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Abstract
Aiming at the fact that the traditional method cannot quickly and accurately judge the power system transient voltage stability, and the deep neural network method needs many sample data sets and has a large number of hyperparameters, a power system transient voltage stability assessment method based on deep forest (gcForest) is proposed. gcForest effectively extracts features through ensemble learning, multi-layer feature transformation, and dynamically adjusting the number of model layers. Finally, this method is applied to transient voltage stability assessment of an electrolytic aluminium load-intensive area in Guangxi Power Grid. The results show that the method has high accuracy, the feasibility and effectiveness of this method are verified. This method can assist dispatchers to judge the transient voltage stability of the power grid.
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Details
1 Guangxi Power Grid Electrical Power Research Institute; Supported by the Science and Technology Project of Guangxi Power Grid (Contract No(0470002019030101DL00006)).
2 State Key Laboratory of HVDC, Electric Power Research Institute, CSG, China; Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System; CSG Key Laboratory of Power System Simulation; Supported by the Science and Technology Project of Guangxi Power Grid (Contract No(0470002019030101DL00006)).