Reducing neural network complexity via optimization algorithms for fault diagnosis in renewable energy systems

This article discusses the challenges involved in diagnosing faults in renewable energy systems. A significant challenge is the high computational demands of the artificial intelligence algorithms needed for grid-connected photovoltaic (GCPV) and wind energy conversion (WEC) systems in fault diagnos...

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Bibliographic Details
Published in:	Ain Shams Engineering Journal Vol. 15; no. 12; p. 103086
Main Authors:	Hajji, Mansour, Hichri, Amal, Yahyaoui, Zahra, Mansouri, Majdi, Bouazzi, Yassine, Rajhi, Wajdi, Ahmad, Ayyaz
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.12.2024 Elsevier
Subjects:	Algorithm complexity Computation time (CT) Deep learning (DL) methods Fault diagnosis (FD) Grid-connected photovoltaic (GCPV) system Memory space (MS) Optimization techniques Wind energy conversion (WEC) system Deep learning (DL) methods Fault diagnosis (FD) Computation time (CT) Algorithm complexity Grid-connected photovoltaic (GCPV) system Optimization techniques Memory space (MS) Wind energy conversion (WEC) system
ISSN:	2090-4479
Online Access:	Get full text
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