An Implementation of Exploratory Start for Power Quality Disturbance Pattern Recognition

Turgay Yalcin, Muammer Ozdemir


Identification of system disturbances and detection of them guarantees smart grids power quality system reliability and long lasting life of the power system. The key goal of this study is to generate non - time consuming features for CPU, for recognizing different types of non-stationary and non-linear smart grid faults based on signal processing techniques. This paper proposes a new solution for real time power system monitoring against power quality faults focusing on voltage sag and noise. EEMD is used for noise reduction with first intrinsic mode function (imf1). Hilbert Huang Transform (HHT) is used for generating instantaneous amplitude (IA) and instantaneous frequency (IF) feature of real time voltage sag power signal. The proposed power system monitoring system is able to detect power system voltage sag disturbances and capable of recognize and remove EMI (Electromagnetic Interference)-Noise.  In this study based on experimental studies, Hilbert Huang based pattern recognition technique was used to investigate power signal to diagnose voltage sag in power grid. SVM and Decision Tree (C4.5) were operated and their achievements were matched for calculation error and CPU time. According to the analysis, decision tree algorithm without dimensionality reduction produces the best solution.

Full Text:



J. Momoh, Smart Grid: Fundamentals of Design and Analysis, First Edition, Institute of Electrical and Electronics Engineers, 2012.

R. Smolenski, "Conducted Electromagnetic Interference (EMI) in Smart Grids ", Springer -Verlag London, 2012.

S. Borlase “Smart Grids: Infrastructure, Technology, and Solutions” , CRC press, 2012.

Report to NIST on the Smart Grids Interoperability Standarts Roadmap, EPRI, 2009.

M. H. J. Bollen and I. Y. H. Guo, Signal Processing of Power Quality Disturbances. New York: Wiley, 2006.

P. F. Ribeiro, C.A. Duque, P. M. Silveria, and A. S. Cerqueira, eds., Power Systems Signal Processing for Smart Grids. Chichester, UK: John Wiley & Sons, Inc., 2013.

A. McEachern, Practical Power Quality: An Update, Large Customer Conference, Power Standards Lab, November 25, 2015.

K.-M. Chang, Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition, Sensors, 10, 6063 - 6080, 2010.

N.E. Huang, Z. Shen., S.R. Long, M.L. Wu, H.H. Shih, Q. Zheng, N.C. Yen, C.C. Tung, H.H. Liu, “The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. Roy. Soc. London A, Vol. 454, pp. 903–995, 1998.

Z. Wu, N.E. Huang, “A study of the characteristics of white noise using the Empirical Mode Decomposition method,” Proc. Roy. Soc. London A, 2002.

S. Baykut, T. Akgül, S. Ergintav, EMD – Based Analysis and Denoising of GPS Data, IEEE 17th Signal Processing and Communications Applications Conference, Antalya, 2009.

T.Yalcin, O.Ozgonenel, Feature vector extraction by using empirical mode decomposition from power quality disturbances, IEEE SIU, Fethiye, Mugla, 2012.

O.Ozgonenel, T. Yalcin, I. Guney, U. Kurt, A New Classification for Power Quality Events in Distribution System, Electric Power System Research (EPSR), 95, 192-199, 2013.

Z.Wu,; N.E. Huang, Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data. Anal., 1, 1–41, 2009.

Z. Wang, Quan Zhu, J. Kiely, R. Luxton, Hilbert Huang transform impedance measurement data for cellular toxicity monitoring. International Conference on Networking, Sensing and Control., pp. 767-772, 2009.

M. Uyar, S.Yildirim, , M.T. Gencoglu, ‘An effective wavelet-based feature extraction method for classification of power quality disturbance signals’, Electr. Power Syst. Res, 78, (10), pp. 1747–1755, 2008.

B. Biswal, PK. Dash, S. Mishra, A hybrid ant colony optimization technique for power signal pattern classification. Expert Syst Appl,38: 6368–75, 2011.

K. K. Hoong, S. P. Lam, C. Y. Chung. An output regulation based unified power quality conditioner with Kalman filters. IEEE Trans Ind Electron, 59 (November (11)): 4248–62, 2012.

I. H.Witten, E. Frank, Data Mining: Pratical Machine Learning Tools and Techniques. San Mateo, CA, USA: Morgan Kaufmann, 2005.

M. T. Hagan, M. B. Menhaj, “Training feedforward networks with the Marquardt algorithm,” IEEE Trans. Neural Netw., , Nov., vol. 5, no. 6, pp. 989– 993, 1994.

R. J. Quinlan, C4.5: Programs for Machine Learning, San Mateo, CA, USA, Morgan Kaufmann, vol. 1, 1993.

S.Mishra, T. Nagwani, “A Review on Detection and Classification Methods for Power Quality Disturbances”, International Journal of Engineering Science and Computing, Volume 6, Issue No. 3, 2016.

F. A. S. Borges, R. A. S. Fernandes, I. N. Silva, C. B. S. Silva, “Feature Extraction and Power Quality Disturbances Classification Using Smart Meters Signals”, IEEE Transactions on Industrial Informatics, Vol. 12, No. 2, 2016.

Ozgonenel O., Thomas D. W. P., Yalcin T., “Superiority of decision tree classifier on complicated cases for power system protection,” in 11th International Conference on Developments in Power Systems Protection, Birmingham, UK, pp. 134–134, 2012.

Mahela O. P. , Shaik A. G. , Gupta N., “A critical review of detection and classification of power quality events”, Renewable and Sustainable Energy Reviews, Volume 41, Pages 495–505, 2015.



  • There are currently no refbacks.

Copyright (c) 2016 Turgay Yalcin, Muammer Ozdemir

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.