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access icon free Cross-border transmission line losses calculation using adaptive Monte–Carlo method

  • Author(s): Ivan Tolić 1 ; Kruno Miličević 2 ; Saša Mujović 3
    • View affiliations
    • Affiliations: 1: Measurement and Metering Department, Croatian Transmission System Operator Ltd., C. Hadrijana 3, Osijek, Croatia ;
      2: Faculty of Electrical Engineering, Computer Science and Information Technology in Osijek, Josip Juraj Strossmayer University in Osijek, Trpimirova bb, Croatia ;
      3: Faculty of Electrical Engineering Podgorica, University of Montenegro, Džordža Vašingtona bb, Podgorica, Montenegro
  • Source: Volume 11, Issue 4, July 2017, p. 400 – 405
    DOI:  10.1049/iet-smt.2016.0367 , Print ISSN 1751-8822, Online ISSN 1751-8830
© The Institution of Engineering and Technology
Received 02/09/2016, Accepted 28/12/2016, Revised 05/12/2016, Published 12/01/2017

This study deals with calculation of transmission line losses. Having in mind that this type of losses, besides the line load and the other external input quantities such as atmospheric conditions, strongly depends on measurement uncertainty as well, the issue can be considered through finding the most convenient approach in calculation of measurement uncertainty. In this respect, two approaches (methods) are the most common: traditional method based on the guide to the expression of uncertainty in measurement (GUM) and the adaptive Monte–Carlo method (AMC). The study reveals the main disadvantages of the GUM, which is so far considered as a most accurate method. The observed drawbacks of the GUM can be successfully overcome by using of the AMC method. The comparison between the methods is performed on 110 kV cross-border transmission line from Croatian Transmission System Operator Ltd. The results show the great difference in the estimated variances, i.e. GUM variances are significantly overestimated what results in incorrect transmission losses allocation procedure with respect to its final financial effect. The study confirms that the AMC method is likely to be more practically suitable method for transmission line resistance and losses calculation.

Inspec keywords: Monte Carlo methods; measurement uncertainty; loss measurement; transmission lines

Other keywords: Croatian Transmission System Operator Ltd; GUM; voltage 110 kV; cross-border transmission line loss calculation; adaptive Monte-Carlo method; Guide to the Expression of Uncertainty in Measurement; AMC method; atmospheric condition

