Power Load Classification Based on Feature Weighted Fuzzy Clustering
The load classification of power users provides basic guidance for the research of power system planning, load forecasting, and time-of-use electricity price. In this paper, the variational modal decomposition(VMD) and fuzzy C-means clustering algorithm(FCM) are used for power load classification. B...
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2022-06-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202006320 |
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| author | Zongbiao MA Su'an XU Shaobin ZHU Jing WANG |
| author_facet | Zongbiao MA Su'an XU Shaobin ZHU Jing WANG |
| author_sort | Zongbiao MA |
| collection | DOAJ |
| description | The load classification of power users provides basic guidance for the research of power system planning, load forecasting, and time-of-use electricity price. In this paper, the variational modal decomposition(VMD) and fuzzy C-means clustering algorithm(FCM) are used for power load classification. Based on the unique feature weight of Euclidean distance in FCM, a feature weighting based VMD-FCM clustering algorithm is proposed using the feature weighting based fuzzy clustering method. According to the measured load data of the power grid, the VMD method can effectively decompose the inherent modality of the data, and the introduced FCM-based weight coefficient significantly improves the algorithm's convergence speed and clustering accuracy. The clustering results show that the proposed VMD-FCM clustering method can effectively distinguish different load types and has practical application values, thereby providing guidance for the design and planning of the power system. |
| format | Article |
| id | doaj-art-8868c3687b6e4f7c81a6f896a7c814d8 |
| institution | OA Journals |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2022-06-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-8868c3687b6e4f7c81a6f896a7c814d82025-08-20T02:04:29ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492022-06-01556253210.11930/j.issn.1004-9649.202006320zgdl-54-12-mazongbiaoPower Load Classification Based on Feature Weighted Fuzzy ClusteringZongbiao MA0Su'an XU1Shaobin ZHU2Jing WANG3College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, ChinaCollege of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, ChinaCollege of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, ChinaCollege of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, ChinaThe load classification of power users provides basic guidance for the research of power system planning, load forecasting, and time-of-use electricity price. In this paper, the variational modal decomposition(VMD) and fuzzy C-means clustering algorithm(FCM) are used for power load classification. Based on the unique feature weight of Euclidean distance in FCM, a feature weighting based VMD-FCM clustering algorithm is proposed using the feature weighting based fuzzy clustering method. According to the measured load data of the power grid, the VMD method can effectively decompose the inherent modality of the data, and the introduced FCM-based weight coefficient significantly improves the algorithm's convergence speed and clustering accuracy. The clustering results show that the proposed VMD-FCM clustering method can effectively distinguish different load types and has practical application values, thereby providing guidance for the design and planning of the power system.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202006320load classificationfuzzy clusteringvariational mode decompositionfeature weightingload characteristic curve |
| spellingShingle | Zongbiao MA Su'an XU Shaobin ZHU Jing WANG Power Load Classification Based on Feature Weighted Fuzzy Clustering Zhongguo dianli load classification fuzzy clustering variational mode decomposition feature weighting load characteristic curve |
| title | Power Load Classification Based on Feature Weighted Fuzzy Clustering |
| title_full | Power Load Classification Based on Feature Weighted Fuzzy Clustering |
| title_fullStr | Power Load Classification Based on Feature Weighted Fuzzy Clustering |
| title_full_unstemmed | Power Load Classification Based on Feature Weighted Fuzzy Clustering |
| title_short | Power Load Classification Based on Feature Weighted Fuzzy Clustering |
| title_sort | power load classification based on feature weighted fuzzy clustering |
| topic | load classification fuzzy clustering variational mode decomposition feature weighting load characteristic curve |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202006320 |
| work_keys_str_mv | AT zongbiaoma powerloadclassificationbasedonfeatureweightedfuzzyclustering AT suanxu powerloadclassificationbasedonfeatureweightedfuzzyclustering AT shaobinzhu powerloadclassificationbasedonfeatureweightedfuzzyclustering AT jingwang powerloadclassificationbasedonfeatureweightedfuzzyclustering |