Amir Hossein Poursaeed is an accomplished researcher in electrical engineering with a specialization in power systems, machine learning applications, and renewable energy integration. Holding a Master’s degree from Lorestan University, he has developed a strong academic foundation complemented by an exceptional research portfolio. His work focuses on power system protection, stability, and optimization using advanced AI techniques such as explainable deep learning and quantum neural networks. With over 17 peer-reviewed journal publications, many in Q1 journals, and multiple IEEE conference contributions, his research demonstrates both depth and innovation. He collaborates with leading academics internationally and has contributed to interdisciplinary studies in environmental modeling and water resource management. Amir’s commitment to cutting-edge research in inverter-based power grids, fault diagnosis, and energy systems places him among the promising young scholars in the field. His achievements reflect a rare blend of technical expertise, research leadership, and forward-looking vision essential for shaping the future of smart grids.
Google Scholar
ORCID Profile
Amir Hossein Poursaeed has a solid educational background in electrical engineering with a focus on power systems. He earned his Master of Science degree from Lorestan University, Iran, where he specialized in Digital Power System Protection and Power System Dynamics. His M.Sc. thesis, supervised by Professor Farhad Namdari, focused on using Support Vector Machines for wide-area protection against voltage and transient instabilities. He previously obtained his Bachelor of Science in Electrical Engineering from the same university, where he explored the optimal placement of phasor measurement units using metaheuristic algorithms. His academic performance was commendable, with a GPA of 18.87/20 in his M.Sc. program, demonstrating both technical strength and research capability. Throughout his education, he consistently focused on high-voltage systems, optimization, and smart grid technologies, laying the foundation for his research in AI-based power system protection and stability. His educational journey highlights a continuous commitment to excellence and innovation in energy systems.
Amir Hossein Poursaeed has developed a robust professional profile centered around advanced power system research and academic collaboration. While specific institutional roles aren’t explicitly mentioned, his extensive list of high-impact publications indicates active involvement in collaborative research projects, particularly with institutions such as Lorestan University and international partners. He has co-authored multiple studies with recognized scholars, including Professor Farhad Namdari and Dr. P.A. Crossley, highlighting his integration into the global research community. His contributions include the design of advanced fault detection systems, AI-driven stability analysis tools, and renewable energy integration models. Additionally, his work in inter-turn fault diagnosis and real-time system protection showcases applied engineering skills with a focus on practical solutions for modern grid challenges. His experience spans theoretical research, model development, and algorithm implementation in live or simulated systems, establishing him as a well-rounded researcher in academia and an emerging leader in AI-enabled power engineering technologies.
Amir Hossein Poursaeed’s research interests are rooted in the intersection of electrical power systems and artificial intelligence. His primary focus includes power system stability, digital protection systems, fault detection, and the integration of renewable energy sources. He is especially passionate about leveraging advanced machine learning and explainable AI techniques for enhancing grid reliability and system monitoring. His recent work involves deep learning, support vector machines, and quantum neural networks applied to inverter-based power systems and DC microgrids—fields gaining global relevance due to the rise of decentralized energy systems. Optimization algorithms, transient analysis, and wide-area protection schemes are other key domains of his expertise. He also extends his knowledge into environmental systems, working on AI-based models for water quality assessment. This multidisciplinary approach underlines his goal of developing intelligent, robust, and real-time frameworks for smart grid operations, making his research both innovative and impactful in addressing contemporary and future challenges in energy systems.
Although specific awards and honors are not listed, Amir Hossein Poursaeed’s academic and research accomplishments position him as a candidate deserving of high recognition. His publication record in prestigious Q1 journals, such as Applied Soft Computing, Energy Reports, and Sustainable Energy Technologies and Assessments, reflects scholarly excellence. His papers have introduced novel contributions to power system protection and AI-based monitoring, often co-authored with leading international experts—an indication of his growing reputation in the field. His research has also been accepted at major IEEE conferences, including the International Universities Power Engineering Conference and the International Conference on Electric Power and Energy Conversion Systems, which highlights peer recognition of his work. Moreover, his interdisciplinary research in water resource management using machine learning models demonstrates his versatility and impact beyond core power engineering. Given these achievements, he is highly deserving of academic awards, particularly those that celebrate emerging researchers and innovators in smart energy systems.
Amir Hossein Poursaeed is an emerging thought leader in the field of power systems and intelligent energy technologies. With a strong educational background and a research focus on AI-driven solutions for grid stability and protection, he has consistently demonstrated excellence in both theoretical innovation and practical application. His contributions span power engineering, machine learning, and even environmental sciences—showcasing his ability to bridge disciplines for impactful solutions. Through numerous high-impact publications and international conference engagements, he has established himself as a respected voice in the global research community. His work addresses critical challenges in inverter-based grids, renewable integration, and real-time monitoring, aligning perfectly with the global shift toward sustainable and resilient energy systems. Amir’s trajectory reflects not only technical brilliance but also research leadership, collaboration, and a vision for smarter, safer, and more efficient power systems. He is undoubtedly a strong candidate for honors such as the Best Researcher Award.
-
Title: An Ultra-Fast Directional Protection Scheme for DC Microgrids Based on High-Order Synchrosqueezing Transform
Authors: A.H. Poursaeed, F. Namdari
Year: 2023
Citations: 7
-
Title: Online Transient Stability Assessment Implementing the Weighted Least-Square Support Vector Machine with the Consideration of Protection Relays
Authors: A.H. Poursaeed, F. Namdari
Year: 2025
Citations: 6
-
Title: A New Strategy for Prediction of Water Qualitative and Quantitative Parameters by Deep Learning-Based Models with Determination of Modelling Uncertainties
Authors: M. Poursaeid, A.H. Poursaeed
Year: 2024
Citations: 6
-
Title: Online Voltage Stability Monitoring and Prediction by Using Support Vector Machine Considering Overcurrent Protection for Transmission Lines
Authors: A.H. Poursaeed, F. Namdari
Year: 2020
Citations: 6
-
Title: High‐Speed Algorithm for Fault Detection and Location in DC Microgrids Based on a Novel Time–Frequency Analysis
Authors: A.H. Poursaeed, F. Namdari
Year: 2024
Citations: 3
-
Title: Hydraulic Modeling of the Water Resources Using Learning Techniques
Authors: M. Poursaeid, A.H. Poursaeed, S. Shabanlou
Year: 2022
Citations: 3
-
Title: Explainable AI-Driven Quantum Deep Neural Network for Fault Location in DC Microgrids
Authors: A.H. Poursaeed, F. Namdari
Year: 2025
Citations: 2
-
Title: Simulation Using Machine Learning and Multiple Linear Regression in Hydraulic Engineering
Authors: M. Poursaeid, A.H. Poursaeed, S. Shabanlou
Year: 2023
Citations: 2
-
Title: Optimized Explainable Tabular Transformer Model for Fault Localization in DC Microgrids
Authors: A.H. Poursaeed, F. Namdari, P.A. Crossley
Year: 2025
Citations: 1
-
Title: Optimal Coordination of Directional Overcurrent Relays: A Fast and Precise Quadratically Constrained Quadratic Programming Solution Methodology
Authors: A.H. Poursaeed, M. Doostizadeh, S. Hossein Beigi Fard, A.H. Baharvand, F. Namdari
Year: 2024
Citations: 1