crossrefEnergies2025-09-01Cited by 6

Research on Transmission Line Icing Prediction for Power System Based on Improved Snake Optimization Algorithm-Optimized Deep Hybrid Kernel Extreme Learning Machine

Guanhua Li, Haoran Chen, Shicong Sun, Tie Guo, Luyu Yang

As extreme weather events become more frequent, the icing of transmission lines in winter has become more common, causing significant economic losses to power systems and drawing increasing attention. However, owing to the complexity of the conductor icing process, establishing high-precision ice thickness prediction models is vital for ensuring the safe and stable operation of power grids. Therefore, this paper proposes a hybrid model combining an improved snake optimization (ISO) algorithm, deep extreme learning machine (DELM), and hybrid kernel extreme learning machine (HKELM). Firstly, based on the analysis of the factors that influence the icing, the temperature, the humidity, the wind velocity, the wind direction, and the precipitation are selected as the weather parameters for the prediction model of the transmission line icing. Secondly, the HKELM is introduced into the regression layer of DELM to obtain the deep hybrid kernel extreme learning machine (DHKELM) model for ice thickness prediction. The SO algorithm is then augmented by incorporating the Latin hypercube sampling technique, t-distribution mutation strategy, and Cauchy mutation, enhancing its convergence. Finally, the ISO-DHKELM model is applied to the icing data of transmission lines in Sichuan Province for experiments. The simulation results indicate that this model not only performs well, but also enhances the accuracy of ice thickness predictions.

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An Integrated CFD–Machine Learning Framework for Flow Assurance Risk Assessment During Hot Oil Commissioning of Subsea Jumpers

To mitigate flow assurance risks during the hot oil commissioning of deepwater jumpers, this study develops a transient displacement risk assessment framework integrating CFD–machine learning surrogate models. A 3D numerical model using VOF and conjugate heat transfer simulated h…

Alternative Thermal Technologies for Industrial Process Heat: Barriers and Opportunities

Energy scarcity and subsequent global fuel market shocks have become a significant concern for the United States. Process heating in industry accounts for over half of all industrial energy usage and is almost entirely (>95%) supplied by natural gas, coal, and byproduct fuels.…

Nonlinear Dynamics and Spatial Correlation Pattern of the Digital Economy on Energy Efficiency: Evidence from Ensemble Learning and Spatio-Temporal Graph Neural Network

Achieving synergy between the digital economy and energy efficiency is pivotal for realizing high-quality development under the “Dual Carbon” targets. However, traditional econometric methods struggle to capture the complex nonlinear and spatio-temporal dependencies inherent in t…

Prediction of Waterflooding Performance with a New Machine Learning Method by Combining Linear Dynamical Systems with Neural Networks

Machine learning methods have gained significant attention in forecasting waterflooding performance in recent years, but their accuracy often remains insufficient for practical field applications. This study proposes a hybrid framework that integrates a linear dynamical system (L…

Machine Learning-Based Lifetime Prediction of Lithium Batteries: A Comparative Assessment for Electric Vehicle Applications

This paper evaluates and compares four data-driven methods (Gaussian Process Regression (GPR), echo state network (ESN), gated recurrent unit (GRU), and long short-term memory (LSTM)) for lithium-ion capacity prognostics adapted to electric vehicle conditions. This comparison aim…