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

Yuan, Yuxuan; Guo, Yifei; Dehghanpour, Kaveh; Wang, Zhaoyu; Wang, Yanchao

doi:10.1109/tpwrs.2021.3081608

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

Journal Article · Tue May 18 00:00:00 EDT 2021 · IEEE Transactions on Power Systems

DOI:https://doi.org/10.1109/tpwrs.2021.3081608· OSTI ID:1866731

^[1]; Guo, Yifei ^[2]; ^[2]; ^[2]; ^[2]

Iowa State Univ., Ames, IA (United States); Iowa State University, Ames, IA (United States)
Iowa State Univ., Ames, IA (United States)

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 data and imperfect data quality could bring great technical challenges for real-time system event identification. To address these challenges, this paper proposes a two-stage learning-based framework. In 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 power events. The proposed method fully builds on and is also tested on a large real-world 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. We report the numerical results validate that our method has high identification accuracy while showing good robustness against poor data quality.

View Accepted Manuscript (DOE)

Research Organization:: Iowa State University, Ames, IA (United States)

Sponsoring Organization:: USDOE Office of Electricity (OE)

Grant/Contract Number:: OE0000910

OSTI ID:: 1866731

Journal Information:: IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 6 Vol. 36; ISSN 0885-8950

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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24 POWER TRANSMISSION AND DISTRIBUTION
42 ENGINEERING
Markov transition field
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