Fusion of Microgrid Control With Model-Free Reinforcement Learning: Review and Vision

She, Buxin; Li, Fangxing; Cui, Hantao; Zhang, Jingqiu; Bo, Rui

doi:10.1109/tsg.2022.3222323

Cited by 22 publications

(2 citation statements)

References 108 publications

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“…Physics Informed AI leverages the fusion between the physics-based models and the AI advances. Physics-Informed Neural Networks (PINN) and Physics-Informed Reinforcement Learning find a range of applications in power systems [125][126][127][128]. In [129], a Distributed Deep Reinforcement Learning (DRL) strategy is used to design an optimal defensive strategy against FDI attacks in microgrids under a few assumptions.…”

Section: Learning-based Cyber Attack Detection and Mitigationmentioning

confidence: 99%

A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids

Beg,

Khan,

Rehman

et al. 2023

Energies

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In this paper, the application and future vision of Artificial Intelligence (AI)-based techniques in microgrids are presented from a cyber-security perspective of physical devices and communication networks. The vulnerabilities of microgrids are investigated under a variety of cyber-attacks targeting sensor measurements, control signals, and information sharing. With the inclusion of communication networks and smart metering devices, the attack surface has increased in microgrids, making them vulnerable to various cyber-attacks. The negative impact of such attacks may render the microgrids out-of-service, and the attacks may propagate throughout the network due to the absence of efficient mitigation approaches. AI-based techniques are being employed to tackle such data-driven cyber-attacks due to their exceptional pattern recognition and learning capabilities. AI-based methods for cyber-attack detection and mitigation that address the cyber-attacks in microgrids are summarized. A case study is presented showing the performance of AI-based cyber-attack mitigation in a distributed cooperative control-based AC microgrid. Finally, future potential research directions are provided that include the application of transfer learning and explainable AI techniques to increase the trust of AI-based models in the microgrid domain.

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Section: Learning-based Cyber Attack Detection and Mitigationmentioning

confidence: 99%

A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids

Beg,

Khan,

Rehman

et al. 2023

Energies

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“…It is essential to note that the synthesis of the above method becomes valid due to the availability of mathematical equations for the system. However, in industrial applications, a perfect physical model of the system may not always be attainable [28]. This limitation is primarily attributed to factors such as uncertainty, complexity, nonlinearity, and the presence of various disturbances.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Enhancement of Photovoltaic System Intermittency and Damping Load Variations in Noninverting Buck-Boost Converters Using Robust Weighted Mixed-Sensitivity Control

Fauzi Iskandar,

Hanadi,

Hartono

et al. 2024

IEEE Access

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Due to the intermittent and uncertainty nature of photovoltaic system, their incorporation within a DC microgrid presents a challenge to the primary control, which directly interfaces with the generation converter. Utilizing a weighted mix-sensitivity control, this study aims to amplify the robustness of a non-inverting buck-boost converter integrated photovoltaic system in addressing the uncertainties and disturbances arising from simultaneous fluctuations in irradiance, temperature, and load. The robust control algorithm was formulated by employing frequency-weighting functions and imposing a requirement for the minimum norm of the transformation matrix to accomplish a robust performance and robust stability. Furthermore, the involvement of reference models into a robust control synthesis offerred additional advantages in enhancing the damping of the system. Consequently, the entire design configuration could effectively establish the converter robustness against both generations and load intermittencies occurring simultaneously. The simulation and experiment result is demonstrated to illustrate the efficiency of the designed algorithm.

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Critical Flow-Based Fast Algorithm for the Total Supply Capability Curve of Distribution Networks

Xiao

She

et al. 2023

Arab J Sci Eng

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Fusion of Microgrid Control With Model-Free Reinforcement Learning: Review and Vision

Cited by 22 publications

References 108 publications

A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids

A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids

Simultaneous Enhancement of Photovoltaic System Intermittency and Damping Load Variations in Noninverting Buck-Boost Converters Using Robust Weighted Mixed-Sensitivity Control

Critical Flow-Based Fast Algorithm for the Total Supply Capability Curve of Distribution Networks

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