A localized parametric time-sheared Gabor atom is derived by convolving a linear frequency modulated factor, modulating in frequency and translating in time to a dilated Gaussian function, which is the generalization ...A localized parametric time-sheared Gabor atom is derived by convolving a linear frequency modulated factor, modulating in frequency and translating in time to a dilated Gaussian function, which is the generalization of Gabor atom and is more delicate for matching most of the signals encountered in practice, especially for those having frequency dispersion characteristics. The time-frequency distribution of this atom concentrates in its time center and frequency center along energy curve, with the curve being oblique to a certain extent along the time axis. A novel parametric adaptive time-frequency distribution based on a set of the derived atoms is then proposed using a adaptive signal subspace decomposition method in frequency domain, which is non-negative time-frequency energy distribution and free of cross-term interference for multicomponent signals. The results of numerical simulation manifest the effectiveness of the approach in time-frequency representation and signal de-noising processing.展开更多
Recent advances in electronics have increased the complexity of radar signal modulation.The quasi-linear frequency modulation(quasi-LFM)radar waveforms(LFM,Frank code,P1−P4 code)have similar time-frequency distributio...Recent advances in electronics have increased the complexity of radar signal modulation.The quasi-linear frequency modulation(quasi-LFM)radar waveforms(LFM,Frank code,P1−P4 code)have similar time-frequency distributions,and it is difficult to identify such signals using traditional time-frequency analysis methods.To solve this problem,this paper proposes an algorithm for automatic recognition of quasi-LFM radar waveforms based on fractional Fourier transform and time-frequency analysis.First of all,fractional Fourier transform and the Wigner-Ville distribution(WVD)are used to determine the number of main ridgelines and the tilt angle of the target component in WVD.Next,the standard deviation of the target component's width in the signal's WVD is calculated.Finally,an assembled classifier using neural network is built to recognize different waveforms by automatically combining the three features.Simulation results show that the overall recognition rate of the proposed algorithm reaches 94.17%under 0 dB.When the training data set and the test data set are mixed with noise,the recognition rate reaches 89.93%.The best recognition accuracy is achieved when the size of the training set is taken as 400.The algorithm complexity can meet the requirements of real-time recognition.展开更多
Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributio...Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.展开更多
