A data-driven heuristic for corrective transmission switching

Abstract

Utilizing flexibility of the transmission network has gained significant attention recently. Prior efforts have shown that various benefits could be achieved by appropriately changing the network topology. This paper focuses on the reliability gains that can be achieved through corrective transmission switching (CTS). A full AC contingency analysis is conducted to identify critical contingencies that would result in violations. CTS is employed on these critical contingencies to test for violation reductions. A data-driven heuristic is proposed in this paper to identify the candidate switching list. This heuristic, also referred to as enhanced data mining (EDM) approach, provides a static lookup table consisting of corrective switching solutions, which is fast and effective. The lookup table can be created through a straightforward data mining technique. Simulations on the TVA system demonstrate the effectiveness and efficiency of the proposed heuristic.