nurdin, achmad wahyu (2025) Implementasi Adaptive Neuro Fuzzy Inference System (ANFIS) Untuk Maximum Power Point Tracker (MPPT) Generator Sinkron Untuk Pembangkit Listrik. Other thesis, institut teknologi sepuiluh nopember.
![[thumbnail of 5009221139-Undergraduate_Thesis.pdf]](http://repository.its.ac.id/style/images/fileicons/text.png) |
Text
5009221139-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only until 1 April 2027. Download (1MB) | Request a copy |
Abstract
Peningkatan kebutuhan energi dan perhatian terhadap keberlanjutan lingkungan telah mendorong pengembangan energi terbarukan, termasuk turbin angin, yang memiliki potensi besar untuk menyediakan energi bersih. Namun, optimalisasi kinerja turbin angin dalam kondisi kecepatan angin yang bervariasi tetap menjadi tantangan utama. Penelitian ini bertujuan untuk mengimplementasikan Adaptive Neuro-Fuzzy Inference System (ANFIS) dalam simulator Maximum Power Point Tracking (MPPT) untuk turbin angin, guna meningkatkan efisiensi dan stabilitas sistem. Penelitian ini mengevaluasi pengaruh duty cycle pada motor driver terhadap daya keluaran generator sinkron. Motor driver dirancang untuk mengatur magnet stator dengan variasi duty cycle sebesar 50%, 60%, 70%, 80%, 90%, dan 100%. Hasil menunjukkan bahwa pada frekuensi putaran motor 20 Hz, daya keluaran menuju beban mencapai nilai tertinggi. Selain itu, implementasi metode MPPT berbasis ANFIS pada generator sinkron menghasilkan daya aktif dalam rentang 73.35 Watt hingga 272.74 Watt, dengan kenaikan daya yang signifikan sebesar343.96 Watt pada frekuensi 20 Hz. Hasil penelitian ini menunjukkan bahwa integrasi ANFIS dalam metode MPPT dapat secara adaptif mengoptimalkan daya keluaran generator sinkron, memungkinkan sistem untuk beroperasi pada titik daya maksimum meskipun dalam kondisi lingkungan yang dinamis. Dengan demikian, implementasi ANFIS dalam turbin angin memberikan solusi cerdas untuk monitoring dan pengendalian, meningkatkan efisiensi sistem energi terbarukan secara keseluruhan.
Kata kunci: ANFIS, MPPT, turbin angin, generator sinkron, duty cycle, energi terbarukan.
=================================================================================================================================
The increasing energy demand and focus on environmental sustainability have driven the development of renewable energy technologies, including wind turbines, which have significant potential to provide clean energy. However, optimizing wind turbine performance under varying wind speeds remains a major challenge. This study aims to implement an Adaptive Neuro-Fuzzy Inference System (ANFIS) in a Maximum Power Point Tracking (MPPT) simulator for wind turbines to enhance system efficiency and stability. The study evaluates the impact of duty cycle variations on a motor driver controlling the stator magnet of a synchronous generator. The motor driver is designed to regulate the stator magnet with duty cycles of 50%, 60%, 70%, 80%, 90%, and 100%. Results indicate that at a motor rotation frequency of 20 Hz, the generator’s output power to the load reaches its peak value. Additionally, the implementation of the MPPT method using ANFIS in the synchronous generator produces active power ranging from 73.35 Watts to 272.74 Watts, with a significant power increase of 343.96 Watts observed at a frequency of 20 Hz. These findings demonstrate that integrating ANFIS into the MPPT method can adaptively optimize the output power of synchronous generators, enabling the system to operate at maximum power points even under dynamic environmental conditions. Therefore, implementing ANFIS in wind turbines offers an intelligent solution for monitoring and control, improving the overall efficiency of renewable energy systems.
Keywords: ANFIS, MPPT, wind turbines, synchronous generator, duty cycle, renewable energy.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | ANFIS, MPPT, turbin angin, generator sinkron, duty cycle, energi terbarukan ANFIS, MPPT, wind turbines, synchronous generator, duty cycle, renewable energy. |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2681.O85 Electric motors, Brushless. |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis |
| Depositing User: | Achmad wahyu nurdin |
| Date Deposited: | 30 Jan 2025 08:47 |
| Last Modified: | 30 Jan 2025 08:47 |
| URI: | http://repository.its.ac.id/id/eprint/117271 |
Actions (login required)
 |
View Item |