Machine learning-based fault detection in transmission lines: A comparative study with random search optimization

Regular and fast monitoring of transmission line faults is of immense importance for the uninterrupted transmission of electrical energy. Rapid detection and classification of faults accelerate the repair process of the system, reducing downtime and increasing the efficiency and reliability of the power system. In this context, machine learning stands out as an effective solution for transmission line fault detection. In this study, fault detection is performed using machine learning techniques such as decision trees, logistic regression, and support vector machines. Random search hyperparameter optimization was applied to improve the performance of the models. The models were trained and tested with data from fault-free and faulted cases. While the support vector machines model showed the lowest performance with 74.19% test accuracy, the logistic regression model achieved 97.01% test accuracy. The decision tree model showed the best performance with low error rates. Error measures such as root mean square error (RMSE) and mean absolute error (MAE) were also used to evaluate the predictive power of the models. This research demonstrates how machine learning-based methods can be effectively used in the detection of transmission line faults and presents the performance of different algorithms in a comparative manner.