Development of lossless compression algorithms for power system operational data

Development of lossless compression algorithms for power system operational data

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Power system measurement is extremely crucial for the stable power system operation and results in the generation of bulk data. This enormous volume of data must be transferred from field devices to the control centre and must be preserved for future references. Analysis of practical generation scheduling and monitoring data indicates its repetitive and slow varying nature. A simple, low-computational compression algorithm, the differential binary encoded algorithm (DBEA), is developed for compressing such information and a high compression ratio is achieved for the majority of practical data sets. To overcome the constraints of the DBEA, extended version of DBEA (E-DBEA) is developed which increases the range of input at the expense of compression ratio. Resumable load data compression algorithm (RLDA) is a differential coding-based algorithm developed for compressing load profile data. Comparison of the performance obtained by the DBEA, E-DBEA and RLDA with practical data sets clearly indicates the effectiveness of the DBEA and E-DBEA. The online test bench of the DBEA and E-DBEA consists of two inter connected PCs, one working as virtual load despatch centre and other as virtual generating station or sub-station. Due to the simplicity of the proposed work, it can be useful for data storage and data transfer both at high-level PCs and low-level microcontrollers.


    1. 1)
      • 1. ‘CERC, Indian Electricity Grid Code, 2010’. Available at, accessed 25 October 2015.
    2. 2)
      • 2. Das, S., Rao, P.S.N.: ‘Arithmetic coding based lossless compression schemes for power system steady state operational data’, Electr. Power Energy Syst., 2012, 43, (1), pp. 4753.
    3. 3)
      • 3. Unterweger, A., Engel, D.: ‘Resumable load data compression in smart grids’, IEEE Trans. Smart Grid, 2015, 6, (2), pp. 919929.
    4. 4)
      • 4. Tong, X., Kang, C., Xia, Q.: ‘Smart metering load data compression based on load feature identification’, IEEE Trans. Smart Grid, 2016, 7, (5), pp. 24142422.
    5. 5)
      • 5. Das, S.: ‘Power system data compression for archiving’. MS thesis (engineering), Indian Institute of Science, November 2007.
    6. 6)
      • 6. Pitt, B.D.: ‘Applications of data mining techniques to electric load profiling’. PhD thesis, University of Manchester Institute of Science and Technology, 2000.
    7. 7)
      • 7. Shaheen, M., Shahbaz, M., Jadoon, K.A.K.: ‘Data mining for wind energy site selection’. Proc. World Congress on Engineering and Computer Science 2012 (WCECS 2012), San Francisco, USA, October 2012, vol. I, available at, accessed 12 March 2016.
    8. 8)
      • 8. Das, S., Rao, P.S.N.: ‘Understanding power system behaviour through mining archived operational data’. Proc. Fifteenth National Power Systems Conf. (NPSC), IIT Bombay, India, December 2008, pp. 248253.
    9. 9)
      • 9. Tcheou, M.P., Lovisolo, L., Ribeiro, M.V., et al: ‘The compression of electric signal waveforms for smart grids: state of the art and future trends’, IEEE Trans. Smart Grid, 2014, 5, (1), pp. 291301.
    10. 10)
      • 10. Khan, J., Bhuiyan, S.M.A., Murphy, G., et al: ‘Embedded-zerotree-wavelet-based data denoising and compression for smart grid’, IEEE Trans. Ind. Appl., 2015, 51, (5), pp. 41904200.
    11. 11)
      • 11. Khan, J., Bhuiyan, S., Murphy, G., et al: ‘Data denoising and compression for smart grid communication’, IEEE Trans. Signal Inf. Process. Over Netw., 2016, 2, (2), pp. 200214.
    12. 12)
      • 12. Tate, J.E.: ‘Preprocessing and Golomb–rice encoding for lossless compression of phasor angle data’, IEEE Trans. Smart Grid, 2016, 7, (2), pp. 718728.
    13. 13)
      • 13. Tse, N.C.F., Chan, J.Y.C., Lau, W.-H., et al: ‘Real-time power-quality monitoring with hybrid sinusoidal and lifting wavelet compression algorithm’, IEEE Trans. Power Deliv., 2012, 27, (4), pp. 17181726.
    14. 14)
      • 14. Mukhopadhyay, S.K., Mitra, M., Mitra, S.: ‘A lossless ECG data compression technique using ASCII character encoding’, Comput. Electr. Eng., 2011, 37, (4), pp. 486497.
    15. 15)
      • 15. Mukhopadhyay, S.K., Mitra, M., Mitra, S.: ‘An ECG signal compression technique using ASCII character encoding’, Measurement, 2012, 45, (6), pp. 16511660.
    16. 16)
      • 16. Mukhopadhyay, S.K., Mitra, M., Mitra, S.: ‘ECG signal compression using ASCII character encoding and transmission via SMS’, Biomed. Signal Proc. Control, 2013, 8, (4), pp. 354363.
    17. 17)
      • 17. Sarkar, S.J., Sarkar, N.K., Banerjee, A.: ‘A novel Huffman coding based approach to reduce the size of large data array’. Proc. IEEE Int. Conf. on Circuit, Power and Computing Technologies (ICCPCT- 2016), Tamilnadu, India, March 2016, pp. 15.
    18. 18)
      • 18. Sarkar, S.J., Kar, K., Das, I.: ‘Basic arithmetic coding based approach for compressing generation scheduling data array’. Proc. 2017 IEEE Calcutta Conf. (CATCON 2017), West Bengal, India, December 2017, pp. 2125.
    19. 19)
      • 19. ‘West Bengal State Load Despatch (WBSLDC) Website’. Available at, accessed February 2015.
    20. 20)
      • 20. ‘Eastern Regional State Load Despatch (ERLDC) Website’. Available at, accessed December 2016.
    21. 21)
      • 21. Li, Z.-N., Drew, M.S., Liu, J.: ‘Fundamentals of multimedia’ (Springer, Cham, Switzerland, 2014, 2nd edn.).

Related content

This is a required field
Please enter a valid email address