scholarly journals Power Quality Disturbance Classification Based on Compressed Sensing and Deep Convolution Neural Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 78336-78346 ◽  
Author(s):  
Jidong Wang ◽  
Zhilin Xu ◽  
Yanbo Che
Keyword(s):  
Neural Networks ◽  
Compressed Sensing ◽  
Power Quality ◽  
Convolution Neural Networks ◽  
Power Quality Disturbance

scholarly journals Incoherence Analysis of RD-AIC-Based Observation Matrix and Its Application in Power Quality Disturbance Signal

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yan Liu ◽  
Wei Tang
Keyword(s):  
Compressed Sensing ◽  
Power Quality ◽  
Random Matrix ◽  
Simple Structure ◽  
Design Principles ◽  
Parameter Design ◽  
Analysis Process ◽  
Power Quality Disturbance ◽  
The Matrix ◽  
Observation Matrix

In the theory of compressed sensing, restricted isometry property (RIP) decides the universality and reconstruction robustness of an observation matrix. At present, an observation matrix based on RD-AIC (RD-AIC-based observation matrix) can compress sparse continuous signals with a simple structure, but RIP analysis of this matrix is lack and challenging to prove. In this paper, this problem is relaxed. Instead, we demonstrate the incoherence analysis process, derive the orthogonality and nonsingularity of the matrix, propose the generalized relevance calculation steps of the matrix, and propose the hardware parameter design principles to improve the incoherence of the matrix. Moreover, compression and reconstruction experiments used in power quality disturbance signals are developed for testing the incoherence. The results show that the RD-AIC-based observation matrix has substantial incoherence under suitable hardware parameters, equivalent to the Gaussian random matrix and the Bernoulli random matrix.

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