Real-Time Power Quality Disturbance Classification Using Convolutional Neural Networks

Budi Yanto Husodo, Kalamullah Ramli, Eko Ihsanto, Teddy Surya Gunawan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

There is a growing interest in disturbance monitoring to maintain power quality. This paper developed a real-time power quality disturbance (PQD) detection system using convolutional neural networks (CNN) due to its fast and accurate feature extraction and classification. First, 29 classes of power quality disturbance were synthetically generated around 5000 samples for each type. Second, an efficient CNN structure was developed to extract unique features. Next, the output of CNNs was then inputted into a fully connected layer with a softmax and classification layer to act as the classifier for 29 classes of PQD signals. Our proposed algorithm was then trained using 80% of the synthetic signals, while 20% of the synthetic signals were used for testing. Experimental results showed that the proposed algorithm produced a good result with the classification accuracy of 97.52% trained using 100 epochs. Furthermore, it requires only 80.96 μs to detect each 16 ms segment of PQD signals.

Original languageEnglish
Title of host publicationRecent Trends in Mechatronics Towards Industry 4.0 - Selected Articles from iM3F 2020
EditorsAhmad Fakhri Ab. Nasir, Ahmad Najmuddin Ibrahim, Ismayuzri Ishak, Nafrizuan Mat Yahya, Muhammad Aizzat Zakaria, Anwar P. P. Abdul Majeed
PublisherSpringer Science and Business Media Deutschland GmbH
Pages715-724
Number of pages10
ISBN (Print)9789813345966
DOIs
Publication statusPublished - 2022
EventInnovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020 - Gambang, Malaysia
Duration: 6 Aug 20206 Aug 2020

Publication series

NameLecture Notes in Electrical Engineering
Volume730
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceInnovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020
Country/TerritoryMalaysia
CityGambang
Period6/08/206/08/20

Keywords

  • Bidirectional long short-term memory
  • Classification
  • Power quality disturbance
  • Recurrent neural network
  • Time-frequency based feature extraction

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