IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i23p4504-d291343.html
   My bibliography  Save this article

Intelligent Fault Classification and Location Identification Method for Microgrids Using Discrete Orthonormal Stockwell Transform-Based Optimized Multi-Kernel Extreme Learning Machine

Author

Listed:
  • Teke Gush

    (College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Korea)

  • Syed Basit Ali Bukhari

    (U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), H-12 Campus, Islamabad 44000, Pakistan)

  • Khawaja Khalid Mehmood

    (U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), H-12 Campus, Islamabad 44000, Pakistan)

  • Samuel Admasie

    (College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Korea)

  • Ji-Soo Kim

    (College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Korea)

  • Chul-Hwan Kim

    (College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Korea)

Abstract

This paper proposes an intelligent fault classification and location identification method for microgrids using discrete orthonormal Stockwell transform (DOST)-based optimized multi-kernel extreme learning machine (MKELM). The proposed method first extracts useful statistical features from one cycle of post-fault current signals retrieved from sending-end relays of microgrids using DOST. Then, the extracted features are normalized and fed to the MKELM as an input. The MKELM, which consists of multiple kernels in the hidden nodes of an extreme learning machine, is used for the classification and location of faults in microgrids. A genetic algorithm is employed to determine the optimum parameters of the MKELM. The performance of the proposed method is tested on the standard IEC microgrid test system for various operating conditions and fault cases, including different fault locations, fault resistance, and fault inception angles using the MATLAB/Simulink software. The test results confirm the efficacy of the proposed method for classifying and locating any type of fault in a microgrid with high performance. Furthermore, the proposed method has higher performance and is more robust to measurement noise than existing intelligent methods.

Suggested Citation

  • Teke Gush & Syed Basit Ali Bukhari & Khawaja Khalid Mehmood & Samuel Admasie & Ji-Soo Kim & Chul-Hwan Kim, 2019. "Intelligent Fault Classification and Location Identification Method for Microgrids Using Discrete Orthonormal Stockwell Transform-Based Optimized Multi-Kernel Extreme Learning Machine," Energies, MDPI, vol. 12(23), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4504-:d:291343
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/23/4504/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/23/4504/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hashim A. Al Hassan & Andrew Reiman & Gregory F. Reed & Zhi-Hong Mao & Brandon M. Grainger, 2018. "Model-Based Fault Detection of Inverter-Based Microgrids and a Mathematical Framework to Analyze and Avoid Nuisance Tripping and Blinding Scenarios," Energies, MDPI, vol. 11(8), pages 1-19, August.
    2. Tusongjiang Kari & Wensheng Gao & Ayiguzhali Tuluhong & Yilihamu Yaermaimaiti & Ziwei Zhang, 2018. "Mixed Kernel Function Support Vector Regression with Genetic Algorithm for Forecasting Dissolved Gas Content in Power Transformers," Energies, MDPI, vol. 11(9), pages 1-19, September.
    3. Hosseini, Seyed Amir & Abyaneh, Hossein Askarian & Sadeghi, Seyed Hossein Hesamedin & Razavi, Farzad & Nasiri, Adel, 2016. "An overview of microgrid protection methods and the factors involved," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 174-186.
    4. Hare, James & Shi, Xiaofang & Gupta, Shalabh & Bazzi, Ali, 2016. "Fault diagnostics in smart micro-grids: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1114-1124.
    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. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    2. Alireza Forouzesh & Mohammad S. Golsorkhi & Mehdi Savaghebi & Mehdi Baharizadeh, 2021. "Support Vector Machine Based Fault Location Identification in Microgrids Using Interharmonic Injection," Energies, MDPI, vol. 14(8), pages 1-14, April.
    3. Raad Salih Jawad & Hafedh Abid, 2023. "HVDC Fault Detection and Classification with Artificial Neural Network Based on ACO-DWT Method," Energies, MDPI, vol. 16(3), pages 1-18, January.
    4. Nastaran Gholizadeh & Petr Musilek, 2021. "Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges," Energies, MDPI, vol. 14(12), pages 1-18, June.
    5. Shahriar Rahman Fahim & Subrata K. Sarker & S. M. Muyeen & Md. Rafiqul Islam Sheikh & Sajal K. Das, 2020. "Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews," Energies, MDPI, vol. 13(13), pages 1-22, July.

