1. Learning-Based Real-Time Event Identification Using Rich Real PMU Data.
- Author
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Yuan, Yuxuan, Guo, Yifei, Dehghanpour, Kaveh, Wang, Zhaoyu, and Wang, Yanchao
- Subjects
PHASOR measurement ,CONVOLUTIONAL neural networks ,PHYSICAL laws ,ALGORITHMS ,SYSTEM identification - 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 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. The numerical results validate that our method has high identification accuracy while showing good robustness against poor data quality. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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