Open Access

ARTICLE

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

Tiankui Sun^*, Shuyi Zhuang, Yongling Lu, Wenqiang Xie, Ning Guo, Sudi Xu

Electric Power Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing, 211103, China

* Corresponding Author: Tiankui Sun. Email:

(This article belongs to the Special Issue: Advanced Energy Management and Process Optimization in Industrial Manufacturing: Towards Smart, Sustainable, and Efficient Production Systems)

Energy Engineering 2026, 123(1), . https://doi.org/10.32604/ee.2025.070213

Received 10 July 2025; Accepted 05 September 2025; Issue published 27 December 2025

Abstract

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.

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