Data analytics for fault prediction and diagnosis in wind turbines

Leahy, Kevin

Data analytics for fault prediction and diagnosis in wind turbines

dc.check.embargoformat	Embargo not applicable (If you have not submitted an e-thesis or do not want to request an embargo)	en
dc.check.info	Not applicable	en
dc.check.opt-out	Not applicable	en
dc.check.reason	Not applicable	en
dc.check.type	No Embargo Required
dc.contributor.advisor	O'Sullivan, Dominic	en
dc.contributor.author	Leahy, Kevin
dc.contributor.funder	Science Foundation Ireland	en
dc.date.accessioned	2019-01-30T11:42:53Z
dc.date.available	2019-01-30T11:42:53Z
dc.date.issued	2018
dc.date.submitted	2018
dc.description.abstract	As feed-in tariffs for wind energy are gradually being replaced by market driven auction-based systems, the need for cost savings at every stage of a wind energy project is more apparent than ever. A proven and effective way of reducing maintenance costs is through a condition-based maintenance (CBM) strategy. By using supervisory control and data acquisition (SCADA) system data instead of retrofitting a dedicated condition monitoring (CM) system, CM functionality can be gained at a fraction of the cost. This thesis investigates using SCADA system data for various levels of CM: fault detection, diagnosis and prediction. First, a case study is presented on using classification techniques for CM using SCADA data. Various methods for dealing with the massive class imbalance seen in fault data are evaluated. It was found that all three levels of CM are possible using classification techniques, though with a high number of false positives. Adding a class-weight to the minority class or undersampling the majority class were found to be the best ways of dealing with class imbalance. Sources of accurate failure data can be difficult to obtain for wind turbines. The second part of this thesis presents a novel way of building a historical failure database using alarm system and availability data. This was shown to produce an accurate database of unplanned stoppages related to assembly-level failures, scheduled maintenance, or grid, noise or shadow-related events. Next, common issues with some of the classification approaches present in the literature are addressed, as well as the lack of demonstration of how these approaches would perform in the field. A formalised framework with a prescribed list of steps following best practice guidelines is presented for performing CM using classification techniques on turbine SCADA data. A case study is performed which uses a sliding window metric to evaluate field performance, showing that such a system is effective at flagging faults in advance, but more data is needed to reduce the false positive rate. It is noted throughout the thesis that turbine alarm systems have some consistent shortcomings, and do not live up to their full potential. Hence, a novel methodology is presented which uses clustering techniques to identify similar sequences of alarms as they occurred during unplanned stoppages. A case study applying the methodology showed that just under half of the 456 stoppages could be sorted into one of fifteen distinct types of alarm sequence.	en
dc.description.status	Not peer reviewed	en
dc.description.version	Accepted Version
dc.format.mimetype	application/pdf	en
dc.identifier.citation	Leahy, K. 2018. Data analytics for fault prediction and diagnosis in wind turbines. PhD Thesis, University College Cork.	en
dc.identifier.endpage	177	en
dc.identifier.uri	https://hdl.handle.net/10468/7375
dc.language.iso	en	en
dc.publisher	University College Cork	en
dc.relation.project	info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2302/IE/Marine Renewable Energy Ireland (MaREI) - The SFI Centre for Marine Renewable Energy Research/	en
dc.rights	© 2018, Kevin Leahy.	en
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/	en
dc.subject	Wind energy	en
dc.subject	Condition monitoring	en
dc.subject	Scada data	en
dc.subject	Machine learning	en
dc.thesis.opt-out	false
dc.title	Data analytics for fault prediction and diagnosis in wind turbines	en
dc.type	Doctoral thesis	en
dc.type.qualificationlevel	Doctoral	en
dc.type.qualificationname	PhD	en
ucc.workflow.supervisor	dominic.osullivan@ucc.ie