Fault Analysis in Transmission Line Using Machine Learning

A machine learning approach for fault analysis in transmission lines is presented based on Support Vector Machine (SVM), a supervised learning algorithm for classification and regression tasks. The proposed system is designed to identify and classify different transmission line fault type while maintaining reliability and safety in the power systems. The approach employs SVM’s ability to work on complex and non linear data to achieve high accuracy and fault detection robustness in the presence of noise and other challenging environments. A methodology is presented that employs an advanced preprocessing techniques for feature extraction from fault signals, improving the SVM model performance. The above solution reduces downtime while contributing to efficient fault diagnosis and increasing stability of electrical networks. The system can be adapted to complex challenges of modern power grids and can contribute to a more intelligible and reliable transmission line operation.