Prayogi, Ramadhan Dwi (2021) Maximum Power Point Tracking dengan Orientasi Arus Riak berbasis Artificial Neural Network (ANN) pada Aplikasi Turbin Angin. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Perkembangan teknologi energi baru dan terbarukan dalam beberapa tahun terakhir ini sangat pesat. Berbagai alternatif cara dalam desain dan instalasi dilakukan untuk mendapatkan hasil yang optimal dan dapat
diimplementasikan di masa mendatang. Salah satu energi baru terbarukan yang memiliki potensi besar yaitu energi angin. Dalampenerapannya, besar daya keluaran yang dihasilkan oleh energi anginbervariasi, tergantung dari besarnya kecepatan angin. Oleh karena itu diperlukan Maximum Power Point Tracking (MPPT) untuk mendapatkan nilai daya maksimum dari pembangkitan energi listrik ini.
Maka dari itu pada penelitian ini didesain MPPT dengan metode alternatif yaitu MPPT dengan Orientasi Arus Riak berbasis ANN. MPPT tersebut akan dibandingkan dengan MPPT dengan metode P&O. Dari hasil penelitian yang dilakukan, didapatkan efisiensi energi pada MPPT dengan Orientasi Arus Riak berbasis ANN yaitu 95,8% saat kecepatan angin meningkat, 98,17% saat kecepatan angin menurun, dan 95,06% saat kecepatan angin konstan. Akurasi tegangan dari MPPT dengan Orientasi Arus Riak berbasis ANN, yaitu 97,26% saat kecapatan angin meningkat, 97,65% saat kecepatan angin menurun, dan 97,48% saat kecepatan angin turun. Hasil efisiensi energi dan akurasi tegangan
output lebih baik dibanding daripada MPPT P&O. Dan didapatkan waktu rata-rata untuk mencapai titik daya maksimum yaitu 0,161 detik, waktu rata-rata 0,324 detik lebih cepat dari MPPT P&O.
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The development of new and renewable energy technology in recent years is very rapid. Various alternative methods in the design and installation are carried out to obtain optimal results and can be implemented in the future. One of the new renewable energy that has great potential is wind energy. In its application, the output power
generated by wind energy varies, depending on the magnitude of the wind speed. Therefore, Maximum Power Point Tracking (MPPT) is needed to get the maximum power value from this electrical energy generation. Therefore, in this study, MPPT was designed with an alternative method, namely MPPT with Ripple Current Orientation
based on ANN. The MPPT will be compared with the MPPT using the P&O method. From the results of the research conducted, the energy efficiency of MPPT with Ripple Current Orientation based on ANN is 95.8% when the wind speed increases, 98.17% when the wind speed decreases, and 95.06% when the wind speed is constant. The voltage
accuracy of MPPT with Ripple Current Orientation based on ANN is 97.26% when the wind speed increases, 97.65% when the wind speed decreases, and 97.48% when the wind speed decreases. The results of energy efficiency and output voltage accuracy are better than MPPT P&O. And the average time to reach the maximum power point is 0.161
seconds, the average time is 0.324 seconds faster than MPPT P&O.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | Turbin Angin, MPPT, Orientasi Arus Riak, Artificial Neural Network (ANN), Boost Converter, Wind Turbine, MPPT, Ripple Current Orientation, Artificial Neural Network (ANN), Boost Converter |
| Subjects: | T Technology > TJ Mechanical engineering and machinery > TJ808 Renewable energy sources. Energy harvesting. T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1007 Electric power systems control |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis |
| Depositing User: | Ramadhan Dwi Prayogi |
| Date Deposited: | 14 Aug 2021 14:09 |
| Last Modified: | 14 Aug 2021 14:09 |
| URI: | http://repository.its.ac.id/id/eprint/86669 |
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