Kholik, Muhammad Abdur Rohman (2021) Desain MPPT Pada PV Grid-Connected Dengan Mempertimbangkan Partial Shading Berbasis Artificial Neural Network (ANN). Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Salah satu renewable energi dengan memanfaatkan energi matahari menjadi energi listrik adalah dengan menggunakan Panel Photovoltaic (PV). Partial Shading adalah suatu keadaan ketika modul PV tidak bisa maksimal dalam menyerap intensitas matahari yang diakibatkan oleh perubahan cuaca lokal maupun keadaan lingkungan sekitar sehingga PV tertutup oleh awan, bayangan gedung atau pepohonan. Kondisi seperti ini dapat mempengaruhi kurva karakteristik dari PV, sehingga terdapat beberapa titik puncak pada kurva yaitu GMPP (Global Maximum Power Point) dan LMPP (Local Maximum Power Point). MPPT (Maximum Power Point Tracking) P&O (Peturb and Observe) biasanya cenderung terjebak dalam kondisi Puncak LMPP yaitu titik puncak yang tidak nyata, untuk mengatasi hal tersebut maka dirancang MPPT berbasis Artificial Neural Network untuk mengatasi Partial Shading. Metode tracking MPPT berbasis ANN yang digunakan pada penelitian ini menggunakan tiga referensi masukan yaitu arus, tegangan dan irradiance dengan keluaran duty cycle. Berdasarkan hasil pengujian pada penelitian ini, didapatkan MPPT berbasis ANN mampu mencapai puncak GMPP (Global Maximum Power Point) dengan signal power steady state dalam waktu rata rata ± 0.02 detik, dengan effisiensi daya yang dihasilkan pada MPPT berbasis ANN sebesar 98.51%, sedangkan untuk MPPT P&O mempunyai effisiensi daya sebesar 92.23%.
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One of the renewable energy by utilizing solar energy into electrical energy is to use Photovoltaic (PV) Panels. Partial shading is a condition when the PV module cannot optimally absorb the intensity of the sun caused by changes in local weather and environmental conditions so that the PV is covered by clouds, shadows of buildings or trees. Conditions like this can affect the characteristic curve of PV, so that there are several peak points on the curve, namely GMPP (Global Maximum Power Point) and LMPP (Local Maximum Power Point). MPPT (Maximum Power Point Tracking) P&O (Peturb and Observe) usually tends to be trapped in the LMPP Peak condition, which is an unreal peak point, to overcome this, an Artificial Neural Network-based MPPT is designed to overcome Partial Shading. The ANN-based MPPT tracking method used in this study uses three input references, namely current, voltage and irradiance with a duty cycle output. Based on the test results in this study, it was found that ANN-based MPPT was able to reach the peak of GMPP (Global Maximum Power Point) with a steady state power signal in an average time of ± 0.02 seconds, with the resulting power efficiency of ANN-based MPPT of 98.51%, while for MPPT P&O has a power efficiency of 92.23%.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | Photovoltaic (PV) ,Maximum Power Point Tracking (MPPT), Partial Shading, Artificial Neural Network (ANN) Photovoltaic (PV), Maximum Power Point Tracking (MPPT), Partial Shading, Artificial Neural Network (ANN) |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1001 Production of electric energy or power 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 |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis |
| Depositing User: | Muhammad Abdur Rohman Kholik |
| Date Deposited: | 15 Aug 2021 05:42 |
| Last Modified: | 29 May 2024 06:12 |
| URI: | http://repository.its.ac.id/id/eprint/86771 |
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