Cyber-attack resilient data-driven approaches for early fault prediction system for wind turbines

Tusher, AS; Rahman, MA; Islam, MR; Hossain, MA; Anwar, A; Hossain, MJ

Cyber-attack resilient data-driven approaches for early fault prediction system for wind turbines

Tusher, AS Rahman, MA Islam, MR Hossain, MA Anwar, A Hossain, MJ

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Sustainable Energy Technologies and Assessments, 2025, 84
Issue Date:: 2025-12-01

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Field	Value	Language
dc.contributor.author	Tusher, AS
dc.contributor.author	Rahman, MA
dc.contributor.author	Islam, MR
dc.contributor.author	Hossain, MA
dc.contributor.author	Anwar, A
dc.contributor.author	Hossain, MJ
dc.date.accessioned	2026-02-18T04:07:32Z
dc.date.available	2026-02-18T04:07:32Z
dc.date.issued	2025-12-01
dc.identifier.citation	Sustainable Energy Technologies and Assessments, 2025, 84
dc.identifier.issn	2213-1388
dc.identifier.issn	2213-1396
dc.identifier.uri	http://hdl.handle.net/10453/193585
dc.description.abstract	Predictive maintenance (PdM) technologies, facilitated by smart sensors and artificial intelligence, are increasingly adopted in smart grids to ensure reliable operation and prevent financial losses and physical damage. However, these data-driven systems remain susceptible to cyber-attacks. This study examines the resilience of machine learning and deep learning models in predicting wind turbine (WT) faults 30 min in advance under False Data Injection Attacks (FDIAs). Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) outperformed Decision Tree Classifier (DTC) and Gradient Boosting Machine (GBM) as fault predictors under normal conditions. Consequently, a comprehensive evaluation was performed using RF, XGBoost, and Long Short-Term Memory (LSTM) models under cyber-attacks, revealing that such attacks can reduce prediction accuracy by up to 15% and recall by 28%. Both autoencoder-enabled LSTM and adversarial training were implemented as defense mechanisms. While the autoencoder improved stability, adversarial training achieved superior robustness, making XGBoost the most resilient model, maintaining reliable fault prediction under cyber threats with almost no loss. Unlike existing studies focusing solely on fault prediction with clean SCADA data, this work integrates cyber-attack resilience assessment, adversarial defense, and structured data processing into a unified framework, bridging the gap between reliability, security, and data quality for trustworthy WT PdM.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Sustainable Energy Technologies and Assessments
dc.relation.isbasedon	10.1016/j.seta.2025.104702
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0905 Civil Engineering, 0906 Electrical and Electronic Engineering
dc.subject.classification	3302 Building
dc.subject.classification	4004 Chemical engineering
dc.subject.classification	4008 Electrical engineering
dc.title	Cyber-attack resilient data-driven approaches for early fault prediction system for wind turbines
dc.type	Journal Article
utslib.citation.volume	84
utslib.for	0905 Civil Engineering
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-02-18T04:07:30Z
pubs.publication-status	Published
pubs.volume	84

Abstract:

Predictive maintenance (PdM) technologies, facilitated by smart sensors and artificial intelligence, are increasingly adopted in smart grids to ensure reliable operation and prevent financial losses and physical damage. However, these data-driven systems remain susceptible to cyber-attacks. This study examines the resilience of machine learning and deep learning models in predicting wind turbine (WT) faults 30 min in advance under False Data Injection Attacks (FDIAs). Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) outperformed Decision Tree Classifier (DTC) and Gradient Boosting Machine (GBM) as fault predictors under normal conditions. Consequently, a comprehensive evaluation was performed using RF, XGBoost, and Long Short-Term Memory (LSTM) models under cyber-attacks, revealing that such attacks can reduce prediction accuracy by up to 15% and recall by 28%. Both autoencoder-enabled LSTM and adversarial training were implemented as defense mechanisms. While the autoencoder improved stability, adversarial training achieved superior robustness, making XGBoost the most resilient model, maintaining reliable fault prediction under cyber threats with almost no loss. Unlike existing studies focusing solely on fault prediction with clean SCADA data, this work integrates cyber-attack resilience assessment, adversarial defense, and structured data processing into a unified framework, bridging the gap between reliability, security, and data quality for trustworthy WT PdM.

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http://hdl.handle.net/10453/193585