Risk and cost optimised condition monitoring system design for marine renewable energy
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Date
03/07/2020Item status
Restricted AccessEmbargo end date
03/07/2022Author
Kenny, Calum Jethro
Metadata
Abstract
Marine Renewable Energy (MRE) has significant potential to contribute to global energy security
and sustainability. However, the high initial Levelised Cost Of Energy (LCOE) of MRE, as well as
issues with survivability and reliability, present challenges for its commercialisation. Predictive
maintenance using Condition Monitoring Systems (CMS) addresses these challenges by improving
availability and reducing operational expenditure. This is achieved by monitoring critical subsystems
for the detection and prediction of faults and allowing for maintenance personnel to take mitigating
action prior to subsystem or component failure.
However, the design and application of CMS in MRE is not standardised due to the relatively
nascent operational experience in the sector, as well as the great variety of Wave Energy Converter
(WEC) operating principles. Furthermore, the balance between risk monitoring and cost reduction in
CMS design is not readily accounted for in current reliability methods. A portfolio approach to
sensor selection and risk reduction is therefore proposed, incorporating a Failure Mode and Effects
Analysis (FMEA) to identify and prioritise risks versus reward. This addresses the risk-reduction and
cost-benefit trade-off in CMS design.
This thesis presents two approaches to optimise risk-management and cost-benefit associated with
a CMS sensor portfolio applied to MRE devices, using both an expert judgement, and a matrix-based
optimisation for comparative purposes. In the former approach, sensor upgrades for an articulated
WEC are divided into functional packages and expert judgement is used to determine sensor
selection for a CMS portfolio based on the practical installation requirements of the sensors.
Using the latter approach, sensor upgrades are made to the cooling system of an operational Tidal
Energy Converter (TEC). Firstly, gathered upgrade data is used in conjunction with a physical model
to observe deviation in cooling system performance. Secondly, changes in relationships between
key cooling system variables are observed across a dataset spanning 6 months. The latter approach
is found to effectively identify key application areas for sensor installation in a quantitative manner.
The addition of sensors in a risk and cost optimised CMS portfolio offers a valuable practical
compromise between: an expert’s best-guess qualitative approach (of limited comparative
experience value in new applications) and extensive lifetime operational models (unavailable or
unvalidated in MRE as data is too sparse). The risk and cost optimised CMS portfolio approach may
be extended beyond MRE and applied to other industries, particularly benefitting practitioners in
industries with relatively little experience in CMS design and operation.