Monitoring and Assessment of Grounding Resistance for Transmission Line Towers Based on Multi-Parameter Fusion Algorithm

The reliable operation of power transmission systems heavily depends on the performance of transmission line towers, with grounding resistance playing a crucial role in ensuring system safety. Traditional methods of monitoring grounding resistance are often limited by their inability to process and integrate diverse data sources in real-time, posing a significant challenge in modern power grids. To address these challenges, this paper proposes a novel multi-parameter fusion algorithm for monitoring and assessing the grounding resistance of transmission line towers. The proposed method integrates various relevant electrical parameters and improves the traditional support vector machine (SVM) by using the least squares method (LS) to reduce computational redundancy. Additionally, the whale optimization algorithm (WOA) is employed to fine-tune the LS-SVM parameters, enhancing the model’s accuracy and real-time performance. Finally, the Long Short-Term Memory (LSTM) network is used to extract temporal features from the classified data, enabling precise predictions of the grounding resistance based on historical trends. The effectiveness of the proposed algorithm is validated through experiments using real-world transmission line data, which demonstrate its superior accuracy and efficiency compared to existing methods.