Authors : Vijayshree G; Sumathi S

Volume/Issue : Volume 10 - 2025, Issue 1 - January

Google Scholar : https://tinyurl.com/3dh4ecj4

Scribd : https://tinyurl.com/2w9p344p

DOI : https://doi.org/10.5281/zenodo.14769427

Abstract : The integration of distributed generation (DG) systems, like solar and wind power, presents significant challenges for power quality, including issues with voltage stability, harmonic distortion, and transient disturbances. Traditional power quality solutions often lack flexibility and adaptability, making machine learning (ML) a promising alternative. This review examines how ML models including neural networks, support vector machines, and deep learning architectures can improve power quality by detecting and mitigating disturbances in real time. Key topics covered include step by step real-time implementation strategies, application of ML and artificial for power quality improvement and its advantages. By highlighting recent advances and identifying research gaps, this review offers insights into the future role of ML in maintaining power quality within DG-integrated smart grids.

Keywords : Power Quality, Distributed Generation, Machine Learning, Deep Learning Algorithm.

References :

1. A. S. Chaitra and H. R. Sudarshana Reddy. Dispersed generations (dg) for improvement of power performance using upqc based on a machine learning. SN Computer Science, 4(3), March 2023.
2. Shiyu Chen, Jian Zhang, Lei Wang, Hongkun Zhang, and Lin Li. Evaluation of power quality and reliability of distributed generation in smart grid. IOP Conference Series Earth and Environmental Science, 632(4):042015, January 2021.
3. Chandan Choubey, K. Ramesh, P. Ravi Kumar, B. L. Gupta, Parul Madan, Ram Bajaj, A. Deepak, and Anurag Shrivastava. Machine learning based power quality enhancement system for renewable energy sources. International Journal of Intelligent Systems and Applications in Engineering, 12(18s):249–258, Mar. 2024.
4. Raghvendraprasad Deshpande. Power quality analysis of electrical distribution systems with asynchronous generators. International Journal of Renewable Energy Research, (v8i4), January 2018.
5. Carlos Alberto Iturrino Garcia, Marco Bindi, Fabio Corti, Antonio Luchetta, Francesco Grasso, Libero Paolucci, Maria Cristina Piccirilli, and Igor Aizenberg. Power quality analysis based on machine learning methods for low-voltage electrical distribution lines. Energies, 16(9):3627, April 2023.
6. Carlos Alberto Iturrino Garcia, Marco Bindi, Fabio Corti, Antonio Luchetta, Francesco Grasso, Libero Paolucci, Maria Cristina Piccirilli, and Igor Aizenberg. Power quality analysis based on machine learning methods for low-voltage electrical distribution lines. Energies, 16(9), 2023.
7. Akanksha Jain and S. C. Gupta. Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms. Frontiers in Energy Research, 12, April 2024.
8. Ramya Kuppusamy, Srete Nikolovski, and Yuvaraja Teekaraman. Review of machine learning techniques for power quality performance evaluation in grid-connected systems. Sustainability, 15(20):15055, October 2023.
9. Ramya Kuppusamy, Srete Nikolovski, and Yuvaraja Teekaraman. Review of machine learning techniques for power quality performance evaluation in grid-connected systems. Sustainability, 15(20), 2023.
10. Zhenjun Ma, Muhammad Bilal Awan, Menglong Lu, Shengteng Li, Muhammad Shahbaz Aziz, Xinlei Zhou, Han Du, Xinyi Sha, and Yixuan Li. An overview of emerging and sustainable technologies for increased energy efficiency and carbon emission mitigation in buildings. Buildings, 13(10), 2023.
11. M. R. Mohanraj and R. Prakash. A unified power quality conditioner for power quality improvement in distributed generation network using adaptive distributed power balanced control (adpbc). Interna- tional Journal of Wavelets Multiresolution and Information Processing, 18(01):1941021, June 2019.
12. Antonio Moreno-Munoz, Juan Jose´ de la Rosa, MA Lopez-Rodriguez, J.-M Flores-Arias, Francisco Bellido, and Manuel Ruiz de Adana. Im- provement of power quality using distributed generation. International Journal of Electrical Power Energy Systems, 32:1069–1076, 12 2010.
13. D. K. Nishad, A. N. Tiwari, Saifullah Khalid, Sandeep Gupta, and Anand Shukla. Ai based upqc control technique for power quality optimization of railway transportation systems. Scientific Reports, 14(1):17935, Aug 2024.
14. Arvind R. Singh, R. Seshu Kumar, Mohit Bajaj, Chetan B. Khadse, and Ievgen Zaitsev. Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources. Scientific Reports, 14(1), August 2024.
15. Malladi Lakshmi Swarupa, Katuri Rayudu, Chava Sunil Kumar, Sree Lakshmi Gundebommu, and P. Kamalakar. Machine learning techniques for power quality enhancement of power distribution sys- tems with facts devices. Engineering Technology Applied Science Research, 14(4):14939–14944, August 2024.
16. Mian Farhan Ullah and Aamir Hanif. Power quality improvement in distribution system using distribution static compensator with super twisting sliding mode control. International Transactions on Electrical Energy Systems, 31(9), July 2021.