Current applications of artificial intelligence in rural power grids

The evolving energy landscape, characterized by decarbonization, the integration of renewable energy sources (RES), and the growing demand for grid reliability and energy efficiency, necessitates a profound transformation of power grids, particularly in rural areas. Conventional methods of power grid management often prove insufficient for these challenges, driving the adoption of artificial intelligence (AI) solutions. This study provides a systematic analysis of key AI applications in power grids, examining the algorithms employed and practical implementation examples from both international and domestic contexts. Drawing on a comprehensive review of literature, the authors have identified four primary application areas: load forecasting, power system and grid optimization, fault detection and equipment monitoring, and optimal resource management within power grids. Within these domains, effective algorithms include deep learning techniques such as LSTM, GRU, and CNN, along with machine learning models like SVM and various metaheuristic methods. Practical examples highlight the diverse deployment of AI, adapting to national power system specificities. For instance, countries with a high share of renewable energy sources (RES) often prioritize AI for load forecasting, while in Russia, the focus is on automating the monitoring and diagnostics of extensive rural grids through computer vision and UAVs. AI is instrumental in the design of Smart Grids, enabling the digital transformation of power infrastructure to enhance efficiency, resilience, and adaptability. However, successful AI integration requires addressing challenges related to reliability, cybersecurity, and the explainability of automation-driven decision-making.

1. Gungor V.C., Sahin D., Kocak T., Ergut S., Buccella C., Cecati C., Hancke G.P. Smart grid technologies: Communication technologies and standards. IEEE Transactions on Industrial Informatics. 2011;7(4):529-539. https://doi.org/10.1109/TII.2011.2166794

2. Egorov A.A. Trends in the world market of information technologies in the energy industry (smart monitoring, digital twins, computer vision, artificial intelligence). Part 1. Avtomatizatsiya i IT v energetike. 2022;12:8-24. (In Russ.)

3. Mozokhin A.E., Shvedenko V.N. Digitization development directions of national and foreign energy systems. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2019;4:657-672. (In Russ.) https://doi.org/10.17586/2226-1494-2019-19-4-657-672

4. Lyandau Yu.V., Temirbulatov A.U. Overview of the application of artificial intelligence technologies in the electric power industry. Innovations & Investment. 2023;8:304-309. (In Russ.)

5. Nikiforov I.K. Prospects for the development of electric power networks in the form of multi-agent systems. Pribory and sistemy. Upravlenie, kontrol, diagnostika. 2022;2:13-21. (In Russ.) https://doi.org/10.25791/pribor.2.2022.1321

6. Hong T., Fan S. Probabilistic electric load forecasting: A tutorial review. International Journal of Forecasting. 2016;32(3):914-938. https://doi.org/10.1016/j.ijforecast.2015.11.011

7. Islam B., Shams F.А. Short-term electrical load demand forecasting based on LSTM and RNN deep neural networks. Mathematical Problems in Engineering. 2022. https://doi.org/10.1155/2022/2316474

8. Bouktif S., Fiaz A., Ouni A., Serhani M.A. Multi-sequence LSTM-RNN deep learning and metaheuristics for electric load forecasting. Energies. 2020;13(2):391. https://doi.org/10.3390/en13020391

9. Bershadsky I.A., Dzhura S.G., Chursinova A.A. The use of artificial intelligence to predict electric power consumption of a power supply company. Nauchnyi vestnik Novosibirskogo gosudarstvennogo tekhnicheskogo universiteta = Science bulletin of the Novosibirsk state technical university. 2020;80(4):7-16. (In Russ.) https://doi.org/10.17212/1814-1196-2020-4-7-16

10. Allaev K.R., Kholiddinov I.Kh., Kholiddinova M.M.k. Electric network performance assessment methodology using fuzzy logic. Elektrichestvo. 2025;2:4-14. (In Russ.) https://doi.org/10.24160/0013-5380-2025-2-4-14

11. Almoataz Y. Abdelaziz, Shady H.E. Abdel A., Anamika Y. Artificial intelligence applications in electrical transmission and distribution systems protection. CRC Press. 2021. 512 p. https://doi.org/10.1201/9780367552374

12. Choi S.L. et al. Generative AI for power grid operations Golden, CO: National Renewable Energy Laboratory. NREL/TP-5D00-91176. 2024. https://www.nrel.gov/docs/fy25osti/91176.pdf

13. Varganova A.V., Khramshin V.R., Radionov A.A. Improving efficiency of electric energy system and grid operating modes: Review of optimization techniques. Energies. 2022;15(19):7177. https://doi.org/10.3390/en15197177

14. Massel L.V. Modern stage of artificial intelligence (AI) development and application of AI methods and systems in power engineering. Information and Mathematical Technologies in Science and Management. 2021;4(24):5-20. (In Russ.) https://doi.org/10.38028/ESI.2021.24.4.001

15. Zhang X., Zhang M., Hu Q. Research on the application of artificial intelligence in operation and maintenance for power equipment. In: IOP Conference Series: Earth and Environmental Science. 2020;617(1):012001. https://doi.org/10.1088/1755-1315/617/1/012001

16. Vinogradov A.V. Configuration management as a concept for developing 0.4 kV rural electric grids. Electrical Engineering and Electrical Equipment in Agriculture. 2024;4(57):65-72. (In Russ.) https://doi.org//10.22314/2658-4859-2024-71-4-65-72

17. Ivanov S.O., Nikandrov M.V., Slavutskij L.A. The statistical characteristics evaluation of protection operation based on a feed forward neural network. Pribory and sistemy. Upravlenie, kontrol, diagnostika. 2023;9:26-31. (In Russ.)

18. Mehinovic A., Grebović S., Fejzić A. et al. Application of artificial intelligence methods for determination of transients in the power system. Electric Power Systems Research. 2023;223:109634. https://doi.org/10.1016/j.epsr.2023.109634

19. Bolshev V.E., Vinogradov A.V. Perspective communication technologies for distribution automation. Vestnik kazanskogo gosudarstvennogo energeticheskogo universiteta. 2019;2(42):65-82. (In Russ.)

20. Mikryukov V.V., Patsev A.A., Seredkin O.A. et al. Neural network application for smart analysis of overhead transmission line’s condition with the use of UAVs. Electric Power. Transmission and Distribution. 2024;6(87):106-109. (In Russ.)

21. Limanovskaya O.V., Titov E.A., Volkova D.I., Lemekh A.V. Algorithm for determining the tilt of support of electric power supplies by methods of deep learning with video data. Vestnik Ivanovskogo gosudarstvennogo energeticheskogo universiteta. 2020;2:72-80. (In Russ.) https://doi.org/10.17588/2072-2672.2020.2.072-080

22. Astapova M.A., Lebedev I.V. Review of approaches to the detection of defects in power transmission line elements in images in the infrared, ultraviolet and visible spectra. Modeling, Optimization and Information Technology. 2020;8(4). (In Russ.) https://doi.org/10.26102/2310-6018/2020.31.4.036

23. Vinogradov A.V., Morenko K.S. Database structure for managing the configuration of a conceptual electrical grid. Electrical Engineering and Electrical Equipment in Agriculture. 2025;3(60):38-45. (In Russ.) https://doi.org/10.22314/2658-4859-2025-72-3-38-45