30 / 2024-07-09 11:50:11
Complex power quality disturbance identification based on GAF-MTF three-channel feature fusion
complex power quality disturbance(PQDs); Dimension transformation; Multi-feature fusion; transformer; Gram Angle field(GAF); Markov transfer field(MTF)
Final Paper
Li Shiheng / the Dongguan Power Supply Bureau of Guangdong Power Grid Co., Ltd.
Huang Zhiwei / LTD.;the Dongguan Power Supply Bureau of Guangdong Power Grid Co.
Yang Zhihua / the Dongguan Power Supply Bureau of Guangdong Power Grid Co., Ltd.
Chen Qinghong / the Dongguan Power Supply Bureau of Guangdong Power Grid Co., Ltd.
Dong Yawen / the Dongguan Power Supply Bureau of Guangdong Power Grid Co., Ltd.
Kaicheng Li / Huazhong University of Science and Technology;HUST;the State Key Laboratory of Advanced Electromagnetic Engineering and Technology
Yuan Wentao / Huazhong University of Science and Technology;the State Key Laboratory of Advanced Electromagnetic Engineering and Technology
Xu Aoao / Huazhong University of Science and Technology;the State Key Laboratory of Advanced Electromagnetic Engineering and Technology
The diversification of equipment in power system and the complexity of operating conditions gradually lead to the development of power quality disturbance to the complex power quality disturbance which is released simultaneously by multiple types of disturbance. It is important to identify the type of complex power quality disturbance reliably and effectively for the stable operation of power grid. In this paper, a composite power quality disturbance identification architecture based on GASF-GADF-MTF three-channel multi-dimensional feature fusion plus transformer is proposed. Firstly, the composite power quality disturbance is converted into RGB three-channel data by using Gram summing field(GASF), Gram difference field(GADF) and Markov transfer field(MTF). The two dimensional image of feature fusion is obtained. Then the transformer network is used to classify and discriminate the two-dimensional images containing the complex power quality disturbance characteristics. Simulation results show that the proposed method is reliable and effective, and has the advantages of good noise robustness and strong generalization ability.
Important Date
  • Conference Date

    Nov 06

    2024

    to

    Nov 08

    2024

  • Sep 15 2024

    Draft paper submission deadline

  • Nov 08 2024

    Registration deadline

Sponsored By
Huazhong University of Science and Technology
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