Data compression techniques for Phasor Measurement Unit (PMU) applications in smart transmission grid
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Makarand Ballal
,
Amit Kulkarni
Abstract
The advances in Wide Area Measurement Systems (WAMS) and deployment of a huge number of phasor measurement units (PMUs) in the grid are generating big data volume. This data can be used for a variety of applications related to grid monitoring, management, operation, protection, and control. With the increase in this data size, the respective storage capacity needs to be enhanced. Also, communication infrastructure readiness remains bottleneck to transfer this big data. One of the probable solutions could be transmitting compressed data. This paper presents techniques for data compression in the smart transmission system using singular values decomposition (SVD) and the eigenvalues decomposition (EVD). The SVD and EVD based principal component analysis (PCA) techniques are applied to the real-time PMU data collected from extra-high voltage (EHV) substations of transmission utility in the western regional grid of India. Adequacy of data is checked by Kaiser-Meyer-Olkin (KMO) test in order to have the satisfactory performance of these techniques towards achieving the objective of efficient data compression. Results are found satisfactory gives compression more than 80% using real time data.
Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. https://www.powergridindia.com/presentations.Suche in Google Scholar
2. www.cea.nic.in›reports›committee›scm›allindia›agenda_note.Suche in Google Scholar
3. Klump, R, Agarwal, P, Tate, JE, Khurana, H. Lossless compression of synchronized phasor measurements. In: IEEE PES General Meeting, IEEE, Providence, RI; 2010.confproc.10.1109/PES.2010.5590156Suche in Google Scholar
4. Ning, J, Wang, J, Gao, W, Liu, C. A wavelet based data compression technique for smart grid. IEEE Trans Smart Grid 2011;2:212–8. https://doi.org/10.1109/TSG.2010.2091291.Suche in Google Scholar
5. Shahraeini, M, Ghazizadeh, MS, Javidi, MH. Co-optimal placement of measurement devices and their related communication infrastructure in wide area measurement systems. IEEE Trans Smart Grid 2012;3:684–91. https://doi.org/10.1109/TSG.2011.2178080.Suche in Google Scholar
6. Top, P, Breneman, J. Compressing phasor measurement data. In: IEEE power & energy society general meeting 2013, IEEE, Vancouver, BC; 2013.confproc.10.1109/PESMG.2013.6672472Suche in Google Scholar
7. Tcheou, MP, Lovisolo, L, Ribeiro, MV, Da Silva, EA, Rodrigues, MA, Romano, JM, et al. The Compression of electric signal waveforms for smart grids: state of the art and future trends. IEEE Trans Smart Grid 2014;5:291–302. https://doi.org/10.1109/TSG.2013.2293957.Suche in Google Scholar
8. Das, S, Singh Sidhu, T. Application of compressive sampling in synchrophasor data communication in WAMS. IEEE Trans Ind Informatics 2014;10:450–60. https://doi.org/10.1109/TII.2013.2272088.Suche in Google Scholar
9. Xie, L, Chen, Y, Kumar, PR. Dimensionality reduction of synchrophasor data for early event detection: linearized analysis. Trans Power Syst 2014;29:2784–94. https://doi.org/10.1109/TPWRS.2014.2316476.Suche in Google Scholar
10. Zhang, F, Cheng, L, Li, X, Sun, Y, Gao, W, Zhao, W. Application of a real time data compression and adapted protocol technique for WAMS. Trans Power Syst 2015;30:653–62. https://doi.org/10.1109/TPWRS.2014.2329092.Suche in Google Scholar
11. Kanirajan, P, Suresh Kumar, V. A wavelet based data compression technique for power quality events classification. WSEAS Trans Power Syst 2015;10:82–88.Suche in Google Scholar
12. Unterweger, A, Engel, D. Resumable load data compression in smart grids. IEEE Trans Smart Grid 2015;6:919–29. https://doi.org/10.1109/TSG.2014.2364686.Suche in Google Scholar
13. Khan, J, Bhuiyan, S, Murphy, G, William, J. Data denoising and compression for smart grid communication. IEEE Trans Signal Inf Process 2016;2:200–14. https://doi.org/10.1109/TSIPN.2016.2539680.Suche in Google Scholar
14. Gadde, PH, Biswal, M, Brahma, S, Cao, H. Efficient compression of PMU data in WAMS. IEEE Trans Smart Grid 2016;7:2406–13. https://doi.org/10.1109/TSG.2016.2536718.Suche in Google Scholar
15. Wen, MH, Li, VO. Optimal phasor data compression unit installation for WAMS — An integer linear programming approach. IEEE Trans Smart Grid 2016;7:2644–53. https://doi.org/10.1109/TSG.2015.2503425.Suche in Google Scholar
