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An Online Power System Stability Monitoring System Using Convolutional Neural Networks

Gupta, Ankita and Gurrala, Gurunath and Sastry, PS (2019) An Online Power System Stability Monitoring System Using Convolutional Neural Networks. In: IEEE TRANSACTIONS ON POWER SYSTEMS, 34 (2). pp. 864-872.

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Official URL: https://doi.org/10.1109/TPWRS.2018.2872505


A continuous Online Monitoring System (OMS) for power system stability based on Phasor Measurements (PMU measurements) at all the generator buses is proposed in this paper. Unlike the state-of-the-art methods, the proposed OMS does not require information about fault clearance. This paper proposes a convolutional neural network, whose input is the heatmap representation of the measurements, for instability prediction. Through extensive simulations on standard IEEE 118-bus and IEEE 145-bus systems, the effectiveness of the proposed OMS is demonstrated under varying loading conditions, fault scenarios, topology changes, and generator parameter variations. Two different methods are also proposed to identify the set of critical generators that are most impacted in the unstable cases.

Item Type: Journal Article
Additional Information: Copyright for this article belongs to IEEE.
Keywords: Transient stability; phasor measurements; convolutional neural networks; principal component analysis
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Depositing User: Id for Latest eprints
Date Deposited: 18 Mar 2019 09:47
Last Modified: 18 Mar 2019 09:47
URI: http://eprints.iisc.ac.in/id/eprint/61923

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