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Parameter estimation of fuzzy sliding mode controller for hydraulic turbine regulating system based on HICA algorithm

Chen, Zhihuan, Yuan, Xiaohui, Yuan, Yanbin, Lei, Xiaohui, Zhang, Binqiao
Renewable energy 2019 v.133 pp. 551-565
algorithms, dynamic models, electric power, fuzzy logic, water power
Global hydropower growth continues to increase with a relative higher rate of total installed capacity in just last 10 years. This expansion means that it is increasingly important to make a further research with respect to various hydropower stations. As the core of hydropower stations, hydraulic turbine regulating system (HTRS) attracts many attentions. In essence, HTRS is a complex nonlinear system that governs the frequency and the electrical power output of hydroelectric unit. The design of the control laws for the HTRS is an important and difficult task. In this study, a hybrid imperialist competitive algorithm (HICA) for the dynamic model of HTRS system is proposed and applied to estimate the parameters of fuzzy sliding mode controller (FSMC). In the proposed approach, sliding mode controller (SMC) is regarded as a robust control technique to the external uncertain load disturbances and fuzzy logic rule provides a better proportional gain and reduces the inherent chattering effect of the SMC controller. The HICA is developed to search optimal values of the control law and the membership functions of fuzzy logic rules. Simulations are carried out to verify the effectiveness of proposed approach, where the results show that compared with parallel PID controller and conventional SMC controller, the designed FSMC controller performs much better in terms of system performance and chattering reduction. Also, the results certify the superiority of the HICA algorithm in estimating the parameters for the proposed controller of HTRS in comparison to other classical evolutionary algorithms, where HICA reduces the value of the objective function by 3.28%, 5.33% and 9.69% compared with ICA, BSA, and PSO under unload condition, and HICA reduces the value of the objective function by 3.69%, 4.01% and 10.70% compared with ICA, BSA, and PSO under load condition.