Maximum wind power tracking for PMSG chaos systems – ADHDP method

• Aiming at the chaotic behavior of PMSG under certain parameters, a method based on ADHDP is proposed to track the maximum wind power point, which can make the system out of chaos and track the maximum power point stably, and the optimal control problems of the complex nonlinear system can be solved effectively.
• Cloud RBF neural network is adopted as the function approximation structure of ADHDP. Cloud model introduces the randomness and fuzziness into new algorithm, at the same time, the new algorithm retains the learning ability and topological relation of RBF neural network.
• This method is realized by using the optimal power–speed curve and the vector control principle, adjusting the stator output voltage to control the electromagnetic and then the wind turbine rotor speed can be operated at the optimal speed that corresponds to the maximum power point, meanwhile, the measurement of wind speed also can be avoided.

Aiming at the chaotic behavior of PMSG under certain parameters, the new ADHDP method based on Cloud RBF neural network is proposed to track the point of maximum wind power, which can make the system out of chaos and track the point of maximum power stably, and the optimal control problems of the complex nonlinear system can be solved effectively. This method is realized by using the optimal power–speed curve and the vector control principle, and by adjusting the stator output voltage to control the electromagnetic torque, then the rotor speed of wind turbine can operate at the optimal speed that corresponds to the point of maximum power, meanwhile, the measure of wind speed also can be avoided. The simulation results show the effectiveness of the proposed method.

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