Journal article 1167 views 100 downloads
A hybrid prognostic methodology for tidal turbine gearboxes
,
Ian Masters ,
Joao Amaral Teixeira
Renewable Energy, Volume: 114, Issue: Part B, Pages: 1051 - 1061
Swansea University Authors:
Michael Togneri , Ian Masters
-
PDF | Version of Record
Released under the terms of a Creative Commons Attribution License (CC-BY).
Download (2.22MB)
DOI (Published version): 10.1016/j.renene.2017.07.093
Abstract
Tidal energy is one of promising solutions for reducing greenhouse gas emissions and it is estimated that 100 TWh of electricity could be produced every year from suitable sites around the world. Although premature gearbox failures have plagued the wind turbine industry, and considerable research ef...
| Published in: | Renewable Energy |
|---|---|
| ISSN: | 0960-1481 |
| Published: |
2017
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa34753 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract: |
Tidal energy is one of promising solutions for reducing greenhouse gas emissions and it is estimated that 100 TWh of electricity could be produced every year from suitable sites around the world. Although premature gearbox failures have plagued the wind turbine industry, and considerable research efforts continue to address this challenge, tidal turbine gearboxes are expected to experience higher mechanical failure rates given they will experience higher torque and thrust forces. In order to minimize the maintenance cost and prevent unexpected failures there exists a fundamental need for prognostic tools that can reliably estimate the current health and predict the future condition of the gearbox.This paper presents a life assessment methodology for tidal turbine gearboxes which was developed with synthetic data generated using a blade element momentum theory (BEMT) model. The latter has been used extensively for performance and load modelling of tidal turbines. The prognostic model developed was validated using experimental data. |
|---|---|
| Keywords: |
Tidal Turbines; Prognosis; Gearbox; Life Prediction; Diagnosis; Health management |
| College: |
Faculty of Science and Engineering |
| Issue: |
Part B |
| Start Page: |
1051 |
| End Page: |
1061 |