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Operation and maintenance costs of floating offshore wind farms (FOWFs) can be reduced by considering appropriate fault detection and isolation (FDI) and fault-tolerant control methods. This paper introduces a FOWF benchmark with the capacity to recreate common fault scenarios on the subsystems composing the turbines. Additionally, the paper proposes a FDI strategy as an example case, based on nonlinear parity equations, obtained from structural analysis and non-faulty data, with an adaptive threshold generation mechanism. The proposed FDI approach is assessed using the benchmark case study, showing that most of the faults can be effectively detected, while showing to be insensitive to the electric generator faults., Peer Reviewed, Postprint (author's final draft)

The potential of Marine Renewable Energy (MRE) systems is usually evaluated based on recent metocean data and assuming the stationarity of the MRE resource. Yet, different studies in the literature have shown long-term resource variations and even the connection between ocean warming and wave power variations. Therefore, it is crucial to accurately characterise the future resource, including these long-term variations. To that end, this paper presents a novel data-driven forecasting approach through the combination of machine-learning (ML) and oceanic engineering concepts. First, the historical resource is characterised in the Bay of Biscay, including the different long-term trends identified based upon the dataset obtained via the SIMAR model ensemble. Secondly, the most relevant features of the metocean dataset are extracted and selected via advanced statistical techniques. Finally, three different ML algorithms are designed, validated and tested. All three ML models demonstrate to adequately represent the overall pattern of the dataset, although showing difficulties with reproducing particular peak values. Accordingly, an alternative interval prediction approach is presented for three different wave height discretisation levels, showing a greater potential for long-term metocean data forecasting.

The energy transition towards resilient and sustainable power plants requires moving from periodic health assessment to condition-based lifetime planning, which in turn, creates new challenges and opportunities for health estimation and prediction. Probabilistic forecasting models are being widely employed to predict the likely evolution of power grid parameters, such as weather prediction models and probabilistic load forecasting models, that precisely impact on the health state of power and energy components. These models synthesize forecasting knowledge and associated uncertainty information, and their integration within asset management practice would improve lifetime estimation under uncertainty through uncertainty-aware probabilistic predictions. Accordingly, this paper presents a probabilistic prognostics method for lifetime planning under uncertainty integrating data-driven probabilistic forecasting models with expert-knowledge based Bayesian filtering methods. The proposed concepts are applied and validated with power transformers operated in two different power generation systems and obtained results confirm that the proposed probabilistic transformer lifetime estimate aids in the decision-making process with informative lifetime distributions and associated confidence intervals.

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.