Neuro-Fuzzy based MPPT for Solar PV Panel Hybrid Cooling System

Abstract

Solar Photovoltaic Panels are conveniently used as an alternative source of energy, nowadays. Most panels have low efficiency due to low energy conversion of photovoltaic cells. The increase in temperature causes deficiency over long period of operation. Cooling systems’ main function is to maintain the operating temperature not to exceed a certain limit. This study provides the comparison of three different setups, which includes the use of a hybrid air-cooling, and water-cooling system and neuro-fuzzy based MPPT charge controller. Experiments are performed at a fixed angle of 15◦ based on location operating simultaneously. Afterwards, data gathered by the current, voltage, temperature and lux sensors are assessed for a cost-benefit analysis. Consequently, the overall efficiency of the three setups were evaluated in consideration with the total costs and losses of each system. The results further showed a significant increase of efficiency for all setups compared to the expected rating of the panel used. Moreover, the outcome shows that the use of water flowing over the front surface with fan cooling at the back while using the neuro-fuzzy based maximum power-point tracking charge controller yields the highest efficiency. The proposed Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT yields a RMSE value of 1.5666e-05.