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

<p>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 systems...

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Bibliographic Details
Main Author: Khaled Dhibi (16891524) (author)
Other Authors: Radhia Fezai (16869888) (author), Majdi Mansouri (16869885) (author), Mohamed Trabelsi (16869891) (author), Kais Bouzrara (16869906) (author), Hazem Nounou (16869900) (author), Mohamed Nounou (3489386) (author)
Published: 2021
Subjects:
Engineering
Electrical engineering
Information and computing sciences
Data management and data science
Machine learning
Circuit faults
Data mining
Fault diagnosis
Feature extraction and selection
Feature extraction
Fault classification
Interval-valued data
Kernel
PV systems
Principal component analysis
Random forest
Radio frequency
Reduced kernel principal component analysis
Support vector machines
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