44,610 research outputs found

    Data-driven online temperature compensation for robust field-oriented torque-controlled induction machines

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    Squirrel-cage induction machines (IMs) with indirect field-oriented control are widely used in industry and are frequently chosen for their accurate and dynamic torque control. During operation, however, temperature rises leading to changes in machine parameters. The rotor resistance, in particular, alters, affecting the accuracy of the torque control. The authors investigated the effect of a rotor resistance parameter mismatch in the control algorithm on the angular rotor flux misalignment and the subsequent deviation of stator currents and motor torque from their setpoints. Hence, an online, data-driven torque compensation to eliminate the temperature effect is proposed to enable robust torque-controlled IMs. A model-based analysis and experimental mapping of the temperature effect on motor torque is presented. A temperature-torque lookup-table is subsequently implemented within the control algorithm demonstrating the ability to reduce the detrimental effect of temperature on torque control. Experimental results on a 5.5 kW squirrel-cage induction motor show that the proposed data-driven online temperature compensation method is able to reduce torque mismatch when compared to having no temperature compensation. Up to 17% torque mismatch is reduced at nominal torque and even up to 23% at torque setpoints that are lower than 20% of the nominal torque. A limited torque error of <1% remains in a broad operating range

    High-frequency issues using rotating voltage injections intended for position self-sensing

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    The rotor position is required in many control schemes in electrical drives. Replacing position sensors by machine self-sensing estimators increases reliability and reduces cost. Solutions based on tracking magnetic anisotropies through the monitoring of the incremental inductance variations are efficient at low-speed and standstill operations. This inductance can be estimated by measuring the response to the injection of high-frequency signals. In general however, the selection of the optimal frequency is not addressed thoroughly. In this paper, we propose discrete-time operations based on a rotating voltage injection at frequencies up to one third of the sampling frequency used by the digital controller. The impact on the rotation-drive, the computational requirement, the robustness and the effect of the resistance on the position estimation are analyzed regarding the signal frequency

    A general magnetic-energy-based torque estimator: validation via a permanent-magnet motor drive

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    This paper describes the use of the current–flux-linkage (ipsii{-}psi ) diagram to validate the performance of a general magnetic-energy-based torque estimator. An early step in the torque estimation is the use of controller duty cycles to reconstruct the average phase-voltage waveform during each pulsewidth-modulation (PWM) switching period. Samples over the fundamental period are recorded for the estimation of the average torque. The fundamental period may not be an exact multiple of the sample time. For low speed, the reconstructed voltage requires additional compensation for inverter-device losses. Experimental validation of this reconstructed waveform with the actual PWM phase-voltage waveform is impossible due to the fact that one is PWM in nature and the other is the average value during the PWM period. A solution to this is to determine the phase flux-linkage using each waveform and then plot the resultant ipsii{-}psi loops. The torque estimation is based on instantaneous measurements and can therefore be applied to any electrical machine. This paper includes test results for a three-phase interior permanent-magnet brushless ac motor operating with both sinusoidal and nonsinusoidal current waveforms

    Observer-based Fault Detection and Diagnosis for Mechanical Transmission Systems with Sensorless Variable Speed Drives

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    Observer based approaches are commonly embedded in sensorless variable speed drives for the purpose of speed control. It estimates system variables to produce errors or residual signals in conjunction with corresponding measurements. The residual signals then are relied to tune control parameters to maintain operational performance even if there are considerable disturbances such as noises and component faults. Obviously, this control strategy outcomes robust control performances. However, it may produce adverse consequences to the system when faults progress to high severity. To prevent the occurrences of such consequences, this research proposes the utilisation of residual signals as detection features to raise alerts for incipient faults. Based on a gear transmission system with a sensorless variable speed drive (VSD), observers for speed, flux and torque are developed for examining their residuals under two mechanical faults: tooth breakage with different degrees of severities and shortage of lubricant at different levels are investigated. It shows that power residual signals can be based on to indicate different faults, showing that the observer based approaches are effective for monitoring VSD based mechanical systems. Moreover, it also shows that these two types fault can be separated based on the dynamic components in the voltage signals

    Improving the torque generation in self-sensing BLDC drives by shaping the current waveform

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    Brushless DC drives are widely used in different fields of application because of their high efficiency and power density. Torque ripple can be considered one of the drawbacks of these drives. This paper proposes a method to reduce the torque ripple in BLDC drives. For this reason, the current amplitude is adapted to the rotor position rather than to be kept constant as done in a conventional commutation method. This is done by computing an optimum reference current based on the phase back-EMF waveform. The proposed approach is implemented in a self-sensing drive so its applicability to self-sensing BLDC motor drives is verified. Simulation and experimental results are given and discussed to show that the proposed method actually is able to improve torque production

    Monitoring of power factor for induction machines using estimation techniques

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    Power factor is a significant element in power systems which is defined as the angle difference between voltages and currents that produces power fluctuation between sources and loads. Since, 40-50% of consumption of electrical power in industry is induction machines which are inductive loads, monitoring of the power factor is necessary in order to protect systems. To monitor the power factor on induction machines, it would require both voltage and current waveforms measurement in order to apply the displacement method which require equipments. In this paper, we present a mathematical method using Kriging to determine the operating power factor for an induction machine. Estimation of the operating power factor would be effectively implemented for under load detection and compensation for improving the power quality. Experimental results will be indicated to substantiate the feasibility of the proposed methods

    Quantifying the commutation error of a BLDC machine using sensorless load angle estimation

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    BLDC motors are often used for high speed applications, for example in pumps, ventilators and refrigerators. For commutation discrete position information is necessary. This feedback is often provided by Hall sensors instead of more expensive encoders. However, even small misalignment of the Hall sensors in low cost BLDC motors can lead to unwanted torque ripples or reduced performance of BLDC motors. This misplacement leads not only to noise and vibrations caused by the torque ripples but also to lower efficiency. In this paper, a self-sensing technique to assess the misalignment is introduced. The objective is to obtain knowledge of the quality of the commutation by quantifying the misalignment. The method used in this paper is based on the fundamental components of voltage and current measurements and only needs the available current and voltage signals and electrical parameters such as resistance and inductance to estimate the misalignment

    Open loop control of a stepping motor with step loss detection and stall detection using back-EMF based load angle estimation

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    Stepping motors are the most used electrical machines for low power positioning. The drive controls the machine so that the rotor performs a fixed angular displacement after each step command pulse. Counting the step command pulses enables open-loop positioning. The vast majority of the stepping motor systems is driven in open-loop. When the rotor hits an obstacle stall occurs. Step loss due to overload is another typical problem with stepping motor driven systems. Both phenomena are not detected in open-loop which causes loss of synchronism. In this paper, a sensorless load angle estimator is used to detect step loss and stall. This algorithm is based on the typical stepping motor drive algorithms and does not depend on mechanical load parameters. The method therefore has a broad industrial relevance
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