Supervised machine learning-based salp swarm algorithm for fault diagnosis of photovoltaic systems

Supervised machine learning-based salp swarm algorithm for fault diagnosis of photovoltaic systems

Abstract The diagnosis of faults in grid-connected photovoltaic (GCPV) systems is a challenging task due to their complex nature and the high similarity between faults. To address this issue, we propose a wrapper approach called the salp swarm algorithm (SSA) for feature selection. The main objectiv...

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Bibliographic Details
Main Authors: Amal Hichri, Mansour Hajji, Majdi Mansouri, Hazem Nounou, Kais Bouzrara
Format: Article
Language:English
Published: SpringerOpen 2024-01-01
Series:Journal of Engineering and Applied Science
Subjects:
Fault diagnosis (FD)
Feature selection (FS)
Photovoltaic (PV) systems
Salp swarm algorithm (SSA)
Supervised machine learning (SML)
Online Access:https://doi.org/10.1186/s44147-023-00344-z

Internet

https://doi.org/10.1186/s44147-023-00344-z

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