引用本文: | 王 飞,王立辉,周少武,赵 才,张志飞.基于双分支并联的特征融合电能质量扰动分类方法[J].电力系统保护与控制,2024,52(5):178-187.[点击复制] |
WANG Fei,WANG Lihui,ZHOU Shaowu,ZHAO Cai,ZHANG Zhifei.A dual-branch parallel-based feature fusion approach for power qualitydisturbance classification[J].Power System Protection and Control,2024,52(5):178-187[点击复制] |
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摘要: |
为了提高对电能质量扰动信号(power quality disturbance signal, PQDs)在受到噪声和异常数据干扰时的分类准确率,提出了一种双分支并联特征融合网络的PQDs分类方法。首先,采用一维残差神经网络和一维卷积神经网络两个分支进行特征提取。然后,通过特征融合模块将这些特征融合在一起。最终,通过分类模块对PQDs进行准确分类。相对于串联神经网络,所提方法融合特征向量,增强了特征的区分度,同时适用于并行计算,进一步提高了识别速度。仿真结果表明,所提方法在叠加信噪比为13 dB、15 dB和18 dB的PQDs分类任务中,识别率均超过95%,此外,该方法对异常数据的分类效果也具有一定的鲁棒性。 |
关键词: 一维卷积神经网络 一维残差神经网络 特征提取 扰动分类 |
DOI:10.19783/j.cnki.pspc.230846 |
投稿时间:2023-07-04修订日期:2023-11-06 |
基金项目:国家自然科学基金项目资助(51277056);湖南省自然科学基金项目资助(2021JJ30280) |
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A dual-branch parallel-based feature fusion approach for power qualitydisturbance classification |
WANG Fei1,WANG Lihui1,ZHOU Shaowu2,ZHAO Cai2,ZHANG Zhifei1 |
(1. School of Mechatronic Engineering and Automation, Foshan University, Foshan 528000, China; 2. School of Mechanical
and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China) |
Abstract: |
To enhance the classification accuracy of power quality disturbance signals (PQDs) in the presence of noise and abnormal data interference, this paper introduces a dual-branch parallel feature fusion network-based classification method. First, feature extraction of two branches is accomplished by using a one-dimensional residual neural network and a one-dimensional convolutional neural network. Then, these extracted features are fused together through a feature fusion module. Finally, a classification module is used to accurately classify PQDs. In contrast to serial neural networks, this approach combines feature vectors enhancing feature distinctiveness and is compatible with parallel computing, further improving recognition speed. Simulation results demonstrate that this method achieves recognition rates exceeding 95% for PQDs classification tasks with signal-to-noise ratios of 13 dB, 15 dB, and 18 dB. Additionally, the method exhibits a certain level of robustness in classifying abnormal data. |
Key words: one-dimensional convolutional neural network one-dimensional residual neural network feature extraction disturbance classification |