PMU-data-driven Event Classification in Power Transmission Grids

Conference · Tue Feb 16 00:00:00 EST 2021 · 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)

DOI:https://doi.org/10.1109/isgt49243.2021.9372227· OSTI ID:1958827

Niazazari, Iman; Liu, Yunchuan; Ghasenikhani, Amir; Biswas, Shuchismita; Livani, Hanif; Yang, Lei; Centeno, Virgilio A.

This paper presents an event classification in transmission grids. The convolutional neural network (CNN)-based classifier is proposed to capture the temporal similarity of time-synchronized data stream from phasor measurement units (PMUs). The proposed CNN is trained using Bayesian optimization to search for the best hyperparameters. The effectiveness of the proposed event classification is validated through the real-world dataset from the U.S. transmission grids. This dataset includes line outage, transformer outage, frequency, and oscillation events. The validation process also includes different PMU outputs, such as voltage magnitude, phase angle, current magnitude, frequency, and rate of change of frequency (ROCOF). The results show that ROCOF gives the best classification performance compared to other PMU outputs. In addition, it is shown that the classifier trained with a larger dataset has higher accuracy. Moreover, the superiority of the proposed method is validated through comparison with other state-of-the-art classification methods.

• Research Organization: Univ. of Nevada, Reno, NV (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: OE0000911
• OSTI ID: 1958827
• Journal Information: 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Conference: 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT),Washington, DC, USA,16-18 February 2021
• Country of Publication: United States
• Language: English