Application of Slantlet Transform Based Support Vector Machine for Power Quality Detection and Classification

Faridah Hanim M. Noh^1,2*, Hajime Miyauchi¹, M. Faizal Yaakub³

¹Department of Frontier Technology for Energy & Devices, Kumamoto University, Kumamoto, Japan.
²Department of Electrical Power Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia.
³Department of Electrical Engineering Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia.

DOI: 10.4236/jpee.2015.34030                

Abstract

Concern towards power quality (PQ) has increased immensely due to the growing usage of high technology devices which are very sensitive towards voltage and current variations and the de-regulation of the electricity market. The impact of these voltage and current variations can lead to devices malfunction and production stoppages which lead to huge financial loss for the production company. The deregulation of electricity markets has made the industry become more competitive and distributed. Thus, a higher demand on reliability and quality of services will be required by the end customers. To ensure the power supply is at the highest quality, an automatic system for detection and localization of PQ activities in power system network is required. This paper proposed to use Slantlet Transform (SLT) with Support Vector Machine (SVM) to detect and localize several PQ disturbance, i.e. voltage sag, voltage swell, oscillatory-transient, odd-harmonics, interruption, voltage sag plus odd-harmonics, voltage swell plus odd-harmonics, voltage sag plus transient and pure sinewave signal were studied. The analysis on PQ disturbances signals was performed in two steps, which are extraction of feature disturbance and classification of the dis- turbance based on its type. To take on the characteristics of PQ signals, feature vector was constructed from the statistical value of the SLT signal coefficient and wavelets entropy at different nodes. The feature vectors of the PQ disturbances are then applied to SVM for the classification process. The result shows that the proposed method can detect and localize different type of single and multiple power quality signals. Finally, sensitivity of the proposed algorithm under noisy condition is investigated in this paper.

Keywords

Features Extraction, Power Quality Disturbances, Slantlet Transform, Support Vector Machine

Conflicts of Interest

The authors declare no conflicts of interest.

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