Deteksi Suara Corona Discharge berdasarkan Noise menggunakan Metode LPC dan Euclidean Distance

NURMIATI PASRA, MIFTAHUL FIKRI, KARTIKA TRESYA MAURIRAYA, TRI RIJANTO, I GUSTI PUTU ASTO BUDITJAHJANTO

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ABSTRAK

Kegagalan isolasi masih sering terjadi pada sistem kelistrikan di Indonesia yang disebabkan oleh fenomena tegangan tinggi seperti corona discharge (CD). Hal ini dikarenakan deteksi dini kegagalan isolasi belum dapat dilakukan. Salah satu bentuk CD ialah suara. Langkah awal untuk mendeteksi dini kegagalan isolasi ialah diperlukannya suatu penelitian yang dapat mengklaster suara CD berdasarkan noise yang merupakan tujuan penelitian ini. Pengamatan dilakukan pada kubikal 20-22 kV dengan cara menaik-turunkan tegangan dengan elektroda jarum-batang sejauh 3 cm. Klasifikasi suara CD ditetapkan menjadi 3 cluster yaitu CD murni, CD disertai noise mendesis dan noise mendesis murni. Clustering dilakukan menggunakan metode linear predictive coding (LPC) sebagai ekstraksi ciri dan Euclidean distance sebagai pencocokan pola hasil ekstraksi. Adapun suhu di dalam kubikal antara 25℃ - 32℃ dan kelembaban berkisar 70% - 90%. Hasil akurasi clustering rata-rata yang diperoleh adalah 100% untuk data training dan 97,78% untuk data testing.

Kata kunci: corona discharge, linear predictive coding, Euclidean distance, kegagalan isolasi

 

ABSTRACT

Insulation failures often occur in the electrical system in Indonesia caused by high voltage phenomena such as corona discharge (CD). This is because early detection of insulation failure cannot be carried out. One form of CD is sound. The first step for early detection of insulation failure is the need for a study that can cluster CD sounds based on noise which is the purpose of this study. Observations were made at 20-22 kV cubical by increasing and decreasing the voltage with a needle-rod electrode as far as 3 cm. The classification of CD sound is set into 3 clusters i.e. pure CD, CD with hissing noise, and pure hissing noise. Clustering is done using linear predictive coding (LPC) as feature extraction and Euclidean distance as pattern matching extraction results. The temperature inside the cubical is between 25℃ - 32℃ and the humidity is around 70% - 90%. The average clustering accuracy results obtained are 100% and 97.78% for training and testing data.

Keywords: author’s guideline, document’s template, format, style, abstract


Kata Kunci


corona discharge; linear predictive coding; Euclidean distance; kegagalan isolasi

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v11i1.72

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