COMPARISON OF CORONA DISCHARGE IDENTIFICATION IN 20 kV CUBICLES BASED ON VOLTAGE AND NOISE USING ED, HMM, AND FCM

Authors

  • Miftahul Fikri ᵃHigh Voltage and High Current Institute, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia ᵇFaculty of Electrical and Renewable Energy, Institut Teknologi PLN, Jakarta Indonesia https://orcid.org/0000-0002-9454-9197
  • Christiono - Faculty of Electrical and Renewable Energy, Institut Teknologi PLN, Jakarta Indonesia
  • Iwa Garniwa Mulyana K. ᵇFaculty of Electrical and Renewable Energy, Institut Teknologi PLN, Jakarta Indonesia ᶜDepartment of Electrical Engineering, Universitas Indonesia, Depok, Indonesia
  • Zulkurnain Abdul-Malek High Voltage and High Current Institute, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Muhammad Luthfiansyah Romadhoni UIP3B Kalimantan, PT PLN (Persero), Kalimantan Indonesia
  • Mona Riza Mohd Esa High Voltage and High Current Institute, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Eko Supriyanto High Voltage and High Current Institute, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.11113/jurnalteknologi.v86.19986

Keywords:

Corona discharge, linear predictive coding, Euclidean distance, hidden Markov model, fuzzy c-means

Abstract

Phenomena such as corona discharge (CD) still occurs in many electrical systems in Indonesia. As a first step for early detection of insulation failure. Identification of CD acoustic in this study namely clustering based on voltage and based on noise. So that the CD acoustic classification is set into 3 clusters. In addition, this study also classifies CD acoustic based on noise with three clusters, namely pure CD, CD with noise, and pure noise. Clustering was performed using the linear predictive coding (LPC) method as feature extraction, then a comparison of pattern matching results of feature extraction was performed using Euclidean distance (ED), hidden Markov model (HMM) and fuzzy cluster mean (FCM). The temperature in the cubical is between 27.5 ℃ - 35.3 ℃ and humidity ranges from 70% - 95%. The results of clustering accuracy on the average base voltage using the ED, HMM and FCM methods were obtained respectively 100%, 100% 93.93% for training data and 80.74%, 84.44%, 80.55% for testing data. While the results of the average base noise clustering accuracy using the ED, HMM and FCM methods were obtained respectively 100%, 100%, 94.69% for training data and 100%, 100%, 100% for testing data.

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Published

2024-08-12

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Section

Science and Engineering

How to Cite

COMPARISON OF CORONA DISCHARGE IDENTIFICATION IN 20 kV CUBICLES BASED ON VOLTAGE AND NOISE USING ED, HMM, AND FCM. (2024). Jurnal Teknologi (Sciences & Engineering), 86(5), 11-22. https://doi.org/10.11113/jurnalteknologi.v86.19986