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

作　　者：Shan Cheng Zihao Yu Ye Liu Xianwang Zuo 

机构地区：[1]College of Electrical Engineering and New Energy,China Three Gorges University,Yichang 443002,Hubei,China

出　　处：《Protection and Control of Modern Power Systems》2022年第1期586-601,共16页现代电力系统保护与控制（英文）

基　　金：funded by the National Natural Science Foundation of China under Grant No.51607105.

摘　　要：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)func-tion 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.

关 键 词：Transient stability assessment MP CNN+GRU Sample misclassification Improved focal loss function 

分 类 号：TM712[电气工程—电力系统及自动化]