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Delegates are invited to meet and discuss with the poster presenters during the poster presentation sessions between 10:30-11:30 and 16:00-17:00 on Thursday, 19 November 2015.

Lead Session Chair:
Stephan Barth, ForWind - Center for Wind Energy Research, Germany
Marco Sepulveda Lloyds Register, United Kingdom
Co-authors:
Mark Spring (1) F Peter Davies (1) Gerard Gaal (1) Marco Sepulveda (2)
(1) Lloyds Register, Bristol, United Kingdom (2) IDCORE, Edinburgh, United Kingdom

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Presenter's biography

Biographies are supplied directly by presenters at EWEA 2015 and are published here unedited

Research Engineer at Lloyd’s Register EMEA and Doctor Candidate in engineering at the Industrial Doctorate Centre of Offshore Renewable Energy Systems (IDCORE) which is a consortium of the University of Edinburgh, University of Strathclyde, University of Exeter, the Scottish Association for Marine Science and HR-Wallingford. He worked for the largest energy conglomerate in Chile, CGE Distribution Company as business development executive of renewable energy and energy efficiency systems. In 2012 participated as energy specialist in an governmental technological mission in China to generate long term commercial relationships between Chilean and Chinese energy technology companies.


Poster

Poster Download poster (15.79 MB)

Abstract

Top 30 Chart for wind turbine failure mechanisms

Introduction

The Top 30 Chart from Lloyd's Register, listing wind turbine failure mechanisms, ranked by probability and consequence: the first reference to help ensure subsequent decisions are rational and unbiased.

Approach

Using a risk-based common format, a database may be created to hold all failure mechanisms.

The initial ranking of failure mechanisms is based on rigorous FMEA, validated and widely-used data available in the public domain and the experience of wind farm operators. The Top 30 Chart is further augmented and updated by the use of on-going measurements from operating wind farms.

Examples are given showing how the Top 30 Chart may be used to identify systems for redesign, upgrade, telemetry, new software algorithms or to re-specify the maintenance plan for a wind farm to reduce unnecessary tasks, prioritise the most urgent tasks, improve usage of vessels, crew and technicians, optimise spare parts held, schedule preventative maintenance to minimise downtime and maximise revenue.

Main body of abstract

Wind turbines can exhibit a very large number of potential failure mechanisms. Examples of the majority of these can be found in wind farm O&M procedures and from the practical experience of operators and O&M contractors. Many tasks prove to be unnecessary. Often technicians are inadequately prepared when they arrive at the turbine, with the wrong tools, spares or training.

In order to ensure a logical and balanced treatment of these large number of failure mechanisms, a risk based approach is proposed. Both quantitative and qualitative inputs can be combined. Bespoke software has been used to convert data into knowledge.

The knowledge database can include events which can be predicted or assessed to a high degree of statistical certainty and those which are relatively less-well known about. The resulting risk-ranking of all failure modes is the basis for a more intelligent approach to many important decision processes such as those during new product design, sensor selection, condition monitoring software configuration, SCADA specification, O&M management specification, O&M task prioritisation. In addition, the risk ranking is expected to change over the life of the wind farm. Risk profiles may be updated, based on time, usage, composite signals derived from operational data, experience of O&M technicians and managers.

Conclusion

A rational, hierarchical and common approach to recording failure mechanisms and assessing their risk at key stages in product life cycle, such as new product design and throughout field operation, has been shown to be helpful in enabling intelligent decisions to be made, regarding design, monitoring, retro-fitting and maintenance.


Learning objectives
The project has enabled the following to be developed.
1. balanced hierarchy of failure mechanisms, kept in a single database, incorporating many disparate sources of data and kept up-to-date as components age, wear, degrade, based on intelligent feedback from the operating plant
2. new trends observed in the measured data
3. new composite signals, derived from multiple data channels, available from operating wind turbines
4. statistically-inspired relegation of unnecessary maintenance tasks, including turbine visits for manual resets
5. predictions about the probability of future failures, showing the advantage in terms of scheduling maintenance tasks, using vessels, staff spares, minimising downtime and maximising revenue
Methods have been shown to demonstrate the benefits of investing in data collection, interpretation and storage. Such benefits include reduction in the cost of maintenance and increase in availability and revenue.