TR2016-128

Dynamic State Estimation Based on Unscented Kalman Filter and Very Short-Term Load and Distributed Generation Forecasting


    •  Sun, H., Feng, G., Nikovski, D.N., "Dynamic State Estimation Based on Unscented Kalman Filter and Very Short-Term Load and Distributed Generation Forecasting", IEEE International Conference on Power System Technology (POWERCON), DOI: 10.1109/POWERCON.2016.7753928, September 2016.
      BibTeX TR2016-128 PDF
      • @inproceedings{Sun2016sep2,
      • author = {Sun, Hongbo and Feng, Guangyu and Nikovski, Daniel N.},
      • title = {Dynamic State Estimation Based on Unscented Kalman Filter and Very Short-Term Load and Distributed Generation Forecasting},
      • booktitle = {IEEE International Conference on Power System Technology (POWERCON)},
      • year = 2016,
      • month = sep,
      • doi = {10.1109/POWERCON.2016.7753928},
      • url = {https://www.merl.com/publications/TR2016-128}
      • }
  • MERL Contacts:
  • Research Area:

    Electric Systems

This paper proposes an unscented Kalman filter (UKF) based dynamic state estimation (DSE) method for distribution systems by incorporating very short-term load and distributed generation (DG) forecasting. Instead of fitting state variables into unrealistic state transition models for the prediction step in UKF, this work forecasts and transforms nodal power injections from both load and DG into state predictions through load flow computation. The impact of bad data and irrational sigma points are mitigated through the sanity check and adjustment to the power injections. The test results on a modified IEEE 123-node test feeder are given to demonstrate the effectiveness of the proposed method.