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