Regenerative Braking Energy Recovery System of Metro Train Based on Dual-Mode Power Management

In order to fully utilize the regenerative braking energy of metro trains and stabilize the metro DC traction busbar voltage,a hybrid regenerative braking energy recovery system with a dual-mode power management strategy is proposed.Firstly,the construction of the hybrid regenerative braking energy recovery system is explained.Then,based on the power demand of low-voltage load in metro stations,a dual-mode power management strategy is proposed to allocate the reference power of each system according to the different working conditions,and the control methods of each system are set.Finally,the correctness and effectiveness of the dual-mode strategy are verified through simulation,and the proposed braking energy utilization scheme is compared with other singleform utilization schemes.The results illustrate that the hybrid system with the dual-mode strategy can effectively recycle the regenerative braking energy of metro train and inhibit the busbar voltage fluctuation;the proposed braking energy utilization scheme has certain advantages on energy recovery and DC bus voltage stabilization compared with other single-form schemes;the proposed power management strategy can correctly allocate the reference power of each system with a lower construction cost.

Increasing realism in modelling energy losses in railway vehicles and their impact to energy-efficient train control

The reduction of energy consumption is an increasingly important topic of the railway system.Energy-efficient train control(EETC)is one solution,which refers to mathematically computing when to accelerate,which cruising speed to hold,how long one should coast over a suitable space,and when to brake.Most approaches in literature and industry greatly simplify a lot of nonlinear effects,such that they ignore mostly the losses due to energy conversion in traction components and auxiliaries.To fill this research gap,a series of increasingly detailed nonlinear losses is described and modelled.We categorize an increasing detail in this representation as four levels.We study the impact of those levels of detail on the energy optimal speed trajectory.To do this,a standard approach based on dynamic programming is used,given constraints on total travel time.This evaluation of multiple test cases highlights the influence of the dynamic losses and the power consumption of auxiliary components on railway trajectories,also compared to multiple benchmarks.The results show how the losses can make up 50%of the total energy consumption for an exemplary trip.Ignoring them would though result in consistent but limited errors in the optimal trajectory.Overall,more complex trajectories can result in less energy consumption when including the complexity of nonlinear losses than when a simpler model is considered.Those effects are stronger when the trajectory includes many acceleration and braking phases.

Abnormal Action Recognition with Lightweight Pose Estimation Network in Electric Power Training Scene

Electric power training is essential for ensuring the safety and reliability of the system.In this study,we introduce a novel Abnormal Action Recognition(AAR)system that utilizes a Lightweight Pose Estimation Network(LPEN)to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios.The LPEN network,comprising three stages—MobileNet,Initial Stage,and Refinement Stage—is employed to swiftly extract image features,detect human key points,and refine them for accurate analysis.Subsequently,a Pose-aware Action Analysis Module(PAAM)captures the positional coordinates of human skeletal points in each frame.Finally,an Abnormal Action Inference Module(AAIM)evaluates whether abnormal fall-down or unauthorized trespass behavior is occurring.For fall-down recognition,three criteria—falling speed,main angles of skeletal points,and the person’s bounding box—are considered.To identify unauthorized trespass,emphasis is placed on the position of the ankles.Extensive experiments validate the effectiveness and efficiency of the proposed system in ensuring the safety and reliability of electric power training.

Analysis of Strength Training in the Training of Shot-Putters

The shot put is a sport that requires very high explosive power and precise technique.Strength training occupies the core position in the training of shot-putters,which can not only improve the throwing distance of athletes but also enhance their competitive state and prevent sports injuries.The purpose of this paper is to analyze the principles,classification,methods,and specific exercises of strength training in the training of shot-putters,in order to provide scientific training guidance for shot-putters.

