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In this letter, a simplified single current loop control scheme for single-phase dual-boost inverter has been developed, combining half cycle modulation and virtual-vector (VV) based model predictive control (MPC). As the control reference can be slipped and allocated into two side boost, only a unified grid current reference is relied on in this proposed control method. Furthermore, with the concept of feedforward control, the global optimal solution space in MPC can be compressed, taking the duty ratio as boundary for the precompacted solution space. Thereby, the improved MPC can select the optimal VV in a smaller and closer subspace. Consequently, much finer control effect can be obtained. Experimental tests are provided to validate the proposed solution and its merits. [ABSTRACT FROM AUTHOR]

The low-frequency current ripple, which is inherent in the two-stage single-phase grid-tied fuel cell power system, gives harmful effects on fuel cell stack itself and causes a reduction in the lifetime, efficiency, and operating performance of system. This article first presents a direct current control scheme to improve the dc and ac power quality with a dc-link voltage notch filer and a proposed improved model predictive control (MPC) method. However, since the number of the switching states is low, the fuel cell dc current and grid current may suffer contaminated and distorted, when the conventional MPC algorithm implementation is straightforward. With the aid of feedforward duty ratio and the rotation angle of reference grid current, the optimization space can be reduced in real time, avoiding evaluating all the feasible virtual-vectors, and then the improved MPC has been proposed. Finally, the correctness and effectiveness is verified by the simulation and experiment results. [ABSTRACT FROM AUTHOR]

The performance of train communication network (TCN) is one of the most important factors to ensure the safety of train operation, and there is a lack of research for the TCN performance currently. To master the state of TCN, a combined method to evaluate and predict the TCN performance is proposed in the paper. First, we select appropriate evaluation indexes and determine their comprehensive weights. Then, the evaluation model of TCN performance is established based on a multidimensional normal cloud model combined with weights. According to the evaluation results, the artificial neural network (ANN) is used to predict the TCN performance to realize the real-time evaluation. Finally, the effectiveness of the evaluation and prediction model of TCN performance is verified by the simulation result, which is obtained from the simulation platform of TCN. The results show that service quality for TCN has been effectively improved, which provides a theoretical reference for evaluation and prediction of TCN performance. [ABSTRACT FROM AUTHOR]

To optimize the maintenance of complex systems of metro trains, the variation trends of the failure rates of components under imperfect maintenance are identified, and the maintenance cycle of components under reliability constraints are determined based on the concept of reliability-centered maintenance. An optimal maintenance plan for the components that is based on maintenance economy and availability then is developed. Lastly, opportunistic maintenance with time dependency is introduced, and the maintenance optimization model for complex systems is constructed for the special application environment and maintenance mode of metro trains. The model constrained by the opportunistic maintenance threshold, ${\Delta}{t}$ , to optimize the maintenance costs and availabilities of metro trains is established. The multicomponent joint opportunistic maintenance model in this article is based on the constraints of reliability and availability, which not only has high economy but also has practical application significance. The door subsystem of a Nanning Rail Transit Line 1 train is simulated as an example in this article. The opportunistic maintenance strategy and preventive group maintenance strategy are compared. The simulation results demonstrate that under the premise that the reliability constraints of components are satisfied, the maintenance of the metro train complex system can be effectively optimized by the model; in addition, the maintenance costs are reduced, the availability of metro trains is improved, and a metro train maintenance plan is scientifically formulated. [ABSTRACT FROM AUTHOR]

