Ms. Yasmine Gaaloul | Photovoltaic systems | Best Researcher Award

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Ms. Yasmine Gaaloul | Photovoltaic systems | Best Researcher Award

PhD student, Higher School of Science and Technology Hammem Sousse, Tunisia

Yasmine Gaaloul is a passionate and innovative Ph.D. student in Physical Engineering, specializing in Energy, based in Sahloul, Sousse, Tunisia. With a sharp focus on advanced energy systems and renewable energy technologies, she brings a solid academic foundation combined with hands-on experience in photovoltaic system diagnostics and energy efficiency. Her academic journey reflects a strong dedication to contributing meaningful solutions in the field of sustainable energy using cutting-edge artificial intelligence techniques. 🌱⚡

Publication Profile

Scopus

Summary of Suitability for Best Researcher Award – Ms. Yasmine Gaaloul

Yasmine Gaaloul is a dedicated and innovative PhD student with a strong specialization in renewable energy systems, particularly in fault diagnosis of photovoltaic (PV) systems in DC microgrids using artificial intelligence (AI). Her research is highly relevant to current global challenges in energy sustainability and smart grid technology.

🎓 Education Background

Yasmine pursued her Bachelor’s Degree in Physics (2017–2020) and then earned a Research Master’s Degree in Physics with an Energy Track from Hammem Sousse Higher School of Science and Technology (2020–2022). She is currently enrolled in a Ph.D. program in Physical Engineering at the same institution, where her research focuses on fault diagnosis in photovoltaic (PV) systems using AI tools. Her academic path shows consistent advancement in renewable energy domains. 🎓🔬📚

💼 Professional Experience

Yasmine has built practical experience through internships and projects focused on energy audits, policy compliance, project management, and the design and implementation of energy systems. Notably, she undertook an application internship in Nuclear Medicine at the Tunisian Hospital-University Center in Sousse. She also possesses strong technical skills in simulation and programming tools such as MATLAB, PVsyst, and BAO EVOLUTION, which enhance her applied research capabilities. 🏥💻🔧

🏆 Awards and Honors

While no formal awards are listed, her work speaks volumes through peer-reviewed publications and technical skills in diagnosing photovoltaic faults using AI — a field of growing significance. Her continued progress and engagement in advanced renewable technologies position her as a competitive researcher with promising prospects. 🥇📈

🔬 Research Focus

Yasmine’s core research revolves around the fault detection and diagnosis of PV systems in DC microgrids using artificial intelligence. She also works on simulation and modeling of energy systems, including photovoltaic modules and converters. Her use of machine learning algorithms like Random Forest and KNN for predictive maintenance of PV systems reflects a modern and sustainable approach to energy reliability and performance optimization. Her work aligns with critical global goals for clean and efficient energy. ⚙️🔋🌞

✅ Conclusion 

Given her specialized research in photovoltaic systems and renewable energy using artificial intelligence, Yasmine Gaaloul is a strong candidate for the Research for Best Researcher Award. Her interdisciplinary approach, combining physical engineering with AI, positions her at the forefront of sustainable energy innovation. Her academic trajectory, technical prowess, and publication record demonstrate exceptional promise and dedication. 🏅🌍💡

📚 Top Publications with Notes

Faults Detection and Diagnosis of a Large-Scale PV System by Analyzing Power Losses and Electric Indicators Computed Using Random Forest and KNN-Based Prediction Models (2023)

This study explores hybrid AI techniques to enhance PV system monitoring. Cited by: X articles

Comparative Performance Analysis of Metaheuristic Optimization Algorithms for Parameter Identification of Photovoltaic Cell/Module (2023)

This comparative work evaluates algorithms for optimal PV module performance. Cited by: X articles

Modeling and Simulation of a Two-Phase Interleaved Boost Converter for Photovoltaic Applications (2022)

Focuses on the design of efficient converters for PV systems. Cited by: X articles

Dr. Dawei Qiu | Smart Grid | Best Researcher Award

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Dr. Dawei Qiu | Smart Grid | Best Researcher Award

Lecturer, University of Exeter, United Kingdom

Dr. Dawei Qiu is a distinguished scholar in smart energy systems, currently serving as a Lecturer at the University of Exeter, UK 🏫. With a strong background in electrical engineering and power systems, he specializes in AI-driven reinforcement learning, market design for low-carbon energy transition, and resilience enhancement of energy systems ⚡. His extensive research contributions in smart grids and power systems have earned him recognition in academia, with a Google Scholar citation count of 2,109, an h-index of 24, and an h10-index of 35 📊.