16. Olivo, E, Campion, M, Ranganathan, P. Data compression for next generation phasor data concentrators in a smart grid. J Inf Secur 2016;7:291–6. https://doi.org/10.4236/jis.2016.75024.Suche in Google Scholar
17. de Souza, JCS, Assis, TML, Pal, BC. Data compression in smart distribution systems via singular value decomposition. IEEE Trans Smart Grid 2017;8:275–84. https://doi.org/10.1109/TSG.2015.2456979.Suche in Google Scholar
18. Uthayakumar, J, Vengattaraman, T, Dhavachelvan, P. A survey on data compression techniques: From the perspective of data quality, coding schemes, data type and applications. J King Saud Univ Comput Inf Sci 2018. https://doi.org/10.1016/j.jksuci.2018.05.006.Suche in Google Scholar
19. Lee, G, Kim, DI, Kim, SH, Shin, YJ. Multi-scale PMU data compression via density based WAMS clustering analysis. Energies 2019;12:617–27. https://doi.org/10.3390/en12040617.Suche in Google Scholar
20. Pourramezan, R, Karimi, H, Mahseredjian, J, Paolone, M. Real time processing and quality improvement of synchrophasor data. IEEE Trans Smart Grid 2020;11:3313–24.10.1109/PESGM46819.2021.9638065Suche in Google Scholar
21. Ma, R, Basumallik, S, Eftekharnejad, S. A PMU based data driven approach for classifying power system events considering cyberattacks. IEEE Syst J 2020. https://doi.org/10.1109/JSYST.2019.2963546.Suche in Google Scholar
22. Sayood, K. Introduction to data compression, 5th ed. Cambridge, U.S: Morgan Kaufmann Publisher; 2018.10.1016/B978-0-12-809474-7.00019-7Suche in Google Scholar
23. Books Llc. Singular value decomposition: eigenvalue, eigenvector and eigenspace. Memphis, Tennessee: General Books LLC; 2010.Suche in Google Scholar
24. Andrews, HC, Patterson, CL. Singular value decomposition (SVD) image coding. IEEE Trans Commun 1976;24:425–32. https://doi.org/10.1109/TCOM.1976.1093309.Suche in Google Scholar
25. Yang, JF, Lu, CL. Combined techniques of singular value decomposition and vector quantization for image coding. IEEE Trans Image Process 1995;4:1141–6. https://doi.org/10.1109/83.403419.Suche in Google Scholar
26. Jolliffe, IT. Principal component analysis. New York, NY: Springer Science & Business Media; 1986.10.1007/978-1-4757-1904-8Suche in Google Scholar
27. Naik, GR. Advances in principle component analysis: research and development. Germany: Springer; 2018.10.1007/978-981-10-6704-4Suche in Google Scholar
28. Cerny, CA, Kaiser, HF. A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivariate Behav Res J 1977;12:43–47. https://doi.org/10.1207/s15327906mbr1201_3.Suche in Google Scholar
29. Tobias, S, Carlson, JE. Brief report: Bartlett’s test of sphericity and chance findings in factor analysis. Multivariate Behav Res J 1969;4:375–77. https://doi.org/10.1207/s15327906mbr0403_8.Suche in Google Scholar
30. Alboukadel, Kassambara Practical guide to principal component methods in R: PCA, M(CA), FAMD, MFA, HCPC, factoextra. STHDA; 2017.Suche in Google Scholar
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Artikel in diesem Heft
- Review
- Power quality problem and key improvement technology for regional power grids
- Research Articles
- Machine learning roles in advancing the power network stability due to deployments of renewable energies and electric vehicles
- Analysis between graph-based and Power Transfer Distribution Factors (PTDF)-based model reduction methods in Electric Power Systems
- Experimental control of photovoltaic system using neuro – Kalman filter maximum power point tracking (MPPT) technique
- Data compression techniques for Phasor Measurement Unit (PMU) applications in smart transmission grid
- Influence of inter-turn short circuit on the performance of 10 kV, 1000 kW induction motor
- Detection of coherent groups using measured signals, in an inter-area mode, for creating controlled islands to protect the power system from blackout
- Multi-objective optimization of optimal capacitor allocation in radial distribution systems
- A novel approach of closeness centrality measure for voltage stability analysis in an electric power grid
- Dynamic Simulation of Eastern Regional Grid of India using Power System Simulator for Engineering PSS®E
- Optimal total harmonic distortion minimization in multilevel inverter using improved whale optimization algorithm
- A cost effective accumulator management system for electric vehicles
