Research Associate
Dr Mohsen Eskandari
- PhD, University of Technology Sydney, Syndey, 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
I was born in Saveh, Iran. I received the B.Sc. and M.Sc. degrees from the Islamic Azad University, Saveh Branch, Saveh, Iran, in 2004 and 2013, and the Ph.D. degree from the University of Technology Sydney, Sydney, Australia, in 2021, all in Electrical Engineering. I am a Research Fellow with the University of New South Wales, Sydney, Australia, since March 2020.
I have over 10 years of experience in different parts of the electrical industry. I have proven skills in handling power systems projects as well as a strong background in the field of automation and control. I also possess valuable experience in relation to power systems at transmission, distribution, and load (industrial and residential) levels.
My research interests include power systems, microgrids, control theory, optimization and AI, UAV navigation, wireless communication, and wireless power transfer.
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
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.
- Impedance Emulation and Shaping for Inverters
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.
- Intelligent Control, Management and Protection Techniques for 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 digital twin (a virtual model) of inverters and microgrids with an intelligent decision-making capability.
- 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 navigation.
- RIS-equipped UAV (RISeUAV) for providing aerial LoS service in modern 5G/6G wireless communication systems.