基于压缩感知的电能质量扰动数据稀疏分析与改进重构算法

doi:10.19595/j.cnki.1000-6753.tces.171096

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Abstract Power quality disturbance (PQD) data based on compressed sensing (CS) is subjected to sparse analysis mostly by using the DFT basis. However, this method incurs the problem of spectrum leakage, which reduces the sparseness of the original data, leads to overestimation of the sparseness in the subsequent reconstruction algorithm. In light of the above problem, based on the DFT sparse analysis of PQD signals, an improved reconstruction algorithm immune to spectrum leakage is proposed. Firstly, the amplitude spectra of four typical PQD models are theoretically deduced, and the relationship between the sparse characteristics of each model and the related parameters is analyzed in detail. Secondly, on the basis of this, the sparse adaptive march pursuit (SAMP) algorithm is improved and the concept of spectral energy difference is proposed. The spectral energy difference can effectively describe the change value of signal spectrum energy during adjacent reconstruction iteration. When the difference is used as the iterative termination condition of the SAMP algorithm, sparseness overestimation caused by spectrum leakage can be avoided and the operation efficiency of the algorithm can be improved. Finally, the contrast experiments have verified the superiority of the improved SAMP algorithm proposed in this paper.

Key words： Compressed sensing power quality disturbance DFT sparsity reconstruction algorithm

Received: 27 July 2017 Published: 14 August 2018

PACS:	TH86
	TM935

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	Liu Yan
	Tang Wei
	Liu Baoquan

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Liu Yan,Tang Wei,Liu Baoquan. Data Sparse Analysis and Improved Reconstruction Algorithm of Power Quality Disturbance Based on Compressed Sensing[J]. Transactions of China Electrotechnical Society, 2018, 33(15): 3461-3470.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.171096 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I15/3461

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