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