Intelligent anomaly detection method for three-phase line loss in medium and low-voltage distribution networks
PAN Wei
ZHANG Tao
ZHANG Zhuo
Abstract：[Objective]In the operation and management of power system,the medium and low-voltage distribution network serves as a key link between power sources and users.Its operating efficiency and stability are directly related to the safety and reliability of the entire power system.Three-phase line loss,as an important indicator of the distribution network's operational efficiency,not only reflects the energy loss during the power transmission process but also directly affects the voltage quality,power consumption,and safe operation of the power grid.However,the three-phase line loss data in the distribution network exhibit complex distribution characteristics,such as multi-modal and asymmetric features.During dynamic changes,it is difficult to accurately capture the inherent patterns and structures in the data,which reduces the accuracy of anomaly detection.Therefore,this paper proposed an intelligent method for the anomaly detection of three-phase line loss in medium and low-voltage distribution networks.[Methods]During the data collection process of the distribution network's three-phase line loss,the data can be influenced by multiple factors such as electromagnetic interference and equipment errors,leading to the presence of significant noise and outliers.These noises not only reduce the signal-to-noise ratio but also obscure the true features of the data,thereby affecting the accuracy of subsequent analysis.Therefore,a radial basis function(RBF)neural network was used to extract features from the collected three-phase line loss data.By performing nonlinear mapping of the input data,the method effectively suppressed the interference from noise,enhancing the signal-to-noise ratio.The preprocessed data were then normalized,which further improved the completeness and accuracy of the data collection.A loop current-based method was employed to decompose the circuits in the distribution network into multiple independent loops.In each loop,the real and imaginary parts of the voltage and current were calculated.By analyzing the temporal and phase variations of these values in detail,the operating status of the circuit was thoroughly understood,and potential anomaly patterns were accurately identified.Based on the real and imaginary part values of the voltage and current on the three-phase branch circuits,a Gaussian mixture distribution model was constructed.This model used multiple Gaussian distributions to describe the complex distribution features of the three-phase line loss data,allowing for more accurate capture of the inherent patterns and structures in the data.The maximum expectation algorithm was then used to fit the normalized line loss rate and construct a hybrid Gaussian model consisting of multiple Gaussian mixture distributions.The likelihood probability function of the eigenvector was calculated,and based on a preset probability threshold,the data were determined to be anomalous or normal.If the likelihood probability was below the threshold,it was classified as anomalous;otherwise,it was considered normal.This approach enabled the identification of line loss anomaly data.[Results]Experimental results show that the proposed method can accurately identify three-phase line loss buses,reducing the risk of misjudgment and missed detections.[Conclusions]This method can promptly detect and address faults in the distribution network,which is of significant importance in improving the operational efficiency and reliability of power systems.
Keywords：medium and low-voltage distribution networkthree-phase line lossGaussian mixture distribution functionabnormal detectionreflux circuit methodnoise interferenceimaginary part valueline loss rate
Publication Date：2025-11-25
Online Publishing Date：2025-12-29(First online date of this platform, not the publication date of the document)
Pages：8( 721-728 )