A Data-Driven Model for Power Loss Estimation of Magnetic Materials Based on Multi-Objective Optimization and Transfer Learning

A Data-Driven Model for Power Loss Estimation of Magnetic Materials Based on Multi-Objective Optimization and Transfer Learning

Li, Z. (TU Delft DC systems, Energy conversion & Storage)
Wang, L. (TU Delft DC systems, Energy conversion & Storage)
Liu, R. (TU Delft DC systems, Energy conversion & Storage)
Mirzadarani, R. (TU Delft High Voltage Technology Group)
Luo, T. (TU Delft High Voltage Technology Group)
Lyu, D. (TU Delft DC systems, Energy conversion & Storage)
Ghaffarian Niasar, M. (TU Delft High Voltage Technology Group)
Qin, Z. (TU Delft DC systems, Energy conversion & Storage)

2024

Traditional methods such as Steinmetz's equation (SE) and its improved variant (iGSE) have demonstrated limited precision in estimating power loss for magnetic materials. The introduction of Neural Network technology for assessing magnetic component power loss has significantly enhanced accuracy. Yet, an efficient method to incorporate detailed flux density information—which critically impacts accuracy—remains elusive. Our study introduces an innovative approach that merges Fast Fourier Transform (FFT) with a Feedforward Neural Network (FNN), aiming to overcome this challenge. To optimize the model further and strike a refined balance between complexity and accuracy, Multi-Objective Optimization (MOO) is employed to identify the ideal combination of hyperparameters, such as layer count, neuron number, activation functions, optimizers, and batch size. This optimized Neural Network outperforms traditionally intuitive models in both accuracy and size. Leveraging the optimized base model for known materials, transfer learning is applied to new materials with limited data, effectively addressing data scarcity. The proposed approach substantially enhances model training efficiency, achieves remarkable accuracy, and sets an example for Artificial Intelligence applications in loss and electrical characteristic predictions with challenges of model size, accuracy goals, and limited data.

Power magnetics
core loss
data-driven method
neural network

http://resolver.tudelft.nl/uuid:6d0be74f-e2d5-4675-9304-a0d12196e79e

https://doi.org/10.1109/OJPEL.2024.3389211

2644-1314

IEEE Open Journal of Power Electronics, 5, 605-617

Institutional Repository

journal article

© 2024 Z. Li, L. Wang, R. Liu, R. Mirzadarani, T. Luo, D. Lyu, M. Ghaffarian Niasar, Z. Qin