Invariant risk minimization based topological anomaly detection of distribution network with high proportion distributed photovoltaic
After high proportion distributed photovoltaic(PV)integrated into the distribution network,the fluc-tuation of its output leads to the deviation of end-user power data,failing to meet the requirements of inde-pendent and identically distributed data in machine learning,which results in slow convergence and low accuracy in topology anomaly detection methods based on learning.To address this,a topology anomaly detection method of distribution network with high-proportion distributed PV is proposed based on IRM(in-variant risk minimization)method.A distribution network data flow(DF)model based on adaptive online deep learning is constructed,utilizing historical measurement data from both users and distribution trans-formers to reflect the power flow mapping relationships between electrical quantities in the limited measure-ment points of the distribution network.Based on the IRM method,a DF model of distribution network is constructed,and an improved IRM-DF model is formed to reduce the impact of data distribution shifts caused by the integration of high proportion distributed PV on the original DF model,thereby improving the accuracy of the model.The IRM-DF model is used to replace the node network equation of distribution network,and the correlation matrix of each node is output.The isolation forest algorithm is used to filter out the anomalies in the matrix and determine the abnormal topology location.Taking the simulated low-voltage distribution network as an example,the accuracy and the effectiveness of the proposed method are validated.
distributed photovoltaicdistribution networkinvariant risk minimizationadaptive deep learningdata flow model
万方数据
维普
