| 引用本文: | 赵 妍,潘 怡,李亚波,等.基于AVMD多尺度模糊熵和VPMCD算法的宽频振荡分类[J].电力系统保护与控制,2024,52(13):179-187.[点击复制] |
| ZHAO Yan,PAN Yi,LI Yabo,et al.Broadband oscillation classification based on AVMD multi-scale fuzzy entropy and the VPMCD algorithm[J].Power System Protection and Control,2024,52(13):179-187[点击复制] |
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| 摘要: |
| 电力系统宽频振荡具有宽频域、非线性和时变性的特点，对振荡分类在准确性、快速性等方面提出了更高的要求。为此，提出一种基于自适应变分模态分解(adaptive variational mode decomposition, AVMD)的多尺度模糊熵(multi-scale fuzzy entropy, MFE)和变量预测模型(variable predictive model-based class discriminate, VPMCD)相结合的宽频振荡分类新方法。首先，对宽频振荡信号进行AVMD，得到固有模态分量(intrinsic mode functions, IMFS)。然后，引入MFE对IMFS进行时域特征描述，同时实现对IMFS构造特征向量的降维处理。最后，采用VPMCD对MFE降维后的特征向量实现宽频振荡的分类检测。通过仿真和实测数据分析，结果表明，所提方法的宽频振荡分类检测准确率比支持向量机(support vector machines, SVM)、BP神经网络方法的分类准确率更高，分类时间更短。 |
| 关键词: 宽频振荡分类 多尺度模糊熵 变分模态分解 变量预测模型 |
| DOI：10.19783/j.cnki.pspc.231191 |
| 投稿时间：2023-09-11修订日期：2024-02-15 |
| 基金项目:国家自然科学基金项目资助(61973072)；吉林省教育厅科学技术研究项目资助(JJKH20240144KJ) |
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| Broadband oscillation classification based on AVMD multi-scale fuzzy entropy and the VPMCD algorithm |
| ZHAO Yan1,PAN Yi2,LI Yabo3,NIE Yonghui4 |
| (1. School of Power Transmission and Distribution Technology, Northeast Electric Power University, Jilin 132012, China;
2. School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China; 3. Hangzhou Fuyang
Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310000, China;
4. Academic Administration Office, Northeast Electric Power University, Jilin 132012, China) |
| Abstract: |
| The broadband oscillation of a power system has the characteristics of wide frequency domain, nonlinearity and time variation. This entails higher requirements for the accuracy and rapidity of the oscillation classification. Therefore, a new broadband oscillation classification method based on adaptive variational mode decomposition (AVMD), multi-scale fuzzy entropy (MFE), and variable prediction model-based class discriminate (VPMCD) is proposed. First, AVMD is performed on the broadband oscillation signal to obtain the intrinsic mode functions (IMFS). Then, MFE is introduced to describe the time-domain features of the IMFS, and dimensionality reduction of the eigenvectors constructed by the IMFS is realized. Finally, the VPMCD is used to realize the classification detection of broadband oscillations on the feature vectors after MFE dimensionality reduction. The simulation and measured data analysis results show that the proposed method has higher detection accuracy and shorter classification time for broadband oscillation than that of the support vector machines (SVM) algorithm and the BP neural network algorithm. |
| Key words: broadband oscillation classification multi-scale fuzzy entropy variational mode decomposition variable predictive model |
