Next Article in Journal
Next Article in Special Issue
Previous Article in Journal
Previous Article in Special Issue
Energies 2014, 7(5), 3104-3120; doi:10.3390/en7053104
Article

Condition Parameter Modeling for Anomaly Detection in Wind Turbines

1
, 1,*  and 2
Received: 20 January 2014; in revised form: 5 April 2014 / Accepted: 30 April 2014 / Published: 6 May 2014
(This article belongs to the Special Issue Wind Turbines 2014)
Abstract: Data collected from the supervisory control and data acquisition (SCADA) system, used widely in wind farms to obtain operational and condition information about wind turbines (WTs), is of important significance for anomaly detection in wind turbines. The paper presents a novel model for wind turbine anomaly detection mainly based on SCADA data and a back-propagation neural network (BPNN) for automatic selection of the condition parameters. The SCADA data sets are determined through analysis of the cumulative probability distribution of wind speed and the relationship between output power and wind speed. The automatic BPNN-based parameter selection is for reduction of redundant parameters for anomaly detection in wind turbines. Through investigation of cases of WT faults, the validity of the automatic parameter selection-based model for WT anomaly detection is verified.
Keywords: wind turbine; SCADA data; parameter selection; anomaly detection wind turbine; SCADA data; parameter selection; anomaly detection
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.

Export to BibTeX |
EndNote


MDPI and ACS Style

Yan, Y.; Li, J.; Gao, D.W. Condition Parameter Modeling for Anomaly Detection in Wind Turbines. Energies 2014, 7, 3104-3120.

AMA Style

Yan Y, Li J, Gao DW. Condition Parameter Modeling for Anomaly Detection in Wind Turbines. Energies. 2014; 7(5):3104-3120.

Chicago/Turabian Style

Yan, Yonglong; Li, Jian; Gao, David W. 2014. "Condition Parameter Modeling for Anomaly Detection in Wind Turbines." Energies 7, no. 5: 3104-3120.


Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert