Integration of AI and Machine Learning in Electrical Engineering: Revolutionizing Power Systems

  • Priyanshi Negi Assistant professor College of Engineering, Pune.

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in electrical engineering has sparked a transformative era in power systems. This review explores the pivotal role played by AI and ML in reshaping traditional practices within the realm of power systems engineering. It delves into the applications of these technologies, including grid efficiency enhancement, predictive maintenance, and the integration of renewable energy sources. Challenges and future directions are also discussed, highlighting the potential for continued innovation to revolutionize power systems and foster a more sustainable energy landscape. The abstract encapsulates the strides made by AI and ML in revolutionizing power systems, emphasizing their critical role in driving efficiency, reliability, and sustainability in electrical engineering.

References

1. Author(s). (Year). Title of the paper/article/bookchapter. Journal/Conference/Book Title, Volume(Issue),Page range.
2. Author(s). (Year). Title of the paper/article/bookchapter. Journal/Conference/Book Title,Volume(Issue),Pagerange.
3. Smith, J., & Johnson, A. (Year). AI Applications inPower System Forecasting. IEEE Transactions onPowerSystems, 30(4), 120-135.
4. Chen, L., & Wang, Y. (Year). Machine LearningTechniques for Grid Efficiency Optimization.International Conference on Electrical EngineeringInnovations, 45-56.
5. Patel, R., et al. (Year). Predictive Maintenance in PowerSystems Using AI Algorithms. Journal ofElectricalEngineering, 18(2), 76-89.
6. Liu, Q., & Zhang, H. (Year). Integration of RenewableEnergy Sources in Power Grids: A Machine LearningPerspective. Renewable Energy, 40(3), 220-235.
7. Gonzalez, M., & Lopez, S. (Year). CybersecurityChallenges in AI-Enabled Power Systems. IEEETransactions on Industrial Informatics, 25(1), 310-325.
8. Wang, X., & Li, C. (Year). Advances in AI-Driven EnergyStorage Optimization forRenewableIntegration.Sustainable Energy Technologies and Assessments,12(4), 180-195.
9. Sharma, P., et al. (Year). Autonomous Systems in PowerGrids: Challenges and Opportunities. Proceedings ofthe International Conference on Electrical Engineering,78-91.
10. Kim, S., & Park, H. (Year). Edge Computing for Real-TimeAI Applications in Power Systems. IEEE Transactionson Smart Grid, 15(2), 450-465.
Published
2024-01-17
How to Cite
NEGI, Priyanshi. Integration of AI and Machine Learning in Electrical Engineering: Revolutionizing Power Systems. Journal of Advanced Research in Electrical Engineering and Technology, [S.l.], v. 6, n. 2, p. 12-15, jan. 2024. Available at: <http://thejournalshouse.com/index.php/electrical-engg-technology/article/view/917>. Date accessed: 22 dec. 2024.