Implementation of ANN for Optimization MPPT Using Zeta Converter

Fossil fuels such as coal, gas and petroleum are non-renewable energy and have a limited amount. This encourages the development of renewable energy as an alternative energy source for electricity generation. One of the renewable energy sources is solar cell or photovoltaic (PV) which utilize solar energy. The problem in the use of PV today is that efficiency is still low at a high cost. One of the efforts to optimize photovoltaic power is to use photovoltaic control devices by applying the Maximum Power Point Tracking (MPPT) technique using the photovoltaic optimization method by imitating human neural networks in processing several conditions and providing solutions from existing reference data. This method is called Artificial Neural Network (ANN). With this method, ANN can reach maximum power from the photovoltaic when there is a change in sunlight intensity which is equipped with a zeta converter to increase or decrease the photovoltaic voltage to supply DC loads. The simulation results show that MPPT-ANN can track power up to 199.95 Watt for a photovoltaic with a capacity of 200 WP with a time of 0.056 seconds, and there is no power oscillation when the MPPT has reached the MPP from the photovoltaic.