Optimizing Step-Size of Perturb & Observe and Incremental Conductance MPPT Techniques Using PSO for Grid-Tied PV System

A maximum power point tracking (MPPT) technique plays an important role to ensure maximum photovoltaic (PV) output power is extracted under stochastic weather conditions. The research to date tends to focus on developing a standalone optimization MPPT algorithm rather than looking into a hybrid MPPT algorithm. This paper introduces particle swarm optimization (PSO) to optimize the maximum PV output power and to determine the best design variable for penalizing the step size of the conventional methods namely the perturb and observe (PO) and the incremental conductance (IC). With the help of the hybrid MPPT algorithm (PSO+IC and PSO+PO), the step size is no longer fixed, and it is changing according to the solar irradiance. To evaluate the proposed hybrid algorithm, a single-stage grid connected PV system is designed for several different scenarios with various weather conditions. The performance of the hybrid MPPT algorithm and the conventional methods is compared. The results demonstrate that the hybrid MPPT algorithm is remarkably better than the conventional methods, especially for PSO+IC, where it only takes 43.4 ms of tracking time and reaches the efficiency of 99.07% under standard test conditions.

Automated summary

This paper introduces a hybrid MPPT algorithm using particle swarm optimization to optimize the maximum PV output power. The algorithm adjusts the step size according to solar irradiance. The results show that the hybrid algorithm outperforms the conventional methods.