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Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm

Author

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  • Nantian Huang

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

  • Hua Peng

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

  • Guowei Cai

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

  • Jikai Chen

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

Abstract

In order to improve the recognition accuracy and efficiency of power quality disturbances (PQD) in microgrids, a novel PQD feature selection and recognition method based on optimal multi-resolution fast S-transform (OMFST) and classification and regression tree (CART) algorithm is proposed. Firstly, OMFST is carried out according to the frequency domain characteristic of disturbance signal, and 67 features are extracted by time-frequency analysis to construct the original feature set. Subsequently, the optimal feature subset is determined by Gini importance and sorted according to an embedded feature selection method based on the Gini index. Finally, one standard error rule subtree evaluation methods were applied for cost complexity pruning. After pruning, the optimal decision tree (ODT) is obtained for PQD classification. The experiments show that the new method can effectively improve the classification efficiency and accuracy with feature selection step. Simultaneously, the ODT can be constructed automatically according to the ability of feature classification. In different noise environments, the classification accuracy of the new method is higher than the method based on probabilistic neural network, extreme learning machine, and support vector machine.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:11:p:927-:d:82438
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    References listed on IDEAS

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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. Nantian Huang & Enkai Xing & Guowei Cai & Zhiyong Yu & Bin Qi & Lin Lin, 2018. "Short-Term Wind Speed Forecasting Based on Low Redundancy Feature Selection," Energies, MDPI, vol. 11(7), pages 1-19, June.
    3. 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.
    4. Chiou-Jye Huang & Ping-Huan Kuo, 2018. "A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems," Energies, MDPI, vol. 11(10), pages 1-20, October.
    5. 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.
    6. 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.
    7. 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.
    8. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks," Sustainability, MDPI, vol. 10(4), pages 1-17, April.

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