Diarrastya, Berliand (2026) Desain Dan Implementasi Multi-Input Quadratic High DC-DC Boost Converter dengan Metode Control Artificial Neural Network untuk Aplikasi Energi Terbarukan. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Dalam penerapan sistem hybrid berbasis Sumber Energi Baru Terbarukan (EBT), salah satu tantangan utama adalah karakteristik tegangan keluaran dari sumber seperti photovoltaic (PV), turbin angin (WT), dan fuel cell (FC) yang relatif rendah serta berfluktuasi mengikuti kondisi lingkungan. Kondisi ini dapat menyebabkan ketidakstabilan tegangan dan menyulitkan pengelolaan aliran daya, terutama ketika beberapa sumber energi dengan karakteristik berbeda diintegrasikan dalam satu sistem. Oleh karena itu, diperlukan konverter DC-DC yang mampu meningkatkan tegangan, menjaga regulasi tegangan keluaran, serta mendukung pengelolaan daya yang fleksibel pada DC Bus sebagai titik integrasi sumber energi. Penelitian ini berfokus pada pemodelan, perancangan, dan implementasi Multi-Input High Step-Up DC-DC Converter dengan sistem kontrol berbasis Artificial Neural Network (ANN) metode Multi-Layer Perceptron (MLP) untuk aplikasi sistem hybrid EBT. Dua topologi yang dikembangkan adalah Ultra-High Step-Up DC-DC Converter dan Multi-Input Single-Output Quadratic Boost Converter (MISO-QBC). Hasil penelitian menunjukkan bahwa kontrol ANN mampu menjaga kestabilan tegangan DC Bus secara efektif. Topologi Ultra-High Step-Up DC-DC Converter berhasil meningkatkan tegangan dari 45 V menjadi 400 V dengan toleransi 2,5%, sedangkan MISO-QBC mampu meningkatkan tegangan dari 24–54 V menjadi 400–600 V dengan toleransi 0,7%.
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In the implementation of hybrid systems utilizing environmentally friendly New and Renewable Energy Sources (NRES), a major challenge arises due to the output voltage characteristics of sources such as photovoltaic (PV), wind turbine (WT), and fuel cell (FC), which typically exhibit low voltage levels and fluctuate according to environmental conditions. These variations lead to voltage instability issues and difficulties in power flow management, particularly when integrating multiple sources with different characteristics. The DC bus plays a crucial role as the main integration point for all energy sources in a hybrid configuration. Therefore, it is essential to employ a converter interface that not only provides significant voltage boosting but also ensures stable output voltage regulation and enables adaptive and independent power distribution from each energy source. The objective of this research is to model, design, and implement a Multi-Input High Step-Up DC-DC Converter architecture equipped with an intelligent control system based on an Artificial Neural Network (ANN) using a Multi-Layer Perceptron (MLP) method, intended for application in hybrid systems consisting of multiple types of renewable energy sources. Two converter topologies are proposed: the Ultra-High Step-Up DC-DC Converter and the Multi-Input Single-Output Quadratic Boost Converter (MISO-QBC). The ANN-based control method effectively enhances the performance of the DC bus, maintaining a high and stable voltage level. The Ultra-High Step-Up DC-DC Converter topology is capable of achieving a ninefold voltage gain, stepping up the input voltage from 45 V to 400 V with a tolerance of 2.5%, while the Multi-Input Single-Output Quadratic Boost Converter (MISO-QBC) topology provides up to a twenty-fourfold voltage gain, boosting the input range of 24–54 V to 400-600 V with a tolerance of 0.7%.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Hybrid, Quadratic Boost Converter, ANN, Multiple Layer Perceptron |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1001 Production of electric energy or power T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1007 Electric power systems control T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2692 Inverters |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis |
| Depositing User: | Berliand Diarrastya |
| Date Deposited: | 21 Jul 2026 04:20 |
| Last Modified: | 21 Jul 2026 04:29 |
| URI: | http://repository.its.ac.id/id/eprint/135926 |
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