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

Author

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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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    1. Raji Atia & Noboru Yamada, 2016. "Distributed Renewable Generation and Storage System Sizing Based on Smart Dispatch of Microgrids," Energies, MDPI, vol. 9(3), pages 1-16, March.
    2. Wei-Tzer Huang & Kai-Chao Yao & Chun-Ching Wu, 2014. "Using the Direct Search Method for Optimal Dispatch of Distributed Generation in a Medium-Voltage Microgrid," Energies, MDPI, vol. 7(12), pages 1-19, December.
    3. Hao Liang & Weihua Zhuang, 2014. "Stochastic Modeling and Optimization in a Microgrid: A Survey," Energies, MDPI, vol. 7(4), pages 1-24, March.
    4. Mirsaeidi, Sohrab & Said, Dalila Mat & Mustafa, Mohd. Wazir & Habibuddin, Mohd. Hafiz & Ghaffari, Kimia, 2016. "Modeling and simulation of a communication-assisted digital protection scheme for micro-grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 867-878.
    5. Ming-Tse Kuo & Shiue-Der Lu, 2013. "Design and Implementation of Real-Time Intelligent Control and Structure Based on Multi-Agent Systems in Microgrids," Energies, MDPI, vol. 6(11), pages 1-15, November.
    6. 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.
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    Cited by:

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    2. 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.
    3. 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.
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    5. 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).
    6. Elnazeer Ali Hamid Abdalla & Perumal Nallagownden & Nursyarizal Bin Mohd Nor & Mohd Fakhizan Romlie & Sabo Miya Hassan, 2018. "An Application of a Novel Technique for Assessing the Operating Performance of Existing Cooling Systems on a University Campus," Energies, MDPI, vol. 11(4), pages 1-24, March.
    7. 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).
    8. 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.

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