Traditional thermal power units are continuously replaced by renewable energies,of which fluctuations and intermittence impose pressure on the frequency stability of the power system.Electrolytic aluminum load(EAL)acc...Traditional thermal power units are continuously replaced by renewable energies,of which fluctuations and intermittence impose pressure on the frequency stability of the power system.Electrolytic aluminum load(EAL)accounts for large amount of the local electric loads in some areas.The participation of EAL in local frequency control has huge application prospects.However,the controller design of EAL is difficult due to the measurement noise of the system frequency and the nonlinear dynamics of the EAL’s electric power consumption.Focusing on this problem,this paper proposes a control strategy for EAL to participate in the frequency control.For the controller design of the EAL system,the system frequency response model is established and the EAL transfer function model is developed based on the equivalent circuit of EAL.For the problem of load-side frequency measurement error,the frequency estimation method based on Kalman-filtering is designed.To improve the performance of EAL in the frequency control,a fuzzy EAL controller is designed.The testing examples show that the designed Kalman-filter has good performance in de-noising the measured frequency,and the designed fuzzy controller has better performance in stabilizing system frequency than traditional methods.展开更多
An improved retrieval method, which uses the solution with a Gaussian constraint as the initial state variables for the Kalman Filtering （KF） method, was developed to retrieve the wet refractivity profiles from slan...An improved retrieval method, which uses the solution with a Gaussian constraint as the initial state variables for the Kalman Filtering （KF） method, was developed to retrieve the wet refractivity profiles from slant wet delays （SWD） extracted by the double-differenced （DD） GPS method. The accuracy of the GPS-derived SWDs is also tested in this study against the measurements of a water vapor radiometer （WVR） and a weather model. It is concluded that the GPS-derived SWDs have similar accuracy to those measured with WVR and are much higher in quality than those derived from the weather model used. The developed method is used to retrieve the 3D wet refractivity distribution in the Hong Kong region. The retrieved profiles agree well with the radiosonde observations, with a difference of about 4 mm km^- 1 in the low levels. The accurate profiles obtained with this method are applicable in a number of meteorological applications.展开更多
Multisensor data fusion has played a significant role in diverse areas ranging from local robot guidance to global military theatre defense etc. Various multisensor data fusion methods have been extensively investigat...Multisensor data fusion has played a significant role in diverse areas ranging from local robot guidance to global military theatre defense etc. Various multisensor data fusion methods have been extensively investigated by researchers, of which Klaman filtering is one of the most important. Kalman filtering is the best-known recursive least mean-square algorithm to optimally estimate the unknown states of a dynamic system, which has found widespread application in many areas. The scope of the work is restricted to investigate the various data fusion and track fusion techniques based on the Kalman Filter methods, then a new method of state fusion is proposed. Finally the simulation results demonstrate the effectiveness of the introduced method.展开更多
为提高零中频接收机中正交(in-phase quadrature,IQ)失配信号校正的收敛速度与鲁棒性,本文将Kalman滤波算法与盲源分离结构结合,提出了一种基于双通道Kalman滤波的校正算法。该算法通过状态空间建模与协方差自适应更新,能够在动态环境...为提高零中频接收机中正交(in-phase quadrature,IQ)失配信号校正的收敛速度与鲁棒性,本文将Kalman滤波算法与盲源分离结构结合,提出了一种基于双通道Kalman滤波的校正算法。该算法通过状态空间建模与协方差自适应更新,能够在动态环境下实现更高效、稳定的参数估计,从而实现对IQ失配信号的有效补偿。将本文算法与最小均方算法(least mean square,LMS)、归一化最小均方算法(normalized least mean square,NLMS)和仿射投影算法(affine projection algorithm,APA)进行对比仿真,结果显示,校正后信号的镜像抑制比(image rejection ratio,IRR)均达到约45 dB,但双通道Kalman滤波算法对应的IRR曲面图更加平滑,同时,16QAM和16PSK调制方式下该算法的误符号率最低,表明本文算法能够有效实现IQ失配校正,具有较好的稳定性。本文算法迭代约50次时,均方误差收敛趋近于0,而LMS、NLMS和APA算法则分别需要迭代约500次、400次和200次才能够收敛,表明该算法具有较好的收敛性。通过参数的敏感性仿真分析,在较大的参数范围内本文算法达到的IRR差别甚微,具有良好的鲁棒性。展开更多
