Akademik Veri Yönetim Sistemi
Journal of scientific reports-A (Online), sa.050, ss.150-168, 2022 (Hakemli Dergi)
In recent years, interest in solar energy has increased due to the increase in power consumption, the inadequacy of fossil resources and the damage it causes to the environment, as it is a natural energy source and is sustainable. Electricity is generated from solar energy using photovoltaic (PV) panel systems, and PV systems can be easily installed anywhere. In the PV panel systems, the power obtained at the panel output decreases and the efficiency decreases due to geographical conditions, environmental factors and system design. Maximum Power Point (MPP) tracking algorithm is used to obtain maximum output power from the PV panel system and to increase system efficiency. In this study, an Adaptive Network Based Fuzzy Inference System (ANFIS) based MPP tracking algorithm has been developed to obtain maximum power continuously from on-grid double stage 2 kW single phase PV inverter. The ANFIS algorithm uses an adaptive neural network to optimize the parameters of the membership function, and is a combination of artificial intelligence and fuzzy logic. In the algorithm, Direct Quadrant (dq) synchronous reference frame transform is used to generate PWM signals of active switches in on-grid single phase PV inverter. In this algorithm, dq control is performed by converting the grid current and its component obtained by 90° time delay from the stationary axis to the synchronous rotating axis. The algorithm developed based on dq and ANFIS provides the power demanded by the AC grid in a stable and continuous, while following the MPP, increasing the power obtained from the PV panel and inverter, providing maximum efficiency. The validity of the developed algorithm was tested using the Matlab/Simulink simulation program. The comparison simulation results with the PandO algorithm confirm the superiority of the developed ANFIS algorithm.