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

Khalfan Al Kharusi (), Abdelsalam El Haffar and Mostefa Mesbah
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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

Energies, 2023, vol. 16, issue 15, 1-22

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.

Keywords: adaptive protection; machine learning; fault detection and classification; inverter-based generators; groups setting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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