Advanced machine learning applications to modern power systems

Feng, Cong; Sun, Mucun; Dabbaghjamanesh, Morteza; Liu, Yuanzhi; Zhang, Jie

doi:10.1016/b978-0-12-820168-8.00007-9

Cited by 2 publications

(1 citation statement)

References 81 publications

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“…Physics-based machine learning algorithms have been widely applied in high-dimensional industrial problems, including aerodynamics [42], air pollution simulation [81], electrical power systems [82,83] or numerical weather prediction (NWP) [84]. Compared to traditional physics-based simulations, machine learning techniques show a significant strength of efficiency, especially when coupling with model reduction approaches, such as POD [81], domain localization [85] or image auto-encoder [86].…”

Section: Computing the Reduced Basismentioning

confidence: 99%

Data-Enabled Physics-Informed Machine Learning for Reduced-Order Modeling Digital Twin: Application to Nuclear Reactor Physics

Gong

Cheng

Chen

et al. 2022

Nuclear Science and Engineering

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This paper proposes an approach that combines reduced-order models with machine learning in order to create physics-informed digital twins to predict high-dimensional output quantities of interest, such as neutron flux and power distributions in nuclear reactor cores. The digital twin is designed to solve forward problems given input parameters, as well as to solve inverse problems given some extra measurements. Offline, we use reduced-order modeling, namely, the proper orthogonal decomposition (POD) to assemble physics-based computational models that are accurate enough for fast predictive digital twin. The machine learning techniques, namely, k-nearest-neighbors (KNN) and decision trees (DT) are used to formulate the input-parameterdependent coefficients of the reduced basis, whereafter the high-fidelity fields are able to be reconstructed. Online, we use the real time input parameters to rapidly reconstruct the neutron field in the core based on the adapted physics-based digital twin. The effectiveness of the framework is illustrated through a real engineering problem in nuclear reactor physics -reactor core simulation in the life cycle of HPR1000 governed by the two-group neutron diffusion equations affected by input parameters, i.e., burnup, control rod inserting step, power level and temperature of the coolant, which shows potential applications for on-line monitoring purpose.

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Section: Computing the Reduced Basismentioning

confidence: 99%

Data-Enabled Physics-Informed Machine Learning for Reduced-Order Modeling Digital Twin: Application to Nuclear Reactor Physics

Gong

Cheng

Chen

et al. 2022

Nuclear Science and Engineering

View full text Add to dashboard Cite

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A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions

Alhamrouni,

Abdul Kahar,

Salem

et al. 2024

Applied Sciences

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This review comprehensively examines the burgeoning field of intelligent techniques to enhance power systems’ stability, control, and protection. As global energy demands increase and renewable energy sources become more integrated, maintaining the stability and reliability of both conventional power systems and smart grids is crucial. Traditional methods are increasingly insufficient for handling today’s power grids’ complex, dynamic nature. This paper discusses the adoption of advanced intelligence methods, including artificial intelligence (AI), deep learning (DL), machine learning (ML), metaheuristic optimization algorithms, and other AI techniques such as fuzzy logic, reinforcement learning, and model predictive control to address these challenges. It underscores the critical importance of power system stability and the new challenges of integrating diverse energy sources. The paper reviews various intelligent methods used in power system analysis, emphasizing their roles in predictive maintenance, fault detection, real-time control, and monitoring. It details extensive research on the capabilities of AI and ML algorithms to enhance the precision and efficiency of protection systems, showing their effectiveness in accurately identifying and resolving faults. Additionally, it explores the potential of fuzzy logic in decision-making under uncertainty, reinforcement learning for dynamic stability control, and the integration of IoT and big data analytics for real-time system monitoring and optimization. Case studies from the literature are presented, offering valuable insights into practical applications. The review concludes by identifying current limitations and suggesting areas for future research, highlighting the need for more robust, flexible, and scalable intelligent systems in the power sector. This paper is a valuable resource for researchers, engineers, and policymakers, providing a detailed understanding of the current and future potential of intelligent techniques in power system stability, control, and protection.

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Advanced machine learning applications to modern power systems

Cited by 2 publications

References 81 publications

Data-Enabled Physics-Informed Machine Learning for Reduced-Order Modeling Digital Twin: Application to Nuclear Reactor Physics

Data-Enabled Physics-Informed Machine Learning for Reduced-Order Modeling Digital Twin: Application to Nuclear Reactor Physics

A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions

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