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eess.SP2026
Flow-PIN: A Two-Stage Power-Flow-Guided Method for System-Wide Multivariate Profile Inpainting in Distribution Networks
Zhenghao Zhou, Yiyan Li, Yike Guo +3
High-quality system measurement data is critical for power distribution system operation. As deep generative models (e.g., GAN, Diffusion, etc.) have been widely studied to solve t…
eess.SY2026
A Physics-guided Fine-tuned LLM-based Framework for Customized Power Distribution System Feeder Generation
Zhenghao Zhou, Yiyan Li, Tao Xu +3
Power distribution system feeder models (e.g., IEEE 33-bus system, IEEE 13-bus system, etc.) are cornerstones for conducting power distribution system studies. As real-world feeder…
eess.SP2026
An LLM-Enabled Frequency-Aware Flow Diffusion Model for Natural-Language-Guided Power System Scenario Generation
Zhenghao Zhou, Yiyan Li, Fei Xie +5
Diverse and controllable scenario generation (e.g., wind, solar, load, etc.) is critical for robust power system planning and operation. As AI-based scenario generation methods are…