Simulation and Experimental validation of Distributed MPPT algorithms for partially shaded Photovoltaic systems

This paper analyzes the performance of two different maximum power point tracking (MPPT) algorithms for photovoltaic (PV) system: artificial neural network (ANN) and adaptive neuro fuzzy inference (ANFIS) as used by an interleaved soft switching boost converter (ISSBC) system with different conditions, such as partially shaded, condition, changing solar insolation and PV cell temperature. However, under partially shaded conditions, when the PV module characteristics get more complex with multiple peaks of output power. Both algorithms are methodically investigated by means of Matlab simulation and hardware experimental validation, compare in terms of parameters tracking speed, power extraction, and harmonic analysis. In this topology, each cascaded H-bridge inverter (CHBMLI) unit is connected to an individual PV module through an interleaved soft switching boost converter (ISSBC). The simulation and hardware results show that ANFIS algorithm is outperforming than the ANN algorithm. Streszczenie. Streszczenie. W artykule analizowane są dwa algorytmy śledzenia maksymalnej mocy (MPPT) stosowane w systemach fotowoltaicznych: jeden (ANN) wykorzystuje sieci neuronowe a drugi wykorzystuje ANFIS. Układ pozwla na załączanie przekształtnika w zależności od warunków, np. zacienienia czy temperatury. Symulacja i eksperymentalna weryfikacja algorytmów śledzenia mocy w systemach fotowoltaicznych.