双制式列车接地系统的车-地联合牵引供电计算

为研究双制式列车在不同供电制式区段下的车体环流和车体—轴端电位分布,针对某型双制式列车构建列车接地系统的链式电路模型,并提出车-地一体化的交、直流区段联合牵引供电计算方法;同时,建立列车接地保护电阻优化模型,并分析2种列车接地系统配置方案.以国内某条双制式线路为算例进行仿真验证,研究结果表明:方案1相较方案2,列车的车体—轴端电位最大值降低36.58%~41.04%,车体环流最大值降低18.49%~22.97%;并且在方案1中,头尾车保护电阻设置为20 mΩ,可使列车的车体—轴端电位最大值为1.15 V,车体电流最大值为50.30 A,达到抑制车体—轴端电位的最优效果.该车-地一体化的交、直流区段联合牵引供电计算方法可适用于单一供电制式或者多供电制式列车接地系统的分析.

Multi-train modeling and simulation integrated with traction power supply solver using simplified Newton–Raphson method

Multi-train modeling and simulation plays a vital role in railway electrification during operation and planning phase. Study of peak power demand and energy consumed by each traction substation needs to be deter- mined to verify that electrical energy flowing in its railway power feeding system is appropriate or not. Gauss-Seidel, conventional Newton-Raphson, and current injection methods are well-known and widely accepted as a tool for electrical power network solver in DC railway power supply study. In this paper, a simplified Newton-Raphson method has been proposed. The proposed method employs a set of current-balance equations at each electrical node instead of the conventional power-balance equation used in the conventional Newton-Raphson method. This concept can remarkably reduce execution time and computing complexity for multi-train simulation. To evaluate its use, Sukhumvit line of Bangkok transit system （BTS） of Thai- land with 21.6-km line length and 22 passenger stopping stations is set as a test system. The multi-train simulation integrated with the proposed power network solver is developed to simulate 1-h operation service of selected 5-min headway. From the obtained results, the proposed method is more efficient with approximately 18 % faster than the conventional Newton-Raphson method and just over 6 % faster than the current injection method.

基于改进LSTM-TCN模型的分布式光伏功率预测研究

光伏发电受天气因素的影响,造成供电的稳定性降低,使得光伏功率预测准确性不高。为保证供电服务质量,提出一种基于改进LSTM-TCN的预测模型。利用灰色关联度算法选取光伏功率影响因素,通过相似度计算,获取分布式光伏功率值,组成训练样本数据。将LSTM与TCN结合,构建改进LSTM-TCN预测模型,并通过模型训练,得到成熟的预测模型。实验结果表明,所研究模型预测结果的平均平方差和平均绝对误差率分别为0.562 6 kW和2.653 2%,远低于另外两种对比方法,表明所提方法的功率预测准确性高。

基于Online-GRU信道预测的星上自适应功率控制方法

针对传统卫星功率控制方法存在资源浪费、时延长的问题,提出一种基于在线-门控循环单元(Online-GRU)信道预测的星上自适应功率控制方法,通过在线训练更新网络参数来解决离线预测算法存在的累积误差的问题。仿真结果表明,提出的在线训练算法比离线算法预测精确度提升了38.30%,相比在线-长短期记忆网络(Online-LSTM)节约了63.21%的训练时间;提出的自适应功率控制方法比固定发射功率的方法节约了55.74%的发射功率;同时,相比基于地面定时反馈信道状态的自适应功率控制方法具备更好的鲁棒性。

城市轨道交通供电-牵引-混合储能系统设计与仿真

为了进一步提高城市轨道交通列车再生制动能量的回收与利用效率,通过对系统功能需求分析,设计一种基于分布式地面混合储能城市轨道交通列车牵引供电系统结构,建立供电网络、列车牵引和储能系统模型。同时针对低通滤波方式导致锂电池和超级电容出现大量功率交换的问题,提出带超级电容补偿的阶梯功率分配法,该方法运算量小且实时性强,能够避免混合储能系统内部功率循环。结合所提模型和策略,利用MATLAB/Simulink进行仿真试验,仿真结果表明混合储能系统能量利用效率提高了1.39%,稳压率提高了12.75%,节能率提高了6.72%。

Variable Parameter Nonlinear Control for Maximum Power Point Tracking Considering Mitigation of Drive-train Load

