Fault Diagnosis of HVDC Transmission System Using Wavelet Energy Entropy and the Wavelet Neural Network

Author(s):  
Cuicui Liu ◽  
Feng Wang ◽  
Fang Zhuo ◽  
Ziqian Zhang
2013 ◽  
Vol 645 ◽  
pp. 316-319
Author(s):  
Su Ping Li ◽  
Yao Ling Fan

This paper presents a novel fault diagnosis method for sensors in air-handling units based on wavelet energy entropy. Instead of directly comparing the numerous data under noise conditions, the wavelet energy entropy deviation is used for the fault detection and diagnosis. The actual Three-level wavelet analysis is used to decompose the measurement data captured from sensors first and then the concept of Shannon entropy is referred to define the wavelet energy entropy. Once the wavelet energy entropy is obtained, whether the sensors are faulty can be confirmed through comparing the deviation of the wavelet energy entropy residual of the measured signal and the estimated one to the preset threshold. Testing results show that the wavelet energy entropy is a sensitive indictor to diagnose the sensor faults. The deviations of wavelet energy entropy of sensors under fault-free conditions and faulty ones all exceed the threshold. The severer the fault is, the larger the residuals of the wavelet energy entropy will be. The results prove that the proposed method is valid and effective for the fault detection and diagnosis of the sensors.


2011 ◽  
Vol 382 ◽  
pp. 163-166
Author(s):  
Qing Xin Zhang ◽  
Jin Li ◽  
Hai Bin Li ◽  
Chong Liu

In the technology of motor fault diagnosis, current monitoring methods have become a new trend in motor fault diagnosis. This paper presents a motor fault diagnosis method based on Park vector and wavelet neural network. This method uses the stator current as the object of study. Firstly, it uses Park vector to deal with the stator current and filter out fundamental frequency component, thus the characteristics component of motor broken-bar will be separated from fundamental frequency component; Secondly, it uses five layers wavelet packet decomposition to pick up fault characteristic signal; Finally, we distinguish the fault by BP neural network, and use the simulation software of MATLAB to realize it. The test results show that: This method can detect the existence of motor broken-bar fault, and has a good value in engineering.


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