Qing Fu

Dr. Qing Fu

Physics School, Sun Yat-sen University, Guangzhou, Guangdong, China
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Academic Career Length

18Years (2009-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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30 September 2018 in IEEE Transactions on Sustainable Energy
Verified
Journal Article
Closed access
YSYinghao SHAN
Yinghao SHAN
Jiefeng HuJiefeng Hu
Jiefeng Hu
KCKa Wing Chan
Ka Wing Chan
QFQing Fu
Qing Fu
Josep M. GuerreroJosep M. Guerrero
Josep M. Guerrero
In renewable energy systems, fluctuating outputs from energy sources and variable power demand may deteriorate the voltage quality. In this paper, a model predictive control strategy without using any proportional-integral-derivative (PID) regulators is proposed. The proposed strategy consists of a model predictive current and power (MPCP) control scheme and a model predictive voltage and power (MPVP) control method. By controlling the bidirectional dc-dc converter of the battery energy storage system based on the MPCP algorithm, the fluctuating output from the renewable energy sources can be smoothed while stable dc-bus voltage can be maintained. Meanwhile, the ac/dc interlinking converter is controlled by using the MPVP scheme to ensure stable ac voltage supply and proper power flow between the microgrid and the utility grid. Then, a system-level energy management scheme is developed to ensure stable operation under different operation modes by considering fluctuating power generation, variable power demand, battery state of charge, and electricity price. Compared with the traditional cascade control, the proposed method is simpler and shows better performance, which is validated in simulation based on a 3.5-MW PV-wind-battery system with real-world solar and wind profiles.
6 November 2024 in International Journal of Hydrogen Energy
Verified
Journal Article
Closed access
Benfei WangBenfei Wang
Benfei Wang
DXDingnan Xin
Dingnan Xin
QFQing Fu
Qing Fu
CZCaizhi Zhang
Caizhi Zhang
1 May 2009
Verified
Conference Paper
Closed access
XCXiaogao Chen
Xiaogao Chen
QFQing Fu
Qing Fu
DWDonghai Wang
Donghai Wang
A photovoltaic (PV) grid-connected power conditioning system with the function of uninterruptible power supply (UPS) is presented in the paper. It can not only realize photovoltaic generation, but also has full function of shunt active power filter (APF). In addition, it can act as UPS when instantly power interruption occurs. The instantaneous reactive power theory is employed in harmonics current detecting and signal synthesis algorithm of reference current. The proposed control strategy has two control modes (Grid connection mode and UPS mode) which can switch to each other automatically. A dual instantaneous loop control strategy is applied to UPS mode so as to obtain fast responses. The whole system is constructed and simulated in Matlab/Simulink environment. The power qualities under different conditions are given and compared. By analysis of the simulation results, it is shown that the proposed system is feasible and practical.
5 May 2023 in Electronics
Verified
Journal Article
Open access
PDF file
HYHANG YANG
HANG YANG
QFQing Fu
Qing Fu
Benfei WangBenfei Wang
Benfei Wang
YCYishan Chen
Yishan Chen
YSyixing su
yixing su
Parallel-connected converters are commonly used in applications such as DC microgrids. The presence of multiple converters allows for the use of the interleaving technique to minimize bus ripple. Most existing interleaving methods rely on communication lines, which are unsuitable for distributed scenarios. This paper presents a method that requires no communication line. The elimination of communication lines reduces the system’s complexity and cost while improving its reliability. We provide an analytical guarantee of stability and validate the control method using phasor simulation and an experimental prototype with three 24 V to 8 V DC-DC buck converters connected in parallel. Measurements show that the proposed method allows the parallel system to maintain an interleaved state in the face of load steps and converters plugged in and out.
12 January 2026 in Sustainable Energy Technologies and Assessments
Verified
Journal Article
Closed access
QFQing Fu
Qing Fu
YWYuxi Wang
Yuxi Wang
Benfei WangBenfei Wang
Benfei Wang
YZYiqi Zeng
Yiqi Zeng
XZXiaoping Zhou
Xiaoping Zhou
ZLZhongyu Lu
Zhongyu Lu
26 July 2025 in Electronics
Verified
Journal Article
Open access
PDF file
LLLiheng Long
Liheng Long
ZCZhiyao Chen
Zhiyao Chen
JWJunqian Wu
Junqian Wu
QFQing Fu
Qing Fu
ZZZirui Zhang
Zirui Zhang
RZRonghui Zhang
Ronghui Zhang
With the rapid growth of urban populations and the expansion of metro networks, accurate energy consumption prediction has become a critical task for optimizing metro operations and supporting low-carbon city development. Traditional statistical and machine learning methods often struggle to model the complex, nonlinear, and time-varying nature of metro energy data. To address these challenges, this paper proposes MTMM, a novel hybrid model that integrates the multi-head attention mechanism of the Transformer with the efficient, state-space-based Mamba architecture. The Transformer effectively captures long-range temporal dependencies, while Mamba enhances inference speed and reduces complexity. Additionally, the model incorporates multivariate energy features, leveraging the correlations among different energy consumption types to improve predictive performance. Experimental results on real-world data from the Guangzhou Metro demonstrate that MTMM significantly outperforms existing methods in terms of both MAE and MSE. The model also shows strong generalization ability across different prediction lengths and time step configurations, offering a promising solution for intelligent energy management in metro systems.