|
|
|
| Power quality disturbance identification of multi-scale fusion depth residual network based on channel selection |
| LIU Wei, WANG Kai |
| College of Electrical and Information Engineering, Northeast Petroleum University, Daqing, Heilongjiang 163318 |
|
|
|
|
Abstract Aiming at the shortcomings of traditional power quality disturbance classification methods that are difficult to manually select features and low accuracy, on the basis of traditional convolutional networks, drawing on the ideas of inception and residuals, combined with hybrid pooling and efficient channel attention mechanism, a power quality disturbance identification method based on channel selection multi-scale fusion deep residual network (CSSF-ResNet) is proposed. Multi-scale convolution is used to extract features of different scales, and global mixed pooling is combined with efficient channel attention mechanism. Feature screening is carried out in the channel dimension. Effective features are mined, and residual connections are introduced to form a CSSF-ResNet. The simulation results show that the proposed method has the advantages of high classification accuracy and strong noise robustness.
|
|
Received: 02 April 2023
|
|
|
|
| Cite this article: |
|
LIU Wei,WANG Kai. Power quality disturbance identification of multi-scale fusion depth residual network based on channel selection[J]. Electrical Engineering, 2023, 24(5): 11-15.
|
|
|
|
| URL: |
|
http://dqjs.cesmedia.cn/EN/Y2023/V24/I5/11
|
[1] 严静, 邵振国. 电能质量谐波监测与评估综述[J]. 电气技术, 2020, 21(7): 1-7.
[2] 李云峰, 高云鹏, 蔡星月, 等. 自适应辛几何模态分解和短时能量差分因子在电能质量扰动检测中的应用[J]. 电工技术学报, 2022, 37(17): 4390-4400.
[3] 吴建章, 梅飞, 郑建勇, 等. 基于改进经验小波变换和XGBoost的电能质量复合扰动分类[J]. 电工技术学报, 2022,37(1): 232-243, 253.
[4] 张萍, 沈新鹤. 基于H∞混合灵敏度的并网逆变器谐振控制策略[J]. 电气技术, 2022, 23(11): 6-12.
[5] 瞿合祚, 李晓明, 陈陈, 等. 基于卷积神经网络的电能质量扰动分类[J]. 武汉大学学报(工学版), 2018, 51(6): 534-539.
[6] 王继东, 张迪. 基于侧输出融合卷积神经网络的电能质量扰动分类方法[J]. 电力自动化设备, 2021, 41(11): 107-112, 126.
[7] 王维博, 张斌, 曾文入, 等. 基于特征融合一维卷积神经网络的电能质量扰动分类[J]. 电力系统保护与控制, 2020, 48(6): 53-60.
[8] SZEGEDY C, LIU Wei, JIA Yangqing, et al.Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recogni- tion (CVPR), Boston, MA, USA, 2015: 1-9.
[9] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al.Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016: 770-778.
[10] WANG Qilong, WU Banggu, ZHU Pengfei, et al.ECA-Net: efficient channel attention for deep con- volutional neural networks[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020: 11531-11539. |
|
|
|
2023, Vol. 24 
