Knowledge Graph-based Portrait Depiction Technology for Substation Equipment
Current state evaluation methods for power equipment mostly depend on expert experience determining the weights and model parameters,and the models are constructed based on idealized hypothesis,resulting in poor application effects.To solve this problem,this paper proposes knowledge graph-based substation equipment portraits.Firstly,it extracts the substation equipment information through analyzing the correlation between various data such as monitoring signals,direct transmission of alarms,protection information,grid currents,and online monitoring and the state of the equipment.Secondly,the data is extracted,cleaned and fused.Finally,the paper uses the knowledge graph to construct the structured semantic database for the substation equipment.Combining with the graph classification,it divides the extracted data into multi-dimensional attribute categories,and analyzes the characteristic of each equipment to construct a substation equipment portrait model.It also uses the fuzzy mathematics analytic hierarchy process to evaluate equipment state.The experimental data shows that the proposed method has higher data cleaning rate and lower fault leakage detection rate,which is conductive to effectively manage the power equipment.
substation equipmentportrait depictionknowledge mapmultidimensional data systemdata mining
万方数据
维普
