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Application of Deep Learning Gated Recurrent Unit in Hybrid Shunt Active Power Filter for Power Quality Enhancement

Author

Listed:
  • Ayesha Ali

    (Department of Electrical Engineering, University of Management and Technology, Lahore 54000, Pakistan)

  • Ateeq Ur Rehman

    (Faculty of Engineering, Uni de Moncton, Moncton, NB E1A3E9, Canada)

  • Ahmad Almogren

    (Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia)

  • Elsayed Tag Eldin

    (Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt)

  • Muhammad Kaleem

    (Department of Electrical Engineering, University of Management and Technology, Lahore 54000, Pakistan)

Abstract

This research work aims at providing power quality improvement for the nonlinear load to improve the system performance indices by eliminating maximum total harmonic distortion (THD) and reducing neutral wire current. The idea is to integrate a shunt hybrid active power filter (SHAPF) with the system using machine learning control techniques. The system proposed has been evaluated under an artificial neural network (ANN), gated recurrent unit, and long short-term memory for the optimization of the SHAPF. The method is based on the detection of harmonic presence in the power system by testing and comparison of traditional pq0 theory and deep learning neural networks. The results obtained through the proposed methodology meet all the suggested international standards of THD. The results also satisfy the current removal from the neutral wire and deal efficiently with minor DC voltage variations occurring in the voltage-regulating current. The proposed algorithms have been evaluated on the performance indices of accuracy and computational complexities, which show effective results in terms of 99% accuracy and computational complexities. deep learning-based findings are compared based on their root-mean-square error (RMSE) and loss function. The proposed system can be applied for domestic and industrial load conditions in a four-wire three-phase power distribution system for harmonic mitigation.

Suggested Citation

  • Ayesha Ali & Ateeq Ur Rehman & Ahmad Almogren & Elsayed Tag Eldin & Muhammad Kaleem, 2022. "Application of Deep Learning Gated Recurrent Unit in Hybrid Shunt Active Power Filter for Power Quality Enhancement," Energies, MDPI, vol. 15(20), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7553-:d:941235
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    References listed on IDEAS

    as
    1. Mihaela Popescu & Alexandru Bitoleanu & Mihaita Linca & Constantin Vlad Suru, 2021. "Improving Power Quality by a Four-Wire Shunt Active Power Filter: A Case Study," Energies, MDPI, vol. 14(7), pages 1-20, April.
    2. Saeed-Ul Hassan & Mubashir Imran & Sehrish Iqbal & Naif Radi Aljohani & Raheel Nawaz, 2018. "Deep context of citations using machine-learning models in scholarly full-text articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1645-1662, December.
    3. Ayesha Khan & Mujtaba Hussain Jaffery & Yaqoob Javed & Jehangir Arshad & Ateeq Ur Rehman & Rabia Khan & Mohit Bajaj & Mohammed K. A. Kaabar, 2021. "Hardware-in-the-Loop Implementation and Performance Evaluation of Three-Phase Hybrid Shunt Active Power Filter for Power Quality Improvement," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-23, October.
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    Cited by:

    1. Guilherme Gonçalves Pinheiro & Robson Bauwelz Gonzatti & Carlos Henrique da Silva & Rondineli Rodrigues Pereira & Bruno P. Braga Guimarães & João Gabriel Luppi Foster & Germano Lambert-Torres & Klever, 2023. "Comparison of Control Techniques for Harmonic Isolation in Series VSC-Based Power Flow Controller in Distribution Grids," Energies, MDPI, vol. 16(6), pages 1-27, March.

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