@article{Wang2026, 
author = {Jiazhou Wang and Shuobin Wang and Xinhua Yan and Yuhang Zhu and Ziteng He and Qing-Shan Jia},
title = {Surrogate-assisted EMT-based transient stability enhancement for renewable energy power systems},
year = {2026},
journal = {Cybernetics and Intelligence},
keywords = {Renewable-dominated power systems, electromagnetic transient simulation, artificial intelligence, fault ride-through, oscillation diagnosis},
url = {https://www.sciopen.com/article/10.26599/CAI.2026.9390017},
doi = {10.26599/CAI.2026.9390017},
abstract = {Electromagnetic transient (EMT) simulation is essential for transient stability analysis in renewable energy power systems, but its high computational cost limits large-scale scenario screening, control tuning, and rapid post-event assessment. This paper presents a surrogate-assisted EMT-based approach to enhance the efficiency of transient stability studies, where data-driven surrogate models are used to assist, rather than replace, EMT simulations. Three representative EMT-based tasks are investigated. For pre-event analysis, voltage-observable surrogate models are used to approximate EMT-derived severity indicators and support efficient vulnerability ranking. For in-event analysis, surrogate-assisted ordinal optimization is employed to tune converter control parameters to improve fault ride-through performance. For post-event analysis, stability-diagnosis models are developed using externally measurable voltage waveforms to detect instability and identify parameter interactions that influence it. Case studies on a representative renewable energy power system show that the proposed approach substantially reduces the number of EMT simulations while preserving key nonlinear transient characteristics relevant to stability assessment.}
}