Karabuk University | Turkey
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Early Academic Pursuits
Dr. Elaheh Yaghoubi's academic journey began with an Associate's degree in Electrical Engineering from University College of Rouzbahan, Iran, where she graduated with a GPA of 3.5. She then pursued a Bachelor's degree in Electrical Engineering at Aryan Institute of Science and Technology University, Iran, achieving a perfect GPA of 4. Following this, she completed her Master's degree in Electrical Engineering at Islamic Azad University in Qaemshahr, Mazandaran, Iran, again with a perfect GPA of 4. Her Master's thesis focused on developing a routing algorithm for a proposed topology for a grid on a large-scale chip to detect errors. Dr. Yaghoubi is currently a Ph.D. candidate in Electronic and Electrical Engineering at Karabuk University in Turkey, where she is working on her thesis titled "Optimal power control of grid-connected distributed generation in a hierarchical framework based on Model Predictive Control."
Professional Endeavors
Dr. Yaghoubi has a diverse professional background that complements her academic achievements. From 2015 to 2018, she served as a Senior Manager at Kati Kabl Tabarestan Factory in Mazandaran, Iran, where she was responsible for quality assurance, inspecting products to ensure high quality, and troubleshooting technical issues. She then worked as a Senior Manager at Rico Electronics Company in Mazandaran, Iran, overseeing product quality assurance and implementing design modifications. From 2019 to 2021, she worked as a Website Designer at WebCore Company in Mazandaran, designing front-end interfaces with HTML, CSS, and JavaScript, and back-end systems with PHP and Laravel. Currently, Dr. Yaghoubi is a Principal Researcher at the Power Electrical Developing Advanced Research (PEDAR) group, focusing on investigation, teaching, and designing.
Contributions and Research Focus
Dr. Yaghoubi's research interests are broad and interdisciplinary, encompassing power system analysis, power system stability, power management, microgrids, smart grids, renewable energies, model predictive controllers (MPC), artificial neural networks, machine learning, deep learning, plasmonic applications, and nano-electronic devices. Her current research work involves optimal power control of grid-connected distributed generation using model predictive control, a topic that is crucial for the advancement of smart grids and renewable energy systems. She has also contributed to the understanding and development of routing algorithms for large-scale chips and has experience in quality control and product management in industrial settings.
Accolades and Recognition
Throughout her academic and professional career, Dr. Yaghoubi has been recognized for her excellence and contributions. She successfully passed her Ph.D. qualification exam with a perfect grade of 4 out of 4. Her consistent academic performance, marked by perfect GPAs during her Bachelor's and Master's studies, reflects her dedication and expertise in her field.
Impact and Influence
Dr. Yaghoubi's work has had a significant impact on both academic and industrial fields. Her research on smart grids, optimization techniques, and model predictive control contributes to the development of more efficient and reliable power systems. Her practical experience in quality control and product management ensures that her research is grounded in real-world applications and industrial standards.
Legacy and Future Contributions
Dr. Yaghoubi's legacy lies in her interdisciplinary approach to electronic and electrical engineering, integrating theoretical research with practical applications. Her work in power systems, renewable energy, and advanced control techniques positions her as a key contributor to the future of smart grid technology and sustainable energy solutions. As she continues her research and professional activities, Dr. Yaghoubi is likely to make further significant contributions to the field, driving innovation and excellence in electronic and electrical engineering.
Notable Publications
A systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavior 2024
A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering 2024
Controlling and tracking the maximum active power point in a photovoltaic system connected to the grid using the fuzzy neural controller 2023 (1)
Tunable band-pass plasmonic filter and wavelength triple-channel demultiplexer based on square nanodisk resonator in MIM waveguide 2022 (9)
Triple-channel glasses-shape nanoplasmonic demultiplexer based on multi nanodisk resonators in MIM waveguide 2021 (11)