Abnormal Node Detection in Distribution Network Based on Terminal Data Mining in Dynamic Topology
Abnormal node detection is used to find the nodes where measured value of electrical parameters such as power and voltage are different from actual value resulted from the anomalies like electricity theft and equipment failure.It is the basis of distribution network situational awareness,operation and management.In this regard,an abnormal detection method is proposed based on terminal data mining.Firstly,by mining power flow constraint relationship between node voltage and power,an anomaly detection system under power flow mapping framework is constructed.And nodes that do not conform to the constraint relationship are judged as anomalies.Secondly,hedge backpropagation and residual neural network is constructed to fit the power flow constraint relationship,which can realize fast and accurate voltage calculation when topology and parameter information of distribution network are missing.Thirdly,the concept of model backtracking and spanning tree algorithm are introduced to identify the common topological structure changes in distribution network.The change information is fed back to the main model to realize the application of the detection in dynamic topology.Finally,the validity of the proposed method is verified by using practical cases and simulation experiments.
abnormal node detectionpower flow mappingterminal data drivendistribution network topology
NETL
NSTL
万方数据
