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Bailu Xiao

Researcher at Oak Ridge National Laboratory

Publications -  32
Citations -  1476

Bailu Xiao is an academic researcher from Oak Ridge National Laboratory. The author has contributed to research in topics: Microgrid & Photovoltaic system. The author has an hindex of 12, co-authored 30 publications receiving 1192 citations. Previous affiliations of Bailu Xiao include University of Tennessee.

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Modular Cascaded H-Bridge Multilevel PV Inverter With Distributed MPPT for Grid-Connected Applications

TL;DR: In this article, a modular cascaded H-bridge multilevel photovoltaic (PV) inverter for single- or three-phase grid-connected applications is presented.
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Modular Multilevel Inverter with New Modulation Method and Its Application to Photovoltaic Grid-Connected Generator

TL;DR: In this article, an improved phase disposition pulse width modulation (PDPWM) was proposed for a modular multilevel inverter which is used for Photovoltaic grid connection.
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A New Selective Loop Bias Mapping Phase Disposition PWM With Dynamic Voltage Balance Capability for Modular Multilevel Converter

TL;DR: This paper presents an improved phase disposition pulsewidth modulation for the modular multilevel converter (MMC) which is based on the selective loop bias mapping (SLBM) method, which has no issues of sorting, which makes it suitable for MMC with a large number of submodules in one leg.
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Microgrid optimal scheduling with chance-constrained islanding capability

TL;DR: In this paper, a new concept, probability of successful islanding (PSI), indicating the probability that a microgrid maintains enough spinning reserve (both up and down) to meet local demand and accommodate local renewable generation after instantaneously islanding from the main grid, is developed.
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Robust optimisation-based microgrid scheduling with islanding constraints

TL;DR: With proper robust level, the solution of the proposed scheduling model ensures successful islanding of the microgrid with minimum load curtailment and guarantees robustness against all possible realisations of the modelled operational uncertainties.