access icon openaccess Investigation of background noise in active distribution network and its impacts on synchrophasor estimation

The next-future extensive diffusion of distributed generation and the increasing energy demands due to new components (e.g. electrical vehicles, energy storages) are changing the characteristics of the power grid at the distribution level, which makes it inevitable to introduce the synchrophasor measurement technology into the distribution network. This study analyses the power spectrum for the background noise of the field measured voltage signal of 0.4 kV in one electrical vehicle charging station. The coloured filter model is established using the autoregressive moving average process, which leads to the conclusion that the coloured noise spectrum is generally concave. The impact of background noise for the phasor estimation is evaluated in the steady state and dynamic state referring to IEEE Std C37.118.1™-2011.

Inspec keywords: electric vehicles; autoregressive moving average processes; IEEE standards; distribution networks; phasor measurement; power grids

Other keywords: phasor estimation; coloured filter model; electrical vehicles; distributed generation; power spectrum; autoregressive moving average process; distribution level; energy storages; synchrophasor estimation; increasing energy demands; study analyses; voltage 0.4 kV; active distribution network; background noise; next-future extensive diffusion; electrical vehicle charging station; synchrophasor measurement technology; coloured noise spectrum; power grid

Subjects: Power system measurement and metering; Other topics in statistics; Distribution networks; Transportation

References

    1. 1)
      • 12. Bi, T., Liu, H., Feng, Q., et al: ‘Dynamic phasor model-based synchrophasor estimation algorithm for M-class PMU’, IEEE Trans. Power Deliv., 2015, 30, (3), pp. 110.
    2. 2)
      • 5. 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. 297298.
    3. 3)
      • 11. Yang, J.Z., Liu, C.W.: ‘A precise calculation of power system frequency’, IEEE Power Eng. Rev., 2007, 21, (4), pp. 7171.
    4. 4)
      • 18. Bollen, M.H.J., Gu, I.Y.H.: ‘Signal processing of power quality disturbances’ (Wiley, Hoboken, NJ, USA, 2006).
    5. 5)
      • 1. Quijano, D.A., Wang, J., Sarker, M.R., et al: ‘Stochastic assessment of distributed generation hosting capacity and energy efficiency in active distribution networks’, IET Gener. Transm. Distrib., 2017, 11, (18), pp. 46174625.
    6. 6)
      • 8. Yan, X., Zhang, T., Zhang, B.: ‘Measurement and research of channel noise distributed characterization in low voltage networks’. IEEE/PES Transmission and Distribution Conf. and Exhibition, Asia and Pacific, Dalian, 2005, pp. 17.
    7. 7)
      • 19. Tripathy, P., Srivastava, S.C., Singh, S.N.: ‘A modified TLS-ESPRIT based method for low-frequency mode identification in power systems utilizing synchrophasor measurements’, IEEE Trans. Power Syst., 2011, 26, (2), pp. 719727.
    8. 8)
      • 14. IEEE Standard for Synchrophasor Measurements for Power Systems, IEEE Standard C37.118.1-2011, 2011.
    9. 9)
      • 13. de la O Serna, J.A.: ‘Dynamic phasor estimates for power system oscillations’, IEEE Trans. Instrum. Meas., 2007, 56, (5), pp. 16481657.
    10. 10)
      • 9. Zhan, L., Liu, Y., Culliss, J., et al: ‘Dynamic single-phase synchronized phase and frequency estimation at the distribution level’, IEEE Trans. Smart Grid, 2015, 6, (4), pp. 20132022.
    11. 11)
      • 7. Zhang, H., Jin, Z., Liu, Y.: ‘Wide-area measurement system light and its application in China’, Autom. Electr. Power Syst., 2014, 38, (22), pp. 8590.
    12. 12)
      • 10. Macii, D., Fontanelli, D., Petri, D., et al: ‘Impact of wideband noise on synchrophasor, frequency and ROCOF estimation’. IEEE Int. Workshop on Aachen Applied Measurements for Power Systems (AMPS), Aachen, 2015, pp. 4348.
    13. 13)
      • 15. IEEE Standard for Synchrophasor Data Transfer for Power Systems, IEEE Standard C37.118.2-2011, 2011.
    14. 14)
      • 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. 19141921.
    15. 15)
      • 2. Van Pham, H., Rueda, J.L., Erlich, I.: ‘Probabilistic evaluation of voltage and reactive power control methods of wind generators in distribution networks’, IET Renew. Power Gener., 2015, 9, (3), pp. 195206.
    16. 16)
      • 17. Jain, S.K., Singh, S.N.: ‘Exact model order ESPRIT technique for harmonics and interharmonics estimation’, IEEE Trans. Instrum. Meas., 2012, 61, (7), pp. 19151923.
    17. 17)
      • 4. Carr, S., Premier, G.C., Guwy, A.J., et al: ‘Energy storage for active network management on electricity distribution networks with wind power’, IET Renew. Power Gener., 2014, 8, (3), pp. 249259.
    18. 18)
      • 16. Chen, C.I., Chen, Y.C.: ‘Comparative study of harmonic and interharmonic estimation methods for stationary and time-varying signals’, IEEE Trans. Ind. Electron., 2014, 61, (1), pp. 397404.
    19. 19)
      • 3. Lei, J., Gong, Q.: ‘Operating strategy and optimal allocation of large-scale VRB energy storage system in active distribution networks for solar/wind power applications’, IET Gener. Transm. Distrib., 2017, 11, (9), pp. 24032411.
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