摘要
Abstract
In order to solve the problems of low recognition rate of electricity theft information and insufficient detection accuracy in the field of electricity theft detection in the current distribution station area,DBN deep learning is introduced to carry out high-precision anti electricity theft detection research in the low-voltage distribution station area.By making full use of the powerful ability of edge computing intelligent terminal,the rapid acquisition and preliminary processing of real-time electricity theft data in low-voltage distribution station area is realized.Through the well-designed adaptive weighted fusion algorithm,the multi-dimensional data from different electric energy metering devices are effectively integrated,which greatly enriches the data base for the analysis of electricity theft.In the stage of data processing and analysis,DBN deep learning model,with its powerful feature extraction and nonlinear mapping ability,carries out deep mining and intelligent recognition of the fused electricity theft information,significantly improving the accuracy and efficiency of electricity theft recognition.The experimental results show that compared with the traditional detection methods,the proposed detection method significantly improves the detection accuracy,realizes the accurate identification and timely response of complex electricity theft,and provides a strong technical support for the safe and stable operation of power system.关键词
DBN深度学习/配电台区/反窃电/高精度/低压Key words
DBN deep learning/distribution panel area/electricity anti-theft/high precision/low voltage分类
动力与电气工程
