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3 results on '"Yihua Ma"'

1. Dynamic equivalent modeling for power converter based on LSTM neural network in wide operating range

Author

Yunlu Li, Guiqing Ma, Junyou Yang, Haixin Wang, Jiawei Feng, and Yihua Ma

Subjects
Power converter, Dynamic modeling, Deep learning, Neural network, Equivalent modeling, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The internal parameters and topology of the power converter are unknown in some practical cases. Existing modeling methods based on impedance frequency scanning method can only guarantee that the dynamic modeling is effective at a single working point. To make the established dynamic model effective in a wide range, an equivalent modeling method for power converter based LSTM Neural Network is presented. At first, the equivalence of black-box modeling problem and deep loop neural network is studied. Then, dynamic modeling method black-box power converter on wide operating range by using LSTM neural network is proposed. Finally, the simulation results under large disturbance and multi-operating points show that the proposed method is effective under wide operation range.

Published
2021
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2. Dynamic equivalent modeling for microgrid based on GRU

Author

Yunlu Li, Junyou Yang, Haixin Wang, Jia Cui, Yihua Ma, and Siyu Huang

Subjects
Dynamic equivalent model, Microgrid, Neural network, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The dynamic behaviors of microgrid become more complicated due to the increasing implementation of distributed energy generation, energy storage. It is significant to study the dynamic response for power planning, analysis, and control of microgrid. Hence, an accurate dynamic equivalent model is essential, since it can help to evaluate the performance by simulation to avoid the loss and danger in practical test. However, the dynamic model based on differential equation cannot be established because of the lack of information in most of time. To build dynamic model when microgrid is a black-box system, a gated recurrent unit based neural network is proposed in this paper. The proposed neural network can be treated as a black-box differential–algebraic equations. The structure design and model training procedure are presented in detail. Study cases are implemented to evaluate the performance of modeling method. The comparison results show that the proposed dynamic modeling method can precisely estimate the dynamic response of microgrid.

Published
2020
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3. Molecular Basis for Sesterterpene Diversity Produced by Plant Terpene Synthases

Author

Juan He, Yong Wang, Zhixi Liu, Ikuro Abe, Jianxu Li, Takaaki Mitsuhashi, Tetsuro Shinada, Peng Zhang, Yihua Ma, Tsutomu Sato, Hai-Li Liu, Hongwei Liu, Qingwen Chen, Yuting Zhang, and Guodong Wang

Subjects
crystal structure, ATP synthase, biology, Plant terpene, Chemistry, Stereochemistry, sesterterpene, Mutagenesis, Brassicaceae, terpene synthase, Cell Biology, Plant Science, biology.organism_classification, Biochemistry, Terpenoid, Terpene synthase, Helix, biology.protein, Arabidopsis thaliana, cyclization mechanism, Molecular Biology, terpenoid, Biotechnology, Research Article
Abstract

Class I terpene synthase (TPS) generates bioactive terpenoids with diverse backbones. Sesterterpene synthase (sester-TPS, C25), a branch of class I TPSs, was recently identified in Brassicaceae. However, the catalytic mechanisms of sester-TPSs are not fully understood. Here, we first identified three nonclustered functional sester-TPSs (AtTPS06, AtTPS22, and AtTPS29) in Arabidopsis thaliana. AtTPS06 utilizes a type-B cyclization mechanism, whereas most other sester-TPSs produce various sesterterpene backbones via a type-A cyclization mechanism. We then determined the crystal structure of the AtTPS18–FSPP complex to explore the cyclization mechanism of plant sester-TPSs. We used structural comparisons and site-directed mutagenesis to further elucidate the mechanism: (1) mainly due to the outward shift of helix G, plant sester-TPSs have a larger catalytic pocket than do mono-, sesqui-, and di-TPSs to accommodate GFPP; (2) type-A sester-TPSs have more aromatic residues (five or six) in their catalytic pocket than classic TPSs (two or three), which also determines whether the type-A or type-B cyclization mechanism is active; and (3) the other residues responsible for product fidelity are determined by interconversion of AtTPS18 and its close homologs. Altogether, this study improves our understanding of the catalytic mechanism of plant sester-TPS, which ultimately enables the rational engineering of sesterterpenoids for future applications., Sesterterpenoids, a relatively new group of terpenoids, are structurally diverse and widely distributed in bacteria, fungi, and the plant kingdom. However, the enzymatic mechanism of sesterterpene synthase (sester-TPS), the first committed biosynthesis step, remains obscure. This study reveals the critical amino acids controlling the substrate and product specificity of plant sester-TPS by determining the first crystal structure of the sester-TPS–FSPP complex.

Published
2020

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