磷酸铁锂(LiFePO_(4),LFP)与镍钴锰酸锂(LiNi_(x)Co_(y)Mn_(2)O_(2),NCM)电池串联构建的混合动力电池系统,是突破传统单一化学体系瓶颈的关键技术。然而,混装电池包中LFP电池具有平坦的电压平台特性,导致全工作区间的荷电状态(state of ...磷酸铁锂(LiFePO_(4),LFP)与镍钴锰酸锂(LiNi_(x)Co_(y)Mn_(2)O_(2),NCM)电池串联构建的混合动力电池系统,是突破传统单一化学体系瓶颈的关键技术。然而,混装电池包中LFP电池具有平坦的电压平台特性,导致全工作区间的荷电状态(state of charge,SOC)估算精度受限,且在多算法切换时易出现SOC跳变现象。为此,本工作提出一种基于开路电压(open circuit voltage,OCV)曲线区间自适应划分的分段融合SOC估算方法。首先,考虑到LFP电池OCV斜率变化特征,设计了分段平滑策略,在高斜率区保持电压特征,在平台区增强平滑效果,并根据平滑OCV曲线的一阶差分斜率,设定自适应斜率阈值,将放电区间划分为前端高斜率区、中间平台区与后端高斜率区,为SOC算法选择提供明确依据;其次,构建分段估算框架:在高斜率区采用改进自适应扩展卡尔曼滤波进行SOC动态跟踪,在平台区则利用混合包中NCM电池的SOC进行映射估算。针对算法切换点SOC跳变问题,进一步提出梯度敏感的S型融合算法(gradient-sensitive adaptive blending,GSAB),该算法通过量化切换点邻域的SOC梯度差异,动态调整融合函数参数以生成平滑过渡权重,抑制切换点的SOC跳变。结果表明,改进自适应扩展卡尔曼滤波算法在NCM电池上的均方根误差相较于传统扩展卡尔曼滤波算法降低63.70%;GSAB策略有效消除了算法切换时的SOC突变,使过渡区波动降低72.42%。最终,在城市道路循环工况下,LFP电池全区间SOC估算的平均绝对误差与均方根误差分别降至1.08%和1.31%,验证了所提方法能有效提升LFP电池SOC全区间估算精度。展开更多
基金funded by Science and Technology Project of State Grid Corporation of China:Research on the Construction and Evaluation Technology of the Data-Driven-based Adjustable Resource Pool of Typical Industrial Enterprises,Grant No.1400-202016386A-0-0-00.
文摘Traditional thermal power units are continuously replaced by renewable energies,of which fluctuations and intermittence impose pressure on the frequency stability of the power system.Electrolytic aluminum load(EAL)accounts for large amount of the local electric loads in some areas.The participation of EAL in local frequency control has huge application prospects.However,the controller design of EAL is difficult due to the measurement noise of the system frequency and the nonlinear dynamics of the EAL’s electric power consumption.Focusing on this problem,this paper proposes a control strategy for EAL to participate in the frequency control.For the controller design of the EAL system,the system frequency response model is established and the EAL transfer function model is developed based on the equivalent circuit of EAL.For the problem of load-side frequency measurement error,the frequency estimation method based on Kalman-filtering is designed.To improve the performance of EAL in the frequency control,a fuzzy EAL controller is designed.The testing examples show that the designed Kalman-filter has good performance in de-noising the measured frequency,and the designed fuzzy controller has better performance in stabilizing system frequency than traditional methods.
文摘An improved retrieval method, which uses the solution with a Gaussian constraint as the initial state variables for the Kalman Filtering （KF） method, was developed to retrieve the wet refractivity profiles from slant wet delays （SWD） extracted by the double-differenced （DD） GPS method. The accuracy of the GPS-derived SWDs is also tested in this study against the measurements of a water vapor radiometer （WVR） and a weather model. It is concluded that the GPS-derived SWDs have similar accuracy to those measured with WVR and are much higher in quality than those derived from the weather model used. The developed method is used to retrieve the 3D wet refractivity distribution in the Hong Kong region. The retrieved profiles agree well with the radiosonde observations, with a difference of about 4 mm km^- 1 in the low levels. The accurate profiles obtained with this method are applicable in a number of meteorological applications.
文摘Multisensor data fusion has played a significant role in diverse areas ranging from local robot guidance to global military theatre defense etc. Various multisensor data fusion methods have been extensively investigated by researchers, of which Klaman filtering is one of the most important. Kalman filtering is the best-known recursive least mean-square algorithm to optimally estimate the unknown states of a dynamic system, which has found widespread application in many areas. The scope of the work is restricted to investigate the various data fusion and track fusion techniques based on the Kalman Filter methods, then a new method of state fusion is proposed. Finally the simulation results demonstrate the effectiveness of the introduced method.
文摘为提高零中频接收机中正交(in-phase quadrature,IQ)失配信号校正的收敛速度与鲁棒性,本文将Kalman滤波算法与盲源分离结构结合,提出了一种基于双通道Kalman滤波的校正算法。该算法通过状态空间建模与协方差自适应更新,能够在动态环境下实现更高效、稳定的参数估计,从而实现对IQ失配信号的有效补偿。将本文算法与最小均方算法(least mean square,LMS)、归一化最小均方算法(normalized least mean square,NLMS)和仿射投影算法(affine projection algorithm,APA)进行对比仿真,结果显示,校正后信号的镜像抑制比(image rejection ratio,IRR)均达到约45 dB,但双通道Kalman滤波算法对应的IRR曲面图更加平滑,同时,16QAM和16PSK调制方式下该算法的误符号率最低,表明本文算法能够有效实现IQ失配校正,具有较好的稳定性。本文算法迭代约50次时,均方误差收敛趋近于0,而LMS、NLMS和APA算法则分别需要迭代约500次、400次和200次才能够收敛,表明该算法具有较好的收敛性。通过参数的敏感性仿真分析,在较大的参数范围内本文算法达到的IRR差别甚微,具有良好的鲁棒性。
