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Model-Based Fault Detection of Inverter-Based Microgrids and a Mathematical Framework to Analyze and Avoid Nuisance Tripping and Blinding Scenarios

Author

Listed:
  • Hashim A. Al Hassan

    (Electric Power Systems Laboratory, Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh 15261, PA, USA)

  • Andrew Reiman

    (Electric Power Systems Laboratory, Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh 15261, PA, USA)

  • Gregory F. Reed

    (Electric Power Systems Laboratory, Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh 15261, PA, USA)

  • Zhi-Hong Mao

    (Electric Power Systems Laboratory, Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh 15261, PA, USA)

  • Brandon M. Grainger

    (Electric Power Systems Laboratory, Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh 15261, PA, USA)

Abstract

Traditional protection methods such as over-current or under-voltage methods are unreliable in inverter-based microgrid applications. This is primarily due to low fault current levels because of power electronic interfaces to the distributed energy resources (DER), and IEEE1547 low-voltage-ride-through (LVRT) requirements for renewables in microgrids. However, when faults occur in a microgrid feeder, system changes occur which manipulate the internal circuit structure altering the system dynamic relationships. This observation establishes the basis for a proposed, novel, model-based, communication-free fault detection technique for inverter-based microgrids. The method can detect faults regardless of the fault current level and the microgrid mode of operation. The approach utilizes fewer measurements to avoid the use of a communication system. Protecting the microgrid without communication channels could lead to blinding (circuit breakers not tripping for faults) or nuisance tripping (tripping incorrectly). However, these events can be avoided with proper system design, specifically with appropriately sized system impedance. Thus, a major contribution of this article is the development of a mathematical framework to analyze and avoid blinding and nuisance tripping scenarios by quantifying the bounds of the proposed fault detection technique. As part of this analysis, the impedance based constraints for microgrid system feeders are included. The performance of the proposed technique is demonstrated in the MATLAB/SIMULINK (MathWorks, Natick, MA, USA) simulation environment on a representative microgrid architecture showing that the proposed technique can detect faults for a wide range of load impedances and fault impedances.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2152-:d:164314
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    References listed on IDEAS

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    1. Mirsaeidi, Sohrab & Said, Dalila Mat & Mustafa, Mohammad Wazir & Habibuddin, Mohammad Hafiz & Ghaffari, Kimia, 2016. "Fault location and isolation in micro-grids using a digital central protection unit," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1-17.
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    Cited by:

    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. 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.

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