Muhammad, Wildan Arinal Haq (2017) Deteksi dan Penentuan Kategori Besaran Arcing pada Hubung Singkat Tegangan Rendah. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Gangguan hubung singkat tegangan rendah menimbulkan fenomena busur api listrik (arcing) yang dapat memicu terjadinya kebakaran apabila langsung bersentuhan dengan benda yang mudah terbakar. Fenomena busur api listrik (arcing) tersebut terjadi karena pengaman yang dipasang (Miniature Circuit Breaker) gagal mendeteksi adanya gangguan hubung singkat yang berlangsung sangat cepat. Tugas akhir ini akan melakukan percobaan pendeteksian arcing hubung singkat pada tegangan rendah dengan melihat nilai arus arcing hubung singkat yang naik secara signifikan pada saat terjadi gangguan hubung singkat, selanjutnya nilai arus arcing yang masuk akan ditrasformasikan menggunakan transformasi wavelet untuk mempermudah melakukan pengamatan karakteristik arus arcing. Terdapat 2 threshold (batas) yang harus dipenuhi agar dapat dikategorikan arcing yang pertama nilai arus HF maksimum adalah 2 A dan jumlah titik gangguan sebanyak 7. Arus arcing hubung singkat yang terdeteksi akan dilakukan penentuan kategori berdasarkan jumlah serabut yang dihubung singkatkan. Parameter-paramter yang diamati pada tiap jumlah serabut terjadi hubung singkat adalah arus HF maksimum, durasi dan energi pada saat arcing hubung singkat terdeteksi. Arcing hubung singkat akan dikategorikan sesuai dengan parameter-parameter tersebut dengan menggunakan algoritma artificial neural network (ANN). Penenetuan kategori besaran arcing hubung singkat menggunakan artificial neural network mempunyai akurasi sebesar 90%.
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Short circuit fault in low voltage is causing arcing phenomenon that able to trigger fire case when contact with flammable material directly. Arcing occurs when protection equipment (Miniature Circuit Breaker) fail to detect short circuit fault because it occurs very fast. This Final Project is doing experiment short circuit arcing detection in low voltage installation and observe the value of short circuit arcing current which increase significantly when short circuit is occurred. The value of arcing current will transform using wavelet transformation for observe the arcing current characteristic easier. There are 2 thresholds must be fulfilled in order to be categorized arcing. First is the value of maximum arcing current high frequency is 2 A and the number of fault point is 7. Short circuit arcing current detected will categorized based on the number of filament shorted circuit. Parameters observed on each number of filament shorted circuit are maximum current high frequency, duration and energy when short circuit arcing detected. Short circuit arcing will categorize based on parameters above using artificial neural network algorithm Level accuracy of.determination category of arcing in low voltage short circuit use artficial neural network algorithm is 90 %
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | Busur Api Listrik Tegangan Rendah, Transformasi Wavelet, Threshold arus HF, Threshold titik gangguan, artificial neural network |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK201 Electric Power Transmission T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK3226 Transients (Electricity). Electric power systems. Harmonics (Electric waves). |
| Divisions: | Faculty of Industrial Technology > Electrical Engineering > 20201-(S1) Undergraduate Thesis |
| Depositing User: | Muhammad Wildan Arinal Haq |
| Date Deposited: | 30 Jan 2018 03:47 |
| Last Modified: | 08 Mar 2019 06:20 |
| URI: | http://repository.its.ac.id/id/eprint/45183 |
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