Drehgeberlose Regelung von Drehfeldmaschinen

Schröder, Dierk; Böcker, Joachim

doi:10.1007/978-3-662-62700-6_7

“…In literature, there exist approaches which deal with the steady-state loss optimization of an induction machine (IM) for a given reference torque with steady-state flux linkage maps [1] or based on an equivalent circuit diagram (ECD) with concentrated elements [2]. The basic idea was introduced in [3] where the optimal operating point (OP) is calculated online by approximations of nonlinear equations and reduction of the problem to the solution of a polynomial of fourth order. By iterative solution of the given problem, the desired optimal reference current is obtained.…”

Section: A State Of the Artmentioning

confidence: 99%

Nonlinear Efficiency-Optimal Model Predictive Torque Control of Induction Machines

Becker,

Stender,

Wallscheid

2023

Preprint

0

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<p>Induction machines (IMs) are widely used in many applications, e.g., electric vehicles or industrial automation, which motivates efficiency-optimal operation for the sake of energy and cost savings. However, a loss-minimal IM control leads to reduced flux operation at partial load, i.e., achievable torque dynamics after load steps are physically limited due to the IM’s large rotor time constant. Hence, designing the IM torque control for minimal settling time is of particular importance to allow both an efficient and sufficiently dynamic drive operation. Against this background, a model predictive control (MPC) framework is proposed, which utilizes a precise model of the IM covering magnetic saturation, iron losses, skin effect and thermal influences. As this model is highly nonlinear, it is iteratively linearized along the predicted control trajectory so that a computationally efficient quadratic program can be defined for the MPC problem. To enable sufficiently long prediction horizons, a hierarchical control structure with the stator currents as the actuation variables of the model predictive torque control is utilized. Thanks to this scalable control approach, the proposed framework can be easily extended to multi-machine drive systems covering higher-dimensional problem spaces. Empirical tests validate the feasibility of the proposed approach both for single as well as multi-machine drive applications.</p>

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“…Hence, the resulting QP's problem space scales only linearly depending on the number of machines considered within an MMDS application. Operating strategies, like [1]- [3], that calculate the optimal steady-state reference currents do scale worse than linear, i.e., the extension towards MMDS applications comes at the expense of disproportional computational load.…”

Section: A Extension Of the Single-machine Equationsmentioning

confidence: 99%

Nonlinear Efficiency-Optimal Model Predictive Torque Control of Induction Machines

Becker,

Stender,

Wallscheid

2023

Preprint

0

View full text Add to dashboard Cite

<p>Induction machines (IMs) are widely used in many applications, e.g., electric vehicles or industrial automation, which motivates efficiency-optimal operation for the sake of energy and cost savings. However, a loss-minimal IM control leads to reduced flux operation at partial load, i.e., achievable torque dynamics after load steps are physically limited due to the IM’s large rotor time constant. Hence, designing the IM torque control for minimal settling time is of particular importance to allow both an efficient and sufficiently dynamic drive operation. Against this background, a model predictive control (MPC) framework is proposed, which utilizes a precise model of the IM covering magnetic saturation, iron losses, skin effect and thermal influences. As this model is highly nonlinear, it is iteratively linearized along the predicted control trajectory so that a computationally efficient quadratic program can be defined for the MPC problem. To enable sufficiently long prediction horizons, a hierarchical control structure with the stator currents as the actuation variables of the model predictive torque control is utilized. Thanks to this scalable control approach, the proposed framework can be easily extended to multi-machine drive systems covering higher-dimensional problem spaces. Empirical tests validate the feasibility of the proposed approach both for single as well as multi-machine drive applications.</p>

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“…9, where k is the discrete-time sampling index. An integral part of the solution is the use of flux prediction based on the machine model (1), which enables minimal flux amplitude tracking errors with extremely small sampling to fundamental frequency ratio (6 < min(T e /T s ) < 7). Fig.…”

Section: A Model Predictive Flux Controlmentioning

confidence: 99%

“…For general MTPC other control strategies (e.g., FOC) would be more desirable. Further detailed information on these operating regions can be found in [1,2].…”

Section: Operating Point Strategymentioning

confidence: 99%

“…Fig. 1: Drive operating regions and limits: a) Inverter output in the αβ-voltage plane, b) IPMSM in the dq-current plane, c) maximum average torque and power output according to voltage utilization two regions: maximum torque-per-current (MTPC) and fluxweakening operation, which culminates in maximum torqueper-flux (MTPF) operation [1,2]. MTPC is applied at low speeds to generate the desired torque output with minimum ohmic losses.…”

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confidence: 99%

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Model Predictive Direct Self-Control for Six-Step Operation of Permanent-Magnet Synchronous Machines

Peña

¹

,

Meyer

²

,

Wallscheid

³

et al. 2023

IEEE Trans. Power Electron.

2

0

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The optimization of voltage utilization can boost electric drives' power and torque capabilities, which is of particular interest in transportation applications. However, this is a weak point of most control approaches, like linear field-oriented control (FOC) methods using standard modulation schemes. This paper introduces the model predictive direct self-control (MPDSC) strategy, a modification of direct self-control (DSC) for its application in a digital implementation to achieve maximum voltage utilization (i.e., six-step operation) for permanent magnet synchronous machines. This solution is suitable for highly utilized machines with heavy magnetic (cross-)saturation and low sampling to fundamental frequency ratio. The modifications include using load angle regulation to control the selected operating point and model prediction to compensate for the actuation delay. The proposed system can achieve six-step operation with accurate torque control and robustness against disturbances and parameter estimation inaccuracies. Comprehensive simulation and experimental results demonstrate the performance of the proposed MPDSC while operating at the voltage constraint. In particular, a transient rise time of 2.3 ms to maximum torque, current reduction for equal torque-speed operating point of up to 18 %, and maximum torque increase for equal current amplitude of up to 15 % compared to conventional FOC have been empirically observed.

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Drehgeberlose Regelung von Drehfeldmaschinen

Cited by 3 publications

References 77 publications

Nonlinear Efficiency-Optimal Model Predictive Torque Control of Induction Machines

Nonlinear Efficiency-Optimal Model Predictive Torque Control of Induction Machines

Nonlinear Efficiency-Optimal Model Predictive Torque Control of Induction Machines

Model Predictive Direct Self-Control for Six-Step Operation of Permanent-Magnet Synchronous Machines

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