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Abstract
Solar energy is the radiant heat and light energy harvested by ultra violet rays to convert into electrical Direct Current (DC). The solar energy stood ahead of other renewable energy as it can produce a constant level of alternating current over the year with minimal harmonic distortions. The renewable energy attracts the energy harvesters as there is rise of deficiency of carbon and reduction of efficiency in thermal energy generation. The concerns associated with the solar power generation are the fluctuation in the generated direct current due to the displacement of sun and deviation in the quantity of solar rays from place to place. This apprehension is overcome by following the technical methods of employing latest technology is determining the optimal position to harvest the solar power at the high rate and forecasting the power generation effectively. This paper proposes a novel hybrid methodology of employing fuzzy based controller to determine the Maximum Power Point Tracking (MPPT) in solar power generation and employing Artificial Intelligence (AI) technology to perform high precision forecasting of power generation. The K-Nearest Neighbor algorithm is a least assumption algorithm is employed in predicting the energy level harvested in the solar Photovoltaic cells. The Artificial Intelligence considers the vital parameters of displacement direction of the sun, temperature, clearness index and humidity in the air. The performance analysis of the proposed methodology is compared with the IEEE standard bus and the prediction is proved to be more precision with a maximum standard deviation of 0.06%.
Highlights
Renewable energy [1] is the thriving and ever green technology which incorporates more innovative technology in recent days for generating clean and non-polluting energy
This paper proposes a novel hybrid methodology of employing fuzzy based controller to determine the Maximum Power Point Tracking (MPPT) in solar power generation and employing Artificial Intelligence (AI) technology to perform high precision forecasting of power generation
Among the different renewable energy generation methodologies, the solar power generation leads the other two methods of power generation as the solar radiation is available throughout the year supported with one time installation cost with minimal maintenance required compared to the other methodologies of wind and tidal power generation
Summary
Renewable energy [1] is the thriving and ever green technology which incorporates more innovative technology in recent days for generating clean and non-polluting energy. The major challenges [6] of the solar power generation are the location of placement of photo voltaic cells to harvest maximum amount of solar radiation. It is not fixed that merely installation of solar photo voltaic cells produce megawatts of electric power but requires a proper identification of location so that to harvest maximum quantity of solar radiations. As the generated power from the solar power system relies on the quantity of the solar radiation, the forecasting of solar incident energy is considered to be more significant for power generation mechanism and for proper load management. Numerous researchers had actively involved in the forecasting process using Artificial Neural Networks (ANN), fuzzy logic and hybrid methodologies for forecasting the solar radiation and the quantity of the output power generation. This research article proposes methodology using K-Nearest Neighbor algorithm [11] of Artificial Intelligence and fuzzy based controller to obtain the Maximum Power Point Tracking process
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