A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.

Torque effect on vibration behavior of high-speed train gearbox under internal and external excitations

The high-speed train transmission system,experiencing both the internal excitation originating from gear meshing and the external excitation originating from the wheel-rail interaction,exhibits complex dynamic behavior in the actual service environment.This paper focuses on the gearbox in the high-speed train to carry out the bench test,in which various operat-ing conditions(torques and rotation speeds)were set up and the excitation condition covering both internal and external was created.Acceleration responses on multiple positions of the gearbox were acquired in the test and the vibration behavior of the gearbox was studied.Meanwhile,a stochastic excitation modal test was also carried out on the test bench under different torques,and the modal parameter of the gearbox was identified.Finally,the sweep frequency response of the gearbox under gear meshing excitation was analyzed through dynamic modeling.The results showed that the torque has an attenuating effect on the amplitude of gear meshing frequency on the gearbox,and the effect of external excitation on the gearbox vibration cannot be ignored,especially under the rated operating condition.It was also found that the torque affects the modal param-eter of the gearbox significantly.The torque has a great effect on both the gear meshing stiffness and the bearing stiffness in the transmission system,which is the inherent reason for the changed modal characteristics observed in the modal test and affects the vibration behavior of the gearbox consequently.

Steady-state voltage-control method considering large-scale wind-power transmission using half-wavelength transmission lines

Half-wavelength transmission can transmit large-scale renewable energy over very long distances.This paper proposes an improved steady-state voltage-control method for half-wavelength transmission systems considering largescale wind-power transmission.First,the unique voltage characteristics of half-wavelength lines are deduced based on the distributed parameter model.In the secondary voltage-control level,reactive power-transmission limits of half-wavelength lines are introduced as another control objective except for tracing the pilot bus voltage reference.Considering the uncertainty and fluctuation of wind power,the overvoltage risk-assessment method of half-wavelength lines is presented to determine specific voltage-control strategies.Simulation results demonstrate that the proposed voltage-control method delivers superior tracking performance according to a voltage reference value and prevents the overvoltage risk of halfwavelength lines effectively in different wind-power penetrations.

Measured load spectra of the bearing in high-speed train gearbox under different gear meshing conditions

The load spectrum is a crucial factor for assess-ing the fatigue reliability of in-service rolling element bear-ings in transmission systems.For a bearing in a high-speed train gearbox,a measurement technique based on strain detection of bearing outer ring was used to instrument the bearing and determine the time histories of the distributed load in the bearing under different gear meshing conditions.Accordingly,the load spectrum of the total radial load car-ried by the bearing was compiled.The mean value and class interval of the obtained load spectrum were found to vary non-monotonously with the speed and torque of gear mesh-ing,which was considered to be caused by the vibration of the shaft and the bearing cage.As the realistic service load input of bearing life assessment,the measured load spectrum under different gear meshing conditions can be used to pre-dict gearbox bearing life realistically based on the damage-equivalent principle and actual operating conditions.

An Improved Coupled Dynamic Modelling for Exploring Gearbox Vibrations Considering Local Defects

Gearbox is a key part in machinery,in which gear,shaft and bearing operate together to transmit motion and power.The wide usage and high failure rate of gearbox make it attract much attention on its health monitoring and fault diagnosis.Dynamic modelling can study the mechanism under different faults and provide theoretical foundation for fault detection.However,current commonly used gear dynamic model usually neglects the influence of bearing and shaft,resulting in incomplete understanding of gearbox fault diagnosis especially under the effect of local defects on gear and shaft.To address this problem,an improved gear-shaft-bearing-housing dynamic model is proposed to reveal the vibration mechanism and responses considering shaft whirling and gear local defects.Firstly,an eighteen degree-of-freedom gearbox dynamic model is proposed,taking into account the interaction among gear,bearing and shaft.Secondly,the dynamic model is iteratively solved.Then,vibration responses are expounded and analysed considering gear spalling and shaft crack.Numerical results show that the gear mesh frequency and its harmonics have higher amplitude through the spectrum.Vibration RMS and the shaft rotating frequency increase with the spalling size and shaft crack angle in general.An experiment is designed to verify the rationality of the proposed gearbox model.Lastly,comprehensive analysis under different spalling size and shaft crack angle are analysed.Results show that when spalling size and crack angle are larger,RMS and the amplitude of shaft rotating frequency will not increase linearly.The dynamic model can accurately simulate the vibration of gear transmission system,which is helpful for gearbox fault diagnosis.

