INVESTIGATION OF POWER SYSTEM TRANSIENT DISTURBANCES IN FREQUENCY AND TIME-FREQUENCY DOMAINS

Yıl 2014, Cilt: 32 Sayı: 2, 154 - 162, 01.06.2014

Ahmet Serdar Yılmaz Ahmet Alkan

Öz

Switching transients are defined as high frequency disturbances in power systems. These events are the important part of the power quality problems. In this study, to determine these transient disturbances, Autoregressive-Burg and Eigenvector techniques are used to obtain power spectral densities on frequency domain and the obtained results are compared. In these frequency domain techniques frequencies of the transient disturbances are defined clearly but the time of the disturbances can't be shown. Time-frequency domain analysis techniques are used in order to show both the frequency and time of the transient disturbances. For this aim, in this study Short time Fourier transforms (STFT) is employed for time-frequency domain analysis of switching transients. Proposed methods separate the frequency components of original signals accurately. Eigenvector and Burg methods have been employed to make a comparison in frequency domain analysis. It can be concluded that Eigenvector method gives a better frequency resolution with sharper frequency peaks.

Anahtar Kelimeler

Power quality, switching transients, burg method, eigenvector method, short time fourier transform.

Kaynakça

• [1] IEEE Standards, IEEE Recommended practice for powering and grounding sensitive electronics equipment. IEEE Std. 1100, 1992.
• [2] Martzloff F., Power quality work at the International Electrotechnical Commission., Proceedings PQA'97, Sweden, 1997.
• [3] Alkan, A., and Yilmaz, A.S., Frequency domain analysis of power system transients using Welch and Yule-Walker AR methods, Energy Conversion and Management, 48, 2129-2135, 2007.
• [4] Bollen, M.H.J., Styvaktakis E, and Gu, I., Characterization and analysis of power system transients, IEEE Trans. on Power Delivery, 20, 2298-2306, 2005.
• [5] Yilmaz, A.S., Alkan, A., and Asyali, M.H., Application of parametric spectral estimation methods on detection of power system harmonics, Electric Power System Research, 78, 683-693, 2008.
• [6] Gu, I. and Bollen, M.H.J, Time-frequency and time-scale domain analysis of voltage disturbances, IEEE Trans. on Power Delivery, 15, 1279-1283, 2000.
• [7] Yilmaz, A.S. And et al, Application of lifting based wavelet transforms to characterize powerquality events, Energy Conversion and Management 48, 112–123, 2007.
• [8] Subasi, A., and et al, Comparison of subspace-based methods with AR parameter methods in epileptic seizure detection, Computers in Biology and Medicine 36, 195–208, 2006.
• [9] Proakis, J.G., and Manolakis, D.G., Digital Signal Processing Principles, Algorithms, and Applications, Prentice-Hall, Englewood Cliffs, NJ, USA, 1996.
• [10] Kay, S.M., and Marple, S.L., Spectrum analysis—a modern perspective, Proc. IEEE 69 1380–1419, 1981.
• [11] Kay, SM., Modern Spectral Estimation: Theory and Application, Prentice-Hall, Englewood Cliffs, NJ, USA, 1988.
• [12] Allen, J.B.,Short term spectral analysis, synthesis, and modification by discrete Fourier transform, IEEE Transactions on Acoustics, Speech, Signal Processing, vol. ASSP-25, 235-238.
• [13] Allen, J.B., Application of the short-time Fourier transform to speech processing and spectral analysis, Proc. IEEE ICASSP-82, 1012-1015, 1982.
• [14] Allen, J.B. and Rabiner, L.R., A unified approach to short-time Fourier analysis and synthesis, Proc. IEEE, 65, 1558-1564, 1977.
• [15] Cohen, L., Time-Frequency Analysis, Englewood Cliffs, Prentice-Hall. NJ, USA, 1995.
• [16] Çankaya, I., and Vatansever F., Fourier ve Dalgacık Dönüşümünün Karşılaştırılması, SAÜ. FBE Dergisi, 6, 3, 16-26, 2002.
• [17] Rioul,O., M.Vetterlı, Wavelets and Signal Processing., IEEE SP Magazine, 14-38, October 1991.