A method for mining over-limit patterns of low voltage in users based on hierarchical affinity propagation clustering
Mining different over-limit patterns of the low voltage is very important to guide the management of low voltage issues in users. Due to the complexity and the ever-changing nature of voltage, the over-limit patterns of low voltage are always inherently unknown in users. A pattern mining method for...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Electric Power Engineering Technology
2025-01-01
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| Series: | 电力工程技术 |
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| Online Access: | https://www.epet-info.com/dlgcjsen/article/abstract/240911910 |
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| author | SHEN Shuhao ZHONG Qing XU Zhong WANG Gang LI Haifeng WANG Longjun |
| author_facet | SHEN Shuhao ZHONG Qing XU Zhong WANG Gang LI Haifeng WANG Longjun |
| author_sort | SHEN Shuhao |
| collection | DOAJ |
| description | Mining different over-limit patterns of the low voltage is very important to guide the management of low voltage issues in users. Due to the complexity and the ever-changing nature of voltage, the over-limit patterns of low voltage are always inherently unknown in users. A pattern mining method for low voltage in users based on hierarchical affinity propagation clustering (HAP) is proposed in this paper. Firstly, large-scale voltage data is clustered into several clusters using the HAP clustering algorithm, and these clusters are regarded as the different over-limit patterns of low voltage. Then, four indices are defined from two aspects of the duration and amplitude to characterize the features of the clusters. The features of the over-limit patterns are then drived by calculating the indices for each cluster. Finally, the proposed method is applied to a real dataset, effectively mining four over-limit patterns of low voltage. The characteristics of different patterns provide the important information for the supervision and analysis of low voltage issues in users, and the priorities of low voltage problems management in users can be well leveled. |
| format | Article |
| id | doaj-art-809e919fa59645ebb905f9a725c5bfd3 |
| institution | Kabale University |
| issn | 2096-3203 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Department of Electric Power Engineering Technology |
| record_format | Article |
| series | 电力工程技术 |
| spelling | doaj-art-809e919fa59645ebb905f9a725c5bfd32025-02-08T08:40:18ZzhoEditorial Department of Electric Power Engineering Technology电力工程技术2096-32032025-01-01441303810.12158/j.2096-3203.2025.01.004240911910A method for mining over-limit patterns of low voltage in users based on hierarchical affinity propagation clusteringSHEN Shuhao0ZHONG Qing1XU Zhong2WANG Gang3LI Haifeng4WANG Longjun5School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, ChinaSchool of Electric Power Engineering, South China University of Technology, Guangzhou 510640, ChinaGuangzhou Power Supply Bureau Co., Ltd., Guangzhou 510600, ChinaSchool of Electric Power Engineering, South China University of Technology, Guangzhou 510640, ChinaSchool of Electric Power Engineering, South China University of Technology, Guangzhou 510640, ChinaSchool of Electric Power Engineering, South China University of Technology, Guangzhou 510640, ChinaMining different over-limit patterns of the low voltage is very important to guide the management of low voltage issues in users. Due to the complexity and the ever-changing nature of voltage, the over-limit patterns of low voltage are always inherently unknown in users. A pattern mining method for low voltage in users based on hierarchical affinity propagation clustering (HAP) is proposed in this paper. Firstly, large-scale voltage data is clustered into several clusters using the HAP clustering algorithm, and these clusters are regarded as the different over-limit patterns of low voltage. Then, four indices are defined from two aspects of the duration and amplitude to characterize the features of the clusters. The features of the over-limit patterns are then drived by calculating the indices for each cluster. Finally, the proposed method is applied to a real dataset, effectively mining four over-limit patterns of low voltage. The characteristics of different patterns provide the important information for the supervision and analysis of low voltage issues in users, and the priorities of low voltage problems management in users can be well leveled.https://www.epet-info.com/dlgcjsen/article/abstract/240911910low voltage usershierarchical affinity propagation (hap) clusteringover-limit patterns of low voltageover-limit durationover-limit voltage amplitudemanagement priority |
| spellingShingle | SHEN Shuhao ZHONG Qing XU Zhong WANG Gang LI Haifeng WANG Longjun A method for mining over-limit patterns of low voltage in users based on hierarchical affinity propagation clustering 电力工程技术 low voltage users hierarchical affinity propagation (hap) clustering over-limit patterns of low voltage over-limit duration over-limit voltage amplitude management priority |
| title | A method for mining over-limit patterns of low voltage in users based on hierarchical affinity propagation clustering |
| title_full | A method for mining over-limit patterns of low voltage in users based on hierarchical affinity propagation clustering |
| title_fullStr | A method for mining over-limit patterns of low voltage in users based on hierarchical affinity propagation clustering |
| title_full_unstemmed | A method for mining over-limit patterns of low voltage in users based on hierarchical affinity propagation clustering |
| title_short | A method for mining over-limit patterns of low voltage in users based on hierarchical affinity propagation clustering |
| title_sort | method for mining over limit patterns of low voltage in users based on hierarchical affinity propagation clustering |
| topic | low voltage users hierarchical affinity propagation (hap) clustering over-limit patterns of low voltage over-limit duration over-limit voltage amplitude management priority |
| url | https://www.epet-info.com/dlgcjsen/article/abstract/240911910 |
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