Renewable energy ship was regarded as one of the ship energy technologies with a good prospect. In order to study the application of solar and wind energy on ships in the marine environment and the impact of ship rolling on the system, the feasibility of applying solar energy and wind energy to ships was analyzed, and the structural composition of ship power system incorporating renewable energy source was studied. The model of the ship power system integrated with renewable energy was built in PSCAD/EMTDC simulation software. The layout of wind power generation system and photovoltaic power generation system was given for the actual ship, and the ship parameters and specific parameters of each simulation module were determined. It can be seen that the rolling of ship will cause fluctuations in the grid-connected power of the photovoltaic power generation system and the wind power generation system from the comparison of the simulation curves. Finally, a simulation experiment is provided to prove the access of the battery can well suppress the grid-connected power fluctuation caused by the rolling of the ship, which has an important impact on the stability of the ship power system with renewable energy.

In the current context of a demand for high-efficiency and high-performance motors, AC copper loss is a key factor limiting the power density improvements of high-speed motors. Due to the interaction of multiple factors affecting AC copper losses, there is a lack of comprehensive and consistent understanding of the loss mechanism. Moreover, existing analysis methods face limitations in either computational efficiency or result accuracy. To address this, this study proposed a fully analytical model for hollow conductor AC loss, derived from a detailed analysis of magnetic and current distributions within hollow conductors under high-frequency armature and air-gap magnetic fields. It also examines the correlation between the armature AC copper loss and induced AC copper loss. The model accounts for the nonlinear effects of the iron core by incorporating an anisotropic equivalent magnetic permeability. The model accuracy was verified under various magnetic field settings and hollow structure designs, with errors within 4% and 6% in comparison to simulation and experimental data, respectively. This study enhances understanding and computational precision of AC copper loss in high-speed motors and provides a refined model for performance analysis and optimization.

In the pursuit of electric motor digitalization and performance enhancement, addressing the inherent complexities of multi-physics and multimodal field performance analysis is essential. Current methodologies often face limitations due to substantial computational resource demands and a tendency towards local optima in high-dimensional problems. This paper proposed a self-adaptive reduced basis algorithm (SARBA), which utilizes a non-intrusive model order reduction technique to reduce computational complexity. To efficiently and accurately extract the reduced basis (RB) that are independent of the spatial distribution and parameters, a hierarchical proper orthogonal decomposition (HPOD) method was proposed as a priori dimensionality reduction, enabling the model to capture global features and achieve low dimensionality in the design space. Furthermore, to balance the trade-off between global exploration and local exploitation in nonlinear spaces, this paper combined adaptive algorithm with radial basis function (RBF) to approximate the RB expansion coefficient vectors for untrained parameters, and dynamically adjusts the training dataset based on expectation improvement criteria, thereby iteratively updating the RBF model. The resulting multi-step collaborative modeling framework facilitates precise field solution predictions from a limited dataset of large-scale simulations. The model’s accuracy and effectiveness were validated through tests on benchmark functions and its application in optimizing the stator magnetic field of high-speed electric motors.

In order to realize the comprehensive utilization of multiple energy sources in renewable energy ships and improve the utilization rate of renewable energy and system economy, this paper studies the matching and optimization of renewable energy power generation system and ship power system. Firstly, the structure of renewable energy ship power system is introduced. Considering the natural conditions and the actual situation of the ship for the first time, the wind power generation, photovoltaic power generation and energy storage devices are configured in the ship power system. The objective function including system annual average cost and system reliability index is established. The constraint conditions are determined, and the system energy scheduling strategy is proposed. Then, the influence of the introduction of renewable energy generation system and energy storage device on the ship power system is analyzed. Finally, particle swarm optimization and improved algorithm are used to optimize and solve the configuration problem. The relationship between the loss of power supply probability and energy excess percentage in ship power system is analyzed for the first time, which provides a reference for the optimal allocation scheme of actual ship. The results are helpful to improve the economy and system reliability of renewable energy ships.

Within large turboalternators, the excessive local temperatures and spatially distributed temperature differences can accelerate the deterioration of electrical insulation as well as lead to deformation of components, which may cause major machine malfunctions. In order to homogenise the stator axial temperature distribution whilst reducing the maximum stator temperature, this paper presents a novel non-uniform radial ventilation ducts design methodology. To reduce the huge computational costs resulting from the large-scale model, the stator is decomposed into several single ventilation duct subsystems (SVDSs) along the axial direction, with each SVDS connected in series with the medium of the air gap flow rate. The calculation of electromagnetic and thermal performances within SVDS are completed by finite element method (FEM) and computational fluid dynamics (CFD), respectively. To improve the optimization efficiency, the radial basis function neural network (RBFNN) model is employed to approximate the finite element analysis, while the novel isometric sampling method (ISM) is designed to trade off the cost and accuracy of the process. It is found that the proposed methodology can provide optimal design schemes of SVDS with uniform axial temperature distribution, and the needed computation cost is markedly reduced. Finally, results based on a 15 MW turboalternator show that the peak temperature can be reduced by 7.3 °C (6.4%). The proposed methodology can be applied for the design and optimisation of electromagnetic-thermal coupling of other electrical machines with long axial dimensions.

The extensive use of finite element models accurately simulates the temperature distribution of electrical machines. The simulation model can be quickly modified to reflect changes in design. However, the long runtime of the simulation prevents any direct application of the optimization algorithm. In this paper, research focused on improving efficiency with which expensive analysis (finite element method) is used in generator temperature distribution. A novel surrogate model based optimization method is presented. First, the Taguchi orthogonal array relates a series of stator geometric parameters as input and the temperatures of a generator as output by sampling the design decision space. A number of stator temperature designs were generated and analyzed using 3-D multi-physical field collaborative finite element model. A suitable shallow neural network was then selected and fitted to the available data to obtain a continuous optimization objective function. The accuracy of the function was verified using randomly generated geometric parameters to the extent that they were feasible. Finally, a multi-objective genetic optimization algorithm was applied in the function to reduce the average and maximum temperature of the machine simultaneously. As a result, when the Pareto front was compared with the initial data, these temperatures showed a significant decrease.