Xiaoxiao Huo | Electric Engineering | Best Researcher Award

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Ms. Xiaoxiao Huo | Electric Engineering | Best Researcher Award

Xiaoxiao Huo at Xiamen University, China

Summary:

Ms. Xiaoxiao Huo is a promising researcher in the field of electrical engineering, currently pursuing her master’s degree at Xiamen University, China. With a strong foundation in power electronics and control theory, she has engaged in various research projects focused on optimizing energy systems and improving power conversion techniques. Ms. Huo is an award-winning student, recognized for her academic excellence and contributions to both research and engineering competitions. She has published several papers on advanced control strategies and holds patents in image recognition technologies for meter reading. Her diverse academic and practical experiences position her as a future leader in energy system optimization and power management.

Professional Profile:

šŸ‘©ā€šŸŽ“Education:

Ms. Xiaoxiao Huo is currently pursuing her M.Eng. in Electrical Engineering at Xiamen University (2022-2025, expected), where she maintains a GPA of 3.6/4.0. She is under the supervision of Dr. Po Li (Xiamen University) and Dr. Zhengmao Li (Aalto University). Her primary coursework includes modern control theory, advanced power electronics, and numerical analysis. She completed her B.Eng. in Electrical Engineering and Automation from South China Agricultural University (SCAU) in 2022 with an average score of 90.91/100. Her undergraduate studies focused on power electronics technology, power system analysis, and electrical machinery theory.

šŸ¢ Professional Experience:

Throughout her academic journey, Ms. Huo has engaged in various research and professional roles. Currently, she is conducting research on multi-energy system planning and operation at Aalto University, where she is developing a planning and operation model for integrated energy systems. In addition, at Xiamen University, she is working on power conversion and energy management strategies for power systems, focusing on model predictive control and its applications. In 2022, Ms. Huo gained hands-on experience as an intern and research assistant at the Guangzhou Institute of Energy Testing, where she designed instrument verification methods using computer vision. She has also been an active volunteer with the Chinese Association of Automation (CAA) since 2021, contributing to academic promotion and conference organization. Her participation in robotics competitions, including the Robomaster visual team, further demonstrates her interest in AI and automation.

Research Interests:

Ms. Huoā€™s research interests lie in the areas of power electronics, control engineering, energy management, and hybrid energy storage systems. Her work focuses on model predictive control, model-free control, observer design, and hardware-in-the-loop (HIL) simulations. She has also contributed to projects involving grid-tied inverters, multi-energy systems, and the optimization of electric power conversion.

Author Metrics:

Ms. Huo has contributed to several significant publications in the fields of energy systems and power electronics. Some of her key works include:

  • Huo, X., & Li, P. (2024). Power Management and Control Strategy Based on Model-Free Control for Hybrid Energy Storage System. Accepted by 2024 9th International Conference on Power and Renewable Energy (ICPRE 2024).
  • Huo, X., & Li, P. Parameter-Free Ultralocal Model-Based Predictive Current Control for Three-Phase Four-Leg Inverters. Submitted to Electric Power Systems Research.
  • Zhang, Y., Huo, X., et al. (2023). Design of an Image Recognition Device for Electronic Water Meter Readings Based on Improved Threading Method. IEEE Chinese Automation Congress (CAC 2023).
  • She is also the co-author of two Chinese patents on image recognition methods for gas and water meter readings.

Top Noted Publication:

Simplified Finite Control Set Model Predictive Control for Single-Phase Grid-Tied Inverters with Twisted Parameters

  • Journal: Electric Power Systems Research
  • Publication Date: January 2025
  • DOI: 10.1016/j.epsr.2024.111063
  • Contributors: Po Li, Xiaoxiao Huo
  • Details: The paper discusses an advanced control method for single-phase grid-tied inverters with twisted parameters, focusing on reducing total harmonic distortion (THD).

Design of an Image Recognition Device for Electronic Water Meter Readings Based on Improved Threading Method

  • Conference: 2023 China Automation Congress (CAC)
  • Publication Date: November 17, 2023
  • DOI: 10.1109/cac59555.2023.10451216
  • Contributors: Yuanming Zhang, Qilun Lu, Xiaoxiao Huo, Yong Wan, Peng Tian
  • Details: This paper focuses on the design of a device utilizing image recognition technology for reading electronic water meters, improving accuracy through an enhanced threading method.

Total Harmonic Distortion Reduction Method of Improved Finite Control Set Model Predictive Control for Single-Phase Inverter with Twisted Parameter

  • Conference: 2023 5th International Conference on Power and Energy Technology (ICPET)
  • Publication Date: July 27, 2023
  • DOI: 10.1109/icpet59380.2023.10367520
  • Contributors: Po Li, Xiaoxiao Huo, Feng Guo
  • Details: This paper presents an improved control method for reducing THD in single-phase inverters with twisted parameters.

Multi-Sampling Rate Finite Control Set Model Predictive Control and Adaptive Method of Single-Phase Inverter

  • Journal: Electronics
  • Publication Date: June 27, 2023
  • DOI: 10.3390/electronics12132848
  • Contributors: Yunfeng She, Xiaoxiao Huo, Xiaoshan Tong, Chunjie Wang, Kunkun Fu
  • Details: The paper discusses multi-sampling rate model predictive control methods for single-phase inverters, including adaptive control to enhance performance.

Research on Quality Control Application of Whole Process Intelligent Manufacturing in Steel Industry 4.0 Based on Big Data Analysis

  • Journal: Journal of Network Intelligence
  • Publication Year: 2022
  • EID: 2-s2.0-85136270844
  • ISSN: 2414-8105
  • Contributors: Zhao, F., Yin, C., Huo, X., Xu, Y.
  • Details: This paper examines the application of big data analysis for quality control in Industry 4.0, focusing on intelligent manufacturing in the steel industry.