Hybrid Red Deer with Moth Flame Optimization for Reconfiguration Process on Partially Shaded Photovoltaic Array

Abstract

ABSTRACT While the partial shading operation is observed in the photovoltaic (PV) panel, the solar radiation strikes the PV modules placed in a non-homogeneous PV array. Most of the array reconfiguration approaches for PV arrays use puzzle-based mathematical techniques to relocate the PV modules. While taking size as the parameter, the existing array reconfiguration approach is not a reliable option for efficient shaded dispersion in large-scale sized PV arrays. The main intent of this paper is to implement a novel array reconfiguration model in PV systems using improved an hybrid meta-heuristic algorithm. In the proposed model, the optimal array reconfiguration is attained by a hybrid meta-heuristic algorithm called Red Deer–Moth–Flame Optimization (RD-MFO), which can prove its excellence in providing the optimal PV array. The proposed objective model with best array reconfiguration is focused on a multi-objective function that covers the constraints like maximizing the power, minimizing efficiency, and minimizing shading loss, and other constraints like fill factor, income generation, and mismatch losses. To validate the reconfiguration model, the proposed approach has experimented on a 9 × 9 PV array with four shade patterns. Furthermore, the comparative analysis of attaining the multi-objective function by the proposed RD-MFO over the conventional meta-heuristic algorithm proves the efficiency of the proposed arrangement. While considering the efficiency of the designed RD-MFO-based PV array for case 4 is 16.923% improved than IPM and 19.230% improved than SGDA. Thus, all the computations have been verified with all the methods, and the suggested model gets superior performance in PV array reconfiguration.