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A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

Author

Listed:
  • Indu Sekhar Samanta

    (Department of Computer Science Engineering, Siksha ‘O’ Anusandhan University, Odisha 751030, India)

  • Subhasis Panda

    (Department of Electrical Engineering, Siksha ‘O’ Anusandhan University, Odisha 751030, India)

  • Pravat Kumar Rout

    (Department of Electrical and Electronics Engineering, Siksha ‘O’ Anusandhan University, Odisha 751030, India)

  • Mohit Bajaj

    (Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun 248002, India
    Graphic Era Hill University, Dehradun 248002, India
    Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan)

  • Marian Piecha

    (Ministry of Industry and Trade, 11015 Prague, Czech Republic)

  • Vojtech Blazek

    (ENET Centre, VSB—Technical University of Ostrava, 70800 Ostrava, Czech Republic)

  • Lukas Prokop

    (ENET Centre, VSB—Technical University of Ostrava, 70800 Ostrava, Czech Republic)

Abstract

Power quality (PQ) monitoring and detection has emerged as an essential requirement due to the proliferation of sensitive power electronic interfacing devices, electric vehicle charging stations, energy storage devices, and distributed generation energy sources in the recent smart grid and microgrid scenarios. Even though, to date, the traditional approaches play a vital role in providing a solution to the above issue, the limitations, such as the requirement of significant human effort and not being scalable for large-scale power systems, force us to think of alternative approaches. Looking at a better perspective, deep-learning (DL) has gained the main attraction for various researchers due to its inherent capability to classify the data by extracting dominating and prominent features. This manuscript attempts to provide a comprehensive review of PQ detection and classification based on DL approaches to explore its potential, efficiency, and consistency to produce results accurately. In addition, this state-of-the-art review offers an overview of the novel concepts and the step-by-step method for detecting and classifying PQ events. This review has been presented categorically with DL approaches, such as convolutional neural networks (CNNs), autoencoders, and recurrent neural networks (RNNs), to analyze PQ data. This paper also highlights the challenges and limitations of using DL for PQ analysis, and identifies potential areas for future research. This review concludes that DL algorithms have shown promising PQ detection and classification results, and could replace traditional methods.

Suggested Citation

  • Indu Sekhar Samanta & Subhasis Panda & Pravat Kumar Rout & Mohit Bajaj & Marian Piecha & Vojtech Blazek & Lukas Prokop, 2023. "A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis," Energies, MDPI, vol. 16(11), pages 1-31, May.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4406-:d:1159439
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    References listed on IDEAS

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    1. Artvin-Darien Gonzalez-Abreu & Miguel Delgado-Prieto & Roque-Alfredo Osornio-Rios & Juan-Jose Saucedo-Dorantes & Rene-de-Jesus Romero-Troncoso, 2021. "A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances," Energies, MDPI, vol. 14(10), pages 1-17, May.
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    5. Zakarya Oubrahim & Yassine Amirat & Mohamed Benbouzid & Mohammed Ouassaid, 2023. "Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review," Energies, MDPI, vol. 16(6), pages 1-41, March.
    6. Fei Mei & Yong Ren & Qingliang Wu & Chenyu Zhang & Yi Pan & Haoyuan Sha & Jianyong Zheng, 2018. "Online Recognition Method for Voltage Sags Based on a Deep Belief Network," Energies, MDPI, vol. 12(1), pages 1-16, December.
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    Cited by:

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    2. Osman Akbulut & Muhammed Cavus & Mehmet Cengiz & Adib Allahham & Damian Giaouris & Matthew Forshaw, 2024. "Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques," Energies, MDPI, vol. 17(10), pages 1-23, May.

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