Islanding Detection Pada Sistem Grid-Photovoltaic Yang Terdistribusi Menggunakan Metode Artificial Neural Network

Mehang, Tirta Samuel (2018) Islanding Detection Pada Sistem Grid-Photovoltaic Yang Terdistribusi Menggunakan Metode Artificial Neural Network. Masters thesis, Institut Teknologi Sepuluh Nopember Surabaya.

[img]
Preview
Text
tesis tanpa watermark.pdf - Accepted Version

Download (4MB) | Preview

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. ================================================================================================================== 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: https://repository.its.ac.id/id/eprint/58996

Actions (login required)

View Item View Item