Perbaikan MPPT Incremental Conductance menggunakan ANN pada Berbayang Sebagian dengan Hubungan Paralel

MUHAMMAD NIZAR HABIBI, DIMAS NUR PRAKOSO, NOVIE AYUB WINDARKO, ANANG TJAHJONO

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ABSTRAK

Algoritma IncrementaL Conductance (IC) adalah algoritma yang bisa diimplementasikan pada sistem Maximum Power Point Tracking (MPPT) untuk mendapatkan daya maksimum dari panel surya. Akan tetapi algoritma MPPT IC tidak bisa bekerja dikondisi berbayang sebagian, karena menimbulkan daya maksimum lebih dari satu. Artificial Neural Network (ANN) bisa mengidentifikasi kurva karakteristik pada kondisi berbayang sebagian dan dapat mengetahui posisi daya maksimum yang sebenarnya. Masukan dari ANN merupakan nilai arus hubung singkat serta tegangan buka dari panel surya, dan keluaran dari ANN adalah nilai duty cycle yang digunakan sebagai posisi awal tracking dari MPPT IC. Data learning didapatkan dari perubahan nilai duty cycle secara manual pada sistem MPPT di berbagai kondisi radiasi. Hasil pengujian menunjukkan algoritma yang diajukan dapat menaikkan energi 5.79% - 13.32% dibandingkan dengan ANN-Perturb and Observe dan ANN-Incremental Resistance dengan durasi 0.6 detik.

Kata kunci: MPPT, Incremental Conductance, Artficial Neural Network, Berbayang Sebagian, Hubungan Paralel

 

ABSTRACT

The Incremental Conductance (IC) algorithm is an algorithm that can be implemented on Maximum Power Point Tracking (MPPT) systems to get maximum power from solar panels. However, the MPPT IC algorithm cannot work in partial shading conditions because it causes more than one maximum power. Artificial Neural Network (ANN) can identify characteristic curves under partial shading conditions and can know the actual maximum power position. The input from ANN is the short circuit current and the open voltage of the solar panel. The output of ANN is the duty cycle value that is used as the initial tracking position of the MPPT IC. Learning data is obtained from manually changing the duty cycle value in the MPPT system in various radiation conditions. The test results show the proposed algorithm can increase energy 5.79% - 13.32% when compared with ANN-Perturb and Observe and ANN-Incremental Resistance with a duration of 0.6 seconds.

Keywords: Maximum Power Point Tracking, Incremental Conductance, Artficial Neural Network, Partial Shading, Parallel Connection


Kata Kunci


MPPT, Incremental Conductance, Artficial Neural Network, Berbayang Sebagian, Hubungan Paralel

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v8i3.546

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