Fault detection method of electric energy meter based on improved clustering algorithm
The traditional fault monitoring method for electric energy metering devices is periodic on-site verification,which has problems such as extensive and non-standard operation management,low work efficiency,difficulty in fault detection and trouble-shooting,and poor monitoring timeliness.In order to solve the problem of low accuracy in fault detection of electric energy metering,an improved clustering algorithm based fault detection method for electric energy metering meters is proposed.This method adopts a clustering algorithm improved by quantum mechanics,combined with the autoregressive integral moving average algorithm to clean the outliers in the data and classify the data.The classified data is input into the Bayesian B-spline fault detection algorithm,and the fault detection of the electric energy meter is completed by calculating the fault rate.The experimental results show that the AUC area of this method is the largest,indicating high detection accuracy,and the number of detected fault points is consistent with the actual situation.The detection time is within 5 seconds,indicating that this method improves both detection accuracy and detection efficien-cy.
data cleaningquantum mechanismdifference measurementbayesian B-spline algorithmweber distributionclustering algorithm
万方数据
维普
