Mehang, Tirta Samuel (2018) Islanding Detection Pada Sistem Grid-Photovoltaic Yang Terdistribusi Menggunakan Metode Artificial Neural Network. Masters thesis, Institut Teknologi Sepuluh Nopember Surabaya.
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Abstract
Sistem Photovoltaic (PV) adalah sistem energi terbarukan yang dapat dihubungkan dengan grid dengan tujuan untuk menambah kapasitas daya sistem. Dampak negatif pada sistem PV terhubung grid yaitu ketika pembangkit utama
berhenti mensuplai beban sedangkan beban masih disuplai oleh sistem PV. Kasus ini didefinisikan sebagai kondisi islanding. Jika kondisi tersebut tidak terdeteksi,
beban akan mengalami gangguan tegangan dan masalah kualitas daya.
Tesis ini menyajikan deteksi islanding menggunakan Artificial Neural Network (ANN). Data pembelajaran ANN dihasilkan dari simulasi tiga skenario utama: powermatch, overvoltage, dan undervoltage. Identifikasi sinyal tegangan pada titik Point Of Common Coupling (PCC) dilakukan untuk mendeteksi apakah sistem tergolong pada kondisi islanding atau non-islanding. Hasil simulasi
menunjukkan bahwa ANN mampu mengenali kondisi normal maupun islanding dengan rentang waktu deteksi antara 0,14 – 0,24 detik.
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Photovoltaic (PV) systems are nowadays one of the most wide-spread
renewable energy systems in the network or grid with one purpose to improve the
reliability of the grid. However, PV systems in the network also contribute a
negative impact as well; when the main grid fails to supply the load and there is a
part of the load energized by the PV systems while being isolated. This case is
defined as islanding. If this condition cannot be detected, the load bus will
experience voltage disturbance and power quality problem.
This thesis presents an islanding detection using Artificial Neural Network
method (ANN). ANN learning data are generated from simulations under three
main scenarios: power match, overvoltage, and undervoltage. Voltage signal at
Point Of Common Coupling (PCC) node in load bus is classified to identify if
system is in islanding condition or not. The simulation results shows that the built
ANN is capable to detect both islanding and non-islanding mode with range of
detection time from 0.14 to 0.24 seconds.
| Item Type: | Thesis (Masters) |
|---|---|
| Additional Information: | RTE 621.312 44 Meh i |
| Uncontrolled Keywords: | Islanding, Photovoltaic, Artificial Neural Networ |
| Subjects: | Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) T Technology > TA Engineering (General). Civil engineering (General) > TA1573 Detectors. Sensors T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1087 Photovoltaic power generation |
| Divisions: | Faculty of Electrical Technology > Electrical Engineering > 20101-(S2) Master Thesis |
| Depositing User: | Tirta Samuel Mehang |
| Date Deposited: | 27 Nov 2020 07:51 |
| Last Modified: | 27 Nov 2020 07:51 |
| URI: | http://repository.its.ac.id/id/eprint/58996 |
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