IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v8y2015i1p549-572d44767.html
   My bibliography  Save this article

Power Quality Disturbances Recognition Based on a Multiresolution Generalized S-Transform and a PSO-Improved Decision Tree

Author

Listed:
  • Nantian Huang

    (School of Electrical Engineering, Northeast Dianli University, Jilin 132012, China)

  • Shuxin Zhang

    (School of Electrical Engineering, Northeast Dianli University, Jilin 132012, China
    These authors contributed equally to this work.)

  • Guowei Cai

    (School of Electrical Engineering, Northeast Dianli University, Jilin 132012, China
    These authors contributed equally to this work.)

  • Dianguo Xu

    (Department of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China)

Abstract

In a microgrid, the distributed generators (DG) can power the user loads directly. As a result, power quality (PQ) events are more likely to affect the users. This paper proposes a Multiresolution Generalized S-transform (MGST) approach to improve the ability of analyzing and monitoring the power quality in a microgrid. Firstly, the time-frequency distribution characteristics of different types of disturbances are analyzed. Based on the characteristics, the frequency domain is segmented into three frequency areas. After that, the width factor of the window function in the S-transform is set in different frequency areas. MGST has different time-frequency resolution in each frequency area to satisfy the recognition requirements of different disturbances in each frequency area. Then, a rule-based decision tree classifier is designed. In addition, particle swarm optimization (PSO) is applied to extract the applicable features. Finally, the proposed method is compared with some others. The simulation experiments show that the new approach has better accuracy and noise immunity.

Suggested Citation

  • Nantian Huang & Shuxin Zhang & Guowei Cai & Dianguo Xu, 2015. "Power Quality Disturbances Recognition Based on a Multiresolution Generalized S-Transform and a PSO-Improved Decision Tree," Energies, MDPI, vol. 8(1), pages 1-24, January.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:1:p:549-572:d:44767
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/8/1/549/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/8/1/549/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Supanat Chamchuen & Apirat Siritaratiwat & Pradit Fuangfoo & Puripong Suthisopapan & Pirat Khunkitti, 2021. "High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm," Energies, MDPI, vol. 14(5), pages 1-18, February.
    2. 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).
    3. Isabel M. Moreno-Garcia & Antonio Moreno-Munoz & Aurora Gil-de-Castro & Math Bollen & Irene Y. H. Gu, 2015. "Novel Segmentation Technique for Measured Three-Phase Voltage Dips," Energies, MDPI, vol. 8(8), pages 1-20, August.
    4. Raquel Martinez & Pablo Castro & Alberto Arroyo & Mario Manana & Noemi Galan & Fidel Simon Moreno & Sergio Bustamante & Alberto Laso, 2022. "Techniques to Locate the Origin of Power Quality Disturbances in a Power System: A Review," Sustainability, MDPI, vol. 14(12), pages 1-27, June.
    5. Lintao Yang & Honggeng Yang & Haitao Liu, 2018. "GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting," Sustainability, MDPI, vol. 10(1), pages 1-16, January.
    6. Nantian Huang & Hua Peng & Guowei Cai & Jikai Chen, 2016. "Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm," Energies, MDPI, vol. 9(11), pages 1-21, November.
    7. 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.
    8. Jingjing Bai & Wei Gu & Xiaodong Yuan & Qun Li & Feng Xue & Xuchong Wang, 2015. "Power Quality Prediction, Early Warning, and Control for Points of Common Coupling with Wind Farms," Energies, MDPI, vol. 8(9), pages 1-18, August.
    9. Guoqing Weng & Feiteng Huang & Jun Yan & Xiaodong Yang & Youbing Zhang & Haibo He, 2016. "A Fault-Tolerant Location Approach for Transient Voltage Disturbance Source Based on Information Fusion," Energies, MDPI, vol. 9(12), pages 1-23, December.
    10. 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.
    11. Misael Lopez-Ramirez & Luis Ledesma-Carrillo & Eduardo Cabal-Yepez & Carlos Rodriguez-Donate & Homero Miranda-Vidales & Arturo Garcia-Perez, 2016. "EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments," Energies, MDPI, vol. 9(7), pages 1-15, July.

    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:8:y:2015:i:1:p:549-572:d:44767. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.