Transformer Fault Diagnosis Based on BP Neural Network by Improved Apriori Algorithm

2020 ◽  
pp. 255-266
Author(s):  
Chang Guoxiang ◽  
Gao Qiaoli ◽  
Gao Xinming ◽  
Cheng Junting
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Apriori Algorithm ◽  
Transformer Fault

Improved PSO-BP neural network for power transformer fault diagnosis

2010 ◽  
Vol 30 (3) ◽  
pp. 783-785 ◽  
Author(s):  
Zhong-yang XIONG ◽  
Qing-bo YANG ◽  
Yu-fang ZHANG
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Power Transformer ◽  
Transformer Fault ◽  
Improved Pso

Transformer Fault Diagnosis Based on Improved Quantum Genetic Algorithm and BP Network

2010 ◽  
Vol 29-32 ◽  
pp. 1543-1549 ◽  
Author(s):  
Jie Wei ◽  
Hong Yu ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fuzzy Theory ◽  
Back Propagation ◽  
Gas Analysis ◽  
Bp Network ◽  
Quantum Genetic Algorithm ◽  
Transformer Fault

Three-ratio of the IEC is a convenient and effective approach for transformer fault diagnosis in the dissolved gas analysis (DGA). Fuzzy theory is used to preprocess the three-ratio for its boundary that is too absolute. As the same time, an improved quantum genetic algorithm IQGA (QGASAC) is used to optimize the weight and threshold of the back propagation (BP). The local and global searching ability of the QGASAC approach is utilized to find the BP optimization solution. It can overcome the slower convergence velocity and hardly getting the optimization of the BP neural network. So, aiming at the shortcoming of BP neural network and three-ratio, blurring the boundary of the gas ratio and the QGASAC algorithm is introduced to optimize the BP network. Then the QGASAC-IECBP method is proposed in this paper. Experimental results indicate that the proposed algorithm in this paper that both convergence velocity and veracity are all improved to some extent. And in this paper, the proposed algorithm is robust and practical.

Improved BP Neural Network for Transformer Fault Diagnosis

2007 ◽  
Vol 17 (1) ◽  
pp. 138-142 ◽  
Author(s):  
Yan-jing SUN ◽  
Shen ZHANG ◽  
Chang-xin MIAO ◽  
Jing-meng LI
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Transformer Fault

Improved BP Neural Network in Diagnosis of Nonlinear Fault

2013 ◽  
Vol 401-403 ◽  
pp. 1055-1058
Author(s):  
Bin Xu ◽  
Xiao Ju Shen ◽  
Wei Ning Xue
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Newton Method ◽  
Bp Neural Network ◽  
Gas Content ◽  
Simulation Experiments ◽  
Nonlinear Characteristics ◽  
Content Ratio ◽  
Diagnosis Model ◽  
Transformer Fault

According to the nonlinear characteristics of transformer fault symptoms and fault types, the application of BP neural network to the problem of transformer fault diagnosis is presented. With a characteristic of the gas content ratio as the input, fault diagnosis model is established by using MATLAB software to achieve improved Newton method. And the simulation experiments show the effectiveness of the model of fault diagnosis.

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