Dr Mohsen Eskandari
Research Associate
- PhD, University of Technology Sydney, Sydney, Australia, 2017 - 2021
- MSc, Islamic Azad University, Saveh Branch, Saveh, Iran, the Islamic Republic of, 2011 - 2013
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BSc, Islamic Azad University, Saveh Branch, Saveh, Iran, the Islamic Republic of, 2000 - 2004
Engineering
Electrical Engineering and Telecommunications
Mohsen was born in Saveh, Iran. He received the B.Sc. and M.Sc. degrees in electrical engineering from Islamic Azad University, Saveh, Iran, in 2004 and 2013, respectively, and the Ph.D. degree in electrical engineering from the University of Technology Sydney (UTS), Sydney, Australia, in 2021. Since March 2020, he has been a Postdoctoral Research Associate with the University of New South Wales, Sydney, Australia. He joined the AUSMURI Neuro-Autonomy project as a Research Fellow in early 2021.
Dr. Mohsen is the founder of MAI OptiTek, offering consultancy and AI services for intelligent grids and networks. He provides technical sub-consultancy services for consultant companies toward digitalization and intelligent grids. Particularly, he provides valuable advice on the design and stability analysis, parameter tuning and registration, and connection test of inverter-based energy resources including PSCAD and PSS®E modeling. He has over ten years of experience in different parts of the electrical industry. He has proven skills in handling electric engineering projects as well as a strong background in the field of automation and control.
His research interests include power systems, renewable energy including inverter-interfaced energy resources, BESSs and microgrids, wireless communication, AI-assisted control-optimization-automation, robotics, and UAV Navigation.
Dr. Eskandari was the recipient of the Higher Degree Research Excellence Award at UTS Faculty of Engineering and Information Technology in 2018 and 2019, in recognition of outstanding academic performance and the best individual HDR project.
E-mail
- Publications
- Media
- Grants
- Awards
- Research Activities
- Engagement
- Teaching and Supervision
- T. Yu, K.Y. Wang, L. Li, M. Eskandari, L.F. Cheng, K. Qu, L. Yin, “Parallel CPSS Structure Based Smart Energy Robotic Dispatcher and its Knowledge Automation Theory”, National Natural Science Foundation of China, $130,000, 2018.
- M. Eskandari, “FEIT HDR Research Collaboration Experience Grant”, University of Technology Sydney, $4,000, 2018.
- UTS International Research Scholarship (IRS) (Nov 2016)
- UTS President’s Scholarship (UTSP) (Nov 2016)
- UTS FEIT Higher Degree by Research Publication Award (Dec 2017)
For publishing high quality, high impact research
- UTS FEIT HDR Research Collaboration Experience Scholarship (Apr 2018)
- UTS FEIT Higher Degree Research Directors Commendation (Nov 2018)
In recognition of outstanding potential and an excellent individual HDR project
- UTS FEIT Higher Degree Research Excellence Award (Dec 2019)
In recognition of outstanding academic performance and the best individual HDR project
- Stabilizing Autonomous Networked Microgrids for Future Resilient Grids
Modern power systems have been developing through high penetration of the power electronic-based renewable energy resources and distributed generation units into conventional power systems. Mixing up the power systems and the power electronics technologies along with the smart grid facilities, the microgrid concept has gained full attention for addressing the resiliency issue of modern power systems through autonomous operation capability. However, it is not an easy task to stabilize an autonomous microgrid due to the dominated inverter-interfaced generation units and complex power flow. The microgrid is an emerging technology that facilitates the integration of renewable resources into power systems, and definitely, would be the cornerstone of future/modern power systems.
Goal
- Frequency and voltage control of inverter-dominated microgrids while preserving power-sharing and securing dynamics stability.
- The impact of battery energy storage systems on the economy dynamics of microgrids with a focus on linking economics (including the steady-state performance) and dynamics (inertia and frequency support).
