Paper Title
A Power Quality Disturbance Classification System Based on Wavelet Transformation and Back Propagation Network
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Registration ID: IJNRD_218947
Published ID: IJNRD2404738
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Keywords
Wavelet Transformation, back propagation, Power quality disturbances, Neural network
Abstract
This paper proposes a method for Power Quality (PQ) disturbances classification based on Wavelet transformation and Backpropagation (BP) algorithm. Several types of PQ disturbance are taken into consideration in this paper. Wavelet transformation is used to construct the feature vector from these PQ disturbances based on Multiresolution analysis (MRA). These feature vectors then are applied to the BP network for training and testing. Comparison research between the proposed method and the existing literature shows that the proposed can provide accurate classification results. The hypothesis test of the average classification accuracy values of different wavelets shows that one can choose any wavelet with a short filter length to achieve a good classification result with a low computational cost. Gaussian white noise and the Monte Carlo method are taken in consideration to simulate the performance of the proposed method in different noise environments.
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How To Cite (APA)
MD RABIUL ISLAM & Shan Gao (April-2024). A Power Quality Disturbance Classification System Based on Wavelet Transformation and Back Propagation Network. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), h311-h321. https://ijnrd.org/papers/IJNRD2404738.pdf
Issue
Volume 9 Issue 4, April-2024
Pages : h311-h321
Other Publication Details
Paper Reg. ID: IJNRD_218947
Published Paper Id: IJNRD2404738
Research Area: Electrical EngineeringÂ
Author Type: Foreign Author
Country: Nanjing, Jiangsu, China
Published Paper PDF: https://ijnrd.org/papers/IJNRD2404738.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404738
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