Since mechanical loads exert a significant influence on the life span of wind turbines, the reduction of transient load on drive-train shaft has received more attention when implementing a maximum power point tracking U+0028 MPPT U+0029 controller. Moreover, a trade-off between the efficiency of wind energy extraction and the load level of drive-train shaft becomes a key issue. However, for the existing control strategies based on nonlinear model of wind turbines, the MPPT efficiencies are improved at the cost of the intensive fluctuation of generator torque and significant increase of transient load on drive train shaft. Hence, in this paper, a nonlinear controller with variable parameter is proposed for improving MPPT efficiency and mitigating transient load on drive-train simultaneously. Then, simulations on FAST U+0028 Fatigue, Aerodynamics, Structures, and Turbulence U+0029 code and experiments on the wind turbine simulator U+0028 WTS U+0029 based test bench are presented to verify the efficiency improvement of the proposed control strategy with less cost of drive-train load. © 2017 Chinese Association of Automation.

西部地区160 km/h动力集中型动车组列车开行需求分析--以昆明局集团公司为例

从完善复兴号列车产品谱系,用好既有路网资源的角度考虑,扩大160km/h复兴号动力集中型动车组列车开行能够提高普速列车开行质效。首先从通达性、运输效率和票价3个方面论证动力集中型动车组列车的优势,然后在对比分析动力集中型动车组列车与其他交通方式基本属性的基础上,构建旅客出行选择的Logit模型,计算得到不同场景下动力集中型动车组列车运输市场分担率,并进一步进行动力集中型动车组客流需求预测和开行方案调整工作。最后以昆明局集团公司为例,根据不同线路实际情况,在通勤、旅游客流需求旺盛的昆明—保山、昆明—成都南、昆明—宣威、昆明—曲靖、昆明—西双版纳每日分别增加列车2.5对、3对、3对、5.5对和1对。

Effect of Aerobic High-Intensity Interval Training on VO2max and Anaerobic Power in Cross-Country Skiers

The purpose of this study was to investigate whether eight weeks of aerobic high-intensity interval training with roller ski is effective in cross-country skiers. 10 male （age, 18.28 ± 2.1 years; height, 171.26 ± 4.12 cm; weight 61.39 ± 6.28 kg） and 8 female （age, 16.05 ± 0.3 years; height, 158.3 ± 6.47 cm; weight, 49.34 ± 0.7 kg） junior cross country skiers completed the study. All skiers performed a 2 × 2-kin all-out uphill intervals with roller-skis, 3 times a week, in addition to their traditional training program. Measurements included VO2max （maximal oxygen uptake）, anaerobic power, and also for 2-km roller ski. All values were listed as pre-to post-test mean （± SD）, significant level, and percentage changes （%）. Pre-to post-testing changes in VO2max, anaerobic power, and also 2-kin roller ski performance were significantly higher during all post-test trials in all groups （P 〈 0.005）. With reference to the training effects found in our study, we suggest that the skiers should integrate the roller ski aerobic high-intensity interval uphill models in their training programs for improving performance.

基于BERT-BiLSTM-CRF的电力事故信息抽取方法

为了探究电力事故规律,建立人身安全预警模型,在大规模事故样本中自动精准抽取信息并进行多维分析十分必要。传统中文信息实体特征抽取的精确度较低,因此,基于新型中文处理的命名实体识别技术,结合多种特定机器学习和深度学习模型,提出一种专用于电网事故领域的BERT-BiLSTM-CRF模型。通过基于转换器的双向编码表示预训练模型输出高质量词向量,利用语义增强掩码策略增强模型深入理解文本整体结构的能力。运用双向长短期记忆网络模型同时捕捉上下文信息,完成特征提取。根据条件随机场模型输出最优预测序列。试验结果表明,专用模型优势显著,其准确率、召回率和F1值均高于3种现有实体识别模型,包括预训练好的基于生成式预训练转换器技术的通用大模型。试验验证了所提方法在处理中文电力事故信息抽取问题时准确度高,具有显著优势。

Dynamic Characteristics of Tracked Vehicle Power Train Considering Road Random Torsional Excitation

The road random torsional excitation is one type of torque rooted from the road roughness and vehicle drive system. This paper aims to study how the road random torsional excitation affects the dynamic characteristics of vehicle power train. The method of simulating the random torsional excitation of tracked vehicle is explored at first. Secondly,the road random torsional excitations under different road roughness,vehicle speeds and pre-tensions are obtained. Thirdly,the dynamic analysis model of tracked vehicle power train is constructed with the consideration of the road random torsional excitation. Eventually,the influences of this excitation on output torque,bearing support force,vibration acceleration and dynamic shear stress of transmission shafts are intensively studied.The research conclusions are helpful to correct and refine the present virtual prototype of tracked vehicle power train.

