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

Read  full  paper  at:

Welcome papers~


Faridah Hanim M. Noh1,2*, 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.


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.


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

Cite this paper

Noh, F. , Miyauchi, H. and Yaakub, M. (2015) Application of Slantlet Transform Based Support Vector Machine for Power Quality Detection and Classification. Journal of Power and Energy Engineering, 3, 215-223. doi:10.4236/jpee.2015.34030.


[1] Bollen, M.H.J. and Gu, I.Y.H. (2006) Signal Processing of Power Quality Disturbances. IEEE Press Series on Power Engineering, John Wiley & Sons Inc., Hoboken.
[2] Thapar, A., Saha, T.K. and Dong, Z.Y. (2004) Investigation of Power Quality Categorisation and Simulating It’s Impact on Sensitive Electronic Equipment. IEEE Power Engineering Society General Meeting, 1, 528-533.
[3] Granados-Lieberman, D., Romero-Troncoso, R.J., Osornio-Rios, R.A., Garcia-Perez, A. and Cabal-Yepez, E. (2011) Techniques and Methodologies for Power Quality Analysis and Disturbance Classification in Power System: A Review. IET Generation Transmission & Distribution, 5, 519-529.
[4] Moussa, A., El-Gammal, M., Abdallah, E.N. and El-Sloud, A.A. (2004) Hardware-Software Structure for On-Line Po- wer Quality Assessment. Proceedings of the 2004 ASME/IEEE Joint, Baltimore, 8 April 2004, 147-152.
[5] Panda, G., Dash, P.K.A., Pradhan, K. and Meher, S.K. (2002) Data Compression of Power Quality Events Using the Slantlet Transform. IEEE Transactions on Power Delivery, 17, 662-667.
[6] Selesnick, I.W. (1999) The Slantlet Transform. IEEE Transactions on Signal Processing, 47, 1304-1313.
[7] Meher, S.K. (2008) A Novel Power Quality Event Classification Using Slantlet Transform and Fuzzy Logic. Proceeding of Power System Technology and IEEE Power India Conference, New Delhi, 12-15 October 2008, 1-4.
[8] Burges, C.J.C. (1998) A Tutorial on Support Vector Machine Pattern Recognition. Data Mining and Knowledge Discovery, 2, 121-167.
[9] Vapnik, V.N. (2000) The Nature of Statistical Learning Theory. 2nd Edition, Springer-Verlag, New York.
[10] Cortes, C. and Vapnik, V.N. (1998) Support Vector Network. Machine Learning, 20, 121-167.


Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out / 更改 )

Twitter picture

You are commenting using your Twitter account. Log Out / 更改 )

Facebook photo

You are commenting using your Facebook account. Log Out / 更改 )

Google+ photo

You are commenting using your Google+ account. Log Out / 更改 )

Connecting to %s