Implementasi Algoritma Kompresi Data untuk Meningkatkan Kinerja Pendeteksian Gangguan Kualitas Daya Listrik
DOI:
https://doi.org/10.18196/mt.v5i1.18386Keywords:
Algoritma Kompresi, CNN, Framework Klasifikasi.Abstract
Resiko terjadinya penurunan kualitas daya listrik dapat terjadi pada banyak tahapan yaitu, produksi, transformasi, distribusi, dan konsumsi energi. Salah satu cara untuk menangani masalah kualitas daya adalah dengan melakukan deteksi dan klasifikasi gangguan kualitas daya atau dalam istilah asing disebut Power Quality Disturbances (PQDs). Namun, penelitian sebelumnya hanya berfokus pada topik berikut: gangguan kebisingan (noise), kegagalan model dalam menggeneralisasi data (overfitting), dan waktu yang diperlukan untuk pelatihan dataset. Sebuah strategi baru disarankan untuk mengatasi masalah ini dengan menggabungkan kompresi dataset sinyal 1-Dimensi dengan algoritma klasifikasi convolutional neural network (CNN). Dua jenis algoritma kompresi yang diusulkan untuk dievaluasi adalah wavelet transform (WT) dan autoencoder. Data yang digunakan untuk evaluasi adalah kumpulan data sintetik menurut standar IEEE-1159, yaitu empat belas tipe PQDs yang berbeda. Selanjutnya, prosedur klasifikasi PQDs akan mengintegrasikan data terkompresi dengan algoritma klasifikasi CNN. Hasil akhir penelitian memperlihatkan, metode yang disarankan menunjukkan bahwa menggabungkan algoritma kompresi autoencoder dan metode klasifikasi CNN dapat mengenali PQDs secara efisien. Bahkan di lingkungan dengan tingkat noise 20db, pemrosesan sinyal PQDs mencapai akurasi hingga 97,14 persen dan berhasil memperkecil overfitting.References
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