Numerical Visualization of Grease Flow in a Gearbox

Lubricating greases are widely used in e.g.open gear drives and gearboxes with difficult sealing conditions.The efficiency and heat balance of grease-lubricated gearboxes depend strongly on the lubrication mechanisms channeling and circulating,for which the grease flow is causal.The computational fluid dynamics opens up the possibility to visualize and understand the grease flow in gearboxes in more detail.In this study,a single-stage gearbox lubricated with an NLGI 1-2 grease was modeled by the finite-volume method to numerically investigate the fluid flow.Results show that the rotating gears influence the grease sump only locally around the gears.For a low grease fill volume,the rotation of the gears is widely separated from the grease sump.For a high grease fill volume,a pronounced geargrease interaction results in a circulating grease flow around the gears.The simulated grease distributions show good accordance with high-speed camera recordings.

Intelligent Fault Diagnosis for Planetary Gearbox Using Transferable Deep Q Network Under Variable Conditions with Small Training Data

Effective fault diagnosis of planetary gearboxes is critical for ensuring the safety and dependability of mechanical drive systems.Nevertheless,variable conditions and inadequate fault data bring huge challenges to its practical fault diagnosis.Taking this into account,this study presents a new intelligent fault diagnosis(IFD)approach for planetary gearbox using a transferable deep Q network(TDQN)that merges deep reinforcement learning(DRL)and transfer learning(TL).First,a DRL environment simulation is designed by a predefined classification Markov decision process.Then,leveraging varied-size convolutions and residual learning,a multiscale residual convolutional neural network agent for TDQN is created to automatically learn meaningful features directly from vibration signals while avoiding model degradation.Next,a large source dataset is obtained from complex conditions,and this agent learns an IFD policy via autonomous interaction with the data environment.Finally,a parameter-based TL strategy is adopted to retrain the model on target datasets with variable conditions and small training data,which is conducted by fine-tuning the model parameters gained from the source task to accomplish target tasks.The results show that this TDQN outperforms not only state-of-the-art methods in a source task with an accuracy of 98.53%but also in two target tasks with 99.63%and 98.37%,respectively.

Multi-objective Optimization of a Two-Stage Helical Gearbox to Improve Efficiency and Reduce Height

The goal of this research is to look at multi-target optimization of a two-stage helical gearbox in order to determine the best key design elements for reducing gearbox height and enhancing gearbox efficiency.To do this,the method known as Taguchi and GRA(Grey Relation Analysis)were used in two stages to address the problem.The single-objective optimization problem was addressed first to close the gap between variable levels,and then the multi-objective optimization problem was solved to determine the best primary design variables.The first and second stage CWFWs(Coefficients of Wheel Face Width),ACS(Permissible Contact Stresses),and first stage gear ratio were also calculated.The study’s findings were utilized to identify the best values for five critical design aspects of a two-stage helical gearbox.

Characterization,identification and life prediction of acoustic emission signals of tensile damage for HSR gearbox housing material

Purpose-This study aims to ensure the operation safety of high speed trains,it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time,yet the traditional tests of mechanical property can hardly meet this requirement.Design/methodology/approach-In this study the acoustic emission(AE)technology is applied in the tensile tests of the gearbox housing material of an high-speed rail(HSR)train,during which the acoustic signatures are acquired for parameter analysis.Afterward,the support vector machine(SVM)classifier is introduced to identify and classify the characteristic parameters extracted,on which basis the SVM is improved and the weighted support vector machine(WSVM)method is applied to effectively reduce the misidentification of the SVM classifier.Through the study of the law of relations between the characteristic values and the tensile life,a degradation model of the gearbox housing material amid tensile is built.Findings-The results show that the growth rate of the logarithmic hit count of AE signals and that of logarithmic amplitude can well characterize the stage of the material tensile process,and the WSVM method can improve the classification accuracy of the imbalanced data to above 94%.The degradation model built can identify the damage occurred to the HSR gearbox housing material amid the tensile process and predict the service life remains.Originality/value-The results of this study provide new concepts for the life prediction of tensile samples,and more further tests should be conducted to verify the conclusion of this research.

面向复杂工况的液力变矩器与变速箱效能优化协同控制策略

针对工程机械在复杂工况下能源利用率不高的问题,提出了一种液力变矩器与变速箱效能优化协同控制策略。首先,在装载机功率与力学平衡的基础上,构造了基于牵引性能理论的装载机动力传动系统仿真平台,并通过装载机循环工况采集到的实验数据验证了其有效性和可靠性;其次,基于发动机与液力变矩器台架实验,研究了ZL50型装载机的牵引特性,对比了其在起步、举升、铲掘、匀速4种不同牵引阶段的动力性能,分析了液力变矩器的闭锁点对装载机动力性、平顺性和经济性的影响;最后,提出了面向复杂工况的液力变矩器与变速箱效能优化协同控制策略,并对优化前后的装载机效能进行了对比分析。研究结果表明:在相同的V型循环工况下,采用所提出的控制策略,装载机等油耗输出能量从372.69 kW·h·mL^(-1)提高到420.51 kW·h·mL^(-1),整机效能提高了约12.8%。所提策略可为大型工程机械传动效率低、燃料消耗大、污染排放严重等问题提供解决方案。

