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.

中文翻译：

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使用基于人工智能的模型预测超导带的非正弦交流损耗


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波。通常交流损耗是针对纯正弦电流计算的，而当电流为非正弦电流时，交流损耗就不再准确。另一方面，大型功率器件冷却系统的效率取决于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，大规模超导器件的设计者对高温超导（HTS）材料的非正弦交流损耗的估计非常感兴趣。本文首先利用H公式有限元法计算了典型高温超导带在畸变电流下的非正弦交流损耗。然后，实施了一系列人工智能 (AI) 模型来预测典型高温超导磁带的交流损耗。为了找到用于非正弦 AC 损耗预测的最佳且更具适应性的 AI 模型，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、基于前馈神经网络的模型、自适应神经模糊推理来评估不同的回归模型基于系统的模型和基于径向基函数神经网络 (RBFNN) 的模型。为了评估所开发模型的稳健性，在实验数据上实施了交叉验证技术。为了比较不同人工智能模型的性能，使用了四种预测指标：泰尔 U 系数（U_Accuracy 和 U_Quality）、均方根误差（RMSE）和回归值（R 值）。获得的结果表明，基于 RBFNN 的模型性能最好，然后是基于 AFIS 的模型。获得的 U 系数和 RMSE 值小于 0。通过使用基于RBFNN的模型进行测试数据，005和R-Value变得接近1，这表明预测性能具有较高的准确性。