Artificial Intelligence Techniques in Smart Grid: A Survey

Omitaomu, Olufemi A.; Niu, Haoran

doi:10.3390/smartcities4020029

Title: Artificial Intelligence Techniques in Smart Grid: A Survey

Journal Article · Thu Apr 22 00:00:00 EDT 2021 · Smart Cities

DOI:https://doi.org/10.3390/smartcities4020029· OSTI ID:1798582

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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1798582

Journal Information:: Smart Cities, Vol. 4, Issue 2; ISSN 2624-6511

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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Multi-View Stacking Ensemble for Power Consumption Anomaly Detection in the Context of Industrial Internet of Things Ouyang, Zhiyou; Sun, Xiaokui; Chen, Jingang IEEE Access, Vol. 6 https://doi.org/10.1109/ACCESS.2018.2805908	journal	January 2018
Real-Time Power System State Estimation and Forecasting via Deep Unrolled Neural Networks Zhang, Liang; Wang, Gang; Giannakis, Georgios B. IEEE Transactions on Signal Processing, Vol. 67, Issue 15 https://doi.org/10.1109/TSP.2019.2926023	journal	August 2019
Parallel Processing of Probabilistic Models-Based Power Supply Unit Mid-Term Load Forecasting With Apache Spark Jiang, Wei; Tang, Haibo; Wu, Lei IEEE Access, Vol. 7 https://doi.org/10.1109/ACCESS.2018.2890339	journal	January 2019
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Coordinated Topology Attacks in Smart Grid Using Deep Reinforcement Learning Wang, Zhenhua; He, Haibo; Wan, Zhiqiang IEEE Transactions on Industrial Informatics, Vol. 17, Issue 2 https://doi.org/10.1109/TII.2020.2994977	journal	February 2021
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Fault Detection, Identification, and Location in Smart Grid Based on Data-Driven Computational Methods Jiang, Huaiguang; Zhang, Jun J.; Gao, Wenzhong IEEE Transactions on Smart Grid, Vol. 5, Issue 6 https://doi.org/10.1109/TSG.2014.2330624	journal	November 2014
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Machine Learning Methods for Attack Detection in the Smart Grid Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatos Tunay IEEE Transactions on Neural Networks and Learning Systems, Vol. 27, Issue 8 https://doi.org/10.1109/TNNLS.2015.2404803	journal	August 2016
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