Use of an artificial neural network for current derivative estimation

Hind, David; Sumner, Mark; Gerada, Chris

doi:10.1109/epe.2013.6634327

Cited by 10 publications

(9 citation statements)

References 20 publications

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“…The use of neural networks (NNs) for position sensorless control to deal with nonlinear elements and to improve control performance has been explored 20–33 . In References 20,21 dealing with induction motors, magnetic flux observers combined with NNs are used for speed estimation with regard to temperature changes and parameter fluctuations, and improvement of control performance is reported.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, References 32,33 consider the use of current slope (derivative) during voltage vector application in low‐speed sensorless control based on saliency propose; specifically, a method is proposed to use NN for removal of detection noise in standard Hall current sensors caused by switching. High‐frequency noise occurs during inverter switching, and this noise superimposes on current sensor signals as well.…”

Section: Introductionmentioning

confidence: 99%

“…The present paper explores application of noise removal method proposed in references 32,33 to three‐shunt current sensing widely employed in home appliances and other applications, and proposes a method to remove switching noise from a three‐shunt current detector by means of a NN, without any special modulation. Some problems arise when the mentioned noise removal technique using NNs is applied to three‐shunt current sensing.…”

Section: Introductionmentioning

confidence: 99%

“…Some problems arise when the mentioned noise removal technique using NNs is applied to three‐shunt current sensing. References 32,33 assume a Hall CT; thus, current noise contains mainly high‐frequency ringing components determined by the circuit pattern, magnitude of stray components in IGBT or other power devices, and dv/dt in switching. On the other hand, with three‐shunt current sensing, detected current is a pulsed signal that switches between zero and current magnitude depending on whether a power device is on or off.…”

Section: Introductionmentioning

confidence: 99%

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Improvement of low‐speed sensorless control with multi‐layer neural network

Maekawa

Tanaka

2022

Electrical Engineering Japan

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In recent years, there has been an increasing demand for position sensorless control in Permanent Magnet Synchronous Motor (PMSM) drives, and various methods have been studied. Switching noise is a problem in the low‐speed sensorless control method that uses the current slope during PWM. Furthermore, another problem is that the inductance does not appear in a sinusoidal distribution owing to magnetic saturation. In this paper, we improve the sensorless control method that estimates the position from the current slope during PWM, which is greatly affected by switching. Additionally, we build a multi‐layer neural network (NN) that directly estimates the position signals by learning a large amount of current data, and verify the driving results in the low‐speed range when the learned NN is incorporated into real‐time control.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improvement of low‐speed sensorless control with multi‐layer neural network

Maekawa

Tanaka

2022

Electrical Engineering Japan

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“…For each derivative, the first 2µs of the corresponding transient current response is stored as the training data. Details of the training method are further discussed in [10]. Provided the ANN configuration is saved, the pre-commissioning routine is required only once for a given motor drive setup, i.e.…”

Section: Proposed Ann Derivative Estimation Techniquementioning

confidence: 99%

Estimating current derivatives for sensorless motor drive applications

Hind

Sumner

Gerada

2015

2015 17th European Conference on Power Electronics and Applications (EPE'15 ECCE-Europe)

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The PWM current derivative technique for sensorless control of AC machines requires current derivative measurements under certain PWM vectors. This is often not possible under narrow PWM vectors due to high frequency (HF) oscillations which affect the current and current derivative responses. In previous work, researchers extended the time that PWM vectors were applied to the machine for to a threshold known as the minimum pulse width (tmin), in order to allow the HF oscillations to decay and a derivative measurement to be obtained. This resulted in additional distortion to the motor current New experimental results demonstrate that an artificial neural network (ANN) can be used to estimate derivatives using measurements from a standard current sensor before the HF oscillations have fully decayed. This reduces the minimum pulse width required and can significantly reduce the additional current distortion and torque ripple.

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Research on Position Estimation Method of PMSM Based on SVPWM Control

Ji,

Chen,

2023

Lecture Notes in Electrical Engineering

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Use of an artificial neural network for current derivative estimation

Cited by 10 publications

References 20 publications

Improvement of low‐speed sensorless control with multi‐layer neural network

Improvement of low‐speed sensorless control with multi‐layer neural network

Estimating current derivatives for sensorless motor drive applications

Research on Position Estimation Method of PMSM Based on SVPWM Control

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