Vulnerability Assessment of the Offshore Wind Farms by Using the Functional Resonance Analysis Method

The German authorities have imposed a legal requirement to expand offshore wind energy generation to a total capacity of 70 GW by the end of 2040, making it a critical source of energy supply.The Europe-wide power outage in 2006 was caused by a poorly planned disconnection of an extra-high voltage line is a good example of the consequences when the energy supply is interrupted.In these kinds of events, intervening promptly and appropriately could be challenging due to the location of the incident.Furthermore, maintenance and repairs can take a long time due to the special technical equipment and thus may limit energy supply during the course of a failure.Apart from that, attacks or accidents can also occur at various places in the infrastructure and may have a wide range of damaging effects.This study presents a method to identify the most vulnerable functions of an Offshore Wind Farm (OWF) whereby the analysis is carried out in two distinctive phases.In the first phase, a Functional Resonance Analysis method (FRAM) is carried out to visualize the wind generation process starting from wind flows to power generation, power transmission and to the onshore substations.In the second phase, a vulnerability assessment using the Krings method is conducted whereby additional factors such as the effect of failures and downtimes are considered in order to define vulnerabilities.The results of this assessment are then compared with vulnerability perceptions of stakeholders in the offshore wind energy sector based on interviews.The study finds that industry stakeholders tend to overrate the vulnerability of offshore wind turbines while the FRAM model indicates a higher vulnerability for offshore platforms.

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.3850/978-981-94-3281-3_esrel-sra-e2025-p7790-cd
Primary Topic
Ocean Waves and Remote Sensing
Type
article
Field-Weighted Citation Impact
0.00

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article

Vulnerability Assessment of the Offshore Wind Farms by Using the Functional Resonance Analysis Method

Frank Sill Torres, Babette Tecklenburg, Sebastian Suhling
Ocean Waves and Remote Sensing
article

Vulnerability Assessment of the Offshore Wind Farms by Using the Functional Resonance Analysis Method

Frank Sill Torres, Babette Tecklenburg, Sebastian Suhling
article en

Abstract

The German authorities have imposed a legal requirement to expand offshore wind energy generation to a total capacity of 70 GW by the end of 2040, making it a critical source of energy supply.The Europe-wide power outage in 2006 was caused by a poorly planned disconnection of an extra-high voltage line is a good example of the consequences when the energy supply is interrupted.In these kinds of events, intervening promptly and appropriately could be challenging due to the location of the incident.Furthermore, maintenance and repairs can take a long time due to the special technical equipment and thus may limit energy supply during the course of a failure.Apart from that, attacks or accidents can also occur at various places in the infrastructure and may have a wide range of damaging effects.This study presents a method to identify the most vulnerable functions of an Offshore Wind Farm (OWF) whereby the analysis is carried out in two distinctive phases.In the first phase, a Functional Resonance Analysis method (FRAM) is carried out to visualize the wind generation process starting from wind flows to power generation, power transmission and to the onshore substations.In the second phase, a vulnerability assessment using the Krings method is conducted whereby additional factors such as the effect of failures and downtimes are considered in order to define vulnerabilities.The results of this assessment are then compared with vulnerability perceptions of stakeholders in the offshore wind energy sector based on interviews.The study finds that industry stakeholders tend to overrate the vulnerability of offshore wind turbines while the FRAM model indicates a higher vulnerability for offshore platforms.

Deutsches Zentrum für Luft- und Raumfahrt
Openalex Percentile: Top 71%
Ocean Waves and Remote Sensing
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