Eigenanalysis of Delayed Networked Microgrids

Wang, Lizhi; Zhou, Yifan; Wan, Wenfeng; Ye, Hua; Zhang, Peng

doi:10.1109/tpwrs.2021.3089040

Cited by 12 publications

(4 citation statements)

References 5 publications

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“…2021; Tang et al 2021; Wang et al. 2021; Zhang 2021; Zhou and Zhang 2021a, Zhou and Zhang 2021b, Zhou, Zhang, and Yue 2021). The project is partnering with a $1B microgrid project at the Energy & Innovation Park (EIP) in New Britain, Connecticut, which aims to transform the traditional manufacturing base of that city to a new digital economy supporting the rapidly growing Data Center sector.…”

Section: Civil/built Infrastructurementioning

confidence: 99%

“…Further, to provide runtime safety assurance despite possible flaws and vulnerabilities in virtualized controllers, a Neural Simplex Architecture is being developed. If a safety violation is imminent, the decision module switches control from the AI-based advanced controller to a verified-safe baseline controller and incrementally retrains the AI-based controller (Jiang et al 2021;Tang et al 2021;Wang et al 2021;Zhang 2021;Zhou and Zhang 2021a, Zhou and Zhang 2021b, Zhou, Zhang, and Yue 2021. The project is partnering with a $1B microgrid project at the Energy & Innovation Park (EIP) in New Britain, Connecticut, which aims to transform the traditional manufacturing base of that city to a new digital economy supporting the rapidly growing Data Center sector.…”

Section: Ai-enabled Provably Resilient Networked Microgridsmentioning

confidence: 99%

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Enabling AI innovation via data and model sharing: An overview of the NSF Convergence Accelerator Track D

et al. 2022

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This article provides a brief overview of 18 projects funded in Track D—Data and Model Sharing to Enable AI Innovation—of the 2020 Cohort of the National Science Foundation's (NSF) Convergence Accelerator (CA) program. The NSF CA is focused on transitioning research to practice for societal impact. The projects described here were funded for one year in phase I of the program, beginning September 2020. Their focus is on delivering tools, technologies, and techniques to assist in sharing data as well as data‐driven models to enable AI innovation. A broad range of domain areas is covered by the funded efforts, spanning across healthcare and medicine, to climate change and disaster, and civil/built infrastructure. The projects are addressing sharing of open as well as sensitive/private data. In September 2021, six of the eighteen projects described here were selected for phase II of the program, as noted in this article.

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Section: Civil/built Infrastructurementioning

confidence: 99%

Section: Ai-enabled Provably Resilient Networked Microgridsmentioning

confidence: 99%

Enabling AI innovation via data and model sharing: An overview of the NSF Convergence Accelerator Track D

et al. 2022

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“…Considering the non-negligible impact of multiple communication and measurement time delays on system stability [68], it is of great significance to analyze the NMG stability in-depth under heterogeneous time delays and investigate the corresponding time-delay compensation control. Existing literature for the time-delayed stability analysis in NMG systems focuses on single-delay issues [22], [31] or multiple-delay issues [15], [69]- [71]. However, only the single-layer control framework for NMG is considered in these works.…”

Section: Systemmentioning

confidence: 99%

“…Similarly, the impact of a single delay on NMG is investigated in reference [31]. Only [70] and [71] conduct multiple time-delay stability analysis for NMG system, while the inner dynamics of MG is simplified and the critical electrical coupling effect among MGs is ignored. [72] and [73] propose robust controllers considering the effect of time delay, but these controllers are only designed and validated under a single-layer control structure.…”

Section: Systemmentioning

confidence: 99%

Modeling, stability analysis and advanced control of cyber-physical microgrid systems

Yao¹

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supervisor, Assoc. Prof. Xu Yan, for his guidance, encouragement, and advice. It has been a great privilege and joy to study under his guidance and supervision. His rigorous academic attitude, infectious enthusiasm, and gentle personality always inspire me in my research and life journey. It is him who makes my Ph.D. journey fulfilling, and it is also him who always encourages me to explore life's possibilities. The limited words hardly express my deep feelings, but I still want to say the Ph.D. journey under his supervision is the best time of my life. Also, I would like to thank my co-supervisor at NTU, Assoc. Prof. Tan Rui, my mentor, Prof. Wang Peng, and my co-supervisor at the Technical University of Denmark, Assoc. Prof. Wu Qiuwei. They provide very valuable suggestions and guidance and give me many supports in my Ph.D. journey. Dr. Wang Yu, Research Fellow of EEE, NTU, has my heartfelt gratitude. He always supports me when I was in tough situations. The research experience shared by him inspires me to pursue more in this field. He teaches me a lot about the detailed algorithms, models, skills, and knowledge, and I really enjoyed the time discussing research issues with him. He is an excellent academic role model for me, and his steady, practical, diligent, and modest qualities will always be worth my lifelong learning.Moreover, I also want to thank my seniors and colleagues, Dr.

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