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| | This paper presents a new method for assessing the harmonic emission level, which based on the robust regression. By utilizing the signals of harmonic voltage and harmonic current which have been measured synchronously at PCC, it applies an iteratively reweighted least squares algorithm to solve regression coefficient: The idea is to use Huber function as the influence function and to compute iteratively with weights. The weights are functions of the residuals from the previous iteration such that t... | | 该文提出了一种基于稳健回归的系统谐波阻抗及用户谐波发射水平的估计方法:利用在公共连接点同步测量得到的谐波电压和谐波电流信号,通过复加权最小二乘迭代算法求解回归系数;利用 Huber 函数作为影响函数进行加权迭代计算,权重为上次迭代的残差函数,以此减少奇异值对回归系数的影响;由回归系数映射出系统谐波阻抗,并跟踪计算用户的谐波发射水平。通过对实验电路的仿真分析以及实际工程实测数据的分析验证了该方法的有效性。 | | 文摘来源 | | It is necessary to separate the emission level of power system and customers. The paper describes a method for estimating power system harmonic impedance and the customer emission level based on binary linear regression. By using field measurements for harmonic voltages and currents at the connection point of the customer,we can estimate the harmonic impedance of power system, and then compute the contribution of a customer's injection. Relative to the presupposition of Double Linear Regression method that ... | | 区分电力系统中系统侧与用户侧谐波发射水平十分必要。该文提出了基于二元线性回归的系统谐波阻抗及用户谐波发射水平的估算方法。根据此方法,利用在公共联接点测量的各次谐波的电压和电流信号,运用二元线性回归统计可得到系统侧的谐波阻抗,进而计算出用户侧谐波发射水平。相对于“双线性回归法”忽略电阻分量,该文提出的方法能够估计出谐波复阻抗中实部与虚部,更为合理。通过对实验电路仿真分析以及实测数据的分析结果,并对比“波动法”的计算结果,验证了其有效性。 | | 文摘来源 | | A new approach for assessing harmonic emission levels from nonlinear loads is proposed with inverse sum of random vectors. The probability models presented are based on the figure characteristics of harmonics from nonlinear load disconnected and connected. The n order moments are used to determine Laguerre polynomials expansion for simulating the sum probability density function (pdf): A recursive algorithm is used to calculate n order moments of measurements on X-Y components. The n order moments of sum of... | | 提出用概率求和估计非线性负荷谐波发射水平的新途径。根据非线性负荷投入前后测量值的数字特征建立逆向求和概率模型,通过测量值的x-y分量的n阶矩与Laguerre多项式逼近求和概率密度函数(pdf:probability density function):用递推算法估计测量统计值各分量的n阶矩,导出矢量和(幅值)的n阶矩,再用参数平方变换合成pdf,引入尺度因子修正截断误差。这种方法求和时不需要各个随机矢量在相位上具有0~2π的概率分布,使用的统计样本值较少。用实际测量值进行的比较分析证明了该方法的有效性。 | | 文摘来源 | |   | | << 更多相关文摘 |
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