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Component reliability test approaches for marine renewable energy

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Listed:
  • Philipp R Thies
  • Lars Johanning
  • Kwaku Ampea Karikari-Boateng
  • Chong Ng
  • Paul McKeever

Abstract

An increasing number of marine renewable energy (MRE) systems are reaching the stage where a working prototype must be demonstrated in operation in order to progress to the next stage of commercial projects. This stage is often referred to as ‘valley of death’ where device developers face the challenge of raising capital needed to demonstrate the prototype. The dilemma is that investors understandably demand a proven track record and demonstrated reliability in order to provide capital. One way to resolve this dilemma is specific component reliability testing that not only satisfies investor expectations but holds the potential to improve and de-risk components for MRE. This paper gives an overview to different component reliability test approaches in established industries and for MRE, covering both wave and tidal energy technologies. There has been notable activity in the research community to develop and implement dedicated component reliability test rigs that allow the investigation and demonstration of component reliability under controlled, yet representative conditions. Two case studies of physical test rigs will illustrate the possible test approaches. The Nautilus Powertrain test rig, a facility at the Offshore Renewable Energy (ORE) Catapult, focuses on the demonstration and testing of drive train components including gearboxes, generators, mechanical couplings and bearings. The Dynamic Marine Component test rig (DMaC) at the University of Exeter aims to replicate the forces and motions for floating offshore applications and their subsystems, including mooring lines and power cables. This paper highlights the relevance of component testing and qualification prior to large-scale commercial deployments and gives an insight to some of the test capabilities available in the sector. Several case studies illustrate the component test approach for tidal energy (Nautilus) and wave energy (DMaC) applications.

Suggested Citation

  • Philipp R Thies & Lars Johanning & Kwaku Ampea Karikari-Boateng & Chong Ng & Paul McKeever, 2015. "Component reliability test approaches for marine renewable energy," Journal of Risk and Reliability, , vol. 229(5), pages 403-416, October.
  • Handle: RePEc:sae:risrel:v:229:y:2015:i:5:p:403-416
    DOI: 10.1177/1748006X15580837
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    1. Kong, C. & Bang, J. & Sugiyama, Y., 2005. "Structural investigation of composite wind turbine blade considering various load cases and fatigue life," Energy, Elsevier, vol. 30(11), pages 2101-2114.
    2. Henderson, Ross, 2006. "Design, simulation, and testing of a novel hydraulic power take-off system for the Pelamis wave energy converter," Renewable Energy, Elsevier, vol. 31(2), pages 271-283.
    3. Kaldellis, J.K. & Kapsali, M., 2013. "Shifting towards offshore wind energy—Recent activity and future development," Energy Policy, Elsevier, vol. 53(C), pages 136-148.
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