scholarly journals THE USE OF DISCRETE WAVELET ANALYSIS OF VIBRO-ACOUSTIC SIGNALS TO DETECT THE POWER SUPPLY ASYMMETRY OF ROTATING ACCOMPANYING ELECTRIC MACHINES

2021 ◽  
pp. 62-70
Author(s):  
Valeriy Graniak ◽  
Oleg Gaidamak
Keyword(s):  
Wavelet Transform ◽  
Frequency Band ◽  
Power Supply ◽  
Supply Voltage ◽  
Acoustic Signals ◽  
Electric Machines ◽  
Electric Machine ◽  
Discrete Wavelet ◽  
Power Asymmetry ◽  
Transform Coefficients

The work shows that among the existing sufficiently described and studied approaches that are suitable for analyzing the temporal realization of a vibosignal, which can be obtained during the operation of a real electric machine, one can single out Fourier transforms and discrete wavelet transformations. An analysis of the descriptions of vibro-acoustic signals given in the literature, caused by the asymmetry of the power supply, shows that this defect leads to the appearance of oscillations that contain a harmonic component localized at the frequency of the supply voltage of the electrical network. This fact justifies the expediency of analyzing the frequency range, which includes the frequency of the supply voltage, and the selection of the mother wavelet, based on the features inherent in a single harmonic oscillation. A method for detecting a defect in the asymmetry of power supply to rotating electric machines of alternating current using a discrete wavelet transformation of a vibro-acoustic signal is proposed. The frequency band has been established, which is advisable to analyze in order to identify the indicated defect. It was found that the detection of a power asymmetry defect with the use of the wavelet transform of the temporal realization of the vibroacoustic signal is advisable to carry out using the Haar maternal wavelet function, which provides a combination of a high degree of affinity of the maternal wavelet with the form of vibration change due to the introduced asymmetry and relative the simplicity of such a transformation. It is shown that when detecting power asymmetry, it is advisable to analyze the behavior of the wavelet coefficients of the frequency band, which includes the frequency of the supply voltage of the electric machine. Since the influence of the indicated defect on other frequency bands will be minimal, the analysis of the behavior of their wavelet transform coefficients in order to identify the indicated defect is ineffective. A numerical criterion for assessing the influence of power asymmetry on the wavelet transform coefficients is proposed in the form of the mean square value of the wavelet coefficients of the informative frequency band in the study of the time interval, which significantly exceeds the period of the supply voltage of the electric machine. It is shown that this criterion has a reduced sensitivity to the impact of non-informative single disturbances that may arise during the operation of an electric machine. Keywords: electric machine, rotor unbalance, defect, burst, wavelet transform.

scholarly journals Dynamic loading of electric machines during testing

2018 ◽  
Vol 7 (2.23) ◽  
pp. 184 ◽  
Author(s):  
Sergey I. Malafeev ◽  
Sergey S. Malafeev
Keyword(s):  
Power Supply ◽  
Energy Efficient ◽  
Electric Energy ◽  
Electrical Energy ◽  
Power Converter ◽  
Electric Machines ◽  
Electric Machine ◽  
Dynamic Mode ◽  
Electrical Machine ◽  
Simple Method

An energy-efficient and simple method of testing electric motors in a dynamic mode is considered. When testing an electrical machine, the rotor is driven into a reciprocating rotary motion. The source of power of the power converter, which controls the electric machine, is a supercapacitor. When the electric machine is accelerated, electric energy is consumed from the supercapacitor, when the machine brakes the kinetic energy of the moving masses is converted into electrical energy, which accumulates in the supercapacitor. The energy loss during the operation of the electromechanical system in the dynamic mode is compensated by recharging the supercapacitor using a controlled rectifier connected to the power supply network.

scholarly journals Identification Method for Series Arc Faults Based on Wavelet Transform and Deep Neural Network

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 142 ◽  
Author(s):  
Qiongfang Yu ◽  
Yaqian Hu ◽  
Yi Yang
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Power Supply ◽  
Distribution Systems ◽  
Deep Neural Network ◽  
Experimental Result ◽  
Discrete Wavelet ◽  
Training Set ◽  
Test Set ◽  
Power Distribution System

The power supply quality and power supply safety of a low-voltage residential power distribution system is seriously affected by the occurrence of series arc faults. It is difficult to detect and extinguish them due to the characteristics of small current, high stochasticity, and strong concealment. In order to improve the overall safety of residential distribution systems, a novel method based on discrete wavelet transform (DWT) and deep neural network (DNN) is proposed to detect series arc faults in this paper. An experimental bed is built to obtain current signals under two states, normal and arcing. The collected signals are discomposed in different scales applying the DWT. The wavelet coefficient sequences are used for forming training set and test set. The deep neural network trained by training set under 4 different loads adaptively learn the feature of arc faults. The accuracy of arc faults recognition is sent through feeding test set into the model, about 97.75%. The experimental result shows that this method has good accuracy and generality under different types of loading.

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