Deep-Learning-Based Koopman Modeling for Online Control Synthesis of Nonlinear Power System Transient Dynamics

Zhao, Tianqiao; Yue, Meng; Wang, Jianhui

doi:10.1109/tii.2023.3240939

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…Wang & Pal, 2023;R. Zhang, Wu, Shao, Li, & Lu, 2018;Zhao, Yue, & Wang, 2023a;. Some papers refer to ANNs generically without specifying the model's architecture, probably referring to single hidden layer MLP Goh et al, 2015;Malbasa et al, 2017;M.V & C.K., 2014;Velayati et al, 2015;Yousefian, Bhattarai, & Kamalasadan, 2017;R.…”

Section: Types Of Techniquementioning

confidence: 99%

“…Zhang et al, 2023aR. Zhang et al, , 2023bZhao et al, 2023aZhao et al, , 2023bZhu, Hill, & Lu, 2021;Zhu & Luo, 2021). The last AI technique found in this review is Quantum Machine Learning (QML) which implements a Variational Quantum Circuit (an ordered sequence of quantum gates characterized by variable parameters that can be tuned during the training procedure) that takes advantage of the ability of quantum machines to perform complex tensor operations and utilize quantum operators (such us superposition and entanglement) to capture complex non-linear behavior is the first (and only work so far) that implements QML for DSA.…”

Section: Types Of Techniquementioning

confidence: 99%

“…Five works that use Reinforcement Learning (RL) were found (Y. Wang & Pal, 2023;Yousefian et al, 2017;Yousefian & Kamalasadan, 2016Zhao et al, 2023a). Finally, only two works based on semisupervised learning were found.…”

Section: Types Of Techniquementioning

confidence: 99%

“…Li et al, 2021;C. Wang et al, 2021;Yousefian et al, 2017;Yousefian & Kamalasadan, 2016Zhao et al, 2023a;Zhou et al, 2016), the 74-bus Nordic System (Naderi et al, 2023), the 77-bus Nordic System , the simplified Australian Electric System of 59 buses and 14 generators (R. Liu et al, 2018;Suprême et al, 2018), the Indian 75-bus system (Chang et al, 2005;Jain et al, 2003), the Iranian 66-bus power grid (H. Mohammadi et al, 2018Mohammadi et al, , 2016, the IEEE standard 118-bus system (Anderson et al, 2023;Dharmapala et al, 2020;Maihemuti et al, 2021;Mukherjee & De, 2020;Nie et al, 2017;Xie & Sun, 2021;Zhao et al, 2023b), the 123-bus IEEE system (Y. Wang & Pal, 2023), the 145-bus IEEE 50-generator test system (Azman et al, 2020;Velayati et al, 2015;Y.…”

Section: Test Systemsmentioning

confidence: 99%

“…Wang et al, 2019;Xie & Sun, 2021) and the activation of under-voltage load disconnection (UVLS) Zhu & Luo, 2021). Other corrective control actions are the modification of control system parameters (Hossain & Kumar, 2023;Yousefian et al, 2017;Yousefian & Kamalasadan, 2016Zhao et al, 2023a) and load shedding (Genc et al, 2010;Naderi et al, 2023;Tian et al, 2019). As indicated earlier, many works only focus on evaluating system stability, without triggering any actions.…”

Section: Type Of Actionmentioning

confidence: 99%

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Artificial Intelligence Techniques for Dynamic Security Assessments – A Survey

Cuevas,

Alvarez-Malebrán,

Rahmann

et al. 2024

Preprint

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The rise of converter interface generation (CIGs) technologies with fast response times has introduced new challenges for the stability of electrical power systems. Traditional dynamic security assessment (DSA) methods of power systems are based on dynamic simulations within a time domain. Because of the high computational efforts and human resources required to carry out these kinds of simulations, they are performed for a limited number of adverse (critical) operational conditions in terms of their stability. However, further incorporation of CIGs in power systems not only results in a shift of the critical operating conditions that may threaten the system’s stability, but also the number of critical conditions increases. Consequently, there is an urgent need to develop new methods for evaluating system stability in a wide range of operating conditions with a reasonable amount of human and computational effort. A complementary 1 strategy to the traditional methods for DSA is to use artificial intelligence (AI) techniques. Unlike traditional methods, AI techniques use a data set that captures the non-linear relationships between the system’s operational conditions and their stability, without needing to solve the algebraic-differential equations modeling the power system. Once these relationships are established, the system’s stability can be evaluated for other operational conditions much faster (within the order of the hundreds of milliseconds) and accurately. This, in turn, allows for the consideration of an extended range of operational conditions for DSA studies. This article surveys the state of the art regarding the use of AI techniques for DSA in electrical power systems. Focus is made on algorithms, the kind of stability being addressed, data processing and applications. Finally, limitations, challenges, and future trends are discussed.

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