Signal Processing-based Artificial Intelligence Approach for Power Quality Disturbance Identification

Md Sadman Sakib, Md Rashidul Islam, S. M.Sazzadul Haque Tanim, Md Shafiul Alam, Md Shafiullah, Amjad Ali

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

1 Scopus citations

Abstract

Power Quality (PQ) disturbance detection is thought to be a very significant service that many utilities provide for their commercial and industrial customers. PQ disturbances affect the load that is connected to the supply, which is troubling to the consumers. Detection and classification of the electrical problem are very difficult to find out which can cause PQ problems. In this paper, the key PQ issues such as voltage sag, voltage swell, voltage interruption, harmonics, and transient events have been tested. It has been demonstrated that a new approach may be used to identify, localize, and examine the probability of classifying distinct forms of PQ disturbances. The basic idea is to divide a disturbance signal into a transparent and comprehensive representation using DWT and ST. Many mathematical processes are utilized to extract features from these decomposed signals. The signal decomposition technique is integrated with the feed-forward neural network model to develop the power quality problem identifier (detection and classification). The simulation results show that the proposed method is effective. The proposed method is also feasible and promising for real-time applications.

Original languageEnglish
Title of host publication2022 International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665469449
DOIs
StatePublished - 2022

Publication series

Name2022 International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2022

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Classification
  • Detection
  • Feature Extraction
  • Feedforward Neural Network (FFNN)
  • Power quality (PQ)
  • Stockwell Transform (ST)
  • Wavelet Transform (WT)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Instrumentation

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