiveness and identification accuracy of the proposed method.

Keywords: whale optimization algorithm; meta-heuristic; improved JAYA algorithm; chaotic mutation; HTGS; parameter identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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