{"type":"ScholarlyArticle","title":"Instability Prediction in Power Systems using Recurrent Neural Networks","authors":[{"name":"Ankita Gupta","slug":"ankita-gupta","given":"Ankita","family":"Gupta","affiliation":"Indian Institute of Science , Bangalore"},{"name":"Gurunath Gurrala","slug":"gurunath-gurrala","given":"Gurunath","family":"Gurrala","affiliation":"Indian Institute of Science , Bangalore"},{"name":"Pidaparthy S Sastry","slug":"pidaparthy-s-sastry","given":"Pidaparthy S","family":"Sastry","affiliation":"Indian Institute of Science , Bangalore"}],"abstract":"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 pre-\r\ndiction 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.","doi":"10.24963/ijcai.2017/249","url":"https://ijcai.org/proceedings/2017/249","pdf":"https://ijcai.org/proceedings/2017/0249.pdf","markdown":"https://ijcai.org/proceedings/2017/249.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/249","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":249,"pages":{"first":1795,"last":1801},"keywords":["Machine Learning: Time-series/Data Streams","Machine Learning: Machine Learning","Machine Learning: Deep Learning","Machine Learning: Neural Networks"],"videos":[],"references":[{"position":1,"raw":"[Cho et al., 2014] Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 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