Using on-line parameter estimation to improve efficiency of IPM machine drives

Kim, Hyun Bae; Hartwig, Johannes; Lorenz, Robert D.

doi:10.1109/psec.2002.1022554

Cited by 39 publications

(12 citation statements)

References 16 publications

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“…The same figure also compares different MTPA curves obtained offline, based on various methods, namely torque measurements, flux maps and flux approximations (polynomial, "linear+saturation" [120] and constant inductance) for an IPMSM. As already shown in [95], the torque vs. current characteristic is a convenient way for comparing the effectiveness of different MTPA solutions, since it clearly reveals how a non-optimal MTPA trajectory results in loss of available torque (at a certain maximum current) and excess of current needed (for obtaining a certain torque). On the other hand, simple comparison between trajectories on the 𝐼 𝑑 𝐼 𝑞 plane (Fig.…”

Section: B Extremum Seekingmentioning

confidence: 99%

“…A common approach to the adaptation of MTPA trajectory during normal operation is based on parameters estimation [3,[95][96][97][98][99][100][101][102]. In this case, the MTPA operating point is determined analytically, applying traditional formulae e.g., (10), but the parameter values (𝐿 𝑑 , 𝐿 𝑞 , Λ 𝑚𝑔 ) are estimated online (based on signals measured by the controller).…”

Section: A Parameters Estimationmentioning

confidence: 99%

“…Moreover, although estimating all magnetic motor parameters would be required, this is not possible under all operating conditions (without any form of signal injection), so a reduced set of parameters is usually updated. In some cases e.g., [95], saturation on the 𝑑-axis is neglected, so that only the 𝑞-axis inductance is adapted online. This simplistic approach is usually effective for IPM machines where mild magnetic saturation occurs.…”

Section: A Parameters Estimationmentioning

confidence: 99%

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Review and Classification of MTPA Control Algorithms for Synchronous Motors

Dianov¹,

Tinazzi

Calligaro

et al. 2022

IEEE Trans. Power Electron.

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This paper discusses the Maximum Torque Per Ampere (MTPA) control of synchronous motors, which have become an indispensable part of highly-efficient motor drives. It explains the nature of torque produced by synchronous motors, ways to find its maximum and algorithms to operate at this point, despite changes of loads and motor parameter variations. The authors propose a classification of the MTPA methods, based on the features of each algorithm or group of similar methods. They demonstrate the conventional control scheme and discuss the modifications necessary for the implementation of each method. This paper reviews existing maximum torque per ampere control algorithms, discusses their pros and cons and suggests possible areas of usage for each group of methods. The authors of the paper share their vast experience in the industry and research aspects, which were obtained by developing industrial, commercial, traction and military drives, and report on their views on the perspective of each method taken into consideration.

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Section: B Extremum Seekingmentioning

confidence: 99%

Section: A Parameters Estimationmentioning

confidence: 99%

Section: A Parameters Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Review and Classification of MTPA Control Algorithms for Synchronous Motors

Dianov¹,

Tinazzi

Calligaro

et al. 2022

IEEE Trans. Power Electron.

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“…On the other hand, the output of the velocity loop is selected as the reference for compensating the dq axis vector synthesized current. These two terms are defined by (9). The performance criterion J is then defined as (10) where the summation is carried out over all patterns in a given set S. The gradient of the performance function with respect to W is computed as ∇ W and W is adjusted along the negative gradient as (11).…”

Section: B Nn Compensator Structurementioning

confidence: 99%

“…Parameter estimation methods [6], [7] require knowledge of certain parameter relationships and may not be accurate in highly saturated motors. Signal injection methods [8], [9] are effective only in low-noise environments and may introduce additional small losses to the system. Perturb and observation methods [10] are sensitive to changes in the load and are limited to steady-state conditions.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Compensator-Based Control for Enhancing IPMSM Dynamics and Copper Loss Efficiency for Air Compressor

Guo,

Sun,

Kawaguchi

et al. 2024

IEEE Access

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Although significant efforts have been made to enhance industrial air conditioning systems, there are still efficiency and transient response issues in vehicle air conditioning systems using IPMSM compressors. This paper focuses on the neural network compensator-based control in an interior permanentmagnet synchronous motor (IPMSM) to address the occurrence of reduced power copper loss efficiency and degraded velocity response in the motor system when confronted with periodic dynamic disturbances of step signals. This paper encompasses two main objectives: the first objective is to introduce a neural network (NN) compensator to improve the power copper loss efficiency. The NN compensator is developed using the velocity loop and current loop control model equation of an IPMSM, and trained to implement optimal compensation control based on the back propagation algorithm. The second objective is to optimize the dynamic performance of velocity response compared to the traditional maximum torque per ampere (MTPA) current control method under step disturbance and dynamic control conditions by building an experimental system for validation, incorporating both hardware and simulation. Another significant advantage is the low computational load introduced by the neural network compensator, rendering it well-suited for implementation within low-order DSP systems. The results indicate that the neural network compensator surpasses conventional MTPA control method in both simulation and hardware-based implementations concerning power copper loss and velocity response in an IPMSM control system.INDEX TERMS Interior permanent-magnet synchronous motor (IPMSM), neural network (NN), back propagation (BP), dq axis synthesis current control

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Fast Parameter Estimation for Adaptive Maximum Torque Per Ampere Control of Interior Permanent Magnet Synchronous Motor Drives

Filho

Vieira

2021

J Control Autom Electr Syst

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Using on-line parameter estimation to improve efficiency of IPM machine drives

Cited by 39 publications

References 16 publications

Review and Classification of MTPA Control Algorithms for Synchronous Motors

Review and Classification of MTPA Control Algorithms for Synchronous Motors

Neural Network Compensator-Based Control for Enhancing IPMSM Dynamics and Copper Loss Efficiency for Air Compressor

Fast Parameter Estimation for Adaptive Maximum Torque Per Ampere Control of Interior Permanent Magnet Synchronous Motor Drives

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