scholarly journals Brushless Synchronous Generator Turn-to-Turn Short Circuit Fault Detection Using Multilayer Neural Network

2018 ◽  
Author(s):  
Pyae Phyo Tun ◽  
Padmanabhan Sampath Kumar ◽  
Ryan Arya Pratama ◽  
Liu Shuyong
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Short Circuit ◽  
Synchronous Generator ◽  
Short Circuit Fault

scholarly journals Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Gayatridevi Rajamany ◽  
Sekar Srinivasan ◽  
Krishnan Rajamany ◽  
Ramesh K. Natarajan
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Induction Motor ◽  
Short Circuit ◽  
Positive Sequence ◽  
Negative Sequence ◽  
Three Phase ◽  
Sequence Components ◽  
Negative Sequence Current ◽  
Short Circuit Fault

The intention of fault detection is to detect the fault at the beginning stage and shut off the machine immediately to avoid motor failure due to the large fault current. In this work, an online fault diagnosis of stator interturn fault of a three-phase induction motor based on the concept of symmetrical components is presented. A mathematical model of an induction motor with turn fault is developed to interpret machine performance under fault. A Simulink model of a three-phase induction motor with stator interturn fault is created for extraction of sequence components of current and voltage. The negative sequence current can provide a decisive and rapid monitoring technique to detect stator interturn short circuit fault of the induction motor. The per unit change in negative sequence current with positive sequence current is the main fault indicator which is imported to neural network architecture. The output of the feedforward backpropagation neural network classifies the short circuit fault level of stator winding.

scholarly journals Symmetrical Loss of Excitation Fault Diagnosis in an Asynchronized High-Voltage Generator

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3054 ◽  
Author(s):  
Yanling Lv ◽  
Yuting Gao ◽  
Jian Zhang ◽  
Chenmin Deng ◽  
Shiqiang Hou
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
High Voltage ◽  
Short Circuit ◽  
Open Circuit ◽  
Voltage Generator ◽  
Three Phase ◽  
Circuit Loss ◽  
High Voltage Generator ◽  
Short Circuit Fault

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.

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