A Maximum Power Point Tracker for Photovoltaic Arrays with Particle Swarm Optimization Technique in cooperation with Fuzzy Cognitive Networks

The ever-increasing demand for low-cost energy and growing concern about environmental issues has generated enormous interest in the utilization of nonconventional energy sources such as the solar energy. The output power of PV arrays is always changing with weather conditions, i.e., solar irradiation and atmospheric temperature. Therefore, to maximize the efficiency of the renewable energy system, it is necessary to track the maximum power point of the PV array. In this paper, a maximum power point tracker using fuzzy set theory is presented to improve energy conversion efficiency. This method is proposed, by using a fuzzy cognitive network, which is in close cooperation with the presented fuzzy controller. The proposed approach is based on the Particle Swarm Optimization method and it is used for the determination of proper weight matrices that lead the Fuzzy Cognitive Map to desired steady states. The method gives a good maximum power operation of any PV. The advantage of the presented method is that the control system can adapt to different changes that might happen during the life cycle of the PV module.