广义正交匹配追踪电能质量信号重构方法
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摘要
针对基于压缩感知的暂态电能质量数据信号恢复效果不佳的问题,提出了基于离散小波稀疏基的广义正交匹配追踪(gOMP)电能质量信号重构方法。当暂态信号出现时,基于离散小波变换的稀疏矩阵可以捕获波形细节。在重构过程中,与OMP相比由于选择了多个正确的索引而不需要附加后续操作,gOMP算法的迭代次数要少得多,而且gOMP可以完好地重建K稀疏电能质量信号。gOMP具有快速处理速度和相当优异的计算复杂性,在电能质量信号重构上具有良好的恢复性能。经过一系列的实验,暂态和稳态电能质量信号都得到了精确的重构,且重构精度大于99. 76%,重构所需时间明显缩短。
Aiming at the problem of poor recovery of transient power quality data signal based on compressive sensing,this paper proposed the power quality signal reconstruction method based on discrete wavelet sparse and the generalized orthogonal matching pursuit( gOMP). When transient signals have appeared,the discrete wavelet sparse based can grab the details at that time. Owing to the selection of multiple "correct"indices with no additional post-processing operation,the gOMP algorithm is finished with the much smaller number of iterations when compared to the OMP,and gOMP can reconstruct K sparse power quality signals well. This paper shows that the gOMP can perfectly reconstruct any K-sparse power quality data. The empirical simulations demonstrate that the gOMP has excellent recovery performance and computational complexity. After a series of experiments,both transient and steady state signals are perfectly reconstructed and reconstruction accuracy is greater than 99. 76%,and refactoring time is significantly reduced.
Aiming at the problem of poor recovery of transient power quality data signal based on compressive sensing,this paper proposed the power quality signal reconstruction method based on discrete wavelet sparse and the generalized orthogonal matching pursuit( gOMP). When transient signals have appeared,the discrete wavelet sparse based can grab the details at that time. Owing to the selection of multiple "correct"indices with no additional post-processing operation,the gOMP algorithm is finished with the much smaller number of iterations when compared to the OMP,and gOMP can reconstruct K sparse power quality signals well. This paper shows that the gOMP can perfectly reconstruct any K-sparse power quality data. The empirical simulations demonstrate that the gOMP has excellent recovery performance and computational complexity. After a series of experiments,both transient and steady state signals are perfectly reconstructed and reconstruction accuracy is greater than 99. 76%,and refactoring time is significantly reduced.
引文
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[2] Miyu Nakao,Takumi Ishihara,Shinya Sugiura. Dual-Mode Time-Domain Index Modulation for Nyquist-Criterion and Faster-Than-Nyquist Single-Carrier Transmissions[J]. IEEE Access,2017,1(1):27659-27667.
[3] Junxin Chen,Yushu Zhang,Leo Yu Zhang. On the Security of Optical Ciphers Under the Architecture of Compressed Sensing Combining With Double Random Phase Encoding[J]. IEEE Photonics Journal,2017,9(4):1943-0655.
[4] D. Donoho. Compressed sensing[J]. IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[5] E. J. Candes,J. Romberg,T. Tao. Robust uncertainty principles:Exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions Inform Theory,2006,52(2):489-509.
[6] Serkan Ozturk,Tian You Yu,Lei Ding. Application of Compressive Sensing to Refractivity Retrieval Using Networked Weather Radars[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(5):2799-2809.
[7] Jian Wang,Seokbeop Kwon,Byonghyo Shim. Generalized Orthogonal Matching Pursuit[J]. IEEE Transactions on Signal Processing,2012,60(12):6202-6216.
[8] Daeyoung Park. Improved Sufficient Condition for Performance Guarantee in Generalized Orthogonal Matching Pursuit[J]. IEEE Signal Processing Letters,2017,24(9):1308-1312.
[9] Joel A. Tropp,Anna C. Gilbert. Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit[J]. IEEE Transactions on Information Theory,2007,53(12):4655-4666.
[10]Deanna Needell,Roman Vershynin. Signal Recovery From Incomplete and Inaccurate Measurements Via Regularized Orthogonal Matching Pursuit[J]. IEEE Journal of Selected Topics in Signal Processing,2010,4(2):310-316.
[11]Thong T. Do,Lu Gan,Nam Nguyen. Sparsity adaptive matching pursuit algorithm for practical compressed sensing[C]. Asilomar Conference on Signals,Systems and Computers,Pacific Grove,California,2008,10:581-587.
[12]Jun Shi,Xiaoping Liu,Xuejun Sha. A Sampling Theorem for Fractional Wavelet Transform With Error Estimates[J]. IEEE Transactions on Signal Processing,2017,65(18):4797-4811.
[13]Thiagarajan J J,Ramamurthy K N,Spanias A. Learning Stable Multilevel Dictionaries for Sparse Representations[J]. IEEE Transactions on Neural Networks&Learning Systems,2015,26(9):1913-1926.
[14]Suliman M A,Alrashdi A M,Ballal T,et al. SNR Estimation in Linear Systems with Gaussian Matrices[J]. IEEE Signal Processing Letters,2017,PP(99):1-1.