Multi-Objective Optimization Method for Power Transformer Design Based on Surrogate Modeling and Hybrid Heuristic Algorithm

In response to the increasing demands for energy conservation and pollution reduction, optimizing transformer design to reduce operational losses and minimize raw material usage has become crucial. This paper introduces an innovative methodology that combines ensemble learning models with hybrid multi-objective optimization heuristic algorithms to optimize leakage impedance deviation, on-load loss, and raw material consumption in power transformers. The stacking ensemble model uses support vector machines, linear regression, decision tree regression, and K-nearest neighbors as base learners, with the extreme learning machine serving as the meta-learner to re-learn outputs from first-level learners. Given the significant impact of hyperparameters on the prediction performance of ensemble learning models, an improved particle swarm optimization method is proposed for effective hyperparameter optimization. To assess the uncertainty of the proposed ensemble learning model, a Kriging surrogate model-based analysis is outlined. Moreover, a powerful multi-objective algorithm that integrates the multi-objective grey wolf optimization (MOGWO) and the non-dominated sorting genetic algorithm-III (NSGA3) is presented for model optimization. This approach demonstrates superior performance compared to mainstream multi-objective optimization algorithms. The effectiveness of this method is further validated through the engineering tests of two real engineering cases. The proposed algorithm can accommodate various design requirements and, under the given constraints, achieve a multi-objective optimization design for power transformers, ensuring optimal performance in different operational scenarios.