基于IPSO算法优化的CNN-LSTM模型及其输电线路覆冰厚度预测方法研究OA

To evaluate in real-time the safety impact of ice thickness on transmission lines under complex meteorological conditions on power systems,this paper proposes an intelligent prediction model that integrates Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM)networks.The Improved Particle Swarm Optimization(IPSO)algorithm is employed to automatically optimize the hyperparameters within the hybrid neural network model,constructing an accurate prediction model for ice thickness on transmission lines.The research results show that the IPSO algorithm can effectively enhance the global search ability of particles,the fitness optimization amplitude is increased by 46.4%and 51.9%compared with the PSO algorithm and the DBO algorithm respectively.Compared with other models,the IPSO-CNN-LSTM hybrid model shows the best prediction performance on the test set,with ERMSE and EMAPE values of 0.408 3 mm and 3.132 6%,respectively,indicating a significant improvement in prediction accuracy.The correlation coefficient R² remains at 0.960 8,demonstrating a high correlation between predicted and actual values and strong data interpretability.In engineering applications,the prediction results of the model can provide reliable data and decision support for the prevention of freezing and thawing of power systems.