IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i13p3460-d380272.html
   My bibliography  Save this article

Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews

Author

Listed:
  • Shahriar Rahman Fahim

    (Department of Electrical and Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh)

  • Subrata K. Sarker

    (Department of Electrical and Electronic Engineering, Varendra University, Rajshahi 6204, Bangladesh)

  • S. M. Muyeen

    (School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth 6845, Australia)

  • Md. Rafiqul Islam Sheikh

    (Department of Electrical and Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh)

  • Sajal K. Das

    (Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh)

Abstract

Accurate fault classification and detection for the microgrid (MG) becomes a concern among the researchers from the state-of-art of fault diagnosis as it increases the chance to increase the transient response. The MG frequently experiences a number of shunt faults during the distribution of power from the generation end to user premises, which affects the system reliability, damages the load, and increases the fault line restoration cost. Therefore, a noise-immune and precise fault diagnosis model is required to perform the fast recovery of the unhealthy phases. This paper presents a review on the MG fault diagnosis techniques with their limitations and proposes a novel discrete-wavelet transform (DWT) based probabilistic generative model to explore the precise solution for fault diagnosis of MG. The proposed model is made of multiple layers with a restricted Boltzmann machine (RBM), which enables the model to make the probability reconstruction over its inputs. The individual RBM layer is trained with an unsupervised learning approach where an artificial neural network (ANN) algorithm tunes the model for minimizing the error between the true and predicted class. The effectiveness of the proposed model is studied by varying the input signal and sampling frequencies. A level of considered noise is added with the sample data to test the robustness of the studied model. Results prove that the proposed fault detection and classification model has the ability to perform the precise diagnosis of MG faults. A comparative study among the proposed, kernel extreme learning machine (KELM), multi KELM, and support vector machine (SVM) approaches is studied to confirm the robust superior performance of the proposed model.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3460-:d:380272
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/13/3460/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/13/3460/
    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. Muhammad Ali & Dae-Hee Son & Sang-Hee Kang & Soon-Ryul Nam, 2017. "An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy," Energies, MDPI, vol. 10(11), pages 1-24, November.
    3. Shuai, Zhikang & Sun, Yingyun & Shen, Z. John & Tian, Wei & Tu, Chunming & Li, Yan & Yin, Xin, 2016. "Microgrid stability: Classification and a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 167-179.
    4. Ying-Yi Hong & Yan-Hung Wei & Yung-Ruei Chang & Yih-Der Lee & Pang-Wei Liu, 2014. "Fault Detection and Location by Static Switches in Microgrids Using Wavelet Transform and Adaptive Network-Based Fuzzy Inference System," Energies, MDPI, vol. 7(4), pages 1-18, April.
    5. 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.
    6. 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. Yijin Li & Jianhua Lin & Geng Niu & Ming Wu & Xuteng Wei, 2021. "A Hilbert–Huang Transform-Based Adaptive Fault Detection and Classification Method for Microgrids," Energies, MDPI, vol. 14(16), pages 1-16, August.
    2. Mohammad Reza Shadi & Hamid Mirshekali & Rahman Dashti & Mohammad-Taghi Ameli & Hamid Reza Shaker, 2021. "A Parameter-Free Approach for Fault Section Detection on Distribution Networks Employing Gated Recurrent Unit," Energies, MDPI, vol. 14(19), pages 1-15, October.
    3. Junho Hong & Dmitry Ishchenko & Anil Kondabathini, 2021. "Implementation of Resilient Self-Healing Microgrids with IEC 61850-Based Communications," Energies, MDPI, vol. 14(3), pages 1-16, January.
    4. Muhammad Umair Safder & Mohammad J. Sanjari & Ameer Hamza & Rasoul Garmabdari & Md. Alamgir Hossain & Junwei Lu, 2023. "Enhancing Microgrid Stability and Energy Management: Techniques, Challenges, and Future Directions," Energies, MDPI, vol. 16(18), pages 1-28, September.
    5. Jannis N. Kahlen & Michael Andres & Albert Moser, 2021. "Improving Machine-Learning Diagnostics with Model-Based Data Augmentation Showcased for a Transformer Fault," Energies, MDPI, vol. 14(20), pages 1-20, October.
    6. 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.
    7. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    8. Khalfan Al Kharusi & Abdelsalam El Haffar & Mostefa Mesbah, 2022. "Fault Detection and Classification in Transmission Lines Connected to Inverter-Based Generators Using Machine Learning," Energies, MDPI, vol. 15(15), pages 1-23, July.
    9. Jong Ju Kim & June Ho Park, 2021. "A Novel Structure of a Power System Stabilizer for Microgrids," Energies, MDPI, vol. 14(4), pages 1-33, February.
    10. 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.
    11. Jorge De La Cruz & Eduardo Gómez-Luna & Majid Ali & Juan C. Vasquez & Josep M. Guerrero, 2023. "Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends," Energies, MDPI, vol. 16(5), pages 1-37, February.
    12. Łukasz Rokicki, 2021. "Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator," Energies, MDPI, vol. 14(19), pages 1-24, October.
    13. Zhen Huang & Xuechun Xiao & Yuan Gao & Yonghong Xia & Tomislav Dragičević & Pat Wheeler, 2023. "Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications," Energies, MDPI, vol. 16(17), pages 1-26, August.

