An Adaptive Control Algorithm For Maximum Power Point Tracking for Wind Energy Conversion Systems
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Wind energy systems are being closely studied because of its benefits as an environmentally friendly and renewable source of energy. Because of its unpredictable availability, power management concepts are essential to extract as much power as possible from the wind when it becomes available. The purpose of this thesis is to presents a new adaptive control algorithm for maximum power point tracking (MPPT) in wind energy systems. The proposed control algorithm allows the generator to track the optimal operation points of the wind turbine system under fluctuating wind conditions and the tracking process speeds up over time. This algorithm does not require the knowledge of intangible turbine mechanical characteristics such as its power coefficient curve, power characteristic or torque characteristic. The algorithm uses its memory feature to adapt to any given wind turbine and to infer the optimum rotor speeds for wind speeds that have not occurred before. The proposed algorithm uses a modified version of Hill Climb Search (HCS) and intelligent memory to implement its power management scheme. This algorithm is most suitable for smaller grid or battery connected wind energy systems. PSIM simulation studies have been done to confirm the effectiveness of the proposed algorithm.