Wind Turbine Planetary Gearbox Fault Diagnosis via Proportion-Extracting Synchrosqueezing Chirplet Transform

Wind turbine planetary gearboxes usually work under time-varying conditions,leading to nonstationary vibration signals.These signals often consist of multiple time-varying components with close instantaneous frequencies.Therefore,high-quality time-frequency analysis(TFA)is needed to extract the time-frequency feature from such nonstationary signals for fault diagnosis.However,it is difficult to obtain high-quality timefrequency representations(TFRs)through conventional TFA methods due to low resolution and time-frequency blurs.To address this issue,we propose a new TFA method termed the proportion-extracting synchrosqueezing chirplet transform(PESCT).Firstly,the proportion-extracting chirplet transform is employed to generate highresolution underlying TFRs.Then,the energy concentration of the underlying TFRs is enhanced via the synchrosqueezing transform.Finally,wind turbine planetary gearbox fault can be diagnosed by analysis of the dominant time-varying components revealed by the concentrated TFRs with high resolution.The proposed PESCT is suitable for achieving high-quality TFRs for complicated nonstationary signals.Numerical and experimental analyses validate the effectiveness of the PESCT in characterizing the nonstationary signals from wind turbine planetary gearboxes.

基于遗传算法的风电齿轮传动系统参数优化设计

齿轮箱是风力发电系统的重要部件,其体积大、重量高等问题制约了风电清洁能源的发展.以1.5 WM风力发电机组为研究对象,建立齿轮传动系统轻量化数学模型,并采用遗传算法进行优化求解.结果表明,采用遗传算法是可行的,优化后齿轮传动系统体积减少了4.59%,这将进一步减小齿轮传动系统箱体体积和总质量.通过计算行星轮系的传动效率,验证了优化结果的可行性,该研究将为风电齿轮传动系统轻量化设计提供新方法.

应用滑动轴承的风电齿轮箱行星轮系动力学建模及解耦方法

在行星轮系动力学建模中,常以非线性油膜力或线性刚度-阻尼形式考虑其对系统动力学特性的影响,前者仿真精度高但计算成本也高,后者计算效率高却忽略了油膜力和轴颈-轴套偏心量的时变性,仿真精度有限。为此,以2MW级风电齿轮箱为研究对象,建立滑动轴承时变线性刚度-阻尼模型,提出计入轴颈-轴套时变偏心量的滑动轴承附加偏心修正力计算方法;利用行星架销轴-行星轮变形协调关系,将时变线性刚度-阻尼模型与附加偏心修正力进行耦合;建立应用滑动轴承的风电齿轮箱行星轮系动力学模型,对比了工况和轴承参数对模型计算精度与系统动态响应的影响,并通过试验加以验证。研究结果表明,齿轮副动态啮合力波动会使滑动轴承刚度-阻尼系数和附加偏心修正力产生周期性变化;在稳定和瞬态工况下,提出的模型可以很好地预测系统响应,尤其是行星轮振动响应;减小滑动轴承宽径比与间隙、增大输入转矩可以改善系统均载性能。

风电机组用滑动轴承研究现状与发展趋势

首先,分析滚动轴承在风电传动系统中应用的局限性,探讨滑动轴承“以滑代滚”的可行性。然后,从风电机组用滑动轴承结构设计、轴承材料、性能分析及优化、试验测试及应用4个关键环节,综述滑动轴承风电应用的现有技术手段和面临的困难。最后,对风电机组滑动轴承的材料改性、一体化设计、延寿技术发展趋势做出展望。

高铁齿轮箱飞溅润滑性能数值模拟与型腔结构参数优化

以高铁齿轮箱齿轮啮合处平均压力和输出端集油盒处润滑油平均体积分数为评价指标,通过流体仿真软件ANSYS CFX,采用浸入实体法,建立了高铁齿轮箱斜齿轮飞溅润滑的CFD数值仿真模型。运用响应面Box-Behnken实验研究得出了径向距离、轴向距离、导油筋宽度对集油盒处润滑油平均体积分数和齿轮啮合处平均压力的二次回归模型,并对设计参数进行了优化。结果显示,最优参数为径向距离22.92 mm、轴向距离50 mm、导油筋宽度14.02 mm时,集油盒处润滑油平均体积分数为0.547,齿轮啮合处平均压力为32.452 kPa。

