充电站电力线载波通信系统匹配阻抗估计新方法研究

The difficulty and key block of Broadband power line carrier communication technology applied in electric vehicle charging station is: The inaccuracy of Impedance estimation restricted its efficiency. This project starts from transmission line theory, Studying the linkage relationship between channel transmission characteristic data and the impedance data, then do inversion analysis and eventually establish a mathematical model of the impedance estimation. The impedance frequency characteristic calculation problem which is usually difficult to be solved directly is converted to a multivariate nonlinear regression problem which can be obtained by the data of transmission characteristic estimation results. Study the linkage model of transmission - impedance under same topology, the mathematical model of the channel transmission characteristics and the input impedance is obtained from the measurement results of the characteristics of the cable and the load, Analysis of the mechanism and influence factors of the variation of the impedance by use of transmission characteristics. Using the new variational mode decomposition algorithm for transmission characteristic curve feature extraction, Combining extreme learning machine to obtain the mathematical modeling problems of matching impedance; The mechanism and time-frequency characteristics of impulse noise in the charging station are studied, A method for estimating the transmission characteristics based on the cyclic spectrum of the received signal under the condition of strong impulse noise; An impedance estimation method suitable for the environment of the charging station is formed in theory. This project has important significance for promotion of high efficiency information transmission by power line communication in the field of electric vehicles and upgrading and development of electric vehicle charging station.

宽带电力线载波通信技术应用于电动车充电站的难点和关键阻碍在于：阻抗估计不准确严重制约其效率。本项目从传输线理论出发，研究信道传输特性数据与阻抗数据的时频联动关系进行反演分析并最终建立阻抗估计的数学模型，将通常难以直接求解得到的阻抗频率特性计算转化为一个可通过传输特性的估计结果得到的非线性反演回归问题。研究同一拓扑的传输-阻抗联动模型，从电缆与负荷等基本元件特性的测量结果得到信道传输特性与输入阻抗的数学模型，分析阻抗随传输特性变化的机理和影响因素；使用新型变分模态分解算法对传输特性曲线进行特征提取，结合极限学习机得到匹配阻抗的数学模型；研究充电站中脉冲噪声的周期循环特性与时频特性，建立基于循环谱的抗噪声传输特性估计方法；从理论上形成适合于充电站环境的、闭环的阻抗估计方法。对于提升电力线通信在电动汽车等领域的高效率信息传输、推动电动车充电站的升级与发展具有重要意义。

宽带电力线载波通信技术应用于电动车充电站以及电网-信息网融合的难点和关键阻碍在于：阻抗估计不准确严重制约其效率。本项目从多导体传输线理论出发，研究信道传输特性数据与阻抗数据的时频联动关系进行反演分析并最终建立阻抗估计的数学模型，将通常难以直接求解得到的实时阻抗频率特性计算转化为一个可通过传输特性的估计结果得到的反演求解问题。首先建立相同拓扑的传输-阻抗联动模型，从电缆与负荷等基本元件特性的测量结果得到信道传输特性与输入阻抗的数学模型，分析阻抗随传输特性变化的机理和影响因素并能得到求解阻抗特性的关键特性；使用新型变分模态分解算法对传输特性曲线进行特征提取，然后使用机器学习技术求解得到匹配阻抗的数学模型中的关键参数，对多种机器学习的应用进行了对比，发现小数据集时使用支持向量机和卷积神经网络可有效得到性能参数，而大数据集时使用KNN算法可简单高效的得到回归参数；研究充电站中脉冲噪声的周期循环特性与时频特性，快速消峰的抗噪声传输特性估计方法；从理论上形成适合于充电站与配电网等树枝分布的阻抗估计方法。对于提升电力线通信在电动汽车和电网-信息网融合领域的高效率信息传输、推动智能电网的升级与发展跨出了坚实的步伐。