Knowledge about the customers’ phase connections is strategic and critical for utility companies. It allows them to optimize maintenance and repair operations, implement load balancing, and detect losses, among other benefits. However, this information may be incomplete or outdated due to the undocumented changes in the Low Voltage network. Several methods have been proposed to estimate it. Methods based on data analysis stand out because they do not require costly specialized equipment. This work presents a new method for Phase Identification and Transformer Substation Detection for single-phase customers. Unlike previous approaches, we address the problem through a heuristic optimization, using an Evolutionary Algorithm based on Deterministic Crowding and correlation analysis on load measurements. The algorithm was designed to work with low penetration of smart meters and missing data, obtaining better results in shorter periods. The method was tested using both a public dataset and a dataset from Tucumán province, Argentina. We obtained an average accuracy above 95% on 21 days if almost 30% of the smart meters are available (200 customers in total). In contrast, only 5 days are required to reach the same accuracy if more than 80% of smart meters are available.

有关客户相连接的知识对于公用事业公司而言具有战略意义且至关重要。它使他们能够优化维护和维修操作、实施负载平衡和检测损失，以及其他好处。但是，由于低压网络中未记录的更改，此信息可能不完整或已过时。已经提出了几种方法来估计它。基于数据分析的方法脱颖而出，因为它们不需要昂贵的专业设备。这项工作为单相客户提供了一种新的相位识别和变电站检测方法。与以前的方法不同，我们通过启发式优化解决问题，使用基于确定性拥挤和负载测量相关性分析的进化算法。该算法旨在处理智能电表渗透率低和数据缺失的情况，在更短的时间内获得更好的结果。该方法使用公共数据集和阿根廷图库曼省的数据集进行了测试。如果近 30% 的智能电表可用（总共 200 个客户），我们在 21 天内获得了 95% 以上的平均准确度。相比之下，如果超过 80% 的智能电表可用，则只需 5 天即可达到相同的精度。