Enhanced Spectral Ensemble Clustering for Fault Diagnosis: Application to Photovoltaic Systems

Enhanced Spectral Ensemble Clustering for Fault Diagnosis: Application to Photovoltaic Systems

The role of clustering in unsupervised fault diagnosis is significant, but different clustering techniques can yield varied results and cause inevitable uncertainty. Ensemble clustering methods have been introduced to tackle this challenge. This study presents a novel integrated technique in the fie...

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Bibliographic Details
Main Authors: Mohsen Zargarani, Claude Delpha, Demba Diallo, Anne Migan-Dubois, Chabakata Mahamat, Laurent Linguet
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Enhanced spectral ensemble clustering (ESEC)
bipartite graph partitioning
eigenvector centrality
neural networks
fault detection and diagnosis (FDD)
photovoltaic (PV) system
Online Access:https://ieeexplore.ieee.org/document/10752920/
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https://ieeexplore.ieee.org/document/10752920/

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