Optimization based mppt for single diode Pv model and iot based fault detection of Pv systems

Abstract

The utilization of solar Photovoltaic (PV) systems has been increased enormously in the past few decades due to less availability of fossil fuels. Most of the applications based on conventional energy sources are moved towards renewable energy to reduce the cost and save the environment. The performance of the solar PV systems must be predicted through accurate simulation designs before proceeding into a real-time application to avoid errors. The parameter estimation of a single diode model becomes a challenging task, owing to limited data availability in datasheets. However, the solar PV plants face numerous failures and faults due to environmental and internal components. Though internal standards are followed, the faults are unavoidable and undetectable which affects the performance of production plants. So, a suitable fault detection mechanism is essential for a solar PV system to detect fault. In this research work, mathematical model is developed for a single diode solar PV system. After developing the mathematical model it is simulated and analyzed using Matlab/Simulink under different environmental conditions. The performance of the system is enhanced by introducing an optimization based Maximum Power Point Tracking (MPPT) strategy. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), BAT optimization, and Grey Wolf Optimization (GWO) model-based MPPT circuits are designed and the performances are comparatively analyzed. Simulation results identify the nonlinear relationship between current-voltage and power-voltage as characteristic curves for different temperature and irradiance values. Maximum power, MPPT power and Tracking Efficiency (TE) are analyzed to verify the optimization based MPPT system. newline