Power Quality (PQ) combined disturbances become common along with ubiquity of voltage flickers and harmonics. This paper presents a novel approach to classify the different patterns of PQ combined disturbances. The cl...Power Quality (PQ) combined disturbances become common along with ubiquity of voltage flickers and harmonics. This paper presents a novel approach to classify the different patterns of PQ combined disturbances. The classification system consists of two parts, namely the feature extraction and the automatic recognition. In the feature extraction stage, Phase Space Reconstruction (PSR), a time series analysis tool, is utilized to construct disturbance signal trajectories. For these trajectories, several indices are proposed to form the feature vectors. Support Vector Machines (SVMs) are then implemented to recognize the different patterns and to evaluate the efficiencies. The types of disturbances discussed include a combination of short-term dis-turbances (voltage sags, swells) and long-term disturbances (flickers, harmonics), as well as their homologous single ones. The feasibilities of the proposed approach are verified by simulation with thousands of PQ events. Comparison studies based on Wavelet Transform (WT) and Artificial Neural Network (ANN) are also reported to show its advantages.展开更多
文摘针对四波横向剪切干涉(quadriwave lateral shearing interference, QLSI)相位重建中现有常规滤波窗提取差分相位时存在易受噪声、频谱泄漏和其他级次频谱串扰影响等问题,提出了一种采用菱形高斯组合窗滤波的相位重建方法。通过由频谱面内的菱形窗和垂直于频谱面的一系列二维高斯窗组合而成的滤波窗,从QLSI干涉图中提取两正交方向的差分相位,最终由两个差分相位通过基于最小二乘的傅里叶变换法重建出待测相位。采用标准样品进行实际测量,比较了使用菱形高斯组合窗和其他四种常规滤波窗滤波对重建相位的重建相位差、均方根误差(root mean square error,RMSE)和峰谷(peak to vally,PV)误差的影响。结果表明:本文提出的方法重建相位的相位差最接近样品标称值,重建相位的RMSE误差和PV误差均取得最小值,可以有效提高相位重建质量。
基金Project (No. 50437010) supported by the Key Program of the Na-tional Natural Science Foundation of China
文摘Power Quality (PQ) combined disturbances become common along with ubiquity of voltage flickers and harmonics. This paper presents a novel approach to classify the different patterns of PQ combined disturbances. The classification system consists of two parts, namely the feature extraction and the automatic recognition. In the feature extraction stage, Phase Space Reconstruction (PSR), a time series analysis tool, is utilized to construct disturbance signal trajectories. For these trajectories, several indices are proposed to form the feature vectors. Support Vector Machines (SVMs) are then implemented to recognize the different patterns and to evaluate the efficiencies. The types of disturbances discussed include a combination of short-term dis-turbances (voltage sags, swells) and long-term disturbances (flickers, harmonics), as well as their homologous single ones. The feasibilities of the proposed approach are verified by simulation with thousands of PQ events. Comparison studies based on Wavelet Transform (WT) and Artificial Neural Network (ANN) are also reported to show its advantages.
