Advances in Compression of Power Quality Signals L. C. M. Andrade, T. Nanjundaswamy, M. Oleskovicz, R. A. S. Fernandes and K. Rose   2019/07/15

Abstract

The emerging technology of smart grids relies heavily on monitoring the distribution networks for disturbances using, for example, the devices installed for measuring power quality signals. Obviously, efficient compression of these electrical signals is of paramount importance to allow fast transmission, remote analysis and automation of response to disturbances, as well as archival storage. While prior approaches to compress electrical signal disturbances employed standard effective components such as wavelet transforms, non-uniform quantizers, and entropy coding, the overall system design was largely ad-hoc in the sense that it did not directly account for or adapt to data statistics. Instead, we propose to jointly design all system modules, including transforms, quantizers and entropy coders, within a genetic algorithm-based optimization framework, while accounting for the variation in statistics across different disturbances, within a two-step “classify then compress” procedure. Specifically, we jointly design the family of wavelets to be employed, the non-uniform quantizer structure, and probability tables for the entropy coder, to optimize the rate-distortion trade-off. Experimental results for 8 classes of commonly occurring power quality disturbances, which were synthetically generated to ensure rich and comprehensive training and test sets, validate the effectiveness of the proposed approach with significant performance gains over prior techniques.

Authors and affiliations

L. C. M. Andrade1, T. Nanjundaswamy3, M. Oleskovicz1, R. A. S. Fernandes2 and K. Rose3
1. Department of Electrical and Computer Engineering, São Carlos School of Engineering, University of São Paulo, São Carlos, SP, Brazil
2. Department of Electrical Engineering, Federal University of São Carlos, São Carlos, SP, Brazil
3. Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA

Key words

Data Compression, Evolutionary Computation, Power Quality, Wavelet Transforms.

References

[1] M. H. J. Bollen, I. Y. H. Gu, S. Santoso, M. F. Mcgranaghan, P. A. Crossley, M. V. Ribeiro and P. F. Ribeiro, "Bridging the gap between signal and power," IEEE Signal Processing Magazine, pp. 12-31, 2009.
[2] L. Liboni, R. Flauzino, I. da Silva, E. Costa and M. Suetake, "Efficient signal processing technique for information extraction and its applications in power systems," Electric Power Systems Research, vol. 141, pp. 538-548, 2016.
[3] A. Vaccaro, I. Pisica, L. L. Lai and A. F. Zobaa, "A review of enabling methodologies for information processing in smart grids," International Journal of Electrical Power & Energy Systems, vol. 107, pp. 516-522, 2019.
[4] F. Samie, L. Bauer and J. Henkel, "Edge computing for smart grid: An overview on architectures and solutions," Power Systems, pp. 21-42, 2019.
[5] P. F. Ribeiro, C. A. Duque, P. M. Ribeiro and A. S. Cerqueira, Power systems signal processing for smart grids, New York: John Wiley & Sons, 2013.
[6] M. P. Tcheou, L. Lovisolo, E. A. B. d. Silva, M. A. M. Rodrigues and P. S. R. Diniz, "Optimum Rate-Distortion Dictionary Selection for Compression of Atomic Decompositions of Electric Disturbance Signals," IEEE Signal Processing Letters, vol. 14, no. 2, pp. 81-84, 2007.
[7] M. P. Tcheou, L. Lovisolo, M. V. Ribeiro, E. A. B. d. Silva, M. A. M. Rodrigues, J. M. T. Romano and P. S. R. Diniz, "The Compression of Electric Signal Waveforms for Smart Grids: State of the Art and Future Trends," IEEE Transactions on Smart Grid, vol. 5, no. 1, pp. 291-302, 2014.
[8] A. S. Spanias, "Speech coding: A tutorial review," Proceedings of the IEEE, vol. 82, no. 10, pp. 1541-1582, 1994.
[9] G. J. Sullivan, "Efficient scalar quantization of exponential and Laplacian random variables," IEEE Transactions on Information Theory, vol. 42, no. 5, pp. 1365--1374, 1996.
[10] J. Chung, E. J. Powers, W. M. Grady and S. C. Bhatt, "Variable rate power disturbance signal compression using embedded zerotree wavelet transform coding," in IEEE Power Engineering Society Winter Meeting, New York, NY, USA, 1999.
[11] C. T. Hsieh and S. J. Huang, "Disturbance data compression of a power system using the Huffman coding approach with wavelet transform enhancement," IEE Proceedings - Generation, Transmission and Distribution, vol. 150, no. 1, pp. 7-14, 2003.
[12] S.-J. Huang and M.-J. Jou, "Application of arithmetic coding for electric power disturbance data compression with wavelet packet enhancement," IEEE Transactions on Power Systems, vol. 19, no. 3, pp. 1334-1341, 2004.
[13] F. Lorio and F. Magnago, "Analysis of data compression methods for power quality events," IEEE Power Engineering Society General Meeting, vol. 1, pp. 504-509, 2004.
[14] K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, 2002.
[15] L. C. M. Andrade, M. Oleskovicz and R. A. S. Fernandes, "Adaptive threshold based on wavelet transform applied to the segmentation of single and combined power quality disturbances," Applied Soft Computing, vol. 38, pp. 967 - 977, 2016.
[16] M. Oleskovicz, D. V. Coury, O. D. Felho, W. F. Usida, A. A. F. M. Carneiro and L. R. S. Pires, "Power quality analysis applying a hybrid methodology with wavelet transforms and neural networks," International Journal of Electrical Power & Energy Systems , vol. 31, no. 5, pp. 206 - 212, 2009.
[17] M. Paez and T. Glisson, "Minimum mean-squared-error quantization in speech PCM and DPCM systems," IEEE Transactions on Communications, vol. 20, no. 2, pp. 225-230, 1972.
[18] R. Reininger and J. Gibson, "Distributions of the two-dimensional DCT coefficients for images,"
IEEE Transactions on Communications, vol. 31, no. 6, pp. 835-839, 1983.
[19] G. J. Sullivan, J. Ohm, W.-J. Han and T. Wiegand, "Overview of the high efficiency video coding (HEVC) standard," IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 12, pp. 1649-1668, 2012.
[20] M. F. Duarte, G. Shen, A. Ortega and R. G. Baraniuk, "Signal compression in wireless sensor networks," Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 370, no. 1958, pp. 118-135, 2012.
[21] P. Murugan, S. Kannan and S. Baskar, "Application of NSGA-II Algorithm to Single-Objective Transmission Constrained Generation Expansion Planning," IEEE Transactions on Power Systems, vol. 24, no. 4, pp. 1790-1797, 2009.
[22] R. B. Agrawal and K. Deb, "Simulated Binary Crossover for Continuous Search Space," Indian Institute of Technology, Kampur, India, 1994.
[23] R. Hooshmand and A. Enshaee, "Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm," Electric Power Systems Research, vol. 80, no. 12, pp. 1552-1561, 2010.
[24] L. C. M. Andrade, M. Oleskovicz and R. A. S. Fernandes, "Analysis of Wavelet Transform applied to the segmentation of disturbance signals with different sampling rates," in IEEE Power Engineering Society General Meeting, National Harbor, MD, USA, 2014.
[25] R. C. Dugan, M. F. McGranaghan, S. Santoso and H. W. Beaty, Electrical Power Systems Quality, Third Edition, New York, NY, USA: McGraw-Hill Education, 2012.