The IES dynamic time-scale scheduling strategy based on multiple load forecasting errors

Considering the complex coupling of multiple energies and the varying load forecasting errors for an integrated energy system(IES),this study proposes a dynamic time-scale scheduling strategy based on long short-term memory(LSTM)and multiple load forecasting errors.This strategy dynamically selects a hybrid timescale which is suitable for a variety of energies for each month.This is obtained by combining the mean absolute percentage error(MAPE)curve of the load forecasting with the error restriction requirements of the dispatcher.Based on the day-ahead scheduling plan,the output of the partial equipment is selectively adjusted at each time-scale to realize multi-energy collaborative optimization and gives full play to the comprehensive advantages of the IES.This is achieved by considering the differences in the response speed for each piece of equipment within the intra-day scheduling.This study uses the IES as an example,and it dynamically determines the time scale of the energy monthly.In addition,this investigation presents a detailed analysis of the output plan of the key equipment to demonstrate the necessity and the advantages of the strategy.

Assessment of the methods for determining net radiation at different time-scales of meteorological variables

When modeling the soil/atmosphere interaction,it is of paramount importance to determine the net radiation flux.There are two common calculation methods for this purpose.Method 1 relies on use of air temperature,while Method 2 relies on use of both air and soil temperatures.Nowadays,there has been no consensus on the application of these two methods.In this study,the half-hourly data of solar radiation recorded at an experimental embankment are used to calculate the net radiation and long-wave radiation at different time-scales(half-hourly,hourly,and daily) using the two methods.The results show that,compared with Method 2 which has been widely adopted in agronomical,geotechnical and geo-environmental applications.Method 1 is more feasible for its simplicity and accuracy at shorter time-scale.Moreover,in case of longer time-scale,daily for instance,less variations of net radiation and long-wave radiation are obtained,suggesting that no detailed soil temperature variations can be obtained.In other words,shorter time-scales are preferred in determining net radiation flux.

A structure-preserving algorithm for time-scale non-shiftedHamiltonian systems

The variational calculus of time-scale non-shifted systems includes both the traditional continuous and traditional significant discrete variational calculus.Not only can the combination ofand∇derivatives be beneficial to obtaining higher convergence order in numerical analysis,but also it prompts the timescale numerical computational scheme to have good properties,for instance,structure-preserving.In this letter,a structure-preserving algorithm for time-scale non-shifted Hamiltonian systems is proposed.By using the time-scale discrete variational method and calculus theory,and taking a discrete time scale in the variational principle of non-shifted Hamiltonian systems,the corresponding discrete Hamiltonian principle can be obtained.Furthermore,the time-scale discrete Hamilton difference equations,Noether theorem,and the symplectic scheme of discrete Hamiltonian systems are obtained.Finally,taking the Kepler problem and damped oscillator for time-scale non-shifted Hamiltonian systems as examples,they show that the time-scale discrete variational method is a structure-preserving algorithm.The new algorithm not only provides a numerical method for solving time-scale non-shifted dynamic equations but can be calculated with variable step sizes to improve the computational speed.

ε-Dependent Controllability for Two Time-Scale Systems

This paper investigates the controllability of two time-scale systems using both the time-scale separation model and the slow-fast order reduction model. This work considers the effect of a singular perturbation parameter on the model transformations to improve the criterion precision. The Maclaurin expansion method and homotopy arithmetic are introduced to obtain t-dependent controllability criteria. Examples indicate that the s-dependent controllability criteria are more accurate and that the controllability of two time-scale systems does not change during model transformations with these more accurate forms.

基于Intrinsic Time-scale Decomposition算法的扰动信号特征量提取问题研究

由于电能质量扰动信号具有非线性的特点,当下算法无法有效提取电能质量扰动信号,采取Intrinsic Timescale Decomposition算法,可以有效提取电能质量扰动信号的频率、幅值、相位等特征量。利用Intrinsic Time-scale Decomposition算法提取电能质量扰动信号的Proper Rotation Component分量,后对Proper Rotation Component分量进行Hilbert变换以求得电能质量扰动信号的相位和瞬时频率,再根据电能质量扰动信号的高频突变点得到电能质量扰动信号的起止时间;根据envelope函数求得衰减因子。利用软件开发程序MATHEMATICA对电能质量扰动信号进行分析,结果表明,Intrinsic Time-scale Decomposition算法可以很好地提取电能质量扰动信号特征量,并且具有极佳的抗噪性能。

