Data-Driven Optimal Voltage Regulation Using Input Convex Neural Networks

Abstract Fast time-scale voltage regulation is needed to enable high penetration of renewables in power distribution networks. A promising approach is to control the reactive power injections of inverters to maintain the voltages. However, existing voltage regulation algorithms require the exact knowledge of line parameters, which are not known for most distribution systems and are difficult to infer. In this work, by utilizing the convexity results of voltage regulation problem, we design an input convex neural network to learn the underlying mapping between the power injections and the voltage deviations. By using smart meter data, our proposed data-driven approach not only accurately fits the system behavior, but also provides a tractable and optimal way to find the reactive power injections. Various numerical simulations demonstrate the effectiveness of the proposed voltage control scheme.

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