This paper introduces a potential learning scheme that can dynamically predict the stability of the reconnection of sub-networks to a main grid. As the future electrical power systems tend towards smarter and greener technology, the deployment of self sufficient networks, or microgrids, becomes more likely. Microgrids may operate on their own or synchronized with the main grid, thus control methods need to take into account islanding and reconnecting of said networks. The ability to optimally and safely reconnect a portion of the grid is not well understood and, as of now, limited to raw synchronization between interconnection points. A support vector machine (SVM) leveraging real-time data from phasor measurement units (PMUs) is proposed to predict in real time whether the reconnection of a sub-network to the main grid would lead to stability or instability. A dynamics simulator fed with pre-acquired system parameters is used to create training data for the SVM in various operating states. The classifier was tested on a variety of cases and operating points to ensure diversity. Accuracies of approximately 85% were observed throughout most conditions when making dynamic predictions of a given network.

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In this paper, learning algorithms are leveraged to advance power system protection. Advancements in power system protection have come in different forms such as the development of new control strategies and the introduction of a new system architecture such as a microgrid. In this paper, we propose two learning schemes to make accurate predictions and optimal decisions related to power system protection and microgrid control. First, we present a neural network approach to learn a classifier that can predict stable reconnection timings for an islanded sub-network. Second, we present a learning-based control scheme for power system protection based on the policy rollout. In the proposed scheme, we incorporate online simulation using the commercial PSS/e simulator. Optimal decisions are obtained in real time to prevent cascading failures as well as maximize the load served. We validate our methods with the dynamics simulator and test cases RTS-96 and Poland.

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