Power Quality Disturbances (PQDs) Classification Analyzed Based on Deep Learning Technique

Clicks: 2
ID: 313257
2022
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #216 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Power Quality (PQ) problems in a distributed generation are mainly appeared due to excess non-linear load in the system. Identification and classification are necessary to ensure the reliability of Power Quality Disturbances (PQDs). This study proposed a signal processing and deep learning approach classify the PQDs by applying Discrete Wavelet Transform (DWT), Multi-Resolution Analysis (MRA) and a one-dimensional Convolutional Neural Network (CNN). For speed up in training, the performance of model a signal processing-based DWT-MRA extracted 54 features and fed it into 1D-CNN. Implementation of 1D-CNN seems more reliable than other machine learning approaches. Simulation results showed good performance and classification of data efficiently. Hence, the proposed approach could open a new era for PQDs in PV/wind smart grid in the near future to obtain more efficient outcomes.
Reference Key
imported_1777060080_69ebc8f03309c Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Asif Nawaz
Journal Journal of Computing & Biomedical Informatics
Year 2022
DOI
10.56979/401/2022/106
URL
https://jcbi.org/index.php/Main/article/view/106
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.