Improved edge lightweight YOLOv4 and its application in on-site power system work

A“cloud-edge-end”collaborative system architecture is adopted for real-time security management of power system on-site work,and mobile edge computing equipment utilizes lightweight intelligent recognition algorithms for on-site risk assessment and alert.Owing to its lightweight and fast speed,YOLOv4-Tiny is often deployed on edge computing equipment for real-time video stream detection;however,its accuracy is relatively low.This study proposes an improved YOLOv4-Tiny algorithm based on attention mechanism and optimized training methods,achieving higher accuracy without compromising the speed.Specifically,a convolution block attention module branch is added to the backbone network to enhance the feature extraction capability and an efficient channel attention mechanism is added in the neck network to improve feature utilization.Moreover,three optimized training methods:transfer learning,mosaic data augmentation,and label smoothing are used to improve the training effect of this improved algorithm.Finally,an edge computing equipment experimental platform equipped with an NVIDIA Jetson Xavier NX chip is established and the newly developed algorithm is tested on it.According to the results,the speed of the improved YOLOv4-Tiny algorithm in detecting on-site dress code compliance datasets is 17.25 FPS,and the mean average precision(mAP)is increased from 70.89%to 85.03%.

基于BERT-BILSTM-CRF模型的电力行业事故文本智能分析

为解决电力行业事故报告文本较长、语义复杂,难以进行有效文本识别问题,提出1种以BERT作为底层的预训练模型,并设计1种双重注意力机制编码器,结合BILSTM-CRF深度挖掘事故文本语义特征,从而实现文本智能分析。首先构建电力词典,通过对BERT预训练,进行BIO标注,然后引入BILSTM-CRF模型实现对文本标签智能分类,最后将该模型与现行其他4种深度学习模型进行对比。研究结果表明:该模型智能识别精确率、召回率及F 1值(查准率)均达到约97%,较其他4种模型中效果最好的模型分别提高0.02,0.03,0.02。研究结果可为电力行业事故报告文本分析提供1种新思路。

Environmental life cycle assessment of Indian coal-fired power plants

Coal is the backbone of the Indian power sector. The coal-fired power plants remain the largest emitters of carbon dioxide, sulfur dioxide and substantial amounts of nitrogen oxides, which are associated with climate and health impacts. Various CO2 mitigation technologies （carbon capture and storage--CCS） and SO2/NOx mitigation technologies （flue gas desulfurization and selective catalytic reduction） have been employed to reduce the environmental impacts of the coal-fired power plants. Therefore, it is imperative to understand the feasibility of various mitigation technologies employed. This paper attempts to perform environmental life cycle assessment （LCA） of Indian coal-fired power plant with and without CO2, SO2 and NOx mitigation controls. The study develops new normalization factors for India in various damage categories, using the Indian emissions and energy consumption data, coupled with the emissions and particulate emission to come up with a final environmental impact of coal-fired electricity. The results show a large degree of dependence on the perspective of assessment used. The impact of sensitivities of individual substances and the effect of plant efficiency on the final LCA results is also studied.

Prediction of photovoltaic power output based on different non-linear autoregressive artificial neural network algorithms

Prediction of power output plays a vital role in the installation and operation of photovoltaic modules.In this paper,two photovoltaic module technologies,amorphous silicon and copper indium gallium selenide installed outdoors on the rooftop of the University of Dodoma,located at 6.5738°S and 36.2631°E in Tanzania,were used to record the power output during the winter season.The average data of ambient temperature,module temperature,solar irradiance,relative humidity,and wind speed recorded is used to predict the power output using a non-linear autoregressive artificial neural network.We consider the Levenberg-Marquardt optimization,Bayesian regularization,resilient propagation,and scaled conjugate gradient algorithms to understand their abilities in training,testing and validating the data.A comparison with reference to the performance indices:coefficient of determination,root mean square error,mean absolute percentage error,and mean absolute bias error is drawn for both modules.According to the findings of our investigation,the predicted results are in good agreement with the experimental results.All the algorithms performed better,and the predicted power out of both modules using the Bayesian regularization algorithm is observed to exhibit good processing capabilities compared to the other three algorithms that are evident from the measured performance indices.

