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

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.

Prediction method of longitudinal surface settlement caused by double shield tunnelling based on deep learning

“…Studies pertaining to longitudinal surface settlement utilise settlement information per ring, thereby significantly increasing the database capacity compared with studies pertaining to the maximum surface settlement 26 . However, the high excavation speed of the shield tunnelling method causes inadequate monitoring of surface settlement, resulting in a limited database capacity compared to other machine learning problems 32 , 33 . Ye et al 34 obtained surface settlement data by tunnelling under ancient towers and compared the prediction accuracy of databases with different capacities.…”

Prediction Method of Longitudinal Surface Settlement Caused by Double Shield Tunnelling Based on Deep Learning

“…Studies pertaining to longitudinal surface settlement utilise settlement information per ring, thereby significantly increasing the database capacity compared with studies pertaining to the maximum surface settlement [26] . However, the high excavation speed of the shield tunnelling method causes inadequate monitoring of surface settlement, resulting in a limited database capacity compared to other machine learning problems [31,32] . Ye, et al [33] obtained surface settlement data by tunnelling under ancient towers and compared the prediction accuracy of databases with different capacities.…”