Radio Interference Prediction of Transmission Lines Based on CEEMD-CNN-BiLSTM-MHA Model

This study proposes a combined prediction model to improve the accuracy of radio interference prediction in transmission lines affected by multiple environmental factors at high altitudes. The model integrates Complementary Ensemble Empirical Mode Decomposition (CEEMD), Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Multi-Head Attention (MHA). First, a long-term observation station was set up at an altitude of 2,420 meters to collect radio interference data 20 meters from the projected edge of the phase conductor. A normality test was performed on the collected data. Spearman correlation analysis was conducted to filter radio interference data and related environmental factors. Second, to address the nonlinear characteristics of radio interference data, CEEMD was used to decompose the data into relatively stationary subsequences, reducing the impact of random fluctuations on the prediction model. Third, these subsequences were fed into the combined model, integrating CNN and BiLSTM, with the MHA mechanism assigning weights to the BiLSTM hidden layers. To improve predictive performance, an Improved Whale Optimization Algorithm (IWOA) was applied to optimize two BiLSTM hyperparameters, yielding the optimal parameter combination. Finally, the overall radio interference prediction was reconstructed by combining the predictions from each subsequence.