异常用电识别特征库构建技术
Construction Technology of Abnormal Electricity Identification Feature Library

文章旨在介绍构建异常用电识别特征库的方法。首先，对典型用电负荷特征进行设计，包括峰值、谷值、平均值、功率因数等指标，并将其存储到数据库中。接着，对异常用电负荷的特征进行分析，如突变、周期性、持续时间等，并开发相应算法对其进行处理和提取，最终将得到的特征存入异常用电负荷特征库中。在异常用电识别特征库实现方面，利用机器学习技术，对所提取的异常用电负荷特征进行训练，并生成识别模型，以便快速准确地检测和识别异常用电。通过文章，可以更好地了解异常用电的特点和规律，为电力系统的安全运行提供可靠保障。
The purpose of this paper is to introduce the method of constructing abnormal electricity identi-fication feature library. Firstly, the typical power load characteristics are designed, including peak value, valley value, average value, power factor and other indicators, and stored in the database. Then, the characteristics of abnormal power load are analyzed, such as mutation, periodicity, duration, etc., and corresponding algorithms are developed to process and extract them. Finally, the obtained features are stored in the abnormal power load feature library. In the realization of the abnormal electricity identification feature library, the machine learning technology is used to train the extracted abnormal electricity load features and generate the recognition model, so as to quickly and accurately detect and identify abnormal electricity. Through this paper, we can better understand the characteristics and laws of abnormal electricity consumption, and provide reliable guarantee for the safe operation of power system.