基于压缩感知的电力系统数据采集与重构算法研究

发布时间：2018-04-13 21:40

本文选题：电力系统信号采集 + 数据压缩　；参考：《哈尔滨工业大学》2014年硕士论文

【摘要】：传统电力数据采集与压缩方法都是基于香农采样定理，对电网信号连续的采集导致数据量极大，占用大量的硬件空间来传输和储存这些数据，造成很大的负担。然而实际上多数电网信号所包含的信息量并不高，在后端压缩处理时大量前端采集的数据将被舍弃，这使得对前端采集硬件的巨大投入成为一种浪费。香农采样定理是不失真重构信号的充分条件，而非必要条件。近年来，压缩感知理论的提出，在对稀疏信号的采集上，突破了香农采样定理。压缩感知摈弃传统方法对信号“先采样后处理”的方式，而是对信号的采样和压缩同时进行，通过少量的数据量重建原始信号，，从而极大减轻硬件负担。本文在研究压缩感知的理论基础上，针对电网信号的特点，设计了基于压缩感知理论的硬件可实现的电力系统数据采集与重构系统，主要内容工作如下： (1)研究压缩感知的理论基础。通过仿真实验证明电网信号满足压缩感知应用的前提条件，可以将压缩感知理论用于电力系统数据的采集与重构。 (2)研究随机解调器的工作原理。针对电网信号的特点，搭建基于压缩感知的单路电网(Analog to Information Converter, AIC)采集系统，通过仿真实验验证系统的正确性。 (3)研究稀疏信号重构算法中的贪婪算法，主要是正交匹配追踪算法、正则化正交匹配追踪算法和压缩采样匹配追踪算法。完成三种算法的编写，通过仿真实验研究算法的重构能力。 (4)合并设计的单路电网AIC采集系统和贪婪算法成为一个整体的基于压缩感知理论的电力系统数据采集与重构系统。用压缩感知方法和提升小波法对加入不同扰动类型的电网信号仿真对比实验。对仿真实验结果进行分析，证明新的系统在采样率远低于传统方法的情况下，可以正确重构电力系统数据。
[Abstract]:The traditional power data acquisition and compression methods are based on Shannon sampling theorem. The continuous acquisition of power network signals leads to a great deal of data, which takes up a large amount of hardware space to transmit and store these data, resulting in a great burden.However, in fact, most power network signals contain not much information, so a large amount of data collected by the front end will be discarded in the back-end compression process, which makes the huge investment in the front-end acquisition hardware become a waste.Shannon sampling theorem is a sufficient condition, not a necessary condition, for signal reconstruction without distortion.In recent years, the theory of compression perception has broken through Shannon's sampling theorem in the collection of sparse signals.Instead of the traditional method of "sampling and then processing", the compression perception is used to sample and compress the signal at the same time, and reconstruct the original signal by a small amount of data, thus greatly reducing the burden on the hardware.Based on the research of compression sensing theory and the characteristics of power system signal, this paper designs a data acquisition and reconfiguration system of power system based on compression sensing theory. The main work is as follows:1) to study the theoretical basis of compressed perception.The simulation results show that the power system signals satisfy the precondition of compression sensing, and the theory of compression sensing can be applied to data acquisition and reconstruction of power system.The working principle of random demodulator is studied.According to the characteristics of power network signals, a single channel power grid Analog to Information conversion system based on compression perception is built, and the correctness of the system is verified by simulation experiments.The greedy algorithms in sparse signal reconstruction are studied, including orthogonal matching tracking algorithm, regularized orthogonal matching tracking algorithm and compressed sampling matching tracking algorithm.The three algorithms are written, and the reconstruction ability of the algorithm is studied by simulation experiments.4) the single channel AIC acquisition system and greedy algorithm are combined into a whole power system data acquisition and reconstruction system based on compression perception theory.The compression sensing method and the lifting wavelet method are used to simulate the power network signal with different disturbance types.The simulation results show that the new system can reconstruct the power system data correctly when the sampling rate is much lower than the traditional method.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM73

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