Search

In the high-frequency (HF) signal injection based position sensorless permanent magnet synchronous motor (PMSM) drives, there is an estimated position error caused by the cross-saturation effect and other factors. In order to suppress the position error, an HF injection angle self-adjustment based online suppression method is proposed in this letter. By tracking the sign of the increment of the absolute back electromotive force in the estimated frame, the criterion of the HF injection angle self-adjustment can be determined online and the position error can be mitigated effectively. This method can avoid the offline measurement or the variation of the operating state, which has a satisfactory suppression effect for the position error under different operating conditions. The effectiveness is verified by experiments on a 2.2-kW PMSM drive platform. [ABSTRACT FROM AUTHOR]

It is important to improve the accuracy of rotor position estimation for the high-performance operation of position sensorless permanent magnet synchronous motor (PMSM) drives. To mitigate the position harmonic error caused by the nonideal factors in high-frequency signal injection based methods, an adaptive quasi-proportional-resonant rotor position observer (AQPRO) for sensorless PMSM drives is proposed in this article. The AQPRO can track the estimation error and generate the compensation value, which can realize online suppression for position error. By analyzing the stability and the influence of parameters of the proposed position observer on the suppression performance, a proportional-resonant gain self-tuning strategy is investigated to improve the suppression effect of the position harmonic error for different loads and speeds. The influence of self-tuning regulator parameters is modeled and analyzed in detail. The proposed method does not require offline detection and has a strong suppression ability for different harmonic error components. The effectiveness is verified by experiments on a 2.2-kW PMSM drive platform. [ABSTRACT FROM AUTHOR]

Accurate rotor position estimation is important to ensure the high-performance operation of position sensorless permanent magnet synchronous motor (PMSM) drives. To suppress the estimated rotor position harmonic error (ERPHE) caused by inverter nonlinearities, flux spatial harmonics, and the current measurement error in high-frequency signal injection-based schemes, an adaptive iterative learning control-based online suppression method is proposed in this article. This method constructs a P-type iterative learning rotor position observer with the forgetting factor to obtain the tracking error and generate the compensation value in real time. The stability, the convergence, and the parameter sensitivity of this observer are analyzed in detail. To improve the suppression effect of the ERPHE for different loads and speeds, an iterative learning gain self-tuning strategy is investigated. The proposed method does not require the offline detection and the discrimination of harmonic frequencies, which has strong suppression ability for different harmonic error components. The effectiveness is verified by experiments on a 2.2-kW interior PMSM drive platform. [ABSTRACT FROM AUTHOR]

Initial rotor position detection is important to ensure the start-up operation of sensorless permanent magnet synchronous motor (PMSM) drives. In order to suppress the annoying noise caused by the additional signal injection during the detection process in the existing methods, a pseudorandom high-frequency (HF) square-wave voltage injection based low-noise initial position detection method is proposed in this article. Two fixed HF square-wave voltage signals are injected into the estimated d-axis randomly. As the magnetic pole position is detected, the magnetic polarity can be identified at the same time, based on the accumulation of the induced random HF current peak pairs without any extra signal. In addition, a saturated peak current delay compensation strategy is proposed to reduce the digital delay effects on the magnetic polarity detection. The proposed method makes the entire detection process simpler and faster. Furthermore, power spectral density (PSD) analysis of random HF currents is adopted to provide the theoretical support for noise reduction. Finally, the proposed method is verified by experiments on a 2.2-kW interior PMSM drive platform. [ABSTRACT FROM AUTHOR]

The acoustic noises caused by the fixed-frequency signal injection scheme limit the applications of saliency tracking based sensorless control methods for interior permanent magnet synchronous machine (IPMSM) drives at low-speed operation. In this paper, a pseudorandom-frequency sinusoidal injection (PRFSI) considering the sample and hold effect in practical applications is proposed to reduce the undesired acoustic noises. Since the power spectral density (PSD) of the excited high-frequency (HF) current could be dispersed and continuously distributed with PRFSI, the additional HF acoustic noises can be reduced. Through the comparative analysis of HF response currents PSD results for PRFSI and fixed-frequency sinusoidal injection (FFSI), the principle of acoustic noises reduction with PRFSI is analyzed theoretically. Furthermore, the optimal selection of the injection signals is derived considering the sample and hold effect. Whereby, a comparative evaluation of the proposed PRFSI versus FFSI is carried out on a 2.2-kW position sensorless IPMSM drive, in terms of position estimation and PSD results, which verifies the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]