Research on Abnormal Detection of Monitoring Information of Power Transmission and Transformation Equipment in the Big Data Environment
In order to effectively address the challenges posed by non-linear relationships in detecting abnormal monitoring information of power transmission and transformation equipment,a method for detecting abnormal monitoring information of power transmission and transformation equipment based on clustering and separation of information features is proposed in the context of big data.Adopting the method of clustering to separate information features,clustering separates the time-domain,frequency-domain,and spatial features of information,and characterizes the monitoring data of power transmission and transformation equipment.Establish a first-order fitting model to fit and process the state parameters of power transmission and transformation equipment,and associate the information features and state parameters obtained through clustering and separation.Implement abnormal detection of monitoring information for power transmission and transformation equipment based on the weight values between features and state parameters.The experimental results show that the outlier probability obtained by applying the proposed method is less than 0.103,which meets the requirements of anomaly detection in monitoring information of power transmission and transformation equipment.
power transmission and transformation equipmentBig data environmentcluster separation information characteristicsweight valueand monitoring information exception
国家科技期刊平台
NSTL
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