For transformer asset management, the dynamic state estimation can track transient phenomena of transformers or extract an unmeasurable quantity such as magnetic flux linkage. Even the extended Kalman filter or unscented Kalman filter, which are widely-used dynamic state estimations, might result in significant estimation errors when dealing with highly nonlinear dynamics such as transformer core saturation. In this sense, this paper proposes a dynamic state estimation based on the weighted least squares, using real-time measurements and the dynamic model induced by numerically integrating differential equations. The proposed method can directly use implicit functions including differential equations while Kalman filters must rearrange the implicit functions into the explicit ones to obtain the prediction process. Further, because the weighted least squares approach is implemented via the Newton iterative method, the estimation accuracy and robustness to various conditions can be improved despite strong nonlinearity. For the numerical integration of dynamics, the computationally efficient trapezoidal rule or the numerically stable quadratic rule is used. This paper presents numerical simulation results, with a comparison with other dynamic state estimators.
ASJC Scopus subject areas
- Control and Systems Engineering
- Signal Processing
- Computer Networks and Communications
- Applied Mathematics