Clustering Suara Corona Discharge berdasarkan Tegangan menggunakan Metode Fuzzy C-Mean

MIFTAHUL FIKRI, CHRISTIONO CHRISTIONO, IWA GARNIWA MULYANA K., KARTIKA TRESYA MAURIRAYA, NURMIATI PASRA, SAMSURIZAL SAMSURIZAL, MUHAMMAD LUTHFIANSYAH ROMADHONI, ANDI AMAR THAHARA

Sari


ABSTRAK

Corona discharge (CD) atau peluahan permukaan merupakan faktor kegagalan isolasi pada sistem kelistrikan yang dipengaruhi oleh kondisi lingkungan tidak menentu dan perlu pemantauan secara terkini. CD menghasilkan gelombang suara yang digunakan sebagai parameter untuk langkah awal mencapai tujuan tersebut, penelitian ini mengkluster suara CD pada terminasi kubikel 20 kV. Penelitian menggunakan elektroda jarum-jarum berjarak 3 cm. Tercatat nilai sebelum breakdown voltage terjadi pada tegangan 33,4 kV, dan pengambilan data terbagi 3 klaster: 20-24 kV, 25-29 kV, 30-33 kV. Proses klasterisasi dengan metode LPC untuk menghasilkan ekstraksi suara. Kemudian menggunakan metode fuzzy c-mean untuk memperoleh akurasi dengan membandingkan pola suara training dan testing. Pada Kelembapan berkisar 70%-95% dengan suhu antara 27,5°C - 35.3°C diperoleh hasil akurasi 96,00% untuk data training dan 80,00% untuk data testing.

Kata kunci: Corona discharge, fuzzy c-mean, linear predictive coding, kegagalan isolasi

 

ABSTRACT

Corona discharge (CD) is a factor in insulation failure in electrical systems, which is affected by uncertain environmental conditions and requires up-to-date monitoring. CD which produces sound waves, is used as a parameter for the initial step to achieve this goal. This research will cluster CD sounds at 20 kV cubicle terminations. The study used electrode needles spaced 3 cm apart. The value recorded before the breakdown voltage occurred was 33.4 kV, and data collection was divided into 3 clusters: 20–24 kV, 25–29 kV, and 30-33 kV. The clustering process with the LPC method produces sound extraction. Then use the fuzzy C-mean method to obtain accuracy by comparing trained and tested sound patterns. At a humidity range of 70%–95% and temperatures between 27.5°C–35.3°C, the results obtained an accuracy of 96.00% for training data and 80.00% for testing data.

Keywords: Corona discharge, fuzzy c-mean, linier predictive coding, insulation failure


Kata Kunci


Corona discharge; fuzzy c-mean; linear predictive coding; kegagalan isolasi

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v11i3.609

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