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.

Phase-locked loops (PLLs) and frequency-locked loops (FLLs) are of importance in power and energy applications. Both technologies have been introduced to speed-sensorless- controlled motor drives, and increasing applications of PLLs and FLLs for speed estimation are foreseen. To enable a proper and good design, a thorough review of the PLL- and FLL-based speed estimation schemes is then provided in this article. It is revealed through the review that many PLL- and FLL-based estimation schemes fail to accurately track a frequency ramp (i.e., obvious estimation errors appear), which may lead to a compromised estimation accuracy when these schemes that are applied in induction motor drives operating during acceleration and deceleration processes. To address this, the proven speed estimation schemes together with new attempts are also presented in this article. Moreover, various challenges to the PLL- and FLL-based speed estimation schemes, including harmonics, dc offsets, and parameter variations, are considered when evaluating these schemes. Solutions to tackle these disturbances are accordingly presented. In addition, two representative estimation schemes are exemplified through experimental tests. Finally, further challenges in using the PLL- and FLL-based schemes for speed estimation are discussed.

A proper thermal network model (TNM) facilitates the thermal analysis of multichip power modules and is beneficial to the reliability evaluation of power converters. However, many TNMs are struggling to provide the accurate thermal analysis while maintaining acceptable computational efficiency. To address this, a TNM based on the Cauer model is proposed in this article, where the Cauer model is obtained with the assistance of the structure function. Then, the thermal coupling effects in multichip power modules are fully characterized. More specifically, the thermal coupling in the proposed TNM is equivalent to the injected power losses from the specific nodes, and an enumeration method is accordingly used to identify the injected nodes and the injected power losses. Moreover, a discretization approach based on the forward Euler method is implemented to enhance the computational efficiency. Subsequently, the junction temperature of the multichip power module is estimated through the signal-flow graph method. Finally, the proposed TNM uses the typical insulated-gate bipolar transistor modules as a case study, and extensive simulations and experimental tests are conducted to validate the effectiveness of the proposed TNM.

Speed-sensorless control of induction motor drives based on the extended state observer (ESO) attracts much popularity due to its key characteristics, e.g., satisfactory estimation performance, and high robustness against disturbances. However, the conventional ESO generally uses fixed high gains to achieve fast convergence. This may bring about the concern of noise sensitivity. Moreover, a proportional-integral (PI)-type speed controller is mostly adopted in speed-sensorless control of induction motor drives, which may cause unsatisfactory system dynamics. To address these, an adaptive active disturbance rejection control (ADRC) strategy is proposed for speed-sensorless induction motor drives in this article. In this scheme, a third-order adaptive ESO (AESO) is first used to estimate the speed, the phase, and the overall disturbance. Then, a speed controller based on the state error feedback control rate (SEFCR) is designed to improve system dynamics. In practice, disturbances like dc offsets may further challenge estimation performance, and hence, a closed-loop flux observer (CLFO) is employed to deal with this issue. Additionally, a parameter sensitivity analysis of the CLFO is also provided to evaluate the performance of the proposed scheme. Finally, extensive experimental tests have been carried out to validate the effectiveness of the proposed.

Junction temperature monitoring (JTM) is essential for reliability evaluation and health management for insulated-gate bipolar transistor (IGBT) modules, and thus is extensively focused on in power electronics converters. However, many JTM methods for IGBT modules are criticized for providing inaccurate junction temperature information with strong load current dependence. To address this, a JTM method based on the turn- off voltage (TOV) and convolutional neural networks (CNN) is proposed in this article. In this method, the TOV is used as the junction temperature indicator, and the characterization behavior of the TOV during turn- off transient process is thoroughly analyzed. Then, the parameter dependence of the TOV is investigated. Considering the proposed JTM method may be subject to the issue of load current dependence and show an undesirable performance, the CNN is adopted to maintain the accuracy of junction temperature prediction due to its excellent global and local feature recognition capability. With this regards, the proposed JTM method enables to provide accurate junction temperature information under different conditions. Finally, the double-pulse tests and experimental tests are carried out to validate the effectiveness of the proposed JTM method.

Active flux model of permanent magnet synchronous motors simplifies the model and eases sensorless control implementation. However, incorporating the current model (CM) that depends on the estimated electrical position for active flux estimation may cause performance degradation. The conventional active flux observer analysis revealed that position error influences sensorless control performance, especially during transient conditions. Thus, two improvement strategies named method-1 and method-2 are proposed in this article. Method-1 is based on an adaptive gain observer-based approach designed to discard the effect of position error. The observer gains are dependent on the estimated reference frame currents, which leads to the observer complexity. Method-2, on the other hand, uses the position error from the estimated active flux to obtain the true CM active flux without changing the conventional observer gains. Thus, the effect of position error on active flux estimation is discarded. Method-1 and method-2 fully decoupled the speed-position identification and the active flux observer. Meanwhile, a feedforward compensated phase-locked loop is adopted for speed-position identification. The proposed sensorless control strategies have been extensively validated using the Texas digital signal processor (TMS320F28335) and RT-LAB real-time simulator. The experimental results indicated that the proposed methods improved the sensorless control and active flux estimation performance.