A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation

A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation

This paper proposes a novel fault detection and diagnosis (FDD) technique for grid-tied PV systems. The proposed approach deals with system uncertainties (current/voltage variability, noise, measurement errors,…) by using an interval-valued data representation, and with large-scale system...

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Bibliographic Details
Main Authors: Khaled Dhibi, Radhia Fezai, Majdi Mansouri, Mohamed Trabelsi, Kais Bouzrara, Hazem Nounou, Mohamed Nounou
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Random forest
interval-valued data
reduced kernel principal component analysis
fault diagnosis
feature extraction and selection
fault classification
Online Access:https://ieeexplore.ieee.org/document/9410264/

Internet

https://ieeexplore.ieee.org/document/9410264/

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