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2 Bulletin of the Polish Academy of Sciences. Technical Sciences

2 2025

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

Özüpak Yıldırım

Bulletin of the Polish Academy of Sciences. Technical Sciences

2025

Vol. 73, nr 2

art. no. e153229

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.

Comparison and optimization of machine learning methods for fault detection in district heating and cooling systems

Çinar Mehmet, Aslan Emrah, Özüpak Yıldırım

Bulletin of the Polish Academy of Sciences. Technical Sciences

2025

Vol. 73, nr 3

art. no. e154063

In this study, the methods used for the detection of sub-station pollution failures in district heating and cooling (DHC) systems are analyzed. In the study, high, medium, and low-level pollution situations are considered and machine learning methods are applied for the detection of these failures. Random forest, decision tree, logistic regression, and CatBoost regression algorithms are compared within the scope of the analysis. The models are trained to perform fault detection at different pollution levels. To improve the model performance, hyperparameter optimization was performed with random search optimization, and the most appropriate values were selected. The results show that the CatBoost regression algorithm provides the highest accuracy and overall performance compared to other methods. The CatBoost model stood out with an accuracy of 0.9832 and a superior performance. These findings reveal that CatBoost-based approaches provide an effective solution in situations requiring high accuracy, such as contamination detection in DHC systems. The study makes an important contribution as a reliable fault detection solution in industrial applications.