Wuhan University of Technology | China
Professor Hengrui Ma, an Associate Professor at Wuhan University of Technology, is an accomplished researcher in the fields of new power systems and integrated energy systems, demonstrating strong academic excellence and impactful scientific contributions. He earned his Ph.D. from Wuhan University and his Bachelor’s and Master’s degrees from North China Electric Power University, and has been recognized with the prestigious provincial “Kunlun Talents” award for his outstanding academic achievements. Professor Ma has led and participated in several national and provincial-level research initiatives, including sub-task leadership roles in the National Key Research and Development Program of China on Smart Grid (2025–2029), major projects funded by the China Southern Power Grid, and China Electric Power Research Institute, showcasing his influential role in advancing smart grid technologies, urban distribution network planning, and power system resilience against extreme weather. His research productivity includes over 80 peer-reviewed publications, with 70+ indexed by SCI/EI, and a total citation count of 2,961 across Google Scholar, CNKI, and IEEE databases, reflecting the widespread recognition of his work. He has contributed to industrial innovation through 110 patent applications and authored the book Motor and Electrical Control Technology (ISBN: 978-7-114-19121-3) published in 2024. His academic excellence has been further honored through prestigious recognitions such as the Qinghai Provincial Natural Science Award, a Gold Medal at the Geneva International Exhibition of Inventions, and the Wiley China Excellent Author Program award supported by Wiley and the Institution of Engineering and Technology (IET). His papers include ESI Highly Cited and Hot Papers, one of China’s Top 100 Most Influential Academic Papers, and an F5000 award-winning paper, all demonstrating exceptional research impact. Beyond his scholarly work, Professor Ma contributes actively to professional communities, serving as Youth Editorial Committee Member of Smart Power, Committee Member of the Boao New Power System Association, and Vice Secretary-General of the IEEE PES China Satellite Technical Committee – Transmission & Distribution Subcommittee (2024–2026). Through his multidisciplinary collaborations and sustained leadership in innovative scientific exploration, he has significantly advanced integrated energy technologies and the transformation of smart grids toward green, resilient, and intelligent infrastructures. By combining academic rigor with industry-oriented outcomes, Professor Ma continues to drive scientific innovation and make notable contributions to national energy development goals and global technological progress.
Profiles: Google Scholar
Featured Publications
Zou, H., Liu, Q., Yuan, A., Chen, S., Ma, H., & Wang, B. (2025). A method for assessing the operational saturation of electric power operators based on the fusion of multi‐source risk factors.
Xue, Z., Wang, B., Ma, H., Zhang, J., Zhang, H., & Zhou, J. (2025). Research on transformer fault diagnosis and maintenance strategy generation based on TransQwen model. Processes, 13(7), 1977.
Yan, Y., Ma, H., Yuan, A., Feng, Z., & Wu, Z. (2025). IES low‐carbon operation strategy based on double incentive GCT‐CET collaboration and demand response.
Hua, S., Jin, S., Song, Z., Liu, X., Wang, S., & Ma, H. (2025). Precision prediction strategy for renewable energy power in power systems—A physical‐knowledge integrated model. Processes, 13(4), 1049.
Hengrui, D. S. M., Wei, W., Lin, C., & Shufu, L. (2025). Two‐stage robust optimization of 5G base stations considering uncertainty of power load and electricity price.
Zhang, J., Wang, B., Ma, H., He, Y., Wang, H., & Zhang, H. (2025). Reliability evaluation method for underground cables based on double sequence Monte Carlo simulation. Processes, 13(2), 505.
Ma, F., Wang, B., Dong, X., Li, M., Ma, H., Jia, R., & Jain, A. (2025). Scene understanding method utilizing global visual and spatial interaction features for safety production. Information Fusion, 114, 102668.
Yuan, A., Ma, H., Yang, C., Xiao, H., Wang, B., Gao, D. W., & La, Q. (2025). Ultra‐short‐term wind power prediction based on digital twins. IET Renewable Power Generation, 19(1), e70155.
Wang, J., Ma, H., Yin, Z., Feng, Z., Yin, T., & Tian, J. (2024). YOLOv8‐CAFMFusion for image segmentation of substation operation elements in complex environments.
Tian, J., Ma, H., Wang, J., Feng, Z., Luo, P., & Yin, T. (2024). Research on real‐time sensing method of three‐dimensional information of substation based on convolutional neural network stereo matching.