为探究电动汽车充电负荷对配电网的影响,提出了一种考虑荷电状态(state of charge,SOC)、实时电价和充电约束的出行链模型来实现电动汽车充电负荷时空分布预测。建立了道路交通网和配电网的耦合模型用以模拟城市路网中电动汽车的出行特...为探究电动汽车充电负荷对配电网的影响,提出了一种考虑荷电状态(state of charge,SOC)、实时电价和充电约束的出行链模型来实现电动汽车充电负荷时空分布预测。建立了道路交通网和配电网的耦合模型用以模拟城市路网中电动汽车的出行特性以及城市供电特点;针对电动汽车的类型和电动汽车用户出行特性对单体电动汽车进行建模,考虑SOC动态变化电动汽车出行链改进后,运用蒙特卡罗法对电动汽车充电负荷进行预测;将基于城市路网预测的电动汽车充电负荷结果归算至配网节点,通过计算时间序列潮流来评估电动汽车的聚合接入对配电网的影响。以某城市实际交通路网进行仿真,仿真结果表明,所提方法能够精确预测城市电动汽车充电负荷的时空分布,且通过时空分布特性分析电动汽车充电负荷对配电网的影响,并将不同类型负荷与电网节点相结合,从而提高了配电网电压节点的利用效率。展开更多
直流潮流控制器是解决环网式直流配电网的线路潮流不完全可控的有效技术手段。然而,现有方法未能充分发掘其在故障限流中的潜力。该文建立了三有源桥串并联潮流控制器(triple active bridge power flow controller,TAB-PFC)的故障模量...直流潮流控制器是解决环网式直流配电网的线路潮流不完全可控的有效技术手段。然而,现有方法未能充分发掘其在故障限流中的潜力。该文建立了三有源桥串并联潮流控制器(triple active bridge power flow controller,TAB-PFC)的故障模量分析模型,提出一种基于TAB-PFC的双极直流配电网主动限流策略。首先阐述了TAB-PFC的限流原理,提出基于TAB-PFC的主动限流控制策略。然后对TAB-PFC不同故障阶段进行建模,并计及极间互感构建含TAB-PFC的双极直流配电网故障模量等效模型。在此基础上,分析不同参数对TAB-PFC的限流能力的影响,为其参数选取提供依据。在MATLAB/Simulink搭建了含TAB-PFC的双极直流配电网模型,验证了所提主动限流策略的有效性及故障等效电路模型和参数分析的正确性。展开更多
在碳中和的背景下,作为一种无污染的可再生能源,氢能在能源转型中占据着越来越重要的地位。传统的氢电耦合直流微电网设计方案中,制氢设备并联嵌入直流微网,并作为一种灵活性负载参与系统调控以平替部分储能的功能。但是碱液电解槽(alka...在碳中和的背景下,作为一种无污染的可再生能源,氢能在能源转型中占据着越来越重要的地位。传统的氢电耦合直流微电网设计方案中,制氢设备并联嵌入直流微网,并作为一种灵活性负载参与系统调控以平替部分储能的功能。但是碱液电解槽(alkaline water electrolyzer,AWE)具有低压大电流的特点。随着直流微电网电压等级的提升,传统的并联结构一方面增加了电力电子装置的电压转换比的需求,另一方面忽略了碱液电解槽的电热特性。针对以上问题,该文提出了一种基于虚拟热敏电阻的串联型氢电耦合直流微电网稳定控制策略。首先,针对碱液电解槽建立了一套等效电热模型以表征最大电流与温度的关系。在此基础上,提出了一种电堆串联结构的碱液电解制氢模块(series-connectedstacks alkaline water electrolysis module,SAWEM)及其控制策略。串联结构能降低单个电堆输入电压,而虚拟热敏电阻控制策略能实现各电堆间精确合理的功率分配,且对直流微网有功率支撑作用。最后,通过简易的光伏制氢硬件实验平台进行了验证,结果表明该控制方法具有良好的实用性和有效性。展开更多
为平抑微源半桥变流器串联星型结构微电网HCSY-MG(half-bridge converter series Y-connection micro-grids)并网系统中微源出力的波动,保证各相直流侧电压之和相等,与并网电流三相平衡,提出1种基于改进近端策略优化PPO(proximal policy...为平抑微源半桥变流器串联星型结构微电网HCSY-MG(half-bridge converter series Y-connection micro-grids)并网系统中微源出力的波动,保证各相直流侧电压之和相等,与并网电流三相平衡,提出1种基于改进近端策略优化PPO(proximal policy optimization)的分布式混合储能系统HESS(hybrid energy storage system)充、放电优化控制策略。在考虑HCSY-MG系统并网电流与分布式HESS特性的条件下,确定影响并网电流的主要系统变量,以及HESS接入系统的最佳拓扑结构。然后结合串联系统的特点,将分布式HESS的充、放电问题转换为深度强化学习的Markov决策过程。同时针对PPO算法中熵损失权重难以确定的问题,提出1种改进的PPO算法,兼顾智能体的收敛性和探索性。最后以某新能源发电基地的典型运行数据为算例,验证所提控制策略的可行性和有效性。展开更多
基金This project was supported by the National Natural Science Foundation of China (60472102)Shanghai Leading Academic Discipline Project (T0103).
文摘A localized parametric time-sheared Gabor atom is derived by convolving a linear frequency modulated factor, modulating in frequency and translating in time to a dilated Gaussian function, which is the generalization of Gabor atom and is more delicate for matching most of the signals encountered in practice, especially for those having frequency dispersion characteristics. The time-frequency distribution of this atom concentrates in its time center and frequency center along energy curve, with the curve being oblique to a certain extent along the time axis. A novel parametric adaptive time-frequency distribution based on a set of the derived atoms is then proposed using a adaptive signal subspace decomposition method in frequency domain, which is non-negative time-frequency energy distribution and free of cross-term interference for multicomponent signals. The results of numerical simulation manifest the effectiveness of the approach in time-frequency representation and signal de-noising processing.
