London (PRWEB) October 22, 2013
Recently, further accuracy and enhancements have been made in wind prediction and measurements, and regulatory regimes show potential signs of stability (particularly in the US following the renewal of the Production Tax Credit). The attention therefore now turns from the top line to the bottom line as project stakeholders seek to increase yields and reduce cost. While these two variables would seem to be in conflict, a carefully constructed O&M programme can achieve precisely that by proactive measures that will increase yield through lower downtime, achieved at a potentially lower cost.
In order to construct and craft an effective O&M programme, an in depth knowledge of the plant is required including service intervals, spares inventory, access to labour, crane hire agreement, access restrictions etc. Typically, there is a reliance on the engineering expertise that resides at owner or project level, leveraging on the experience of the engineers who have managed and worked on Wind Turbines for many years, in some cases since the dawn of the industry itself. While the input of project engineers is essential, the application of data analytics to O&M can assist in pinpointing loss drivers from a time and cost perspective. These outputs can then be used and applied by experienced engineers to reconfigure and optimise existing O&M programmes.
There is a huge amount of data now available from a Wind Energy Project, including (but not limited to):
SCADA Data whether in real time or reported out on a specific interval (e.g. 10 minutes) or change-only basis.
CMS Data whether from vibration monitoring, acoustic emissions or lube oil monitoring.
Work Order Data detailing where, when and how much each repair costs.
Anemometer Data extracted from the SCADA but processed separately.
Making sense of the vast quantities of data, which could be up to 1TB of data per week in raw unprocessed form, is an overly complex and time consuming task for manual analysis. The application of dedicated, sophisticated analytics to this data assists the engineer in making sense of the data and provides useable outputs to (re)configure O&M programmes.
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