Design Of UPQC for Grid Connected PV System Using Artificial Neural Network for Power Quality Improvement

  • Unique Paper ID: 168953
  • PageNo: 40-48
  • Abstract:
  • This paper focuses on the design and implementation of a Unified Power Quality Conditioner (UPQC) integrated with a solar photovoltaic (PV) system to enhance power quality through an Artificial Neural Network (ANN) controller. The proposed ANN controller replaces the conventional Proportional-Integral (PI) controller to achieve superior Total Harmonic Distortion (THD) performance. The solar PV system employs the Perturb and Observe (P&O) technique for Maximum Power Point Tracking (MPPT), which ensures optimal energy extraction even under variable environmental conditions. The system uses p-q theory for the control strategy, enabling the mitigation of voltage and current issues, such as harmonics, sags, and swells. The entire configuration is developed and analyzed using MATLAB/Simulink, allowing for comprehensive modeling and performance evaluation. Simulation results indicate that the ANN-based controller enhances the system's ability to manage power quality disturbances more effectively compared to conventional methods. The proposed UPQC tied solar PV system serves as a robust solution for integrating renewable energy sources into the grid, addressing power quality challenges, and ensuring reliable energy supply in grid-connected applications. This study highlights the potential of ANN controllers in improving the efficiency and stability of renewable energy-based power quality conditioning systems.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{168953,
        author = {kota swapna and M.Gopichand Naik},
        title = {Design Of UPQC for Grid Connected PV System Using Artificial Neural Network for Power Quality Improvement},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {40-48},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168953},
        abstract = {This paper focuses on the design and implementation of a Unified Power Quality Conditioner (UPQC) integrated with a solar photovoltaic (PV) system to enhance power quality through an Artificial Neural Network (ANN) controller. The proposed ANN controller replaces the conventional Proportional-Integral (PI) controller to achieve superior Total Harmonic Distortion (THD) performance. The solar PV system employs the Perturb and Observe (P&O) technique for Maximum Power Point Tracking (MPPT), which ensures optimal energy extraction even under variable environmental conditions. The system uses p-q theory for the control strategy, enabling the mitigation of voltage and current issues, such as harmonics, sags, and swells. The entire configuration is developed and analyzed using MATLAB/Simulink, allowing for comprehensive modeling and performance evaluation. Simulation results indicate that the ANN-based controller enhances the system's ability to manage power quality disturbances more effectively compared to conventional methods. The proposed UPQC tied solar PV system serves as a robust solution for integrating renewable energy sources into the grid, addressing power quality challenges, and ensuring reliable energy supply in grid-connected applications. This study highlights the potential of ANN controllers in improving the efficiency and stability of renewable energy-based power quality conditioning systems.},
        keywords = {UPQC, Solar PV, Aritificial neural network (ANN), Power Quality, P-Q theory, PI controller.},
        month = {November},
        }

Cite This Article

swapna, K., & Naik, M. (2024). Design Of UPQC for Grid Connected PV System Using Artificial Neural Network for Power Quality Improvement. International Journal of Innovative Research in Technology (IJIRT), 11(6), 40–48.

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