Performance evaluation and extraction of global power maxima under shading scenarios and load Variation: An experimental study

Abstract

Due to environmental degradation, depletion of fossil resources and contaminants in the air, energy structure reform and development of sustainable environment friendly are approaching in the past few years, in which several renewable energy technologies such as solar, wind, bio and hydropower as efficient alternative energy have drawn considerable attention. Solar Photovoltaic (SPV) systems, in particular, have huge benefits for society and the economy as an essential means of extracting and utilization. However, these systems suffer from extracting maximum power from variable irradiance due to shading scenarios and variable load. As a result of PSCs, many LMPPs and GMPPs exist on their power voltage (P-V) characteristics. Thus, MPTT techniques are implemented in these systems to resolve these issues. Several researchers have conducted substantial research and discussion on these techniques to improve SPV system performances, but the practical applicability of MPPT approaches faces multiple obstacles. This study evaluates the performances of three such MPPT metaheuristic approaches, i.e., umbrella optimization technique (UOT), particle swarm optimization (PSO) and teaching learning-based optimization (TLBO) experimentally in real-time environment on small hardware under PSCs and load variation scenarios. Each technique is tested on a standalone SPV system incorporating an Inverse Single-Ended Primary Inductor Converter (SEPIC) and Luo DC-DC converter separately on various parameters of SPV systems such as tracking time, tracking efficiency, output power, output current and computational complexity, etc. With inverse SEPIC DC converter, UOT boosts GMPP up to 3.8% to 3.9% while the tracking time is reduced or faster by 20.11% to 27.7% compared to PSO & TLBO techniques. It also shows 98.6% average tracking efficiency under considered PSCs. On the other hand, UOT achieves 4.22% to 5.5% higher GMPP and tracking time is reduced by 38.38% to 57.87% with the Luo DC converter compared to PSO & TLBO techniques. UOT shows 98.19% average tracking efficiency under considered PSCs with this converter. Moreover, UOT can also maintain 3.80% to 6.48% high output current in the SPV system under varying load conditions, with stabilization time reduced to 34.06% to 35.80% compared to PSO & TLBO techniques. The further practical applicability of the study is tested on a 300 W standalone SPV system. On this system, UOT again shows its better tracking performance by boosting 3.5% to 3.64% GMPP as well as the system output current. It also takes 30.86% less tracking time with 98.56% average tracking efficiency compared to PSO and TLBO. Experimental validation of all three metaheuristic approaches on low and high-rated systems confirms the supremacy of UOT over PSO & TLBO techniques in terms of tracking time, tracking efficiency, output power and current under different working scenarios.