Cover Image

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

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....

Full description

Saved in:
Bibliographic Details
Main Authors: Satpathy P.R., Ramachandaramurthy V.K., Sharma R., Thanikanti S.B., Bhowmik P., Sinha S.
Other Authors: 57195339278
Format: Conference paper
Published: Institute of Electrical and Electronics Engineers Inc. 2025
Subjects:
Energy
Machine-learning
Mismatch
Multiple-peak
Optimal power
Partial shading
Photovoltaic arrays
Photovoltaics
Power curves
Power- generations
Genetic programming
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-36924
record_format dspace
spelling my.uniten.dspace-369242025-03-03T15:45:49Z A Genetic Programming-Machine Learning Based Optimal Power Generation Approach for PV Arrays During Partial Shading Satpathy P.R. Ramachandaramurthy V.K. Sharma R. Thanikanti S.B. Bhowmik P. Sinha S. 57195339278 6602912020 57196545270 56267551500 57196457126 57209803221 Energy Machine-learning Mismatch Multiple-peak Optimal power Partial shading Photovoltaic arrays Photovoltaics Power curves Power- generations Genetic programming 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 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. Final 2025-03-03T07:45:49Z 2025-03-03T07:45:49Z 2024 Conference paper 10.1109/SEFET61574.2024.10718002 2-s2.0-85208925929 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208925929&doi=10.1109%2fSEFET61574.2024.10718002&partnerID=40&md5=5b6b5c98bfea78cc553196784c25ab07 https://irepository.uniten.edu.my/handle/123456789/36924 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Energy
Machine-learning
Mismatch
Multiple-peak
Optimal power
Partial shading
Photovoltaic arrays
Photovoltaics
Power curves
Power- generations
Genetic programming
spellingShingle Energy
Machine-learning
Mismatch
Multiple-peak
Optimal power
Partial shading
Photovoltaic arrays
Photovoltaics
Power curves
Power- generations
Genetic programming
Satpathy P.R.
Ramachandaramurthy V.K.
Sharma R.
Thanikanti S.B.
Bhowmik P.
Sinha S.
A Genetic Programming-Machine Learning Based Optimal Power Generation Approach for PV Arrays During Partial Shading
description 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 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.
author2 57195339278
author_facet 57195339278
Satpathy P.R.
Ramachandaramurthy V.K.
Sharma R.
Thanikanti S.B.
Bhowmik P.
Sinha S.
format Conference paper
author Satpathy P.R.
Ramachandaramurthy V.K.
Sharma R.
Thanikanti S.B.
Bhowmik P.
Sinha S.
author_sort Satpathy P.R.
title A Genetic Programming-Machine Learning Based Optimal Power Generation Approach for PV Arrays During Partial Shading
title_short A Genetic Programming-Machine Learning Based Optimal Power Generation Approach for PV Arrays During Partial Shading
title_full A Genetic Programming-Machine Learning Based Optimal Power Generation Approach for PV Arrays During Partial Shading
title_fullStr A Genetic Programming-Machine Learning Based Optimal Power Generation Approach for PV Arrays During Partial Shading
title_full_unstemmed A Genetic Programming-Machine Learning Based Optimal Power Generation Approach for PV Arrays During Partial Shading
title_sort genetic programming-machine learning based optimal power generation approach for pv arrays during partial shading
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2025
_version_ 1825816078381481984
score 13.244413

Similar Items