ABSTRACT Conventional wire condition monitoring methods often lack the precision and reliability required for real-time assessment in complex environments, leading to undetected failures and compromised power transmission safety. To address these limitations, the article presents an XGBoost (Extreme Gradient Boosting) – LSTM (Long and Short-Term Memory) hybrid model for wire state monitoring using Beidou GNSS (Global Navigation Satellite System) and IMU (Inertial Measurement Unit) sensors. The system tracks wire vibration, tilt, rotation, bending, and stretching in real time. GNSS provides positional data, and IMU records acceleration and angular velocity. The Kalman filter cleans data, and XGBoost and LSTM are used for feature selection and temporal modelling. The combined model achieves 94.1% accuracy overall, with XGBoost at 92.4% and LSTM at 90.9%. Classification accuracy is 95.5% for vibration, 94.7% for bending, and 94.3% for stretching. The experiment also showed that the model has stable performance in different temperature environments, with accuracy fluctuating between 94.0% and 94.2% within the temperature range of −5℃ to 50℃. In general, the XGBoost LSTM model has achieved high-precision and high stability monitoring in wire condition monitoring, which is significantly better than the traditional single model. This study improves the accuracy of wire condition monitoring and has significant practical application value and strong stability.
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