Deep Convolution Neural Network Based Fault Detection and Identification for Modular Multilevel Converters

2018 ◽  
Author(s):  
Xiangshuai Qu ◽  
Bin Duan ◽  
Qiaoxuan Yin ◽  
Mengjun Shen ◽  
Yinxin Yan
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Convolution Neural Network ◽  
Multilevel Converters ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Modular Multilevel Converters ◽  
Deep Convolution Neural Network

Fault Detection Method of Power Insulator Based on Deep Convolution Neural Network

2021 ◽  
Author(s):  
Yan Wang ◽  
Weijie Zhang
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Detection Method ◽  
Convolution Neural Network ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Region Selection ◽  
Function Design ◽  
Deep Convolution Neural Network ◽  
Anchor Design

Aiming at the problem of low detection accuracy of traditional power insulator fault detection methods, a power insulator fault detection method based on deep convolution neural network is designed. For the training of deep convolution neural network, the fault detection of power insulator based on deep convolution neural network is realized by anchor design, loss function design, candidate region selection mechanism establishment and sharing convolution features. The experimental results show that the fault detection method of power insulator based on deep convolution neural network is more accurate than the traditional method, and the detection time is less.

scholarly journals A Neuro-Augmented Observer for Robust Fault Detection in Nonlinear Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Huajun Gong ◽  
Ziyang Zhen
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Fault Detection ◽  
Rbf Neural Network ◽  
Estimation Error ◽  
Accurate Estimation ◽  
Detection Scheme ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Approach Zero

A new fault detection method using neural-networks-augmented state observer for nonlinear systems is presented in this paper. The novelty of the approach is that instead of approximating the entire nonlinear system with neural network, we only approximate the unmodeled part that is left over after linearization, in which a radial basis function (RBF) neural network is adopted. Compared with conventional linear observer, the proposed observer structure provides more accurate estimation of the system state. The state estimation error is proved to asymptotically approach zero by the Lyapunov method. An aircraft system demonstrates the efficiency of the proposed fault detection scheme, simulation results of which show that the proposed RBF neural network-based observer scheme is effective and has a potential application in fault detection and identification (FDI) for nonlinear systems.

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