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author:

Hu, Litao (Hu, Litao.) [1] | Wang, Huaiyuan (Wang, Huaiyuan.) [2] (Scholars:王怀远) | Dang, Ran (Dang, Ran.) [3] | Tong, Haoxuan (Tong, Haoxuan.) [4] | Zhang, Yang (Zhang, Yang.) [5]

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Abstract:

In order to solve the problem of transient stability assessment deviation caused by the imbalance of power system sample quantity and quanlity, starting from the training process of assessment model, the gradient norm of samples to model parameters is obtained by the pre-training model, and the mean ratio of gradient norm is introduced to quantify the imbalance of samples. Compared with the prior information, the mean ratio of gradient norm comprehensively considers the imbalance between the sample quantity and quality. An imbalanced correction method based on the cost-sensitive method is proposed, which is used to improve the assessment preference of the model and realize a preferable correction effect. The simulative results of IEEE 39-bus system and East China Power System verify the effectiveness of the proposed method. © 2024 Electric Power Automation Equipment Press. All rights reserved.

Keyword:

Deep learning Power quality Signal encoding System stability Transients

Community:

  • [ 1 ] [Hu, Litao]Fujian Key Laboratory of New Energy Generation and Power Conversion, College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China
  • [ 2 ] [Wang, Huaiyuan]Fujian Key Laboratory of New Energy Generation and Power Conversion, College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China
  • [ 3 ] [Dang, Ran]Shaanxi Aircraft Industry Limited Liability Company, Hanzhong; 723000, China
  • [ 4 ] [Tong, Haoxuan]Taining County Power Supply Branch, State Grid Fujian Electric Power Co., Ltd., Sanming; 354400, China
  • [ 5 ] [Zhang, Yang]Fujian Key Laboratory of New Energy Generation and Power Conversion, College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China

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Source :

Electric Power Automation Equipment

ISSN: 1006-6047

CN: 32-1318/TM

Year: 2024

Issue: 4

Volume: 44

Page: 156-163 and 177

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WoS CC Cited Count:

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ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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