Online state estimation of a microgrid using particle filtering

Aristotelis E. Thanos, Nurcin Celik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Recent technical advances in power systems on communications, computation and generation technologies have collectively lead to the development of microgrids. However, these microgrids are still heavily challenged by the state estimation problem which traditionally exists in power grids. State estimation in these systems is especially crucial due to the impact it has to the power flow control and the security of the system. In this work, we introduce a novel algorithm for online state estimation of microgrids using particle filtering. The proposed algorithm is fed by a database receiving data from electrical and environmental sensors in real time. The performance of the proposed algorithm is first validated through synthetic experiments. Then, the experiments are conducted using real data obtained from a benchmark low voltage microgrid. The experiments reveal that the proposed algorithm is able to achieve state estimations that are very close to the actual states (in terms of power injections). This way, significant improvement is premised in the functional performance of microgrids while savings are encountered in computational resource utilization. As part of its future venues, proposed particle filter-based state estimation algorithm will be embedded into a dynamic data driven adaptive simulation framework that is being designed for the power control and management of microgrids.

Original languageEnglish
Title of host publicationIIE Annual Conference and Expo 2013
PublisherInstitute of Industrial Engineers
Number of pages10
StatePublished - Jan 1 2013
EventIIE Annual Conference and Expo 2013 - San Juan, Puerto Rico
Duration: May 18 2013May 22 2013


OtherIIE Annual Conference and Expo 2013
Country/TerritoryPuerto Rico
CitySan Juan


  • Data driven simulations
  • Microgrids
  • Particle filtering
  • Real-time estimation
  • State estimation

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


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