Kalman-filtering approach to power-system-state estimation
This dissertation contains a development of a Kalman-filtering strategy of estimating the dynamic state of an electric-power system. Based on the principles of nonlinear Kalman-filtering theory, a dynamic state estimation algorithm is developed which traces the operation of the power system while processing discrete-measurement data in an optimal fashion. Furthermore, the algorithm is shown to possess the statistical properties necessary for detection and identification of grossly incorrect measurement data. The proposed dynamic estimator has several advantages over the well-known weighted-least-squares static-state estimators in regard to its accuracy, real-time implementation, and the required redundancy in the processed measurement data. The performance of the estimator is highly dependent on observability in the filtering process; thus, the problem of evaluating and improving observability in state estimation is considered in depth. An example is included that illustrates results derived in the dissertation and demonstrates the accuracy and applicability of the proposed scheme.
- Research Organization:
- Akron Univ., OH (USA)
- OSTI ID:
- 7225860
- Country of Publication:
- United States
- Language:
- English
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