most citedAdaptive Spatial-Temporal Graph Learning-Enabled Short-Term Voltage Stability Assessment against Time-Varying Topological Conditions

2 citations · 2 across the 4 of their papers we have counts for

collaborators

6 papers

eess.SY2026

Intelligent Domain Adaptation for Power System Transient Stability Assessment Under Varying Operating Scenarios

Yuan Yang, Lipeng Zhu, Chao Deng +3

While deep learning-based transient stability assessment (TSA) approaches have exhibited great potential in power system stability monitoring, they are prone to undergo performance…

eess.SY2026

Impedance Modeling and Stability Analysis of Droop-Controlled Inverter Under Unbalanced Power Grid Operating Conditions

Qiang Zeng, Lipeng Zhu, Yang Li +6

With the growing integration of renewable energy sources into power grids, the risks of oscillation caused by interactions between grid-tied inverters and the grids are becoming in…

eess.SY20262 cited

Adaptive Spatial-Temporal Graph Learning-Enabled Short-Term Voltage Stability Assessment against Time-Varying Topological Conditions

Chao Deng, Lipeng Zhu, Chang Liu +5

The emerging deep learning (DL) technology has recently exhibited great potential in data-driven short-term voltage stability (SVS) assessment of complex power grids. However, with…

cs.CL2026

Lightweight Multimodal LLM-Enabled Cost-Effective Defect Grading of Power Transmission Equipment

Tao Wang, Lipeng Zhu, Jiayong Li +2

Defect grading of power transmission equipment (DGPTE) is crucial to the stability of electric energy transmission. Although existing machine learning methods exhibit strong capabi…

eess.SY2025

Quantitative Damping Calculation and Compensation Method for Global Stability Improvement of Inverter-Based Systems

Yang Li, Zenghui Zheng, Xiangyang Wu +4

Small-signal stability issues-induced broadband oscillations pose significant threats to the secure operation of multi-inverter systems, attracting extensive research attention. Re…

eess.SP2025

Three-Stage Composite Outlier Identification of Wind Power Data: Integrating Physical Rules with Regression Learning and Mathematical Morphology

Limengqian Zheng, Lipeng Zhu, Weijia Wen +2

Existing studies on identifying outliers in wind speed-power datasets are often challenged by the complicated and irregular distributions of outliers, especially those being densel…