Abstract Induction machines are often preferred in industrial applications at present. Therefore, it is an important problem to know the electrical parameters of induction machines correctly. Electrical parameters of induction motors can be obtained experimentally by performing DC test, no-load rotor test, and locked rotor test. Furthermore, equivalent circuit parameters of the induction machines can be estimated with high accuracy using the data from machine manufacturers. In this study, equivalent circuit parameters of squirrel-cage induction motors have been successfully estimated by using Feed Forward Neural Network (FFNN) for single-cage and double-cage models. Although there is a feed forward neural network study in the literature, the training process of the neural network structures was carried out with unmemorized training. In addition to FFNN, an unmemorized Elman Neural Network (ENN) structure has been proposed to solve this problem. The proposed methods were compared with the literature for both models, and their performances were examined. The obtained FFNN results suggest that the proposed method performed better results than both adaptive neuro fuzzy inference system (ANFIS) and artificial neural network (ANN) for the single-cage model. In the double-cage model, FFNN performed better than ANN but relatively weaker than ANFIS. The results of the ENN structure are close to ANFIS for both single-cage and double-cage models.

中文翻译：

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基于非记忆训练神经网络的鼠笼式感应电机参数估计比较研究

摘要 目前，工业应用中往往首选感应电机。因此，正确了解感应电机的电气参数是一个重要的问题。感应电动机的电气参数可以通过直流试验、空载转子试验和堵转试验等实验获得。此外，感应电机的等效电路参数可以使用机器制造商提供的数据进行高精度估计。在这项研究中，通过使用前馈神经网络 (FFNN) 对单笼和双笼模型成功地估计了鼠笼式感应电机的等效电路参数。虽然文献中有前馈神经网络研究，但神经网络结构的训练过程是通过无记忆训练进行的。除了 FFNN，还提出了一种未记忆的 Elman 神经网络 (ENN) 结构来解决这个问题。将所提出的方法与两种模型的文献进行了比较，并检查了它们的性能。获得的 FFNN 结果表明，对于单笼模型，所提出的方法比自适应神经模糊推理系统 (ANFIS) 和人工神经网络 (ANN) 执行更好的结果。在双笼模型中，FFNN 的表现优于 ANN，但相对弱于 ANFIS。ENN 结构的结果对于单笼和双笼模型都接近于 ANFIS。获得的 FFNN 结果表明，对于单笼模型，所提出的方法比自适应神经模糊推理系统 (ANFIS) 和人工神经网络 (ANN) 执行更好的结果。在双笼模型中，FFNN 的表现优于 ANN，但相对弱于 ANFIS。ENN 结构的结果对于单笼和双笼模型都接近于 ANFIS。获得的 FFNN 结果表明，对于单笼模型，所提出的方法比自适应神经模糊推理系统 (ANFIS) 和人工神经网络 (ANN) 执行更好的结果。在双笼模型中，FFNN 的表现优于 ANN，但相对弱于 ANFIS。ENN 结构的结果对于单笼和双笼模型都接近于 ANFIS。