J
Jianhui Wang
Researcher at Southern Methodist University
Publications - 177
Citations - 8839
Jianhui Wang is an academic researcher from Southern Methodist University. The author has contributed to research in topics: Electric power system & AC power. The author has an hindex of 41, co-authored 175 publications receiving 4835 citations. Previous affiliations of Jianhui Wang include Argonne National Laboratory & University of Texas at San Antonio.
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Robust Risk-Constrained Unit Commitment with Large-scale Wind Generation: An Adjustable Uncertainty Set Approach
TL;DR: In this paper, a robust risk-constrained unit commitment (RRUC) formulation is proposed to cope with large-scale volatile and uncertain wind generation, where the wind generation uncertainty set in RRUC is adjustable via choosing diverse levels of operational risk.
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Capacity Planning of Energy Hub in Multi-Carrier Energy Networks: A Data-Driven Robust Stochastic Programming Approach
TL;DR: A data-driven two-stage robust stochastic programming model for energy hub capacity planning with distributional robustness guarantee is proposed and transformed into an equivalent convex program with a nonlinear objective and linear constraints, and is solved by an outer-approximation algorithm that entails solving only linear program.
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A Novel Event Detection Method Using PMU Data With High Precision
TL;DR: Numerical simulations on the real-time and synthetic PMU data show that the DPSDT method can accurately detect the start-time of an event and the event placement with relatively high precision.
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Wind Power Providing Flexible Ramp Product
TL;DR: This paper attempts to explore the mechanism and economic impacts of including wind power producers (WPPs) as ramp capacity providers and a two-stage stochastic real-time unit commitment model considering ramp capacity adequacy is formulated.
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Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach
Ying Zhang,Jianhui Wang,Bo Chen +2 more
TL;DR: A data-driven learning-based algorithm for detecting unobservable FDIAs in distribution systems using autoencoders for efficient dimension reduction and feature extraction of measurement datasets and integrates the autoen coders into an advanced generative adversarial network framework.