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

Author:

Zheng Chen1,Li Qionglin1,Liu Shuming1,Dai Shuangyin1,Zhang Bo1,Liu Yajuan2

Affiliation:

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

2. 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.

Funder

National Natural Science Foundation of China

Science and Technology Program of the State Grid Corporation

Publisher

MDPI AG

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