VCS-based system is a common form of AC/DC interconnected microgrid due to its flexibility in power supply and control. However, the line impedance between DC bus of microgrid and DC terminal of VSC can not be neglected, which significantly deteriorates system stability. This article focuses on the input impedance of VSC seen from the DC side and the output impedance of rest part of VSC-based system to analyze the stability of the system. Furthermore, a feedforward compensation impedance reshaping method is proposed to enhance stability margins when power flows back through VSC. Simulation results confirm the effectiveness of this proposed method.

Most of the mechanical fault diagnosis methods of induction motors (IMs) are based on vibration signals. However, vibration sensors are expensive and require direct contact with IMs. This article proposes a data-driven mechanical fault diagnosis method for IMs using stator current signals, which is more convenient and low cost, since no additional sensors are required. Aiming at the weak representation of mechanical faults in current signals, an intelligent noise elimination method based on noise reconstruction model is proposed to improve the signal-to-noise ratio. Through the automatic feature extraction and classification of the residual current envelope spectrum, high diagnosis accuracy can be obtained even if some differences have existed among samples. The effectiveness of the proposed method is verified by high accuracy diagnosis results on two experimental platforms. The results show that the average diagnostic accuracy of the proposed algorithm for one bearing fault and two eccentricity faults can reach 96%. Even if the fault type and the fault degree are distinguished at the same time, an accuracy of 90% can be achieved for six kinds of bearing faults.

An average torque distribution (ATD) strategy for low-speed operation of three-phase switched reluctance motor (SRM) with hall-effect rotor position sensor, as well as a calibration method for the sensor, are proposed. The sensor can only detect unaligned and aligned positions of all phases, and these positions evenly divides an electrical cycle into six position sectors. These positions are fully utilized by shaping a phase current as a four-step waveform and controlling motor average torque in each sector. In commutation sectors, a motor average torque reference is distributed into two phase average torque references. The distribution is optimized offline to minimize torque ripple and copper losses based on the phase torque characteristics. Compared with the torque control schemes using square-wave current references, the proposal can greatly reduce torque ripple without or with only a small penalty on copper losses. Based on difference among the aligned position detection errors of the hall-effect sensor, a calibration procedure for the sensor is developed. This procedure can ensure the absolute value of the average of these errors less than the maximum difference among these errors without high-resolution position signal as reference. Experimental results demonstrate effectiveness of the proposed ATD strategy and the calibration.

In this paper, a predictive phase current control (PCC) scheme based on a local linear phase voltage model for a switched reluctance motor is proposed. The current is controlled by regulating the average voltage through PWM, ensuring a fixed switching frequency. A linear model is proposed to approximate the relationship between the voltage and the current slope in a short period. By using the voltage and current slope information in the previous control cycle, the intercept and slope of the model can be identified online. In the previous control cycle, the phase voltage changes from zero to positive or negative DC-link voltage, and then the identified model is used to predict the average voltage required in the next PWM cycle for the actual current so as to accurately track its reference. The effectiveness of the proposed PCC was verified experimentally. The results demonstrate that the proposed control scheme can significantly reduce the current and torque ripples compared to hysteresis control with the same sampling rate. The proposed PCC is easy to implement, does not need to obtain the motor characteristics in advance and is not sensitive to the changes in characteristic parameters caused by motor aging, etc. It is relatively suitable for applications that need to accurately track the given current curve.

