Data-driven identification of household-transformer relationships in power distribution networks using Hausdorff similarity assessment
Precisely identifying the household-transformer relationship is of significant importance for both the stability of the power system and the quality of customer electricity consumption. However, the complex network structures and frequent reconfigurations may lead to inaccurate records of household-...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1233827/full |
| _version_ | 1797752191692308480 |
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| author | Yuru Zhu Yuru Zhu Xiu Yang Haitao Yan |
| author_facet | Yuru Zhu Yuru Zhu Xiu Yang Haitao Yan |
| author_sort | Yuru Zhu |
| collection | DOAJ |
| description | Precisely identifying the household-transformer relationship is of significant importance for both the stability of the power system and the quality of customer electricity consumption. However, the complex network structures and frequent reconfigurations may lead to inaccurate records of household-transformer relationships. In this paper, a novel data-driven similarity assessment solution is proposed to enhance the accuracy and scalability of identifying household-transformer relationships. Initially, a data processing method based on dynamic temporal regularization with sliding windows is employed to optimize dataset quality as well as enhance the efficiency of data processing. Then, a two-stage solution is proposed for identifying the household-transformer relationship. The first stage involves initial normalized clustering based on the basic information of power distribution substations, while the second stage assesses the similarity between households and transformer operational states based on Hausdorff distance. The superior performance of the proposed method is extensively assessed through real historical datasets, compared to benchmarks. |
| first_indexed | 2024-03-12T16:59:36Z |
| format | Article |
| id | doaj.art-48710b4722b342789b5e6b8bae034388 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-03-12T16:59:36Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-48710b4722b342789b5e6b8bae0343882023-08-07T15:42:37ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-08-011110.3389/fenrg.2023.12338271233827Data-driven identification of household-transformer relationships in power distribution networks using Hausdorff similarity assessmentYuru Zhu0Yuru Zhu1Xiu Yang2Haitao Yan3Shanghai University of Electric Power, Shanghai, ChinaState Grid Jiangsu Electric Power Co., Ltd., Haian Power Supply Branch, Nantong, ChinaShanghai University of Electric Power, Shanghai, ChinaState Grid Jiangsu Electric Power Co., Ltd., Haian Power Supply Branch, Nantong, ChinaPrecisely identifying the household-transformer relationship is of significant importance for both the stability of the power system and the quality of customer electricity consumption. However, the complex network structures and frequent reconfigurations may lead to inaccurate records of household-transformer relationships. In this paper, a novel data-driven similarity assessment solution is proposed to enhance the accuracy and scalability of identifying household-transformer relationships. Initially, a data processing method based on dynamic temporal regularization with sliding windows is employed to optimize dataset quality as well as enhance the efficiency of data processing. Then, a two-stage solution is proposed for identifying the household-transformer relationship. The first stage involves initial normalized clustering based on the basic information of power distribution substations, while the second stage assesses the similarity between households and transformer operational states based on Hausdorff distance. The superior performance of the proposed method is extensively assessed through real historical datasets, compared to benchmarks.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1233827/fullhousehold-transformer relationship identificationlow-voltage distribution networkHausdorff distancedata qualityclustering algorithms |
| spellingShingle | Yuru Zhu Yuru Zhu Xiu Yang Haitao Yan Data-driven identification of household-transformer relationships in power distribution networks using Hausdorff similarity assessment Frontiers in Energy Research household-transformer relationship identification low-voltage distribution network Hausdorff distance data quality clustering algorithms |
| title | Data-driven identification of household-transformer relationships in power distribution networks using Hausdorff similarity assessment |
| title_full | Data-driven identification of household-transformer relationships in power distribution networks using Hausdorff similarity assessment |
| title_fullStr | Data-driven identification of household-transformer relationships in power distribution networks using Hausdorff similarity assessment |
| title_full_unstemmed | Data-driven identification of household-transformer relationships in power distribution networks using Hausdorff similarity assessment |
| title_short | Data-driven identification of household-transformer relationships in power distribution networks using Hausdorff similarity assessment |
| title_sort | data driven identification of household transformer relationships in power distribution networks using hausdorff similarity assessment |
| topic | household-transformer relationship identification low-voltage distribution network Hausdorff distance data quality clustering algorithms |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1233827/full |
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