Alireza FOROUGHI, Ebrahim MOHAMMADI, Saeid ESMAEILI

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

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.

Anahtar Kelimeler:

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

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

Keywords:

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

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In this study, another voltage sag source (S4) , voltage sag due to reacceleration of large motors after faults clearance, is considered, which was not considered in [10]. It is obvious from the results that the SVM is stronger than the PNN for classifying voltage sag sources. 8. Conclusions
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