A Novel Approach: Effective Compressive Sensing in Power Network Problem

Indrarini Dyah Irawati, Sugondo Hadiyoso, Gelar Budiman

Abstract

A sparse power matrix is a type of two-dimensional data that conveys the amount of electric power each node, connected to an electric power meter sensor. The need for sensors increases in large power networks. This is problematic due to the increase in the number of sensors resulting in increased costs, the installation is more complicated, attenuation of sensor power, and requires maintenance. In order to reduce the use of sensors and increase the effectiveness of power to the sensors, electrical power measurements can be carried out at certain nodes. This also increases performance due to an efficient communication channel bandwidth and reduces power system data storage. This paper estimates the minimum required sensors on a power grid using compressive sensing (CS). The present study proposes a novel reconstruction algorithm for CS, the effective-OMP (E-OMP), to address this power network problem. E-OMP determines the minimum number of sensors in use. The study simulations reveal that E-OMP can save power from sensor use and preserve storage space. The proposed reconstruction algorithm generates NMSE less than 0.05 and has guaranteed high reconstruction accuracy compared to other ones (OMP, IRLS, and L1 reconstruction algorithms.

 

 

Keywords: compressive sensing, effective-OMP, power network, reconstruction.


Full Text:

PDF


References


GLOBAL SUSTAINABLE ELECTRICITY PARTNERSHIP. New electricity frontiers: Harnessing the role of low-carbon electricity uses in a digital era, 2018. https://www.globalelectricity.org/content/uploads/New-electricity-frontiers-report.pdf

GIELEN D., BOSHELL F., SAYGIN D., BAZILIAN M. D., WAGNER N., and GORINIR. The role of renewable energy in the global energy transformation. Energy Strategy Reviews, 2019, 24(1): 38-50.https://doi.org/10.1016/j.esr.2019.01.006

ALAM M. S., & AREFIFARS. A. Energy Management in Power Distribution Systems: Review, Classification, Limitations and Challenges. IEEE Access, 2019, 7: 92979-93001. https://doi.org/10.1109/ACCESS.2019.2927303

SCHNEIDER K. P., LAVAL S., HANSEN J., MELTON R. B., PONDER L., and FOX L. A distributed power system control architecture for improved distribution system resiliency. IEEE Access, 2019, 7: 9957-9970. https://doi.org/10.1109/ACCESS.2019.2891368

RANI M., DHOK S. B., and DESHMUKHR. B. A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications. IEEE Access, 2018, 6: 4875-4894. https://doi.org/10.1109/ACCESS.2018.2793851

QUINSAC A. B. C., & KOUAMÉ D. Frequency domain compressive sampling for ultrasound imaging. Advances in Acoustics and Vibration, 2012, 2012: 231317. https://doi.org/10.1155/2012/231317

KOLODZIEJ S. P., AZNAVEH M., BULLOCK M., DAVID J., DAVIS T. A., HENDERSON M., HU Y., and SANDSTROM R. The Suite Sparse Matrix Collection Website Interface. Journal of Open Source Software, 2019, 4(35): 1244-1248.

MISHRA A., THAKKAR F., MODI C., and KHER R. ECG Signal Compression Using Compressive Sensing and Wavelet Tranform. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, California, 2012, pp. 3404-3407. https://doi.org/10.1109/EMBC.2012.6346696

OLETIC D., SKRAPEC M., and BILAS V. Monitoring Respiratory Sounds: Compressed Sensing Reconstruction via OMP on Android Smartphone. In: GODARA B., & NIKITA K.S. (eds.) Wireless Mobile Communication and Healthcare. MobiHealth 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Vol. 61. Springer, Berlin, Heidelberg, 2013: 114-121. https://doi.org/10.1007/978-3-642-37893-5_13

OLETIC D., SKRAPEC M., and BILAS V. Prototype of Respiratory Sounds Monitoring System Based on Compressive Sampling. In: ZHANG Y. T. (ed.) The International Conference on Health Informatics. IFMBE Proceedings, Vol 42. Springer, Cham, 2014: 92-95.https://doi.org/10.1007/978-3-319-03005-0_24

