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

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dc.citedby0
dc.contributor.authorSatpathy P.R.en_US
dc.contributor.authorRamachandaramurthy V.K.en_US
dc.contributor.authorSharma R.en_US
dc.contributor.authorThanikanti S.B.en_US
dc.contributor.authorBhowmik P.en_US
dc.contributor.authorSinha S.en_US
dc.contributor.authorid57195339278en_US
dc.contributor.authorid6602912020en_US
dc.contributor.authorid57196545270en_US
dc.contributor.authorid56267551500en_US
dc.contributor.authorid57196457126en_US
dc.contributor.authorid57209803221en_US
dc.date.accessioned2025-03-03T07:45:49Z
dc.date.available2025-03-03T07:45:49Z
dc.date.issued2024
dc.description.abstractSolar 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 9?9 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 38.92% and 19.62% than the conventional and existing reconfiguration techniques. ? 2024 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/SEFET61574.2024.10718002
dc.identifier.scopus2-s2.0-85208925929
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85208925929&doi=10.1109%2fSEFET61574.2024.10718002&partnerID=40&md5=5b6b5c98bfea78cc553196784c25ab07
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36924
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024
dc.subjectEnergy
dc.subjectMachine-learning
dc.subjectMismatch
dc.subjectMultiple-peak
dc.subjectOptimal power
dc.subjectPartial shading
dc.subjectPhotovoltaic arrays
dc.subjectPhotovoltaics
dc.subjectPower curves
dc.subjectPower- generations
dc.subjectGenetic programming
dc.titleA Genetic Programming-Machine Learning Based Optimal Power Generation Approach for PV Arrays During Partial Shadingen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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