A New Elman Neural Network-Based Control Algorithm for Adjustable-Pitch Variable-Speed Wind-Energy Conversion Systems

This paper presents an improved Elman neural network (IENN)-based algorithm for optimal wind-energy control with maximum power point tracking. An online training IENN controller using back-propagation (BP) learning algorithm with modified particle swarm optimization (MPSO) is designed to allow the pitch adjustment for power regulation. The node connecting weights of the IENN are trained online by BP methodology. MPSO is adopted to adjust the learning rates in the BP process to improve the learning capability. Performance of the proposed ENN with MPSO is verified by many experimental results.