Publication Profile

Google Scholar

🎓 Education

Dr. Qiu holds a Ph.D. in Electrical Engineering from Imperial College London (2016–2020) 🎓, where he conducted pioneering research on local flexibility’s impact on electricity retailers under the supervision of Prof. Goran Strbac. Prior to this, he completed his M.Sc. in Power System Engineering from University College London (2014–2015) and obtained his B.Eng. in Electrical and Electronic Engineering from Northumbria University at Newcastle (2010–2014) ⚙️. His academic journey has been shaped by esteemed mentors, including Dr. Ben Hanson and Dr. Zhiwei (David) Gao, IEEE Fellow.

💼 Experience

Dr. Qiu’s professional career spans academia and research institutions, where he has contributed significantly to energy systems innovation 🌍. Before joining the University of Exeter in 2024, he was a Research Fellow at Imperial College London (2023–2024), specializing in market design for low-carbon energy systems. He also served as a Research Associate at the same institution from 2020 to 2023 🔬. His work in smart grids and energy resilience has been instrumental in shaping sustainable and intelligent power infrastructure.

🏆 Awards and Honors

Dr. Qiu’s research excellence has been acknowledged through various accolades 🏅. His contributions to smart energy systems, AI-driven reinforcement learning, and low-carbon market design have positioned him as a leading researcher in the field. His studies have been published in top-tier journals, and his work has received high citations, demonstrating its impact on the global research community 🌟.

🔬 Research Focus

Dr. Qiu’s research is centered on leveraging artificial intelligence and reinforcement learning for power and energy applications 🤖. His work explores market mechanisms for cost-effective and sustainable energy transitions, as well as the resilience enhancement of energy systems in response to climate change 🌍. His expertise in AI-driven optimization and machine learning applications in energy systems makes him a key contributor to the advancement of smart grid technologies.

🔚 Conclusion

Dr. Dawei Qiu is a leading researcher in smart energy systems, with a strong academic background and impactful contributions to power systems engineering 🔬. His expertise in AI-driven market optimization, reinforcement learning, and resilient energy systems has made him a valuable asset to the research community 🌍. With his ongoing work at the University of Exeter, he continues to drive innovation in low-carbon and intelligent energy solutions ⚡.

🔗 Publications

A knowledge-based safe reinforcement learning approach for real-time automatic control in a smart energy hub – Applied Energy (Under review, 2025) 🔗 Link

Enhanced Meta Reinforcement Learning for Resilient Transient Stabilization – IEEE Transactions on Power Systems (Under review, 2025) 🔗 Link

Machine learning-based economic model predictive control for energy hubs with variable energy efficiencies – Energy (First round revision, 2024) 🔗 Link

A Review of Resilience Enhancement Measures for Hydrogen-penetrated Multi-energy Systems – Proceedings of the IEEE (Under review, 2025) 🔗 Link

Coordinated Optimal Dispatch Based on Dynamic Feasible Operation Region Aggregation – IEEE Transactions on Smart Grid (First round revision, 2024) 🔗 Link

A Sequential Multi-Agent Reinforcement Learning Method for Coordinated Reconfiguration of Substation and MV Distribution Networks – IEEE Transactions on Power Systems (Under review, 2024) 🔗 Link

Enhancing Microgrid Resilience through a Two-Layer Control Framework for Electric Vehicle Integration and Communication Load Management – IEEE Internet of Things Journal (Under review, 2024) 🔗 Link

Coordinated Electric Vehicle Control in Microgrids Towards Multi-Service Provisions: A Transformer Learning-based Risk Management Strategy – Energy (Under review, 2024) 🔗 Link

Adaptive Resilient Control Against False Data Injection Attacks for a Multi-Energy Microgrid Using Deep Reinforcement Learning – IEEE Transactions on Network Science and Engineering (Under review, 2024) 🔗 Link

Jin Wang | Renewable Energy Technologies Award | Best Researcher Award

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Dr. Jin Wang | Renewable Energy Technologies Award | Best Researcher Award

Doctoral candidate, Taiyuan University of Technology, China

Jin Wang, a doctoral candidate at Taiyuan University of Technology, is a promising researcher in the field of electrical engineering. Born on June 1, 1996, Jin is a member of the Han nationality and is deeply focused on coastal renewable energy generation. He is working towards his Ph.D. in Electrical Engineering and has actively contributed to advancing knowledge in energy systems. With a strong academic foundation and hands-on research experience, Jin is making significant strides in his field. 🧑‍🎓⚡🌍

Publication Profile

ORCID

Education:

Jin Wang completed his undergraduate degree in Electrical Engineering at Taiyuan University of Technology in 2020. Following this, he embarked on his Ph.D. journey at the same university, with an expected completion date in 2025. His academic career is distinguished by a dedication to innovation and the pursuit of sustainable energy solutions. 🎓🔌

Experience:

Jin Wang has been involved in a variety of research projects at Taiyuan University of Technology, with a focus on energy systems for polar regions and coastal renewable energy generation. He has also gained practical experience by contributing to national and provincial-level projects, particularly on clean, low-carbon energy systems. 💼🌱

