A neural fuzzy based maximum power point tracker for a photovoltaic system

The global electrical energy consumption is steadily rising and therefore there is need to increase the power generation capacity. The required capacity increase can be based on renewable energy. Photovoltaic energy remains a largely unexploited renewable energy source due to low conversion efficiency of the photovoltaic modules. To maximize the power derived from the PV systems it is important to operate the panel at its optimal power point by use of a maximum power point tracker (MPPT). MPPTs find and maintain operation at the maximum power point, using an MPPT algorithm. This paper presents a high performance tracking of maximum power delivered from photovoltaic systems using adaptive neural fuzzy inference systems (ANFIS). This method combines the learning abilities of artificial neural networks and the ability of fuzzy logic to handle imprecise data. It is therefore able to handle non linear and time varying problems hence making it suitable for this work. It is expected that this method will be able to accurately track the maximum power point. This will ensure efficient use of PV systems and therefore leading to reduced cost of electricity. The performance of the proposed method was compared to that of a fuzzy logic based MPPT to demonstrate its effectiveness over other previously used MPPT techniques.