F
Fankun Bu
Researcher at Iowa State University
Publications - 34
Citations - 786
Fankun Bu is an academic researcher from Iowa State University. The author has contributed to research in topics: Smart meter & Photovoltaic system. The author has an hindex of 9, co-authored 31 publications receiving 413 citations.
Papers
More filters
Journal ArticleDOI
A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems
TL;DR: The critical topics of DSSE, including mathematical problem formulation, application of pseudo-measurements, metering instrument placement, network topology issues, impacts of renewable penetration, and cyber-security are discussed.
Journal ArticleDOI
A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems
TL;DR: In this article, a review of the literature on state estimation in power systems is presented, including mathematical problem formulation, application of pseudo-measurements, metering instrument placement, network topology issues, impacts of renewable penetration, and cyber-security.
Journal ArticleDOI
A Data-Driven Game-Theoretic Approach for Behind-the-Meter PV Generation Disaggregation
TL;DR: A novel game-theoretic learning process is proposed to adaptively generate optimal composite exemplars using the constructed library of candidate exemplars, through repeated evaluation of disaggregation residuals.
Proceedings ArticleDOI
A Time-Series Distribution Test System Based on Real Utility Data
TL;DR: An important uniqueness of this grid model is it has one-year smart meter measurements at all nodes, thus bridging the gap between existing test feeders and quasi-static time-series based distribution system analysis.
Journal ArticleDOI
A Game-Theoretic Data-Driven Approach for Pseudo-Measurement Generation in Distribution System State Estimation
TL;DR: In this paper, a game-theoretic expansion of relevance vector machines (RVMs) is proposed to estimate the nodal power consumption and quantify its uncertainty while reducing the prohibitive computational burden of model training for large AMI datasets.