te.ghanbari

RESEARCH PROFILE

Teymoor Ghanbari | Electrical Engineering
Power Systems • Power Electronics • Energy Storage • EVs • Condition Monitoring

Research Expertise

My research has developed around a common objective: improving the reliability, safety, monitoring, and energy performance of modern electrical and power-electronic systems. My work combines power-system protection, transient phenomena, electrical sensing, condition monitoring, power electronics, energy storage, electric vehicles, and intelligent diagnostic methods.

My experience includes the design and experimental development of fault- and inrush-current limiters; sensor-based monitoring of electrical machines and power-electronic converters; battery and supercapacitor state/health estimation; hybrid energy-storage systems; EV energy management; and AI/ML-based fault and aging detection. This work has resulted in more than 100 journal papers, 50+ conference papers, six patents, and extensive supervision of MSc and PhD research.

A particularly relevant aspect of my research is the combination of theoretical analysis with experimental implementation, including the development of sensing methods, prototypes, monitoring algorithms, and energy-management strategies.

Selected Research Contributions

·       Intelligent condition monitoring: Development of online methods for detecting faults, degradation, and aging in electrical and power-electronic components, including converter capacitors, batteries, supercapacitors, and electrical machines.

·       Advanced electrical sensing: Development and application of Rogowski-coil-based sensing techniques for fault diagnosis, transient measurement, and condition monitoring.

·       Power-system protection: Development of AC/DC fault-current limiting and transient-limiting technologies applicable to microgrids and distribution systems.

·       Energy storage: Research on battery, supercapacitor, and flywheel technologies, including SOH/SOE estimation, early aging detection, sizing, and coordinated energy management.

·       Electric vehicles: Research covering EV propulsion and regenerative braking, battery/supercapacitor energy management, wireless charging, fast charging, and hybrid battery/flywheel storage.

·       AI and predictive methods: Application of machine learning and data-driven approaches to fault detection, battery aging/RUL prediction, and monitoring of renewable-energy and power-electronic systems.

Current Research Interests

My current research is increasingly focused on the intersection of electrification, renewable energy, energy storage, power electronics, and intelligent condition monitoring.

Battery health monitoring, early aging detection and RUL estimation • Hybrid battery–supercapacitor–flywheel energy-storage systems • EV energy management, charging and high-power-density storage • Condition monitoring and reliability of power-electronic converters • Protection and fault diagnosis of PV systems, microgrids and DC networks • AI/ML-based predictive maintenance and fault detection • Sensor-based monitoring of electrical machines and power-system components.

Recent work includes machine-learning-based modeling of DC series arc faults in PV systems, EV energy-management and storage research, flywheel energy storage, battery aging/RUL-related research, and advanced DC microgrid fault detection.

Potential Contribution to Research

The research’s expanding activities in renewable electricity, solar-energy utilization, electrification, energy storage, smart-grid technologies, and sustainable transportation provide several natural points of interaction with my research background.

I would be particularly interested in developing collaborative research around:

·       Renewable Energy & PV Systems — condition monitoring, fault detection, protection, and improved reliability of PV and power-electronic systems.

·       Energy Storage — battery health monitoring, early aging detection, RUL estimation, and intelligent management of battery/supercapacitor storage for renewable-energy applications.

·       EVs & Charging Infrastructure — battery/energy-management strategies, EV charging systems, hybrid energy storage, and power-electronic reliability.

·       Smart Grids & Microgrids — fault detection, protection, transient management, DC microgrids, and integration of distributed energy resources.

·       Intelligent Condition Monitoring — development of practical sensor- and AI-based approaches for predictive maintenance of electrical assets.

My previous projects have included battery-management-system development, PV fault-ride-through assessment, battery RUL prediction, hybrid battery/flywheel energy storage for EVs, and monitoring of electrical and power-electronic components. These experiences could provide a basis for joint research proposals, experimental projects, student research, and industry-oriented collaborations.

Selected Publications

1. Mehraban, A., Farjah, E. & Ghanbari, T. “AI-Based Control of Storage Capacity in High-Power-Density Energy Storage Systems, Used in Electric Vehicles.” IEEE Transactions on Transportation Electrification, 2023.

2. “Online Parameter Estimation for Supercapacitor State-of-Energy and State-of-Health Determination in Vehicular Applications.” IEEE Transactions on Industrial Electronics, 2019.

3. “Rogowski Search Coil for Stator Fault Diagnosis.” IEEE Sensors Journal, 2013.

4 “Solid-State Fault Current Limiter for Microgrid.” IEEE Transactions on Power Delivery, 2013.

Research Profiles

Google Scholar: https://scholar.google.com/citations?user=nYAb_aAAAAAJ&hl=en

ORCID: https://orcid.org/my-orcid?orcid=0000-0002-2185-5388

Power Systems | Power Electronics | Energy Storage | Electric Vehicles | Condition Monitoring | Renewable Energy | Microgrids | Intelligent Diagnostics