Line-line fault detection and classification for photovoltaic systems using ensemble learning model based on I-V characteristics

Aref Eskandari, Jafar Milimonfared, Mohammadreza Aghaei (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

The fault diagnosis of photovoltaic (PV) arrays aims to increase the reliability and service life of PV systems. Line-Line (LL) faults may remain undetected under low mismatch level and high impedance due to low currents of faults, resulting in power losses and fire potential disaster. This paper proposes a novel and intelligent fault diagnosis method based on an ensemble learning model and Current-Voltage (I-V) characteristics to detect and classify LL faults at the DC side of PV systems. For this purpose, first, the key features are extracted via analyzing I-V characteristics under various LL fault events and normal operation. Second, a feature selection algorithm has been applied to select the best features for each learning algorithm in order to reduce the amount of data required for the learning process. Third, an ensemble learning model is developed that combines several learning algorithms based on the probabilistic strategy to achieve superior diagnostic performance. Here, we find an excellent agreement between simulation and experimental results that the proposed method can obtain higher accuracy in detecting and classifying the LL faults, even under low mismatch levels and high fault impedances. In addition, the comparison results demonstrate that the performance of the proposed method is better than individual machine learning algorithms, so that the proposed method precisely detects and classifies LL faults on PV systems under the different conditions with an average accuracy of 99% and 99.5%, respectively.

Original languageEnglish
Pages (from-to)354-365
Number of pages12
JournalSolar Energy
Volume211
DOIs
Publication statusPublished - 15 Nov 2020

Keywords

  • Ensemble learning model
  • Fault detection and classification
  • Feature selection
  • Line-Line faults
  • Machine learning
  • Photovoltaic monitoring

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