In load rejection transitional processes in pumped-storage plants (PSPs), the process of closing pump turbines, including guide vane (GVCS) and ball valve closing schemes (BVCS), is crucial for controlling pulsating pressures and water hammer. Extreme pressures generated during the load rejection process may result in fatigue damage to turbines, and cracks or even bursts in the penstocks. In this study, the closing schemes for pump turbine guide vanes and ball valves are optimized to minimize water hammer and pulsating pressures. A model is first developed to simulate water hammer pressures and to estimate pulsating pressures at the spiral case and draft tube of a pump turbine. This is combined with genetic algorithms (GA) or non-dominated sorting genetic algorithm II (NSGA-II) to realize single- or multi-objective optimizations. To increase the applicability of the optimized result to different scenarios, the optimization model is further extended by considering two different load-rejection scenarios: full load-rejection of one pump versus two pump turbines, simultaneously. The fuzzy membership degree method provides the best compromise solution for the attained Pareto solutions set in the multi-objective optimization. Employing these optimization models, robust closing schemes can be developed for guide vanes and ball valves under various design requirements.
Model simulation and control of pumped storage unit (PSU) are essential to improve the dynamic quality of power station. Only under the premise of the PSU models reflecting the actual transient process, the novel control method can be properly applied in the engineering. The contributions of this paper are that (1) a real-time accurate equivalent circuit model (RAECM) of PSU via error compensation is proposed to reconcile the conflict between real-time online simulation and accuracy under various operating conditions, and (2) an adaptive predicted fuzzy PID controller (APFPID) based on RAECM is put forward to overcome the instability of conventional control under no-load conditions with low water head. Respectively, all hydraulic factors in pipeline system are fully considered based on equivalent lumped-circuits theorem. The pretreatment, which consists of improved Suter-transformation and BP neural network, and online simulation method featured by two iterative loops are synthetically proposed to improve the solving accuracy of the pump-turbine. Moreover, the modified formulas for compensating error are derived with variable-spatial discretization to improve the accuracy of the real-time simulation further. The implicit RadauIIA method is verified to be more suitable for PSUGS owing to wider stable domain. Then, APFPID controller is constructed based on the integration of fuzzy PID and the model predictive control. Rolling prediction by RAECM is proposed to replace rolling optimization with its computational speed guaranteed. Finally, the simulation and on-site measurements are compared to prove trustworthy of RAECM under various running conditions. Comparative experiments also indicate that APFPID controller outperforms other controllers in most cases, especially low water head conditions. Satisfying results of RAECM have been achieved in engineering and it provides a novel model reference for PSUGS.
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