基于压缩传感的舰船电网电能质量数据获取方法研究

Ship integrated power system has the physical characteristics of denser grid capacity, more nonlinear loads, and larger impulse to grid, which will cause severe power quality problems. Thus building a power quality monitoring system and obtaining timely, accurate power quality information are the preconditions of improving power quality in the island power system of a ship. Data acquisition method is one of the key theories and techniques of power quality monitoring system. Traditional methods for acquisition and compression of power quality signals are based on Nyquist sampling theory, which are faced with such troubles as high sampling rate,waste of sampling resources and high attainable cost for hardware. To solve these problems, based on compressed sensing theory a method for compressed sampling and reconstruction of power quality data is studied here. Different from the traditional signal acquisition process, compressed sensing, which is a new theory that captures and represents compressible signals at a sampling rate significantly below the Nyquist rate. It firstly employs non-adaptive linear projections that preserve the structure of the signal, and then the signal reconstruction is conducted using an optimization process from these projections. Compressed sensing has been a research hotspot and a new theoretical framework in the field of signal processing. This technique also is in the initial stage in the field of electronics and electrical engineering. Three problems will be studied in our research: (1)signal sparse representation.How to construct theredundant dictionary, which match with the time-frequency character of power quality data. Also ,fast algorithm of sparse decomposition will be studied; (2)design of measurement matrix.How to build appropriate random measure matrix to realize low-rate sampling. (3)reconstruction algorithm matching with sparse decomposition.

舰船综合电力系统电网容量密度大、非线性负荷多、负荷对电网的冲击大，电能质量问题严重，必须建立电能质量监测系统，获得及时准确的电能质量信息，以改善电网电能质量。数据获取是电能质量监测的关键技术，传统电能质量数据获取以Nyquist采样定理为基础，存在采样率高、采样资源浪费和硬件实现成本高的问题。压缩传感是一种全新的数据采集和编码理论，它以远低于Nyquist采样频率的非适应性测量和优化方法高概率重构信号，合并采样和压缩过程，是信号处理领域的研究热点和新的框架，在电气电子工程领域的应用处于起步阶段。本项目研究基于压缩传感的舰船电网电能质量数据获取方法，理论上要解决三个问题：（1）构造适合于舰船电能质量信号时频特征的冗余字典，将信号进行稀疏表示，并设计快速有效的稀疏分解算法；（2）根据冗余字典原子库构成，构造合适的测量矩阵获取观测向量，实现信号低速采样；（3）与稀疏分解相对应的快速重构算法的实现。

数据获取是电能质量监测的关键技术，本项目研究基于压缩传感的舰船电网电能质量数据获取方法，期望能获得较传统数据压缩方法更优的性能，解决传统的电能质量信号采集压缩方法所面临的采样率高、采样资源浪费及硬件实现成本高的问题。.本项目主要研究内容包括以下三点：（1）构造适合于舰船电能质量信号时频特征的冗余字典将信号进行稀疏表示；（2）构造合适的测量矩阵获取观测向量，实现信号低速采样；（3）与稀疏分解相对应的快速重构算法的实现。在电能质量的稀疏表示问题上，本项目采用小波、小波包、DCT、DFT等多个基函数构成一个混合基作为冗余字典来适应电能质量信号的时频特征，解决了单一的正交基不能很好的匹配所要分解的信号的问题。研究结果表明，在以l2相对恢复误差作为衡量指标时，采用冗余字典实现信号的稀疏分解，不仅能够更好的匹配电能质量信号，也能得到更少的原子数，也即信号在冗余字典下会更稀疏。在构造测量矩阵问题上，设计了正交高斯分布随机信号作为测量矩阵，实现了信号的压缩采样。研究结果表明，由部分DCT矩阵构成的测量矩阵由于与信号稀疏分解的冗余字典有相关性，不能成功恢复信号；正交高斯随机测量矩阵在重构时的迭代搜索次数、搜索时间上均优于高斯随机测量矩阵，伯努利随机测量矩阵、随机0-1矩阵。在重构算法的实现问题上，采用正交匹配追踪（OMP）算法实现信号重构。研究结果表明，OMP算法在重构信噪比、搜索时间上均优于迭代重加权最小二乘法（IRLS）、凸优化法（CVX）和基追踪（BP）法。.仿真结果表明，对于谐波信号，同一压缩率下压缩传感重构SNR要大于小波变换和DCT变换；对于暂态扰动信号，压缩传感重构SNR小于小波变换和DCT变换。分析认为，在预构的冗余字典下信号的稀疏度仍然不够，需要采用学习字典实现稀疏编码。