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.
Papers
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Journal ArticleDOI
Detection and Characterization of Intrusions to Network Parameter Data in Electric Power Systems
Daniel K. Molzahn,Jianhui Wang +1 more
TL;DR: An algorithm for detecting and characterizing cyberattacks to network parameter data, with specific application to optimal power flow problems, is proposed.
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State-of-the-art short-term electricity market operation with solar generation: A review
TL;DR: In this article, a thorough review of the electricity markets worldwide with solar energy is provided, and a variety of proposed mathematical solutions to the problem are also discussed, with the focus on the uncertainty-based market operations.
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Generalized Graph Laplacian Based Anomaly Detection for Spatiotemporal MicroPMU Data
TL;DR: In this article, a novel anomaly detection method using the generalized graph Laplacian (GGL) matrix was developed to visualize the spatio-temporal relationship of distribution-level phasor measurement unit ( $\boldsymbol{\mu }$ PMU) data.
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Black-Start and Service Restoration in Resilient Distribution Systems with Dynamic Microgrids
TL;DR: In this paper, the authors proposed a restoration procedure that adopts self-organizing inverters as BS units and achieves distribution system restoration in the context of dynamic microgrids, i.e., MGs with dynamic and adjustable boundaries nested in the distribution systems.
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An Adjustable Chance-Constrained Approach for Flexible Ramping Capacity Allocation
TL;DR: A concept of conditional distributions of wind power ramping is proposed, which is empirically verified by using simulation and real-world data and an adjustable chance-constrained approach to optimally allocate FRC reserves is developed.