Line Faults Classification Using Machine Learning on Three Phase Voltages Extracted from Large Dataset of PMU Measurements

Conference · Tue Jan 04 04:00:00 EST 2022 · Proceedings of the Annual Hawaii International Conference on System Sciences Proceedings of the 55th Hawaii International Conference on System Sciences

DOI:https://doi.org/10.24251/hicss.2022.425· OSTI ID:1874493

Otudi, Hussain ^[1]; Dokic, Tatjana; Mohamed, Taif; Kezunovic, Mladen; Hu, Yi; Obradovic, Zoran

---

1. Texas A&M University

An end-to-end supervised learning method was developed to classify transmission line faults in a two-year field-recorded dataset that includes synchronized measurements of three-phase voltages recorded by 38 phasor measurement units (PMUs) sparsely located in the US Western Grid interconnection. Statistical analysis was performed to extract features from this large dataset to train the support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) classifiers. The training further leverages a simulated dataset from a synthetic grid with 12 PMUs to increase the number of types of faults infrequently seen in the field-recorded dataset. Training the classification models with the combined dataset resulted in a classification accuracy of 98.58%. This is a significant improvement over 86.87% to 87.17% accuracy obtained by relying on the field-recorded dataset alone.

• Research Organization: Texas A&M Engineering Experiment Station
• Sponsoring Organization: U.S. Department of Energy
• DOE Contract Number: OE0000913
• OSTI ID: 1874493
• Conference Information: Journal Name: Proceedings of the Annual Hawaii International Conference on System Sciences Proceedings of the 55th Hawaii International Conference on System Sciences
• Country of Publication: United States
• Language: English