Techniques for improving filters in power grid contingency analysis

Authors:
Robert Adolf;David Haglin;Mahantesh Halappanavar;Yousu Chen;Zhenyu Huang
Affiliations:
Pacific Northwest National Laboratory, Richland, Washington;Pacific Northwest National Laboratory, Richland, Washington;Pacific Northwest National Laboratory, Richland, Washington;Pacific Northwest National Laboratory, Richland, Washington;Pacific Northwest National Laboratory, Richland, Washington
Venue:
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Year:
2011

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Abstract

Electrical power grid contingency analysis aims to understand the impact of potential component failures and assess a system's capability to tolerate them. The computational resources needed to explore all potential x-component failures, for modest sizes of x 1, is not feasible due to the combinatorial explosion of cases to consider. A common approach for addressing the large workload is to select the most severe x-component failures to explore (a process we call filtering). It is important to assess the efficacy of a filter; in particular, it is necessary to understand the likelihood that a potentially severe case is filtered out. A framework for assessing the quality/performance of a filter is proposed. This framework is generalized to support resource-aware filters and multiple evaluation criteria.