Detection of Wind Turbine Power Performance Abnormalities Using Eigenvalue Analysis

Georgios Alexandros Skrimpas, Christian Walsted Sweeney, Kun Saptohartyadi Marhadi, Bogi Bech Jensen, Nenad Mijatovic, Joachim Holbøll

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Abstract

Condition monitoring of wind turbines is a field of continu- ous research and development as new turbine configurations enter into the market and new failure modes appear. Systems utilising well established techniques from the energy and in- dustry sector, such as vibration analysis, are commercially available and functioning successfully in fixed speed and vari- able speed turbines. Power performance analysis is a method specifically applicable to wind turbines for the detection of power generation changes due to external factors, such as ic- ing, internal factors, such as controller malfunction, or delib- erate actions, such as power de-rating. In this paper, power performance analysis is performed by sliding a time-power window and calculating the two eigenvalues corresponding to the two dimensional wind speed - power generation dis- tribution. The power is classified into five bins in order to achieve better resolution and thus identify the most proba- ble root cause of the power deviation. An important aspect of the proposed technique is its independence of the power curve provided by the turbine manufacturer. It is shown that by detecting any changes of the two eigenvalues trends in the five power bins, power generation anomalies are consistently identified
Original languageEnglish
Title of host publicationProceedings of the 2014 Annual Conference of the Prognostics and Health Management Society
Number of pages7
Publication date2014
Publication statusPublished - 2014
Event2014 Annual Conference of the Prognostics and Health Management Society - Fort Worth, TX, United States
Duration: 29 Sep 20142 Oct 2014
http://www.phmsociety.org/events/conference/phm/14

Conference

Conference2014 Annual Conference of the Prognostics and Health Management Society
CountryUnited States
CityFort Worth, TX
Period29/09/201402/10/2014
Internet address

Bibliographical note

Georgios Alexandros Skrimpas et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Cite this

Skrimpas, G. A., Sweeney, C. W., Marhadi, K. S., Jensen, B. B., Mijatovic, N., & Holbøll, J. (2014). Detection of Wind Turbine Power Performance Abnormalities Using Eigenvalue Analysis. In Proceedings of the 2014 Annual Conference of the Prognostics and Health Management Society