• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2022, Vol. 43 ›› Issue (2): 63-69.doi: 10.12204/j.issn.1000-7229.2022.02.008

• Smart Grid • Previous Articles     Next Articles

Multi-task Transient Stability Assessment Based on Sub-layer of Hybrid Gated Recurrent Unit

SUN Lixia(), PENG Jiajie(), TIAN Yiyun(), CHEN Xinling(), YUAN Huan()   

  1. College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
  • Received:2021-07-22 Online:2022-02-01 Published:2022-03-24
  • Contact: SUN Lixia E-mail:Lixiasun@hhu.edu.cn;921939688@qq.com;1450313963@qq.com;1803466824@qq.com;1829036536@qq.com
  • Supported by:
    National Natural Science Foundation of China(51707056)


In the related research of transient power-angle stability assessment and transient voltage stability assessment, independent assessment models are usually constructed, respectively, which hinders the information sharing among different tasks and wastes computing and storage resources. Considering the similarities and differences among different assessment tasks, in order to better realize the two assessments at the same time, a multi-task transient stability assessment model based on sub-layer of hybrid gated recurrent unit (GRU) is proposed in this paper. Because the power system transient process has obvious time-series characteristics, the GRU sub-layer is used to extract the time-series characteristics of the measured data efficiently. The gating mechanism is introduced into the model structure to automatically adjust the proportion of each sub-layer in constructing the feature representation of different evaluation tasks, which not only promotes the information sharing among different tasks, but also weakens the negative impact of the differences among different tasks on model training. The experimental results in IEEE 39-bus test system show that the model proposed in this paper has better evaluation performance and computing speed.

Key words: transient stability assessment, deep learning, multi-task learning, gated recurrent unit (GRU)

CLC Number: