Line Faults Classification Using Machine Learning on Three Phase Voltages Extracted from Large Dataset of PMU Measurements
- Temple Univ., Philadelphia, PA (United States); Texas A&M Engineering Experiment
- Texas A & M Univ., College Station, TX (United States)
- Quanta Technology, Raleigh, NC (United States)
- Temple Univ., Philadelphia, PA (United States)
An end-to-end supervised learning method is developed to classify transmission line faults in a twoyear field-recorded dataset that includes synchronized measurements of three-phase voltages recorded by 38 Phasor Measurement Units (PMU) sparsely located in in the US Western Grid interconnection. Statistical analysis is performed to extract features from this large dataset to train Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) classifiers initially. The training further leverages a simulated dataset from a synthetic grid with 12 PMUs to increase the number of faults of types infrequently seen in the field-recorded dataset. Training the classification models with the combined dataset resulted in a classification accuracy of 97.7%. This is a significant improvement over 89.7% to 92.5% accuracy obtained by relying on the field-recorded dataset alone.
- Research Organization:
- Texas A & M Univ., College Station, TX (United States). Texas A & M Engineering Experiment Station
- Sponsoring Organization:
- USDOE Office of Electricity (OE)
- Grant/Contract Number:
- OE0000913
- OSTI ID:
- 1891312
- Journal Information:
- Proceedings of the Annual Hawaii International Conference on System Sciences, Journal Name: Proceedings of the Annual Hawaii International Conference on System Sciences Vol. N/A; ISSN 2572-6862
- Publisher:
- University of Hawaii at Manoa LibraryCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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