Porous structure based on triply periodic minimal surface (TPMS) has received extensive attention due to its excellent performance and lightweight. This study aimed to propose three novel types of composite TPMS-based porous structures (PI-type, PIP-type, and PN-type) inspired by the microscopic porous structure of bones and investigate their crashworthiness properties. The finite element method was used to simulate the deformation process of porous structures, and the finite element models were validated by the quasi-static compression experiment on PI structures manufactured by 3D printing. The simulation results revealed that the thickness ratio of the inner and outer surfaces significantly influenced the structure's deformation modes and energy absorption capability. The PI structures with thickness ratios of 0.3 and 0.4 (PI-0.3 and PI-0.4) and the PIP structures with thickness ratios of 0.4 and 0.5 (PIP-0.4 and PIP-0.5) were found to have higher energy absorption capacity and efficiency and lower peak crushing force than traditional foam structures. Especially, the energy absorption capacity and efficiency of PI-0.4 are 10.8% and 12.1% higher than those of the foam, respectively. It was found that the PI structure with the thickness ratio of 0.4 has the best crashworthiness, evaluating the crashworthiness indicators (PCF, SEA, MCF, and CFE) by the complex proportional assessment (CPA) method. Moreover, the theoretical formula of the average crush force of the structure was predicted based on the Gibson empirical formula, which can provide a guideline for the crashworthiness design of TPMS-based porous structures.

An n/2-sensor-based and an (m + 1)/2-sensor-based phase current detection scheme are proposed for even-numbered switched reluctance motors (EMSRMs) with n phases and odd-numbered switched reluctance motors (OMSRMs) with m phases. For the EMSRMs, the phases are divided into n/2 groups each of which includes two phases furthest from each other, and the lower dc bus is split into n/2 + 1 buses such that the currents through the lower switches of a group flow through a bus whose current is detected by a sensor. For the OMSRMs, the phases are divided into (m + 1)/2 groups and the currents through the lower switches of a group are detected by a multiplexed sensor without converter modification; the phase grouping is generalized as an optimization problem considering the volume and measuring range of the sensors. The schemes can detect the magnetization and freewheeling phase currents under multiphase excitation without pulse injection and voltage penalty. Compared to the existing schemes using cross-winding sensors, the proposed schemes can increase the motor torque by extending the phase conduction region. In addition, the proposed scheme for EMSRMs can combine the low-cost low-side shunt current sensing technique, and the proposed scheme for OMSRMs can increase the current sensing resolution. Simulations are carried out to validate the two proposed schemes. The proposed (m + 1)/2-sensor-based scheme is further verified experimentally.

In intelligent fault diagnosis, transfer learning can reduce the requirement of sufficient labeled data and the same data distribution. However, for the diagnosis of a new machine, there are still some limitations, such as low accuracy or the demand for some labeled data with fault information in the new machine. In this article, we propose a stack autoencoder transfer learning algorithm based on the class separation and domain fusion (SAE-CSDF) to solve these problems. According to the characteristics of bearing faults, the proposed weighted domain fusion strategy can ensure the direction and balance in the transfer process. The proposed class separation degree can improve the accuracy of the target domain indirectly by extending the differences between the classes in the source domain. The effectiveness of the SAE-CSDF is verified via the mutual transfer of two public datasets and one laboratory dataset. The results show that the accuracy of the algorithm can reach 97% in the transfer between different machines, even if there is no labeled fault data in the new machine.

离网式光伏发电系统能在一定程度上解决电网覆盖不全面的问题.传统的两电平功率变换器拓扑结构无法满足高压大功率的场合,其功率器件损耗较大,影响发电装置的效率.因此提出一种二极管箝位式三电平变换拓扑为光伏发电功率变换装置的主电路,采用三相直-交功率变换器SVPWM调制.建立直-交功率变换器的数学模型,选用电压外环和电流内环的PI数字控制策略.通过仿真及实验测试,分析结果表明,设计的直-交功率变换器输出波形正弦度好,谐波含量小,电能质量高,验证了所搭建控制系统的有效性.

In this article, an improved adaptive control strategy for the divided capacitor voltages of a full-bridge three-level (FBTL) dc/dc converter is put forward. First, a switching strategy is proposed and the principles of the switching strategy are analyzed in detail. Second, the self-balance characteristics in four cases and the control strategy under large perturbation of the divided capacitor voltages in three cases are analyzed. Third, two methods to detect the divided capacitor voltages and one method to calculate the threshold voltages to convert different cases are investigated. Finally, a 600-KVA FBTL dc/dc based on MATLAB/SIMULINK and a 100-VA FBTL dc/dc converter experimental prototype are built and tested and the results verify the theoretical analysis.

