LAPSE:2020.0708
Published Article
LAPSE:2020.0708
Wind Turbine Condition Monitoring Strategy through Multiway PCA and Multivariate Inference
June 23, 2020
This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA) data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA). Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated as a random process given the random nature of the turbulent wind. The objective is to decide if the multivariate distribution that is obtained from the wind turbine to be analyzed (healthy or not) is related to the baseline one. To achieve this goal, a test for the equality of population means is performed. Finally, the results of the test can determine that the hypothesis is rejected (and the wind turbine is faulty) or that there is no evidence to suggest that the two means are different, so the wind turbine can be considered as healthy. The methodology is evaluated on a wind turbine fault detection benchmark that uses a 5 MW high-fidelity wind turbine model and a set of eight realistic fault scenarios. It is noteworthy that the results, for the presented methodology, show that for a wide range of significance, α ∈ [ 1 % , 13 % ] , the percentage of correct decisions is kept at 100%; thus it is a promising tool for real-time wind turbine condition monitoring.
Keywords
condition monitoring, Fault Detection, multivariate statistical hypothesis testing, principal component analysis, wind turbine
Suggested Citation
Pozo F, Vidal Y, Salgado Ó. Wind Turbine Condition Monitoring Strategy through Multiway PCA and Multivariate Inference. (2020). LAPSE:2020.0708
Author Affiliations
Pozo F: Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, Spain [ORCID]
Vidal Y: Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, Spain [ORCID]
Salgado Ó: Mechanical Engineering, IK4-Ikerlan, J.M. Arizmendiarrieta 2, 20500 Arrasate (Gipuzkoa), Spain [ORCID]
[Login] to see author email addresses.
Journal Name
Energies
Volume
11
Issue
4
Article Number
E749
Year
2018
Publication Date
2018-03-26
Published Version
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en11040749, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2020.0708
This Record
External Link

doi:10.3390/en11040749
Publisher Version
Download
Files
[Download 1v1.pdf] (591 kB)
Jun 23, 2020
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
418
Version History
[v1] (Original Submission)
Jun 23, 2020
 
Verified by curator on
Jun 23, 2020
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2020.0708
 
Original Submitter
Calvin Tsay
Links to Related Works
Directly Related to This Work
Publisher Version