2008
DOI: 10.1049/iet-gtd:20070071
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Application of support vector machines for fault diagnosis in power transmission system

Abstract: Post-fault studies of recent major power failures around the world reveal that maloperation and/or improper coordination of protection system were responsible to some extent. When a major power disturbance occurs, protection and control action are required to stop the power system degradation, restore the system to a normal state and minimise the impact of the disturbance. However, this has indicated the need for improving protection coordination by additional post-fault and corrective studies using intelligen… Show more

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Cited by 73 publications
(37 citation statements)
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“…Hence, Gaussian kernel function is reasonably used in this paper. To obtain a good performance, some parameters in SVM have to be chosen carefully [50]. These parameters include:…”
Section: Svmsmentioning
confidence: 99%
“…Hence, Gaussian kernel function is reasonably used in this paper. To obtain a good performance, some parameters in SVM have to be chosen carefully [50]. These parameters include:…”
Section: Svmsmentioning
confidence: 99%
“…Recently, the classifiers, such as Bayesian networks, [2][3][4] artificial neural networks, and support vector machine (SVM), have been widely applied in fault diagnosis field, among which SVM is a machine learning method based on structure risk minimization principle, and it can solve the classification problems with small training samples, high dimensions, and nonlinearity. Until now, SVM has been applied in fault diagnosis of rolling-element bearing, 1 fault diagnosis of turbo-pump rotor, 5 fault diagnosis of rotor-bearing system, 6 fault diagnosis of power transmission system, 7 and so on. Relevance vector machine (RVM) is an intelligent learning technique based on sparse Bayesian framework.…”
Section: Introductionmentioning
confidence: 99%
“…This balance gives a better generalization performance than other neural network models. For the past several years, SVM has been successfully applied in solving a large range of practical problems in different areas [15][16][17][18][19][20][21][22][23].…”
Section: Introductionmentioning
confidence: 99%