Comparative analysis of evolutionary-based maximum power point tracking for partial shaded photovoltaic

Prisma Megantoro, Hafidz Faqih Aldi Kusuma, Lilik Jamilatul Awalin, Yusrizal Afif, Dimas Febriyan Priambodo, Pandi Vigneshwaran

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


The characteristics of the photovoltaic module are affected by the level of solar irradiation and the ambient temperature. These characteristics are depicted in a V-P curve. In the V-P curve, a line is drawn that shows the response of changes in output power to the level of solar irradiation and the response to changes in voltage to ambient temperature. Under partial shading conditions, photovoltaic (PV) modules experience non-uniform irradiation. This causes the V-P curve to have more than one maximum power point (MPP). The MPP with the highest value is called the global MPP, while the other MPP is the local MPP. The conventional MPP tracking technique cannot overcome this partial shading condition because it will be trapped in the local MPP. This article discusses the MPP tracking technique using an evolutionary algorithm (EA). The EAs analyzed in this article are genetic algorithm (GA), firefly algorithm (FA), and fruit fly optimization (FFO). The performance of MPP tracking is shown by comparing the value of the output power, accuracy, time, and tracking effectiveness. The performance analysis for the partial shading case was carried out on various populations and generations.

Original languageEnglish
Pages (from-to)5717-5729
Number of pages13
JournalInternational Journal of Electrical and Computer Engineering
Issue number6
Publication statusPublished - Dec 2022


  • Evolutionary algorithm
  • Maximum power point tracking
  • Optimization
  • Photovoltaic
  • Renewable energy


Dive into the research topics of 'Comparative analysis of evolutionary-based maximum power point tracking for partial shaded photovoltaic'. Together they form a unique fingerprint.

Cite this