Timeseries Fault Classification in Power Transmission Lines by Non-Intrusive Feature Extraction and Selection Using Supervised Machine Learning

This paper presents a supervised machine learning approach using eight popular classifiers for fault classification in power transmission lines. The classification of faults, indicated by the behavior of the electrical signals associated with them, plays a pivotal role in maintaining the reliability, stability, and security of electrical grids. However, most of the previous studies on fault analysis in power systems relied on proprietary data and lack of standardized benchmarks, hindering the comparison of algorithms and making performance more erratic. Moreover, the nonavailability of labeled data for all types of faults is the most problematic. This paper proposes to perform fault classification on a data set created from a real-time Simulink model to standardize performance and advance research in this area. A new strategy for non-intensive feature extraction is applied using relatively simpler techniques, eliminating computationally expensive techniques such as wavelets. Feature selection through dimensionality reduction techniques is used to improve model performance and more efficient use of computational resources. The performance of the learning algorithms (e.g. Decision Tree, Random Forest, etc.) has been analyzed with various preprocessing techniques (e.g. data scaling, transformation, etc.) and tuning of parameters, focusing on the accuracy and computational time (
$T_{c}$
), for performance generalization and efficiency. Performing specific operations on data in sequence of steps provided flexibility and adaptability in processing the data, making it easy to train, evaluate, and validate the learning algorithms. The results demonstrated that the proposed scheme can be effectively used for fault classification with high accuracy and significant reductions in
$T_{c}$
under various operating conditions. The study also determined the best estimator for each classifier when building and training the classifier models, offering a variety of options. Logistic Regression, Random Forest and Support Vector Machine were the outperforming classifiers and proved their potential for classifying faults in electric power transmission lines.