Combination of a Rabbit Optimization Algorithm and a Deep-Learning-Based Convolutional Neural Network-Long Short-Term Memory-Attention Model for Arc Sag Prediction of Transmission Lines

Arc droop presents significant challenges in power system management due to its inherent complexity and dynamic nature. To address these challenges in predicting arc sag for transmission lines, this paper proposes an innovative time-series prediction model, AROA-CNN-LSTM-Attention(AROA-CLA). The model aims to enhance arc sag prediction by integrating a convolutional neural network (CNN), a long short-term memory network (LSTM), and an attention mechanism, while also utilizing, for the first time, the adaptive rabbit optimization algorithm (AROA) for CLA parameter tuning. This combination improves both the prediction performance and the generalization capability of the model. By effectively leveraging historical data and exhibiting superior time-series processing capabilities, the AROA-CLA model demonstrates excellent prediction accuracy and stability across different time scales. Experimental results show that, compared to traditional and other modern optimization models, AROA-CLA achieves significant improvements in RMSE, MAE, MedAE, and R2 metrics, particularly in reducing errors, accelerating convergence, and enhancing robustness. These findings confirm the effectiveness and applicability of the AROA-CLA model in arc droop prediction, offering novel approaches for transmission line monitoring and intelligent power system management.