Surrogate-assisted EMT-based transient stability enhancement for renewable energy power systems

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.