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A Novel Approach for Microgrid Protection Based upon Combined ANFIS and Hilbert Space-Based Power Setting

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  • Ali Hadi Abdulwahid

    (School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Department of Engineering Electrical Power, Engineering Technical College, Southern Technical University, Basrah 61001, Iraq)

  • Shaorong Wang

    (School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Nowadays, the use of distributed generation (DG) has increased because of benefits such as increased reliability, reduced losses, improvement in the line capacity, and less environmental pollution. The protection of microgrids, which consist of generation sources, is one of the most crucial concerns of basic distribution operators. One of the key issues in this field is the protection of microgrids against permanent and temporary failures by improving the safety and reliability of the network. The traditional method has a number of disadvantages. The reliability and stability of a power system in a microgrid depend to a great extent on the efficiency of the protection scheme. The application of Artificial Intelligence approaches was introduced recently in the protection of distribution networks. The fault detection method depends on differential relay based on Hilbert Space-Based Power (HSBP) theory to achieve fastest primary protection. It is backed up by a total harmonic distortion ( THD ) detection method that takes over in case of a failure in the primary method. The backup protection would be completely independent of the main protection. This is rarely attained in practice. This paper proposes a new algorithm to improve protection performance by adaptive network-based fuzzy inference system (ANFIS). The protection can be obtained in a novel way based on this theory. An advantage of this algorithm is that the protection system operates in fewer than two cycles after the occurrence of the fault. Another advantage is that the error detection is not dependent on the selection of threshold values, and all types of internal fault can identify and show that the algorithm operates correctly for all types of faults while preventing unwanted tripping, even if the data were distorted by current transformer (CT) saturation or by data mismatches. The simulation results show that the proposed circuit can identify the faulty phase in the microgrid quickly and correctly.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:12:p:1042-:d:84898
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    References listed on IDEAS

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    Cited by:

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    2. Patnaik, Bhaskar & Mishra, Manohar & Bansal, Ramesh C. & Jena, Ranjan K., 2021. "MODWT-XGBoost based smart energy solution for fault detection and classification in a smart microgrid," Applied Energy, Elsevier, vol. 285(C).
    3. 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.
    4. Cristian Cepeda & Cesar Orozco-Henao & Winston Percybrooks & Juan Diego Pulgarín-Rivera & Oscar Danilo Montoya & Walter Gil-González & Juan Carlos Vélez, 2020. "Intelligent Fault Detection System for Microgrids," Energies, MDPI, vol. 13(5), pages 1-21, March.
    5. Stephen Ntiri Asomani & Jianping Yuan & Longyan Wang & Desmond Appiah & Kofi Asamoah Adu-Poku, 2020. "The Impact of Surrogate Models on the Multi-Objective Optimization of Pump-As-Turbine (PAT)," Energies, MDPI, vol. 13(9), pages 1-29, May.
    6. Ali Hadi Abdulwahid & Shaorong Wang, 2018. "A Novel Method of Protection to Prevent Reverse Power Flow Based on Neuro-Fuzzy Networks for Smart Grid," Sustainability, MDPI, vol. 10(4), pages 1-19, April.
    7. En-Chih Chang, 2018. "Study and Application of Intelligent Sliding Mode Control for Voltage Source Inverters," Energies, MDPI, vol. 11(10), pages 1-14, September.
    8. Patnaik, Bhaskar & Mishra, Manohar & Bansal, Ramesh C. & Jena, Ranjan Kumar, 2020. "AC microgrid protection – A review: Current and future prospective," Applied Energy, Elsevier, vol. 271(C).

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