斜齿轮啮合弯矩对风电齿轮箱行星轮滑动轴承瞬态润滑性能影响分析

风电齿轮箱行星轮滑动轴承常被设计为以行星轮内孔和销轴分别作为轴套和轴颈,然而斜齿轮啮合弯矩容易使行星轮与销轴之间产生轴线不对中,导致边缘接触风险高,影响运行寿命。以6 MW级传动链齿轮箱行星轮滑动轴承为研究对象,考虑滑动轴承径向载荷、弯矩以及转速的动态影响,建立了行星轮滑动轴承瞬态摩擦-动力学耦合模型,并以风电机组传动链SIMPACK动力学模型提取的行星轮动态啮合力与时变转速作为行星轮滑动轴承的载荷与运动边界输入,分析了斜齿轮啮合弯矩、输入扭矩和滑动轴承半径间隙对行星轮滑动轴承润滑性能的影响规律,并进行了实验验证。研究结果表明,行星轮动态啮合力及其产生的啮合弯矩会造成行星轮轴心位置与偏斜角产生动态循环变化,并且随着载荷的增加,行星轮滑动轴承油膜/固体接触压力与不对中弯矩会逐渐增大;减小行星轮滑动轴承半径间隙可以有效提高其瞬态润滑性能。

新型轿车电动玻璃升降器箱与盖注塑模设计

新型轿车电动玻璃升降器的塑料箱与盖,是升降器传动机构的组装与支撑件。箱与盖的孔与孔位精度,决定着传动机构的性能和精度。在提供了样品的情况下,接收方提出供给方所生产的升降器要与进口产品能通用的要求。箱与盖注塑模设计的过程中,根据样品上模具结构成型痕迹和注塑件的形体分析,确定了注塑模的结构。实践证明,所加工的箱和盖与样品达到了一致,实现了与进口件混装的要求。

基于IPSO-MCKD的汽车变速箱轴承故障诊断

针对车辆在城区运行过程中频繁启停造成变速箱滚动轴承故障易发的问题,在轴承故障诊断中引入最大相关峭度反卷积(Maximum Correlation Kurtosis Deconvolution,MCKD)的方法,为了避免过于依赖人工选择MCKD算法中滤波器系数和移位数,提出了一种参数自适应的最大相关峭度反卷积的故障诊断方法。该方法以输入信号的包络谱中最大相关峭度为目标函数,采用改进后的粒子群优化(Improved Particle Swarm Optimization,IPSO)算法优化MCKD中的滤波器系数和位移数,最后通过对故障信号的包络谱进行分析,提取轴承的故障特征。仿真和试验的结果表明,该方法可以有效降低环境中的噪声干扰,准确从强噪声中提取故障特征,实现故障诊断。

基于雪消融算法优化长短时网络的齿轮箱油温预警方法

风电机组数据的复杂多变导致所采集数据的数据特征之间深度冗余。数据特征深度冗余导致常规预测模型容易陷入局部最优,进而使模型预测精度较低。为提高齿轮箱油温预测精度,首先,采用随机森林进行特征提取,利用改进的自适应噪声集合经验模态分解对选定的特征数据进行分解,使用主成分分析法对数据进行降维处理;然后,运用雪消融优化算法来搜索长短期记忆递归神经网络模型最佳超参数设置。实验结果证明,该方法可以有效提高齿轮箱油温异常预警模型的精度。

改进MFO-LSTM网络的风电机组齿轮箱故障预警研究

风电机组齿轮箱在数据采集与监控系统(SCADA)的帮助下,通过监控齿轮箱油温是否超过阈值实现故障报警,其判断精度不高且问题发现不及时,因此使用长短期记忆网络模型(LSTM)融合SCADA数据实现对齿轮箱油温状态的预测。用齿轮箱正常运行状态下的数据训练LSTM模型,计算油温预测值与真实值之间的残差,根据正态分布的原则设置残差的上下预警阈值,用来对齿轮箱故障进行预警。为简化训练模型的复杂度,在SCADA数据中选用与齿轮箱油温相关性较为密切的参数作为LSTM模型的输入项。为降低因LSTM模型超参数设置不当造成的预测准确度表现不佳,提出改进飞蛾火焰算法(MFO)与LSTM的组合模型,在保留MFO算法强大的全局搜索能力的同时,使其避免陷入局部搜索的陷阱,通过改进MFO对LSTM模型参数进行迭代优化,最终构建合适的模型。最后通过某风电机组SCADA数据验证该方法能够有效预警齿轮箱的故障,并且与其他方法相比准确度更高,预警更及时,迭代效果更好。