Detail Improvement of Doernenburg Ratio Method Dissolved-gas Analysis using 5-fold Cross Validation Artificial Neural Network (article of International Journal of Advanced Science and Technology (IJAST) Vol. 29 No. 07s Special Issue 2020) Bibliografi Author: Bachri, Karel Octavianus ; Khayam, Umar ; Soedjarno, Bambang Anggoro ; Kartawidjaja, Maria Angela Topik: Dissolved Gas Analysis; Doernenburg Ratio Method; Artificial Neural Network; Cross Validation; JABFUNG-FT-KOB-2023-21 Bahasa: (EN )     Penerbit: Science and Engineering Research Support Society     Tempat Terbit: Tasmania    Tahun Terbit: 2020     Jenis: Article - diterbitkan di jurnal ilmiah internasional Fulltext: B21_IJAST_SS_17620-Article_Text-26275-1-10-20200523.pdf (510.31KB; 1 download) [Informasi yang berkaitan dengan koleksi ini di internet] Abstract Dissolved-gas Analysis (DGA) is the common method in determining fault occurring in a transformer. Fault is identified based on the composition of dissolved gases. This paper discusses the improvement of conventional DGA interpretation method using 5-fold cross validation Artificial Neural Network (ANN). Conventional ANN requires a large number of data, while collecting data requires amount of times. This can be overcome using cross validation method. data is divided into five groups. Four of which are selected as train data, while the other one is used as test data. These processes are repeated until all groups are used as test data. Using cross validation, a valid and consistent decision can be made using limited amount of data. Opini Anda Klik untuk menuliskan opini Anda tentang koleksi ini! Lihat Sejarah Pengadaan  Konversi Metadata   Kembali

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