Normalization Process Based on Kernel Ridge Regression Applied on Wind Turbine IAS Monitoring - Archive ouverte HAL
Communication Dans Un Congrès Année : 2019

Normalization Process Based on Kernel Ridge Regression Applied on Wind Turbine IAS Monitoring

Résumé

Variable speed wind turbines use the available wind resource more efficiently than a fixed speed wind turbine, especially during light wind conditions. This enhancement forces the monitoring methods to deal with these large variations in speed and torque, since the conditions are seldom if ever stationary. The unsteady behavior of these wind turbines is also a difficulty in terms of long term diagnostic, since the comparison of successive measurements is usually performed under the same operating conditions. Normalization of the indicators according to well-chosen variables might bring a valuable tool regarding several aspects. In this paper, the attention is focused on the regression process using classical machine learning tools. The difficulty is to design a process able to efficiently estimate the behavior of any indicator depending on the environmental conditions. Indeed, indicator multivariate laws are expected to present extremely varied shapes, and using common linear regression technique can hardly solve this issue. Kernel machines are therefore presented in this paper as an efficient solution to normalize the indicators, and will be shown to ease the health monitoring of the wind turbine shaft line on a practical case based on instantaneous angular speed signals. The example presents the distinctive feature to have a defect visible only specific operating conditions. This operating conditions being unknown a priori, this example clearly enlightens the need of such a regression tool.
Fichier non déposé

Dates et versions

hal-04670563 , version 1 (12-08-2024)

Identifiants

Citer

Hugo André, Flavien Allemand, Ilyes Khelf, Adeline Bourdon, Didier Remond. Normalization Process Based on Kernel Ridge Regression Applied on Wind Turbine IAS Monitoring. ADVANCES IN CONDITION MONITORING OF MACHINERY IN NON-STATIONARY OPERATIONS (CMMNO 2018), Jun 2018, GEWERBESTRASSE 11, CHAM, CH-6330, Switzerland. pp.44-53, ⟨10.1007/978-3-030-11220-2\_6⟩. ⟨hal-04670563⟩
0 Consultations
0 Téléchargements

Altmetric

Partager

More