Accurate Parameter Estimation of a Hydro-Turbine Regulation System Using Adaptive Fuzzy Particle Swarm Optimization

Parameter estimation is an important part in the modeling of a hydro-turbine regulation system (HTRS), and the results determine the final accuracy of a model. A hydro-turbine is normally a non-minimum phase system with strong nonlinearity and time-varying parameters. For the parameter estimation of such a nonlinear system, heuristic algorithms are more advantageous than traditional mathematical methods. However, most heuristics based algorithms and their improved versions are not adaptive, which means that the appropriate parameters of an algorithm need to be manually found to keep the algorithm performing optimally in solving similar problems. To solve this problem, an adaptive fuzzy particle swarm optimization (AFPSO) algorithm that dynamically tunes the parameters according to model error is proposed and applied to the parameter estimation of the HTRS. The simulation studies show that the proposed AFPSO contributes to lower model error and higher identification accuracy compared with some traditional heuristic algorithms. Importantly, it avoids a possible deterioration in the performance of an algorithm caused by inappropriate parameter selection.