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

Chen Zheng (), Qionglin Li, Shuming Liu, Shuangyin Dai, Bo Zhang and Yajuan Liu
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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

Energies, 2024, vol. 17, issue 13, 1-15

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.

Keywords: power quality disturbance; transient signal; convolutional neural networks; transformer network; feature fusion strategy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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