Incorporating network uncertainties in voltage dip state estimation

G. Ye (Corresponding author), M. Nijhuis, V. Cuk, J.F.G. Cobben

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)
220 Downloads (Pure)


The magnitudes of voltage dips on non-monitored buses can be estimated by applying Bayes's theorem with measured voltage dips on a limited number of monitored buses. Bayes's theorem is used to obtain the grid conditions, including the network impedance, load, distributed generation vector, and short-circuit location, from which the voltage magnitudes along the feeder can be calculated. In order to implement this approach effectively, it is necessary to understand which parameters among the grid conditions are more significant for the estimation performance and how the uncertainties associated with these parameters affect the estimation results. Moreover, since the measurement structure provides observations to calculate the posterior distribution from the prior distribution of the grid conditions. The measurement parameters, quantities, and accuracy levels are varied in simulations to obtain an optimum measurement structure balancing between the cost and the measurement performance. This paper presented the results of the sensitivity studies which have been done on the IEEE 13-Bus and 123-Bus test systems. The results shows the performance of the voltage dip state estimation approach is less sensitive to the uncertainty level of the load while more sensitive to those of the DG, the short-circuit power, the zero sequence impedance of the cable and the short-circuit resistance. The voltage measurement accuracy is more important than that of current and phase angles. The proposed method shows much stronger robustness compared to the conventional one and the estimation performance can be improved through several measures, e.g. adding more measurement points.

Original languageEnglish
Pages (from-to)888-896
Number of pages9
JournalInternational Journal of Electrical Power and Energy Systems
Publication statusPublished - 1 Dec 2019


  • Bayesian inference
  • Estimation
  • Population Monte Carlo (PMC) sampling
  • Sensitivity
  • Voltage dip


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