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arXiv · 2006.10121

Learning-Based Real-Time Event Identification Using Rich Real PMU Data

Yuxuan Yuan·Yifei Guo·Kaveh Dehghanpour·Zhaoyu Wang·Yanchao Wang
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Abstract

A large-scale deployment of phasor measurement units (PMUs) that reveal the inherent physical laws of power systems from a data perspective enables an enhanced awareness of power system operation. However, the high-granularity and non-stationary nature of PMU time series and imperfect data quality could bring great technical challenges to real-time system event identification. To address these issues, this paper proposes a two-stage learning-based framework. At the first stage, a Markov transition field (MTF) algorithm is exploited to extract the latent data features by encoding temporal dependency and transition statistics of PMU data in graphs. Then, a spatial pyramid pooling (SPP)-aided convolutional neural network (CNN) is established to efficiently and accurately identify operation events. The proposed method fully builds on and is also tested on a large real dataset from several tens of PMU sources (and the corresponding event logs), located across the U.S., with a time span of two consecutive years. The numerical results validate that our method has high identification accuracy while showing good robustness against poor data quality.

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Yuxuan Yuan, Yifei Guo, Kaveh Dehghanpour, Zhaoyu Wang, Yanchao Wang. 2020-06-17. Learning-Based Real-Time Event Identification Using Rich Real PMU Data. https://doi.org/10.1109/tpwrs.2021.3081608

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