Efficient Method for Fault Classification in Transmission Line Using Kernel Naive Bayes Classifier

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Pazoki Mohammad

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Journal Paper

Paper Information

Journal: Modeling in Engineering Year:2018 | Volume:16 | Issue:52 Page(s): 119-129

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Information Journal Paper

Title

Efficient Method for Fault Classification in Transmission Line Using Kernel Naive Bayes Classifier

Author(s)

Pazoki Mohammad | Issue Writer Certificate

Keywords

Transmission lineQ1

Fault classificationQ1

Pattern recognitionQ2

Abstract

In this paper, using Pattern recognition method all fault type is classified. Firstly, feature vectors obtained from sequence components of current and/or voltage signals are normalized by efficient technique. Afterwards, the proposed supervising function applies Kernel Naive Bayes classifier. The classification method through tuning of kernel function bandwidth s suitable for a complex and non-linear feature spaces. The signal processing procedures is done by using minimum sampling frequency hence the output of conventional current and voltage transformers can be utilized. Moreover, the performance of proposed Pattern recognition methodology is evaluated from different point of views. The achieved results indicate that the proposed fault classifier has acceptable performance even in the noisy conditions.

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APA: Copy

Pazoki, Mohammad. (2018). Efficient Method for Fault Classification in Transmission Line Using Kernel Naive Bayes Classifier. JOURNAL OF MODELING IN ENGINEERING, 16(52 ), 119-129. SID. https://sid.ir/paper/357836/en

Vancouver: Copy

Pazoki Mohammad. Efficient Method for Fault Classification in Transmission Line Using Kernel Naive Bayes Classifier. JOURNAL OF MODELING IN ENGINEERING[Internet]. 2018;16(52 ):119-129. Available from: https://sid.ir/paper/357836/en

IEEE: Copy

Mohammad Pazoki, “Efficient Method for Fault Classification in Transmission Line Using Kernel Naive Bayes Classifier,” JOURNAL OF MODELING IN ENGINEERING, vol. 16, no. 52 , pp. 119–129, 2018, [Online]. Available: https://sid.ir/paper/357836/en

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