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

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 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 languageEnglish
Title of host publication2010 International Conference on Power System Technology
Subtitle of host publicationTechnological Innovations Making Power Grid Smarter, POWERCON2010
DOIs
Publication statusPublished - 1 Dec 2010
Event2010 International Conference on Power System Technology: Technological Innovations Making Power Grid Smarter, POWERCON2010 - Hangzhou, China
Duration: 24 Oct 201028 Oct 2010

Conference

Conference2010 International Conference on Power System Technology: Technological Innovations Making Power Grid Smarter, POWERCON2010
CountryChina
CityHangzhou
Period24/10/1028/10/10

Keywords

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

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Fuel Technology

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