Transformer fault prediction based on particle swarm optimization and SVM

2011 ◽  
Author(s):  
Yan Zhang ◽  
Bide Zhang ◽  
Zichun Pei ◽  
Yan Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fault Prediction ◽  
Swarm Optimization ◽  
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.

Research on Fault Prediction Method of Power Electronic Circuits Based on Particle Swarm Optimization RBF Neural Network

2014 ◽  
Vol 687-691 ◽  
pp. 3354-3360 ◽  
Author(s):  
Shen Yu Wang ◽  
Dan Jiang Chen ◽  
Yin Zhong Ye
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Rbf Neural Network ◽  
Particle Swarm ◽  
Fault Prediction ◽  
Power Electronic ◽  
Electronic Circuits ◽  
Characteristic Parameters ◽  
Swarm Optimization ◽  
Power Electronic Circuits

Aiming at the issue of fault prediction technique of power electronic circuits, a method based on characteristic parameter data and Particle Swarm Optimization RBF(Radial Basis Function) Neural Network for the fault prediction of power electronic circuits was proposed. Taking the Buck converter circuit as an example,the fault prediction of power electronic circuits was achieved. Firstly,the output voltage was selected as monitoring signal, then the average voltage and ripple voltage were extracted as characteristic parameters. Lastly Particle Swarm Optimization RBF Neural Network was used to predict the fault. The experimental results show that the Particle Swarm Optimization RBF Neural Network is more accurate in predicting than the only RBF Neural Network.The new method can trace the characteristic parameters’ trend and can be effectively applied in fault prediction of power electronic circuits.

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