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Bin Zhao
Researcher at Chinese Academy of Sciences
Publications - 21
Citations - 304
Bin Zhao is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Transformer & Converters. The author has an hindex of 6, co-authored 21 publications receiving 176 citations. Previous affiliations of Bin Zhao include Technical University of Denmark & Nanyang Technological University.
Papers
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AI Algorithm-Based Two-Stage Optimal Design Methodology of High-Efficiency CLLC Resonant Converters for the Hybrid AC–DC Microgrid Applications
TL;DR: The leakage inductances of a planar transformer are used as the resonant inductances and the magnetic design of the CLLC resonant converter based on artificial intelligence (AI) algorithm is proposed.
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Stability-Constraining-Dichotomy-Solution-Based Model Predictive Control to Improve the Stability of Power Conversion System in the MEA
TL;DR: This paper theoretically presents how to add the system stability item to the MPC cost function in an analytic way instead of using the traditional “experience-based” weight method and proposes a large signal stability constraining dichotomy solution (SCDS)-based model predictive control (MPC).
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Analysis and Performance of LCLC Resonant Converters for High-Voltage High-Frequency Applications
TL;DR: In this article, the LCLC resonant converter with zero current switching and zero voltage switching was investigated for two-stage high-voltage (several kilovolts) high-frequency (Several hundred kilohertzes) applications.
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An Improved Partially Interleaved Transformer Structure for High-Voltage High-Frequency Multiple-Output Applications
TL;DR: The proposed partially interleaved structure features lower leakage inductance, smaller AC capacitance and lower rate of AC-DC resistance, which is suitable for high-frequency high-efficiency applications.
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Simplified Resonant Parameter Design of the Asymmetrical CLLC -Type DC Transformer in the Renewable Energy System via Semi-Artificial Intelligent Optimal Scheme
TL;DR: A semi-artificial intelligence (semi-AI)-based simplified parameter design approach is put forward in this paper for ACLLC-type DCT, which replaces all unknown parameters with two intermediate parameters through certain manipulations, and then utilizes a very simple computer-assisted procedure to optimally design the parameters of the ACLLCs.