IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i24p4732-d296908.html
   My bibliography  Save this article

Power Quality Disturbances Classification via Fully-Convolutional Siamese Network and k-Nearest Neighbor

Author

Listed:
  • Ruijin Zhu

    (Electric Engineering College, Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China)

  • Xuejiao Gong

    (Electric Engineering College, Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China)

  • Shifeng Hu

    (Electric Engineering College, Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China)

  • Yusen Wang

    (School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden)

Abstract

The classification of disturbance signals is of great significance for improving power quality. The existing methods for power quality disturbance classification require a large number of samples to train the model. For small sample learning, their accuracy is relatively limited. In this paper, a hybrid algorithm of k-nearest neighbor and fully-convolutional Siamese network is proposed to classify power quality disturbances by learning small samples. Multiple convolutional layers and full connection layers are used to construct the Siamese network, and the output result of the Siamese network is used to judges the category of the signal. The simulation results show that: For small sample sizes, the accuracy of the proposed approach is significantly higher than that of the existing methods. In addition, it has a strong anti-noise ability.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4732-:d:296908
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/24/4732/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/24/4732/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yusen Wang & Wenlong Liao & Yuqing Chang, 2018. "Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting," Energies, MDPI, vol. 11(8), pages 1-14, August.
    2. 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.
    3. Huihui Wang & Ping Wang & Tao Liu, 2017. "Power Quality Disturbance Classification Using the S-Transform and Probabilistic Neural Network," Energies, MDPI, vol. 10(1), pages 1-19, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cheng-I Chen & Sunneng Sandino Berutu & Yeong-Chin Chen & Hao-Cheng Yang & Chung-Hsien Chen, 2022. "Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid," Energies, MDPI, vol. 15(7), pages 1-16, March.
    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. 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.
    4. Jiajun Cai & Kai Zhang & Hui Jiang, 2023. "Power Quality Disturbance Classification Based on Parallel Fusion of CNN and GRU," Energies, MDPI, vol. 16(10), pages 1-12, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Igual, R. & Medrano, C., 2020. "Research challenges in real-time classification of power quality disturbances applicable to microgrids: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    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.
    3. Paolo Castello & Carlo Muscas & Paolo Attilio Pegoraro & Sara Sulis, 2019. "PMU’s Behavior with Flicker-Generating Voltage Fluctuations: An Experimental Analysis," Energies, MDPI, vol. 12(17), pages 1-14, August.
    4. Delong Cai & Kaicheng Li & Shunfan He & Yuanzheng Li & Yi Luo, 2018. "On the Application of Joint-Domain Dictionary Mapping for Multiple Power Disturbance Assessment," Energies, MDPI, vol. 11(2), pages 1-17, February.
    5. Pu Zhao & Qing Chen & Kongming Sun & Chuanxin Xi, 2017. "A Current Frequency Component-Based Fault-Location Method for Voltage-Source Converter-Based High-Voltage Direct Current (VSC-HVDC) Cables Using the S Transform," Energies, MDPI, vol. 10(8), pages 1-15, July.
    6. Paula Remigio-Carmona & Juan-José González-de-la-Rosa & Olivia Florencias-Oliveros & José-María Sierra-Fernández & Javier Fernández-Morales & Manuel-Jesús Espinosa-Gavira & Agustín Agüera-Pérez & José, 2022. "Current Status and Future Trends of Power Quality Analysis," Energies, MDPI, vol. 15(7), pages 1-18, March.
    7. 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.
    8. Jessica Wojtkiewicz & Matin Hosseini & Raju Gottumukkala & Terrence Lynn Chambers, 2019. "Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units," Energies, MDPI, vol. 12(21), pages 1-13, October.
    9. 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.
    10. Juan-José González de-la-Rosa & Manuel Pérez-Donsión, 2020. "Special Issue “Analysis for Power Quality Monitoring”," Energies, MDPI, vol. 13(3), pages 1-6, January.
    11. Khan, Zulfiqar Ahmad & Khan, Shabbir Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2024. "DSPM: Dual sequence prediction model for efficient energy management in micro-grid," Applied Energy, Elsevier, vol. 356(C).
    12. Alexandre Serrano-Fontova & Pablo Casals Torrens & Ricard Bosch, 2019. "Power Quality Disturbances Assessment during Unintentional Islanding Scenarios. A Contribution to Voltage Sag Studies," Energies, MDPI, vol. 12(16), pages 1-21, August.
    13. Leijiao Ge & Tianshuo Du & Changlu Li & Yuanliang Li & Jun Yan & Muhammad Umer Rafiq, 2022. "Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications," Energies, MDPI, vol. 15(23), pages 1-24, November.
    14. Do-In Kim, 2021. "Complementary Feature Extractions for Event Identification in Power Systems Using Multi-Channel Convolutional Neural Network," Energies, MDPI, vol. 14(15), pages 1-15, July.
    15. Andi A. H. Lateko & Hong-Tzer Yang & Chao-Ming Huang, 2022. "Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method," Energies, MDPI, vol. 15(11), pages 1-21, June.
    16. 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.
    17. Ying Cao & Xi Wang & Li Zhu & Hongwei Wang & Xiaoning Wang, 2023. "A Meta-Learning-Based Train Dynamic Modeling Method for Accurately Predicting Speed and Position," Sustainability, MDPI, vol. 15(11), pages 1-20, May.
    18. Kewei Cai & Belema Prince Alalibo & Wenping Cao & Zheng Liu & Zhiqiang Wang & Guofeng Li, 2018. "Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network," Energies, MDPI, vol. 11(11), pages 1-18, November.
    19. 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.
    20. Yuan-Kang Wu & Cheng-Liang Huang & Quoc-Thang Phan & Yuan-Yao Li, 2022. "Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints," Energies, MDPI, vol. 15(9), pages 1-22, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4732-:d:296908. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.