Graduate Thesis Or Dissertation

 

Analysis of Learning Schemes for Power Systems Secure Control Public Deposited

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/qb98mm229

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  • As the future electrical power systems tend towards smarter and greener technology, thedeployment of self-sufficient networks, or microgrids, becomes more likely. Microgridsmay operate on their own or synchronized with the main grid, thus control methodsneed to take into account islanding and reconnecting said networks. Isolation of subnetworks may be necessary to protect either the main grid or the subnetworks themselves.With the ever growing concern of cyber-attacks on power systems, the ability to isolatenetwork locations potentially targeted by adversaries, or exhibiting signs of cascadingfailures, is necessary. It is possible to create unique attacks that may leverage networkoperating points to maximize damage to the main grid even when the attack is confinedto a microgrid. Upon isolation of a subnetwork, a control technique must be used tosafely reconnect it to the main grid. The ability to optimally and safely reconnect aportion of the grid is not well understood and, as of now, limited to raw synchronizationbetween interconnection points. A support vector machine (SVM) leveraging real-timedata from phasor measurement units (PMUs) is proposed to predict in real time whetherthe reconnection of a sub-network to the main grid would lead to stability or instability. A dynamics simulator fed with pre-acquired system parameters is used to createtraining data for the SVM in various operating states. The classifier was tested on avariety of cases and operating points to ensure diversity. Accuracies of approximately85% were observed throughout most conditions when making dynamic predictions of agiven network.
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  • 2017-11-08 to 2018-06-26

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