Abnormal identification method of operation and maintenance signal of distribution network intelligent fusion terminal based on big data
The intelligent integration of distribution network terminal operation and maintenance signals is diverse,with different numerical ranges and fluctuation characteristics,which increases the difficulty of identification.To improve accuracy,this article adopts naive Bayesian algorithm for big data analysis.By preprocessing the operation and maintenance signal,including noise filtering and feature extraction,the signal is transformed into a form suitable for anomaly identification.Based on Bayesian theory and independent assumption of feature conditions,parallelized classification of signals is carried out to identify abnormal operation and maintenance signals.The experimental results show that the correlation coefficient of the design method is as high as 0.968,which proves its significant identification effect and provides strong support for the operation and maintenance management of intelligent integration terminals in the distribution network.
big dataintelligent fusion terminal of distribution networkoperation and maintenance signalabnormal identification