An intelligent maximum power tracking control strategy for wind-driven IG system using MPSO algorithm

This paper presents the design of an on-line training fuzzy neural network (FNN) using back-propagation learning algorithm with modified particle swarm optimization (MPSO) regulating controller for the induction generator (IG). The MPSO is adopted in this study to adapt the learning rates in the back-propagation process of the FNN to improve the learning capability. The proposed output maximization control is achieved without mechanical sensors such as the wind speed or position sensor, and the new control system will deliver maximum electric power with light weight, high efficiency, and high reliability. The estimation of the rotor speed is designed on the basis of the sliding mode control theory.