基金This work was supported by the National Natural Science Foundation of China(91538201)the Taishan Scholar Project of Shandong Province(ts201511020)the project supported by Chinese National Key Laboratory of Science and Technology on Information System Security(6142111190404).
文摘Recent advances in electronics have increased the complexity of radar signal modulation.The quasi-linear frequency modulation(quasi-LFM)radar waveforms(LFM,Frank code,P1−P4 code)have similar time-frequency distributions,and it is difficult to identify such signals using traditional time-frequency analysis methods.To solve this problem,this paper proposes an algorithm for automatic recognition of quasi-LFM radar waveforms based on fractional Fourier transform and time-frequency analysis.First of all,fractional Fourier transform and the Wigner-Ville distribution(WVD)are used to determine the number of main ridgelines and the tilt angle of the target component in WVD.Next,the standard deviation of the target component's width in the signal's WVD is calculated.Finally,an assembled classifier using neural network is built to recognize different waveforms by automatically combining the three features.Simulation results show that the overall recognition rate of the proposed algorithm reaches 94.17%under 0 dB.When the training data set and the test data set are mixed with noise,the recognition rate reaches 89.93%.The best recognition accuracy is achieved when the size of the training set is taken as 400.The algorithm complexity can meet the requirements of real-time recognition.
文摘Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.
文摘草地地上生物量(Aboveground Biomass,AGB)是衡量草地生态系统功能和质量的核心指标。然而,在呼伦贝尔草原长时序、大尺度的AGB反演中,由于部分年份样本点稀少甚至缺失,年际反演模型的精度难以保证,影响了对草原生态系统动态变化的准确评估。为解决这一关键问题,本文提出了一种基于精度分配权重的年际优化反演模型,旨在提高AGB反演精度,并通过模型结果分析呼伦贝尔草地AGB的时空变化特征。首先,以2003年、2004年、2009年和2010年的Landsat-5的Level-2数据为数据源,计算得到植被指数;以遥感影像时间为基准提取气象数据,结合野外样地实测草地AGB数据进行相关性分析,提取相关性最高的植被指数、气温和降水数据,利用偏最小二乘(Partial Least Squares Regression,PLSR)算法构建得出三年的草地AGB反演模型;然后对三年草地AGB反演模型采用简单平均法、加权平均线性法和精度分配权重法进行评估,结果表明精度分配权重模型为最优模型;再利用实测AGB对最优模型进行对比分析评估模型精度;最后,利用得到的最优模型对呼伦贝尔草地AGB进行长时序生物量反演并分析草地AGB时空变化特征。