A Complex Least Squares Enhanced Smart DFT Technique for Power System Frequency Estimation
A Complex Least Squares Enhanced Smart DFT Technique for Power System Frequency Estimation
复制标题
用于电力系统频率估计的复杂最小二乘增强智能DFT技术
DOI：
10.1109/tpwrd.2015.2418778
复制
发表时间：
2017-06-01
影响因子：
4.4
通讯作者：
Mandic, Danilo P.
中科院分区：
文献类型：
--
作者：
Xia, Yili;He, Yukun;Mandic, Danilo P.
为了提高智能离散傅里叶变换（SDFT）算法在噪声和谐波污染情况下的频率估计精度，提出了一种复值最小二乘（CLS）算法框架。首先建立了由原始SDFT算法所采用的连续DFT基本分量之间的基本时间序列关系，当存在噪声或意外的高阶谐波时不成立，从而导致次优估计性能。为了消除这些对频率估计的不利影响，接下来通过模型失配误差向量来量化关系破裂的程度。CLS技术，然后采用最小化的均方模型偏差时，SDFT电压建模是次优的。所提出的CLS增强SDFT（CLS-SDFT）方法被证明是更准确的比原来的严重噪声和谐波失真的环境中，在线频率估计的典型场景。SDFT框架的好处是通过各种电力系统条件下的模拟以及实际测量来验证的。
A complex-valued least-squares (CLS) framework is proposed in order to enhance the accuracy of the smart discrete Fourier transform (SDFT) algorithms for power system frequency estimation in the presence of noise and harmonic pollution. It is first established that the underlying time-series relationship among the consecutive DFT fundamental components employed by the original SDFT algorithms does not hold when noises or unexpected higher order harmonics are present, resulting in suboptimal estimation performances. To eliminate these adverse effects on the frequency estimation, the degree of the relationship breakdown is next quantified via a model mismatch error vector. The CLS technique is then employed to minimize the mean-square model deviation when the SDFT voltage modelling is suboptimal. The proposed CLS-enhanced SDFT (CLS-SDFT) methods are shown to be more accurate than the original ones in heavily noisy and harmonic-distorted environments, typical scenarios in online frequency estimation. The benefits of the SDFT framework are verified by simulations for various power system conditions, as well as for real-world measurements.