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Abstract
Currently, most wind power is generated by variable-speed wind energy conversion systems (WECSs), in which the rotational speeds of the shafts can be controlled to generate the maximum power. An effective maximum power point tracking (MPPT) control algorithm is an essential part of the control system of a modern variable-speed WECS. The performance of some traditional MPPT methods largely depends on wind speed information obtained from an anemometer. This, however, increases the capital, installation, and operational costs of the WECS. In addition, the measured wind speed may not be accurate for high-performance MPPT control. Therefore, the development of wind speed sensorless MPPT methods is necessary. Moreover, an electromechanical position sensor is commonly used to obtain accurate information of the generator’s rotor position to achieve high-performance control of the WECS. The use of the position sensor not only increases the cost, size, weight, and hardware wiring complexity of the WECS but also causes degradation of the mechanical robustness of the WECS. To overcome these drawbacks, the development of rotor position sensorless control methods for WECSs is required.
In this work, two intelligent wind speed sensorless MPPT algorithms for variable-speed WECSs based on the reinforcement learning (RL) method and a new rotor position estimation algorithm for sensorless vector control of the doubly-fed induction generator (DFIG) are proposed. First, a model-free Q-learning algorithm is designed for the controller of a WECS to learn an optimum speed-power curve online for MPPT control of the WECS. Second, an artificial neural network-based RL MPPT algorithm is developed for permanent magnet synchronous generator (PMSG)-based WECSs. The proposed two MPPT algorithms are applicable for both the DFIG and PMSG-based WECSs. Both algorithms have online learning ability and do not require knowledge of the wind turbine parameters or wind speed. Finally, a new adaptive sliding-mode observer (SMO)-based rotor position estimation algorithm for sensorless vector control of DFIGs is proposed. The proposed method is robust to the machine parameter uncertainty and has satisfactory performances under all the test conditions, which ensures the reliability of the sensorless control of the DFIG-based WECSs connecting to weak power grids.
