Efficient Framework to Manipulate Data Compression and Classification of Power Quality Disturbances for Distributed Power System

Mariana Syamsudin, Cheng-I Chen (), Sunneng Sandino Berutu () and Yeong-Chin Chen
Additional contact information
Mariana Syamsudin: Department of Electrical Engineering, Politeknik Negeri Pontianak, Pontianak 78124, Indonesia
Cheng-I Chen: Department of Electrical Engineering, National Central University, Taoyuan 320, Taiwan
Sunneng Sandino Berutu: Department of Information and Technology, Immanuel Christian University, Yogyakarta 55571, Indonesia
Yeong-Chin Chen: Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan

Energies, 2024, vol. 17, issue 6, 1-20

Abstract: There is some risk of power quality disturbances at many stages of production, transformation, distribution, and energy consumption. The cornerstone for dealing with power quality problems is the characterization of power quality disturbances (PQDs). However, past research has focused on a narrow topic: noise disruption, overfitting, and training time. A new strategy is suggested to address this problem that combines efficient one-dimensional dataset compression with the convolutional neural network (CNN) classification algorithm. First, three types of compression algorithms: wavelet transform, autoencoder, and CNN, are proposed to be evaluated. According to the IEEE-1159 standard, the synthetic dataset was built with fourteen different PQD types. Furthermore, the PQD classification procedure integrated compressed data with the CNN classification algorithm. Finally, the suggested method demonstrates that combining CNN compression and classification methods can efficiently recognize PQDs. Even in noisy environments, PQD signal processing achieved up to 98.25% accuracy and managed the overfitting.

Keywords: power quality disturbance; data compression; classification; convolutional neural network; distributed power system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/6/1396/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/6/1396/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:6:p:1396-:d:1356880

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().