Belachew Desalegn ^* Bimrew Admasu ^*

• Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, Ethiopia

Recent research works are generally underlying that the intermittent characteristics of sustainable energy sources pose great challenges to the efficiency and cost competitiveness of sustainable energy harvesting technologies. Hence, modern sustainable energy systems need to implement a stringent power management strategy to achieve the maximum possible green electricity production with reducing costs. Due to the above-mentioned characteristics of sustainable energy sources, the power management systems have nowadays become increasingly sophisticated. For addressing the analysis, scheduling and control problems of future sustainable power systems, conventional model-based methods are totally inefficient as they fail to handle irregular electric power disturbances in renewable energy generations. Consequently, with the advent of smart grids in the recent years, the power system operators have become to rely on smart metering and advanced sensing devices for gathering more big data. This in turn facilitates the application of advanced machine learning algorithms, which can ultimately cause to generate useful information by learning from massive data without assumptions and simplifications in handling the most irregular operating behaviors of the power systems. This paper aims to explore various application objectives of some machine learning algorithms that primarily apply to wind energy conversion systems. In addition, an enhanced PI (2DoF) algorithm is particularly introduced, and implemented in a DFIG-based WECS to enhance the reliability of power production. A main contribution of this article is to leverage the superior qualities of the PI (2DoF) algorithm for enhanced performance, stability, and robustness of the WECS under uncertainties. Finally, the effectiveness of the study is demonstrated by developing a virtual reality in MATLAB-SIMULINK environment.