@article{Chen2026, 
author = {Tingwei Chen and Kai Sun},
title = {A comprehensive survey on generative AI for power systems: Methods, applications, and emerging trends},
year = {2026},
journal = {Cybernetics and Intelligence},
keywords = {power system, GAN, diffusion model, LLM, GenAI, VAE},
url = {https://www.sciopen.com/article/10.26599/CAI.2026.9390018},
doi = {10.26599/CAI.2026.9390018},
abstract = {The increasing complexity of modern power systems has created a strong demand for advanced data-driven tools. Generative Artificial Intelligence (GenAI) has been applied to a range of problems, including power flow analysis, stability analysis, fault diagnosis, and power system planning and operation. Recent studies have explored Generative Adversarial Network (GAN)-, Variational Autoencoder (VAE)-, diffusion-, and Large Language Model (LLM)-based approaches across these domains, but a systematic and unified review remains lacking. This survey fills that gap with a structured overview of GenAI architectures and their applications in power flow analysis, stability analysis, fault diagnosis, and planning and operation. Three principal findings emerge from this analysis. Tier 1 (established): In data-centric support tasks such as scenario generation, data augmentation, measurement reconstruction, and natural-language-to-model interfaces, GenAI provides consistent advantages over conventional methods. Tier 2 (conditional): In core analytical tasks such as classical transient stability assessment, AC power flow approximation, and component-level fault diagnosis, GenAI achieves competitive performance. Physical constraint enforcement and cross-system generalization, however, remain open problems that condition practical adoption. Tier 3 (not yet application-ready): Safety-critical autonomous real-time grid control and analysis of converter-dominated wideband dynamics remain out of reach for current GenAI methods. Scarce domain-specific data, the absence of formal feasibility guarantees, and strict latency requirements combine to form a fundamental barrier. These findings show what GenAI may or may not yet contribute to power systems and motivate the challenges and future directions outlined in the final section.}
}