Power system transient stability assessment based on the multiple paralleled convolutional neural network and gated recurrent unit

Abstract

Abstract In order to accurately evaluate power system stability in a timely manner after faults, and further improve the feature extraction ability of the model, this paper presents an improved transient stability assessment (TSA) method of CNN + GRU. This comprises a convolutional neural network (CNN) and gated recurrent unit (GRU). CNN has the feature extraction capability for a micro short-term time sequence, while GRU can extract characteristics contained in a macro long-term time sequence. The two are integrated to comprehensively extract the high-order features that are contained in a transient process. To overcome the difficulty of sample misclassification, a multiple parallel (MP) CNN + GRU, with multiple CNN + GRU connected in parallel, is created. Additionally, an improved focal loss (FL) function which can implement self-adaptive adjustment according to the neural network training is introduced to guide model training. Finally, the proposed methods are verified on the IEEE 39 and 145-bus systems. The simulation results indicate that the proposed methods have better TSA performance than other existing methods.

Keywords

• Transient stability assessment
• MP CNN + GRU
• Sample misclassification
• Improved focal loss function