Power Grid Fault Diagnosis and Early Warning Method Based on Artificial Intelligence Technology
In the fault diagnosis of smart power grid,it is difficult to accurately locate the fault area due to the variety of uncer-tain information and a large number of equipment parameters.Based on the traditional Bayesian network algorithm,the princi-pal component analysis method(PCA)is introduced to realize the fault location and early warning.Using PCA,this paper ex-tracts the characteristic index of the equipment components,and constructs the set of potential fault components.The Bayesian method is adopted to integrate electrical parameters,state variables and switch signals and improve the information utilization efficiency of multiple data sources,so as to accurately identify the fault area.Taking a typical power grid as an example to sim-ulate the typical faults of the power grid and locate the fault area.The experimental results show that the algorithm determining the fault probability of the components is 0.886 72,which can accurately locate the area where the power grid fault occurs,and will have a good application prospect in the actual power system fault diagnosis.
power grid faultBayesian judgmentPCAfault location
万方数据
维普
