Hardiana, Tiara Oktavia (2018) Perancangan Kontrol Charging Battery Menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS) Pada PV Berbasis Solar Tracker Satu Poros. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
PV panel merupakan penghasil energi listrik yang sangat ramah lingkungan dan mudah dalam pengunaannya. Penggunaan PV panel cocok untuk mensuplai beban puncak atau pada malam hari, dengan menggunakan baterai sebagai penyimpanan energi. Namun dalam pengaplikasiannya diperlukan manajemen pengisian baterai agar dapat terkontrol serta baterai dapat berusia panjang. Solusi untuk permasalahan manajemen baterai dilakukan melalui penelitian ini yang membahas mengenai sistem pengisian baterai. Konverter DC-DC yang digunakan adalah tipe SEPIC. Kontrol tegangan pengisian baterai menggunakan Adaptive Neuro Fuzzy Inference System (ANFIS). Pada simulasi kondisi cerah kontrol ANFIS mampu menjejak set point tegangan charging dan didapat respon tegangan dengan nilai rise time sebesar 0,0028s, maximum overshoot sebesar 0,027%, peak time sebesar 0,008s, dan settling time sebesar 0,0193s. Saat pengisian baterai PV solar tracker mendapat peningkatan 0,25% dibandingkan dengan PV panel fixed. Disebabkan PV solar tracker mampu mengikuti arah posisi matahari sehingga didapat nilai irradiasi dan suhu maksimum yang berpengaruh pula terhadap tegangan input dan arus input yang masuk pada konverter. ========================================================================================================================
PV panel is an energy generator that is very environmentally friendly and easy to use. The use of PV panels is suitable for supplying peak loads or at night, using batteries for energy storage. However, in its application required the management of charging the battery to be controlled and the battery can be long-lived. The solution to battery management problems is done through this research which discusses the battery charging system. The DC-DC converter used is SEPIC type. Control the battery charging voltage using Adaptive Neuro Fuzzy Inference System (ANFIS). In the simulation of bright conditions ANFIS control able to set the set point of charging voltage and obtained response voltage with rise time value of 0,0028s, maximum overshoot of 0,027%, peak time of 0,008s, and settling time of 0,0193s. When the solar PV diesel battery charger gets a 0.25% increase compared to fixed panel PV. Because solar PV tracker is able to follow the direction of the sun position so it gets the value of irradiation and maximum temperature that also affect the input voltage and input current in converter.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | set point, rise time, peak time, solar tracker, SEPIC |
| Subjects: | Q Science > QA Mathematics > QA9.64 Fuzzy logic T Technology > TJ Mechanical engineering and machinery > TJ810.5 Solar energy |
| Divisions: | Faculty of Industrial Technology > Physics Engineering > 30201-(S1) Undergraduate Thesis |
| Depositing User: | TIARA OKTAVIA HARDIANA |
| Date Deposited: | 12 Jul 2021 22:45 |
| Last Modified: | 12 Jul 2021 22:45 |
| URI: | http://repository.its.ac.id/id/eprint/55043 |
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