FAULT-PROOFING THE GRID: MACHINE LEARNING-BASED ENSEMBLE MODELS FOR TRANSMISSION LINE FAILURE DETECTION

Abstract

Transmission line failures pose significant risks to the reliability and safety of electrical power systems, often resulting in costly outages and prolonged system downtimes. Early and accurate detection of potential failures is crucial to ensure uninterrupted power delivery and facilitate efficient maintenance planning. In this project, we present a robust classification framework for transmission line failure prediction using ensemble machine learning techniques, with a focus on enhancing predictive accuracy and model robustness. The study begins with comprehensive Exploratory Data Analysis (EDA) to uncover underlying patterns and relationships within the dataset. Using statistical summaries, correlation analysis, and visualization techniques, key features influencing transmission line failures are identified. This analysis is instrumental in refining the dataset and improving model performance. A Decision Tree Classifier is initially employed as the baseline model due to its simplicity and interpretability. However, it demonstrates limitations such as overfitting and poor generalization on complex or noisy data. To address these challenges, we propose a Random Forest Classifier as the improved model. By aggregating predictions from multiple decision trees, the Random Forest significantly enhances classification accuracy, mitigates overfitting, and better captures feature interactions. Experimental results show that the Random Forest Classifier consistently outperforms the Decision Tree across all major performance metrics, confirming its effectiveness in accurately predicting transmission line failures. This project underscores the value of ensemble methods in critical infrastructure applications and demonstrates how data-driven approaches can be leveraged to build intelligent fault detection systems in the power sector.