Developing an Improved ANN Algorithm Assisted by a Colony of Foraging Ants for MPP Tracking of Grid Interactive Solar Powered Arc Welding Machine

In this article, a metaheuristic optimized multilayer Feed-forward Artificial Neural Network (ANN) controller is proposed to extract the maximum power from available solar energy for a three-phase shunt active power filter (APF) grid connected photovoltaic (PV) system supplying an arc welding machine. Firstly, in order to improve the maximum power point (MPP) delivered by PV arrays and to overcome the drawbacks in the Incremental Conductance (INC) method, a hybrid MPPT controller is designed. The proposed approach abbreviated as ANN-ACO MPPT controller is based on an ant colony optimization (ACO) algorithm which is useful to train the developed ANN and to evolve the connection weights and biases to get the optimal values of duty cycle converter corresponding to the MPP of PV array. Secondly, aiming to meet the various grid requirements such as power quality (PQ) improvement, distortion free signals etc., a shunt APF is utilized, and a direct power control (DPC) is designed for distributing the solar energy between the DC-link capacitor, arc welding machine and the AC grid. Finally, the performance of proposed control system is confirmed by simulation tests on a 12.2 kW PV system. The simulation results demonstrate that the deigned ANN-ACO MPPT controller can provide a better MPP tracking with a faster speed and a high robustness with a minimal steady-state oscillation than those obtained with the conventional INC method.