MATHEMATICAL MODELLING AND OPTIMIZATION OF POLY-CRYSTALLINE PHOTOVOLTAIC MODULE

This work proposes modeling and optimization of poly-crystalline photovoltaic (PV) modules, validated with experiment, using single diode (SDM) and double diode model(DDM). The PV cell is treated as an equivalent electrical circuit with series and shunt resistance. The weather data like temperature and irradiance are used as input variables. The operating current and maximum power point are obtained using three variables: current, voltage, and power. An experiment was set up in Nagpur city, on the Deccan plateau situated in central India. This place is suited for PV installation due to the high average solar insolation period throughout the year. The outputs of the PV model are optimized using a genetic algorithm (GA) and simulated annealing (SA) algorithm and validated against the experiments with variable load conditions. The metaheuristics optimization techniques worked well for this model and improved the accuracy and precision of the current-voltage (I-V) and power voltage (P-V) curves. The present work compares the errors of the SA and GA algorithms used for extracting the five key points of (I -V) curves using the normalized sum of squared errors (NSSE). The proposed model optimization results are in close agreement with experimental results.