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Abstract
The thesis, titled "Artificial Intelligence (AI) based Control and Monitoring of Renewable EnergySystems in Microgrids," proposes advanced AI-driven control strategies to enhance the stability, efficiency, and reliability of microgrids integrating renewable energy sources like solar and wind. It introduces novel integral reinforcement learning (IRL) methods for robust power converter control, a data-driven linear quadratic regulator (LQR) for large-signal stability, and a generative physics-informed machine learning (GPIML) technique for accurate health monitoring of DC-link capacitors. The findings bridge the gap between theoretical AI applications and industrial implementation, contributing significantly to the sustainable operation of modern power systems.
| Original language | English |
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| Qualification | Doctor of Philosophy |
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| Award date | 10 Mar 2025 |
| DOIs | |
| Publication status | Unpublished - 2025 |
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- THESIS - DOCTOR OF PHILOSOPHY - QIE Tianhao - 2024_
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Generative Physics Informed Machine Learning Method for DC-Link Capacitance Estimation
Qie, T., Zhang, X., Xiang, C., Zhao, S., Jiang, C., Iu, H. H. C. & Fernando, T., 2025, In: IEEE Transactions on Industrial Electronics. 72, 5, p. 5461-5471 11 p.Research output: Contribution to journal › Article › peer-review
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A Novel Data-Driven Large Signal Stabilizer for Interleaved DC/DC Boost Converter With Constant Power Load
Qie, T., Zhang, X., Jiang, W., Ye, J., Iu, H. H. C. & Fernando, T., 2024, In: IEEE Transactions on Industrial Electronics. 71, 11, p. 14308-14317 10 p.Research output: Contribution to journal › Article › peer-review
3 Citations (Scopus) -
A Novel Data-Driven Linear Quadratic Regulator for Interleaved DC/DC Boost Converter
Qie, T., Zhang, X., Zhu, J., Vilathgamuwa, M., Iu, H. H. C. & Fernando, T., 1 May 2024, In: IEEE Transactions on Power Electronics. 39, 5, p. 5400-5410 11 p.Research output: Contribution to journal › Article › peer-review
3 Citations (Scopus)