A flexibility-based approach for the design and management of floating offshore wind farms

Torres-Rincón, Samuel; Bastidas‐Arteaga, Emilio; Sánchez-Silva, Mauricio

doi:10.1016/j.renene.2021.04.121

Cited by 7 publications

(2 citation statements)

References 46 publications

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“…However, this trend of increasing numbers of offshore wind farms puts pressure on local water traffic management. For example, the newly added obstacles increase difficulties of navigation for passing vessels [3] and improve the difficulties of Search and Rescue (SAR). Once an accident happens, it results in water pollution, significant damage of facilities and other catastrophic causalities and economic losses.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Method for Ship Detection and Traffic Monitoring in Offshore Wind Farm Waters

Liu¹,

Hu²,

Ji³

et al. 2023

Preprint

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Newly build offshore wind farms (OWFs) render a collision risk between ships and installations. The paper proposed a real-time traffic monitoring method based on machine vision and deep learning technology to improve the efficiency and accuracy of the traffic monitoring system in the vicinity of offshore wind farms. Specifically, the method employs real automatic identification system (AIS) data to train a machine vision model, which is then used to identify passing ships in OWF waters. Furthermore, the system utilizes stereo vision techniques to track and locate the positions of passing ships. The method is tested in offshore water in China to validate its reliability. The results prove that the system sensitively detects the dynamic information of the passing ships, such as the distance between ships and OWFs, ship speed and course. Overall, this study provides a novel approach to enhancing the safety of OWFs, which is increasingly important as the number of such installations continues to grow. By employing advanced machine vision and deep learning techniques, the proposed monitoring system offers an effective means of improving the accuracy and efficiency of ship monitoring in challenging offshore environments.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning Method for Ship Detection and Traffic Monitoring in Offshore Wind Farm Waters

Liu¹,

Hu²,

Ji³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…However, this trend of increasing numbers of offshore wind farms puts pressure on local water traffic management. For example, the newly added obstacles increase the difficulties for both the navigation of passing vessels [3] and of Search and Rescue. Once an accident happens, it results in water pollution, significant damage to facilities and other catastrophic casualties and economic losses.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Method for Ship Detection and Traffic Monitoring in an Offshore Wind Farm Area

Liu

et al. 2023

JMSE

View full text Add to dashboard Cite

Newly built offshore wind farms (OWFs) create a collision risk between ships and installations. The paper proposes a real-time traffic monitoring method based on machine vision and deep learning technology to improve the efficiency and accuracy of the traffic monitoring system in the vicinity of offshore wind farms. Specifically, the method employs real automatic identification system (AIS) data to train a machine vision model, which is then used to identify passing ships in OWF waters. Furthermore, the system utilizes stereo vision techniques to track and locate the positions of passing ships. The method was tested in offshore waters in China to validate its reliability. The results prove that the system sensitively detects the dynamic information of the passing ships, such as the distance between ships and OWFs, and ship speed and course. Overall, this study provides a novel approach to enhancing the safety of OWFs, which is increasingly important as the number of such installations continues to grow. By employing advanced machine vision and deep learning techniques, the proposed monitoring system offers an effective means of improving the accuracy and efficiency of ship monitoring in challenging offshore environments.

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Integrating systems thinking and flexibility in infrastructure management

Acuña-Coll

Sánchez-Silva

2023

Innov. Infrastruct. Solut.

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Infrastructure systems are subjected to demands imposed by extreme natural events and increasing socioeconomic pressures. The latter are derived, for example, from a rise in the export of goods and services, and population growth, which is also associated with problems such as access to housing and basic sanitation. According to the World Risk Report 2016, inadequate infrastructure increases the frequency of losses to life and property, as well as the risk that an extreme natural event might become a disaster. Thus, frequently, traditional infrastructure systems design and management have fail to meet the stakeholders’ expectations. The main problems emerge from decisions based on the assumption that systems are static, instead of changing continuously, and on uncertainty management. This paper presents, first, a conceptual proposal that combines systems thinking and flexibility as the main pillars to facilitate systems to respond and change over time. Systems thinking recognizes the dynamic evolution of systems and helps understand how processes and subprocesses interact at different levels (i.e., hierarchical arrangement of processes). Flexibility, which is the ability of a system to respond or change, is crucial to managing better risks and varying demands, thus fostering resilience to disasters and climate change. Incorporating flexibility in the development of infrastructure contributes to enhancing the management of planned or unplanned events, taking advantage of investment/business opportunities, reducing possible cost overruns, and handling the perceptions and interests of stakeholders. Based on the conceptual framework, the second part of the paper presents a proposal to integrate these concepts into a physical and financial infrastructure model. The outcome of this model is a set of management policies that render the actions that best reflect the interest of stakeholders. The conceptual ideas and the proposed model are illustrated with two examples showing the value of integrating systems thinking and flexibility in infrastructure management over time.

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A flexibility-based approach for the design and management of floating offshore wind farms

Cited by 7 publications

References 46 publications

A Deep Learning Method for Ship Detection and Traffic Monitoring in Offshore Wind Farm Waters

A Deep Learning Method for Ship Detection and Traffic Monitoring in Offshore Wind Farm Waters

A Deep Learning Method for Ship Detection and Traffic Monitoring in an Offshore Wind Farm Area

Integrating systems thinking and flexibility in infrastructure management

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