Perancangan Sistem Pembangkit Listrik Tenaga Surya Skala Kecil Berbasis Solar Tracker Menggunakan Kontrol Adaptrive Neuro-Fuzzy Inference System (ANFIS) Sebagai Sumber Energi Listrik Bagi Rumah Tangga

Shoffiana, Nur Alfiani (2024) Perancangan Sistem Pembangkit Listrik Tenaga Surya Skala Kecil Berbasis Solar Tracker Menggunakan Kontrol Adaptrive Neuro-Fuzzy Inference System (ANFIS) Sebagai Sumber Energi Listrik Bagi Rumah Tangga. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Penggunaan panel surya sebagai bentuk implementasi energi terbarukan masih menghadapi kendala dalam mengoptimalkan efisiensi kinerja panel. Perencanaan dan implementasi yang baik sangat diperlukan untuk memaksimalkan daya yang dihasilkan. Salah satu upaya untuk meningkatkan produksi energi listrik pada panel surya yaitu penggunaan solar tracker pada PLTS. Penelitian ini dirancang sistem pembangkit listrik tenaga surya berbasis solar tracker menggunakan kontrol Adaptive Neuro-Fuzzy Inference System (ANFIS) sebagai sumber energi listrik bagi rumah tangga. Kontrol ANFIS yang digunakan yaitu ANFIS-hybrid dengan tipe fungsi keanggotaan segitiga untuk input dan konstan untuk output. Aspek-aspek yang ditinjau antara lain passive continuous single axis solar tracker, passive continuous double axis solar tracker, passive discrete single axis solar tracker, dan passive discrete double axis solar tracker. Hasil respon sistem dengan performa terbaik yaitu pada sumbu azimuth kontrol ANFIS 7 MF dengan Ess : 0.03%, Rise Time : 48.8917 s, Maximum Overshoot : 0.6745%, dan Settling Time : 60.7161 s. Performa peningkatan energi terbaik diperoleh pada penggunaan passive discrete single axis solar tracker dengan kontrol ANFIS yang menghasilkan energi 14.97% lebih besar dibandingkan dengan kontrol Fuzzy. Total energi yang diperoleh yaitu 1162.65 Wh

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The use of solar panels as a form of renewable energy implementation still faces obstacles in optimizing panel performance efficiency. Good planning and implementation are needed to maximize the power generated. One of the efforts to increase the production of electrical energy on solar panels is the use of solar trackers on solar power plants. This research designed a solar tracker-based solar power generation system using Adaptive Neuro-Fuzzy Inference System (ANFIS) control as an alternative source of electrical energy for households. The ANFIS control used is ANFIS-hybrid with triangular membership function type for input and constant for output. The aspects reviewed include passive continuous single axis solar tracker, passive continuous double axis solar tracker, passive discrete single axis solar tracker, and passive discrete double axis solar tracker. The results of the system response with the best performance are on the azimuth axis of ANFIS 7 MF control with Ess: 0.03%, Rise Time: 48.8917 s, Maximum Overshoot: 0.6745%, and Settling Time: 60.7161 s. The best energy increase performance is obtained in the use of passive discrete single axis solar tracker with ANFIS control which produces 14.97% more energy than Fuzzy control. The total energy obtained is 1162.65 Wh.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Pembangkit Listrik Tenaga Surya (PLTS), Solar Tracker, Passive, Adaptive Neuro-Fuzzy Inference System (ANFIS), Solar Power Plant, Solar Tracker, Passive, Adaptive Neuro-Fuzzy Inference System (ANFIS)
Subjects:	Q Science > QC Physics > QC100.5 Measuring instruments (General) T Technology > T Technology (General) > T57.62 Simulation T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1007 Electric power systems control T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1056 Solar power plants. Ocean thermal power plants T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1087 Photovoltaic power generation T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2692 Inverters T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2945 Lead-acid batteries.
Divisions:	Faculty of Industrial Technology > Physics Engineering > 30101-(S2) Master Thesis
Depositing User:	Nur Alfiani Shoffiana
Date Deposited:	08 Aug 2024 08:32
Last Modified:	08 Aug 2024 08:32
URI:	http://repository.its.ac.id/id/eprint/115087

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