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    A study of deep neural network controller based power quality improvement of hybrid pv/wind systems by using smart inverter
    (Altınbaş Üniversitesi, 2021) Ab-Belkhir, Adel; Bayat, Oğuz
    Nowadays, the world is witnessing uncontrollable changes, such as global warming and climate change because of excessive fossil fuel utilization for transportation and power generation; therefore, the developed world is mainly concentrating on alternative resource development; for which, they have taken major research and development initiatives. Generally, certain alternative power generation sources, including wind, solar, and hydropower are not detrimental to nature. For this reason, solar and wind power have been declared as useful alternative energy resources, and besides, they are abundant. This thesis shows the investigation of the performances of wind energy systems and photovoltaic cells when weather conditions keep on changing. The findings of this research provide the basis for developing an advanced intelligent control system to maximize power generation. For renewable energy sources, the MPPT controller is essential because weather conditions are mostly unpredictable. This thesis has been written with a major objective to suggest a new algorithm, which is based on deep neural network (DNN), and to apply it for maximum power point tracking (MPPT). MATLAB was used to simulate this project for wind-based power generation systems and photovoltaic (PV) cells. An advanced DNN controller was developed for reducing the THD value and improving the output power quality of a microgrid-integrated hybrid wind/PV power generation system. The performance of the proposed system was tested and analyzed in several possible operating conditions using MATLAB to assure its functionality. In the end, we also analyzed the results of simulations using the IEEE 1547 standard.