Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine

2016 ◽  
Vol 23 (2) ◽  
pp. 1198-1206 ◽  
Author(s):  
Jinzhong Li ◽  
Qiaogen Zhang ◽  
Ke Wang ◽  
Jianyi Wang ◽  
Tianchun Zhou ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Support Vector ◽  
Dissolved Gas ◽  
Transformer Fault

Power Transformer Fault Diagnosis Based on Least Squares Support Vector Machine and Particle Swarm Optimization

2011 ◽  
Vol 50-51 ◽  
pp. 624-628
Author(s):  
Xin Ma
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Power Transformer ◽  
Particle Swarm ◽  
Support Vector ◽  
Risk Minimization ◽  
Swarm Optimization ◽  
Transformer Fault

Dissolved gas analysis (DGA) is an important method to diagnose the fault of power t ransformer. Least squares support vector machine (LS-SVM) has excellent learning, classification ability and generalization ability, which use structural risk minimization instead of traditional empirical risk minimization based on large sample. LS-SVM is widely used in pattern recognition and function fitting. Kernel parameter selection is very important and decides the precision of power transformer fault diagnosis. In order to enhance fault diagnosis precision, a new fault diagnosis method is proposed by combining particle swarm optimization (PSO) and LS-SVM algorithm. It is presented to choose σ parameter of kernel function on dynamic, which enhances precision rate of fault diagnosis and efficiency. The experiments show that the algorithm can efficiently find the suitable kernel parameters which result in good classification purpose.

New Methold on Power Transformer Fault Diagnosis

2012 ◽  
Vol 217-219 ◽  
pp. 2623-2628
Author(s):  
Nan Lan Wang ◽  
Ming Shan Cai
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Wavelet Neural Network ◽  
Bp Algorithm ◽  
Oil Analysis ◽  
Wavelet Network ◽  
Dissolved Gas ◽  
Transformer Fault

This paper improves the simple genetic algorithm and combines genetic algorithm with BP algorithm to the wavelet neural network in the power transformer fault diagnosis by dissolved gas-in-oil analysis, Simulation result shows the problem was solved that wavelet network settles into local small extremum so easily that the network surging will increase and the network will not be convergent if the initialization is unreasonable, and overcomes the shortcoming that the speed is too slow if use genetic algorithm to train neural network independently.

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