缺血预处理提升运动表现--方法、应用及机制

背景:缺血预处理的保护效应在医学领域已经得到广泛证实和应用,但在运动领域的使用方法、干预部位、施加压力和时长尚无统一定论,对于不同运动类型的运动表现的作用效果在相关研究间也存在一定的差异。目的:阐述国内外关于缺血预处理提升运动表现的研究现状,为缺血预处理在运动领域的应用提供最佳方法和理论支持。方法:在中国知网、万方数据、维普、Web of Science、EBSCO、PubMed和Cochrane数据库进行文献检索,以“缺血预处理、预适应、预处理、血流限制”和“运动、运动表现”为中文检索词,以“Remote conditioning,Remote ischemic conditioning,transient limb ischemia,muscle ischemia,ischemic preconditioning”“exercise performance,sport,exercise,athletes”为英文检索词,最终纳入69篇文献进行综述分析。结果与结论:①缺血预处理的干预方式分为远程缺血预处理和局部缺血预处理,常见施加部位为上臂或大腿中下1/3交界处,双侧同时或交替进行皆可,压力为220 mmHg(1 mmHg=0.133 kPa),干预时长常采用4×5 min。②缺血预处理在运动科学领域的研究集中于自行车、游泳、跑步和抗阻训练等,该方法对提升有氧耐力、无氧耐力和力量耐力的运动表现效果显著,但其对爆发力的影响仍存在争议。③缺血预处理的主要作用是在应激条件下激发人体内源性保护机制,促进阿片、缓激肽及腺苷等物质的释放,增强线粒体生物合成,抑制疲劳信号转导,进而提升运动表现。④目前缺血预处理提升运动表现的机制研究还不够深入,建议今后进一步探讨其发挥积极效应可能机制的同时,也应关注其是否有负面效应,以期为该方法科学合理地应用提供依据。

Numerical investigation on the aerodynamic characteristics of high-speed train under turbulent crosswind

Increasing velocity combined with decreasing mass of modern highspeed trains poses a question about the influence of strong crosswinds on its aerodynamics. Strong crosswinds may affect the running stability of high speed trains via the amplified aerodynamic forces and moments. In this study, a simulation of turbulent crosswind flows over the leading and end cars of ICE2 highspeed train was performed at different yaw angles in static and moving ground case scenarios. Since the train aerodynamic problems are closely associated with the flows occurring around train, the flow around the train was considered as incompressible and was obtained by solving the incom pressible form of the unsteady Reynoldsaveraged Navier Stokes （RANS） equations combined with the realizable kepsilon turbulence model. Important aerodynamic coef ficients such as the side force and rolling moment coeffi cients were calculated for yaw angles ranging from 30° to 60° and compared with the results obtained from wind tunnel test. The dependence of the flow structure on yaw angle was also presented. The nature of the flow field and its structure depicted by contours of velocity magnitude and streamline patterns along the train＇s crosssection were presented for different yaw angles. In addition, the pressure coefficient around the circumference of the train at dif ferent locations along its length was computed for yaw angles of 30° and 60°, The computed aerodynamic coef ficient outcomes using the realizable kepsilon turbulencemodel were in good agreement with the wind tunnel data. Both the side force coefficient and rolling moment coeffi cients increase steadily with yaw angle till about 50° before starting to exhibit an asymptotic behavior. Contours of velocity magnitude were also computed at different cross sections of the train along its length for different yaw angles. The result showed that magnitude of rotating vortex in the lee ward side increased with increasing yaw angle, which leads to the creation of a lowpressure region in the lee ward side of the train causing high side force and roll moment. Generally, this study shows that unsteady CFD RANS methods combined with an appropriate turbulence model can present an important means of assessing the crucial aerodynamic forces and moments of a highspeed train under strong crosswind conditions.