In offshore wind farm systems, the submarine cable topology architecture consists primarily of two core components: collector submarine cables and transmission submarine cables, which undertake the functions of power collection and transmission, respectively. For the collector submarine cables, this study established a COMSOL electromagnetic-thermal coupling model to calculate the structural losses and ampacity characteristics of the cable layers under multi-circuit laying conditions. The analysis shows that losses are directly related to wind turbine current and losses in the direct-buriedsection. For the transmission submarine cables, electromagnetic-thermal-fluid multiphysics coupling models were constructed for both direct-buried and J-tube laying conditions. The study systematically investigated the influence mechanisms of ambient temperature, wind speed, and solar radiation intensity on ampacity, and established a multivariate linear regression prediction model for ampacity under environmental constraints in the J-tube section.

Wind power is an exceptionally clean source of energy, its rational utilization can fundamentally alleviate the energy, environment, and development problems, especially under the goals of “carbon peak” and “carbon neutrality”. A combined short-term wind power prediction based on LSTM artificial neural network has been studied aiming at the nonlinearity and volatility of wind energy. Due to the large amount of historical data required to predict the wind power precisely, the ambient temperature and wind speed, direction, and power are selected as model input. The CEEMDAN has been introduced as data preprocessing to decomposes wind power data and reduce the noise. And the PSO is conducted to optimize the LSTM network parameters. The combined prediction model with high accuracy for different sampling intervals has been verified by the wind farm data of Chongli Demonstration Project in Hebei Province. The results illustrate that the algorithm can effectively overcome the abnormal data influence and wind power volatility, thereby provide a theoretical reference for precise short-term wind power prediction.