Browsing by Keyword "Mooring lines"
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Item Condition monitoring of mooring systems for Floating Offshore Wind Turbines using Convolutional Neural Network framework coupled with Autoregressive coefficients(2024-06-15) Sharma, Smriti; Nava, Vincenzo; RENOVABLES OFFSHOREThis research presents a novel approach proposed for the monitoring of mooring systems in Floating Offshore Wind Turbines (FOWTs), employing a combination of Convolutional Neural Networks (CNNs) and Auto-Regressive (AR) models. CNN finds broad application in monitoring intricate structures, as they adeptly handle noisy response data without necessitating profound domain expertise. The precision of CNNs relies on the extraction of meaningful features from input data, necessitating meticulous data curation and labeling for optimal computational efficiency and accurate estimation. Emphasis is placed on the preference for feature-rich small datasets over voluminous yet sparse datasets, aiming to enable CNNs to discern crucial patterns more effectively and mitigate issues such as overfitting and extensive preprocessing. The novelty of the proposed approach lies in the integration of AR models, which serve to compress data and enhance damage-sensitive characteristics in the input for CNNs. This integration involves deploying regression models fitted to historical responses, parameterized with AR coefficients sensitive to damage, and further classifying severity using CNNs. The sequential nature of this approach addresses challenges such as vanishing/exploding gradients, particularly for extended historical data, while also attenuating the impact of noise and irrelevant information through data compression. The study explores the effectiveness of the coupled AR-CNN method in monitoring FOWT mooring lines, with a specific focus on two levels of damage identification: detection with classification and damage severity across diverse damage and operational scenarios. The modified methodology exhibits superior outcomes by conducting a performance analysis against traditional CNNs and other machine-learning methods, highlighting the potential of the AR-CNN strategy to improve the precision of FOWT mooring line condition monitoring. These findings underscore the AR-CNN strategy's potential to enhance the accuracy of FOWT mooring line condition monitoring.Item Control co-design for wave energy farms: Optimisation of array layout and mooring configuration in a realistic wave climate(2024-06) Peña-Sanchez, Yerai; García-Violini, Demián; Penalba, Markel; Zarketa-Astigarraga, Ander; Ferri, Francesco; Nava, Vincenzo; Ringwood, John V.; RENOVABLES OFFSHOREThis paper presents a novel Control Co-Design (CCD) methodology aimed at economically optimising the layout of wave energy converter (WEC) arrays. CCD ensures the synergy of optimised WEC and array parameters with the final control strategy, resulting in a comprehensive and efficient design of the array. By integrating a spectral-based control strategy into the array layout design, this study pursues the twin objectives of maximising energy absorption while reducing costs. To prove the performance of the proposed CCD methodology, an application case is proposed where the inter-device distance, alignment, and mooring configuration of a five-device array, considering realistic wave scenarios, are optimised. Energy capture and system cost evaluations are conducted, with results emphasising the significance of incorporating advanced control strategies in the design phase to improve energy absorption and reduce costs. With the application case, the study demonstrates that the optimal layout of a WEC array considering economic factors may differ from the optimal from purely technical factors, such as energy absorption, in the analysed case.Item MONITORING MOORING (MONIMOOR) LINES OF FLOATING STRUCTURES USING DEEP LEARNING-BASED APPROACHES(2023) Sharma, Smriti; Nava, Vincenzo; Gorostidi, Nicolas; RENOVABLES OFFSHOREThe renewable energy sector, specifically offshore wind energy, has grown rapidly in Europe in recent years, owing to the lower energy costs. A typical Floating Offshore Wind Turbine (FOWT) system is comprised of several coupled subsystems that should jointly ensure its integrity under nominal operating conditions as well as sustainability under extreme conditions or prolonged usage. From the reliability perspective, one of the most critical subsystems is the mooring system, which keeps the platform floating in a stable condition. Eventually, monitoring the mooring lines is important to ensure the safety and serviceability of FOWT throughout its service life. This article describes a comprehensive model for assisting businesses in planning real-time monitoring of the FOWT. The proposal combines an Auto-regressive model (AR) with a Convolutional neural network (CNN) in a near real-time approach for damage detection in FOWT. The CNN-based approach monitors and subsequently identifies anomalies in the AR model coefficients of the motion prediction model apriori trained for the FOWT platform under its undamaged condition. Accordingly, a model to predict the motion of a semi-submersible FOWT platform is prepared to employ undamaged time history response (single point displacements and rotations) and the optimal AR coefficients are identified under all sea states and damage conditions. The proposed deep learning-based CNN is further employed to attribute these coefficients to different damage/health states of the platform. The effectiveness of the proposed approach is validated through numerical simulations using NREL's open-source wind turbine simulation tool OpenFAST. In the numerical model, various scenarios are simulated in an attempt to replicate real damage scenarios under varying metocean conditions while taking into account the plausible failure mechanisms in the mooring lines. The strategy of combining AR and CNN in a novelty detection-based methodology performs admirably in damage identification and classification.Item Monitoring Mooring Lines of Floating Offshore Wind Turbines: Autoregressive Coefficients and Stacked Auto-Associative-Deep Neural Networks(British Institute of Non-Destructive Testing, 2024) Sharma, Smriti; Nava, Vincenzo; RENOVABLES OFFSHOREThis study introduces a pioneering monitoring system designed to mitigate operational costs and enhance the sustainability of Floating Offshore Wind Turbines (FOWT). The proposed framework combines Autoregressive models with a Stacked Auto-Associative-based Deep Neural Network (AANN-DNN) to detect and classify damages in mooring systems of FOWTs. By extracting damage-sensitive features (DSFs) using the AR models from time-series data and employing unsupervised learning in the auto-associative neural network, followed by supervised training with DNN, the approach demonstrates exceptional accuracy in damage identification and classification. Numerical simulations conducted using NREL’s OpenFAST software under diverse metocean conditions validate the method’s efficacy, offering a promising solution for efficient FOWT mooring line monitoring.