Hierarchical Deep Learning Machine for Power System Online Transient Stability Prediction

• Published in: IEEE transactions on power systems Vol. 35; no. 3; pp. 2399 - 2411
• Main Authors: Zhu, Lipeng, Hill, David J., Lu, Chao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Convolutional neural networks; Deep learning; Machine learning; On-line systems; phasor measurements; Power system stability; Regression models; Stability analysis; Thermal stability; trajectories; Trajectory; Transient analysis; Transient stability
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2019.2957377

This paper develops a hierarchical deep learning machine (HDLM) to efficiently achieve both quantitative and qualitative online transient stability prediction (TSP). For the sake of improving its online efficiency, multiple generators' fault-on trajectories as well as the two closest data-points in pre-/post-fault stages are acquired by PMUs to form its raw inputs. An anti-noise graphical transient characterization technique is tactfully designed to transform multiplex trajectories into 2-D images, within which system-wide transients are concisely described. Then, following the divide-and-conquer philosophy, the HDLM trains a two-level convolutional neural network (CNN) based regression model. With stability margin regressions hierarchically refined, it manages to perform reliable and adaptive online TSP almost immediately after fault clearance. Test results on the IEEE 39-bus test system and the real-world Guangdong Power Grid in South China demonstrate the HDLM's superior performances on both stability status and stability margin predictions.