Transient stability multi-swing step-out prediction with online anomaly detection

Takuya Omi; Hiroto Kakisaka; Tomomi Sadakawa; Shinichi Iwamoto

doi:10.1109/TENCON.2016.7848624

Transient stability multi-swing step-out prediction with online anomaly detection

Takuya Omi, Hiroto Kakisaka, Tomomi Sadakawa, Shinichi Iwamoto

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Electric power systems are becoming more complex making them more difficult to control. Owing to recent developments in information and communication technologies, power system data have become available online. In this paper, we propose a method that can predict transient stability multi-swing step-out using 'anomaly detection with data mining'. In particular, we focus our attention on the theory of ChangeFinder, which uses the SDAR algorithm and the two-stage learning model. The generator phase angles are obtained from transient stability simulations. They are passed as inputs to the ChangeFinder and the multi swing step-out can be detected. The validity of the proposed method is verified through simulations on the IEEJ 10 machine 47 bus-system.

Original language	English
Title of host publication	Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3123-3126
Number of pages	4
ISBN (Electronic)	9781509025961
DOIs	https://doi.org/10.1109/TENCON.2016.7848624
Publication status	Published - 2017 Feb 8
Event	2016 IEEE Region 10 Conference, TENCON 2016 - Singapore, Singapore Duration: 2016 Nov 22 → 2016 Nov 25

Other

Other	2016 IEEE Region 10 Conference, TENCON 2016
Country/Territory	Singapore
City	Singapore
Period	16/11/22 → 16/11/25

Keywords

Anomaly Detection
Data Mining
Multi-Swing Step-out
Online Analysis
Transient Stability

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/TENCON.2016.7848624

Cite this

Transient stability multi-swing step-out prediction with online anomaly detection. / Omi, Takuya; Kakisaka, Hiroto; Sadakawa, Tomomi et al.
Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 3123-3126 7848624.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Omi, T, Kakisaka, H, Sadakawa, T & Iwamoto, S 2017, Transient stability multi-swing step-out prediction with online anomaly detection. in Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016., 7848624, Institute of Electrical and Electronics Engineers Inc., pp. 3123-3126, 2016 IEEE Region 10 Conference, TENCON 2016, Singapore, Singapore, 16/11/22. https://doi.org/10.1109/TENCON.2016.7848624

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title = "Transient stability multi-swing step-out prediction with online anomaly detection",

abstract = "Electric power systems are becoming more complex making them more difficult to control. Owing to recent developments in information and communication technologies, power system data have become available online. In this paper, we propose a method that can predict transient stability multi-swing step-out using 'anomaly detection with data mining'. In particular, we focus our attention on the theory of ChangeFinder, which uses the SDAR algorithm and the two-stage learning model. The generator phase angles are obtained from transient stability simulations. They are passed as inputs to the ChangeFinder and the multi swing step-out can be detected. The validity of the proposed method is verified through simulations on the IEEJ 10 machine 47 bus-system.",

keywords = "Anomaly Detection, Data Mining, Multi-Swing Step-out, Online Analysis, Transient Stability",

author = "Takuya Omi and Hiroto Kakisaka and Tomomi Sadakawa and Shinichi Iwamoto",

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doi = "10.1109/TENCON.2016.7848624",

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booktitle = "Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016",

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AU - Omi, Takuya

AU - Kakisaka, Hiroto

AU - Sadakawa, Tomomi

AU - Iwamoto, Shinichi

PY - 2017/2/8

Y1 - 2017/2/8

N2 - Electric power systems are becoming more complex making them more difficult to control. Owing to recent developments in information and communication technologies, power system data have become available online. In this paper, we propose a method that can predict transient stability multi-swing step-out using 'anomaly detection with data mining'. In particular, we focus our attention on the theory of ChangeFinder, which uses the SDAR algorithm and the two-stage learning model. The generator phase angles are obtained from transient stability simulations. They are passed as inputs to the ChangeFinder and the multi swing step-out can be detected. The validity of the proposed method is verified through simulations on the IEEJ 10 machine 47 bus-system.

AB - Electric power systems are becoming more complex making them more difficult to control. Owing to recent developments in information and communication technologies, power system data have become available online. In this paper, we propose a method that can predict transient stability multi-swing step-out using 'anomaly detection with data mining'. In particular, we focus our attention on the theory of ChangeFinder, which uses the SDAR algorithm and the two-stage learning model. The generator phase angles are obtained from transient stability simulations. They are passed as inputs to the ChangeFinder and the multi swing step-out can be detected. The validity of the proposed method is verified through simulations on the IEEJ 10 machine 47 bus-system.

KW - Anomaly Detection

KW - Data Mining

KW - Multi-Swing Step-out

KW - Online Analysis

KW - Transient Stability

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Transient stability multi-swing step-out prediction with online anomaly detection

Abstract

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Keywords

ASJC Scopus subject areas

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