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首页> 外文期刊>IEEE transactions on industrial informatics >Connectivity Verification in Distribution Systems Using Smart Meter Voltage Analytics: A Cloud-Edge Collaboration Approach
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Connectivity Verification in Distribution Systems Using Smart Meter Voltage Analytics: A Cloud-Edge Collaboration Approach

机译：使用智能仪表电压分析的分配系统中的连接验证：云边缘协作方法

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摘要

Distribution topology is oftentimes changed to cope with the development of local power load. Therefore, connectivity verification has become a critical task for optimal grid operation. In this article, a novel cloud-edge collaboration approach is presented to identify outlier users and correct connections. In this article, based on the smart meter voltage analytics, an affinity propagation clustering-based local outlier factor (AP-LOF) algorithm is proposed for the voltage outlier identification and verification of the edge transformer. Compared to traditional methods, it can effectively identify the outlier user groups with high internal voltage correlation. Besides, a recommendation mechanism is developed in the cloud center, which repositions the identified outlier users by coordinating the information exchange between the edge transformers and the cloud center. Numerical tests are conducted using the actual smart meter voltage data. The results show that the proposed AP-LOF algorithm exhibits a better performance, which is suitable for the identification of various outlier users. Compared to a centralized architecture, 66% savings in calculation time is achieved by the cloud-edge collaboration approach. It further demonstrates the effectiveness and practicability of the proposed method in terms of identification accuracy and verification efficiency.
机译：分销拓扑结构通常改变为应对局部电力负荷的发展。因此，连接验证已成为最佳网格操作的关键任务。在本文中，提出了一种新颖的云边缘协作方法，以识别异常值用户和正确的连接。在本文中，基于智能仪表电压分析，提出了基于亲和传播聚类的本地异常因素（AP-LOF）算法，用于电压异常识别和边缘变压器的验证。与传统方法相比，它可以有效地识别具有高内部电压相关性的异常用户组。此外，在云中心开发了推荐机制，通过协调边缘变压器和云中心之间的信息交换来重新定位所识别的异常值用户。使用实际智能仪表电压数据进行数值测试。结果表明，所提出的AP-LOF算法表现出更好的性能，适用于识别各种异常用户。与集中式架构相比，通过云边缘协作方法实现计算时间的66％。它进一步展示了所提出的方法在识别准确性和验证效率方面的有效性和实用性。

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