| A load classification framework based on VMD and singular value energy difference spectrum | |
| Chen, Shuo1; Guo, Kunya1; Zeng, Peng2; Lv, Xunming1; Jia, Zhida1; Yang JY(杨俊友)1 | |
| Department | 工业控制网络与系统研究室 |
| Conference Name | 3rd IEEE International Conference on Energy Internet, ICEI 2019 |
| Conference Date | May 27-31, 2019 |
| Conference Place | Nanjing, China |
| Author of Source | Global Energy Interconnection Research Institute ; North China Electric Power University |
| Source Publication | Proceedings - IEEE International Conference on Energy Internet, ICEI 2019 |
| Publisher | IEEE |
| Publication Place | New York |
| 2019 | |
| Pages | 398-402 |
| Indexed By | EI |
| EI Accession number | 20193607392979 |
| Contribution Rank | 2 |
| ISSN | 978-1-7281-1493-4 |
| Keyword | VMD energy difference spectrum load classification compactness |
| Abstract | High load data dimension and insufficient sample characteristics challenge the load clustering accuracy. For this challenge, this paper proposes a load analysis method based on variational mode decomposition (VMD) and the energy difference spectrum of singular value (EDSSV). Firstly, the load data is decomposed by VMD algorithm. Simultaneously, the lowest frequency intrinsic function in the decomposition results is selected for EDSSV. The decomposition manifests load sample characteristics and reduces the load data dimension. Furthermore, the singular value is used to obtain an energy difference spectrum curve by EDSSV, which converts curve features to energy features and reduces the amount of data. The k-means clustering result of the original sample and the result of the energy difference spectrum curve are compared through compactness index. Compared with the k-means method, the result of clustering index CP with the proposed method is reduced by 0.0627, which shows that the clustering accuracy is improved. Also, the load data dimension is reduced by about 80%. |
| Language | 英语 |
| Document Type | 会议论文 |
| Identifier | http://ir.sia.cn/handle/173321/25617 |
| Collection | 工业控制网络与系统研究室 |
| Corresponding Author | Chen, Shuo |
| Affiliation | 1.State Grid Liaoning Electric Power Co. Ltd., Shenyang, China 2.Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China |
| Recommended Citation GB/T 7714 | Chen, Shuo,Guo, Kunya,Zeng, Peng,et al. A load classification framework based on VMD and singular value energy difference spectrum[C]//Global Energy Interconnection Research Institute, North China Electric Power University. New York:IEEE,2019:398-402. |
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| A load classificatio（513KB） | 会议论文 | 开放获取 | CC BY-NC-SA | View Download | ||
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