    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. 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.
    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. Antoine Boche & Clément Foucher & Luiz Fernando Lavado Villa, 2022. "Understanding Microgrid Sustainability: A Systemic and Comprehensive Review," Energies, MDPI, vol. 15(8), pages 1-29, April.
    5. Haifeng Liang & Yue Dong & Yuxi Huang & Can Zheng & Peng Li, 2018. "Modeling of Multiple Master–Slave Control under Island Microgrid and Stability Analysis Based on Control Parameter Configuration," Energies, MDPI, vol. 11(9), pages 1-18, August.
    6. 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.
    7. Hirsch, Adam & Parag, Yael & Guerrero, Josep, 2018. "Microgrids: A review of technologies, key drivers, and outstanding issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 402-411.
    8. Miguel Carpintero-Rentería & David Santos-Martín & Josep M. Guerrero, 2019. "Microgrids Literature Review through a Layers Structure," Energies, MDPI, vol. 12(22), pages 1-22, November.
    9. 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.
    10. 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).
    11. 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.
    12. Ali Hadi Abdulwahid & Shaorong Wang, 2016. "A Novel Approach for Microgrid Protection Based upon Combined ANFIS and Hilbert Space-Based Power Setting," Energies, MDPI, vol. 9(12), pages 1-25, December.
    13. Ricardo Granizo Arrabé & Carlos Antonio Platero Gaona & Fernando Álvarez Gómez & Emilio Rebollo López, 2016. "Novel Auto-Reclosing Blocking Method for Combined Overhead-Cable Lines in Power Networks," Energies, MDPI, vol. 9(11), pages 1-20, November.
    14. Tu, Chunming & He, Xi & Shuai, Zhikang & Jiang, Fei, 2017. "Big data issues in smart grid – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1099-1107.
    15. 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.
    16. Neto, Pedro Bezerra Leite & Saavedra, Osvaldo R. & Oliveira, Denisson Q., 2020. "The effect of complementarity between solar, wind and tidal energy in isolated hybrid microgrids," Renewable Energy, Elsevier, vol. 147(P1), pages 339-355.
    17. Serban, Ioan, 2018. "A control strategy for microgrids: Seamless transfer based on a leading inverter with supercapacitor energy storage system," Applied Energy, Elsevier, vol. 221(C), pages 490-507.
    18. 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.
    19. Veerapandiyan Veerasamy & Noor Izzri Abdul Wahab & Rajeswari Ramachandran & Muhammad Mansoor & Mariammal Thirumeni & Mohammad Lutfi Othman, 2018. "High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 11(12), pages 1-24, November.
    20. Tito G. Amaral & Vitor Fernão Pires & Armando Cordeiro & Daniel Foito & João F. Martins & Julia Yamnenko & Tetyana Tereschenko & Liudmyla Laikova & Ihor Fedin, 2023. "Incipient Fault Diagnosis of a Grid-Connected T-Type Multilevel Inverter Using Multilayer Perceptron and Walsh Transform," Energies, MDPI, vol. 16(6), pages 1-18, March.

    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:13:y:2020:i:13:p:3460-:d:380272. 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.