Toward Automated Decision-Making in Power Systems Wide-Area Protection Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/rf55zb39z

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  • In recent years there have been many improvements in the reliability of critical infrastructure systems. Despite these improvements and despite targeted efforts to improve the operation and control of the electric grid, the power systems industry has seen relatively small advances in this regard. For instance, today's power system is increasingly affected by power quality deficiencies, a high number of local and regional contingencies, malfunctions in equipment, and severe emergency cascading outages. This research proposes an automated decision-making framework for protecting the power network from such events. Because automated responses to emergency situations are dependent on an observable system, this work first proposes a synchrophasor data analysis methodology that leverages statistical correlation techniques in order to identify data inconsistencies, power system events, and malicious cyber-attacks. The results of this preliminary identification method show that decorrelation of PMU data streams around a network may be a valid means of initiating further automated protection and control. Assuming a robust and automated wide-area monitoring methodology, this research also proposes a novel, algorithmic approach to selecting Remedial Action Schemes (RASs) in order to optimize the operation of the power network during and after a contingency. Specifically, this work implements an algorithm called policy-switching to consolidate traditional load shedding and islanding schemes into a robust protection policy. In order to model and simulate the functionality of the proposed power systems protection algorithm, this work conducts Monte-Carlo, time-domain simulations using Siemens PSS/E. The algorithm is tested with experiments on the IEEE 39-bus model as well as the 2383-bus Polish model, demonstrating that this protection framework achieves optimal power system performance via automated decision-making.
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  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2015-06-16T18:41:47Z (GMT) No. of bitstreams: 2 license_rdf: 1379 bytes, checksum: da3654ba11642cda39be2b66af335aae (MD5) MeierRichardJ2015.pdf: 9698440 bytes, checksum: c91653208a03c59e6aa0c748382c344a (MD5)
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