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access icon free Application of random matrix model in multiple abnormal sources detection and location based on PMU monitoring data in distribution network

  • Author(s): Yingjie Yan 1 ; Yadong Liu 1 ; Jian Fang 2 ; Pandi Vijayakumar 3 ; Padmanaban Sanjeevikumar 4 ; Xiuchen Jiang 1
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  • Source: Volume 14, Issue 26, 29 December 2020, p. 6476 – 6483
    DOI:  10.1049/iet-gtd.2020.0755 , Print ISSN 1751-8687, Online ISSN 1751-8695
© The Institution of Engineering and Technology
Received 21/04/2020, Accepted 13/10/2020, Revised 31/07/2020, Published 05/01/2021

With the conversion of the global power economy and energy structure, access to a large amount of renewable energy has led to a decrease in power system inertia. The slight abnormal disturbance in the distribution network may have a significant impact on social and economic development. Aim at enhancing power stability and system resiliency; this study focuses on the detection and location of multiple abnormal sources in the distribution network. Most traditional methods use models relying on precise line parameters, subject to poor adaptability to the distribution network with a large number of nodes, and rapidly changing topology. Therefore, this study proposes a novel random matrix model, driven by monitoring data from phasor measurement units distributed on the overhead transmission lines. In this model, linear shrinkage (LS) theory, and Marchenko–Pastur law are combined for noise reduction to ensure the dynamic character and anti-noise ability. Moreover, data dimensions and sample points may be at the same level in an extensive scale network. The LS and standard condition number rule (SCN) are used for estimating the number of abnormal sources. Finally, the effectiveness of this paper's model is verified in PSCAD. The results indicate that the method has specific dynamic performance and anti-noise ability.

Inspec keywords: power distribution lines; power distribution faults; renewable energy sources; random processes; power system CAD; power overhead lines; socio-economic effects; matrix algebra; power distribution economics; phasor measurement; power system stability

Other keywords: multiple abnormal sources detection; overhead transmission lines; antinoise ability; power system inertia; linear shrinkage theory; renewable energy; social development; random matrix model; noise reduction; phasor measurement units; power system resiliency; PMU monitoring data; standard condition number rule; PSCAD; global power economy; power stability; data dimensions; energy structure; Marchenko–Pastur law; power distribution network disturbance; SCN; economic development

Subjects: Power engineering computing; Power system measurement and metering; Algebra; Power system control; Other topics in statistics; Distribution networks; Overhead power lines; Power system management, operation and economics; Algebra; Other topics in statistics