针对全桥三电平(FBTL)DC-DC变换器两个占空比协调优化控制、传输功率太小以及钳位电容电压平衡等问题,该文提出一种基于查表法的FBTL DC-DC变换器优化控制策略.首先,详细分析FBTL DC-DC变换器输入、输出特性,并设计一种钳位电容电压自平衡调制策略.其次,在所设计的钳位电容电压自平衡调制策略基础上,提出一种抗钳位电容电压扰动控制策略,并根据钳位电容输入功率差与电压最大调节能力的关系计算出占空比必须满足的条件.然后,建立一种以变压器输入电压谐波为目标函数,功率传输能力、钳位电容电压调节能力等为约束条件的优化模型,进而得到FBTL DC-DC变换器的优化控制策略.优化占空比离线计算,然后存储在数字控制系统里,控制时直接采用查表法得到最优占空比.所提出的方法无需变换器精确模型,控制过程简单、高效,控制效果优于传统控制策略.最后,在Matlab/Simulink平台和一台实验样机上验证了所设计的优化控制策略的正确性和有效性.

开关磁阻电机采用传统型转矩分配策略时,电流峰值过高、电流可控性下降导致其出现严重的转矩脉动.针对这一问题,提出了基于遗传算法的开关磁阻电机电流变化率和铜耗的综合优化方法.在直线型转矩分配函数的基础上,根据电流输出波形与转矩分配函数的关系提出了二次型补偿曲线.选择电流变化率和铜耗作为优化目标,利用遗传算法的全局寻优能力,对新型转矩分配函数的参数进行优化,得到最优的转矩分配函数曲线,从而降低了换向时的电流峰值.仿真和实验结果表明,新型转矩分配函数能够有效地改善各相给定电流波形,降低电流控制难度,从而抑制转矩脉动.

内置式永磁同步电机无位置传感器低速控制主要采用高频正弦信号注入方法,但估计转子位置需应用滤波器提取高频信号,滤波器使用会延迟信号、降低电流环带宽,严重影响动态控制性能。一种改进的高频方波注入法被提出以克服上述问题,该方法将高频信号注入与矢量控制分周期进行,无需低通和带通滤波器。但是该方法在估计转子位置时受逆变器非线性影响较大,容易出现较大的估计误差。针对上述问题,该文提出一种注入幅值自适应的可抑制逆变器非线性的新型高频方波注入法。该方法通过变化的注入电压幅值抑制逆变器非线性造成的高频电压矢量信号畸变,提高转子位置角度估计精度。仿真及对比实验结果证明,该方法可将位置估计误差从原有的平均误差±11°降低到了平均误差±3°,具有良好的稳定性和鲁棒性。

针对基于反电动势(EMF)模型法的内置式永磁电机(IPMSM)无位置传感器控制中,逆变器的非线性和磁场空间谐波会引起反电动势产生6k±1次谐波,最终导致估算的转子位置中存在6k次脉动,降低转子位置估算精度的问题,提出一种双线性递归最小二乘(BRLS)自适应滤波的转子位置估算方法.该方法通过滑模观测器获取反电动势,然后依据信号增强的原理,由双线性递归最小二乘自适应滤波器通过在线更新滤波器系数,跟踪并滤除反电动势估算值中指定的谐波分量,进而抑制转子位置中6k次谐波脉动.实验结果表明,所提出的自适应滤波的转子位置估算方法收敛速度快、精度高,能可靠抑制谐波并提高转子位置估算精度.

