Abstract
The paper is concerned with the calculation of parameters for the squirrel-cage induction motor using a pre-trained neural network. The relevance of this work lies in the potential use of the neural network as a tool for the accurate T-equivalent circuit parameters identification during motor fault diagnostics or its initial commissioning. The aim of the study is to evaluate the parameter identification accuracy for its subsequent use as a diagnostic tool. The methods employed include machine learning (neural networks), electrical machine and electric drive theory, mathematical modeling of transient processes and experiments (no-load test and locked-rotor test) conducted on a test bench. The neural network training was based on the data from 4A series induction motors with a power range from 90 W to 200 kW. The developed approach, utilizing a trained neural network, is tailored to specific motor types and enables the equivalent circuit parameters identification for subsequent analysis and diagnostics. The paper describes the parameter identification process using the neural network, detailing its structure, input and output data and tuning parameters. A methodology for verifying the identified parameters adequacy through mathematical modeling of transient processes was developed. The identification accuracy is assessed on a real-world Siemens induction motor (which was not included in the training dataset). The optimal neural network configuration in terms of the neurons number in the hidden layer was selected. Conclusions were drawn regarding the accuracy and applicability of the neural network as a tool for identifying equivalent circuit parameters.
Keywords
squirrel-cage induction motor, equivalent circuit, motor parameters, neural network, parameter identification
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