Dropout and Pruned Neural Networks for Fault Classification in Photovoltaic Arrays

Sunil Rao, Gowtham Muniraju, Cihan Tepedelenlioglu, Devarajan Srinivasan, Govindasamy Tamizhmani, Andreas Spanias

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Automatic detection of solar array faults reduces maintenance costs and increases efficiency. In this paper, we address the problem of fault detection, localization, and classification in utility-scale photovoltaic (PV) arrays using machine learning methods. More specifically, we develop a series of customized neural networks for detection and classification of solar array faults. We evaluate fault detection and classification using metrics such as accuracy, confusion matrices, and the Risk Priority Number (RPN). We examine and assess the use of customized neural networks with dropout regularizers. We develop and evaluate neural network pruning strategies and illustrate the trade-off between fault classification model accuracy and algorithm complexity. Our approach promises to elevate the performance and robustness of PV arrays and compares favorably against existing methods.

Original languageEnglish (US)
Article number9525102
Pages (from-to)120034-120042
Number of pages9
JournalIEEE Access
Volume9
DOIs
StatePublished - 2021

Keywords

  • Dropout neural networks
  • machine learning
  • photovoltaic panel fault detection
  • pruned neural networks
  • solar array fault classification

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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