References

    1. 1)
      • 12. Wu, J., Ota, K., Dong, M., et al: ‘Big data analysis-based security situational awareness for smart grid’, IEEE Trans. Big Data, 2018, 4, (3), pp. 408–417.
    2. 2)
      • 35. Sun, H., Xu, T., Guo, Q., et al: ‘Analysis on blackout in Great Britain power grid on August 9th, 2019 and its enlightenment to power grid in China’, Proc. CSEE, 2019, 21, (39), pp. 6183–6191.
    3. 3)
      • 20. He, X., Ai, Q., Qiu, R., et al: ‘A big data architecture design for smart grids based on random matrix theory’, IEEE Trans. Smart Grid, 2017, 8, (2), pp. 674–686.
    4. 4)
      • 33. Naik, B., Obaidat, M.S., Nayak, J., et al: ‘Intelligent secure ecosystem based on metaheuristic and functional link neural network for edge of things’, IEEE Trans. Ind. Inf., 2020, 16, (3), pp. 1947–1956.
    5. 5)
      • 6. Zhong, Z., Xu, C., Billian, B.J., et al: ‘Power system frequency monitoring network (FNET) implementation’, IEEE Trans. Power Syst., 2005, 20, (4), pp. 1914–1921.
    6. 6)
      • 9. Tajdinian, M., Allahbakhshi, M., Mohammadpourfard, M., et al: ‘Probabilistic framework for transient stability contingency ranking of power grids with active distribution networks: application in post disturbance security assessment’, IET Gener. Transm. Distrib., 2020, 14, (5), pp. 719–727.
    7. 7)
      • 3. Bie, Z., Lin, Y., Li, G., et al: ‘Battling the extreme: a study on the power system resilience’, Proc. IEEE, 2017, 105, (7), pp. 1253–1266.
    8. 8)
      • 2. Wen, Y., Li, W., Huang, G., et al: ‘Frequency dynamics constrained unit commitment with battery energy storage’, IEEE Trans. Power Syst., 2016, 31, (6), pp. 5115–5125.
    9. 9)
      • 15. Moradifar, A., Akbari Foroud, A., Gorgani Firouzjah, K.: ‘Intelligent localisation of multiple non-linear loads considering impact of harmonic state estimation accuracy’, IET Gener. Transm. Distrib., 2017, 11, (8), pp. 1943–1953.
    10. 10)
      • 5. Zhou, N., Luo, L., Sheng, G., et al: ‘Power distribution network dynamic topology awareness and localization based on subspace perturbation model’, IEEE Trans. Power Syst., 2020, 35, (2), pp. 1479–1488.
    11. 11)
      • 7. Lee, L., Centeno, V.: ‘Comparison of μPMU and PMU’. Proc. Conf. on Clemson University Power Systems, Charleston, SC, USA, 2018, pp. 1–6.
    12. 12)
      • 34. ‘IEEE Recommended Practice for Monitoring Electric Power Quality’, IEEE Std 1159–2009 (revision of IEEE STD 1159–1995), 2009, pp. 1–94.
    13. 13)
      • 28. Wen, Y., Meng, H., Fang, M., et al: ‘Fault location method based on full waveform information for distribution networks’. Proc. Conf. on Condition Monitoring and Diagnosis, Perth WA, 2018, pp. 1–5.
    14. 14)
      • 32. Yan, Y., Sheng, G., Wang, H., et al: ‘The key state assessment method of power transmission equipment using big data analyzing model based on large dimensional random matrix’, Proc. CSEE, 2016, 2, (36), pp. 435–445.
    15. 15)
      • 36. Tong, J., Schreier, P. J., Guo, Q., et al: ‘Shrinkage of covariance matrices for linear signal estimation using cross-validation’, IEEE Trans. Signal Process., 2016, 64, (11), pp. 2965–2975.
    16. 16)
      • 31. Yan, Y., Sheng, G., Qiu, R., et al: ‘Big data modeling and analysis for power transmission equipment: a novel random matrix theoretical approach’, IEEE Access, 2018, 6, pp. 7148–7156.
    17. 17)
      • 18. Meier, A., Stewart, E., Mceachern, A., et al: ‘Precision micro synchrophasors for distribution systems: a summary of applications’, IEEE Trans. Smart Grid, 2017, 8, (6), pp. 2926–2936.
    18. 18)
      • 14. Nassif, A. B., Yong, J., Mazin, H., et al: ‘An impedance-based approach for identifying interharmonic sources’, IEEE Trans. Power Deliv., 2011, 26, (1), pp. 333–340.
    19. 19)
      • 10. Wei, D., Wang, B., Liu, D., et al: ‘A method for WAMS big data modeling and abnormal data detection with large random matrices’, Proc. CSEE, 2019, 1, (35), pp. 59–66.
    20. 20)
      • 1. Mithulananthan, N., Shah, R., Lee, K.Y.: ‘Small-disturbance angle stability control with high penetration of renewable generations’, IEEE Trans. Power Syst., 2014, 3, (29), pp. 1463–1472.
    21. 21)
