Power system state estimation

Sekaran, Kamini Kousalya (2008) Power system state estimation. Masters thesis, Memorial University of Newfoundland.

[English] PDF - Accepted Version Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Download (6MB)

Abstract

Monitoring and control of a complex interconnected power system requires the accurate estimate of its states. Different meters are placed at the various substations and the measurements are transmitted to the central control center. However, it is likely that there may be errors associated with the measurements and in some cases, some measurements may not be available. Power system state estimation is a technique by which the state of a power system (usually magnitude and angle of bus voltages) is determined using raw measurements. The results of the state estimation are used for realtime security analysis, optimal power flow, etc. These are also used in calculating the line power flows between buses, by which the system operators will be able to conclude if any line is overloaded, and then take necessary action to prevent any mishap from happening. In the initial part of this thesis, State Estimation (SE) based on Weighted Least Squares (WLS) technique and bad data detection, identification, and elimination are presented. The bad data detection and identification are facilitated by the chi-squared test and normal residual methods. The WLS, chi-Squared test and normal residual methods are implemented in Matlab and tested using different power system models. Case studies demonstrate that the WLS technique is reliable in estimating state variables of a power system. Chi-squared and Normal residual methods detect and identify bad data efficiently. -- In the second part of this thesis, the transmission line reactance parameter error estimation is formulated and implemented using the residual sensitivity analysis for various power system models. The estimation involves two steps: the error identification and estimation of the parameter implemented using Matlab. The identification of the parameter error is facilitated by the normalized residual technique, and the parameter error estimation is facilitated using the residual sensitivity analysis. The parameters estimated are reliable and close to the actual (true) value. In the final part of this thesis the measurements considered to be available for state estimation are a few synchronized phasor measurements in addition to the conventional measurement data, to enhance the performance of the state estimator, for a very large power system. The phasor measurement, when present in sufficient numbers, with other measurements, improves the accuracy of the SE. Matlab is used to implement multi-area SE using various case studies.

Actions (login required)

View Item