Awards and Honors:

Jin has received several certifications that highlight his commitment to excellence. These include his achievements in English proficiency (CET-6, CET-4), as well as certifications in hydrogen energy technology and industrial technology enhancement. 🏅🎖️

Research Focus:

Jin’s research primarily revolves around coastal renewable energy generation, with specific attention to energy systems in extreme environments, such as polar regions. His work includes projects on energy systems for coastal research stations, hybrid energy systems, and proton exchange membrane fuel cells (PEMFC) in standalone systems. 🔋🌊

Conclusion:

Jin Wang is a dedicated researcher with a clear focus on sustainable energy solutions for challenging environments. His academic background, along with his involvement in cutting-edge research projects, positions him to make significant contributions to the field of renewable energy. 🌍💡

Publications:

Application and effect analysis of renewable energy in a small standalone automatic observation system deployed in the polar regions – AIP Advances, 2022, Author ranking: 1

Improving Proton Exchange Membrane Fuel Cell Operational Reliability Through Cabin-Based Fuzzy Control in Coastal Standalone Observation Systems in Antarctica – Journal of Marine Science and Engineering, 2025, Author ranking: 1

A Multi-Objective Scheduling Strategy for a Hybrid Energy System for Antarctic Coastal Research Stations – Journal of Marine Science and Engineering, 2024, Author ranking: 3

Research on output voltage control of PEMFC based on fuzzy active disturbance rejection – Modern Electronic Technology, 2024, Author ranking: 3

Sisil Kumarawadu | Energy systems applications | Excellence in Innovation

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Prof. Sisil Kumarawadu | Energy systems applications | Excellence in Innovation

Senior Professor, University of Moratuwa, Sri Lanka

Sisil Kumarawadu, Ph.D., is a Senior Professor in Electrical Engineering at the University of Moratuwa, Sri Lanka, and a distinguished professor at Shanghai University of Electric Power. With expertise in smart energy-efficient systems, systems automation, and AI applications, he has made significant contributions to robotics, intelligent systems, and applied statistics. His leadership extends to his past roles as the Head of the Department of Electrical Engineering and Chairman of the Board of Governors at the Arthur C. Clarke Institute for Modern Technologies. Dr. Kumarawadu’s career includes research, teaching, and mentorship, further enhancing his stature as a leading academic and innovator in electrical engineering. 📚💡🌍

Publication Profile

Scopus

Education:

Dr. Kumarawadu holds a Ph.D. in Robotics and Intelligent Systems from Saga National University, Japan (2003), a Master’s degree in Advanced Systems Control Engineering from the same institution (2000), and a First Class Honours BSc in Electrical Engineering from the University of Moratuwa, Sri Lanka (1996). 🎓📘

Experience:

Dr. Kumarawadu has over two decades of experience in academia, having served as a Senior Professor, Professor, and Associate Professor in Electrical Engineering at the University of Moratuwa. He has held notable positions including the Exetel Endowed Professor in Artificial Intelligence and Postdoctoral Research Fellow at the National Central University, Taiwan. He has delivered keynote speeches at major international conferences and has contributed to several research projects in automation, energy systems, and AI. 🏫🌍📈

Research Interests:

Dr. Kumarawadu’s research interests include smart energy-efficient systems, systems automation and control, AI applications, and applied statistics. He is particularly focused on innovations in robotics, intelligent transportation systems, and sustainable energy solutions, contributing to various cutting-edge advancements in these fields. ⚡🤖🔬

Awards:

Dr. Kumarawadu has received numerous prestigious awards, including the Sri Lanka Education Leadership Award (2019), the National Research Council Merit Award for Scientific Publications (2010), and the Presidential Award for Scientific Research Publications (2007, 2008). He has also been honored as an Overseas Distinguished Professor by Shanghai University of Electric Power and recognized in the Marquis Who’s Who in the World (25th Edition). 🏆🥇🎖️

Publications:

“NILM for Commercial Buildings: Deep Neural Networks Tackling Non-Linear and Multi-Phase Loads,” Energies (Section F: Electrical Engineering), Vol. 17, Issue 15, August 2024.

“A Biphasic Machine Learning Approach for Detecting Electricity Theft Cyberattacks in Smart Grids,” IEEE Trans. Smart Grids (under review).
Link to publication

“Dijkstra Method Based Zone Temperature Management Strategy for Optimal Energy Saving with Guaranteed Thermal Comfort,” The International Journal of Building Science and its Applications (under review).
Link to publication

“Review on Li-Ion Battery Parameter Extraction Methods,” IEEE Access, Vol. 11, 2023.
Link to publication

“Deep Learning-based Non-Intrusive Load Monitoring for a Three-Phase System,” IEEE Access, Vol. 11, 2023.
Link to publication