| 引用本文: | 宋铁维,施伟锋,毕 宗,谢嘉令.基于2D—ResNet的船舶电力系统电能质量扰动识别[J].电力系统保护与控制,2022,50(10):94-104.[点击复制] |
| SONG Tiewei,SHI Weifeng,BI Zong,XIE Jialing.Shipboard power quality disturbance recognition based on a two dimensional residual network[J].Power System Protection and Control,2022,50(10):94-104[点击复制] |
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| 摘要: |
| 为实现船舶电力系统电能质量扰动准确识别，结合深度学习提出基于二维残差网络(2D-ResNet)的电能质量扰动识别方法。首先将电能质量一维时间序列通过距离矩阵转化为二维平面图，随后将图像送入所提二维残差网络中提取特征。最终输出特征图通过线性层分类器得到识别结果，实现船舶电力系统电能质量扰动的在线识别。与现有特征提取方法相比，不同信噪比下该方法扰动识别准确率均最高。信噪比为20 dB时，单标签分类平均准确率为93.86%，多标签分类平均F1-score为96.52%，证明了2D-ResNet能有效提取扰动特征且对噪声具备鲁棒性。对于未知复合扰动，单标签分类器识别失败，而多标签分类器准确识别出扰动中的未知成分，且F1-score达到93%，证明了多标签分类适用于未知复合扰动识别。 |
| 关键词: 船舶电力系统 电能质量 二维残差网络 扰动识别 单标签分类 多标签分类 |
| DOI：DOI: 10.19783/j.cnki.pspc.211021 |
| 投稿时间：2021-08-03修订日期：2021-10-15 |
| 基金项目:上海市科技计划项目资助(20040501200) |
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| Shipboard power quality disturbance recognition based on a two dimensional residual network |
| SONG Tiewei,SHI Weifeng,BI Zong,XIE Jialing |
| (Department of Electrical Automation, Shanghai Maritime University, Shanghai 201306, China) |
| Abstract: |
| For accurate classification, a power quality disturbance recognition method of a shipboard power system based on a two dimensional residual network (2D-ResNet) is proposed. First, the one-dimensional power quality time series is transformed into a two-dimensional image by a distance matrix, and then the image is sent to the proposed 2D-ResNet to extract features. Then an output feature map is used to obtain the recognition results through the linear layer classifier to realize on-line recognition of power quality disturbances in a shipboard power system. Compared with existing feature extraction methods, this method has the highest accuracy of disturbance recognition under different signal-to-noise ratio (SNR). When the SNR is 20 dB, the average accuracy of single-label classification is 93.86%, and the average F1-score of multi-label classification is 96.52%. This proves that the 2D-ResNet can effectively extract features and is robust to noise. A single-label classifier fails to recognize unknown compound disturbance, while the multi-label classifier accurately recognizes the unknown components in the disturbance signal, and the F1-score reaches 93%, which proves that the multi-label classification is suitable for the recognition of unknown compound disturbance.
This work is supported by the Shanghai Science and Technology Committee Foundation (No. 20040501200). |
| Key words: shipboard power system power quality two dimensional residual network disturbance identification single-label classification multi-label classification |
