Data mining and Statistical Review of Optimization Techniques of Hybrid Renewable Energy Systems
Muhammad Imran Khan

Muhammad Imran Khan, Department of Electrical Engineering, University of Wollongong, Dubai.
Manuscript received on November 18, 2020. | Revised Manuscript received on December 10, 2020. | Manuscript published on December 10, 2021. | PP: 181-192 | Volume-10 Issue-2, December 2020 | Retrieval Number: 100.1/ijitee.B82531210220| DOI: 10.35940/ijitee.B8253.1210220
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Abstract: The renewable energy becomes the second largest source of global electricity worldwide. However, the intermittent nature of RESs (RES)causes great challenges and severed problems regarding system security and reliability. Combining two or more RES in hybrid renewable topologies can overcome these problems and improve the power quality, reliability and increasing the overall system efficiency especially when the combined sources have a complementary nature for each other. For example, Solar Photovoltaic and Wind energy have a complementary nature since they can complement each other in partial failure time and in turn, increases the reliability of the overall system. Optimization techniques are essentially required to optimally coordinate between the combined energy sources, reduce the total system cost, and maximize the extracted power and consequently increasing the total efficiency. Therefore, this paper has a twofold aim which is conducting comprehensive review of the optimization techniques, Software and tools, topologies of hybrid renewable energy systems (HRES)and then applying data mining and statistical calculations to predict the most suitable optimization techniques for a hybrid system composed of Solar PV, Wind Turbine, and battery bank. 
Keywords: Hybrid renewable energy, Photovoltaic, Wind energy, Wind turbine, Data mining, Statistics.
Scope of the Article: Data Mining