Mahmudah, Norma (2021) Sistem Manajemen Energi Terbarukan Dengan Prediksi Keluaran Daya PV, Pelepasan Beban, Dan Kondisi Pengisian Baterai Pada Sistem Off-Grid. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Pembangkit listrik merupakan kebutuhan yang sangat penting dalam kehidupan sahari-hari, Sehingga banyak yang mempertimbangkan energi terbarukan untuk mensuplai kekurangan daya. Sumber energi yang paling efektif adalah Photovoltaic (PV) dan Wind Turbin dan Baterai sebagai penyimpanan sumber energi sehingga perlu adanya Energy Management System (EMS). EMS diusulkan untuk starategi managemen kontrol antara energi terbarukan (PV,Wind Turbin), Baterai, dan beban komersial. Pada penelitihan ini diusulkan melakukan EMS pada sistem yang terdiri dari Energi terbarukan (PV dan Wind),baterai, dan beban. Terdapat tiga pertimbangan utama dalam menentukan EMS pada sistem off-grid. Pertama memprediksi daya keluaran PV dan beban dengan mempertimbangkan parameter irradiance, temperatur PV dan Temperatur sekitar (ambient) menggunakan Artificial Neural Network (ANN) yaitu Cascade Forward Neural Network dengan algoritma Levenberg-marquard yang cepat dan stabil. Strategi kedua adalah mengunakan algoritma rule-base untuk menjaga State of Charge (SOC) agar tidak mengalami kondisi overcharging/overdischarging. Yang ketiga melakukan pelepasan beban untuk mengoptimalkan charge ke baterai. Penelitihan ini dilakukan dengan mengunakan Simulink matlab.
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Power generation is a very important requirement in everyday life, so many consider renewable energy to supply power shortages. The most effective energy sources are Photovoltaic (PV) and Wind Turbines and Batteries as a storage source of energy so that an Energy Management System (EMS) is needed. EMS is proposed for control management strategies between renewable energy (PV, Wind Turbines), batteries, and commercial loads. In this research, it is proposed to carry out EMS on a system consisting of renewable energy (PV and Wind), batteries, and loads. There are three main considerations when determining an EMS in an off-grid system. First, predict the output power of PV and load by considering the parameters of irradiance, PV temperature and ambient temperature using an Artificial Neural Network (ANN), namely the Cascade Forward Neural Network with the fast and stable Levenberg-marquard algorithm. The second strategy is to use a rule base algorithm to keep the State of Charge (SOC) from experiencing overcharging / overdischarging conditions. The third performs load shedding to optimize the charge to the battery. This research was carried out using the Simulink Matlab.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Artificial Neural Network, Cascade Forward Neural Network, Levenberg-marquard, Photovoltaic (PV), Energy Management System (EMS), State of Charge (SOC) dan Load Shedding, Artificial Neural Network, Cascade Forward Neural Network, Levenberg-marquard, Photovoltaic (PV), Energy Management System (EMS), State of Charge (SOC) and Load Shedding |
| Subjects: | T Technology > TJ Mechanical engineering and machinery > TJ808 Renewable energy sources. Energy harvesting. T Technology > TJ Mechanical engineering and machinery > TJ828 Wind turbines T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1087 Photovoltaic power generation |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20101-(S2) Master Thesis |
| Depositing User: | Norma Mahmudah |
| Date Deposited: | 24 Feb 2021 08:08 |
| Last Modified: | 03 Jul 2024 19:51 |
| URI: | http://repository.its.ac.id/id/eprint/82764 |
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