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.
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Adolf, R., Haglin, D., Halappanavar, M., Chen, Y., Huang, Z. (2011). Techniques for Improving Filters in Power Grid Contingency Analysis. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2011. Lecture Notes in Computer Science(), vol 6871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23199-5_44
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DOI: https://doi.org/10.1007/978-3-642-23199-5_44
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