The traditional real-time load warning method for distribution transformers has problems such as low recall rate, low warning accuracy, and long warning time, which may lead to potential equipment failures or overload situations not being detected and dealt with in a timely manner, increasing the safety risk of transformer operation and potentially causing safety issues such as equipment damage, fire, or power outage. Therefore, a real-time early warning method of distribution transformer load considering meteorological factor data is designed. The meteorological factor data are collected by the light sensor, humidity sensor, temperature sensor and rainfall sensor, and the load data collection architecture is built by the load monitor, central master station and maintenance station to realize the load data collection of the distribution transformer. The K-nearest neighbor (KNN) method is used to process the missing values of the data, and the LOF algorithm is used to determine the local outliers and eliminate the outliers in the data set to achieve data cleaning. Considering the load loss, hot spot temperature and meteorological factors of the distribution transformer, an early warning model is built, and the cleaned data are input into the model to realize Real-time early warning of the distribution transformer load. The experimental results show that the recall rate of this method varies from 95% to 97%, the accuracy rate of early warning is always above 94%, and the maximum value of early warning time is 0.63s. Having good early warning ability.

传统的配电变压器负荷实时预警方法存在召回率低、预警准确率低、预警时间长等问题，可能导致潜在的设备故障或过载情况不能及时发现和处理，增加变压器运行的安全风险，可能导致设备损坏、火灾、停电等安全问题。因此，设计了一种考虑气象因素数据的配电变压器负荷实时预警方法。通过光传感器、湿度传感器、温度传感器和雨量传感器采集气象因素数据，通过负荷监测仪、中心主站和维护站构建负荷数据采集架构，实现配电变压器负荷数据采集。采用K近邻（KNN）方法处理数据的缺失值，利用LOF算法确定局部异常值，消除数据集中的异常值，实现数据清洗。综合考虑配电变压器负荷损耗、热点温度和气象因素，建立预警模型，将清洗后的数据输入到模型中，实现配电变压器负荷的实时预警。实验结果表明，该方法的召回率在95%~97%之间，预警准确率始终在94%以上，预警时间最大值为0.63s。具有良好的预警能力。