A novel Prairie Dog Optimization Algorithm (PDOA) based MPPT controlling mechanism for grid-PV systems

Abstract

<p>Recently, the solar photovoltaic (PV) systems are increasingly used in many application systems, due to their efficiency and cost-effectiveness. Still, extracting the maximum energy from PV panels under varying climatic conditions is one of the most significant problems. For this purpose, various optimization based MPPT controlling mechanisms are developed in the conventional works for obtaining the maximum energy yield. However, it suffers with the major problems of low convergence, computational burden, high time consumption to reach the optimal solution, and lack of efficiency. Therefore, this research work objects to implement a novel and recently developed optimization technique, named as, Prairie Dog Optimization Algorithm (PDOA) for MPPT controlling. With superior tracking efficiency and enhanced speed, it helps to extract the greatest power from the PV panels. In order to manage the output of PV with less switching stress and loss, a high gain interleaved SEPIC DC-DC converter is also used. A better power quality is ensured by using the voltage source inverter to lower harmonic distortion levels. The significance of the proposed study is to develop a novel optimization based MPPT controlling technique to obtain the maximum energy yield form the PV panels. The PDOA technique is not previously used for power extraction and MPP tracking, due to its intelligent best optimum solutions with high convergence, the proposed work uses the PDOA technique to optimally identify MPP. Also, the voltage boosting and conversion is performed with the use of high gain interleaved SEPIC converter with increased efficiency. Performance study evaluates and compares the simulation outcomes and the efficacy of the suggested regulating topology using a variety of metrics, including IV & PV characteristics, output voltage, output power, THD, grid output, peak time, settling time, and others.</p>