Application of Support Vector Machines to the induction motor parameters identification

Abstract The paper presents the application of the Support Vector Machines (SVM) to identify the parameters of the induction machine. The problem is identical to the regression task, solved here with the help of multiple SVM modules – each identifying the separate system’s parameter. The work regime of the induction motor and the significance of its accurate modelling are introduced. The application of SVM for the task is discussed, both as the standalone regression method and combined with the preceding classification approach (such as decision trees). Methods of measuring the regression accuracy in both scenarios are introduced. Experimental results of the model identification are presented in detail and discussed. The SVM optimization is performed, including selection of the kernel and its parameters’ values, maximizing the diagnostic accuracy. The paper is concluded with results discussion, conclusions and future prospects.

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