Power transformer fault diagnosis based on support vector machine with cross validation and genetic algorithm

2011 ◽  
Author(s):  
JinLiang Yin ◽  
YongLi Zhu ◽  
GuoQin Yu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Cross Validation ◽  
Power Transformer ◽  
Support Vector ◽  
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.

scholarly journals Improved Genetic Algorithm and XGBoost Classifier for Power Transformer Fault Diagnosis

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhanhong Wu ◽  
Mingbiao Zhou ◽  
Zhenheng Lin ◽  
Xuejun Chen ◽  
Yonghua Huang
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Gas Analysis ◽  
Diagnostic Methods ◽  
Identification Accuracy ◽  
Basic Unit ◽  
Support Vector ◽  
Improved Genetic Algorithm ◽  
Transformer Fault

Power transformer is an essential component for the stable and reliable operation of electrical power grid. The traditional transformer fault diagnostic methods based on dissolved gas analysis are limited due to the low accuracy of fault identification. In this study, an effective transformer fault diagnosis system is proposed to improve identification accuracy. The proposed approach combines an improved genetic algorithm (IGA) with the XGBoost to form a hybrid diagnosis network. The combination of the improved genetic algorithm and the XGBoost (IGA-XGBoost) forms the basic unit of the proposed method, which decomposes and reconstructs the transformer fault recognition problem into several minor problems IGA-XGBoosts can solve. The results of simulation experiments show that the IGA performs excellently in the combined optimization of input feature selection and the XGBoost parameter, and the proposed method can accurately identify the transformer fault types with an average accuracy of 99.2%. Compared to IEC ratios, dual triangle, support vector machine and common vector approach the diagnostic accuracy of the proposed method is improved by 30.2, 47.2, 11.2, and 3.6%, respectively. The proposed method can be a potential solution to identify the transformer fault types.

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