Power system static state estimation using Kalman filter algorithm

• Published in: International journal for simulation and multidisciplinary design optimization Vol. 7; p. A7
• Main Authors: Saikia, Anupam, Mehta, Ram Krishna
• Format: Journal Article
• Language: English
• Published: Les Ulis EDP Sciences 2016
• Subjects: Algorithms; Computer simulation; Electric power systems; Kalman filter algorithm; Kalman filters; Newton-Raphson method; Numerical stability; Power system state estimation; Random noise; State estimation; Weight least square method
• ISSN: 1779-627X; 1779-6288; 1779-6288
• DOI: 10.1051/smdo/2016007

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.