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Condition Assessment and Analysis of Bearing of Doubly Fed Wind Turbines Using Machine Learning Technique

Aiman Abbas Mahar, Nayyar Hussain Mirjat (nayyar.hussain@faculty.muet.edu.pk), Bhawani S. Chowdhry, Laveet Kumar, Quynh T. Tran and Gaetano Zizzo (gaetano.zizzo@unipa.it)
Additional contact information
Aiman Abbas Mahar: Department of Electronic Engineering, Mehran University of Engineering & Technology, Jamshoro 76062, Pakistan
Nayyar Hussain Mirjat: Department of Electrical Engineering, Mehran University of Engineering & Technology, Jamshoro 76062, Pakistan
Bhawani S. Chowdhry: NCRA-HHRCMS Lab, Mehran University of Engineering and Technology, Jamshoro 76062, Pakistan
Laveet Kumar: Department of Mechanical Engineering, Mehran University of Engineering & Technology, Jamshoro 76062, Pakistan
Quynh T. Tran: Institute of Energy Science, Vietnam Academy of Science and Technology, Hanoi 10000-04, Vietnam
Gaetano Zizzo: Department of Engineering, University of Palermo, 90128 Palermo, Italy

Energies, 2023, vol. 16, issue 5, 1-16

Abstract: Condition monitoring of wind turbines is progressively increasing to maintain the continuity of clean energy supply to power grids. This issue is of great importance since it prevents wind turbines from failing and overheating, as most wind turbines with doubly fed induction generators (DFIG) are overheated due to faults in generator bearings. Bearing fault detection has become a main topic targeting the optimum operation, unscheduled downtime, and maintenance cost of turbine generators. Wind turbines are equipped with condition monitoring devices. However, effective and reliable fault detection still faces significant difficulties. As the majority of health monitoring techniques are primarily focused on a single operating condition, they are unable to effectively determine the health condition of turbines, which results in unwanted downtimes. New and reliable strategies for data analysis were incorporated into this research, given the large amount and variety of data. The development of a new model of the temperature of the DFIG bearing versus wind speed to identify false alarms is the key innovation of this work. This research aims to analyze the parameters for condition monitoring of DFIG bearings using SCADA data for k-means clustering training. The variables of k are obtained by the elbow method that revealed three classes of k (k = 0, 1, and 2). Box plot visualization is used to quantify data points. The average rotation speed and average temperature measurement of the DFIG bearings are found to be primary indicators to characterize normal or irregular operating conditions. In order to evaluate the performance of the clustering model, an analysis of the assessment indices is also executed. The ultimate goal of the study is to be able to use SCADA-recorded data to provide advance warning of failures or performance issues.

Keywords: bearings; condition monitoring; DFIG; K means; SCADA data; wind turbine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (1)

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