Thesis
Lyapunov exponents over variable window sizes for prediction of rotor angle stability
Washington State University
Master of Science (MS), Washington State University
2013
Handle:
https://hdl.handle.net/2376/104164
Abstract
In recent years, Phasor Measurement Units (PMUs) have been widely adopted on power systems and advanced communication technologies are improved significantly. As a result, power system dynamics can now be monitored using real-time data provided by PMUs. The power industry needs to find a way to take advantages of the large amounts of data. A PMU-based method using Maximal Lyapunov Exponent (MLE) to determine the stability of a system following a disturbance has been established in previous research. This thesis proposes a new method to determine the proper time window of the MLE in an on-line environment. Spectral analysis is applied to the oscillation waveforms to calculate the size of time windows of the MLE. The starting window size is the inverse of the most dominant frequency component, but all windows are saved for later use. Two MLEs are calculated using the same window size sometime later. The consistency of the two MLEs indicates that sufficient information has been included in the first time window to characterize the system dynamics. Otherwise, the window size needs to be updated for the operating condition. The window size can be increased by choosing the next smallest window saved earlier. This method increases the accuracy of prediction given by the MLE. At the same time, the computational burden does not increase significantly and, therefore, it does not compromise the effectiveness of this method. A case study using a 200-bus system is presented to validate the feasibility of the proposed method.
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Details
- Title
- Lyapunov exponents over variable window sizes for prediction of rotor angle stability
- Creators
- Haosen Guo
- Contributors
- Chen-Ching Liu (Degree Supervisor)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University; [Pullman, Washington] :
- Identifiers
- 99900525288701842
- Language
- English
- Resource Type
- Thesis