Benefits of Direct Phase Voltage Measurement in the Rotor Initial Position Detection for Permanent-Magnet Motor Drives

Oboe, Roberto; Tinazzi, Fabio; Zigliotto, M.

doi:10.1109/tie.2015.2448514

Cited by 36 publications

(5 citation statements)

References 24 publications

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“…IV. The motor phaseto-phase voltages (two out of three) were measured with a custom digital measurement system, based on fast PWM signal oversampling, post-processed by a dedicate FPGA chip [22].…”

Section: Resultsmentioning

confidence: 99%

Magnetic Modeling of Synchronous Reluctance and Internal Permanent Magnet Motors Using Radial Basis Function Networks

Ortombina

Tinazzi

Zigliotto

2018

IEEE Trans. Ind. Electron.

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The general trend towards more-intelligent energy-aware ac drives is driving the development of new motor topologies and advanced model-based control techniques. Among the candidates, pure reluctance and anisotropic permanent magnet motors are gaining popularity, despite their complex structure. The availability of accurate mathematical models that describe these motors is essential to the design of any model-based advanced control. This paper focuses on the relations between currents and flux linkages, which are obtained through innovative radial-basis function neural networks. These special drive-oriented neural networks take as inputs the motor voltages and currents, returning as output the motor flux linkages, inclusive of any nonlinearity and cross-coupling effect. The theoretical foundations of the radial basis function networks, the design hints and a commented series of experimental results on a real laboratory prototype are included in the paper. The simple structure of the neural network fits for implementation on standard drives. The online training and tracking will be the next steps in FPGAbased control systems.Index Terms-Permanent magnet motors, reluctance motor, artificial neural networks, magnetic flux linkages. NOMENCLATURE Variables names convention:• variable with accent : estimated quantities • bold lowercase variable: vectorial quantities Symbols used:flux linkages τ (t) Electromagnetic torque d,q Voltage estimation errors N g K Number of Gaussian functions b k Proportional coefficient of the first layer d max Diameter of the quadratic training region n k , x k , a k Input, centre in (d, q) reference frame and output of the k th Gaussian functions w d,q k Proportional coefficients of the second layer M Number of steady state training points L. Ortombina, F. Tinazzi and M. Zigliotto are with

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Section: Resultsmentioning

confidence: 99%

Magnetic Modeling of Synchronous Reluctance and Internal Permanent Magnet Motors Using Radial Basis Function Networks

Ortombina

Tinazzi

Zigliotto

2018

IEEE Trans. Ind. Electron.

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“…The voltage measurements for the SynR 1 were carried out by means of a dedicated board based on high sampling frequency (1 MHz) and FPGA [15]. The expanded uncertainty of the measurement equipment is estimated at 11.7 • 10 −3 U dc .…”

Section: Uncertaintiesmentioning

confidence: 99%

On the true maximum efficiency operations of synchronous motor drives

Tinazzi¹,

Sandulescu²,

Peretti³

et al. 2017

2017 IEEE 12th International Conference on Power Electronics and Drive Systems (PEDS)

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Energy efficient AC drives for synchronous machines are key components in modern mechatronic systems, including applications of renewable energy and transportation. It is possible that the next generation of synchronous motors may be of the reluctance-prevalent type, due to the ecological footprint and cost of permanent magnet materials. When a reluctance torque is available, the torque is usually regulated according to the maximum torque per ampere principle which minimises the copper losses for a given torque request. However, iron and additional losses also contribute to the total machine losses, although their influence on the best current vector trajectory for total loss minimisation is not clear yet. The aim of this paper is to discuss and quantitatively evaluate the effect of iron and additional losses on the efficiency of different synchronous machines, looking for the true maximum efficiency. An extended batch of measurements performed on different types of machines with low power rating are discussed in detail.

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“…Although this method is simple, the pulse duration must be tuned, and this approach is valid only for PMSMs with a large saliency. A current pulse method was proposed in [4 ], which does not depend on the saliency; however, direct phase voltage measurement is required.…”

Section: Introductionmentioning

confidence: 99%

Auto‐calibration of position offset for PMSM drives with uncertain parameters

Choi

Kim

et al. 2020

Electron. lett.

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This Letter proposes a novel technique for automatic position offset calibration for permanent‐magnet synchronous machine (PMSM) drives. Contrary to existing methods, the proposed technique estimates and compensates for the position offset without using machine true parameters or signal injection, which is possible under the condition of zero‐current control at high enough speed where parameter‐dependent terms are eliminated. To achieve this condition using uncertain parameters, speed control with absolute value feedback and model‐free current control are devised. Additionally, under this condition, not only the misalignment of the position sensor but also the sensor delay can be considered as the position offset, which improves the accuracy of the calibration. The proposed approach will be verified by experimental results.

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Benefits of Direct Phase Voltage Measurement in the Rotor Initial Position Detection for Permanent-Magnet Motor Drives

Cited by 36 publications

References 24 publications

Magnetic Modeling of Synchronous Reluctance and Internal Permanent Magnet Motors Using Radial Basis Function Networks

Magnetic Modeling of Synchronous Reluctance and Internal Permanent Magnet Motors Using Radial Basis Function Networks

On the true maximum efficiency operations of synchronous motor drives

Auto‐calibration of position offset for PMSM drives with uncertain parameters

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