A Learning Scheme for Microgrid Islanding and Reconnection

TL;DR

This paper proposes a real-time learning scheme using SVMs and PMU data to predict the stability of microgrid reconnection, enhancing safety and efficiency in smart grid operations.

Contribution

It introduces a novel SVM-based method leveraging real-time PMU data for dynamic stability prediction during microgrid reconnection.

Findings

Achieved approximately 85% accuracy in stability predictions.

Validated the method across diverse operating conditions.

Demonstrated the potential for real-time microgrid management.

Abstract

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.