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Chen-Ching Liu
Researcher at Virginia Tech
Publications - 274
Citations - 14290
Chen-Ching Liu is an academic researcher from Virginia Tech. The author has contributed to research in topics: Electric power system & Electricity market. The author has an hindex of 57, co-authored 269 publications receiving 12126 citations. Previous affiliations of Chen-Ching Liu include Washington State University & Purdue University.
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Computation of Milestones for Decision Support During System Restoration
TL;DR: In this paper, a generic restoration milestone (GRM) based decision support tool is proposed to reduce the restoration time and improve the reliability of a power system by synthesizing a specific restoration strategy based on GRMs.
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Intruders in the Grid
TL;DR: A power grid is a critical infrastructure that relies on supervisory control and data acquisition systems for monitoring, control, and operation and the use of firewalls has become a widely adopted access control method against intruders.
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DGs for Service Restoration to Critical Loads in a Secondary Network
TL;DR: A resilience-oriented method to determine restoration strategies for secondary network distribution systems after a major disaster is proposed, incorporating technical issues associated with secondary networks, limits on DG capacity and generation resources, dynamic constraints, and operational limits.
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An Expert System as a Dispatchers' Aid for the Isolation of Line Section Faults
TL;DR: An expert system to aid decision-making in an operating center environment is developed in this study and main issues in the expert system development, such as rule base organization, implementation in control center, interface with a database, and computational efficiency, are discussed.
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Coherency and aggregation techniques incorporating rotor and voltage dynamics
TL;DR: In this paper, the effect of voltage variations on the coherency of generators due to weak coupling between real power and voltage has been considered and a new method to match the power flow conditions for generator aggregation is presented.