Convolutional neural network‐based power system frequency security assessment

Abstract

Abstract 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.

Keywords

• error analysis
• measurement errors
• power engineering computing
• power generation planning
• power grids
• power system security