AN INTELLIGENT OPTIMUM RELATION BASED MPPT CONTROLLER FOR A WIND GENERATOR SYSTEM

Abstract

Abstract: Permanent magnet synchronous generators in wind generation systems are equipped with two sets of converters connected back to back. For maximum power transfer under variable wind speed conditions, the generator side as well as grid side converter reference settings have to be optimized. In this article, the generator side converter speed settings are determined through an adaptive neuro-fuzzy algorithm for extracting maximum power from wind. The algorithm contains two series neuro-fuzzy networks. Wind power and corresponding generator speeds against wind speed are trained by the first network. The second network trains wind speed against optimum generator rotor speeds. An optimum relation based (ORB) tracking algorithm is used to determine the grid side converter (inverter) DC voltage reference settings. Converter DC power and current ratios are compared with pre-computed optimum values. Following any change in the system, the desired DC voltage settings are determined through a simple perturb and observe technique. The MPPT algorithms were tested on the wind generation system model which includes the detailed dynamics of the various components. Studies show that with incorporation of the proposed algorithms, delivery of power to the grid is achievable with maximum possible value of power coefficient. The MPPT techniques using neuro-fuzzy and ORB techniques do not require measurement of wind speed or any system parameter. Keywords Renewable energy, Wind generator, MPPT, ANFIS, PMSG, Optimum rotor speed