An artificial neural network approach for sensorless speed estimation via rotor slot harmonics

In this paper, a sensorless speed estimation method with an artificial neural network for squirrel cage induction motors is presented. Motor current is generally used for sensorless speed estimation. Rotor slot harmonics are available in the frequency spectrum of the current. The frequency components of these determined harmonics are used to estimate the speed of the motor in which the number of rotor slots is given. In the literature, individual algorithms have been used to calculate the speed from the slot harmonics. Unlike the literature, in the proposed method, an artificial neural network is used to extract the speed from the rotor slot harmonic components in the spectrum. This experimental study is carried out to prove the method under steady-state conditions. The experimental results show that the proposed method is suitable for speed estimation and its average error is below 1.5 rpm.

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