Energy management and intelligent power control of a stand-alone wind energy conversion system with battery storage

This present paper considers a novel strategy for energy management and intelligent power control of a stand-alone electric generation system (EGS). The considered system consists of a wind energy conversion system, which contains a wind turbine with permanent magnet synchronous generator associated with a battery storage system and a direct current (DC) load. According to different climatic changes, load variations, and battery state of charge, the considered EGS is studied as a switched and uncertain nonlinear system. The main control objectives are, first, to regulate the wind power generation to satisfy the required power and second, to maintain the battery state of charge within a certain limits to extend its life cycle. Thus, a strategy of energy management is proposed based on different modes of system operation. Furthermore, a neural network-based integral sliding mode controller (NN-ISMC) is developed, as a robust and intelligent control method, to satisfy the reference power of each operation areas and to improve the robustness and the stability of such stand-alone system under high perturbations and external disturbances. The analytical stability of the proposed approach is demonstrated and assured using Lyapunov function method. In addition, the simulation results, realized using Matlab software, show that the suggested NN-ISMC control strategy ensures faster transient response and smaller steady-state error performances, compared to the other presented methods.

Automated summary

This paper introduces a novel strategy for energy management and intelligent power control of a stand-alone electric generation system using a neural network-based integral sliding mode controller. The proposed approach aims to improve system performance and stability under high perturbations and external disturbances.