- Developing accurate physics-aware mathematical models of microgrids for paving the path toward the Digital Twin of microgrids.
Hypothesis
Toward Net-Zero carbon emission energy and power systems.
The microgrid concept is the key solution to address the resiliency issue of modern power systems.
Resilient grids with autonomous multi-microgrid systems
- Intelligent Autonomy for Autonomous Vehicles and Systems
Neuro-autonomy: neuro-inspired perception, navigation, and spatial awareness for autonomous robots, vehicles, and systems.
Goal
- Autonomous UAV/Robotics navigation.
- Deep visual SLAM.
- A unified module for perception (including localization), path planning, and path tracking (control), to convert raw sensory (visual) data to navigation control signals.
- Impedance Emulation and Shaping for Inverter-Interfaced Energy Resources
The electric components are identified by their impedances and the impedance model of an electric system reveals its characteristics. The dominant inductive impedance of bulk power systems in generation and transmission levels and its compliance with the f-P and V-Q control loops have resulted in harmony for the stable operation of bulk power systems for more than a century. However, inverters reveal arbitrary impedance characteristics, depending on their controllers, that have put the stability of power systems at risk. This project introduces a new concept for developing and designing inverters based on the impedance shaping concept.
Goal
Developing and designing impedance shaping-based controllers to solve stability issues of modern grids hosting inverter-interfaced distributed energy resources.
Hypothesis
A universal controller for inverters utilizing impedance shaping consistent with inertia-impedance strength indexes of electrical grids.
- Revisiting and quantifying inverter-dominated grids' inertia and impedance strength indexes
The impacts of the inverters's controllers on the grid strength indexes should be clarified and quantified. The existing virtual impedance and virtual inertia solution fail to stabilize the future grids and require effective solutions.
Goal
Stabilizing the inverter-dominated grids.
Hypothesis
Exploring milestones toward Net-Zero emission electric power and energy systems with 100% inverter-based resources.
- Studying the bidirectional impact of grid-connected grid-forming inverters and the grid
The grid-connection of grid-forming inverters, mostly for connecting battery energy storage systems, is a trending solution to address weak grid stability issues.
Goal
The impacts of the inverter controller on the grid inertia and impedance.
Controller design to stabilize the inverter and strengthen the grid.
Hypothesis
The interaction of grid-forming inverters through the grid can raise critical stability issues.
- Intelligent Control, Management and Protection Techniques for Inverters-based Energy Resources and Microgrids
Studying the application of deep learning AI-based techniques for addressing uncertainties, complexities, nonlinearities, and computational hardness of conventional methods for control, energy management, and protection of inverter-interface energy resources and (micro) grids.
Goal
- Exploring potential applications of deep learning AI in modern grids including inverter-interfaced renewable energy resources and distributed generation units.
- Handling large-scale data associated with high penetration of renewable energy resources, behind-the-meter batteries, and EVs.
- Identifying unrecognized features of modern power systems hidden in time series data given by frequency measurements and PMUs.
- Developing an intelligent inverter stability tool (IST).
- Utilizing AI to complement physics-aware transients-dynamics-economics models in the context of Digital Twin.
Hypothesis
Developing a digital twin (a virtual model) of inverters and microgrids with an intelligent decision-making capability.
- Wireless Communication Aided Accurate Localization for Autonomous Intelligent Vehicles and UAVs
Lidar and vision-based SLAM methods need sensor fusion for intelligent vehicles for safe path planning and path tracking for autonomous driving in dense urban environments. Modern wireless communication networks with fine beamforming capability of massive multi-input-multi-output antennas can assist existing visual SLAMs to create accurate results.
Goal
- Utilizing the power of mmWave communication for accurate localization.
- RIS-equipped UAV (RISeUAV) for providing aerial LoS service in modern 5G/6G wireless communication systems.
- RIS-assisted localization for UAVs and intelligent vehicles.