Ice Cover Prediction for Transmission Lines Based on Feature Extraction and an Improved Transformer Scheme

Abstract

Frequent and severe icing on transmission lines poses a serious threat to the stability and safe operation of the power system. Meteorological data, inherently stochastic and uncertain, requires effective preprocessing and feature extraction to ensure accurate and efficient prediction of transmission line icing thickness. We address this challenge by leveraging the meteorological features of icing phenomena and propose a novel feature preprocessing method that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and spectral clustering. This method effectively preprocesses raw time series data, extracts key features, and constructs a similarity matrix and feature vector. The resulting feature vector serves as a new data representation, facilitating cluster analysis to isolate meteorological and icing-related features specific to transmission lines. Subsequently, we introduce an enhanced Transformer model for predicting transmission line icing thickness. The proposed model leverages the extracted meteorological and icing features by independently embedding variable tokens for each input feature. This approach improves the model’s prediction accuracy under multiple feature inputs, leading to more effective learning. The experimental results demonstrate that the performance of the proposed prediction algorithm is better than the three baseline algorithms (hybrid CEEMDAN and LSTM (CEEMDAN-LSTM), hybrid CEEMDAN, spectral clustering, and LSTM (CEEMDAN-SP-LSTM), and hybrid CEEMDAN, spectral clustering, and Transformer (CEEMDAN-SP-Transformer)) under multiple feature inputs and different parameter settings.