    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. Noor Hussain & Mashood Nasir & Juan Carlos Vasquez & Josep M. Guerrero, 2020. "Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems–A Review," Energies, MDPI, vol. 13(9), pages 1-31, May.
    2. Shahriar Rahman Fahim & Subrata K. Sarker & S. M. Muyeen & Md. Rafiqul Islam Sheikh & Sajal K. Das, 2020. "Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews," Energies, MDPI, vol. 13(13), pages 1-22, July.
    3. Chen, Yizhong & He, Li & Li, Jing, 2017. "Stochastic dominant-subordinate-interactive scheduling optimization for interconnected microgrids with considering wind-photovoltaic-based distributed generations under uncertainty," Energy, Elsevier, vol. 130(C), pages 581-598.
    4. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    5. El-Sharafy, M. Zaki & Farag, Hany E.Z., 2017. "Back-feed power restoration using distributed constraint optimization in smart distribution grids clustered into microgrids," Applied Energy, Elsevier, vol. 206(C), pages 1102-1117.
    6. Kara Mostefa Khelil, Chérifa & Amrouche, Badia & Benyoucef, Abou soufiane & Kara, Kamel & Chouder, Aissa, 2020. "New Intelligent Fault Diagnosis (IFD) approach for grid-connected photovoltaic systems," Energy, Elsevier, vol. 211(C).
    7. Harrou, Fouzi & Sun, Ying & Taghezouit, Bilal & Saidi, Ahmed & Hamlati, Mohamed-Elkarim, 2018. "Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches," Renewable Energy, Elsevier, vol. 116(PA), pages 22-37.
    8. Katja Sirviö & Kimmo Kauhaniemi & Aushiq Ali Memon & Hannu Laaksonen & Lauri Kumpulainen, 2020. "Functional Analysis of the Microgrid Concept Applied to Case Studies of the Sundom Smart Grid," Energies, MDPI, vol. 13(16), pages 1-31, August.
    9. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Akif Zia Khan & Dong Ryeol Shin, 2017. "Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective," Energies, MDPI, vol. 10(4), pages 1-47, April.
    10. Edmilson Bermudes Rocha Junior & Oureste Elias Batista & Domingos Sávio Lyrio Simonetti, 2022. "Differential Analysis of Fault Currents in a Power Distribution Feeder Using abc , αβ0 , and dq0 Reference Frames," Energies, MDPI, vol. 15(2), pages 1-22, January.
    11. Barra, P.H.A. & Coury, D.V. & Fernandes, R.A.S., 2020. "A survey on adaptive protection of microgrids and distribution systems with distributed generators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    12. Lee, J. & Razeghi, G. & Samuelsen, S., 2022. "Generic microgrid controller with self-healing capabilities," Applied Energy, Elsevier, vol. 308(C).
    13. Andrés A. Zúñiga & Alexandre Baleia & João Fernandes & Paulo Jose Da Costa Branco, 2020. "Classical Failure Modes and Effects Analysis in the Context of Smart Grid Cyber-Physical Systems," Energies, MDPI, vol. 13(5), pages 1-26, March.
    14. Xiangbing Gao & Bo Jia & Gen Li & Xiaojing Ma, 2022. "Calorific Value Forecasting of Coal Gangue with Hybrid Kernel Function–Support Vector Regression and Genetic Algorithm," Energies, MDPI, vol. 15(18), pages 1-15, September.
    15. Shobole, Abdulfetah Abdela & Wadi, Mohammed, 2021. "Multiagent systems application for the smart grid protection," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    16. Dagar, Annu & Gupta, Pankaj & Niranjan, Vandana, 2021. "Microgrid protection: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    17. Sergio Danilo Saldarriaga-Zuluaga & Jesús María López-Lezama & Nicolás Muñoz-Galeano, 2020. "Optimal Coordination of Overcurrent Relays in Microgrids Considering a Non-Standard Characteristic," Energies, MDPI, vol. 13(4), pages 1-18, February.
    18. José de Jesús Jaramillo Serna & Jesús M. López-Lezama, 2019. "Calculation of Distance Protection Settings in Mutually Coupled Transmission Lines: A Comparative Analysis," Energies, MDPI, vol. 12(7), pages 1-32, April.
    19. Giovanni Betta & Domenico Capriglione & Luigi Ferrigno & Marco Laracca & Gianfranco Miele & Nello Polese & Silvia Sangiovanni, 2021. "A Fault Diagnostic Scheme for Predictive Maintenance of AC/DC Converters in MV/LV Substations," Energies, MDPI, vol. 14(22), pages 1-23, November.
    20. Das, Saborni & Hazra, Abhik & Basu, Mousumi, 2018. "Metaheuristic optimization based fault diagnosis strategy for solar photovoltaic systems under non-uniform irradiance," Renewable Energy, Elsevier, vol. 118(C), pages 452-467.

    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:23:p:4504-:d:291343. 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.