Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions

Abstract

The application of small-scale electrical systems is widespread and the integration of Maximum Power Point Tracking (MPPT) control for Photovoltaic systems with battery applications further enhances the techno-economic feasibility of renewable systems. For this purpose, a novel MPPT control system using Dynamic Group based cooperation optimization (DGBCO) algorithm is utilized for PV systems. The population in the DGBCO is divided into exploration and exploitation groups. Due to effective mathematical modeling, the drawbacks of existing MPPT control techniques are undertaken. The drawbacks of modern MPPT control become prominent under partial shading conditions (PSC) which give rise to power loss, random fluctuations, and slow control action. The DGBCO is implemented using a search and skip mechanism which significantly enhances the performance of the MPPT controller and improves the efficiency of PV systems. The results are compared with recently developed Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CS), DragonFly Optimizer (DFO), and Particle Swarm Optimization (PSO) techniques. The operating conditions case studies include fast varying irradiance and PS with skewed GM. The DGBCO based MPPT control technique is also validated by the experimental setup. The results are compared using statistical and analytical indices such as tracking time, settling time, power tracking efficiency, total energy, RMSE, MAE, and RE. The results show the superior performance of the proposed DGBCO. Relatively, 2%–8% higher energy harvest, and up to 60% faster tracking time helps to achieve up to 99.86% power tracking efficiency in both transient and steady-state control operation. Lower values of statistical metrices i.e. RMSE, MAE, and SR indicate the robustness and effective mathematic modeling of DGBCO for effective MPPT of PV systems under PS conditions.