Prediction of Nonsinusoidal AC Loss of Superconducting Tapes Using Artificial Intelligence-Based Models
Prediction of Nonsinusoidal AC Loss of Superconducting Tapes Using Artificial Intelligence-Based Models
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DOI：
10.1109/access.2020.3037685
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
M. Yazdani-Asrami;Mehran Taghipour-Gorjikolaie;W. Song;Min Zhang;W. Yuan
中科院分区：
文献类型：
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作者：
M. Yazdani-Asrami;Mehran Taghipour-Gorjikolaie;W. Song;Min Zhang;W. Yuan
由于电力电子设备和非线性负载的广泛使用，现代电网中的电流不再是正弦的。通常交流损耗是针对纯正弦电流计算的，而当电流为非正弦时，交流损耗不再准确。另一方面，大型电力设备中冷却系统的效率取决于在设计阶段对交流损耗引起的热负荷的准确估计和预测。因此，高温超导材料的非正弦交流损耗的估算对于大型超导装置的设计者来说具有重要意义。本文首先采用H列式有限元法计算了一种典型高温超导带材在畸变电流作用下的非正弦交流损耗。然后，一系列的人工智能（AI）模型被实施来预测一个典型的HTS磁带的交流损耗。为了找到最好的和更自适应的人工智能模型的非正弦交流损耗预测，不同的回归模型进行评估，使用支持向量机回归模型，广义线性回归模型，决策树回归模型，前馈神经网络的模型，自适应神经模糊推理系统的模型，径向基函数神经网络（RBFNN）的模型。为了评估开发的模型的鲁棒性交叉验证技术的实验数据上实施。为了比较不同AI模型的性能，使用了四个预测指标：Theil的U系数（U_Accuracy和U_Quality）、均方根误差（RMSE）和回归值（R值）。结果表明，最好的性能属于基于RBFNN的模型，然后是基于ANFIS的模型。通过对测试数据的检验，得到的U系数和RMSE值均小于0.005，R值接近1，具有较高的预测精度。
Current is no longer sinusoidal in modern electric networks because of widespread use of power electronic-based equipments and nonlinear loads. Usually AC loss is calculated for pure sinusoidal current, while it is no longer accurate when current is nonsinusoidal. On the other hand, efficiency of cooling system in large scale power devices is dependent on accurate estimation and prediction of the heat load caused by AC loss in design stage. Therefore, estimation of nonsinusoidal AC loss of high temperature superconducting (HTS) material would be of great interest for designers of large-scale superconducting devices. In this paper, at first nonsinusoidal AC loss of a typical HTS tape was calculated under distorted currents using H-formulation finite element method. Then, a range of artificial intelligence (AI) models were implemented to predict AC loss of a typical HTS tape. In order to find the best and more adaptive AI model for nonsinusoidal AC loss prediction, different regression models are evaluated using Support Vector Machine regression model, Generalized Linear regression model, Decision Tree regression model, Feed Forward Neural Network based model, Adaptive Neuro Fuzzy Inference System based model, and Radial Basis Function Neural Network (RBFNN) based model. In order to evaluate robustness of developed models cross-validation technique is implemented on experimental data. To compare the performance of different AI models, four prediction measures were used: Theil’s U coefficients (U_Accuracy and U_Quality), Root Mean Square Error (RMSE) and Regression value (R-value). Obtained results show that best performance belongs to RBFNN based model and then ANFIS based model. U coefficients and RMSE values are obtained less than 0.005 and R-Value is become close to one by using RBFNN based model for testing data, which indicates high accuracy prediction performance.