7 citations · 9 across the 7 of their papers we have counts for
9 papers
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…
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…
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…
A Causal-Guided Multimodal Large Language Model for Generalized Power System Time-Series Data Analytics
Zhenghao Zhou, Yiyan Li, Xinjie Yu +6
Power system time series analytics is critical in understanding the system operation conditions and predicting the future trends. Despite the wide adoption of Artificial Intelligen…
Deep-Learning-based Frequency-Domain Watermarking for Energy System Time Series Data Asset Protection
Zhenghao Zhou, Yiyan Li, Xinjie Yu +4
Data has been regarded as a valuable asset with the fast development of artificial intelligence technologies. In this paper, we introduce deep-learning neural network-based frequen…
A Two-Stage AI-Powered Motif Mining Method for Efficient Power System Topological Analysis
Yiyan Li, Zhenghao Zhou, Jian Ping +3
Graph motif, defined as the microstructure that appears repeatedly in a large graph, reveals important topological characteristics of the large graph and has gained increasing atte…
A Glass-Box Deep-Learning Method for Electrical Energy System Modeling Based on Kolmogorov-Arnold Network
Zhenghao Zhou, Yiyan Li, Zelin Guo +2
Deep learning methods have been widely used as an end-to-end modeling strategy of electrical energy systems because of their conveniency and powerful pattern recognition capability…
A Neural-Network-Embedded Equivalent Circuit Model for Lithium-ion Battery State Estimation
Zelin Guo, Yiyan Li, Zheng Yan +1
Equivalent Circuit Model(ECM)has been widelyused in battery modeling and state estimation because of itssimplicity, stability and interpretability.However, ECM maygenerate large es…
Unsupervised and Interpretable Synthesizing for Electrical Time Series Based on Information Maximizing Generative Adversarial Nets
Zhenghao Zhou, Yiyan Li, Runlong Liu +2
Generating synthetic data has become a popular alternative solution to deal with the difficulties in accessing and sharing field measurement data in power systems. However, to make…