Machine learning-based statistical testing hypothesis for fault detection in photovoltaic systems

R. Fazai, K. Abodayeh, Majdi Mansouri, M. Trabelsi, Hazem Nounou, Mohamed Nounou, G. E. Georghiou

Research output: Contribution to journalArticle

2 Citations (Scopus)


In this paper, we consider a machine learning approach merged with statistical testing hypothesis for enhanced fault detection performance in photovoltaic (PV) systems. The developed method makes use of a machine learning based Gaussian process regression (GPR) technique as a modeling framework, while a generalized likelihood ratio test (GLRT) chart is applied to detect PV system faults. The developed GPR-based GLRT approach is assessed using simulated and real PV data through monitoring the key PV system variables (current, voltage, and power). The computation time, missed detection rate (MDR), and false alarm rate (FAR) are computed to evaluate the fault detection performance of the proposed approach.

Original languageEnglish
Pages (from-to)405-413
Number of pages9
JournalSolar Energy
Publication statusPublished - 15 Sep 2019



  • Fault detection
  • Gaussian process regression (GPR)
  • Generalized likelihood ratio test (GLRT)
  • Machine learning
  • Photovoltaic (PV) systems

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Materials Science(all)

Cite this