2020
DOI: 10.1016/j.apenergy.2020.115733
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Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions

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Cited by 190 publications
(38 citation statements)
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“…This not only leads to increased costs, but also limits the size of the data being processed. Therefore, adaptive algorithms and AI-based coordination mechanisms are needed to achieve flexibility and distributed data management [80][81][82].…”
Section: From Traditional Methods To Artificial Intelligencementioning
confidence: 99%
“…This not only leads to increased costs, but also limits the size of the data being processed. Therefore, adaptive algorithms and AI-based coordination mechanisms are needed to achieve flexibility and distributed data management [80][81][82].…”
Section: From Traditional Methods To Artificial Intelligencementioning
confidence: 99%
“…Considering the need of tackling challenges posed by the intermittence and variability of renewable power generation on the one hand (Qadir et al, 2021 ) and the increasing complexity of the diverse renewable energy sources as the main players in the electric networks with energy storages on the other (F. Ahmad, Almuayqil, et al, 2021 ; Ahmad, Zhang, et al, 2021 ; Ahmad et al, 2019 ; T. Ahmad, Almuayqil, et al, 2021 ; Ahmad, Zhang, et al, 2021 ; Dreglea et al, 2021 ; Kalair et al, 2021 ; Lee & He, 2021 ), the crucial contribution of digitisation and AI to the R&D of the smart grid, the backbone of the SD of our decarbonised, 100% renewable energy future, is evident and paramount. At the same time, case studies also identified challenges the current grid with some smart grid components faces, such as insufficient data to meet high requirements on data, imbalanced learning, interpretability of AI, difficulties in transfer learning, the robustness of AI to communication quality, and the robustness against attack or adversarial events (Aly, 2020 ; Kempitiya et al, 2020 ; Schappert & von Hauff, 2020 ; Shi et al, 2020 ).…”
Section: Artificial Intelligence and Sustainable Development Researchmentioning
confidence: 99%
“…µ 11 , µ 12 , µ 13 , µ 21 , µ 22 , and µ 23 are weighting coefficients. Using the artificial intelligence optimization algorithm changes the weighting coefficient to select the optimal performance combination, including system stability, frequency adjustment accuracy, and low energy consumption of the control system [26].…”
Section: Design Of Robust Controller For Lfc In Mgmentioning
confidence: 99%