基于数据驱动的低压台区拓扑识别技术研究

该文基于数据驱动的方法,提出一种低压台区拓扑识别技术,以解决传统手动勘测和模型匹配方法存在的效率低、成本高等问题。采用智能电表等物联网设备获取低压电网中的实时用电数据。然后,通过数据挖掘和机器学习技术,分析电流、电压、功率等多源数据,提取潜在的拓扑信息。在此基础上,结合拓扑分析算法,建立低压台区拓扑模型。通过对实际低压台区的案例研究,验证所提出方法的有效性和鲁棒性。与传统方法相比,该技术能够更加实时、准确地识别低压台区的拓扑结构,为电力系统的监测、运行和维护提供新的手段。该研究对于推动电力系统智能化发展,提高配电网的可靠性和可控性具有重要的实际应用意义。

Based on data-driven methods,this paper proposes a low-voltage substation topology recognition technology to solve the problems of low efficiency and high cost in traditional manual survey and model matching methods,and it is proposed to use smart meters and other IoT devices to obtain real-time electricity consumption data in the low-voltage power grid.Then,through data mining and machine learning techniques,analyze multi-source data such as current,voltage,and power to extract potential topology information.On this basis,combined with topology analysis algorithms,a low-voltage substation topology model was established.The effectiveness and robustness of the proposed method have been verified through case studies of actual low-voltage substation areas.Compared with traditional methods,this technology can more real-time and accurately identify the topology structure of low-voltage substations,providing a new means for monitoring,operation,and maintenance of power systems.This study has important practical application significance for promoting the intelligent development of power systems and improving the reliability and controllability of distribution networks.