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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Mariana Syamsudin & Cheng-I Chen & Sunneng Sandino Berutu & Yeong-Chin Chen, 2024. "Efficient Framework to Manipulate Data Compression and Classification of Power Quality Disturbances for Distributed Power System," Energies, MDPI, vol. 17(6), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1396-:d:1356880
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    References listed on IDEAS

    as
    1. Dash, P.K. & Prasad, Eluri N.V.D.V. & Jalli, Ravi Kumar & Mishra, S.P., 2022. "Multiple power quality disturbances analysis in photovoltaic integrated direct current microgrid using adaptive morphological filter with deep learning algorithm," Applied Energy, Elsevier, vol. 309(C).
    2. María Pérez-Ortiz & Silvia Jiménez-Fernández & Pedro A. Gutiérrez & Enrique Alexandre & César Hervás-Martínez & Sancho Salcedo-Sanz, 2016. "A Review of Classification Problems and Algorithms in Renewable Energy Applications," Energies, MDPI, vol. 9(8), pages 1-27, August.
    3. Xiaoyao Huang & Tianbin Hu & Chengjin Ye & Guanhua Xu & Xiaojian Wang & Liangjin Chen, 2019. "Electric Load Data Compression and Classification Based on Deep Stacked Auto-Encoders," Energies, MDPI, vol. 12(4), pages 1-17, February.
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