A Genetic Programming-Machine Learning Based Optimal Power Generation Approach for PV Arrays During Partial Shading

Created by W.Langdon from gp-bibliography.bib Revision:1.8344

@InProceedings{Satpathy:2024:SEFET,

• author = "Priya Ranjan Satpathy and Vigna Kumaran Ramachandaramurthy and Renu Sharma and Sudhakar Babu Thanikanti and Pritam Bhowmik and Sayantan Sinha",
• title = "A Genetic Programming-Machine Learning Based Optimal Power Generation Approach for {PV} Arrays During Partial Shading",
• booktitle = "2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation (SEFET)",
• year = "2024",
• month = jul,
• keywords = "genetic algorithms, genetic programming, Photovoltaic systems, Costs, Prevention and mitigation, Transportation, Switches, Genetics, Reliability engineering, Complexity theory, Time-domain analysis, Dispersion, photovoltaic, energy, partial shading, multiple peaks, mismatch",
• DOI = "doi:10.1109/SEFET61574.2024.10718002",
• abstract = "Solar photovoltaic (PV) energy systems are highly influenced to the environmental partial shading that diminishes the power generation to the most. Various mitigation techniques have been presented in the past but, each exhibits limitations in terms of application, cost, adaptability and complexity. Array reconfiguration have a wide acceptance as the cost-effective way of enhancing the power generation during partial shading but, the major drawback lies in the effective shade dispersion, faster operation, reliability and implementation. Hence, considering these constraints, this paper suggests an array reconfiguration technique that uses the genetic programming-machine learning (GP-ML) approach for efficient operation of PV arrays during partial shading scenarios. The proposed approach uses a lower switch count to enhance the power generation of the PV arrays and reduces the possibility of non-convex power curves during shading. The validation is carried out in the simulation using a 9times9 PV array under complex shading cases and compared with conventional and existing reconfiguration techniques using power curves and various parameters. From the analysis, it is discovered that the proposed approach enhances the average power generation of the PV array to 38.92percent and 19.62percent than the conventional and existing reconfiguration techniques.",
• notes = "Also known as \cite{10718002}",

}

Genetic Programming entries for Priya Ranjan Satpathy Vigna Kumaran Ramachandaramurthy Renu Sharma Sudhakar Babu Thanikanti Pritam Bhowmik Sayantan Sinha

Citations