EEG Feature Learning Model Based on Intrinsic Time-Scale Decomposition and Adaptive Huber Loss

According to the World Health Organization,about 50 million people worldwide suffer from epilepsy.The detection and treatment of epilepsy face great challenges.Electroencephalogram(EEG)is a significant research object widely used in diagnosis and treatment of epilepsy.In this paper,an adaptive feature learning model for EEG signals is proposed,which combines Huber loss function with adaptive weight penalty term.Firstly,each EEG signal is decomposed by intrinsic time-scale decomposition.Secondly,the statistical index values are calculated from the instantaneous amplitude and frequency of every component and fed into the proposed model.Finally,the discriminative features learned by the proposed model are used to detect seizures.Our main innovation is to consider a highly flexible penalization based on Huber loss function,which can set different weights according to the influence of different features on epilepsy detection.Besides,the new model can be solved by proximal alternating direction multiplier method,which can effectively ensure the convergence of the algorithm.The performance of the proposed method is evaluated on three public EEG datasets provided by the Bonn University,Childrens Hospital Boston-Massachusetts Institute of Technology,and Neurological and Sleep Center at Hauz Khas,New Delhi(New Delhi Epilepsy data).The recognition accuracy on these two datasets is 98%and 99.05%,respectively,indicating the application value of the new model.

AN IMPROVED ALGORITHM OF GMM VOICE CONVERSION SYSTEM BASED ON CHANGING THE TIME-SCALE

This paper improves and presents an advanced method of the voice conversion system based on Gaussian Mixture Models(GMM) models by changing the time-scale of speech.The Speech Transformation and Representation using Adaptive Interpolation of weiGHTed spectrum(STRAIGHT) model is adopted to extract the spectrum features,and the GMM models are trained to generate the conversion function.The spectrum features of a source speech will be converted by the conversion function.The time-scale of speech is changed by extracting the converted features and adding to the spectrum.The conversion voice was evaluated by subjective and objective measurements.The results confirm that the transformed speech not only approximates the characteristics of the target speaker,but also more natural and more intelligible.

电力市场多时间尺度决策的抽蓄电站容量优化配置

针对抽蓄电站市场定位不明确和投资回报难以保障的问题,提出一种电力市场多时间尺度决策的抽蓄电站容量优化配置方法。上层以抽蓄电站投资回报率最大为目标建立容量规划模型;下层模拟抽蓄电站参与市场,在第1阶段中长期市场中,风力发电、光伏发电计及风险约束租赁一定容量的抽蓄,组成风−蓄、光−蓄联盟,与火电共同向电网报量报价,建立以电网购电成本最小为目标的统一出清模型;第2阶段现货市场中,考虑中长期市场出清约束,抽蓄电站利用租赁后剩余容量参与电能量市场和辅助服务市场,建立以电网购电成本和辅助服务费用之和最小为目标的出清模型。将中长期市场和现货市场的出清结果反馈给上层,进一步优化抽蓄电站容量配置。算例分析表明所提模型能够有效促进风电和光伏的消纳,充分利用抽蓄电站的灵活性资源,提升其盈利能力。

一种二维时频多尺度熵的滚动轴承故障诊断方法

多尺度熵是一种有效表征一维振动信号复杂性和不规则程度的非线性动力学方法,但其只考虑了信号的时域复杂性,而忽略了频域信息。为了综合利用振动信号时频域信息和量度时频分布的复杂性特征,将二维多尺度熵引入到滚动轴承的故障诊断中,提出了一种基于二维时频多尺度熵和萤火虫算法优化支持向量机的滚动轴承故障诊断方法。首先,采用连续小波变换将一维时间序列转换为时频图像;其次,计算时频图像的二维多尺度熵值;再次,将得到的二维多尺度熵值输入到萤火虫优化的支持向量机中进行分类和预测;最后,通过滚动轴承试验数据分析验证所提方法的有效性。结果表明:论文所提方法能够准确地识别滚动轴承的故障类型和故障程度。

Real-time Subsynchronous Control Interaction Monitoring Using Improved Intrinsic Time-scale Decomposition

