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个人简介
The core area of my research is 'Control systems applications'. This encompasses system modelling, control development and model-based fault diagnosis. The specific application areas of my core area in the order of priority are as follows:
Solar farm fault detection and diagnosis: The theme of this work is to use control theoretic concepts to devise methods for automatic detection and diagnosis of underperforming solar panels in large solar farm spread over hectares of land. Underperformance in solar panels could be due to faults, degradation or panel soiling. The work involves extensive experimental work on an experimental setup at UQ and ongoing field trials at Gatton Solar farm. This work is of significant industrial and commercialisation potential as underperforming solar panels have major economic and safety consequences in large solar farms.
Control of grid-connected inverters: The main objective here is to apply advanced control theory for the control of grid-connected inverters. Power quality, stability and performance outcomes associated with grid connected inverters is heavily reliant on underlying control algorithms. Despite significant advancements in inverter technologies, the underlying control methods continue to be rudimentary. This research theme is aimed at applying advanced modern control methods to tackle operational challenges associated with growing number of grid connected inverters.
Control algorithms for demand side management: Demand side management is a key tool in the effective and efficient management of future power networks comprising of distributed and intermittent energy resources. However, most of the existing demand side potential to improve power network performance remains untapped mostly due to lack of adequate control algorithms. This reserach theme is to develop novel control methods based on advanced control theory for effective and robust management of distributed energy resources.
Solar farm fault detection and diagnosis: The theme of this work is to use control theoretic concepts to devise methods for automatic detection and diagnosis of underperforming solar panels in large solar farm spread over hectares of land. Underperformance in solar panels could be due to faults, degradation or panel soiling. The work involves extensive experimental work on an experimental setup at UQ and ongoing field trials at Gatton Solar farm. This work is of significant industrial and commercialisation potential as underperforming solar panels have major economic and safety consequences in large solar farms.
Control of grid-connected inverters: The main objective here is to apply advanced control theory for the control of grid-connected inverters. Power quality, stability and performance outcomes associated with grid connected inverters is heavily reliant on underlying control algorithms. Despite significant advancements in inverter technologies, the underlying control methods continue to be rudimentary. This research theme is aimed at applying advanced modern control methods to tackle operational challenges associated with growing number of grid connected inverters.
Control algorithms for demand side management: Demand side management is a key tool in the effective and efficient management of future power networks comprising of distributed and intermittent energy resources. However, most of the existing demand side potential to improve power network performance remains untapped mostly due to lack of adequate control algorithms. This reserach theme is to develop novel control methods based on advanced control theory for effective and robust management of distributed energy resources.
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IEEE ACCESS (2024): 2778-2792
M. Imran Azim, Gayan Lankeshwara,Wayes Tushar,Rahul Sharma ,Mollah R. Alam,Tapan Kumar Saha,Mohsen Khorasany,Reza Razzaghi
IEEE Trans. Smart Gridno. 2 (2024): 1978-1990
INTERNATIONAL JOURNAL OF CONTROLpp.1-16, (2023)
IEEE Transactions on Power Systemsno. 99 (2023): 1-12
International Journal of Electrical Power & Energy Systems (2023): 109267
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iScienceno. 7 (2023): 107194-107194
IEEE Transactions on Smart Gridno. 99 (2023): 1-1
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