结果表明:(1)植被指数NDPI、气温和降水数据与草地AGB之间的相关性较高,分别为0.67、0.26和0.29;(2)三种模型中,精度分配权重回归模型拟合效果最好(R2为0.67),对比分析的精度也优于其余两种模型;(3)草地AGB空间分布呈现由西向东逐渐增多的趋势,且该趋势在大多数年份中保持一致,尤其在1996年、2013年和2018年表现最为明显。然而,在1997年和2007年,该趋势的变化幅度较小或出现一定的波动,表现出不同的空间分布特征;时间分布上,除了1997年和2007年草地AGB的量较低(整体在30kg/30m2以下),2019年草地AGB的量较高(整体在65kg/30m2左右),其他年份的草地AGB总体较为稳定,波动范围在(45±10)kg/30m2左右,未出现明显的波动。综上,以精度分配权重模型能够解决在反演草地AGB过程中存在部分年份样本点稀少甚至缺失的问题,研究结果为准确估算呼伦贝尔草原长时序、大尺度草地AGB、碳储量等研究提供重要参考。
文摘为探究电动汽车充电负荷对配电网的影响,提出了一种考虑荷电状态(state of charge,SOC)、实时电价和充电约束的出行链模型来实现电动汽车充电负荷时空分布预测。建立了道路交通网和配电网的耦合模型用以模拟城市路网中电动汽车的出行特性以及城市供电特点;针对电动汽车的类型和电动汽车用户出行特性对单体电动汽车进行建模,考虑SOC动态变化电动汽车出行链改进后,运用蒙特卡罗法对电动汽车充电负荷进行预测;将基于城市路网预测的电动汽车充电负荷结果归算至配网节点,通过计算时间序列潮流来评估电动汽车的聚合接入对配电网的影响。以某城市实际交通路网进行仿真,仿真结果表明,所提方法能够精确预测城市电动汽车充电负荷的时空分布,且通过时空分布特性分析电动汽车充电负荷对配电网的影响,并将不同类型负荷与电网节点相结合,从而提高了配电网电压节点的利用效率。
文摘直流潮流控制器是解决环网式直流配电网的线路潮流不完全可控的有效技术手段。然而,现有方法未能充分发掘其在故障限流中的潜力。该文建立了三有源桥串并联潮流控制器(triple active bridge power flow controller,TAB-PFC)的故障模量分析模型,提出一种基于TAB-PFC的双极直流配电网主动限流策略。首先阐述了TAB-PFC的限流原理,提出基于TAB-PFC的主动限流控制策略。然后对TAB-PFC不同故障阶段进行建模,并计及极间互感构建含TAB-PFC的双极直流配电网故障模量等效模型。在此基础上,分析不同参数对TAB-PFC的限流能力的影响,为其参数选取提供依据。在MATLAB/Simulink搭建了含TAB-PFC的双极直流配电网模型,验证了所提主动限流策略的有效性及故障等效电路模型和参数分析的正确性。
文摘在碳中和的背景下,作为一种无污染的可再生能源,氢能在能源转型中占据着越来越重要的地位。传统的氢电耦合直流微电网设计方案中,制氢设备并联嵌入直流微网,并作为一种灵活性负载参与系统调控以平替部分储能的功能。但是碱液电解槽(alkaline water electrolyzer,AWE)具有低压大电流的特点。随着直流微电网电压等级的提升,传统的并联结构一方面增加了电力电子装置的电压转换比的需求,另一方面忽略了碱液电解槽的电热特性。针对以上问题,该文提出了一种基于虚拟热敏电阻的串联型氢电耦合直流微电网稳定控制策略。首先,针对碱液电解槽建立了一套等效电热模型以表征最大电流与温度的关系。在此基础上,提出了一种电堆串联结构的碱液电解制氢模块(series-connectedstacks alkaline water electrolysis module,SAWEM)及其控制策略。串联结构能降低单个电堆输入电压,而虚拟热敏电阻控制策略能实现各电堆间精确合理的功率分配,且对直流微网有功率支撑作用。最后,通过简易的光伏制氢硬件实验平台进行了验证,结果表明该控制方法具有良好的实用性和有效性。
文摘为平抑微源半桥变流器串联星型结构微电网HCSY-MG(half-bridge converter series Y-connection micro-grids)并网系统中微源出力的波动,保证各相直流侧电压之和相等,与并网电流三相平衡,提出1种基于改进近端策略优化PPO(proximal policy optimization)的分布式混合储能系统HESS(hybrid energy storage system)充、放电优化控制策略。在考虑HCSY-MG系统并网电流与分布式HESS特性的条件下,确定影响并网电流的主要系统变量,以及HESS接入系统的最佳拓扑结构。然后结合串联系统的特点,将分布式HESS的充、放电问题转换为深度强化学习的Markov决策过程。同时针对PPO算法中熵损失权重难以确定的问题,提出1种改进的PPO算法,兼顾智能体的收敛性和探索性。最后以某新能源发电基地的典型运行数据为算例,验证所提控制策略的可行性和有效性。