Subjects: Monte Carlo methods; Phase and gain measurement

References

    1. 1)
      • 5. Cox, M.G., Siebert, B.R.L.: ‘The use of a Monte Carlo method for evaluating uncertainty and expanded uncertainty’, Metrologia, 2006, 43, pp. S178–S188.
    2. 2)
      • 6. Ferrero, A., Salicone, S.: ‘Fully comprehensive mathematical approach to the expression of uncertainty in measurement’, IEEE Trans. Instrum. Meas., 2006, 55, (3), pp. 706–712.
    3. 3)
      • 22. Harris, P.M., Cox, M.G.: ‘On a Monte Carlo method for measurement uncertainty evaluation and its implementation’, Metrologia, 2014, 51, pp. S176–S182.
    4. 4)
      • 19. Hall, B.D.: ‘Evaluating methods of calculating measurement uncertainty’, Metrologia, 2008, 45, (2), pp. L5–L8.
    5. 5)
      • 18. JCGM: ‘Evaluation of measurement data – supplement 1 to the ‘guide to the expression of uncertainty in measurement’ – propagation of distributions using a Monte Carlo method’ (2008).
    6. 6)
      • 2. ENTSO-E: ‘ENTSO-E Overview of transmission tariffs in Europe: Synthesis 2015’ (2015).
    7. 7)
      • 9. Esward, T.J., de Ginestous, A., Harris, P.M., et al: ‘A Monte Carlo method for uncertainty evaluation implemented on a distributed computing system’, Metrologia, 2007, 44, (5), pp. 319–326.
    8. 8)
      • 14. Chen, H.-C., Wu, P.-C., Huang, J.-Y., et al: ‘Uncertainty analysis for measurement of measurand’, Measurement, 2010, 43, (9), pp. 1250–1254.
    9. 9)
      • 38. Vardi, M.Y.: ‘Automatic verification of probabilistic concurrent finite-state programs’. 26th Annual Symp. on Foundations of Computer Science, 1985. 1985, pp. 327–338.
    10. 10)
      • 13. Pavese, F.: ‘On the degree of objectivity of uncertainty evaluation in metrology and testing’, Meas. J. Int. Meas. Confed., 2009, 42, (9), pp. 1297–1303.
    11. 11)
      • 23. Bich, W.: ‘From errors to probability density functions. Evolution of the concept of measurement uncertainty’, IEEE Trans. Instrum. Meas., 2012, 61, (8), pp. 2153–2159.
    12. 12)
      • 1. ‘European network of transmission system operators for electricity’. Available at https://www.entsoe.eu/Pages/default.aspx, accessed April 2016.
    13. 13)
      • 28. Azpurua, M., Tremola, C., Paez, E.: ‘Comparison of the GUM and Monte Carlo methods for the uncertainty estimation in electromagnetic compatibility testing’, Prog. Electromagn. Res., 2011, 34, (September), pp. 125–144.
    14. 14)
      • 33. Modrić, Ž., Kovač, Z., Fekete, K.: ‘Determination of energy interchanged on the tie lines—some practical issues’, J. Energy Power Eng., 2014, 8, pp. 948–956.
    15. 15)
      • 35. Ramboz, J.D., Petersons, O.: ‘A calibration service for currentTransformers’ (NIST Special Publication 250-36, 1991).
    16. 16)
      • 21. Harris, P., Matthews, C.E., Cox, M.G., et al: ‘Summarizing the output of a Monte Carlo’, Metrologia, 2014, 51, pp. 243–252.
    17. 17)
      • 17. Vilbaste, M., Slavin, G., Saks, O., et al: ‘Can coverage factor 2 be interpreted as an equivalent to 95% coverage level in uncertainty estimation? Two case studies’, Meas. J. Int. Meas. Confed., 2010, 43, (3), pp. 392–399.
    18. 18)
      • 20. Wang, C.M., Iyer, H.K.: ‘On non-linear estimation of a measurand’, Metrologia, 2012, 49, pp. 20–26.
    19. 19)
      • 34. Landes&Gyr AG: ‘High precision metering ZMQ202/ZFQ202 user manual’ (2005).
    20. 20)
      • 7. Giordani, A., Mari, L.: ‘Measurement, models, and uncertainty’, IEEE Trans. Instrum. Meas., 2012, 61, (8), pp. 2144–2152.
    21. 21)
      • 31. JCGM: ‘JCGM 102: 2011 evaluation of measurement data – supplement 2 to the ‘guide to the expression of uncertainty in measurement’ – extension to any number of output quantities’ (2011).
    22. 22)
      • 25. Cox, M.G., Harris, P.M.: ‘Software specifications for uncertainty evaluation’ (2006).
    23. 23)
      • 36. Ferrero, A., Prioli, M., Salicone, S.: ‘The construction of joint possibility distributions of random contributions to uncertainty’, IEEE Trans. Instrum. Meas., 2014, 63, (1), pp. 80–88.
    24. 24)
      • 10. Rodríguez-Gonzálvez, P., González-Aguilera, D., Hernández-López, D., et al: ‘Accuracy assessment of airborne laser scanner dataset by means of parametric and non-parametric statistical methods’, IET Sci. Meas. Technol., 2015, 9, (4), pp. 505–513.
    25. 25)
      • 30. Hall, B.D.: ‘Expanded uncertainty regions for complex quantities’, Metrologia, 2013, 50, (5), pp. 490–498.
    26. 26)
      • 24. Bich, W.: ‘Uncertainty evaluation by means of a Monte Carlo approach’. BIPM Workshop 2 on CCRI, 2008.
    27. 27)
      • 12. Hall, B.D.: ‘Notes on complex measurement uncertainty – Part 2’ (2010), 2012.
    28. 28)
      • 8. JCGM 100:2008: ‘Evaluation of measurement data—guide to the expression of uncertainty in measurement’ (2008).
    29. 29)
      • 37. Mathworks: ‘dfittool’. Available at http://www.mathworks.com/help/stats/dfittool.html, accessed June2016.
    30. 30)
      • 15. Ferrero, A., Salicone, S.: ‘Uncertainty: only one mathematical approach to its evaluation and expression?’, IEEE Trans. Instrum. Meas., 2012, 61, (8), pp. 2167–2178.
    31. 31)
      • 16. Tokarska, M.: ‘Evaluation of measurement uncertainty of fabric surface resistance implied by the van der pauw equation’, IEEE Trans. Instrum. Meas., 2014, 63, (6), pp. 1593–1599.
    32. 32)
      • 27. Dobrić, G., Žarković, M., Šošić, D.: ‘Fuzzy-based Monte Carlo simulation for harmonic load flow in distribution networks’, IET Gener. Transm. Distrib., 2015, 9, (3), pp. 267–275.
    33. 33)
      • 4. Solaguren-Beascoa Fernández, M., Alegre Calderón, J.M., Bravo Díez, P.M.: ‘Implementation in MATLAB of the adaptive Monte Carlo method for the evaluation of measurement uncertainties’, Accredit. Qual. Assur., 2009, 14, (2), pp. 95–106.
    34. 34)
      • 26. Bertrand-Krajewski, J.-L., Ribeiro, A.S., Almeida, M.d.C.: ‘Evaluation of uncertainties in measurements’ (2011).
    35. 35)
      • 11. Couto, P.R.G., Damasceno, J.C., Oliveira, S.P.: ‘Monte Carlo simulations applied to uncertainty in measurement’. in Prof. Wai Kin (Victor), Chan (Ed.): ‘Theory and Applications of Monte Carlo Simulations’ (InTech, 2013, 1st edn.), pp. 27–51.
    36. 36)
      • 32. Nachtigalová, I., Suchánek, M.: ‘Measurement uncertainty evaluation using Monte Carlo method'no date, 0, pp. 1–7.
    37. 37)
      • 3. Basil, M., Papadopoulos, C., Sutherland, D., et al: ‘Application of probabilistic uncertainty methods (Monte- Carlo simulation) in flow measurement uncertainty estimation’. Flow Measurement 2001 – Int. Conf. Application, 2001, pp. 1–21.
    38. 38)
      • 29. Wübbeler, G., Harris, P.M., Cox, M.G., et al: ‘A two-stage procedure for determining the number of trials in the application of a Monte Carlo method for uncertainty evaluation’, Metrologia, 2010, 47, (3), pp. 317–324.
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