Design of 400 V-10 kV Multi-Voltage Grades of Dual Winding Induction Generator for Grid Maintenance Vehicle

To ensure an uninterrupted power supply, mobile power sources (MPS) are widely deployed in power grids during emergencies. Comprising mobile emergency generators (MEGs) and mobile energy storage systems (MESS), MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies, offering advantages such as flexibility and high resilience through electricity delivery via transportation networks. This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator (DWIG) intended for MEG applications, employing an improved particle swarm optimization (PSO) algorithm based on a back-propagation neural network (BPNN). A parameterized finite element (FE) model of the DWIG is established to derive constraints on its dimensional parameters, thereby simplifying the optimization space. Through sensitivity analysis between temperature rise and electromagnetic loss of the DWIG, the main factors influencing the machine’s temperature are identified, and electromagnetic loss is determined as the optimization objective. To obtain an accurate fitting function between electromagnetic loss and dimensional parameters, the BPNN is employed to predict the nonlinear relationship between the optimization objective and the parameters. The Latin hypercube sampling (LHS) method is used for random sampling in the FE model analysis for training, testing, and validation, which is then applied to compute the cost function in the PSO. Based on the relationships obtained by the BPNN, the PSO algorithm evaluates the fitness and cost functions to determine the optimal design point. The proposed optimization method is validated by comparing simulation results between the initial design and the optimized design.