Transient stability assessment base on fusion of critical generator information and state plane images
Aiming at the problem that traditional machine learning methods based on power system time-series data are inadequate in mining the deep coupling information of the system,this paper introduces the physical information of instability mode as a priori knowledge into machine learning,and proposes a transient stability assessment method that incorporates the information of critical generators.The method calculates the contribution of each generator's power flow to the line through the power flow tracing to derive the system critical generator weights.According to the morphology,the state plane trajectory images are feature-enhanced according to the key generator weights.Simulation results under IEEE 39-bus system and IEEE 145-bus system show that the proposed method has better evaluation performance than the traditional evaluation method;the constructed state plane image samples have smaller occupation space and better classification performance than the traditional time series image samples.
万方数据
维普
