Maximum power point neuro-fuzzy tracker for photovoltaic arrays

Every photovoltaic (PV) array has unique point at which maximum power can be generated and extracted for different atmospheric conditions and output load. The maximum power point tracking is therefore critical for the success of PV arrays. All methods proposed earlier suffer from slow response; they oscillate around the maximum power point and ignoring the atmospheric conditions changing. This paper presents an intelligent method of maximum power point tracking for photovoltaic systems. It is based on tracking the maximum power point by monitoring the voltage and current of the solar array and adjusting the duty cycle of the PWM switching signal of a buck-boost DC/DC converter. Both conventional fuzzy logic controller and neuro-fuzzy controller are implemented to evaluate PV system performance. Functional neuro-fuzzy controller has advantages over that of fuzzy logic both in speed and generalization features. Obtained results show that the neuro-fuzzy controller can deal with different load and weather conditions and deliver more power from the photovoltaic systems.