Design and development of optimized neural network controller models for doubly fed induction generator in wind energy conversion systems

Abstract

Energy is evolved into one of the most prominent and imperative facet of the Blue Planet in the recent decades. Predominant energy production is relied on conventional non-renewable energy resources. However, they cause irregular weather patterns, global warming, environmental pollution and rigorously affect the habitual cyclic of the living organisms on the earth. Hence, an alternative sort of energy resources should be adopted in order to tackle the energy crisis and their associated concerns. In turn, the utilization of renewable energy resources arrived into existence in the energy generation sector. Specifically Wind Energy Conversion System (WECS) paves the way for even more substantial green energy production with an infinite period of time and without threatening the livelihood of the biosphere. Moreover, the technological advancement made in their configurations lead them to operate more reliably and cost-effectively. In wind energy systems, a wound-rotor asynchronous machine namely Doubly-Fed Induction Generator (DFIG) driven by a Variable-Speed Wind Turbine (VSWT) equipped with power electronic converter topology has been a widely accepted configuration for attaining variable-speed wind energy conversion. Although, there is some issues concerning utility grid integration of DFIG based wind energy systems, such as poor power quality, uncertain electric grid voltage, variations in active and reactive power flow and variations in switching operations of power converter topology. In addition, there is still lack in the operation of DFIG system in terms of an advanced performance. Those issues can be addressed by modelling a suitable controller model for DFIG system. Thus, this research work is carried out earnestly and is intended to configure an optimized controller model that is applicable for DFIG based wind energy system driven by VSWT, so as to confer a valuable contribution inthe renewable energy sector newline