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Title: Data-driven operations and maintenance for offshore wind farms : tools and methodologies
Author: Koltsidopoulos Papatzimos, Alexios
ISNI:       0000 0004 8509 2759
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2020
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Offshore wind assets have reached multi-GW scale and additional capacity is being installed and developed. To achieve demanding cost of energy targets, awarded by competitive auctions, the operations and maintenance (O&M) of these assets have to become increasingly efficient, whilst ensuring compliance and effectiveness. Existing offshore wind farm assets generate a significant amount of inhomogeneous operational data. These data contain rich information about the condition of the assets, which are rarely fully utilized by the operators and service providers. This thesis provides useful methodologies and tools that can help wind farm owners, operators and service providers to reduce their O&M costs by better managing their data, integrating processes and providing data-driven decision making. The developed methodologies and tools are being presented through several case studies, showing the effectiveness of the solutions and their potential cost reduction opportunities. These are split into the following four themes:i. Data management techniques, methodologies and case studies, aiming to improve data collection and data integration strategies for a data informed decision making. Processes and best practices for workflow improvements and automated datacollection and standardization.iii. Data analytics including reliability, diagnostic and prognostic methodologies and case studies.iv. Maintenance planning including enhanced planning strategies, decision support frameworks and optimized maintenance operations. All of the above frameworks, methodologies and case studies are linked together as they provide insights for data-driven decision making, which results in better informed and thus less costly maintenance strategies. The methodologies and case studies presented will assist in creating data-driven O&M processes and allowing the full utilization of the produced offshore wind farm data.
Supervisor: Thies, Philipp ; Kurt, Rafet ; Jeffrey, Henry Sponsor: Engineering and Physical Sciences Research Council (EPSRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
Keywords: offshore wind turbines ; offshore wind ; operations and maintenance ; cost reduction ; reliability ; data analytics ; maintenance optimization ; decision-making