Open Access

ARTICLE

Offshore Wind Turbines Anomalies Detection Based on a New Normalized Power Index

Bassel Weiss¹, Segundo Esteban^2,*, Matilde Santos³

1 Faculty of Informatics, Complutense University of Madrid, Madrid, 28040, Spain
2 Faculty of Physics, Complutense University of Madrid, Madrid, 28040, Spain
3 Institute of Knowledge Technology, Complutense University of Madrid, Madrid, 28040, Spain

* Corresponding Author: Segundo Esteban. Email:

(This article belongs to the Special Issue: Intelligent Control and Machine Learning for Renewable Energy Systems and Industries)

Computer Modeling in Engineering & Sciences 2025, 144(3), 3387-3418. https://doi.org/10.32604/cmes.2025.070070

Received 07 July 2025; Accepted 20 August 2025; Issue published 30 September 2025

Abstract

Anomaly detection in wind turbines involves emphasizing its ability to improve operational efficiency, reduce maintenance costs, extend their lifespan, and enhance reliability in the wind energy sector. This is particularly necessary in offshore wind, currently one of the most critical assets for achieving sustainable energy generation goals, due to the harsh marine environment and the difficulty of maintenance tasks. To address this problem, this work proposes a data-driven methodology for detecting power generation anomalies in offshore wind turbines, using normalized and linearized operational data. The proposed framework transforms heterogeneous wind speed and power measurements into a unified scale, enabling the development of a new wind power index (WPi) that quantifies deviations from expected performance. Additionally, spatial and temporal coherence analyses of turbines within a wind farm ensure the validity of these normalized measurements across different wind turbine models and operating conditions. Furthermore, a Support Vector Machine (SVM) refines the classification process, effectively distinguishing measurement errors from actual power generation failures. Validation of this strategy using real-world data from the Alpha Ventus wind farm demonstrates that the proposed approach not only improves predictive maintenance but also optimizes energy production, highlighting its potential for broad application in offshore wind installations.

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