Convolutional neural network-based power system frequency security assessment

Weak inertia characteristics of power systems with high penetrations of renewables have become a prominent problem for frequency security. To solve this problem, a convolutional neural network (CNN)-based deep learning approach is applied to realize rapid frequency security assessment (FSA). First, the time series frequency security feature is autonomously mined from the wide-area measurement data to serve as the input data. By doing so, the complex construction process of frequency security feature quantity is avoided. A deep learning structure is then used to establish a non-linear mapping relationship between time series features and frequency security indicators to realize end-to-end power system frequency security prediction. Next, the evaluation accuracy of the proposed approach is optimized by tuning the key parameters in the CNN-based evaluation model. Through data measurement error analysis and a wind penetration sensitivity study, the anti-interference performance of the proposed evaluation model is demonstrated. Finally, the effectiveness of the CNN-based FSA is verified by case studies of a modified 16-machine 68-node system and the China Southern Power Grid.