基于高维数学形态学和形态学小波的电力系统故障信号特征提取

项目名称： 基于高维数学形态学和形态学小波的电力系统故障信号特征提取

项目编号： No.51207058

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 电气科学与工程学科

项目作者： 季天瑶

作者单位： 华南理工大学

项目金额： 17万元

中文摘要： 电力系统故障信号特征提取是继电保护和电能质量分析的重要问题。然而半个多世纪以来，对其处理一直依赖于傅里叶变换和小波变换等传统方法。这些方法需要通过复杂的积分计算将信号转换到频域进行分析，且无法反映信号的瞬时信息。数学形态学主要分析和处理信号的波形，然而其绝大多数应用为图像处理，在高维空间中的应用尚有待开发，且缺乏对频率信息的分析。针对这些问题，本项目提出基于高维数学形态学和形态学小波的故障信号特征提取方法，将对空间变换、高维数学形态学、特征提取、形态学小波、形态学在时频域间沟通等问题进行深入理论研究，针对电力系统故障信号的特点开发快速准确的瞬时特征提取算法，拟解决的问题包括去除故障信号的直流偏移、检测和补偿二次电流饱和、检测暂态信号、检测和定位电能扰动并进行分类、分析谐波成分。另外对高维数学形态学及数学形态学在时频域间的联系的研究是一个崭新的方向，研究成果会对信号处理领域产生重要影响。

中文关键词： 数学形态学；空间变换；特征提取；电力系统保护；电能质量分析

英文摘要： Power system fault signal feature extraction is an important issue for protective relaying and power quality analysis. However, for over half a century it has been relied on traditional signal processing techniques such as Fourier transform and Wavelet transform. They focus on the frequency information of a signal by transferring it to the frequency domain, which requires complex integral calculation, and cannot reflect the shape information of the signal or obtain the knowledge of the transients. Mathematical morphology (MM), on the other hand, processes and analyses the geometrical structures of a signal, yet it is mainly concerned with image processing. Its potential has not been fully explored for applications in a high dimensional domain, and its ability for frequency analysis has never been attempted. In order to tackle these issues, this project will propose fault signal feature extraction algorithms based on high-dimensional MM and morphological wavelet, and will research on the topics of space transform, high-dimensional morphological operators, feature extraction, morphological wavelet, and MM's relationship between time and frequency domains. Targeting the characteristic of power system fault signal, a set of transient feature extraction algorithm will be developed, in order to solve the problems such

英文关键词： Mathematical morphology；Space transform；Feature extraction；Power system protection；Power quality analysis