| 引用本文: | 徐明昕,赵 健,王小宇,等.基于电压聚类和关联卷积的配电网户变关系识别方法[J].电力系统保护与控制,2022,50(4):103-111.[点击复制] |
| XU Mingxin,ZHAO Jian,WANG Xiaoyu,et al.Transformer-customer identification method for a low-voltage distribution networkbased on voltage clustering and incidence convolution[J].Power System Protection and Control,2022,50(4):103-111[点击复制] |
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| 摘要: |
| 准确的低压配电网户变关系是电力营销管理和台区线损治理的重要基础，传统的户变关系识别方法排查成本高、识别效果欠佳，无法适用于规模日趋庞大的低压配电网。在此背景下，提出了一种基于智能电表量测数据和用户档案信息的低压配电网户变关系识别方法。首先利用用户地理位置信息实现邻近用户的初步合并，再基于GMM聚类算法对电压时序数据进行聚类划分，用户划分结果作为下一步的迭代初值。然后基于能量供需平衡建立配变与用户的关联卷积识别模型实现低压配电台区户变关系的辨识。最后，在实际的低压配电系统中验证了该方法在提升户变关系识别效率和准确率等方面具有显著优势，具备一定的实践应用价值和工程指导作用。 |
| 关键词: 低压配电台区 户变关系 电压相关性 能量供需平衡 聚类 |
| DOI：DOI: 10.19783/j.cnki.pspc.210618 |
| 投稿时间：2021-05-23修订日期：2021-06-29 |
| 基金项目:国家自然科学基金资助项目(51907114)；上海市科学技术委员会“扬帆计划”(19YF1416900)；上海市教育发展基金会和上海市教育委员会“曙光计划”(18SG50) |
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| Transformer-customer identification method for a low-voltage distribution networkbased on voltage clustering and incidence convolution |
| XU Mingxin,ZHAO Jian,WANG Xiaoyu,LI Liang,XUAN Yi,XU Xianghai |
| (1. College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China;
2. State Grid Zhejiang Electric Power Company, Hangzhou 310014, China) |
| Abstract: |
| Having an accurate transformer-customer relationship is key for power marketing management and loss management. The traditional identification method of transformer-customer relationship has high cost and is poor in performance, and cannot be applied to the increasingly large-scale low-voltage distribution network. This paper proposes a method to identify the transformer-customer relationship in a low-voltage distribution network. First, the geographic location information of customers is used to realize the initial merging of neighboring customers. Then, based on a Gaussian mixed model (GMM) clustering algorithm, voltage time series data are clustered and divided. The customer segment result is used as the next iteration initial value. Finally, an incidence convolution identification model based on the balance of energy supply and demand is established to identify the connection relationship between the transformers and customers. The method proposed in this paper is studied in an actual low-voltage distribution network. The method has significant advantages in improving the efficiency and accuracy of transformer-customer relationship identification, and has practical application value and offers engineering guidance.
This work is supported by the National Natural Science Foundation of China (No. 51907114). |
| Key words: low-voltage distribution network transformer-customer relationship voltage correlation energy conservation clustering algorithm |
