Grey wolf optimization-based fractional MPPT for enhancing performance and efficiency of photovoltaic systems

The photovoltaic (PV) cell is used to transform solar energy into electrical energy which is utilized by us. However, the amount of received solar energy is not enough to meet the needs. In such scenarios, the concept of maximum power point tracking (MPPT) technique is introduced which makes use of controller for maximizing power based on voltage and current conditions. Since conventional MPPT techniques are not adaptive to optimize power from PV system due to slower response time, high ripple factor, and large number of oscillations. Thus, the given paper makes use of a hybrid fractional order proportional integral derivative (FOPID) controller along with grey wolf optimization (GWO) algorithm for tracking the maximum power point in PV cell by simulating the results in MATLAB/Simulink. This assessment included a comparison with existing MPPT techniques such as Perturb and Observe (P&O), Incremental Conductance (IC), fractional short circuit current (FSCC), fractional open circuit voltage (FOCV) and fuzzy logic control (FLC). The simulation results confirmed that the proposed work outperforms the existing traditional MPPT techniques in terms of rise time (m-sec), power output (W) and efficiency (%).