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• W2218617729 abstract "A robust neural-network-method applied to speed estimation in line-connected three-phase induction motors is presented.A comparative study of single and multiple current sensor applied to speed estimation is presented.Proposed method is validated using simulation and experimental results considering unbalance operation conditions and wide range of load torque.Proposed method is embedded in a digital processor and validated in a test bench. Estimating the electrical and mechanical parameters involved in three-phase induction motors is frequently employed to avoid measuring every variable in the process. Among mechanical parameters, speed is an important variable: it is involved in control, diagnosis, condition monitoring, and can be measured or estimated by sensorless methods. These technologies offer advantages when compared with direct measurement, such as lower cost or more robust systems. This paper proposes the use of artificial neural networks to estimate rotor speed by using current sensors for balanced and unbalanced voltage sources with a wide mechanical load range in a line-connected induction motor. This paper also presents two case analyses: (i) a single current sensor; and (ii) a multiple currents sensors. Simulation and experimental results are presented to validate the proposed approach. A neural speed estimator embedded in a digital processor is also presented." @default.
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• W2218617729 date "2016-03-01" @default.
• W2218617729 modified "2023-09-24" @default.
• W2218617729 title "Neural speed estimator for line-connected induction motor embedded in a digital processor" @default.
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• W2218617729 doi "https://doi.org/10.1016/j.asoc.2015.12.033" @default.
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