Stator reference frame approach for DC injection-based stator resistance estimation in electric drives

Zanuso, Giovanni; Peretti, Luca; Sandulescu, Paul

doi:10.1109/peds.2015.7203391

Cited by 14 publications

(11 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The stator resistance R s has to be known with good accuracy, to compute (11). A precise online tracking algorithm, based on a modified version of that proposed in [23], was…”

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.

View full text Add to dashboard Cite

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

show abstract

“…The stator resistance R s has to be known with good accuracy, to compute (11). A precise online tracking algorithm, based on a modified version of that proposed in [23], was…”

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.

View full text Add to dashboard Cite

show abstract

“…The algorithm requires the a-priori knowledge of a precise and updated value of the stator resistance R s , which can be obtained by modern algorithms as for example those described in [19] and [20]. The training algorithm description is reported in Sect.…”

Section: Basics Of the Rbf-based Synr Motor Modelmentioning

confidence: 99%

“…The left-hand term of (19) is greater than zero for current phase angles below the correct one, and viceversa. Actually, it can been found that the mismatch computed by (19) is proportional to the required current phase angle modification. This is exploited by making the current phase modification proportional to the result of (19).…”

Section: The Mtpa Tracking Algorithmmentioning

confidence: 99%

“…It is worth to remark that the RBF network weights were initialised supposing known and constant inductances L d , L q (TABLE II), so that the 3D plots λ d , λ q as function of currents were two simple inclined planes. The MTPA algorithm represented by (19) and (20) is then evaluated on the actual current request and the estimated flux linkages. The algorithm updates the phase ϑ * of the reference current vector, according to (19), first approaching and then maintaining the SynR motor drive in a dynamically updated MTPA condition.…”

Section: Online Adaptive Mtpa Controlmentioning

confidence: 99%

“…The MTPA algorithm represented by (19) and (20) is then evaluated on the actual current request and the estimated flux linkages. The algorithm updates the phase ϑ * of the reference current vector, according to (19), first approaching and then maintaining the SynR motor drive in a dynamically updated MTPA condition. As an example, Fig.…”

Section: Online Adaptive Mtpa Controlmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Maximum Torque per Ampere Control of Synchronous Reluctance Motors by Radial Basis Function Networks

Ortombina

Tinazzi

Zigliotto

2019

IEEE J. Emerg. Sel. Topics Power Electron.

View full text Add to dashboard Cite

As neodymium and other rare earth materials become a critical commodity, the exploitation of reluctance torque in synchronous motors is certainly an interesting option. Alternative motor topologies span from internal permanent magnet motors to pure reluctance machines. Anyway, any anisotropic structure suffers of some magnetic nonlinearity that call for more sophisticated models to get an efficient torque control. Neural network based algorithms are good candidates for modelling the current-to-flux linkages curves of synchronous reluctance motors, but so far their use was limited by the inherent complexity and the computational burden. This paper proposes the use of a special kind of neural networks, namely, the radial basis function networks, to get the magnetic model of any synchronous motor, including saturation and cross-coupling effects. Through experimental evidence, it will be shown that the structure is light enough to be implemented, trained and self-updated online on standard high-end ac drives. The model is used to track online the maximum torque-per-ampere working point of a synchronous reluctance motor drive.

show abstract

Model-based synchronous machine control with compensation of model inaccuracies and instantaneous flux weakening capabilities

Zanuso¹,

Peretti²,

Sandulescu³

2016

8th IET International Conference on Power Electronics, Machines and Drives (PEMD 2016)

View full text Add to dashboard Cite

This paper proposes a model-based current/torque control strategy for synchronous machines, where the magnetic model mismatches are compensated in real time and the flux weakening operation is obtained without the need of an external voltage regulation loop. Starting from an initial (and potentially wrong) knowledge of the non-linear current-to-flux linkage relation in the form of a look-up-table, a combination of feedforward actions, compensating mechanisms and binary searches are used to achieve the target during real-time operation. The algorithm is implemented in a laboratory test bench and successfully verified with experiments on an 11-kW synchronous reluctance machine, proving the feasibility of the proposed approach.

show abstract

Stator reference frame approach for DC injection-based stator resistance estimation in electric drives

Cited by 14 publications

References 5 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

Adaptive Maximum Torque per Ampere Control of Synchronous Reluctance Motors by Radial Basis Function Networks

Model-based synchronous machine control with compensation of model inaccuracies and instantaneous flux weakening capabilities

Contact Info

Product

Resources

About