Machine learning strategies for power systems

Abstract

As renewable energy penetration into the United States power system continues to increase, maintaining situational awareness of the complex power grid becomes increasingly challenging. Machine learning models developed from classical optimization theory have been investigated and implemented across many practical disciplines. This dissertation addresses adapting some of these models to create data-driven approaches trained by real power system signals, offering unique advantages and improvements over existing methods. In the first part of the dissertation, the use of constrained machine learning models for power system stability is investigated and a convolutional neural network is developed into a classifier for use in transient stability assessment. The second part of this dissertation deals with the challenge of reliable Th\'evenin equivalent model estimation. An algorithm for estimating equivalent values from existing voltage and current measurements is developed and tested using real power system data. This allows for the supervised training of a recurrent neural network toward Th\'evenin equivalent regression.