Dr. Phuong Nguyen-Thanh | Engineering | Best Researcher Award
Dr. Phuong Nguyen-Thanh, National Kaohsiung University of Science and Technology, Taiwan
Phuong Nguyen Thanh is a dedicated Postdoctoral Researcher at the Energy Technology Research Center (ETRIC) in the Department of Electrical Engineering at the National Kaohsiung University of Science and Technology. Born on April 14, 1988, in Vietnam, he has developed a strong academic and professional background in electrical engineering and energy systems. His core skills encompass independent research, teaching various subjects in English, and programming in multiple languages, including JavaScript and MATLAB. With a passion for renewable energy integration, Phuong aims to contribute significantly to advancements in power systems. He is fluent in Vietnamese and English and has basic proficiency in Chinese. His professional journey reflects a commitment to fostering knowledge and innovation in the field of electrical engineering.
Professional Profile
Summary of Suitability for the Research for Best Researcher Award
Phuong Nguyen Thanh’s expertise in Electrical Engineering and Energy Technology positions him as a highly suitable candidate for the Research for Best Researcher Award. His background as a Postdoc Researcher at the Energy Technology Research Center (ETRIC), combined with his significant teaching experience at Nha Trang University, demonstrates a strong blend of academic knowledge and practical research ability. His PhD from National Kaohsiung University of Science and Technology further emphasizes his dedication to advancing energy systems, a critical area in modern research.
Education
Phuong Nguyen Thanh earned his Doctor of Philosophy in Electrical Engineering from the National Kaohsiung University of Science and Technology in 2022, where he focused on integrating artificial intelligence and deep learning algorithms in power systems. Before that, he completed his Master’s degree in Electronic and Computer Engineering at RMIT Vietnam in November 2014, a branch of the renowned Royal Melbourne Institute of Technology in Australia. His academic foundation includes a Bachelor’s degree in Electrical Engineering from the University of Vietnam Technology, where he specialized in Energy Systems, graduating in December 2010. Phuong’s strong educational background equips him with the theoretical knowledge and practical skills essential for addressing complex challenges in energy technology, paving the way for his impactful research in renewable energy and smart systems.
Experience
Phuong Nguyen Thanh has a rich professional experience in academia and research. He is currently a Postdoctoral Researcher at the Energy Technology Research Center (ETRIC), National Kaohsiung University of Science and Technology, where he applies AI and deep learning algorithms to enhance power systems since September 2022. Prior to this role, he served as a full-time lecturer at Nha Trang University, Vietnam, from 2010 to January 2019, where he taught courses on Power Systems, Power Dispatching Strategies, and Programming on Microcontrollers. His teaching methods emphasized practical applications and hands-on learning, fostering a deep understanding of electrical engineering among his students. Phuong has demonstrated his commitment to education and research by developing various applications for smartphones on Android and iOS platforms, as well as engaging in independent research projects that focus on renewable energy solutions and smart grid technologies.
Awards and Honors
Phuong Nguyen Thanh has received several accolades for his contributions to the field of electrical engineering and renewable energy. His academic excellence was recognized with a scholarship for his Ph.D. studies at the National Kaohsiung University of Science and Technology, underscoring his commitment to advancing knowledge in energy technology. During his Master’s program at RMIT Vietnam, he was awarded a merit scholarship for outstanding performance in his coursework. Phuong has also been acknowledged for his innovative research projects, which have been presented at various national and international conferences. His dedication to teaching has earned him positive evaluations from students and faculty alike, reflecting his effective communication skills and passion for educating the next generation of engineers. Phuong’s continued pursuit of research excellence is expected to yield further recognition as he contributes to significant advancements in renewable energy integration and smart systems.
Research Focus
Phuong Nguyen Thanh’s research focus lies in the integration of renewable energy technologies and the application of artificial intelligence (AI) in power systems. His recent work involves exploring deep learning algorithms to enhance the efficiency and reliability of energy distribution networks. Phuong is particularly interested in developing smart grid solutions that can adapt to dynamic energy demands while optimizing the use of renewable sources such as solar and wind power. He aims to create innovative algorithms for energy management systems that can facilitate the seamless integration of distributed energy resources. Additionally, Phuong’s research investigates the implementation of microcontroller programming in energy systems to improve automation and control. Through his work at the Energy Technology Research Center (ETRIC), he seeks to address the challenges of renewable energy integration and contribute to the development of sustainable energy solutions for the future.
Publication Top Notes
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Hourly load prediction based feature selection scheme and hybrid CNN-LSTM method for building’s smart solar microgrid
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A cloud 15kV-HDPE insulator leakage current classification based improved particle swarm optimization and LSTM-CNN deep learning approach
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Advanced AIoT for failure classification of industrial diesel generators based hybrid deep learning CNN-BiLSTM algorithm
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Real-time AIoT anomaly detection for industrial diesel generator based an efficient deep learning CNN-LSTM in industry 4.0
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Novel cloud-AIoT fault diagnosis for industrial diesel generators based hybrid deep learning CNN-BGRU algorithm