AI-Based fault detection and predictive maintenance for power transmission networks in remote areas using catboost for enhanced reliability

Abstract

Power transmission network plays a crucial role in maintaining the stability and reliability of electrical systems. Fault detection and predictive maintenance are essential to ensure continuous operation and minimize downtimes, but traditional fault detection methods face challenges, particularly in remote areas where manual inspections are impractical. This paper presents a framework to enhance the steadiness and efficacy of power transmission networks through advanced fault detection and predictive maintenance. The proposed framework begins with data collection from power transmission sensors, including voltage, current, and temperature readings, along with historical fault records. Next, data pre-processing is performed using median imputation to handle missing values and categorical encoding to transform non-numeric data into numerical form. Feature extraction follows, where time-domain features like Peak-to-Peak Value, RMS, and Zero-Crossing Rate are computed to detect potential faults. The CatBoost model is then trained on the extracted features, and hyperparameter optimization is conducted using the Coati Optimization Algorithm. Once trained, the model performs fault detection and prediction, identifying faults such as Transformer Failures, Overheating, and Line Breakages. The model is assessed using metrics like accuracy of 99.42%, precision of 99.37%, recall of 99.40%, and F1-score of 99.38%. The framework achieves high performance in detecting faults and can be deployed in power transmission systems for proactive maintenance, reducing reliance on manual inspections, improving system reliability, and addressing challenges in remote locations.