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author:

Wang, Huaiyuan (Wang, Huaiyuan.) [1] (Scholars:王怀远) | Chen, Qifan (Chen, Qifan.) [2]

Indexed by:

EI PKU CSCD

Abstract:

From the two aspects of model construction and characteristic quantities extraction, a transient stability discriminant model with better noise immunity is proposed. A stacked variational auto-encoder is adopted to construct the assessment model. Besides, a L2 regularization method is introduced in the trai-ning process, which enhances the generalization ability of the stability discriminant model. Meanwhile, the characteristic quantities extraction time of the proposed method is different from the traditional method. By setting the threshold of the maximum power angle difference of all generators, when the system develops to the threshold, the characteristic quantities extraction is carried out. The simulative results based on IEEE 39-bus system show that the miscalculation of the stability assessment model is greatly reduced with the proposed characteristic quantities extraction method. Meanwhile the reasonable threshold will not affect the start of real-time control methods, and the noise immunity ability of the model can be also strengthened. © 2019, Electric Power Automation Equipment Press. All right reserved.

Keyword:

Convergence of numerical methods Deep learning Electric power systems Electric power system stability Extraction Learning systems Power quality Real time control Signal encoding Stability Transient analysis

Community:

  • [ 1 ] [Wang, Huaiyuan]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China
  • [ 2 ] [Chen, Qifan]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China

Reprint 's Address:

  • 王怀远

    [wang, huaiyuan]college of electrical engineering and automation, fuzhou university, fuzhou; 350116, china

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Source :

Electric Power Automation Equipment

ISSN: 1006-6047

Year: 2019

Issue: 12

Volume: 39

Page: 134-139

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 10

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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