Small disturbance stability evaluation method considering the saturation impact of synchronous machine
Currently,the small disturbance stability evaluation methods based on machine learning only use steady power flow data to build machine learning models.Therefore,the saturation characteristics of synchronous machine,an important factor affecting the analysis results of small disturbance stability,are not fully considered.Once the saturation characteristics of a synchronous machine change due to retrofit,replacement and other factors,the physical quantities reflecting the stability of small disturbance,such as characteristic values,minimum damping ratio,may also change whereas the corresponding analysis results based on power flow data remain unchanged.Thus,the stability of small disturbance is not accurately reflected.To address the issue,this paper proposes a small disturbance stability evaluation method with full considerations of the saturation impact of synchronous machines.The method integrates Convolutional Neural Network (CNN)and Graph Convolutional Neural Network (GCN)and then a model called CNN-GCN is built to improve the feature extraction ability of machine learning model.In this model,saturation coefficient of machine and steady-state power flow data are employed to predict the minimum damping ratio.The effectiveness and superiority of the proposed method are validated by the IEEE14-node system.
synchronous machine saturationsmall signal stabilitymachine learningfusion model
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