Identifikasi Gangguan Degradation Fault pada Photovoltaic Array berbasis Artificial Neural Network

SUHARININGSIH SUHARININGSIH, EPYK SUNARNO, MUTIARA NADHIFAH SALSABILA, DIMAS OKKY ANGGRIAWAN, EKA PRASETYONO

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ABSTRAK

Energi terbarukan sudah mulai mendominasi dunia sejak puluhan tahun lalu, terutama listrik tenaga surya. Pada setiap instalasi PV terdapat gangguan yang sering terjadi, salah satunya adalah degradation fault. degradation fault merupakan jenis gangguan berupa perubahan warna pada lapisan Ethylene Vinyl Acetate dari yang berwarna putih menjadi kuning hingga kecoklatan. Perubahan warna tersebut disebabkan oleh usia pemakaian dan suhu yang terlalu panas dan dapat menyebabkan penurunan arus yang sangat drastis. Kejadian ini mengakibatkan penurunan Isc mencapai 13%. Hal ini tidak baik jika terus dibiarkan pada instalasi solar panel. Oleh karena itu, pada jurnal ini akan membahas pengidentifikasian degradation fault pada array PV dengan Artificial Neural Network. ANN akan mengidentifikasi adanya penurunan arus pada PV array. Dari hasil yang didapatkan bahwa penurunan arus mencapai 12% dan dapat mengidentifkasi adanya degradation fault.

Kata kunci: degradation fault, discoloration, Ethylene Vinyl Acetate , short circuit current, artificial neural network

 

ABSTRACT

Renewable energy has started to dominate the world since decades ago, especially solar electricity. In every PV installation there are disturbances that often occur, one of which is a degradation fault. Degradation fault is a type of disturbance in the form of discoloration of the Ethylene Vinyl Acetate layer from white to yellow to brownish. The discoloration is caused by age of use and temperatures that are too hot and can cause a very drastic decrease in current. This incident resulted in a decrease in Isc reaching 13%. This is not good if it continues to be left on solar panel installations. Therefore, this journal will discuss the identification of degradation faults in PV arrays with Artificial Neural Networks. ANN will identify a decrease in current in the PV array. From the results obtained that the decrease in current reaches 12% and can identify a degradation fault.

Keywords: degradation fault, discoloration, Ethylene Vinyl Acetate , short circuit current, artificial neural network


Kata Kunci


degradation fault; discoloration; Ethylene Vinyl Acetate; short circuit current; artificial neural network

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v12i1.36

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