Fault Detection of Wind Turbine Induction Generators through Current Signals and Various Signal Processing Techniques

Abstract

In the wind industry (WI), a robust and effective maintenance system is essential. To minimize the maintenance cost, a large number of methodologies and mathematical models for predictive maintenance have been developed. Fault detection and diagnosis are carried out by processing and analyzing various types of signals, with the vibration signal predominating. In addition, most of the published proposals for wind turbine (WT) fault detection and diagnosis have used simulations and test benches. Based on previous work, this research report focuses on fault diagnosis, in this case using the electrical signal from an operating WT electric generator and applying various signal analysis and processing techniques to compare the effectiveness of each. The WT used for this research is 20 years old and works with a squirrel-cage induction generator (SCIG) which, according to the wind farm control systems, was fault-free. As a result, it has been possible to verify the feasibility of using the current signal to detect and diagnose faults through spectral analysis (SA) using a fast Fourier transform (FFT), periodogram, spectrogram, and scalogram.

Centrifugal Pump Fault Detection with Convolutional Neural Network Transfer Learning

“…BRBs and/or short circuit between turns faults: For every turn, the tracked MCSA plot produces a slightly altered version of every cycle and causes an elliptical shape of the Park transform [ 21 ]. The difference can also be seen more clearly between healthy and a BRB fault problem in [ 20 ].…”

“…The presented approach was inspired by the discussion of DQ patterns and related motor conditions by Irfan et al in [ 11 , 20 , 21 ], as well as a Park vector application for DQ [ 22 ]. Irfan et al [ 11 ] used a three-phased 1.5 hp, 3450 rpm and 60 Hz centrifugal pump and proposed an electric diagnostic technique to detect CP faults without needing extra sensors.…”

“…The dataset included data from non-faulty operational pumps; pumps with diagnosed faults; and intentionally damaged pumps, including damage such as impeller damage, stator winding faults, excessive vibrations, cavitations and/or bearing damage faults. A novel fault detection method is then proposed based on DQ pattern analysis that built upon work by [ 11 , 20 , 21 , 22 ]. The method extracts DQ patterns and feeds them into convolutional neural network transfer learning for analysis to provide the binary classification of different type of faults.…”

Transformer Core Fault Diagnosis via Current Signal Analysis with Pearson Correlation Feature Selection

“…The first method used short-time Fourier transform (STFT) analysis on stator phase current signals for demagnetization fault diagnosis in permanent magnet synchronous motor (PMSM) drive systems, with high accuracy results obtained using k-nearest neighbors (KNNs) and multilayer perceptron (MLP) models. Similarly, in [28], they explored fault diagnosis in wind turbines (WTs) using electrical signals from the generator of a 20-year-old operating WT. For this, signal analysis techniques such as fast Fourier transform (FFT) and periodogram were employed to compare the effectiveness of spectral analysis methods, demonstrating the feasibility of using current signals for fault detection.…”

An Entropy-Based Condition Monitoring Strategy for the Detection and Classification of Wear Levels in Gearboxes

“…As the gearboxes are commonly connected to an electric motor, the motor current signature analysis (MCSA) technique has been widely used for the detection of faults in gearboxes. These techniques use frequency domain transforms as the fast Fourier transform (FFT) [ 10 , 11 ], or time–frequency transformation as wavelets [ 12 ], multiple signal classification (MUSIC) [ 13 ], and empirical mode decomposition (EMD) [ 14 ] to extract behavioral patterns that change from one operating condition to another. Additionally, the information obtained from the domain transforms usually works along with some artificial intelligence techniques, such as artificial neural networks (ANNs) [ 15 ] and fuzzy systems [ 16 ] that work as classifiers to perform automatic detection of the type or severity of fault that is present in the gearbox.…”