NDLI: A Data-Mining Approach to Monitoring Wind Turbines

Content Provider	IEEE Xplore Digital Library
Author	Kusiak, A. Verma, A.
Copyright Year	2010
Abstract	The rapid expansion of wind farms has generated interest in operations and maintenance. An operating wind turbine undergoes various state changes, including transformation from a normal to a fault mode. Condition-based maintenance tools are needed to identify potential faults in the system. The prediction of turbine fault modes is of particular interest. In this research, data-mining algorithms are employed to construct prediction models for wind turbine faults. A three-stage prediction process is followed: 1) prediction of a fault of any kind; 2) prediction of specific faults of the system; and 3) identification on unseen faults. A comparative analysis of various data-mining algorithms is reported based on the data collected at a large wind farm. Random forest algorithm models provided the best accuracy among all algorithms tested. The robustness of the predictive model is validated for faults that have occurred at turbines with previously unseen data. The research results discussed in this paper have been derived from data collected at 17 wind turbines.
Starting Page	150
Ending Page	157
Page Count	8
File Size	1421836
File Format	PDF
ISSN	19493029
Volume Number	3
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wind turbines Prediction algorithms Maintenance engineering Accuracy Data mining Monitoring Predictive models wind turbine states multiclass classification prediction wind turbine
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Fault Monitoring of Wind Turbine Generator Brushes: A Data-Mining Approach

Prediction of Status Patterns of Wind Turbines: A Data-Mining Approach

A Data-Driven Approach for Monitoring Blade Pitch Faults in Wind Turbines

Monitoring Wind Turbine Vibration Based on SCADA Data

On-condition maintenance for wind turbines

An Approach for Condition-Based Maintenance Optimization Applied to Wind Turbine Blades

Assessment of Condition Monitoring Techniques for Offshore Wind Farms

Comparison of Wind Turbine Performance Prediction and Measurement

Modeling and Prediction of Gearbox Faults With Data-Mining Algorithms

A Data-Mining Approach to Monitoring Wind Turbines

Similar Documents

Fault Monitoring of Wind Turbine Generator Brushes: A Data-Mining Approach

Prediction of Status Patterns of Wind Turbines: A Data-Mining Approach

A Data-Driven Approach for Monitoring Blade Pitch Faults in Wind Turbines

Monitoring Wind Turbine Vibration Based on SCADA Data

On-condition maintenance for wind turbines

An Approach for Condition-Based Maintenance Optimization Applied to Wind Turbine Blades

Assessment of Condition Monitoring Techniques for Offshore Wind Farms

Comparison of Wind Turbine Performance Prediction and Measurement

Modeling and Prediction of Gearbox Faults With Data-Mining Algorithms

A Data-Mining Approach to Monitoring Wind Turbines