Earlier detection of risk of blackout by real-time dynamic security assessment based on extreme learning machines

Y. Xu; Z. Y. Dong; K. Meng; Zhao Xu; R. Zhang; Andrew Y. Wu; K. P. Wong

doi:10.1109/POWERCON.2010.5666055

Earlier detection of risk of blackout by real-time dynamic security assessment based on extreme learning machines

Y. Xu, Z. Y. Dong, K. Meng, Zhao Xu, R. Zhang, Andrew Y. Wu, K. P. Wong

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

7 Citations (Scopus)

Abstract

The lack of real-time tools capable of detecting risk of blackouts is one of the contribution factors to the recent large blackouts occurred around the world. In terms of dynamic security assessment (DSA), artificial intelligence and data mining techniques have been widely applied to facilitate very fast DSA for enhanced situational awareness of insecurity. However, many of the current state-of-the-art models usually sutTer from excessive training time and complex parameters tuning problems, leading to their inefficiency for real-time implementation. In this paper, a new DSA method using Extreme Learning Machine (ELM) is proposed, which has significantly improved the learning speed and can therefore provide earlier detection of the risk of blackout. The proposed method is examined on the New England 39-bus test system, and compared with other state-of-the-art methods in terms of computation time and accuracy. The simulation results show that the ELM-based DSA method possesses superior computation speed and acceptably high accuracy.

Original language	English
Title of host publication	2010 International Conference on Power System Technology
Subtitle of host publication	Technological Innovations Making Power Grid Smarter, POWERCON2010
DOIs	https://doi.org/10.1109/POWERCON.2010.5666055
Publication status	Published - 1 Dec 2010
Event	2010 International Conference on Power System Technology: Technological Innovations Making Power Grid Smarter, POWERCON2010 - Hangzhou, China Duration: 24 Oct 2010 → 28 Oct 2010

Conference

Conference	2010 International Conference on Power System Technology: Technological Innovations Making Power Grid Smarter, POWERCON2010
Country/Territory	China
City	Hangzhou
Period	24/10/10 → 28/10/10

Keywords

Blackout prevention
Dynamic security assessment
Extreme learning machine (ELM)
Intelligent system

ASJC Scopus subject areas

Energy Engineering and Power Technology
Fuel Technology

More information

10.1109/POWERCON.2010.5666055

Cite this

Xu, Y., Dong, Z. Y., Meng, K., Xu, Z., Zhang, R., Wu, A. Y., & Wong, K. P. (2010). Earlier detection of risk of blackout by real-time dynamic security assessment based on extreme learning machines. In 2010 International Conference on Power System Technology: Technological Innovations Making Power Grid Smarter, POWERCON2010 Article 5666055 https://doi.org/10.1109/POWERCON.2010.5666055

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title = "Earlier detection of risk of blackout by real-time dynamic security assessment based on extreme learning machines",

abstract = "The lack of real-time tools capable of detecting risk of blackouts is one of the contribution factors to the recent large blackouts occurred around the world. In terms of dynamic security assessment (DSA), artificial intelligence and data mining techniques have been widely applied to facilitate very fast DSA for enhanced situational awareness of insecurity. However, many of the current state-of-the-art models usually sutTer from excessive training time and complex parameters tuning problems, leading to their inefficiency for real-time implementation. In this paper, a new DSA method using Extreme Learning Machine (ELM) is proposed, which has significantly improved the learning speed and can therefore provide earlier detection of the risk of blackout. The proposed method is examined on the New England 39-bus test system, and compared with other state-of-the-art methods in terms of computation time and accuracy. The simulation results show that the ELM-based DSA method possesses superior computation speed and acceptably high accuracy.",

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Xu, Y, Dong, ZY, Meng, K, Xu, Z, Zhang, R, Wu, AY & Wong, KP 2010, Earlier detection of risk of blackout by real-time dynamic security assessment based on extreme learning machines. in 2010 International Conference on Power System Technology: Technological Innovations Making Power Grid Smarter, POWERCON2010., 5666055, 2010 International Conference on Power System Technology: Technological Innovations Making Power Grid Smarter, POWERCON2010, Hangzhou, China, 24/10/10. https://doi.org/10.1109/POWERCON.2010.5666055

Earlier detection of risk of blackout by real-time dynamic security assessment based on extreme learning machines. / Xu, Y.; Dong, Z. Y.; Meng, K. et al.
2010 International Conference on Power System Technology: Technological Innovations Making Power Grid Smarter, POWERCON2010. 2010. 5666055.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Dong, Z. Y.

AU - Meng, K.

AU - Xu, Zhao

AU - Zhang, R.

AU - Wu, Andrew Y.

AU - Wong, K. P.

PY - 2010/12/1

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Earlier detection of risk of blackout by real-time dynamic security assessment based on extreme learning machines

Abstract

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Keywords

ASJC Scopus subject areas

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