This paper presents a speed estimation scheme by combing second-order sliding-mode observer (SMO) the model reference adaptive system (MRAS) in the sensorless-vector-controlled linear induction motor (LIM) drives for medium-low speed maglev applications. A state space-vector model of the LIM considering the dynamic end effects is rearranged in order to be represented by the form of the super-twisting algorithm (STA). Then, a STA-SMO based stator current observer of the LIM with Popov's hyperstability theory is designed, which is used to replace the reference model of the MRAS-based scheme. Correspondingly, the actual stator current model is regarded as the adaptive model of the MRAS for speed estimation. Meanwhile, because the variation of stator resistance associated with stator temperature may lead to large estimated error and even system instability, a parallel stator resistance on-line identification scheme is adopted in the proposed speed estimation scheme to reduce the speed error and improve the stability of the sensorless control system. Compared to the speed estimation schemes based on Luerberger observer with MRAS and the single-manifold SMO, the proposed speed estimation scheme exhibits better estimated performance. The effectiveness and feasibility of the proposed speed estimation scheme have been verified by simulation and hardware-in-the-loop (HIL) tests.

This paper presents a model-based open-circuit fault diagnosis approach for single-phase three-level neutral-point-clamped (3LNPC) converters in electric railway application. The diagnosis algorithm, which only requires the signals existing in the control system, not only detects open-circuit faults but also identifies the faulty device among the transistors and clamping diodes. The mixed logical dynamic (MLD) model of the converter is built to estimate the grid current. The residual generated from the measured current subtracting the estimated one is analyzed under different open-circuit faults. According to the characteristics of the residual changing rate, the proposed approach allows fault localization. The proposed method is effective both in traction and regenerative braking operation and has fast diagnosis speed. Hardware-in-the-loop (HIL) experiments are carried out to verify the effectiveness of the proposed fault diagnosis algorithm.

This paper proposes a novel disturbance-observer-based complementary sliding-mode (DO-CSM) speed controller for the field-oriented controlled permanent-magnet synchronous motor (FOC-PMSM) drive system. At first, the rotor speed dynamics of the PMSM drive system considering the lumped disturbance, which consists of external disturbances, parametric uncertainties and unmodeled dynamics, is presented. Then, the classic CSM speed controller using the saturation function and a typical artificial neural network (ANN)-based CSM speed controller, i.e., the Elman NN (ENN)-based intelligent CSM (ENN-ICSM) speed controller for the FOC-PMSM drive system are reviewed. Afterward, the design of the proposed DO-CSM speed controller, which combines the CSM controller using the signum function with the super-twisting sliding-mode observer (STSMO), is presented. In such a speed controller, the rotor speed tracking is accomplished by the adopted CSM controller, whereas the STSMO is employed to estimate and compensate the lumped disturbance in the rotor speed dynamics. Finally, experimental comparisons among the proposed DO-CSM speed controller, the classic CSM speed controller and three selected ENN-ICSM speed controllers are carried out. Experimental results verify the effectiveness and superiority of the proposed DO-CSM speed controller.

In power electronics and power systems, the dual-phase-locked loop (DPLL) is a well-accepted approach to maintain zero steady-state errors when subjecting to a frequency ramp input. This distinctive characteristic of the DPLL may make it as a promising candidate for speed estimation in speed-sensorless drive systems. Considering this issue, a DPLL-based speed estimation scheme for linear induction motor (LIM) speed-sensorless drive systems is presented in this paper to achieve accurate speed estimation. The basics of the DPLL and the direct application of the DPLL scheme are provided. However, in practice, the direct application of the DPLL scheme may degrade the performance of the entire system, which includes the non-idealities in the secondary electromotive force (EMF) signals, such as harmonics and parameter variations. Accordingly, the pre-filter and the amplitude normalization (AN) scheme are employed to mitigate the effect of non-idealities. Meanwhile, the small-signal model of the DPLL scheme is presented, enabling the analysis of the dynamic performance and the comparison of the synchronous reference frame phase locked-loop (SRF-PLL) scheme. In addition, a parameter tuning guideline of the proposed DPLL scheme is detailed by using the derived small signal model. The performance of the DPLL scheme is investigated by experimental tests.

The dc-link oscillation is a typical phenomenon happening in the metro traction drive system (MTDS) due to the negative input impedance associated with the constant power load, which is considered as a thorny problem that needs to be resolved well. This paper proposes an active damping stabilization scheme based on inverter power regulation that can effectively eliminate the dc-link oscillation of the MTDS. The proposed stabilization scheme is mainly composed of two parts: 1) the generation of damping power; and 2) the inverter power control loop. The damping power, linked to the deviation of the dc-link voltage through the gain units, is fed back to the vector-controlled induction motor drive system to establish the inverter power control loop. From this control loop, the added q -axis current is obtained, which is regarded as the stabilization term. The additional stabilization term is combined with the q-axis current command of the induction motor for achieving the improvements of the drive performance and the system stability. The effectiveness of the proposed active damping stabilization scheme is verified by simulation and hardware-in-the-loop tests.