PV Array Fault Detection using Radial Basis Networks

Emma Pedersen, Sunil Rao, Sameeksha Katoch, Kristen Jaskie, Andreas Spanias, Cihan Tepedelenlioglu, Elias Kyriakides

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

An increase in grid-connected photovoltaic arrays creates a need for efficient and reliable fault detection. In this paper, machine learning strategies for fault detection are presented. An Artificial Neural Network was studied with the goal of detecting three photovoltaic module conditions. In addition, an unsupervised approach was successfully implemented using the-means clustering algorithm, successfully detecting arc and ground faults. To distinguish and localize additional faults such as shading and soiling, a supervised approach is adopted using a Radial Basis Function Network. A solar array dataset with voltage, current, temperature, and irradiance was examined. This dataset had labeled data with normal conditions and faults due to soiling and shading. A radial basis network was trained to classify faults, resulting in an error rate below 2% on synthetic data with realistic levels of noise.

Original languageEnglish (US)
Title of host publication10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728149592
DOIs
StatePublished - Jul 2019
Event10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019 - Patras, Greece
Duration: Jul 15 2019Jul 17 2019

Publication series

Name10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019

Conference

Conference10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019
CountryGreece
CityPatras
Period7/15/197/17/19

Keywords

  • Fault detection
  • Machine learning
  • Radial basis networks
  • Solar energy

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Information Systems and Management
  • Media Technology
  • Education

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  • Cite this

    Pedersen, E., Rao, S., Katoch, S., Jaskie, K., Spanias, A., Tepedelenlioglu, C., & Kyriakides, E. (2019). PV Array Fault Detection using Radial Basis Networks. In 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019 [8900710] (10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IISA.2019.8900710