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.

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.

scholarly journals Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Chun Yan ◽  
Meixuan Li ◽  
Wei Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Rapid Determination ◽  
Oil Analysis ◽  
Dissolved Gas ◽  
Adaboost Algorithm ◽  
Diagnosis Model ◽  
The Stability ◽  
Transformer Fault

Dissolved gas-in-oil analysis (DGA) is a powerful method to diagnose and detect transformer faults. It is of profound significance for the accurate and rapid determination of the fault of the transformer and the stability of the power. In different transformer faults, the concentration of dissolved gases in oil is also inconsistent. Commonly used gases include hydrogen (H2), methane (CH4), acetylene (C2H2), ethane (C2H6), and ethylene (C2H4). This paper first combines BP neural network with improved Adaboost algorithm, then combines PNN neural network to form a series diagnosis model for transformer fault, and finally combines dissolved gas-in-oil analysis to diagnose transformer fault. The experimental results show that the accuracy of the series diagnosis model proposed in this paper is greatly improved compared with BP neural network, GA-BP neural network, PNN neural network, and BP-Adaboost.

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

scholarly journals Transformer Fault Diagnosis Model Based on Improved Gray Wolf Optimizer and Probabilistic Neural Network

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3029
Author(s):  
Yichen Zhou ◽  
Xiaohui Yang ◽  
Lingyu Tao ◽  
Li Yang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diagnostic Accuracy ◽  
Probabilistic Neural Network ◽  
Diagnostic Methods ◽  
Local Optimum ◽  
Gray Wolf ◽  
Gray Wolf Optimizer ◽  
Diagnosis Model ◽  
Transformer Fault

Dissolved gas analysis (DGA) based in insulating oil has become a more mature method in the field of transformer fault diagnosis. However, due to the complexity and diversity of fault types, the traditional modeling method based on oil sample analysis is struggling to meet the industrial demand for diagnostic accuracy. In order to solve this problem, this paper proposes a probabilistic neural network (PNN)-based fault diagnosis model for power transformers and optimizes the smoothing factor of the pattern layer of PNN by the improved gray wolf optimizer (IGWO) to improve the classification accuracy and robustness of PNN. The standard GWO easily falls into the local optimum because the update mechanism is too single. The update strategy proposed in this paper enhances the convergence ability and exploration ability of the algorithm, which greatly alleviates the dilemma that GWO is prone to fall into local optimum when dealing with complex data. In this paper, a reliability analysis of thirteen diagnostic methods is conducted using 555 transformer fault samples collected from Jiangxi Power Supply Company, China. The results show that the diagnostic accuracy of the IGWO-PNN model reaches 99.71%, which is much higher than that of the traditional IEC (International Electrotechnical Commission) three-ratio method. Compared with other neural network models, IGWO-PNN also has higher diagnostic accuracy and stability, and is more applicable to the field of transformer fault diagnosis.

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