电力系统信号与数据的压缩传感技术综述

随着智能电网建设及电力系统自动化、智能化水平的不断提高,电力系统信号分析与数据处理方法在电力系统中的重要作用进一步凸显。压缩传感(CS)是一种基于信号的稀疏特性,将利用奈奎斯特采样定理的信号采样过程转化为基于优化计算恢复信号的观测过程的新兴信号处理方式,并广泛应用于信号/图像处理、医疗成像与无线通信等领域。基于压缩传感的电力系统信号分析与数据处理方法具有采样速率低、高压缩比,以及便于提取信号特征等优点,因而,在电力系统中具有广泛的应用前景。描述了压缩传感理论框架,并对该理论在电力系统信号分析与数据处理中的应用进行详细综述,其中主要围绕电能质量分析、故障分析、电力系统模态识别、电力系统预测、数据传输,以及智能电网等方面进行评述,并结合压缩传感在电力系统信号分析与数据处理领域的发展状况,对其发展前景进行展望。

Survey of compressed sensing technology for signal and data of power system

As the automation and intelligence for power grid and power system improves continuously, the function of power system signal analysis and data processing methods is further highlighted. Compressed sensing is a new signal processing method based on signal sparseness, which transforms the signal sampling process using Nyquist sampling theorem into the observation process based on optimized computation for restoring signal. It is widely used in signal / image processing, medical treatment imaging, wireless communication and etc. Compressed sensing based power system signal analysis and data processing method has the advantages of low sampling rate, high compression ratio, easy to extract signal characteristics and etc. Therefore, it has a wide application prospect in power system. The main purpose of this paper is to provide a theoretical framework of compressed sensing, and to summarize its applications in power system signal analysis and data processing. The main aspects are as follows: power quality analysis, fault analysis, power system modal identification, power system prediction, data transmission, smart grid and etc, and combined with the development of compressed sensing in power system signal analysis and data processing, its development prospect is expected.