Abstract
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.
Highlights
System identification is an important branch of modern control theory
To avoid parameter selection and overcome the premature convergence problem, an adaptive fuzzy particle swarm optimization (AFPSO) based on a fuzzy inference system, variable neighborhood search strategy and hybrid evolution is proposed in this paper and applied to the parameter estimation of nonlinear hydro-turbine regulation system (HTRS)
In order to improve the global search ability of PSO and provide an effective mutation mechanism, this paper proposes a hybrid evolutionary strategy that combines the advantages of PSO and firefly algorithm (FA)
Summary
System identification is an important branch of modern control theory. The principle is to find a mathematical model that best fits the dynamic characteristics of a real system in a specified model set according to the input and output data of the system [1]. The heuristic algorithm, known as particle swarm optimization (PSO), was developed a number of years back and has been successfully used to solve various practical problems faced by renewable energy systems, such as parameter estimation, optimization control and energy management [10,11,12,13,14,15,16]. To avoid parameter selection and overcome the premature convergence problem, an adaptive fuzzy particle swarm optimization (AFPSO) based on a fuzzy inference system, variable neighborhood search strategy and hybrid evolution is proposed in this paper and applied to the parameter estimation of nonlinear HTRS. (2) Adopt a comprehensive objective function that considers output error and correlation coefficient to make the parameter estimation more reliable; (3) In order to reflect the dynamic characteristics of a real system, a general simulation model of nonlinear HTRS is established. Performance of thePerformance new algorithm evaluated in Section by comparative studies comparative on the parameter of HTRS underand twoconclusions operating are conditions, the parameterstudies estimation of HTRS underestimation two operating conditions, drawn inand the conclusions final section.are drawn in the final section
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