Fathulloh, Muhammad Aryo Aji (2025) Pengembangan Kontrol Lapisan Internal Pada Virtual Synchronous Generator Menggunakan Adaptif Neuro-Fuzzy Inference Sistem Untuk Memperkokoh Integrasi Jaringan. Masters thesis, InstitutTeknologi SepuluhNopember.
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Abstract
Seiring dengan meningkatnya integrasi energi terbarukan, menjaga stabilitas jaringan listrik menjadi semakin menantang akibat tidak adanya inersia pada sistem berbasis inverter. Virtual Synchronous Generator (VSG) menawarkan solusi dengan meniru karakteristik inersia dan redaman dari generator konvensional. Namun, kinerja VSG sangat bergantung pada pengendalian lapisan dalam (inner loop), yang harus mampu mengatur arus dan tegangan secara presisi. Pengendali Proportional-Integral (PI), memiliki keterbatasan dalam menangani dinamika sistem yang tak linier dan berubah-ubah seiring waktu. Studi ini mengembangkan strategi pengendalian inner loop yang canggih untuk VSG dengan menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS). Dengan menggabungkan kemampuan pembelajaran jaringan saraf tiruan dan penalaran logika fuzzy, ANFIS memungkinkan penyesuaian parameter control secara waktu yang berubah-ubah, sehingga meningkatkan respons sistem dan regulasi daya. Hasil simulasi menunjukkan bahwa pengendali berbasis ANFIS memiliki kinerja yang lebih unggul dibandingkan pengendali PI. Overshoot maksimum daya dengan pengendali PI dan ANFIS berturut-turut adalah 2.8 MW dan 2.2 MW. Temuan ini menunjukkan bahwa ANFIS dapat meredam osilasi dengan meningkatkan rasio redaman (damping ratio). Oleh karena itu menjadi solusi pengendalian yang lebih cerdas dan adaptif untuk memastikan integrasi sumber energi terbarukan ke dalam jaringan listrik secara handal.
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As renewable energy integration increases, maintaining grid stability becomes increasingly challenging due to the absence of inertia in inverter-based systems. The Virtual Synchronous Generator (VSG) offers a solution by mimicking the inertia and damping of conventional generators. However, its performance relies heavily on inner loop control, which must regulate current and voltage precisely. Traditional PI controllers, while simple, are limited in managing nonlinear and time-varying system dynamics. This study introduces an advanced inner loop control strategy for VSG using the Adaptive Neuro-Fuzzy Inference System (ANFIS). By combining neural network learning with fuzzy logic reasoning, ANFIS enables real-time adjustment of control parameters, improving system response and regulation. The simulation results indicate that the ANFISbased controller demonstrates superior performance compared to the conventional PI controller. The maximum power overshoot observed with the PI and ANFIS controllers is 2.8 MW and 2.2 MW, respectively. These findings suggest that ANFIS is capable of effectively damping system oscillations by enhancing the damping ratio. Consequently, it serves as a more intelligent and adaptive control solution to ensure the reliable integration of renewable energy sources into the power grid.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | (Generator Sinkron Virtual, Sistem Inferensi Neuro-Fuzzy Adaptif, Pengendalian Lapisan Dalam, Pengendalian Cerdas, Stabilitas Jaringan Listrik, (VSG, ANFIS, Inner Loop Control, Intelligent Control, Grid Stability |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1010 Electric power system stability. Electric filters, Passive. T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK3226 Transients (Electricity). Electric power systems. Harmonics (Electric waves). |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20101-(S2) Master Thesis |
| Depositing User: | Fathulloh Muhammad Aryo Aji |
| Date Deposited: | 22 Jul 2025 05:49 |
| Last Modified: | 22 Jul 2025 05:49 |
| URI: | http://repository.its.ac.id/id/eprint/119909 |
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