Automatic Detection of Power Quality Disturbance Using Convolutional Neural Network Structure with Gated Recurrent Unit


Yiğit E., Özkaya U., Öztürk Ş., Singh D., Gritli H.

Mobile Information Systems, vol.0, no.0, pp.1-11, 2021 (SCI-Expanded)

  • Publication Type: Article / Article
  • Volume: 0 Issue: 0
  • Publication Date: 2021
  • Doi Number: 10.1155/2021/7917500
  • Journal Name: Mobile Information Systems
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.1-11
  • Bursa Uludag University Affiliated: Yes

Abstract

Power quality disturbance (PQD) is essential for devices consuming electricity and meeting today’s energy trends. *is study

contains an effective artificial intelligence (AI) framework for analyzing single or composite defects in power quality. A con-

volutional neural network (CNN) architecture, which has an output powered by a gated recurrent unit (GRU), is designed for this

purpose. *e proposed framework first obtains a matrix using a short-time Fourier transform (STFT) of PQD signals. *is matrix

contains the representation of the signal in the time and frequency domains, suitable for CNN input. Features are automatically

extracted from these matrices using the proposed CNN architecture without preprocessing. *ese features are classified using the

GRU. *e performance of the proposed framework is tested using a dataset containing a total of seven single and composite

defects. *e amount of noise in these examples varies between 20 and 50 dB. *e performance of the proposed method is higher

than current state-of-the-art methods. *e proposed method obtained 98.44% ACC, 98.45% SEN, 99.74% SPE, 98.45% PRE,

98.45% F1-score, 98.19% MCC, and 93.64% kappa metric. A novel power quality disturbance (PQD) system has been proposed,

and its application has been represented in our study. *e proposed system could be used in the industry and factory.