Context-informed conditional anomaly detection approach for wave power plants: The case of air turbines. (1st June 2022) - Aizpurua, Jose Ignacio; Penalba, Markel; Kirillova, Natalia; Lekube, Jon; Marina, Dorleta
Abstract: The reliability and energy production of wave power plants (WPPs) depend on sea-state conditions, operation efficiency and degradation of its constituent assets. Air turbines are key assets for the efficient and reliable operation of WPPs and ensuring their correct operation leads to enhance the efficiency of WPPs. However, the lack of run-to-failure data and scarce fault records hampers the development of predictive condition monitoring solutions. In this context, focusing on unsupervised health monitoring methods, this paper presents an air turbine conditional anomaly detection (CAD) approach with a practical case study tested and validated on the Mutriku wave power plant. In contrast to anomaly detection models, which model the health-state without taking into account the influence of the operating context, the proposed CAD approach learns the expected air turbine operation conditioned on specific sea-states information modelled through wave energy flux concepts. This is achieved through an ensemble of Gaussian Mixture models and the expectation–maximization algorithm. Results show that, the integration of sea-states in the anomaly detection learning process improves the discrimination capability of the CAD model compared with the anomaly detection model without sea-state information, reducing false positive events and improving the accuracy of the CAD model. Highlights: A novel context-aware conditional anomaly detection approach is presented for air turbines. Sea-states and air turbine power curves models are correlated using statistical learning methods. Conditional anomaly occurrence probabilities are inferred for health monitoring purposes. The approach is validated using the Mutriku wave power plant case study. Integration of sea-states in the anomaly detection learning process improves the discrimination capability of the anomaly detection model reducing false positive events.