ZHU J., CHEN C., SU S., and CHANGZ. Compressive Sensing of Multichannel EEG Signals via lq Norm and Schatten-p Norm Regularization. Mathematical Problems in Engineering, 2016, 2016: 2189563. https://doi.org/10.1155/2016/2189563

LAKSHMINARAYANA M., & SARVAGYAM. MICCS: A novel framework for medical image compression using compressive sensing. International Journal of Electrical and Computer Engineering, 2018, 8(5): 2818-2828. http://doi.org/10.11591/ijece.v8i5.pp2818-2828

IRAWATI I. D., HADIYOSO S., and HARIYANIY. S. Multi-wavelet level comparison on compressive sensing for MRI image reconstruction. Bulletin of Electrical Engineering and Informatics, 2020, 9(4): 1461-1467. https://doi.org/10.11591/eei.v9i4.2347

OROVIĆ I., PAPIĆ V., IOANA X. L. C., and STANKOVIĆ S. Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations. Mathematical Problems in Engineering, 2016, 2016: 7616393. https://doi.org/10.1155/2016/7616393

RAGAB M., OMER O. A., and ABDEL-NASSER M. Compressive sensing MRI reconstruction using empirical wavelet transform and grey wolf optimizer. Neural Computing and Applications, 2018, 7: 1-20. https://doi.org/10.1007/s00521-018-3812-7

USMAN K., GUNAWAN H., and SUKSMONOA. B. Compressive sensing reconstruction algorithm using L1-norm minimization via L2-norm minimization. International Journal on Electrical Engineering and Informatics, 2018, 10(1): 37-50. https://doi.org/10.15676/ijeei.2018.10.1.3

IRAWATI I. D., SUKSMONO A. B., and EDWARDI. J. M. An Interpolation Comparative Analysis for Missing Internet Traffic Data. Proceedings of the 3rd International Conference on Electronics, Communications and Control Engineering, Bali, 2020, pp. 26-30. https://doi.org/10.1145/3396730.3396740

IRAWATI I. D., SUKSMONO A. B., and EDWARDI. J. M. Measurement Matrix for Sparse Internet Data based Compressive Sampling. Proceedings of the IEEE International Computer Science and Engineering Conference, Chiang Mai, 2018, pp. 1-6. https://doi.org/10.1109/ICSEC.2018.8712812

NIE L., JIANG D., and GUOL. End-to-end network traffic reconstruction via network tomography based on compressive sensing. Journal of Network and Systems Management, 2015, 23: 709-730. https://doi.org/10.1007/s10922-014-9314-8

JIANG D., WANG W., SHI L., and SONGH. A Compressive Sensing-Based Approach to End-to-End Network Traffic Reconstruction. IEEE Transactions on Network Science and Engineering, 2020, 7(1): 507-519.

YIN M., YU K., and WANGZ. Compressive Sensing Based Sampling and Reconstruction for Wireless Sensor Array Network. Mathematical Problems in Engineering, 2016, 2016: 9641608. https://doi.org/10.1155/2016/9641608

IRAWATI I. D., SUKSMONO A. B., and EDWARDI. J. M. Enhanced OMP and Bilinear Interpolation in Missing Traffic Reconstruction based on Sparse SVD. Proceedings of the 26th IEEE International Conference on Telecommunications, Hanoi, 2019.

TEHEOU, M., LOVISOLO L., RIBEIRO M. V., DA SILVA E. A. B., RODRIGUES M. A. M., ROMANO J. M. T., and DINIZP. S. R. The Compression of Electric Signal Waveforms for Smart Grids: State of the Art and Future Trends. IEEE Transactions on Smart Grid, 2014, 5(1): 291-301. https://doi.org/10.1109/TSG.2013.2293957

GEREK O. N., & ECED. G. Compression of power quality event data using 2D representation. Electric Power Systems Research, 2008, 78(6): 1047-1052. https://doi.org/10.1016/j.epsr.2007.08.006

ZHANG D., BI Y., and ZHAOJ. A new data compression algorithm for power quality online monitoring. Proceedings of the International Conference on Sustainable Power Generation and Supply, Nanjing, 2009, pp. 1-4. https://doi.org/10.1109/SUPERGEN.2009.5347884

