基于稀疏分解和复合熵编码的电能质量扰动数据高效压缩算法

doi:10.19595/j.cnki.1000-6753.tces.221653

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摘要

对于电能质量扰动信号压缩问题,压缩比和重构误差是一对互相矛盾的指标。传统的压缩算法难以同时满足高压缩比和低重构误差的要求。为了同时提高压缩比和减小重构误差,该文提出了一种基于稀疏分解、哈夫曼编码和长行程编码的混合压缩算法。首先,该文使用基于联合字典的稀疏分解算法,将电能质量扰动信号中的暂态分量和稳态分量进行分离;其次,对暂态分量使用小波分析、哈夫曼编码、长行程编码算法对其进行编码压缩。对稳态分量,即基波和谐波分量,则保留其大于设定阈值的部分,进而完成对信号的压缩。仿真信号和实测信号的实验结果表明该算法较对比算法具有更高的压缩比和更低的重构误差,同时证明了对采样率和高斯白噪声具有较强的抗干扰能力。

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关键词 ：信号压缩, 电能质量扰动, 哈夫曼编码, 长行程编码, 小波分析, 稀疏分解

Abstract：

The power quality disturbance (PQD) data are useful information for the power grid. Generally, different PQD waveforms correspond to different faults in the power grid, so the data can be used to distinguish the types of PQD signals. For example, the transient oscillation signal is caused by heavy load switching, the voltage notch originates from phase commutation of power electronic devices, voltage sag or interruption occurs because of short circuit faults, and the impulse signal is due to the thunder striking the lines, etc. In addition, the PQD waveforms can be used to locate the disturbance of the distribution network. However, with the scale of the power network increasing, PQD events are inevitably becoming more frequent than ever before. If there are many power grid monitoring devices in a certain distribution grid network, the bandwidth of Ethernet will be tremendous to transmit and receive the uncompressed PQD data, and the disk space for storage will also be huge, which leads to high expense on Ethernet and disk. Therefore, a highly efficient compression algorithm for PQD data calls for much attention and has become more urgent than ever before.
The principle of the algorithm can be described as follow. First, this paper uses joint dictionary based sparse decomposition algorithm to separate the transient and steady-state components in PQD, secondly, the transient components are compressed by wavelet analysis, Huffman coding and run-length coding algorithms, for the steady-state component, i.e. the fundamental and harmonic components, the values that are greater than the threshold is reserved, and the compression is completed. To evaluate the proposed method, two indicators, compression ratio (CR) and percentage of root-mean-square difference (PRD) are utilized. Three kind of PQD signals, including impulse signal, sag with transient oscillation, and harmonic with decaying amplitude are generated in Matlab and compressed. The CR and PRD of impulse signal of the proposed algorithm is 27.64 and 1.19%, which are better than that of the competing methods. Similarly, the CR and PRD of sag with transient oscillation of the proposed algorithm is 31.12 and 1.88%, and the CR and PRD of harmonic with decaying amplitude of the proposed algorithm is 7.98 and 2.02%, showing better compression results. What’s more, a real-life sag signal measured from Power System Dynamic Simulation Laboratory, which is affiliated with Huazhong University of Science and Technology, are compressed and recovered. The CR are as high as 50, while the PRD is less than 2%, which are better than the results of sparse decomposition.
The following conclusions can be drawn from the simulation and experiment results: (1) Compared with the competing methods such as “FFT+WT”, “sparse decomposition”, and OCSVM, the compression ratio (CR) in the proposed method is much higher than that of the compared methods without PRD declining significantly. Therefore, it is appropriate to apply the proposed method to the real-life power quality signals. (2) the experiment performed in the Dynamic power system simulation laboratory results show that the CR is higher than the competing methods, which showed better application prospect.

Key words： Signal compression power quality disturbance Huffman coding long-run coding wavelet analysis sparse decomposition

收稿日期: 2022-08-30

PACS:

TM71

基金资助:

国家自然科学基金资助项目（52077089）

通讯作者: 李开成男,1962年生,教授,博士生导师,研究方向为电子式互感器、电能质量信号分析。E-mail：likaicheng@hust.edu.cn

作者简介: 肖贤贵男,1988年生,博士研究生,研究方向为电能质量扰动分析及信号处理、机器学习及优化方法在电能质量分析中的应用。E-mail：xiaoxiangui@hust.edu.cn

引用本文:

肖贤贵, 李开成, 贺才郡, 宋朝霞, 董宇飞. 基于稀疏分解和复合熵编码的电能质量扰动数据高效压缩算法[J]. 电工技术学报, 0, (): 221653-221653. Xiao Xiangui, Li Kaicheng, He Caijun, Song Zhaoxia, Dong Yufei. A Highly Efficient Compression Algorithm for Power Quality Disturbance Data Using Sparse Decomposition and Hybrid Entropy Encoding. Transactions of China Electrotechnical Society, 0, (): 221653-221653.

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