Power system transient stability assessment based on Powershap feature selection
To further improve the accuracy and reliability of transient stability assessment(TSA),a feature selection method(Powershap)based on the combination of statistics and Shapley values is proposed,and a power system transient stability assessment model is established.Firstly,the input feature set is constructed based on the steady-state components during the operation of the power system.Powershap is used to divide the dataset into multiple subsets for training,and key feature sets are selected.Then,multiple CatBoost models are trained using key feature sets and transient stability assessments are conduct to generate transient stability assessment models.Finally,simulation experiments are conducted on the New England 10-machine 39-node system and the New England 54-machine 118-node system with the addition of new energy generation,and evaluation results are provided.The experiments show that,in the 10-machine 39-node system in New England,using the Powershap feature selection method for classification can achieve an accuracy of 99.79%.On the improved New England 54-machine 118-node system,its accuracy can reach 99.49%,indicating that the method can effectively perform transient stability assessment of power systems.It is verified that the proposed TSA model has good robustness and generalization ability.
power systemtransient stability assessmentfeature selectionPowershapCatBoost