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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
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    Citations

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. 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.
    10. 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.
    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.

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