A Comprehensive Review of Most Competitive Maximum Power Point Tracking Techniques for Enhanced Solar Photovoltaic Power Generation

A major design challenge for a grid-integrated photovoltaic power plant is to generate maximum power under varying loads, irradiance, and outdoor climatic conditions using competitive algorithm-based controllers. The objective of this study is to review experimentally validated advanced maximum power point tracking algorithms for enhancing power generation. A comprehensive analysis of 14 of the most advanced metaheuristics and 17 hybrid homogeneous and heterogeneous metaheuristic techniques is carried out, along with a comparison of algorithm complexity, maximum power point tracking capability, tracking frequency, accuracy, and maximum power extracted from PV systems. The results show that maximum power point tracking controllers mostly use conventional algorithms; however, metaheuristic algorithms and their hybrid variants are found to be superior to conventional techniques under varying environmental conditions. The Grey Wolf Optimization, in combination with Perturb & Observe, and Jaya-Differential Evolution, is found to be the most competitive technique. The study shows that standard testing and evaluation procedures can be further developed for comparing metaheuristic algorithms and their hybrid variants for developing advanced maximum power point tracking controllers. The identified algorithms are found to enhance power generation by grid-integrated commercial solar power plants. The results are of importance to the solar industry and researchers worldwide

A Novel Metaheuristic Approach for Solar Photovoltaic Parameter Extraction Using Manufacturer Data

Solar photovoltaic (PV) panel parameter estimation is vital to manage solar-based microgrid operations, for which several techniques have been developed. Solar cell modeling using metaheuristic algorithms is found to be one of the accurate techniques. However, it requires experimental datasets, which may not be available for most of the industrial modules. Therefore, this study proposed a new model to estimate the solar parameters for two types of PV panels using manufacturer datasheets only. In addition, two optimization techniques called particle swarm optimization (PSO) and genetic algorithm (GA) were also investigated for solving this problem. The predicted results showed that GA is more accurate than PSO, but PSO is faster. The new model was tested under different solar radiation conditions and found to be accurate under all conditions, with an error which varied between 7.6212 × 10−4 under standard testing conditions and 0.0032 at 200 W/m2 solar radiation. Further comparison of the proposed method with other methods in the literature showed its capability to compete with other models despite not using experimental datasets. The study is of significance for the sustainable energy management of newly established commercial PV micro grids

A Novel Metaheuristic Approach for Solar Photovoltaic Parameter Extraction Using Manufacturer Data

Solar photovoltaic (PV) panel parameter estimation is vital to manage solar-based microgrid operations, for which several techniques have been developed. Solar cell modeling using metaheuristic algorithms is found to be one of the accurate techniques. However, it requires experimental datasets, which may not be available for most of the industrial modules. Therefore, this study proposed a new model to estimate the solar parameters for two types of PV panels using manufacturer datasheets only. In addition, two optimization techniques called particle swarm optimization (PSO) and genetic algorithm (GA) were also investigated for solving this problem. The predicted results showed that GA is more accurate than PSO, but PSO is faster. The new model was tested under different solar radiation conditions and found to be accurate under all conditions, with an error which varied between 7.6212 × 10−4 under standard testing conditions and 0.0032 at 200 W/m2 solar radiation. Further comparison of the proposed method with other methods in the literature showed its capability to compete with other models despite not using experimental datasets. The study is of significance for the sustainable energy management of newly established commercial PV micro grids