The optimization of guide vane closing schemes (OGVCS) of pumped storage hydro units (PSHUs) is a cooperative control and optimal operation research field in renewable energy power generation technology. This paper presents an OGVCS model of PSHUs considering the rise rate of the unit rotational speed, the specific node pressure of each hydraulic unit, as well as various complicated hydraulic and mechanical constraints. The OGVCS model is formulated as a multi-objective optimization problem to optimize conflicting objectives, i.e., unit rotational speed and water hammer pressure criteria. In order to realize an efficient solution of the OGVCS model, an enhanced multi-objective bacterial-foraging chemotaxis gravitational search algorithm (EMOBCGSA) is proposed to solve this problem, which adopts population reconstruction, adaptive selection chemotaxis operator of local searching strategy and elite archive set to efficiently solve the multi-objective problem. In particular a novel constraints-handling strategy with elimination and local search based on violation ranking is used to balance the various hydraulic and mechanical constraints. Finally, simulation cases of complex extreme operating conditions (i.e., load rejection and pump outage) of a 'single tube-double units' type PSHU system are conducted to verify the feasibility and effectiveness of the proposed EMOBCGSA in solving OGVCS problems. The simulation results indicate that the proposed EMOBCGSA can provide a lower rise rate of the unit rotational speed and smaller water hammer pressure than other methods established recently while considering various complex constraints in OGVCS problems.
Accurate parameter identification of pump turbine governing system (PTGS) is of great importance to the precise modeling of pumped storage unit. As PTGS is characterized by uncertainties and strong nonlinear characteristics, it is difficult to identify its parameters. To solve the parameter identification problem for PTGS, an improved backtracking search algorithm (IBSA) is proposed by combining the original BSA with the orthogonal initialization technique, the chaotic local search operator, the elastic boundary processing strategy and the adaptive mutation scale factor. The proposed IBSA algorithm for parameter identification of PTGS was applied on an illustrative example to demonstrate its accuracy and efficiency. The simulation results have shown that IBSA performed better compared with the particle swarm optimization, the gravitational search algorithm and the original BSA in regard to solution quality and parameter identification accuracy.
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