Fault pattern recognition method for the high voltage circuit breaker based on the incremental learning algorithms for SVM

Geng, Pulong; Song, Jiancheng; Xu, Chuanfu; Zhao, Yu

doi:10.1109/cmd.2016.7757917

Cited by 7 publications

(2 citation statements)

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“…SVM is a kind of machine learning algorithm based on statistical learning theory, Vapnik-Chervonenkis theory, and structural risk minimization. It has unique advantages in solving small sample, non-linear, and high-dimensional pattern recognition problems and has been widely used in the fields of pattern recognition and regression analysis [25,36].…”

Section: Foundamentals Of Svmmentioning

confidence: 99%

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Entropy SVM–Based Recognition of Transient Surges in HVDC Transmissions

Luo

Yao

Liu

et al. 2018

Entropy

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Protection based on transient information is the primary protection of high voltage direct current (HVDC) transmission systems. As a major part of protection function, accurate identification of transient surges is quite crucial to ensure the performance and accuracy of protection algorithms. Recognition of transient surges in an HVDC system faces two challenges: signal distortion and small number of samples. Entropy, which is stable in representing frequency distribution features, and support vector machine (SVM), which is good at dealing with samples with limited numbers, are adopted and combined in this paper to solve the transient recognition problems. Three commonly detected transient surges-single-pole-to-ground fault (GF), lightning fault (LF), and lightning disturbance (LD)-are simulated in various scenarios and recognized with the proposed method. The proposed method is proved to be effective in both feature extraction and type classification and shows great potential in protection applications.

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Section: Foundamentals Of Svmmentioning

confidence: 99%

“…SVMs, which are proposed based on statistical learning theory, reveal better capability in solving classification problems with a small size of samples. For fault classification of power systems, where the number of samples are limited, SVM could be a better choice [25,26].…”

Section: Introductionmentioning

confidence: 99%