With increasing requirements for the amount of data transmitted by train communication networks (TCN), the industrial Ethernet has become a solution with large transmission rates and high compatibility. To ensure safety and real-time communication, the delay of data transmission must be strictly limited in TCN. However, the delay caused by data transmission conflicts is still a problem in the current Ethernet research, which cannot meet the demands of real-time transmission between network devices. To solve the time-delay problem, an improved adaptive genetic algorithm (IAGA) for topology optimization is proposed, in which a new optimization model is established based on the train Ethernet topology. Compared with two traditional algorithms in solving the optimization model, the IAGA achieved better performance in terms of convergence speed and solution accuracy. To verify the effect of the optimized model, the theoretical calculation, simulation, and experiment based on a communication platform are carried out for the real-time index. Experimental results indicated that the end-to-end delay of the optimized network is significantly reduced. In designing and solving the topology optimization problem of TCN, the proposed model and algorithm effectively realized real-time improvement of the data transmission. [ABSTRACT FROM AUTHOR]

With the continuous development of rail transit fully automatic operation, the urgent need to improve train operation safety makes obstacle detection become the research focus. In this work, a flexible and efficient multiscale one-stage object detector FE-YOLO was proposed for image obstacle detection. The feature extraction network is composed of attention module, downsampling module, residual block, spatial pyramid pooling (SPP) module, and so on. A repeatable bidirectional cross-scale path aggregation module was designed as the core of the feature fusion network. The dataset RT2021 of rail transit obstacles was constructed based on the real scene. The mean of average precision (mAP), detection time, iteration time, parameters, and anti-interference ability were used to compare FE-YOLO with other classic object detectors. The results showed that FE-YOLO has the best comprehensive performance. The mAP can reach 92.57%, and the single-frame detection time on the onboard embedded device is up to 0.0989 s. The ablation experiment verified the effectiveness of each module of FE-YOLO and has the best generalization performance on the PASCAL VOC dataset. Finally, a rail transit obstacle detection system was developed, and multiple sensors were used to improve the detection accuracy. Experiments showed that the detection system can work normally in different environments. [ABSTRACT FROM AUTHOR]

When an existing photovoltaic (PV) system is upgraded to a residential PV/battery system, the single-phase PV inverter under both input conditions of battery and PV should be properly controlled to restrain the input current ripple and grid-current harmonics. To do this, equivalent circuits of PV array and Li-ion battery pack are first constructed and respectively analyzed. The analysis results show that the input current under the battery pack may contain serious ripple component due to the low internal impedance of the battery pack, which cannot suppress the ripple current caused by the inherent power coupling problem of the grid-connected single-phase inverter. Then, based on the small signal model of the boost dc–dc convertor, a novel active control method is proposed for mitigating the input current ripple, which adopts double-channel current feedbacks including an additional ripple current feedback channel and the normal one. To provide the feedback signal, a third-order general integrator is introduced to extract the current ripple. Besides, a proportional-resonant controller is used to restrain the grid-current harmonics. Finally, the control parameters are obtained using MATLAB toolbox, and the proposed control strategies are validated by the experimental results on a 5 kW prototype. [ABSTRACT FROM AUTHOR]

In this paper, a combined reactive power modulation and grid current distortion improvement approach is proposed for an H6 transformer-less full-bridge single-phase photovoltaic (PV) grid-connected inverter. H6 transformer-less inverters with traditional modulation and control strategies may not satisfy the requirement of reactive power compensation or may result in more severe zero-crossing current distortion. Therefore, contrary to the traditional modulation, a novel reactive power injection space vector pulse width modulation (SVPWM) is proposed, which consists of two operation stages—inverter modulation and reactive power modulation. The implementation of SVPWM for reactive power modulation using a digital signal processor is also investigated. Furthermore, to suppress the current zero-crossing distortion in the reactive power injection mode, a global sliding mode function based on the proportion-integration-resonance current controller is designed, and the control law of the global sliding mode is derived. Using the segment modulation and grid current distortion improvement approach, the function of reactive current injection is implemented in commercial PV inverters, and the total harmonic distortion of the grid current is decreased significantly by more than 5% in the low-power segment, under the operating conditions of a lagging or leading power factor of 0.95. The effectiveness and feasibility of the proposed approach are verified through simulation and experiment using a 5-kVA prototype. [ABSTRACT FROM AUTHOR]