Abstract
This paper proposes the application of formal methods for knowledge discovery from large quantity of data to reduce the complexity of Power Flow (PF) and Optimal Power Flow (OPF) problems. In particular, a knowledge-based paradigm for PF and OPF analyses is used to extract complex features, hidden relationships, and useful hypotheses potentially describing regularities in the problem solutions from operation data-sets. This is realized by designing a knowledge-extraction process based on Principal Components Analysis (PCA). The structural knowledge extracted by this process is then used to project the problem equations into a domain in which these equations can be solved more effectively. In this new domain, the cardinality of the PF and OPF problem is sensibly reduced and, consequently, the problem solutions can be obtained more efficiently. The effectiveness of the proposed framework is demonstrated with numerical results obtained for realistic power networks for several operating conditions.
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