The denoising and detection of transient disturbances are two important subjects for power quality monitoring and analysis. To effectively denoise and detect transient disturbances under noisy conditions, an improved ...The denoising and detection of transient disturbances are two important subjects for power quality monitoring and analysis. To effectively denoise and detect transient disturbances under noisy conditions, an improved iterative adaptive kernel regression method is proposed in this paper. The proposed method has advantages that itdoes not need to estimate the noise variance or a filter threshold, and has both denoising and detection capabilities for transient disturbances. Simulation results demonstrate that the proposed method provides excellent denoising effects, which can not only suppress noise effectively but also preserve disturbance features of sudden change points well. Additionally, it provides good detection and location performance for single and combined transient disturbances, even under strong noise conditions. Finally, the effectiveness of the proposed method is further verified by using real disturbance data.展开更多
针对含噪声的暂态电能质量扰动检测问题,提出了一种基于小波自适应去噪的改进HT-LMD(HilbertHuang and Local Mean Decomposition)分解检测方法。分析了局部均值分解检测扰动的优缺点以及噪声对LMD检测方法的影响,提出了采用小波分解与...针对含噪声的暂态电能质量扰动检测问题,提出了一种基于小波自适应去噪的改进HT-LMD(HilbertHuang and Local Mean Decomposition)分解检测方法。分析了局部均值分解检测扰动的优缺点以及噪声对LMD检测方法的影响,提出了采用小波分解与重构和自适应阈值技术以及基于正交性判据(Orthogonality Criterion,OC)新的HT-LMD检测方法。小波自适应去噪技术能减弱噪声对LMD分解影响,正交性判据能减少分解的迭代次数。典型暂态电能质量扰动模拟信号和实测信号的检测结果表明,所提方法能在有效提高LMD方法检测电能质量扰动效果同时很好地保留原有暂态扰动信号奇异性特征,提高了检测和定位精度。展开更多
针对电能质量的扰动检测问题,以电流信号为研究对象,提出结合形态滤波与多分辨率奇异值分解(singular value decomposition,SVD)包的电能质量扰动检测算法。根据形态学滤波器计算特点,采用余弦结构元素,对滤除噪声后的信号构造矩阵进行...针对电能质量的扰动检测问题,以电流信号为研究对象,提出结合形态滤波与多分辨率奇异值分解(singular value decomposition,SVD)包的电能质量扰动检测算法。根据形态学滤波器计算特点,采用余弦结构元素,对滤除噪声后的信号构造矩阵进行多分辨率SVD包分解,通过分解后的高频分量特征检测扰动,结合自适应阈值判断是否发生扰动,利用仿真对其进行验证。仿真实验结果表明:该算法相较于普通形态学与SVD方法有更好的抗噪能力,且可实现对扰动信号的快速、准确定位。展开更多
基金supported in part by the NationalKey R&D Program of China (No. 2016YFB1200401, No. 2017YFB1201103)in part by the Program for Application of Cophase Power Supply Technology (No. 2018002)
文摘The denoising and detection of transient disturbances are two important subjects for power quality monitoring and analysis. To effectively denoise and detect transient disturbances under noisy conditions, an improved iterative adaptive kernel regression method is proposed in this paper. The proposed method has advantages that itdoes not need to estimate the noise variance or a filter threshold, and has both denoising and detection capabilities for transient disturbances. Simulation results demonstrate that the proposed method provides excellent denoising effects, which can not only suppress noise effectively but also preserve disturbance features of sudden change points well. Additionally, it provides good detection and location performance for single and combined transient disturbances, even under strong noise conditions. Finally, the effectiveness of the proposed method is further verified by using real disturbance data.
文摘针对含噪声的暂态电能质量扰动检测问题,提出了一种基于小波自适应去噪的改进HT-LMD(HilbertHuang and Local Mean Decomposition)分解检测方法。分析了局部均值分解检测扰动的优缺点以及噪声对LMD检测方法的影响,提出了采用小波分解与重构和自适应阈值技术以及基于正交性判据(Orthogonality Criterion,OC)新的HT-LMD检测方法。小波自适应去噪技术能减弱噪声对LMD分解影响,正交性判据能减少分解的迭代次数。典型暂态电能质量扰动模拟信号和实测信号的检测结果表明,所提方法能在有效提高LMD方法检测电能质量扰动效果同时很好地保留原有暂态扰动信号奇异性特征,提高了检测和定位精度。
文摘针对电能质量的扰动检测问题,以电流信号为研究对象,提出结合形态滤波与多分辨率奇异值分解(singular value decomposition,SVD)包的电能质量扰动检测算法。根据形态学滤波器计算特点,采用余弦结构元素,对滤除噪声后的信号构造矩阵进行多分辨率SVD包分解,通过分解后的高频分量特征检测扰动,结合自适应阈值判断是否发生扰动,利用仿真对其进行验证。仿真实验结果表明:该算法相较于普通形态学与SVD方法有更好的抗噪能力,且可实现对扰动信号的快速、准确定位。
