AC Loss Prediction Model of a 150 kJ HTS SMES Based on Multi-Scale Model and Artificial Neural Networks

Abstract

The calculation of ac loss is an essential part in the thermal stability estimation of the high-temperature superconductor (HTS) superconducting magnetic energy storage system (SMES). In this paper, we propose an artificial neural network (ANN) prediction model to calculate the ac loss in real-time and apply the ANN prediction model to a 150 kJ self-made HTS SMES. The ANN prediction model is composed of a multi-scale model and an ANN. An ac loss data set calculated with the multi-scale model is used to train the ANN, and a validation data set is used to test the accuracy of the ANN after training. By comparing with the homogenization model, we find that the multi-scale model is reliable when used for ac loss calculation and suitable for building ac loss data set due to its high calculation speed. The ANN prediction model is verified to be reliable for fast estimation of the ac loss, and its accuracy improves as the scale of the training data set increases.