DOI:https://doi.org/10.1007/s40565-018-0467-4 |
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Transient power quality disturbance denoising and detectionbased on improved iterative adaptive kernel regression |
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Author:
Yan WANG1,2 , Qunzhan LI1
, Fulin ZHOU1
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Author Affiliation:
1. School of Electrical Engineering, Southwest Jiaotong
University, Chengdu 611756, China
2. College of Electrical & Information Engineering, Southwest
Minzu University, Chengdu 610041, China
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Foundation: |
This work is supported in part by the National
Key R&D Program of China (No. 2016YFB1200401, No.
2017YFB1201103), and in part by the Program for Application of
Cophase Power Supply Technology (No. 2018002). |
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Abstract: |
The denoising and detection of transient disturbances are two important subjects for power quality monitoring and analysis. To effectively denoise and detect
transient disturbances under noisy conditions, an improved
iterative adaptive kernel regression method is proposed in
this paper. The proposed method has advantages in that it
does not need to estimate the noise variance or a filter
threshold, and has both denoising and detection capabilities
for transient disturbances. Simulation results demonstrate
that the proposed method provides excellent denoising
effects, which can not only suppress noise effectively but
also preserve disturbance features of sudden change points
well. Additionally, it provides good detection and location
performance for single and combined transient disturbances, even under strong noise conditions. Finally, the
effectiveness of the proposed method is further verified by
using real disturbance data. |
Keywords: |
Transient power quality disturbance, Noise
variance, Filter threshold, Denoising, Sudden change point,
Detection, Location |
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Online Time:2019/05/14 |
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