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

Tan Ding,Nan Zhang,Chaoshun Li,Li Chang,Chen Feng

doi:10.3390/en11092367

Tan Ding, Nan Zhang + Show 3 more

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https://doi.org/10.3390/en11092367

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Journal: Energies	Publication Date: Sep 7, 2018
Citations: 14	License type: CC BY 4.0

Affiliation: Huazhong University of Science and Technology

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.

Highlights

The hydraulic turbine governing system (HTGS) is the core control system of hydroelectric generating units (HGUs), which undertakes the tasks of load and frequency adjustment
Identification methods based on meta-heuristic algorithms have been developed in recent years, which treat the problem of parameter identification as an optimization problem [11]
A hybrid movement strategy is applied on Mixed-Strategy-based Whale Optimization Algorithm (MSWOA), in which a dynamic ratio based on improved JAYA algorithm is applied on the strategy of searching for prey and a chaotic dynamic weight is applied on the strategies of bubble-net attacking and encircling prey

Summary

Introduction

The hydraulic turbine governing system (HTGS) is the core control system of hydroelectric generating units (HGUs), which undertakes the tasks of load and frequency adjustment. The HTGS is a non-linear and non-minimum phase system [1,2,3,4,5,6,7] Traditional identification methods, such as least squares method [8], input response method [9], and maximum likelihood estimate (MLE) [10], have been applied in parameter identification of HTGS in the past years, but there are too many restrictions on these methods. It is difficult to apply these methods in HTGS parameter identification if nonlinearities have been considered To conquer these problems, identification methods based on meta-heuristic algorithms have been developed in recent years, which treat the problem of parameter identification as an optimization problem [11]. Because meta-heuristic algorithms are global optimization methods, Energies 2018, 11, 2367; doi:10.3390/en11092367 www.mdpi.com/journal/energies

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