Abstract
Electrification was the leading drive in the modern world in its engineering achievements of the twentieth century. Electricity, as an unavoidable characteristic, invisibly fuses into the fabric of modern society. Smart grid integrates electricity and PS network communication that delivers digital information about the operator's and consumers' real-time network operations. Interdisciplinary research areas like communication, automation, sensor and control include smart grid technology. Application-centered network communication architecture must be developed for the Smart Grid to support traditional applications for the smart grid which evolves. The conversion to a smart grid is therefore inevitable and involves the integration of intelligence into the existing electronic grid (IEDs). The current PS infrastructure in smart grid is being upgraded by integrating distributed energy resources, advanced automated control and prediction systems to ensure optimal energy use, making the PS reliable and safer. This study introduces a proposed approach for data compression based on a combined binary regression wavelet-surrogate tree and a hybrid thresholding method. The results show that implementing the proposed data compression approach can result in a substantial reduction in the number of messages exchanged between the IEDS and the SCADA within intelligentsia substation automation in the smart grids by the General Object-Oriented Substation Event (Goose).
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References
Aleshinloye A, Bais A (2018) Modified differential golomb arithmetic lossless compression algorithm for smart grid applications. In: 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), pp. 1–6, https://doi.org/10.1109/ISGTEurope.2018.8571649.
Amulya, MP, Bhide SR, Bhat SS (2017) Experimenting with IEC 61850 and GOOSE messaging. In: 2017 4th International Conference on Power, Control Embedded Systems (ICPCES), pp. 1–6. https://doi.org/10.1109/ICPCES.2017.8117641.
Bhuiyan S, Khan J, Murphy G (2018) Weighted entropy for data compression in smart grid. IEEE Ind Appl Soc Annu Meet (IAS) 2018:1–6. https://doi.org/10.1109/IAS.2018.8544486
Bhuiyan BA, Absar MW, Roy A (2017) Performance comparison of various wavelets in compression of PMU generated data in smart grid. In: 2017 3rd International Conference on Electrical Information and Communication Technology (EICT), pp. 1–6. https://doi.org/10.1109/EICT.2017.8275177.
Chen Z, Lee C (2013) Multi-GPU acceleration for smart grid data compression. In: 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing, pp. 1808–1813. https://doi.org/10.1109/HPCC.and.EUC.2013.259.
Chowdhury MR, Tripathi S, De S (2021) Adaptive multivariate data compression in smart metering internet of things. IEEE Trans Industr Inf 17(2):1287–1297. https://doi.org/10.1109/TII.2020.2981382
Chren S, Rossi B, Pitner T (2016) Smart grids deployments within EU projects: the role of smart meters. In: 2016 Smart Cities Symposium Prague (SCSP), Prague, Czech Republic, pp. 1–5. https://doi.org/10.1109/SCSP.2016.7501033
de Souza JCS, Assis TML, Pal BC (2017) Data compression in smart distribution systems via singular value decomposition. IEEE Trans Smart Grid 8(1):275–284. https://doi.org/10.1109/TSG.2015.2456979
Gillis JM, Alshareef SM, Morsi WG (2016) Nonintrusive load monitoring using wavelet design and machine learning. IEEE Trans Smart Grid 7(1):320–328. https://doi.org/10.1109/TSG.2015.2428706
Huang W (2018) Learn IEC 61850 configuration in 30 minutes. In: 2018 71st Annual Conference for Protective Relay Engineers (CPRE), College Station, TX, pp. 1–5. https://doi.org/10.1109/CPRE.2018.8349803
Karthika S, Rathika P (2019) An efficient data compression algorithm for smart distribution systems using singular value decomposition. In: 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), 2019, pp. 1–7. https://doi.org/10.1109/INCOS45849.2019.8951340.
Khan J, Bhuiyan SMA, Murphy G, Arline M (2015) Embedded-zerotree-wavelet-based data denoising and compression for smart grid. In: IEEE Transactions on Industry Applications, vol. 51, no. 5, pp. 4190–4200. https://doi.org/10.1109/TIA.2015.2420613.
Mateo C, Talavera JA (2018) Short-time Fourier transform with the window size fixed in the frequency domain. (Report). Digit Signal Process 77:13–21. https://doi.org/10.1016/j.dsp.2017.11.003
Tcheou MP et al (2014) The compression of electric signal waveforms for smart grids: state of the art and future trends. IEEE Trans Smart Grid 5(1):291–302. https://doi.org/10.1109/TSG.2013.2293957
Zhao Q, Dai W (2015) A wavelet denoising method of new adjustable threshold. In: 2015 IEEE 16th International Conference on Communication Technology (ICCT), pp. 684–688. https://doi.org/10.1109/ICCT.2015.7399929.
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Subbarao, D., Kumar, A.S., Bikshapthi, S.K. et al. Analysis of data compression techniques in smart grids for optimising mean-square-error. Appl Nanosci 13, 1591–1599 (2023). https://doi.org/10.1007/s13204-021-02028-7
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DOI: https://doi.org/10.1007/s13204-021-02028-7