Commercial photovoltaic performance optimization using genetic algorithms

Manjeevan Seera, Choo Jun Tan, Kuldeep Kaur Randhawa, Kok-Keong Chong

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

Abstract

The irradiance from the sun or solar spectral irradiance can have significant variance in different locations due to the latitude, humidity, and cosine effect of incident sunlight. The performance of the outdoor photovoltaic (PV) modules is greatly influenced by the solar spectrum. In this study, the effects of the local spectral irradiance on outdoor PV modules are of interest. With similar irradiance and operating temperature, the performances of the PV modules at different locations differ as compared with that of the benchmark AM1.5G results. In order to predict the actual PV module performance under local climate conditions, two locations in Peninsular Malaysia are considered. Twelve types of solar PV modules from different manufacturers and materials are analysed. Two sets of experiments were conducted using variants of Genetic Algorithms, where the Power Conversion Efficiency (PCE) at different irradiance levels is first considered. Results from the study show that there is a gap between the AM1.5G results and the outdoor results from the locations being analysed.
Original languageEnglish
Title of host publicationArtificial Intelligence and Environmental Sustainability
Subtitle of host publicationChallenges and Solutions in the Era of Industry 4.0
EditorsHui Lin Ong, Ruey-an Doong, Raouf Naguib, Chee Peng Lim, Atulya K. Nagar
Place of PublicationSingapore Singapore
PublisherSpringer
Chapter6
Pages117-130
Number of pages14
Edition1st
ISBN (Electronic)9789811914348
ISBN (Print)9789811914331
DOIs
Publication statusPublished - 2022

Publication series

NameAlgorithms for Intelligent Systems
PublisherSpringer
ISSN (Print)2524-7565
ISSN (Electronic)2524-7573

Keywords

  • Genetic algorithm
  • Optimization
  • Photovoltaic module
  • Power conversion efficiency

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