Power Quality Disturbance Signal Denoising Based on Overcomplete Representation

2021 ◽  
Author(s):  
Lei Chen ◽  
Jianjun Xu ◽  
Shuang Chen ◽  
Hui Yang ◽  
Linhu Liu
Keyword(s):  
Power Quality ◽  
Signal Denoising ◽  
Power Quality Disturbance ◽  
Overcomplete Representation

Improved Threshold Function for Power Quality Disturbance Signal Denoising

2014 ◽  
Vol 687-691 ◽  
pp. 3636-3639
Author(s):  
Xiao Long Fan ◽  
Wei Cheng Xie ◽  
De Lei Sha ◽  
Yu Li
Keyword(s):  
Power Quality ◽  
Threshold Value ◽  
Threshold Function ◽  
Signal Denoising ◽  
Wavelet Coefficients ◽  
Wavelet Shrinkage ◽  
Comparison Results ◽  
Power Quality Disturbance ◽  
Detection And Localization ◽  
Suppress Noise

Mutation point feature of power quality (PQ) disturbance signals are very conducive to the wavelet-based detection and localization of PQ events, but the PQ signals are often disturbed by noise. In order to suppress noise and keep mutation points, an improved threshold function was proposed. According to the fact that the wavelet coefficients of signal and noise distributed on different scale, the threshold σj2lnk amended by to calculate threshold value for each scale adaptively. (k is the number of wavelet coefficients at level j). In simulation, four type of PQ signals and three degrading degrees are testing; meanwhile, four existing algorithm with wavelet shrinkage are performed for comparison. Results reveal that the proposed scheme not only suppresses noise of PQ signal well but also keeps the mutation points nicely.

scholarly journals Detector of power quality disturbance signal with random phase offset

2019 ◽  
Vol 16 (22) ◽  
pp. 20190401-20190401
Author(s):  
Jeonghwa Yoo ◽  
Sangho Choe
Keyword(s):  
Power Quality ◽  
Random Phase ◽  
Phase Offset ◽  
Power Quality Disturbance

scholarly journals High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1238
Author(s):  
Supanat Chamchuen ◽  
Apirat Siritaratiwat ◽  
Pradit Fuangfoo ◽  
Puripong Suthisopapan ◽  
Pirat Khunkitti
Keyword(s):  
Feature Selection ◽  
Power Quality ◽  
Distribution System ◽  
Classification Accuracy ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Electrical Distribution ◽  
Power Quality Disturbance ◽  
Optimal Feature Selection ◽  
Optimal Feature

Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system’s performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system.

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