Power System Event Identification based on Deep Neural Network with Information Loading
This work addresses the need for reliable transmission systems in the power industry, but it is incremental as it builds on existing CNN methods with specific enhancements.
The paper tackled the problem of online power system event identification and classification by developing a deep neural network approach that achieved highly accurate results, as demonstrated on real-world data from the U.S. power grid.
Online power system event identification and classification is crucial to enhancing the reliability of transmission systems. In this paper, we develop a deep neural network (DNN) based approach to identify and classify power system events by leveraging real-world measurements from hundreds of phasor measurement units (PMUs) and labels from thousands of events. Two innovative designs are embedded into the baseline model built on convolutional neural networks (CNNs) to improve the event classification accuracy. First, we propose a graph signal processing based PMU sorting algorithm to improve the learning efficiency of CNNs. Second, we deploy information loading based regularization to strike the right balance between memorization and generalization for the DNN. Numerical studies results based on real-world dataset from the Eastern Interconnection of the U.S power transmission grid show that the combination of PMU based sorting and the information loading based regularization techniques help the proposed DNN approach achieve highly accurate event identification and classification results.