This thesis proposes a framework for detection and identification of system topological changes in near real-time that utilizes the statistical properties of electricity generation and demand, which are assumed to be known. Instead of relying on offline models as with traditional methods, the proposed method is model-free, and exploits the high-speed synchronized measurements provided by phasor measurement units (PMUs). In this framework, a statistical quickest change algorithm is applied to the voltage phase angle measurements collected from PMUs to detect the change-point that corresponds to the system topology change instant. An advantage of this algorithm is that the operator also has full control over the tradeoff between detection delay and false alarm rate. Additionally, a full measurement set is not necessary for its implementation and good results can be achieved even for a few PMU measurements. A scheme for systematic PMU bus selection is presented along with a method to partition the power system such that the aforementioned algorithm for line outage detection can be applied in parallel to each area, allowing for even faster detection. The optimal partitioning scheme is formulated as an integer program and solved using a greedy algorithm. In the second half of the thesis, an adaptive line outage detection algorithm that accounts for the transient dynamics following a line outage is proposed. A more accurate governor power flow model of the power system is used. This new algorithm is shown to have better performance compared to existing algorithms for line outage detection. In order to lend support for the work done in this thesis, case studies are done through simulations on standard IEEE test systems.
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Real-time power system topology change detection and identification
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