• EN
  • DA

Danish NationalResearch Database

  • Publications
  • Researchers
Example Finds records
water{} containing the word "water".
water supplies"{}" containing the phrase "water supplies".
author:"Doe, John"author:"{}" containing the phrase "Doe, John" in the author field.
title:IEEEtitle:{} containing the word "IEEE" in the title field.
bech{} containing the word "bech".
marie bech"{}" containing the phrase "marie bech".
orcid:0000-0002-5429-5292orcid:{} Having a particular ORCID
Need more help? Advanced search tutorial
  • Selected (0)
  • History

Condition monitoring with wind turbine SCADA data using Neuro-Fuzzy normal behavior models

    • Save to Mendeley
    • Export to BibTeX
    • Export to RIS
    • Email citation
Authors:
  • Schlechtingen, Meik ;
    Close
    Department of Mechanical Engineering, Technical University of Denmark
  • Santos, Ilmar
    Close
    Orcid logo0000-0002-8441-5523
    Department of Mechanical Engineering, Technical University of Denmark
DOI:
10.1115/GT2012-68011
Abstract:
This paper presents the latest research results of a project that focuses on normal behavior models for condition monitoring of wind turbines and their components, via ordinary Supervisory Control And Data Acquisition (SCADA) data. In this machine learning approach Adaptive Neuro-Fuzzy Interference System (ANFIS) models are employed to learn the normal behavior in a training phase, where the component condition can be considered healthy. In the application phase the trained models are applied to predict the target signals, e.g. temperatures, pressures, currents, power output, etc. The behavior of the prediction error is used as an indicator for normal and abnormal behavior, with respect to the learned behavior. The advantage of this approach is that the prediction error is widely decoupled from the typical fluctuations of the SCADA data caused by the different turbine operational modes. To classify the component condition Fuzzy Interference System (FIS) structures are used. Based on rules that are established with the prediction error behavior during faults previously experienced and generic rules, the FIS outputs the component condition (green, yellow and red). Furthermore a first diagnosis of the root cause is given. In case of fault patterns earlier unseen the generic rules allow general statements about the signal behavior which highlight the anomaly. Within the current research project this method is applied to 18 onshore turbines of the 2 MW class operating since April 2009. First results show that the proposed method is well suited to closely monitor a large variety of signals, identify anomalies and correctly classify the component condition. The accuracy of the normal behavior models developed is high and small signal behavior changes become recognizable. The result of the automatic analysis is given in graphical and text format. Within the paper examples of real faults are provided, showing the capabilities of the method proposed. The method can be applied both to existing and new built turbines without the need of any additional hardware installation or manufacturers input.
ISBN:
9780791844724
Type:
Conference paper
Language:
English
Published in:
Journal of Alloys and Compounds, 2012, p. 717-726
Main Research Area:
Science/technology
Conference:
ASME Turbo Expo 2012
Publisher:
American Society of Mechanical Engineers
Submission year:
2012
ID:
244356981

Full text access

  • Doi Get publisher edition via DOI resolver
Checking for on-site access...

On-site access

At institution

  • Technical university of dk

Metrics

Feedback

Sitemap

  • Search
    • Statistics
    • Tutorial
    • Data
    • FAQ
    • Contact
  • About
    • Institutions
    • Release History
    • Cookies and Personal Data
  • Open Access
    • The Danish Open Access Indicator

Copyright © 1998–2018.

Fivu en