RT Conference Proceedings
T1 MONITORING MOORING (MONIMOOR) LINES OF FLOATING STRUCTURES USING DEEP LEARNING-BASED APPROACHES
A1 Sharma, Smriti
A1 Nava, Vincenzo
A1 Gorostidi, Nicolas
AB The 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.
SN 2623-3347
YR 2023
FD 2023
LK https://hdl.handle.net/11556/3933
UL https://hdl.handle.net/11556/3933
LA eng
NO Sharma , S , Nava , V & Gorostidi , N 2023 , ' MONITORING MOORING (MONIMOOR) LINES OF FLOATING STRUCTURES USING DEEP LEARNING-BASED APPROACHES ' , COMPDYN Proceedings .
NO Publisher Copyright: © 2023 COMPDYN Proceedings. All rights reserved
NO This study is funded by the Spanish Ministry of Economic Affairs and Digital Transformation under the Recovery, Transformation and Resilience Plan in the call of R&D Missions in the Artificial Intelligence 2021 Programme, in the framework of IA4TES project (Artificial Intelligence for Sustainable Energy Transition) with reference number MIA.2021.M04.008. The authors also would like to acknowledge the Spanish Ministry of Science and Innovation projects with references TED2021-132783B-I00 and PID2019-108111RB-I00 (FEDER/AEI); the “BCAM Severo Ochoa” accreditation of excellence CEX2021-001142-S / MICIN / AEI / 10.13039/501100011033; and the Basque Government through the BERC 2022-2025 program.
DS TECNALIA Publications
RD 28 sept 2024