# Power System Event Identification based on Deep Neural Network with Information Loading

arXiv: https://arxiv.org/abs/2011.06718 · HTML: https://mlsanity.com/paper/2011.06718/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [Robust Event Classification Using Imperfect Real-world PMU Data](https://mlsanity.com/paper/2110.10128/) (2021)
- [A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data](https://mlsanity.com/paper/2202.06836/) (2022)
- [Adversarial Purification for Data-Driven Power System Event Classifiers with Diffusion Models](https://mlsanity.com/paper/2311.07110/) (2023)
- [GraphPMU: Event Clustering via Graph Representation Learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements](https://mlsanity.com/paper/2205.13116/) (2022)
- [pmuBAGE: The Benchmarking Assortment of Generated PMU Data for Power System Events -- Part I: Overview and Results](https://mlsanity.com/paper/2204.01095/) (2022)
- [pmuBAGE: The Benchmarking Assortment of Generated PMU Data for Power System Events](https://mlsanity.com/paper/2210.14204/) (2022)
- [A Semi-Supervised Approach for Power System Event Identification](https://mlsanity.com/paper/2309.10095/) (2023)

## Abstract (arXiv)

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.