A novel meta-heuristic optimization algorithm based MPPT control technique for PV systems under complex partial shading condition

Abstract

Abstract The need to combat the increase in global warming is well taken by solar energy lead renewable energy resources. The techno-economic feasibility of solar systems in the form of photovoltaic (PV) generation is highly dependent upon its operating conditions. The nonlinear control problem is further worsened by partial shading (PS) environment causing major power losses. Bio-inspired maximum power point tracking (MPPT) control techniques, in literature, exhibit some major common drawbacks such as high tracking and settling time, oscillations at global maxima (GM), and local maxima (LM) trapping under PS conditions. This paper presents a novel search and rescue (SRA) optimization algorithm based MPPT control of PV systems to circumvent these shortcomings. Enhancement in performance of PV systems, fast and effective tracking of GM and very low oscillations at GM are the improvements exhibited by the proposed technique. Comprehensive case study-wise comparison of the SRA technique is made with recently developed grasshopper optimization (GHO), grey wolf optimization (GWO), particle swarm optimization (PSO), Cuckoo Search (CS), and PSO-gravitational search (PSOGS) based MPPT techniques that elaborate the qualitative, quantitative and statistical viewpoints. The experimental verification and field atmospheric data of Islamabad, the capital city of Pakistan, is utilized to validate the practicality of the proposed SRA based MPPT controller in real-world applications. As compared to the above-mentioned MPPT techniques, the proposed SRA achieves up to 8% more power and 5% more energy. Furthermore, the settling time and tracking time are shortened by up to 72% and 180% respectively. The simplicity of implementation, robustness, and up to 99.93% power tracking efficiency in steady-state are the prominent features of the proposed SRA control strategy.