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A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances

Author

Listed:
  • Artvin-Darien Gonzalez-Abreu

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

  • Miguel Delgado-Prieto

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

  • Roque-Alfredo Osornio-Rios

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

  • Juan-Jose Saucedo-Dorantes

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

  • Rene-de-Jesus Romero-Troncoso

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

Abstract

Monitoring electrical power quality has become a priority in the industrial sector background: avoiding unwanted effects that affect the whole performance at industrial facilities is an aim. The lack of commercial equipment capable of detecting them is a proven fact. Studies and research related to these types of grid behaviors are still a subject for which contributions are required. Although research has been conducted for disturbance detection, most methodologies consider only a few standardized disturbance combinations. This paper proposes an innovative deep learning-based diagnosis method to be applied on power quality disturbances, and it is based on three stages. Firstly, a domain fusion approach is considered in a feature extraction stage to characterize the electrical power grid. Secondly, an adaptive pattern characterization is carried out by considering a stacked autoencoder. Finally, a neural network structure is applied to identify disturbances. The proposed approach relies on the training and validation of the diagnosis system with synthetic data: single, double and triple disturbances combinations and different noise levels, also validated with available experimental measurements provided by IEEE 1159.2 Working Group. The proposed method achieves nearly a 100% hit rate allowing a far more practical application due to its capability of pattern characterization.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2839-:d:554965
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    References listed on IDEAS

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    1. Misael Lopez-Ramirez & Eduardo Cabal-Yepez & Luis M. Ledesma-Carrillo & Homero Miranda-Vidales & Carlos Rodriguez-Donate & Rocio A. Lizarraga-Morales, 2018. "FPGA-Based Online PQD Detection and Classification through DWT, Mathematical Morphology and SVD," Energies, MDPI, vol. 11(4), pages 1-15, March.
    2. 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.
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    4. Ruijin Zhu & Xuejiao Gong & Shifeng Hu & Yusen Wang, 2019. "Power Quality Disturbances Classification via Fully-Convolutional Siamese Network and k-Nearest Neighbor," Energies, MDPI, vol. 12(24), pages 1-12, December.
    5. 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.
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    7. 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.
    8. 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. Arafat, M.Y. & Hossain, M.J. & Alam, Md Morshed, 2024. "Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    2. 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.
    3. 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.
    4. Tiago Pinto, 2023. "Artificial Intelligence as a Booster of Future Power Systems," Energies, MDPI, vol. 16(5), pages 1-4, February.
    5. Sergey Zhironkin & Elena Dotsenko, 2023. "Review of Transition from Mining 4.0 to 5.0 in Fossil Energy Sources Production," Energies, MDPI, vol. 16(15), pages 1-35, August.
    6. Andrej Brandis & Denis Pelin & Zvonimir Klaić & Damir Šljivac, 2022. "Identification of Even-Order Harmonics Injected by Semiconverter into the AC Grid," Energies, MDPI, vol. 15(5), pages 1-18, February.

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