Speed-Sensorless Vector Control of an Induction Generator Including Stray Load and Iron Losses and Online Parameter Tuning

Bašić, Mateo; Vukadinović, Dinko; Grgić, Ivan; Bubalo, Matija

doi:10.1109/tec.2019.2952666

Cited by 15 publications

(12 citation statements)

References 21 publications

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“…In the case of MRAS, the impact of variations in R s , R r , L m , iron losses, and SLLs has been extensively addressed in many studies, and a number of solutions have been proposed for their compensation and/or online adaptation. Online tuning of R s and/or R r is reported in previous studies, 5,[11][12][13] compensation of magnetic saturation, 2,5,[13][14][15] and compensation of iron losses. 5,11,13,14,16 In, Baši c et al, 14 an MRAS scheme is proposed which includes compensation of magnetic saturation, stray-load, and iron losses.…”

Section: Introductionmentioning

confidence: 60%

“…In this context, fieldoriented control (FOC) is the most used one, mainly due to its high-performances in terms of fast dynamic response and accurate decoupled control of induction machine (IM) flux and torque. 5 It is well-known that speed signal acquisition is an essential task for the execution of IM's FOC, either in motor or generator mode. The speed signal can be provided by a physical sensor.…”

Section: Introductionmentioning

confidence: 99%

“…Among MBEs, model reference adaptive system (MRAS) and sliding mode observers (SMOs) are more practical in common engineering applications and are attracting more and more attention. 2,5,[10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] They are both designed on the basis of the IM dynamic mathematical model. The accuracy of the estimated speed strongly depends on the accuracy with which the utilized IM model describes the real IM behavior.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Advanced sliding mode speed observer with compensated stray‐load and iron losses for a stand‐alone wind turbine‐driven induction generator

Aberbour

Bašić

Vukadinović

2022

Circuit Theory & Apps

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Model‐based speed estimation schemes in sensorless field‐oriented control (FOC) of induction machines (IMs) are designed on the basis of a corresponding mathematical model. Thus, a reliable and robust sensorless controller requires using a more detailed IM model. The omission of nonlinear phenomena in real‐time application, such as magnetic saturation, iron losses, and stray‐load losses, certainly leads to inaccuracy in the estimated speed due to the existence of the mentioned phenomena in the IM. In this context, we address here the problem of neglecting the stray‐load and iron losses in a model‐based speed observer, synthesized by means of sliding mode theory, and we evaluate the impact of this omission on the execution of the utilized FOC and maximum power point tracking (MPPT) algorithm applied for a stand‐alone wind energy conversion system (WECS). The performance of the proposed sliding mode speed observer (SMSO), derived from the IM model including the above mentioned nonlinear phenomena, is experimentally evaluated over wide ranges of wind speed and IM magnetizing flux. It is also compared with the model reference adaptive system (MRAS) speed estimator, which is derived from the same IM model, as well as to the simplified SMSO, designed based on the IM model without stray‐load and iron losses. The obtained results show that the proposed SMSO has greater robustness with respect to abrupt variations of the rotor flux in comparison with the MRAS speed estimator, whereas it ensures significantly more stable operation and lower speed estimation error in comparison with the simplified SMSO.

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Section: Introductionmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Advanced sliding mode speed observer with compensated stray‐load and iron losses for a stand‐alone wind turbine‐driven induction generator

Aberbour

Bašić

Vukadinović

2022

Circuit Theory & Apps

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“…Model Reference Adaptive System (MRAS)-based methods can be also counted among computationally undemanding techniques with a simple structure and implementation [21,22,36,37]. Usually, they are used to identify one or two parameters because it is difficult to find a stable adaptive law [5].…”

Section: Introductionmentioning

confidence: 99%

Induction Machine On-Line Parameter Identification for Resource-Constrained Microcontrollers Based on Steady-State Voltage Model

Košťál

Kobrle

2021

Electronics

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This paper presents a new, computationally modest on-line identification method for the simultaneous estimation of the rotor resistance and magnetizing inductance of an induction machine suitable for electric drives that use an indirect field-oriented control strategy (IFOC), and their control hardware is equipped with a resource-constrained microcontroller. Such drives can be found both in the manufacturing industry and railway traction vehicles in the thousands, having either older control hardware that cannot cope with computationally excessive identification methods or being in cost-sensitive applications, thus being equipped with a low-cost microcontroller. IFOC is a very common control strategy for such drives due to its good dynamic properties and comparatively simple implementation. However, it is sensitive to inaccuracies of rotor resistance and magnetizing inductance. These two parameters change during the operation of the drive, being influenced by the temperature, frequency, and saturation of the magnetic circuit. Improper values of parameters in the controller can degrade the performance of IFOC, resulting in slower acceleration or unnecessary oversaturation of the machine. Respecting these changes can therefore bring significant benefits such as the good dynamic properties of the drive, which can shorten operations in the manufacturing industry or travel times of vehicles. A number of on-line identification methods for monitoring the parameter changes have been published so far, but the majority of them are demanding on microcontroller time or its memory. The proposed method, on the contrary, is comparatively simple and thus easy for implementation with low requirements to the microcontroller. Therefore, it is suitable for both upgrades of existing drives or new low-cost applications. Derivation of the method from the mathematical model and the final algorithm for the microcontroller are presented. The performance of the proposed method is validated with experimental results obtained with a 3.5 kW induction machine drive with an industrial microcontroller during a warming test and under various loads and frequencies.

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“…Although offline identification methods have high accuracy, the signal process load is heavy. Online identification methods have been widely used in sensorless and adaptive control [13], [14]. Most of the online identification methods focus on identifying one or two parameters while keeping the other parameters fixed [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Voltage Vector Directional Control for IPMSM Based on MTPA Strategy

Song

Zhao

et al. 2020

IEEE Access

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Space vector pulse width modulation (SVPWM) is used in motor control because of its high dynamic response and flexible control. Maximum torque per ampere (MTPA) strategy has become the most commonly used choice for internal permanent magnet synchronous motor (IPMSM). However, complicated coordinate transformations are required in the usual SVPWM algorithm and the performance of the MTPA algorithm can be affected by the parameters variation. This paper proposes a voltage vector directional control (VVDC) method based on the MTPA strategy. The inverse Park transformation is avoided in the proposed control scheme. And the proposed algorithm not only is briefer and clearer in the sector judgment process but also has lesser calculation during the duty cycle generation. In order to realize the MTPA strategy, two voltage feedback closed loops are designed to compensate for the d-axis and q-axis currents. A simple and effective multi-parameter identification method is also presented to solve the parameters variation problem. The effectiveness of the proposed method is verified by simulation and experiments.INDEX TERMS IPMSM, MTPA, motor parameter identification, voltage vector control.

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Speed-Sensorless Vector Control of an Induction Generator Including Stray Load and Iron Losses and Online Parameter Tuning

Cited by 15 publications

References 21 publications

Advanced sliding mode speed observer with compensated stray‐load and iron losses for a stand‐alone wind turbine‐driven induction generator

Advanced sliding mode speed observer with compensated stray‐load and iron losses for a stand‐alone wind turbine‐driven induction generator

Induction Machine On-Line Parameter Identification for Resource-Constrained Microcontrollers Based on Steady-State Voltage Model

Voltage Vector Directional Control for IPMSM Based on MTPA Strategy

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