Application of a robust algorithm for dynamic state estimation of a power system

In this paper, a computation algorithm using the exponential function to increase the robustness of the dynamic state estimation is proposed. The merit of this approach lies in its immunity to the polluted measurements, while the implementation of the method is not complicated when compared with other methods. To validate the effectiveness of the proposed method, it was tested through several example power systems under different scenarios that include normal operation, bad measurements, sudden load change, and topology error conditions. From test results, they help support the feasibility of the method for state estimation applications.

Nomenclature

Time instant.

Total number of time instants.

Number of state variables.

Number of measurement variables.

State vector.

Predicted state vector.

Estimated state vector.

True state vector.

Measurement vector.

True measurement vector.

Measurement error vector.

Covariance matrix of .

State error vector.

Covariance matrix of .

Covariance matrix of .

Covariance matrix of error vector.

Weighting matrix .

Nonlinear measurement function.

Jacobian matrix .

Kalman gain matrix.