DASH P. K., PANIGRAHI B. K., SAHOO D. K., and PANDA G. Power Quality Disturbance Data Compression, Detection, and Classification using Integrated Spline Wavelet and S-Transform. IEEE Power Engineering Review, 2002, 22(7): 595-600. https://doi.org/10.1109/MPER.2002.4312423

SANTOSO S., POWERS E. J., and GRADY W. M. Power Quality Disturbance Data Compression Using Wavelet Transform Methods. IEEE Transactions on Power Delivery, 1997, 12(3): 1250-1257. https://doi.org/10.1109/61.637001

NING J., WANG J., GAO W., and LIU C. A Wavelet-based Data Compression Technique for Smart Grid. IEEE Transactions on Smart Grid, 2011, 2(1): 212-218. https://doi.org/10.1109/TSG.2010.2091291

LITTLER T. B., & MORROWD. J. Wavelets for the Analysis and Compression of Power System Disturbances. IEEE Transactions on Power Delivery, 1999, 14(2): 358-364. https://doi.org/10.1109/61.754074

RIBEIRO M. V., PARK S. H., ROMANO J. M. T., and MITRAS. K. A novel MDL-based compression method for power quality applications. IEEE Transactions on Power Delivery, 2007, 22(1): 27-36. https://doi.org/10.1109/TPWRD.2006.887091

LOVISOLO L., DA SILVA E. A. B., RODRIGUES M. A. M., and DINIZP. S. R. Efficient Coherent Adaptive Representations of Monitored Electric Signals in Power Systems Using Damped Sinusoids. IEEE Transactions on Signal Processing, 2005, 53(10): 3831-3846. https://doi.org/10.1109/TSP.2005.855400

TCHEOU M. P., LOVISOLO L., DA SILVA E. A. B., RODRIGUES M. A. M., and DINIZP. S. R. Optimum rate-distortion dictionary selection for compression of atomic decompositions of electric disturbance signals. IEEE Signal Processing Letters, 2007, 14(2): 81-84. https://doi.org/10.1109/LSP.2006.882117

DONOHO D. Compressed Sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. https://doi.org/10.1109/TIT.2006.871582

QING A., HONGTAO Z., ZHIKUN H., and ZHIWENC. A compression approach of power quality monitoring data based on two dimension DCT. Proceedings of the 3rd International Conference on Measuring Technology and Mechatronics Automation, Shangshai, 2011. https://doi.org/10.1109/ICMTMA.2011.12

DAI S., LIU W., WANG Z., LI K., ZHU P., and WANG P. An Efficient Lossless Compression Method for Periodic Signals Based on Adaptive Dictionary Predictive Coding. Applied Sciences, 2020, 10: 4918. https://doi.org/10.3390/app10144918

MALLAT S., & ZHANG Z. Matching Pursuits with Time-Frequency Dictionaries. IEEE Transactions on Signal Processing, 1993, 41(12): 3397-3415. https://doi.org/10.1109/78.258082

TROPP J. A., & GILBERT A. C. Signal Recovery from Random Measurement via Orthogonal Matching Pursuit. IEEE Transaction on Information Theory, 2007, 53(12): 4655-4666. https://doi.org/10.1109/TIT.2007.909108

CANDES E. The Restricted Isometry Property and Its Implications for Compressed Sensing. Comptes Rendus Mathematique, 2008, 346(9-10): 589-592.

DAVIS T. A., & HU Y. The University of Florida Sparse Matrix Collection. ACM Transactions on Mathematical Software, 2011, 38(1): 1-28. https://doi.org/10.1145/2049662.2049663

BUDIMAN G., SUKSMONO A. B., and DANUDIRDJO D. Compressive Sampling with Multiple Bit Spread Spectrum-Based Data Hiding. Applied Sciences, 2020, 10(12): 4338. https://doi.org/10.3390/app10124338

IRAWATI I. D., HADIYOSO S., BUDIMAN G., and MULYANA A. Lifting Wavelet Transform in Compressive Sensing for MRI Reconstruction. Journal of Southwest Jiaotong University, 2020, 55(5). http://jsju.org/index.php/journal/article/view/726


Refbacks

  • There are currently no refbacks.