基于压缩感知的电力系统谐波分析

发布时间：2018-01-15 07:44

本文关键词：基于压缩感知的电力系统谐波分析　出处：《天津大学》2014年硕士论文　论文类型：学位论文

【摘要】：电能质量信息采集和数据分析是保证供电质量、提升电网运行效率和建设未来智能电网的基础性支撑,其中谐波信息的采集和分析至关重要。高密度的信息采集使数据量呈爆炸式增长,因此该研究领域已试图将压缩感知引入谐波等数据的信息采集,以克服传统Nyquist采样的缺陷和高密集信息采集的数据存储问题,更加适应电网监测环境。本文立足于研究现状,在研究探讨和仿真分析了谐波数据压缩采集和重构过程中稀疏基、测量矩阵和恢复算法选取的基础上,阐述和提出了一种“基于压缩感知的谐波分析方法”和一种“适用于电网谐波信号的压缩感知改进恢复算法”。首先,本文分析出谐波信号在DFT基下具有良好稀疏性,阐述了一种压缩感知谐波分析方法的实现框架,该框架在信号重构过程同时实现谐波分量检测功能。实验表明,本文提出的压缩感知谐波分析方法的检测结果能够满足国家标准的精度要求。特别地,当压缩比为30%时,该谐波分析框架可将频率、幅值和相位的检测误差保持在10-3、0.15%和0.1o以内,满足国家标准较高精度的检测要求。其次,考虑到实际电网的谐波信号中,谐波分量的幅值能量远远低于基波分量,进一步分析出谐波信号的“基波分量稀疏度”性质。在此性质基础上,设计了一种适用于谐波信号的压缩感知恢复算法,通过滤除压缩信号中的基波分量实现算法对谐波分量的性能优化。实验表明,相比于SPG算法,该恢复算法对频率检测误差最多可降低0.001Hz,对幅值检测误差最多可降低约0.15个百分点,对相位检测误差最多可降低约0.24o,同时在重构性能上信噪比可提升2~8dB。这说明该恢复算法能够进一步提升检测和重构性能。
[Abstract]:Power quality information collection and data analysis is the basic support to ensure the quality of power supply, improve the efficiency of power grid and build the future smart grid. The acquisition and analysis of harmonic information is very important. The high density of information collection makes the amount of data increase explosively, so the research field has tried to introduce compression perception into the information collection of data such as harmonics. In order to overcome the defects of traditional Nyquist sampling and the data storage problem of high-density information collection, and adapt to the monitoring environment of power grid, this paper is based on the current research situation. On the basis of researching and simulating the selection of sparse basis, measurement matrix and recovery algorithm in the process of harmonic data compression and reconstruction. This paper describes and proposes a method of harmonic analysis based on compression sensing and an improved recovery algorithm of compression sensing for harmonic signals in power system. In this paper, we analyze the good sparsity of harmonic signal under DFT, and discuss the realization framework of a compression sensing harmonic analysis method. The experimental results show that the proposed method can meet the precision requirements of national standards. When the compression ratio is 30, the harmonic analysis frame can keep the detection error of frequency, amplitude and phase within 10 ~ (-3) ~ 0.15% and 0.1 o. It meets the requirement of high precision detection of national standard. Secondly, considering the actual harmonic signal, the amplitude energy of harmonic component is much lower than that of fundamental wave component. The properties of fundamental component sparsity of harmonic signal are further analyzed. Based on this property, a compression perceptual recovery algorithm for harmonic signal is designed. The performance optimization of harmonic component is realized by filtering the fundamental component of compression signal. The experiment shows that compared with SPG algorithm, the recovery algorithm can reduce the frequency detection error by 0.001Hz at most. The maximum amplitude detection error can be reduced by 0.15 percentage points, and the phase detection error can be reduced by about 0.24o. At the same time, the SNR can be improved by 2 ~ 8 dB in the reconstruction performance, which shows that the recovery algorithm can further improve the detection and reconstruction performance.
【学位授予单位】：天津大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM711

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