Next Article in Journal
Dual Search Maximum Power Point (DSMPP) Algorithm Based on Mathematical Analysis under Shaded Conditions
Next Article in Special Issue
Overview of Modelling and Advanced Control Strategies for Wind Turbine Systems
Previous Article in Journal
Forecasting China’s Annual Biofuel Production Using an Improved Grey Model
Previous Article in Special Issue
An Experimental Study on the Effects ofWinglets on the Wake and Performance of a ModelWind Turbine
Article Menu

Export Article

Open AccessArticle
Energies 2015, 8(10), 12100-12115; doi:10.3390/en81012100

Integrating Auto-Associative Neural Networks with Hotelling T2 Control Charts for Wind Turbine Fault Detection

Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung City 41170, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Frede Blaabjerg
Received: 6 September 2015 / Revised: 9 October 2015 / Accepted: 19 October 2015 / Published: 23 October 2015
(This article belongs to the Special Issue Wind Turbine 2015)
View Full-Text   |   Download PDF [1073 KB, uploaded 23 October 2015]   |  

Abstract

This paper presents a novel methodology to detect a set of more suitable attributes that may potentially contribute to emerging faults of a wind turbine. The set of attributes were selected from one-year historical data for analysis. The methodology uses the k-means clustering method to process outlier data and verifies the clustering results by comparing quartiles of boxplots, and applies the auto-associative neural networks to implement the residual approach that transforms the data to be approximately normally distributed. Hotelling T2 multivariate quality control charts are constructed for monitoring the turbine’s performance and relative contribution of each attribute is calculated for the data points out of upper limits to determine the set of potential attributes. A case using the historical data and the alarm log is given and illustrates that our methodology has the advantage of detecting a set of susceptible attributes at the same time compared with only one independent attribute is monitored. View Full-Text
Keywords: wind energy; fault detection; auto-associative neural networks; hotelling T2 control charts wind energy; fault detection; auto-associative neural networks; hotelling T2 control charts
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Yang, H.-H.; Huang, M.-L.; Yang, S.-W. Integrating Auto-Associative Neural Networks with Hotelling T2 Control Charts for Wind Turbine Fault Detection. Energies 2015, 8, 12100-12115.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top