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Adaptive Machine-Learning-Based Transmission Line Fault Detection and Classification Connected to Inverter-Based Generators

Author

Listed:
  • Khalfan Al Kharusi

    (Electrical and Computer Engineering, Sultan Qaboos University, P.O. Box 33, Muscat 123, Oman)

  • Abdelsalam El Haffar

    (Electrical and Computer Engineering, Sultan Qaboos University, P.O. Box 33, Muscat 123, Oman)

  • Mostefa Mesbah

    (Electrical and Computer Engineering, Sultan Qaboos University, P.O. Box 33, Muscat 123, Oman)

Abstract

Adaptive protection schemes have been developed to address the problem of behavior-changing power systems integrated with inverter-based generation (IBG). This paper proposes a machine-learning-based fault detection and classification technique using a setting-group-based adaptation approach. Multigroup settings were designed depending on the types of power generation (synchronous generator, PV plant, and type-3 wind farm) connected to a transmission line in the 39-Bus New England System. For each system topology, an optimized pretrained ensemble tree classifier was used. The adaptation process has two phases: an offline learning phase to tune the classifiers and select the optimum subset of features, and an online phase where the circuit breaker (CB) status and the active output power of the generators are continuously monitored to identify the current system topology and to select the appropriate setting group. The proposed system achieved an average accuracy of 99.4%, a 99.5% average precision, a 99.9% average specificity, and a 99.4% average sensitivity of classification. The robustness analysis was conducted by applying several fault scenarios not considered during training, which include different transmission network configurations and different penetration levels of IBGs. The case of incorrect selection of the appropriate setting group resulting from selecting the wrong topology is also considered. It was noticed that the performance of developed classifiers deteriorates when the transmission network is reconfigured and the incorrect setting group is selected.

Suggested Citation

  • Khalfan Al Kharusi & Abdelsalam El Haffar & Mostefa Mesbah, 2023. "Adaptive Machine-Learning-Based Transmission Line Fault Detection and Classification Connected to Inverter-Based Generators," Energies, MDPI, vol. 16(15), pages 1-22, August.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5775-:d:1209367
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    References listed on IDEAS

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    1. Sumei Liu & Tianshu Bi & Yanlin Liu, 2017. "Theoretical Analysis on the Short-Circuit Current of Inverter-Interfaced Renewable Energy Generators with Fault-Ride-Through Capability," Sustainability, MDPI, vol. 10(1), pages 1-15, December.
    2. 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.
    3. Aushiq Ali Memon & Kimmo Kauhaniemi, 2020. "An Adaptive Protection for Radial AC Microgrid Using IEC 61850 Communication Standard: Algorithm Proposal Using Offline Simulations," Energies, MDPI, vol. 13(20), pages 1-31, October.
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    Cited by:

    1. Petar Sarajcev & Dino Lovric, 2024. "Machine Learning Classifier for Supporting Generator’s Impedance-Based Relay Protection Functions," Energies, MDPI, vol. 17(8), pages 1-16, April.

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