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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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
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    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.

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