Maximum wind power tracking based on cloud RBF neural network

Zhong-Qiang Wu, Wen-Jing Jia, Li-Ru Zhao and Chang-Han Wu

Renewable Energy, 2016, vol. 86, issue C, 466-472

Abstract: Based on the mathematical model of Permanent magnet synchronous generator (PMSG), maximum wind power tracking control strategy without wind speed detection is analyzed and a controller based on cloud RBF neural network and approximate dynamic programming is designed to track the maximum wind power point. Optimal power-speed curve and vector control principles are used to control the electromagnetic torque by approximate dynamic programming controller to adjust the voltage of stator, so the speed of wind turbine can be operated at the optimal speed corresponding to the best power point. Cloud RBF neural network is adopted as the function approximation structure of approximate dynamic programming, and it has the advantage of the fuzziness and randomness of cloud model. Simulation results show that the method can solve the optimal control problem of complex nonlinear system such as wind generation and track the maximum wind power point accurately.

Keywords: Maximum wind power point; Cloud model; RBF neural network; Approximate dynamic programming (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:86:y:2016:i:c:p:466-472

DOI: 10.1016/j.renene.2015.08.039

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