IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i11p927-d82438.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/11/927/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/9/11/927/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mahela, Om Prakash & Shaik, Abdul Gafoor & Gupta, Neeraj, 2015. "A critical review of detection and classification of power quality events," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 495-505.
    2. 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.
    3. Saqib, Muhammad A. & Saleem, Ali Z., 2015. "Power-quality issues and the need for reactive-power compensation in the grid integration of wind power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 51-64.
    4. Ting-Chia Ou & Wei-Fu Su & Xian-Zong Liu & Shyh-Jier Huang & Te-Yu Tai, 2016. "A Modified Bird-Mating Optimization with Hill-Climbing for Connection Decisions of Transformers," Energies, MDPI, vol. 9(9), pages 1-12, August.
    5. Ou, Ting-Chia & Hong, Chih-Ming, 2014. "Dynamic operation and control of microgrid hybrid power systems," Energy, Elsevier, vol. 66(C), pages 314-323.
    6. Lerman, Robert I. & Yitzhaki, Shlomo, 1984. "A note on the calculation and interpretation of the Gini index," Economics Letters, Elsevier, vol. 15(3-4), pages 363-368.
    7. Mingchao Xia & Xiaoliang Li, 2013. "Design and Implementation of a High Quality Power Supply Scheme for Distributed Generation in a Micro-Grid," Energies, MDPI, vol. 6(9), pages 1-21, September.
    8. Hong, Chih-Ming & Ou, Ting-Chia & Lu, Kai-Hung, 2013. "Development of intelligent MPPT (maximum power point tracking) control for a grid-connected hybrid power generation system," Energy, Elsevier, vol. 50(C), pages 270-279.
    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. 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. 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.
    6. 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.
    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. 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.

    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. Pengfei Wang & Jialiang Yi & Mansoureh Zangiabadi & Pádraig Lyons & Phil Taylor, 2017. "Evaluation of Voltage Control Approaches for Future Smart Distribution Networks," Energies, MDPI, vol. 10(8), pages 1-17, August.
    2. Sarid, A. & Tzur, M., 2018. "The multi-scale generation and transmission expansion model," Energy, Elsevier, vol. 148(C), pages 977-991.
    3. Hongyue Li & Xihuai Wang & Jianmei Xiao, 2018. "Differential Evolution-Based Load Frequency Robust Control for Micro-Grids with Energy Storage Systems," Energies, MDPI, vol. 11(7), pages 1-19, June.
    4. Chen, J.J. & Zhao, Y.L. & Peng, K. & Wu, P.Z., 2017. "Optimal trade-off planning for wind-solar power day-ahead scheduling under uncertainties," Energy, Elsevier, vol. 141(C), pages 1969-1981.
    5. Yuan Hong & Shengbin Wang & Ziyue Huang, 2017. "Efficient Energy Consumption Scheduling: Towards Effective Load Leveling," Energies, MDPI, vol. 10(1), pages 1-27, January.
    6. Xiaolian Zhang & Can Huang & Sipeng Hao & Fan Chen & Jingjing Zhai, 2016. "An Improved Adaptive-Torque-Gain MPPT Control for Direct-Driven PMSG Wind Turbines Considering Wind Farm Turbulences," Energies, MDPI, vol. 9(11), pages 1-16, November.
    7. Reza Sirjani, 2017. "Optimal Capacitor Placement in Wind Farms by Considering Harmonics Using Discrete Lightning Search Algorithm," Sustainability, MDPI, vol. 9(9), pages 1-20, September.
    8. Andrés Henao-Muñoz & Andrés Saavedra-Montes & Carlos Ramos-Paja, 2018. "Optimal Power Dispatch of Small-Scale Standalone Microgrid Located in Colombian Territory," Energies, MDPI, vol. 11(7), pages 1-20, July.
    9. Carlos Robles Algarín & John Taborda Giraldo & Omar Rodríguez Álvarez, 2017. "Fuzzy Logic Based MPPT Controller for a PV System," Energies, MDPI, vol. 10(12), pages 1-18, December.
    10. Mohammed Elsayed Lotfy & Tomonobu Senjyu & Mohammed Abdel-Fattah Farahat & Amal Farouq Abdel-Gawad & Hidehito Matayoshi, 2017. "A Polar Fuzzy Control Scheme for Hybrid Power System Using Vehicle-To-Grid Technique," Energies, MDPI, vol. 10(8), pages 1-25, July.
    11. Fathabadi, Hassan, 2016. "Novel high-efficient unified maximum power point tracking controller for hybrid fuel cell/wind systems," Applied Energy, Elsevier, vol. 183(C), pages 1498-1510.
    12. Hu, Luoke & Liu, Ying & Lohse, Niels & Tang, Renzhong & Lv, Jingxiang & Peng, Chen & Evans, Steve, 2017. "Sequencing the features to minimise the non-cutting energy consumption in machining considering the change of spindle rotation speed," Energy, Elsevier, vol. 139(C), pages 935-946.
    13. Jaewan Suh & Sungchul Hwang & Gilsoo Jang, 2017. "Development of a Transmission and Distribution Integrated Monitoring and Analysis System for High Distributed Generation Penetration," Energies, MDPI, vol. 10(9), pages 1-15, August.
    14. Qinliang Tan & Yihong Ding & Yimei Zhang, 2017. "Optimization Model of an Efficient Collaborative Power Dispatching System for Carbon Emissions Trading in China," Energies, MDPI, vol. 10(9), pages 1-19, September.
    15. Changcheng Li & Jinghan He & Pei Zhang & Yin Xu, 2017. "A Novel Sectionalizing Method for Power System Parallel Restoration Based on Minimum Spanning Tree," Energies, MDPI, vol. 10(7), pages 1-21, July.
    16. Wang, Jianzhou & Yang, Wendong & Du, Pei & Li, Yifan, 2018. "Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system," Energy, Elsevier, vol. 148(C), pages 59-78.
    17. Geng, Zhiqiang & Yang, Xiao & Han, Yongming & Zhu, Qunxiong, 2017. "Energy optimization and analysis modeling based on extreme learning machine integrated index decomposition analysis: Application to complex chemical processes," Energy, Elsevier, vol. 120(C), pages 67-78.
    18. Azcarate, I. & Gutierrez, J.J. & Lazkano, A. & Saiz, P. & Redondo, K. & Leturiondo, L.A., 2016. "Towards limiting the sensitivity of energy-efficient lighting to voltage fluctuations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1384-1395.
    19. Yongsheng Cao & Guanglin Zhang & Demin Li & Lin Wang & Zongpeng Li, 2018. "Online Energy Management and Heterogeneous Task Scheduling for Smart Communities with Residential Cogeneration and Renewable Energy," Energies, MDPI, vol. 11(8), pages 1-20, August.
    20. Chettibi, N. & Mellit, A., 2018. "Intelligent control strategy for a grid connected PV/SOFC/BESS energy generation system," Energy, Elsevier, vol. 147(C), pages 239-262.

    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:9:y:2016:i:11:p:927-:d:82438. 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.