The Hanning self-convolution window (HSCW) is proposed in this paper. And the phase difference correction algorithm based on the discrete spectrum and the HSCW is given. The HSCW has a low peak side lobe level, a high...The Hanning self-convolution window (HSCW) is proposed in this paper. And the phase difference correction algorithm based on the discrete spectrum and the HSCW is given. The HSCW has a low peak side lobe level, a high side lobe roll-off rate, and a simple spectrum representation. Hence, leakage errors and harmonic interferences can be considerably reduced by weighting samples with the HSCW, the parameter estimation by the HSCW-based phase difference correction algorithm is free of solving high order equations, and the overall method can be easily implemented in embedded systems. Simulation and application results show that the HSCW-based phase difference correction algorithm can suppress the impacts of fundamental frequency fluctuation and white noise on harmonic parameter estimation, and the HSCW is advantageous over existing combined cosine windows in terms of harmonic analysis performance.展开更多
丰富的历史风速数据是开展海岛微电网规划工作的前提。为此,针对待规划海岛无历史风速数据的问题,提出了一种利用周边海岛风速时空相关性估计目标海岛长期风速序列的方法。首先,结合滑动窗和云模型,自适应划分周边海岛风速序列的时序区...丰富的历史风速数据是开展海岛微电网规划工作的前提。为此,针对待规划海岛无历史风速数据的问题,提出了一种利用周边海岛风速时空相关性估计目标海岛长期风速序列的方法。首先,结合滑动窗和云模型,自适应划分周边海岛风速序列的时序区间;其次,根据各时序区间内风速云模型数字特征的余弦相似度,匹配周边海岛各分段风速序列间的相似性转移关系(similarity transfer relationship,STR);最后,考虑STR与海岛空间位置关系,以权重表示各STR对目标海岛风速序列估计的影响,进而依据各STR及其权重估计目标海岛的长期风速序列。研究结果表明:相较于利用皮尔逊相关系数(Pearson correlation coefficient,PCC)计算各天风速序列间的相关性,进而估计海岛长期风速序列的方法,使用所提方法得到的估计结果与实际序列间的平均绝对误差、均方根误差和PCC分别约改善了7.31%、17.98%和0.46%,所提方法能够实现较高准确度的海岛长期风速序列估计。论文研究可为历史风速数据缺失情况下开展海岛风速预测工作提供参考。展开更多
基金Supported by the National Natural Science Foundation of China (Grant No.60872128)
文摘The Hanning self-convolution window (HSCW) is proposed in this paper. And the phase difference correction algorithm based on the discrete spectrum and the HSCW is given. The HSCW has a low peak side lobe level, a high side lobe roll-off rate, and a simple spectrum representation. Hence, leakage errors and harmonic interferences can be considerably reduced by weighting samples with the HSCW, the parameter estimation by the HSCW-based phase difference correction algorithm is free of solving high order equations, and the overall method can be easily implemented in embedded systems. Simulation and application results show that the HSCW-based phase difference correction algorithm can suppress the impacts of fundamental frequency fluctuation and white noise on harmonic parameter estimation, and the HSCW is advantageous over existing combined cosine windows in terms of harmonic analysis performance.
文摘丰富的历史风速数据是开展海岛微电网规划工作的前提。为此,针对待规划海岛无历史风速数据的问题,提出了一种利用周边海岛风速时空相关性估计目标海岛长期风速序列的方法。首先,结合滑动窗和云模型,自适应划分周边海岛风速序列的时序区间;其次,根据各时序区间内风速云模型数字特征的余弦相似度,匹配周边海岛各分段风速序列间的相似性转移关系(similarity transfer relationship,STR);最后,考虑STR与海岛空间位置关系,以权重表示各STR对目标海岛风速序列估计的影响,进而依据各STR及其权重估计目标海岛的长期风速序列。研究结果表明:相较于利用皮尔逊相关系数(Pearson correlation coefficient,PCC)计算各天风速序列间的相关性,进而估计海岛长期风速序列的方法,使用所提方法得到的估计结果与实际序列间的平均绝对误差、均方根误差和PCC分别约改善了7.31%、17.98%和0.46%,所提方法能够实现较高准确度的海岛长期风速序列估计。论文研究可为历史风速数据缺失情况下开展海岛风速预测工作提供参考。
文摘针对经验模态分解(Empirical Mode Decomposition,EMD)中存在的边界效应及边界发散现象随着筛选层次的增加而增加的问题,提出一种利用延拓与可变余弦窗相结合的改进新方法。首先对信号进行延拓处理,增加一定长度的数据,实现延拓数据与原始信号交界处的光滑过度。其次,根据信号边界的发散程度,在逐层提取各阶本征模函数(Intrinsic Model Function,IMF)之前,在信号两端加上宽度可变的余弦窗函数,使得每一个IMF分量边界发散问题最小化,保证信号有效数据的正确分解,实现EMD边界处理算法的改进。仿真和实例信号分析表明,该方法能较好地抑制EMD边界效应,有效地提取故障信号中的特征信息。
