Transient Stability Assessment for Power System Based on One-dimensional Convolutional Neural Network

The process of a system encountering transient faults is evolving over time. And its time dimension information is difficult to be captured using a transient stability assessment method based on traditional machine learning, which limits the improvement of assessment performance. Focusing on the above problem, a transient stability assessment method based on one-dimensional convolutional neural network (1D-CNN) is proposed, which can specifically be used for automatically extracting sequential features in the transient process based on measurement data at the bottom, so as to achieve the target of accurately specifying the system transient stability process with the help of its unique one-dimensional convolution and pooling operation performance. A 1D-CNN model with four convolutional layers that is applicable for transient stability assessment is designed to implement the end-to-end 'sequential feature extraction + transient stability classification'. Moreover, the reliability of the assessment result is strengthened by adjusting key parameters of the model to improve the recall rate of the samples losing stability. According to the simulation experiment of New England 10-machine 39-bus test system, it shows that the proposed method can judge transient stability more accurately in a shorter responding time in comparison to the transient stability assessment method based on traditional machine learning, well satisfying the accuracy and rapidity required in the online transient stability assessment. © 2019 Automation of Electric Power Systems Press.