2018 ◽  
Vol 10 (7) ◽  
pp. 168781401878612 ◽  
Author(s):  
Yu-Tung Chen ◽  
Jui-Chien Lai ◽  
Yu-Ming Jheng ◽  
Cheng-Chien Kuo ◽  
Hong-Chan Chang

In this article, the insulation fault detection of high-voltage motors by the artificial neural network algorithm is used. The proposed method can evaluate the status of operating motor without interrupting the normal operation. According to the measurement of partial discharge information, this research establishes the relationship of stator failures and pattern features. This study uses common high-voltage motor stator fault types to experimentally produce four types of stator test models with insulation defects; these models are compared with a healthy motor model. Through the learning of the artificial neural network, the experimental results show that the artificial neural network–based stator fault diagnosis system proposed in this article has a recognition rate as high as 90% when the conjugate gradient algorithm is used, and there are 20 neurons in the hidden layer.


Author(s):  
Abdullahi Abubakar Masud ◽  
Firdaus Muhammad-Sukki ◽  
Ricardo Albarracin ◽  
Jorge Alfredo Ardila-Rev ◽  
Siti Hawa Abu-Bakar ◽  
...  

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3054 ◽  
Author(s):  
Yanling Lv ◽  
Yuting Gao ◽  
Jian Zhang ◽  
Chenmin Deng ◽  
Shiqiang Hou

As a new type of generator, an asynchronized high-voltage generator has the characteristics of an asynchronous generator and high voltage generator. The effect of the loss of an excitation fault for an asynchronized high-voltage generator and its fault diagnosis technique are still in the research stage. Firstly, a finite element model of the asynchronized high-voltage generator considering the field-circuit-movement coupling is established. Secondly, the three phase short-circuit loss of excitation fault, three phase open-circuit loss of excitation fault, and three phase short-circuit fault on the stator side are analyzed by the simulation method that is applied abroad at present. The fault phenomenon under the stator three phase short-circuit fault is similar to that under the three phase short-circuit loss of excitation. Then, a symmetrical loss of the excitation fault diagnosis system based on wavelet packet analysis and the Back Propagation neural network (BP neural network) is established. At last, we confirm that this system can eliminate the interference of the stator three phase short-circuit fault, accurately diagnose the symmetrical loss of the excitation fault, and judge the type of symmetrical loss of the excitation fault. It saves time to find the fault cause and improves the stability of system operation.


Sign in / Sign up

Export Citation Format

Share Document