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Instability prediction in power systems using recurrent neural networks

Gupta, A and Gurrala, G and Sastry, PS (2017) Instability prediction in power systems using recurrent neural networks. In: 26th International Joint Conference on Artificial Intelligence, 19 - 25 August 2017, Melbourne, pp. 1795-1801.

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Official URL: https://doi.org/10.24963/ijcai.2017/249


Recurrent Neural Networks (RNNs) can model temporal dependencies in time series well. In this paper we present an interesting application of stacked Gated Recurrent Unit (GRU) based RNN for early prediction of imminent instability in a power system based on normal measurements of power system variables over time. In a power system, disturbances like a fault can result in transient instability which may lead to blackouts. Early prediction of any such contingency can aid the operator to take timely preventive control actions. In recent times some machine learning techniques such as SVMs have been proposed to predict such instability. However, these approaches assume availability of accurate fault information like its occurrence and clearance instants which is impractical. In this paper we propose an Online Monitoring System (OMS), which is a GRU based RNN, that continuously keeps predicting the current status based on past measurements. Through extensive simulations using a standard 118-bus system, the effectiveness of the proposed system is demonstrated. We also show how we can use PCA and predictions from the RNN to identify the most critical generator that leads to transient instability.

Item Type: Conference Paper
Publication: IJCAI International Joint Conference on Artificial Intelligence
Publisher: International Joint Conferences on Artificial Intelligence
Additional Information: The copyright for this article belongs to International Joint Conferences on Artificial Intelligence.
Keywords: Forecasting; Learning systems; Stability, Critical generators; Early prediction; Extensive simulations; Machine learning techniques; On-line monitoring system; Preventive control; Recurrent neural network (RNNs); Transient instability, Recurrent neural networks
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 20 Jul 2022 10:20
Last Modified: 20 Jul 2022 10:20
URI: https://eprints.iisc.ac.in/id/eprint/74912

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