A historical data-driven unscented Kalman filter for distribution system state estimation

With the proliferation of intermittent generations in distribution system, it is vital to monitor the system states based on real-time data. This paper presents a historical data-driven unscented kalman filter for distribution system state estimation. The aim of the proposed method is to exploit the historical data to improve the accuracy of state prediction and filtering. The system model in prediction stage is built by time series process and its parameters are estimated by Huber estimation. In filtering stage, the nonlinear measurement equations are addressed by unscented kalman filter. This paper mainly investigates the predicting and filtering performance of the proposed method in different distribution systems. The results of the proposed method compared with weighted least squares (WLS) and extended kalman filter (EKF) are presented along with briefly conclusions.

[1]  Atif S. Debs,et al.  A Dynamic Estimator for Tracking the State of a Power System , 1970 .

[2]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .

[3]  Balasubramaniam Natarajan,et al.  State Estimation and Voltage/VAR Control in Distribution Network With Intermittent Measurements , 2014, IEEE Transactions on Smart Grid.

[4]  J. B. A. London,et al.  Using pseudo and virtual measurements in distribution system state estimation , 2014, 2014 IEEE PES Transmission & Distribution Conference and Exposition - Latin America (PES T&D-LA).

[5]  Hui Li,et al.  A branch-current-based state estimation for distribution systems non-measurement loads , 2004, IEEE Power Engineering Society General Meeting, 2004..

[6]  S. M. Shahidehpour,et al.  State estimation for electric power distribution systems in quasi real-time conditions , 1993 .

[7]  M.D. Ilic,et al.  Electric power system static state estimation through Kalman filtering and load forecasting , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[8]  Gustavo Valverde,et al.  Unscented kalman filter for power system dynamic state estimation , 2011 .

[9]  Ali Abur,et al.  Robust linear state estimation with equality constraints , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[10]  J. Teng,et al.  Distribution system state estimation , 1995 .

[11]  Faridoon Shabaninia,et al.  Kalman-filter algorithm and PMUs for state estimation of distribution networks , 2014, Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014).

[12]  I. Rhodes.  A tutorial introduction to estimation and filtering , 1971 .

[13]  A. P. A. D. Silva,et al.  State forecasting in electric power systems , 1983 .

[14]  P. Komarnicki,et al.  Practical experiences on PMU-based linear state estimation in distribution grids , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[15]  G. Strbac,et al.  Distribution System State Estimation Using an Artificial Neural Network Approach for Pseudo Measurement Modeling , 2012, IEEE Transactions on Power Systems.

[16]  Conversion and delivery of electrical energy in the 21st century , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[17]  Suresh Vadhva,et al.  State Estimation of a Distribution System Using WLS and EKF Techniques , 2015, 2015 IEEE International Conference on Information Reuse and Integration.