Convolutional Neural Network-based Detection of Voltage Anomalies
Metering voltage anomalies directly affect the distribution line loss,and manually identification of voltage a-nomalies is inferior in efficiency.To address this problem,a voltage anomaly detection method based on convolutional neu-ral network was proposed.First the voltage data were unified into three-phase data and the voltage differences among the three phases were calculated and standardized.Then the three-phase voltage was scaled to preserve the size information of the voltage.Subsequently the standardized voltage differences and the scaled voltage data were combined into 6-channel da-ta.Finally,using the convolutional neural network,a voltage anomaly classification model was designed and trained,which could distinguish voltage anomaly states.Experimental results showed that the overall accuracy and recall rate of this method can reach 97％,indicating high accuracy.
electric meterconvolutional neural networkvoltage anomaly detectiondistribution line loss
万方数据
维普
