Fault detection in photovoltaic systems using machine learning technique

Khadija Attouri, Mansour Hajji, Majdi Mansouri, Mohamed Faouzi Harkat, Abdelmalek Kouadri, Hazem Nounou, Mohamed Nounou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

To ensure high reliability of the Grid-Connected Photovoltaic (GCPV) systems, promptly faults detection, diagnosis and automatic process monitoring are essential tools to keep the PV and the grid network under optimal functioning. Regardless of fault types, incipient faults are usually more difficult to detect and accurately isolate. As an alternative and effective method, the principal components analysis (PCA) is proposed to extract and select more relevant features and support vector machines (SVM) technique is applied to quickly detect the faults that occur in a GCPV system. The T2 and squared weighted errors (SWE) statistics, generally used as fault detection indices, are appropriately extracted and selected within the PCA framework. Both of these features are fed to a SVM classifier handling the incipient fault detection. This task is carried out on a simulated GCPV operating under maximum power point trackers (MPPT) and matching realistic outdoor to demonstrate the effectiveness and robustness of the proposed technique.

Original languageEnglish
Title of host publicationProceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages207-212
Number of pages6
ISBN (Electronic)9781728110806
DOIs
Publication statusPublished - 20 Jul 2020
Externally publishedYes
Event17th International Multi-Conference on Systems, Signals and Devices, SSD 2020 - Sfax, Tunisia
Duration: 20 Jul 202023 Jul 2020

Publication series

NameProceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020

Conference

Conference17th International Multi-Conference on Systems, Signals and Devices, SSD 2020
Country/TerritoryTunisia
CitySfax
Period20/07/2023/07/20

Keywords

  • Grid-connected PV systems
  • Machine learning (ML)
  • fault detection
  • feature extraction and selection
  • principal component analysis (PCA)

Fingerprint

Dive into the research topics of 'Fault detection in photovoltaic systems using machine learning technique'. Together they form a unique fingerprint.

Cite this