Application of Hilbert--Huang transform and support vector machine for detection and classification of voltage sag sources

Authors

Alireza Foroughi
Ebrahim Mohammadi
SAEID ESMAEILI

DOI

10.3906/elk-1210-60

Abstract

Power quality disturbances, including voltage sag, swell, harmonics, flicker, and notch, are one of the main concerns for industries and electrical equipment. Among these disturbances, voltage sag, due to its irrecoverable economic effects on industries, is particularly important. In this paper, the detection and classification of voltage sag sources containing motor starting, short circuit, transformer energizing, and the reacceleration of motors after fault clearance using the Hilbert--Huang transform (HHT) and support vector machine (SVM) are studied. A voltage sag waveform includes several oscillating modes; for separating these oscillating modes, which are called intrinsic mode functions (IMFs), empirical mode decomposition is used. Next, by applying the HHT to these IMFs, some required features of each IMF are extracted. Finally, these features are given to the SVM for classification. The results of this classification method as compared with other methods show the high efficiency of the proposed method.

Keywords

Voltage sag classification, Hilbert--Huang transform, support vector machine, empirical mode decomposition, intrinsic mode function, power quality

First Page

1116

Last Page

1129

Recommended Citation

Foroughi, Alireza; Mohammadi, Ebrahim; and ESMAEILI, SAEID (2014) "Application of Hilbert--Huang transform and support vector machine for detection and classification of voltage sag sources," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 22: No. 5, Article 2. https://doi.org/10.3906/elk-1210-60
Available at: https://journals.tubitak.gov.tr/elektrik/vol22/iss5/2