Design of intelligent controllers for wind generation system with sensorless maximum wind energy control

This paper presents the design of an on-line training recurrent fuzzy neural network (RFNN) controller with a high-performance model reference adaptive system (MRAS) observer for the sensorless control of a induction generator (IG). The modified particle swarm optimization (MPSO) is adopted in this study to adapt the learning rates in the back-propagation process of the RFNN to improve the learning capability. By using the proposed RFNN controller with MPSO, the IG system can work for stand-alone power application effectively. 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 based on the MRAS control theory. A sensorless vector-control strategy for an IG operating in a grid-connected variable speed wind energy conversion system can be achieved.