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The Complex Power Quality Disturbance Recognition Method Based on Deep Learning
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Yaping DENG1, Hao JIA2, Xiaohui ZHANG2, Xiangqian TONG1, Lu WANG3
Electric Drive | 2024, 54(3) : 76 - 83
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Electric Drive | 2024, 54(3): 76-83
The Complex Power Quality Disturbance Recognition Method Based on Deep Learning
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Yaping DENG1, Hao JIA2, Xiaohui ZHANG2, Xiangqian TONG1, Lu WANG3
Affiliations
  • 1 School of Electrical Engineering，Xi'an University of Technology，Xi'an 710054，Shaanxi，China
  • 2 School of Automation and Information Engineering，Xi'an University of Technology，Xi'an 710048，Shaanxi，China
  • 3 School of Electrical and Computer Engineering，University of Waterloo，Waterloo N2L3G1，Canada
Published: 2024-03-20 doi: 10.19457/j.1001-2095.dqcd24607
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The accurate recognition of power quality disturbance（PQD） is one of the main problems to be solved after PQD occurrence， which is of great importance for responsibility dividing and power market reform process accelerating. Massive quantities of power quality monitoring data prepare the ground for the recognition of PQD. Since the electrical characteristic is different for different PQD， the waveform difference between different power quality disturbances can be employed for the recognition of PQD. Combing the deep learning， the method for the recognition of complex PQD via bidirectional independently recurrent neural network（Bi-IndRNN）was proposed. In this way， the intrinsic characteristic of PQD was extracted， the internal correspondence between the input sequence and the output sequence was established， the dependence of the analysis result on the physical characteristic quantity was overcome， and the recognition accuracy of PQD was improved. The results illustrate that the diversity of complex PQD can be effectively responded， where the intrinsic characteristic hidden in complex PQD signal can be extracted directly， resulting in high accuracy.

power quality disturbance（PQD） recognition  /  bidirectional independently recurrent neural network（Bi-IndRNN）  /  deep Learning
Yaping DENG, Hao JIA, Xiaohui ZHANG, Xiangqian TONG, Lu WANG. The Complex Power Quality Disturbance Recognition Method Based on Deep Learning[J]. Electric Drive, 2024 , 54 (3) : 76 -83 . DOI: 10.19457/j.1001-2095.dqcd24607
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Article Info
doi: 10.19457/j.1001-2095.dqcd24607
  • Receive Date：2022-09-05
  • Online Date：2025-12-12
  • Published：2024-03-20
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  • Received：2022-09-05
  • Revised：2022-12-08
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Affiliations
    1 School of Electrical Engineering，Xi'an University of Technology，Xi'an 710054，Shaanxi，China
    2 School of Automation and Information Engineering，Xi'an University of Technology，Xi'an 710048，Shaanxi，China
    3 School of Electrical and Computer Engineering，University of Waterloo，Waterloo N2L3G1，Canada
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https://castjournals.cast.org.cn/joweb/dqcd/EN/10.19457/j.1001-2095.dqcd24607
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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