Building an Air Turbine Conditional Anomaly Detection Approach for Wave Power Plants

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Published Nov 24, 2021
Jose Ignacio Aizpurua Markel Penalba Natalia Kirillova Illart Alcorta Jon Lekube Dorleta Marina

Abstract

The MutrikuWave Power Plant (WPP) is a wave energy conversion plant based on the oscillating water column technology (OWC). The energy production and the health state of the plant are directly dependent on the sea-state conditions along with component-specific operation efficiency and failure modes. In this context, this paper presents a preliminary air turbine conditional anomaly detection (CAD) approach for condition monitoring of the Mutriku WPP. The proposed approach is developed based on an ensemble of Gaussian Mixture models, where each anomaly detection model learns the expected air turbine operation conditioned on specific seastates information. Early results show that the integration of sea-states in the anomaly detection learning process improves the discrimination capability of the anomaly detection model.

How to Cite

Aizpurua, J. I., Penalba, M., Kirillova, N., Alcorta, I., Lekube, J., & Marina, D. (2021). Building an Air Turbine Conditional Anomaly Detection Approach for Wave Power Plants. Annual Conference of the PHM Society, 13(1). https://doi.org/10.36001/phmconf.2021.v13i1.3028
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Keywords

Wave energy, anomaly detection, Framework, Air turbine

References
Aizpurua, J. I., & Catterson, V. M. (2015). Towards a methodology for design of prognostic systems. In Annual conference of the prognostics and health management society 2015 (pp. 504–517).
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Lekube, J., Ajuria, O., Ibeas, M., Igareta, I., & Gonzalez, A. (2018). Fatigue and aerodynamicloss in wells turbines: Mutriku wave power plant case. In International conference on ocean energy, cherbourg, france.
Torre-Enciso, Y., Ortubia, I., De Aguileta, L. L., & Marqués, J. (2009). Mutriku wave power plant: from the thinking out to the reality. In Proceedings of the 8th european wave and tidal energy conference, uppsala, Sweden (Vol. 710, pp. 319–329).
Section
Poster Presentations