State estimation is an important tool for power system monitoring and the present study involves integrating phasor measurement units in the state estimation process. Based on measurements taken throughout the network, the role of a state estimator is to estimate the state variables of the power system while checking that these estimates are consistent with the measurement set. In the case of power system state estimation, the state variables are the voltage phasors at each network bus.\
The classical state estimator currently used is based on SCADA (Supervisory Control and Data Acquisition) measurements. Weaknesses of the SCADA measurement system are the asynchronicity of the measurements, which introduce errors in the state estimation results during dynamic events on the electrical network.\
Wide-area monitoring systems, consisting of a network of Phasor Measurement Units (PMU) provide synchronized phasor measurements, which give an accurate snapshot of the monitored part of the network at a given time. The objective of this thesis is to integrate PMU measurements in the state estimator. The proposed state estimators use PMU measurements exclusively, or both classical and PMU measurements.\
State estimation is particularly useful to filter out measurement noise, detect and eliminate bad data. A sensitivity analysis to measurement errors is carried out for a state estimator using only PMU measurements and a classical state estimator. Measurement errors considered are Gaussian noise, systematic errors and asynchronicity errors. Constraints such as zero injection buses are also integrated in the state estimator. Bad data detection and elimination can be done before the state estimation, as in pre-estimation methods, or after, as in post-estimation methods. For pre-estimation methods, consistency tests are used. Another proposed method is validation of classical measurements by PMU measurements. Post-estimation is applied to a measurement set which has asynchronicity errors. Detection of a systematic error on one measurement in the presence of Gaussian noise is also analysed. \
The state estimation problem can only be solved if the measurements are well distributed over the network and make the network observable. Observability is crucial when trying to solve the state estimation problem. A PMU placement method based on metaheuristics is proposed and compared to an integer programming method. The PMU placement depends on the chosen objective. A given PMU placement can provide full observability or redundancy. The PMU configuration can also take into account the zero injection nodes which further reduce the number of PMUs needed to observe the network. Finally, a method is proposed to determine the order of the PMU placement to gradually extend the observable island. \
State estimation errors can be caused by erroneous line parameter or bad calibration of the measurement transformers. The problem in both cases is to filter out the measurement noise when estimating the line parameters or calibration coefficients and state variables. The proposed method uses many measurement samples which are all integrated in an augmented state estimator which estimates the voltage phasors and the additional parameters or calibration coefficients.
| Identifer | oai:union.ndltd.org:BICfB/oai:ulb.ac.be:ETDULB:ULBetd-05302011-101958 |
| Date | 01 June 2011 |
| Creators | Hurtgen, Michaël |
| Contributors | Lobry, Jacques, Sezi, Tevfik, Gyselinck, Johan, Van Cutsem, Thierry, Maun, Jean-Claude, Kinnaert, Michel |
| Publisher | Universite Libre de Bruxelles |
| Source Sets | Bibliothèque interuniversitaire de la Communauté française de Belgique |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://theses.ulb.ac.be/ETD-db/collection/available/ULBetd-05302011-101958/ |
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