- Artificial Intelligence (AI) based Control and Fault Monitoring of Batteries in Microgrids
The project focuses on advanced control and fault monitoring of batteries in microgrid context. Microgrids battery storage systems are frequently charged and discharged at high ramp rate. Such a usage pattern brings the challenges to fast reference tracking control and battery health. To overcome these challenges, this project will explore the AI based control and fault monitoring methods that possess the advantages of model independency, autonomous gain tuning and high computational efficiency. The investigated methods will be applied to produce excellent battery performance in microgrids and report battery faults when necessary.
- Payment type
- Fortnightly Stipend
- Value unit
- Per annum
- Basis of award
- Academic Achievement
Candidates should hold a minimum Bachelor/Honours degree or equivalent in electrical engineering. Strong mathematical background, i.e., optimization algorithm, and some knowledge of battery energy storage system are needed. Some knowledge of control engineering in power systems and AI techniques background will be highly desirable. Relative battery storage application project is required. Candidates will need to be very capable, self-motivated and keen to engage with the local and international science and engineering community. The project manager reserves the right to make an appointment before the closing date if a suitable candidate is identified.
- Australian Citizen, Australian Permanent Resident, International
- Study area
- Electrical Engineering
- Commencement date
- Applications open
- Applications close
- Tenable At
- Currently available
- Application status
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