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Advances in Power Quality Analysis Techniques for Electrical Machines and Drives: A Review

Author

Listed:
  • Artvin-Darien Gonzalez-Abreu

    (HSPdigital CA-Mecatronica, Engineering Faculty, Autonomous University of Queretaro, San Juan del Rio 76807, Mexico)

  • Roque-Alfredo Osornio-Rios

    (HSPdigital CA-Mecatronica, Engineering Faculty, Autonomous University of Queretaro, San Juan del Rio 76807, Mexico)

  • Arturo-Yosimar Jaen-Cuellar

    (HSPdigital CA-Mecatronica, Engineering Faculty, Autonomous University of Queretaro, San Juan del Rio 76807, Mexico)

  • Miguel Delgado-Prieto

    (MCIA Research Center Department of Electronic Engineering, Technical University of Catalonia (UPC), 08034 Barcelona, Spain)

  • Jose-Alfonso Antonino-Daviu

    (Instituto Tecnológico de la Energía, Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain)

  • Athanasios Karlis

    (Department of Electrical & Computer Engineering, Democritus University of Thrace, 69100 Komotini, Greece)

Abstract

The electric machines are the elements most used at an industry level, and they represent the major power consumption of the productive processes. Particularly speaking, among all electric machines, the motors and their drives play a key role since they literally allow the motion interchange in the industrial processes; it could be said that they are the medullar column for moving the rest of the mechanical parts. Hence, their proper operation must be guaranteed in order to raise, as much as possible, their efficiency, and, as consequence, bring out the economic benefits. This review presents a general overview of the reported works that address the efficiency topic in motors and drives and in the power quality of the electric grid. This study speaks about the relationship existing between the motors and drives that induces electric disturbances into the grid, affecting its power quality, and also how these power disturbances present in the electrical network adversely affect, in turn, the motors and drives. In addition, the reported techniques that tackle the detection, classification, and mitigations of power quality disturbances are discussed. Additionally, several works are reviewed in order to present the panorama that show the evolution and advances in the techniques and tendencies in both senses: motors and drives affecting the power source quality and the power quality disturbances affecting the efficiency of motors and drives. A discussion of trends in techniques and future work about power quality analysis from the motors and drives efficiency viewpoint is provided. Finally, some prompts are made about alternative methods that could help in overcome the gaps until now detected in the reported approaches referring to the detection, classification and mitigation of power disturbances with views toward the improvement of the efficiency of motors and drives.

Suggested Citation

  • Artvin-Darien Gonzalez-Abreu & Roque-Alfredo Osornio-Rios & Arturo-Yosimar Jaen-Cuellar & Miguel Delgado-Prieto & Jose-Alfonso Antonino-Daviu & Athanasios Karlis, 2022. "Advances in Power Quality Analysis Techniques for Electrical Machines and Drives: A Review," Energies, MDPI, vol. 15(5), pages 1-26, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1909-:d:764836
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    References listed on IDEAS

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    1. Juan Carlos Bravo-Rodríguez & Francisco J. Torres & María D. Borrás, 2020. "Hybrid Machine Learning Models for Classifying Power Quality Disturbances: A Comparative Study," Energies, MDPI, vol. 13(11), pages 1-20, June.
    2. 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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    7. Ngo Minh Khoa & Le Van Dai, 2020. "Detection and Classification of Power Quality Disturbances in Power System Using Modified-Combination between the Stockwell Transform and Decision Tree Methods," Energies, MDPI, vol. 13(14), pages 1-30, July.
    8. Wang, Shouxiang & Chen, Haiwen, 2019. "A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network," Applied Energy, Elsevier, vol. 235(C), pages 1126-1140.
    9. Yue Shen & Muhammad Abubakar & Hui Liu & Fida Hussain, 2019. "Power Quality Disturbance Monitoring and Classification Based on Improved PCA and Convolution Neural Network for Wind-Grid Distribution Systems," Energies, MDPI, vol. 12(7), pages 1-26, April.
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    Cited by:

    1. Eduardo Perez-Anaya & Arturo Yosimar Jaen-Cuellar & David Alejandro Elvira-Ortiz & Rene de Jesus Romero-Troncoso & Juan Jose Saucedo-Dorantes, 2024. "Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN," Energies, MDPI, vol. 17(4), pages 1-17, February.
    2. Piotr Gnaciński & Marcin Pepliński & Adam Muc & Damian Hallmann & Piotr Jankowski, 2023. "Effect of Ripple Control on Induction Motors," Energies, MDPI, vol. 16(23), pages 1-12, November.
    3. Armenia Androniceanu & Ioana-Catalina Enache & Elena-Narcisa Valter & Florin-Felix Raduica, 2023. "Increasing Energy Efficiency Based on the Kaizen Approach," Energies, MDPI, vol. 16(4), pages 1-24, February.
    4. Alberto Gudiño-Ochoa & Jaime Jalomo-Cuevas & Jesús Ezequiel Molinar-Solís & Raquel Ochoa-Ornelas, 2023. "Analysis of Interharmonics Generation in Induction Motors Driven by Variable Frequency Drives and AC Choppers," Energies, MDPI, vol. 16(14), pages 1-26, July.
    5. Karol Jakub Listewnik, 2022. "A Method for the Evaluation of Power-Generating Sets Based on the Assessment of Power Quality Parameters," Energies, MDPI, vol. 15(14), pages 1-24, July.

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