Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87742
Title: Hybrid method for power system transient stability prediction based on two-stage computing resources
Authors: Tang, Yi
Li, Feng
Wang, Qi
Xu, Yan
Keywords: Trajectory Fitting (TF)
Extreme Learning Machine (ELM)
Issue Date: 2018
Source: Tang, Y., Li, F., Wang, Q., & Xu, Y. (2018). Hybrid method for power system transient stability prediction based on two-stage computing resources. IET Generation, Transmission & Distribution, 12(8), 1697-1703.
Series/Report no.: IET Generation, Transmission & Distribution
Abstract: Accurate and prompt transient stability prediction is one of the effective ways to reduce the risk of blackout or cascading failures. In an effort to achieve improvements in time efficiency and prediction accuracy, a new transient stability prediction method combining trajectory fitting (TF) and extreme learning machine (ELM) based on two-stage process, named hybrid method, is proposed here. ELM-based method is implemented in central station to ensure the time efficiency, while TF-based method is adopted in local station to guarantee the accuracy. Furthermore, data corruption is taken into consideration to assure the robustness of the proposed algorithm. The hybrid method is validated with the New England 39-bus test system and the simulation results indicate its effectiveness and reliability.
URI: https://hdl.handle.net/10356/87742
http://hdl.handle.net/10220/45474
ISSN: 1751-8687
DOI: 10.1049/iet-gtd.2017.1168
Rights: © 2018 Institution of Engineering and Technology. This paper was published in IET Generation, Transmission and Distribution and is made available as an electronic reprint (preprint) with permission of Institution of Engineering and Technology. The published version is available at: [http://dx.doi.org/10.1049/iet-gtd.2017.1168]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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