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Power Quality Disturbance Classification Based on Graph Convolutional Neural Networks and Gramian Angular Field
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Guanglei HUANG1, Qidong TIAN1, Zhixian LIN1, Weinan ZHENG1, Te XU1, Bingran LI2
Electric Drive | 2024, 54(3) : 84 - 90
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Electric Drive | 2024, 54(3): 84-90
Power Quality Disturbance Classification Based on Graph Convolutional Neural Networks and Gramian Angular Field
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Guanglei HUANG1, Qidong TIAN1, Zhixian LIN1, Weinan ZHENG1, Te XU1, Bingran LI2
Affiliations
  • 1 Shenzhen Power Supply Bureau Co.，Ltd.，Shenzhen 310030，Guangdong，China
  • 2 State Grid Jiangsu Electric Power Co.，Ltd.，Suzhou 215000，Jiangsu，China
Published: 2024-03-20 doi: 10.19457/j.1001-2095.dqcd24578
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Due to the extensive addition of new energy systems， the number and types of power quality disturbances in the system are also increased accordingly. However， the traditional power quality disturbance （PQD） classification method has the problem of low accuracy and efficiency， and it is difficult to adapt to the existing power quality management of power systems with high new energy penetration. Therefore， a PQD classification method based on graph convolutional neural networks （GCNNs） and Gramian angular field （GAF） was proposed. First， the original PQD signal was normalized and polar coordinate transformation was processed， then GAF was used to graphically transform different kinds of PQD one-dimensional signals to generate two-dimensional images containing different PQD features， and finally， GCNNs were used to train and classify the different kinds of PQD images to achieve the classification of different PQDs. In the experiment part， the IEEE-39 node system was used to simulate and simulate different types of PQD curves， and the method proposed was used for verification. The experiment results show that the proposed method can automatically extract and optimize the features， and meet the high efficiency and accuracy of PQD identification and classification.

power quality disturbance（PQD）  /  graph convolutional neural networks （GCNNs）  /  Gramian angular field （GAF）  /  disturbance classification
Guanglei HUANG, Qidong TIAN, Zhixian LIN, Weinan ZHENG, Te XU, Bingran LI. Power Quality Disturbance Classification Based on Graph Convolutional Neural Networks and Gramian Angular Field[J]. Electric Drive, 2024 , 54 (3) : 84 -90 . DOI: 10.19457/j.1001-2095.dqcd24578
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Year 2024 volume 54 Issue 3
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Article Info
doi: 10.19457/j.1001-2095.dqcd24578
  • Receive Date：2022-08-22
  • Online Date：2025-12-12
  • Published：2024-03-20
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  • Received：2022-08-22
  • Revised：2022-10-20
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Affiliations
    1 Shenzhen Power Supply Bureau Co.，Ltd.，Shenzhen 310030，Guangdong，China
    2 State Grid Jiangsu Electric Power Co.，Ltd.，Suzhou 215000，Jiangsu，China
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https://castjournals.cast.org.cn/joweb/dqcd/EN/10.19457/j.1001-2095.dqcd24578
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