Fuzzy Based MPPT and Solar Power Forecasting Using Artificial Intelligence

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%.

ANN for Temperature and Irradiation Prediction and Maximum Power Point Tracking Using MRP-SMC

“…In parallel, ref. [41] used a fuzzy method to find the Maximum Power Point (MPP), while [42] used mathematical models for gradient optimization specifically adapted to photovoltaic panels. Despite their effectiveness, these methods do not address stochastic challenges due to the dynamic nature of the environment.…”

“…For example, as reported in [43], physical and indirect forecasting methods struggle to accurately predict future weather conditions. However, in [41], artificial intelligence emerged as a promising avenue for forecasting.…”