Three-phase PWM inverter fed induction motor drive system is widely applied in high power drive applications. Sensor faults are very common in the drive system which, once occur, might result in degraded system performance or even system shutdown. In order to rapidly and accurately diagnose the sensor faults, this paper proposes an intelligent time-adaptive data-driven method to identify the fault location and fault type of sensors in drive system. An emerging machine learning technology named extreme learning machine (ELM) is applied to learn the sensor fault dataset, an ensemble ELM classifier is then designed to improve diagnostic accuracy, based on which a time-adaptive fault diagnosis process is proposed to achieve a high and balanced diagnostic accuracy and speed. As a data-driven method, the proposed method only employs the phase current, DC-link voltage, and speed signals as the inputs to the ensemble ELM classifiers, and requires no additional sensors and other hardware. Simulated and experimental tests show that the proposed method can rapidly and accurately detect the fault sensor location and identify offset fault, stuck fault, and noise faults with an average diagnostic accuracy of 98% and average decision time of 10ms after the fault occurs. Moreover, such diagnosis method is robust to the fluctuation of catenary voltage and DC-link voltage, fault severity, and variation of model parameters, speed, and load.

Icariside II (IS) is a metabolite of icariin, which is derived from Herba Epimedii. In the present study, the antiproliferative effects of IS on A375 human melanoma cells were examined in vitro and a possible mechanism through the ROS-p38-p53 pathway is discussed. A cell WST-8 assay revealed that treatment with IS markedly reduced cell viability from 77 to 21% (25 and 100 µM, respectively), and cell counting demonstrated that IS treatment reduced cell proliferation. IS treatment also induced cell cycle arrest of A375 cells at the G0/G1 and G2/M transitions and inhibited the expression of cell-cycle related proteins, including cyclin E, cyclin-dependent kinase 2 (CDK2), cyclin B1 and phosphorylated cyclin-dependent kinase 1 (P-CDK1). In this study, it was determined that IS inhibits cell proliferation and induces cell cycle arrest through the generation of reactive oxygen species and activation of p38 and p53. These findings were further supported by the evidence that pretreatment with N-acetyl-L-cysteine, SB203580 or pifithrin-α significantly blocked IS-induced reduction of cell viability, increase of cell death and cell cycle arrest. In conclusion, IS inhibits cell proliferation and induces cell cycle arrest. Crucially, it was confirmed that these effects were mediated at least in part by activating the ROS-p38-p53 pathway.

Active damping is a common way to stabilize the current control of LCL-filtered converters. In this paper, the stable region of −180º-phase-crossing is firstly identified within a predefined range of grid impedance and LCL parameter variations. Once the phase of the current control loop is in the identified region, a stabilization control can be attained. Subsequently, digital filters can be adopted to achieve active damping by reshaping the openloop phase. Various digital filters are selected and benchmarked in this paper. It is confirmed that the all-pass filter has a unity gain and adjustable lagging phase before the Nyquist frequency, thereby being a promising solution to the phase reshaping. Therefore, the all-pass filter is employed to move the phase of the open-loop control (i.e., −180°-phase crossing) into the targeted region for active damping. Notably, the current controller and the all-pass filter-based active damping can be separately designed, indicating the easy implementation of the active damping. Experimental tests demonstrate that the proposed method can ensure the system stability over a wide range of parameter variations (e.g., grid impedance changes and LCL-filter parameter drifts) while maintaining fast dynamics with the grid-side current control.

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