Fault diagnosis is an important technology in the development of modern industrial safety. Vibration information is commonly used to determine the state of bearings. Driven by big data, deep learning brings new opportunities to fault diagnosis. As an unsupervised deep learning algorithm, a stacked autoencoder (SAE) can relieve the pressure of labelling data. Due to the diversity and variability of the actual fault diagnosis distribution, an optimized transfer learning (TL) algorithm is proposed to solve the domain adaptation. By directly inheriting features obtained from the pre-training process in the source domain and changing only the fine-tuning process, the complexity of the algorithm is reduced. Considering the data reconstruction ability and robustness, a sparse stacked denoising autoencoder (SSDAE) is proposed for feature extraction, which can indirectly improve the diagnostic accuracy in the target domain. The results for data from the Case Western Reserve University Bearing Data Center show that the proposed SSDAE-TL algorithm is feasible and easy to implement for the fault diagnosis of bearings. (C) 2019 Elsevier Ltd. All rights reserved.

For the permanent magnet synchronous motor (PMSM) control system of the mine traction electric locomotive (MTEL), the fluctuation of the load will lead to the resonance of the velocity of the MTEL and result in mechanical damage. To solve this problem, a disturbance observer-based complementary sliding mode controller (DOB-CSMC) design method is proposed in this paper. A mathematical model of PMSM is first established. Then, a disturbance observer is designed to reconstruct the load disturbances. In order to realize the static and dynamic tracking performance of the PMSM system, a saturation function-based complementary sliding mode controller is designed and the reconstructed disturbance is introduced into the controller to counteract the influence of external disturbance. Finally, the simulation and experimental results show that the DOB-CSMC method has a smaller overshoot and a shorter settling time compared with the traditional SMC method.

In order to analysis the output dynamic characteristics of dc offshore wind farm, a multimachine representation dynamic equivalent method based on an improved fuzzy C-means (IFCM) clustering algorithm is proposed. First, characteristic variables which can characterize the dynamic characteristics of the input and state performance are researched. Second, an IFCM clustering algorithm is first put forward. Third, the dc offshore wind generators (DCOWGs) are divided into many groups by analyzing the characteristic variables data with the IFCM. Finally, DCOWGs in the same group are equivalent as one DCOWG to analyze the dynamic characteristics. Simulation results from MATLAB/SIMULINK verify the theoretical analysis.

研究了一种用于双PWM变换器的直流母线功率平衡控制方法.该方法根据网侧和机侧变流器数学模型推导出各自的功率微分方程,并在网侧变流器控制中引入机侧变流器功率微分,使得网侧变流器功率及时跟踪机侧变流器功率让直流母线电容功率保持不变;为简化负载功率微分的计算,永磁同步电机采用IP控制方法.同时,为了更好地稳定直流母线电压,在网侧变流器的外环控制中采用直流母线电压的控制方法,从而在直流母线电容容量较小时,直流母线电压仍保持稳定.仿真及实验结果验证了所提出的控制方法的有效性及优越性.

研究一种基于干扰观测器滑模控制(DOB-SMC)方法.为实现永磁同步电动机良好的静态和动态跟踪性能,设计了一种基于指数趋近律的滑模控制器,并证明了滑模控制器的渐近稳定性.引入基于DOB的滑模控制器以补偿负载干扰,从而保证系统的鲁棒性.仿真与实验证明,该方法提高了永磁同步电动机控制系统速度跟踪精度,并且对负载干扰具有较好的抑制效果.

Conventional position sensorless control strategies for permanent-magnet synchronous machine (PMSM) operating in the low-speed rang rely on its rotor spatial saliency. But in the case of SPMSM with symmetric rotor structure, magnet saturation induced saliency have been utilized to detect the rotor position. In the same time, filters are needed to extract the position information and fundamental current which will degrade the performance of current loop and position observer because of the time delay caused by filters. To overcome the problem, a novel position sensorless control strategy of SPMSM for low-speed operation based on square wave voltage injection is proposed, the rotor position can be extracted from the induced high frequency current signals in the stationary reference frame by separating the FOC period and the voltage injection period. The proposed method may also be further developed to inject two opposite voltage vectors to eliminate the inverter nonlinearity effect. In the end of the paper, the experimental results confirm the reliability of the proposed method by a 2.2kW SPMSM position sensorless control system.