Dynamic Power Tracking Using Hybrid Firefly-Grey Wolf Optimization Algorithm for Photovoltaic Array under Inhomogeneous Insolation

Due to environmental effects in the form of partial shading, photovoltaic (PV) arrays usually exhibit several power peaks in their power-voltage (PV) characteristic curves. The traditional Maximum PowerPoint Tracking (MPPT) algorithms have trouble finding global maximum powerpoint and tend to become trapped at local maxima, resulting in low efficiency. In order to overcome this, a new hybrid optimization algorithm between FireflyAlgorithm (FA) and GreyWolf Optimization (GWO) is suggested. This hybrid FA-GWO method combines the fast local search of FA and the strong global search of GWO, and can converge faster and have higher tracking performance in partial shading environment. A simplified hill-climbing (HC) strategy is incorporated in the algorithm to further decrease convergence time when it comes to sustained shading patterns. The feasibility of the proposed hybrid MPPT is tested on PV arrays rated 1 kW and 2 kW to show a convergence time of 0.2 sec and a tracking efficacy of 99.5 percent under normal shading configuration. The robustness of the scheme is confirmed in diverse shading conditions and dynamic load variations and it is greater to other hybrid MPPT techniques interms of tracking performance, dynamic performance, and convergence rate.