In recent years,subsynchronous control interaction(SSCI)has frequently taken place in renewable-connected power systems.To counter this issue,utilities have been seeking tools for fast and accurate identification of SSCI events.The main challenges of SSCI monitoring are the time-varying nature and uncertain modes of SSCI events.Accordingly,this paper presents a simple but effective method that takes advantage of intrinsic time-scale decomposition(ITD).The main purpose is to improve the accuracy and robustness of ITD by incorporating the least-squares method.Results show that the proposed method strikes a good balance between dynamic performance and estimation accuracy.More importantly,the method does not require any prior information,and its performance is therefore not affected by the frequency constitution of the SSCI.Comprehensive comparative studies are conducted to demonstrate the usefulness of the method through synthetic signals,electromagnetic temporary program(EMTP)simulations,and field-recorded SSCI data.Finally,real-time simulation tests are conducted to show the feasibility of the method for real-time monitoring.

Robust missile autopilot design based on dynamic surface control

Since the dynamical system and control system of the missile are typically nonlinear, an effective acceleration tracking autopilot is designed using the dynamic surface control(DSC)technique in order to make the missile control system more robust despite the uncertainty of the dynamical parameters and the presence of disturbances. Firstly, the nonlinear mathematical model of the tail-controlled missile is decomposed into slow acceleration dynamics and fast pitch rate dynamics based on the naturally existing time scale separation. Secondly, the controller based on DSC is designed after obtaining the linear dynamics characteristics of the slow and fast subsystems. An extended state observer is used to detect the uncertainty of the system state variables and aerodynamic parameters to achieve the compensation of the control law. The closed-loop stability of the controller is derived and rigorously analyzed. Finally, the effectiveness and robustness of the design is verified by Monte Carlo simulation considering different initial conditions and parameter uptake. Simulation results illustrate that the missile autopilot based DSC controller achieves better performance and robustness than the other two well-known autopilots.The method proposed in this paper is applied to the design of a missile autopilot, and the results show that the acceleration tracking autopilot based on the DSC controller can ensure accurate tracking of the required commands and has better performance.

一种3自由度并联柔索驱动柔性操作臂的建模与控制

以一种3自由度并联柔索驱动机器人为研究对象,研究这种并联柔性系统的控制规律。考虑到运动描述的唯一性及易测性,取约束关节的位置作为广义关节变量,用以描述操作臂的运动状态。在此基础上分析了操作臂的运动学和静力学关系,建立了并联柔性系统的动力学模型。然后,以轨迹控制为目标,分别基于刚性模型和柔性模型设计控制器,并对系统的性能进行分析和仿真。结果表明:基于刚性模型控制器的性能较差,而基于柔性模型的奇异摄动方法则可以获得较好的控制效果。

基于无源性理论的开关磁阻直线电机位置控制

基于能量耗散理论和开关磁阻电机原理,提出了一种开关磁阻直线电机(LSRM)的非线性控制器。因电气时间常数远小于机械时间常数,LSRM可视为两时间尺度系统,并可分解为反馈连接的电气和机械两个无源子系统。分别对两个子系统进行无源性控制器设计,因反馈连接的无源子系统仍然保证无源性,从而保证整个系统的全局稳定性,试验结果表明该方法具有优良的性能和鲁棒性,并易于实现。

一种利用动量轮的弹头姿态控制系统概念研究

对一种以动量轮为执行机构的具有中性静稳定外形的再入弹头姿态控制系统进行了概念研究。从动量矩定理和中性静稳定原理阐述了这种控制方案从理论上是可行的。推导了动量轮控制弹头的姿态动力学方程,并基于分离时间尺度方法设计了姿态控制系统。对该姿态控制系统仿真研究表明:1)姿态角跟踪回路性能良好;2)在不需要太大的动量轮控制输入力矩的情况下,能实现滚动通道稳定控制;3)俯仰和偏航通道的正弦跟踪需要相对较大动量矩的动量轮作为执行机构来实现高精度跟踪控制。

垂直激励圆柱形容器中的表面波结构

本文利用奇异摄动理论的两时间变量展开法 ,研究了垂直强迫圆柱形容器中的单一水表面驻波模式。假设流体是无粘、不可压且运动是无旋的 ,在忽略了表面张力的影响下 ,得到一个具有立方项以及底部驱动项影响的非线性振幅方程。对上述方程进行了数值计算 ,研究了特定 ( 9,6)模式的表面驻波结构和特性 ,如驻波的节点分布及驻波随某些参数的变化规律等 ,从计算的等高线的图象来看 。

