基于大数据分析的高比例分布式电源配电网故障定位方法

Fault Location Method of High-Proportion Distributed Power Distribution Network Based on Big Data Analysis

文章提出一种基于大数据分析的高比例分布式电源配电网故障定位方法,旨在解决传统电力系统中故障定位效率低下、人工判断主观性强等问题。通过分布式电源配电网中的传感器节点采集实时数据,利用大数据分析技术进行数据处理和特征提取。基于机器学习算法建立故障定位模型,使用训练集对模型进行训练和优化。最后,使用测试数据集对模型进行验证,通过准确率、召回率和F1值等指标评估方法的性能。

This paper proposes a fault location method for high-proportion distributed power distribution network based on big data analysis,aiming at solving the problems of low efficiency of fault location and strong subjectivity of manual judgment in traditional power systems.Real-time data are collected by sensor nodes in the distributed power distribution network,and data processing and feature extraction are carried out by using big data analysis technology.The fault location model is established based on machine learning algorithm,and the model is trained and optimized by training set.Finally,the model is verified by test data sets,and the performance of the method is evaluated by accuracy,recall and F1 value.