      • 13. Ren, J., Venkata, S. S., Sortomme, E.: ‘An accurate synchrophasor based fault location method for emerging distribution systems’, IEEE Trans. Power Deliv., 2014, 29, (1), pp. 297–298.
    22. 22)
      • 30. Lin, J., Sheng, G., Yan, Y., et al: ‘Online monitoring data cleaning of transformer considering time series correlation’. Proc. Conf. on IEEE/PES Transm. Distrib., Denver CO, 2018, pp. 1–9.
    23. 23)
      • 38. Jiang, H., Tang, X., Lv, W., et al: ‘Blind multi-target detection for bistatic MIMO radar based on random matrix theory’. Proc. IEEE China Summit & Int. Conf. on Signal and Information Processing, Chengdu, China, 2015.
    24. 24)
      • 21. Hajian, M., Ranjbar, A., Amraee, T., et al: ‘Optimal placement of PMUs to maintain network observability using a modified BPSO algorithm’, Int. J. Electr. Power Energy Syst., 2011, 1, (33), pp. 28–34.
    25. 25)
      • 29. Yan, Y., Sheng, G., Cheng, Y., et al: ‘An method for anomaly detection of state information of power equipment based on big data analysis’, Proc. CSEE, 2015, 35, (1), pp. 52–59.
    26. 26)
      • 17. Meier, A., Culler, D., Mceachern, A., et al: ‘Micro-synchrophasors for distribution systems’. Proc. Conf. on IEEE Innovative Smart Grid Technologies, Washington D.C., USA, 2014, pp. 1–5.
    27. 27)
      • 4. Panteli, M., Mancarella, P.: ‘The grid: stronger, bigger, smarter?: presenting a conceptual framework of power system resilience’, IEEE Power Energy Mag., 2015, 13, (3), pp. 58–66.
    28. 28)
      • 11. Sun, C., Wang, X., Zheng, Y., et al: ‘Early warning system for spatiotemporal prediction of fault events in a power transmission system’, IET Gener. Transm. Distrib., 2019, 13, (21), pp. 4888–4899.
    29. 29)
      • 23. Wang, X, Xie, X., Zhang, S., et al: ‘Micro-PMU for distribution power lines’, CIRED Open Access Proc. J., 2017, 10, (1), pp. 333–337.
    30. 30)
      • 39. Jiang, H., Li, Y., Han, J.: ‘Blind target detection for MIMO radar based on random matrix theory under correlated noise’. Proc. Conf. on Radar, Guangzhou China, 2016.
    31. 31)
      • 40. Jiang, H., Zhang, W., Li, Y.: ‘Target detection and RCS amplitude estimation in large-scale MIMO radar using free probability theory’. Proc. IEEE Global Conf. on Signal Information Processing, Washington, DC, USA, 2016.
    32. 32)
      • 8. Liu, W., Zhang, D., Ding, Y., et al: ‘Power grid vulnerability identification methods based on random matrix theory and entropy theory’, Proc. CSEE, 2017, 20, (37), pp. 5893–5901.
    33. 33)
      • 19. Qiu, R., Hu, Z., Li, H., et al: ‘Cognitive radio communication and networking: principles and practice’ (Wiley Press, Hoboken, NJ, USA, 2012).
    34. 34)
      • 24. Liu, Y., Sheng, G., Wang, Y., et al: ‘Current transformer draw-out power supply design based on power-controlled method’, Autom. Electr. Power Syst., 2010, 34, (3), pp. 70–74.
    35. 35)
      • 37. Liu, Y., Sun, X., Zhao, S.: ‘Source enumeration via GBIC with a statistic for sphericity test in white Gaussian and non-Gaussian noise’, IET Radar Sonar Navig., 2017, 11, (9), pp. 1333–1339.
    36. 36)
      • 26. Xie, X., Liu, Y., Yue, H., et al: ‘A novel transient fault current sensor based on the PCB Rogowski coil for overhead transmission lines’, Sensors, 2016, (16), 5 p. 742.
    37. 37)
      • 22. Ahmadi, A., AlinejadBeromi, Y., Moradi, M.: ‘Optimal PMU placement for power system observability using binary particle swarm optimization and considering measurement redundancy’, Expert Syst. Appl., 2011, 6, (38), pp. 7263–7269.
    38. 38)
      • 27. Du, Y., Liu, Y., Shao, Q., et al: ‘Single line-to-ground faulted line detection of distribution systems with resonant grounding based on feature fusion framework’, IEEE Trans. Power Deliv., 2019, 34, (4), pp. 1766–1775.
    39. 39)
      • 25. Xie, X., Liu, Y., Liu, Z., et al: ‘Design of high-frequency differential winding PCB Rogowski coil’, Chin. J. Sci. Instrum., 2015, 36, (4), pp. 886–894.
    40. 40)
      • 16. Li, X., Zhou, M., Luo, Y.: ‘A disturbance source location method on the low frequency oscillation with time-varying steady-state points’, CES Trans. Electron. Mach. Syst., 2018, 2, (2), pp. 226–231.
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