含外激励van der Pol-Mathieu方程的非线性动力学特性分析

本文针对含有自激励,参数激励和外激励等三种激励联合作用下van der Pol-Mathieu方程的周期响应和准周期运动进行分析,发现其准周期运动的频谱中含有均匀边频带这一新的特性.首先,采用传统的增量谐波平衡法(IHB法)分析了van der Pol-Mathieu方程的周期响应,得到了其非线性频率响应曲线;再利用Floquet理论对周期解进行稳定性分析,得到了两种类型的分岔及它们的位置.然后,基于van der Pol-Mathieu方程准周期运动的频谱中边频带相邻频率之间是等距的且含有两个不可约的基频的特性(其中一个基频是已知的,另一个基频事先是未知的),推导了相应的两时间尺度IHB法,精确计算出van der Pol-Mathieu方程的准周期运动的另一个未知基频和所有的频率成份及其对应的幅值,尤其在临界点附近处的准周期运动响应.得到的准周期运动结果和利用四阶龙格-库塔(RK)数值法得到的结果高度吻合.最后,研究发现了含外激励van der Pol-Mathieu方程在不同激励频率时的一些丰富而有趣的非线性动力学现象.

基于奇异摄动的捷联惯导伪圆锥误差补偿算法

提出了一种新的高精度捷联惯导系统（SINS）姿态更新算法——基于奇异摄动理论的算法。该方法是对伪圆锥误差较大的传统Savage姿态更新算法改进，它运用奇异摄动理论抑制载体真实的角运动频率与宽频带噪声的频率混叠在一起时出现的圆锥误差放大现象——伪圆锥误差，同时将陀螺输出分为快变部分和慢变部分，利用陀螺敏感的载体角运动与陀螺非理想输出信号的两时间尺度特性补偿伪圆锥误差。该算法通过可调参数以控制任意频段的伪圆锥误差的影响，实现算法精度的提高。仿真结果表明，此新方法与目前高精度的Savage四子样算法相比不仅能够提高姿态更新的精度和速率，而且具有良好的灵活性和较小的计算量。

湍流-化学双时间尺度PDF湍流燃烧模型及其检验

高精度航空发动机燃烧室数值模拟需要准确描述湍流和详细化学反应机理之间的强烈非线性相互作用,采用概率密度函数湍流燃烧模型(PDF)的大涡模拟方法(LES)可望达到此目的,现有PDF小尺度混合模型需要考虑化学反应的影响.本文在前人研究基础上,对小尺度混合模型中的混合时间尺度进行了修正,提出3种含湍流-化学反应双时间尺度的PDF模型,在基于LES-PDF方法的Aero Engine Combustor Simulation Code(AECSC)程序基础上,对Sandia实验室的甲烷射流火焰Flame D和Flame E进行了数值模拟,其瞬态结果显示,几种模型都能预测射流火焰的瞬时流动状态.将计算得到的标量平均值统计结果与实验数据进行对比,结果表明,采用算术平均修正的PDF模型计算结果与实验数据最接近.湍流-化学反应双时间尺度PDF模型需要进一步检验.

非线性插值精细积分法在刚柔耦合弹簧摆中的应用

刚柔耦合多体系统变量的特点为既有大范围慢变量,又有小幅度快变量,它们相互耦合,构成时变强非线性的高维动力学方程。由于这一特点往往给系统的数值模拟带来困境,需要对这一特点进行更深入的数值分析。以双时间尺度变量弹簧摆作为研究模型,采用一种三次Lagrange插值精细积分法进行数值计算,该方法是一个显式单步预测一校正的有效算法,能够自起步,且具有精度高、计算量小的特点。将该精细积分法与四阶Runge-Kutta法从能量守恒及计算结果准确度两方面进行比较,结果表明在计算系统快变量的响应时,精细积分法优于四阶Runge-Kutta法。对弹簧摆系统进行动力学行为分析,以大频率比及初始大摆角作为控制参数,研究系统的复杂动力学行为,给出了一定范围内不同动力学性态对应的参数域。