A Comparative Study of Maximum Power Tracking of Turbines of Wind Energy Conversion Systems

Wind Energy Conversion System (WECS) is widely attracting the power producers and achieved rapid growth in recent years. Wind turbines are used to produce electricity from wind energy and use it as a substitute for conventional energy sources. Even though, wind power conversion system undergoes challenges which make it less potent compared to non-renewable resources. The challenges include less efficiency and deviation from peak power under varying wind speed. Researchers have tried to relieve these issues to work on the effectiveness and keep up with optimum output power considerably under varying conditions. Hence the idea of Maximum Power Point Tracking (MPPT) arised, yet most of these researchers are discussed on conventional MPPT methods which have many disadvantages including power oscillations and tracking speed. This work introduces a MPPT technique for WECS based on Artificial Neural Network (ANN) along with a DC to DC boost converter. To ensure the effectiveness, the proposed method is differentiated with conventional and perturb and observe method. The system consist of a wind turbine incorporate with a permanent magnet synchronous generator (PMSG), AC to DC diode bridge rectifier and a DC to DC boost converter. Recently, PMSG has been extensively used in wind energy conversion system due to its high performance, low inertia, high torque, better network compatibility and relatively low maintenance costs.