Int. J. Simul. Multisci. Des. Optim.
Volume 7, 2016
|Number of page(s)||7|
|Published online||07 December 2016|
Power system static state estimation using Kalman filter algorithm
Electrical Department, North Eastern Regional Institute of Science and Technology (NERIST), Itanagar, Arunachal Pradesh
* e-mail: firstname.lastname@example.org
Accepted: 26 October 2016
State estimation of power system is an important tool for operation, analysis and forecasting of electric power system. In this paper, a Kalman filter algorithm is presented for static estimation of power system state variables. IEEE 14 bus system is employed to check the accuracy of this method. Newton Raphson load flow study is first carried out on our test system and a set of data from the output of load flow program is taken as measurement input. Measurement inputs are simulated by adding Gaussian noise of zero mean. The results of Kalman estimation are compared with traditional Weight Least Square (WLS) method and it is observed that Kalman filter algorithm is numerically more efficient than traditional WLS method. Estimation accuracy is also tested for presence of parametric error in the system. In addition, numerical stability of Kalman filter algorithm is tested by considering inclusion of zero mean errors in the initial estimates.
Key words: Power system state estimation / Weight least square method / Kalman filter algorithm
© A. Saikia & R.K. Mehta, Published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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