- Research Article
- Open Access
Detection of Disturbances in Voltage Signals for Power Quality Analysis Using HOS
EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 059786 (2007)
This paper outlines a higher-order statistics (HOS)-based technique for detecting abnormal conditions in voltage signals. The main advantage introduced by the proposed technique refers to its capability to detect voltage disturbances and their start and end points in a frame whose length corresponds to, at least, samples or of the fundamental component if a sampling rate equal to Hz is considered. This feature allows the detection of disturbances in submultiples or multiples of one-cycle fundamental component if an appropriate sampling rate is considered. From the computational results, one can note that almost all abnormal and normal conditions are correctly detected if s256, 128, 64, 32, and 16 and the SNR is higher than 25 dB. In addition, the proposed technique is compared to a root mean square (rms)-based technique, which was recently developed to detect the presence of some voltage events as well as their sources in a frame whose length ranges from up to one-cycle fundamental component. The numerical results reveal that the proposed technique shows an improved performance when applied not only to synthetic data, but also to real one.
Ribeiro MV: Signal processing techniques for power line communication and power quality applications, Ph.D. dissertation. University of Campinas (UNICAMP), São Paulo, Brasil, April 2005.
Duque CA, Ribeiro MV, Ramos FR, Szczupak J: Power quality event detection based on the divide and conquer principle and innovation concept. IEEE Transactions on Power Delivery 2005,20(4):2361-2369. 10.1109/TPWRD.2005.855478
Gu IYH, Ernberg N, Styvaktakis E, Bollen MHJ: A statistical-based sequential method for fast online detection of fault-induced voltage dips. IEEE Transactions on Power Delivery 2004,19(2):497-504. 10.1109/TPWRD.2003.823199
Karimi M, Mokhtari H, Iravani MR: Wavelet based on-line disturbance detection for power quality applications. IEEE Transactions on Power Delivery 2000,15(4):1212-1220. 10.1109/61.891505
Mokhtari H, Karimi-Ghartemani M, Iravani MR: Experimental performance evaluation of a wavelet-based on-line voltage detection method for power quality applications. IEEE Transactions on Power Delivery 2002,17(1):161-172. 10.1109/61.974204
Ece DG, Gerek ON: Power quality event detection using joint 2-D-wavelet subspaces. IEEE Transactions on Instrumentation and Measurement 2004,53(4):1040-1046. 10.1109/TIM.2004.831137
Lu C-W, Huang S-J: An application of B-spline wavelet transform for notch detection enhancement. IEEE Transactions on Power Delivery 2004,19(3):1419-1425. 10.1109/TPWRD.2004.829131
Abdel-Galil TK, El-Saadany EF, Salama MMA: Power quality event detection using Adaline. Electric Power Systems Research 2003,64(2):137-144. 10.1016/S0378-7796(02)00173-6
Dash PK, Chilukuri MV: Hybrid S-transform and Kalman filtering approach for detection and measurement of short duration disturbances in power networks. IEEE Transactions on Instrumentation and Measurement 2004,53(2):588-596. 10.1109/TIM.2003.820486
Gu IY-H, Styvaktakis E: Bridge the gap: signal processing for power quality applications. Electric Power Systems Research 2003,66(1):83-96. 10.1016/S0378-7796(03)00074-9
Zhang H, Liu P, Malik OP: Detection and classification of power quality disturbances in noisy conditions. IEE Proceedings: Generation, Transmission and Distribution 2003,150(5):567-572. 10.1049/ip-gtd:20030459
Huang S-J, Yang T-M, Huang J-T: FPGA realization of wavelet transform for detection of electric power system disturbances. IEEE Transactions on Power Delivery 2002,17(2):388-394. 10.1109/61.997905
Castaldo D, Gallo D, Landi C, Testa A: A digital instrument for nonstationary disturbance analysis in power lines. IEEE Transactions on Instrumentation and Measurement 2004,53(5):1353-1361. 10.1109/TIM.2004.830596
Yang H-T, Liao C-C: A de-noising scheme for enhancing wavelet-based power quality monitoring system. IEEE Transactions on Power Delivery 2001,16(3):353-360. 10.1109/61.924810
Anderson BDO, Moore JB: Optimal Filtering. Prentice-Hall, Englewood Cliffs, NJ, USA; 1979.
Nikias CL, Petropulu AP: Higher-Order Spectra Analysis—A Nonlinear Signal Processing Framework. Prentice Hall, Englewood Cliffs, NJ, USA; 1993.
Mendel JM: Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications. Proceedings of the IEEE 1991,79(3):278-305. 10.1109/5.75086
Ribeiro MV, Marques CAG, Duque CA, Cerqueira AS, Pereira JLR: Power quality disturbances detection using HOS. IEEE Power Engineering Society General Meeting, June 2006, Monreal, QC, Canada 6.
McDonough RN, Whalen AD: Detection of Signals in Noise. Academic Press, London, UK; 1995.
Van Trees HL: Detection, Estimation and Modulation Theory—Parts I. Springer, New York, NY, USA; 1968.
Van Trees HL: Detection, Estimation and Modulation Theory—Parts III. Springer, New York, NY, USA; 1971.
Fishler M, Messer H: Transient signal detection using prior information in the likelihood ratio test. IEEE Transactions on Signal Processing 1993,41(6):2177-2192. 10.1109/78.218145
Colonnese S, Scarano G: Transient signal detection using higher order moments. IEEE Transactions on Signal Processing 1999,47(2):515-520. 10.1109/78.740134
Giannakis GB, Tsatsanis MK: Signal detection and classification using matched filtering and higher order statistics. IEEE Transactions on Acoustics, Speech, and Signal Processing 1990,38(7):1284-1296. 10.1109/29.57557
Nikias CL, Mendel JM: Signal processing with higher-order spectra. IEEE Signal Processing Magazine 1993,10(3):10-37. 10.1109/79.221324
Kauraniemi J, Laakso TI, Hartimo I, Ovaska SJ: Delta operator realizations of direct-form IIR filters. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 1998,45(1):41-52. 10.1109/82.659455
Middleton RH, Goodwin GC: Improved finite word length characteristics in digital control using delta operators. IEEE Transactions on Automatic Control 1986,31(11):1015-1021. 10.1109/TAC.1986.1104162
Theodoridis S, Koutroumbas K: Pattern Recognition. Academic Press, San Diego, Calif, USA; 1999.
Haykin S: Adaptive Filter Theory. Prentice-Hall, Englewood Cliffs, NJ, USA; 1996.
Jain AK, Duin RPW, Mao J: Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000,22(1):4-37. 10.1109/34.824819