Firdaus, Rama Kurniawan (2019) Deteksi dan Klasifikasi Gangguan Kualitas Daya Berbasis S-Transform dan Artificial Neural Network. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Kualitas daya listrik menjadi hal yang perlu diperhatikan seiring
dengan perkembangan teknologi dimana peralatan listrik modern yang
memiliki kepekaan tinggi terhadap gangguan kualitas daya listrik.
Gangguan kualitas daya listrik dapat berdampak pada
ketidaksempurnaan operasi peralatan listrik, menyebabkan kerusakan
peralatan listrik yang berujung pada kerugian ekonomi. Penurunan
kualitas daya listrik disebabkan oleh gangguan yang berupa voltage sag,
voltage swell, gangguan sesaat, harmonisa, flicker yang akan
menyebabkan kegagalan operasi, ketidakstabilan sistem, memperpendek
umur peralatan, dan lain sebagainya.
Pada penelitian ini menggunakan S-transform untuk
mengekstraksi informasi atau fitur dari sinyal gangguan kualitas daya.
Beberapa jenis gangguan kualitas daya memilki sinyal dengan
karakteristik non-stasioner. S-Transform memiliki kemampuan untuk
mengekstrak fitur yang terkandung dalam sinyal arus dan tegangan yang
terdampak gangguan kualitas daya dengan karakteristik non-stasioner
dengan proses komputasi yang ringkas serta penggunaan memori yang
rendah bila dibandingkan dengan metode lain. Hasil ekstraksi fitur
dengan S-Transform selanjutnya akan digunakan untuk input Artificial
Neural Netwok (ANN). Dalam penelitian ini ANN memegang peranan
dalam pengklasifikasian gangguan kualitas daya.
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Power quality has become an important issue for electric utilities
and its customers. In this modern era, many electric devices is very
sensitive to power quality disturbance when in operation. The
disturbance may affect the operation of electric utilities and its
customers that lead to breakdown and economical issue. Power quality
degradation caused by sag, swell, harmonics, flicker, notching and spike
will reduce lifetime of the electric devices.
In this research using S-transform to extract the features that
contained in the power quality disturbance signal. A few of power
quality disturbance event have non-stationary signal characteristic. Stransform
has ability to extracting the features from the signal that
affected by the disturbance with the low computational and low memory
usage compared with other signal extracting method. The extracting
result from the S-transform will be used as input of the Artificial Neural
Network (ANN). The ANN has a role in this research to recognize the
power quality disturbance event from the signal
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSE 621.31 Fir d-1 2019 |
| Uncontrolled Keywords: | Gangguan kualitas daya, S-transform, Artificial Neural Network (ANN) |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1007 Electric power systems control |
| Divisions: | Faculty of Industrial Technology > Electrical Engineering > 20201-(S1) Undergraduate Thesis |
| Depositing User: | Rama Kurniawan Firdaus |
| Date Deposited: | 11 Jun 2021 08:27 |
| Last Modified: | 11 Jun 2021 08:27 |
| URI: | http://repository.its.ac.id/id/eprint/60507 |
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