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Power Quality Transient Disturbance Diagnosis Based on Dynamic Large Convolution Kernel and Multi-Level Feature Fusion Network

Author

Listed:
  • Chen Zheng

    (Electric Power Research Institute, State Grid Henan Electric Power Company, No. 85 Songshan South Road, Erqi District, Zhengzhou 450001, China)

  • Qionglin Li

    (Electric Power Research Institute, State Grid Henan Electric Power Company, No. 85 Songshan South Road, Erqi District, Zhengzhou 450001, China)

  • Shuming Liu

    (Electric Power Research Institute, State Grid Henan Electric Power Company, No. 85 Songshan South Road, Erqi District, Zhengzhou 450001, China)

  • Shuangyin Dai

    (Electric Power Research Institute, State Grid Henan Electric Power Company, No. 85 Songshan South Road, Erqi District, Zhengzhou 450001, China)

  • Bo Zhang

    (Electric Power Research Institute, State Grid Henan Electric Power Company, No. 85 Songshan South Road, Erqi District, Zhengzhou 450001, China)

  • Yajuan Liu

    (Zhengzhou Power Supply Bureau, State Grid Henan Electric Power Company, Zhengzhou 450001, China)

Abstract

Power quality is an important metric for the normal operation of a power system, and the accurate identification of transient signals is of great significance for the improvement of power quality. The diverse types of power system transient signals and strong characteristic coupling brings new challenges to the analysis and identification of power system transient signals. In order to enhance the identification accuracy of transient signals, one method of power system transient signal identification is proposed based on a dynamic large convolution kernel and multilevel feature fusion network. First, the more fine-grained and more informative features of the transient signals are extracted by the dynamic large convolution kernel feature extraction module. Then, the multi-scale local features are adaptively fused by the multilevel feature fusion module. Finally, the fused features are reduced in dimension by the fully connected layer in the classification module and fed into the SoftMax layer for transient signal type detection. The proposed method can effectively improve the small receptive field problem of convolutional neural networks and the lack of ability of Transformer network in extracting local context information. Compared with five other power quality transient disturbance identification models, the experimental results show that the proposed method has better diagnostic accuracy and anti-noise capability.

Suggested Citation

  • Chen Zheng & Qionglin Li & Shuming Liu & Shuangyin Dai & Bo Zhang & Yajuan Liu, 2024. "Power Quality Transient Disturbance Diagnosis Based on Dynamic Large Convolution Kernel and Multi-Level Feature Fusion Network," Energies, MDPI, vol. 17(13), pages 1-15, July.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3227-:d:1426701
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    References listed on IDEAS

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    1. Surendra Singh & Avdhesh Sharma & Akhil Ranjan Garg & Om Prakash Mahela & Baseem Khan & Ilyes Boulkaibet & Bilel Neji & Ahmed Ali & Julien Brito Ballester, 2023. "Power Quality Detection and Categorization Algorithm Actuated by Multiple Signal Processing Techniques and Rule-Based Decision Tree," Sustainability, MDPI, vol. 15(5), pages 1-30, February.
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