An adaptively fast fuzzy fractional order PID control for pumped storage hydro unit using improved gravitational search algorithm. (1st March 2016) - Xu, Yanhe; Zhou, Jianzhong; Xue, Xiaoming; Fu, Wenlong; Zhu, Wenlong; Li, Chaoshun
Highlights: 'S' characteristic of pumped storage hydro unit is considered. Proposing an adaptively fast fuzzy fractional order PID control method. Adding guide vane opening error tracking element for the control method. Establishing a novel pump-turbine model with complete characteristic curves. The bacterial-foraging chemotaxis gravitational search algorithm is proposed. Abstract: With increasing wind or solar farm and pump storage plant integrations, more and more large-scale renewable energy integration systems are taking shape gradually, which brings up challenges to optimized control these systems. Especially, due to the reversible properties of pumped storage hydro unit (PSHU), it is apt to fall into the 'S' characteristic area which can lead to a wide range of unit rotational speed oscillation under no-load condition at middle-low water head. In order to solve these unstable running problems effectively, an adaptively fast fuzzy fractional order PID (AFFFOPID) control method for PSHU is proposed in this paper. A combination of fuzzy logic controller and fractional order PID is adopted by the proposed control method, and error tracking element of PSHU guide vane opening is added to improve the regulation rate. Then, an accurate mathematical model of pump-turbine with complete characteristic curves is established for the PSHU regulating system (PSHURS). Meantime, a novel bacterial-foraging chemotaxis gravitational search algorithm (BCGSA) is used for optimized parameters selection of AFFFOPID method. The BCGSA which is based on the standard gravitational search algorithm accelerates convergence speed with a combination of the Pbest – Gbest -guided strategy and adaptive elastic-ball method. Chemotaxis operator of bacterial foraging algorithm is also added into the BCGSA which is devised to step out the local optimal with a certain probability. Furthermore, simulation tests under different running conditions are utilized to demonstrate the feasibility and robustness of the AFFFOPID. The results are analyzed in detail, which demonstrates the pump-turbine model and the proposed AFFFOPID method are practicable. Comparison with other methods clearly shows that AFFFOPID using BCGSA outperforms the others in most cases.