Perancangan Smart Sensor Active Berbasis Adaptive Neuro Fuzzy Inference System (ANFIS) untuk Mendeteksi Posisi Matahari pada Photovoltaic

Sitanggang, Vinar Shinta Saitama (2024) Perancangan Smart Sensor Active Berbasis Adaptive Neuro Fuzzy Inference System (ANFIS) untuk Mendeteksi Posisi Matahari pada Photovoltaic. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Solar tracker aktif adalah solar tracker yang mendeteksi posisi matahari dan mengikuti pergerakan matahari dengan mengubah posisi panel surya agar selalu menghadap matahari. Sensor LDR (Light Dependent Resistor) merupakan salah satu komponen elektronika yang digunakan pada solar tracker aktif. Pada penelitian ini terdapat 3 skema penempatan sensor LDR dan dilakukan optimisasi penempatan sensor LDR berbasis Adaptive Neuro Fuzzy Inference System (ANFIS). Input dari metode ANFIS adalah radiasi matahari, intensitas cahaya, suhu panel, tegangan dan arus yang dihasilkan oleh panel surya sedangkan output-nya adalah daya yang dihasilkan oleh panel surya. Parameter MSE, RMSE, dan MAE digunakan sebagai parameter performa ANFIS. Dari hasil optimisasi ANFIS, dirancang solar tracker dengan smart sensor active yang memiliki skema switching antara dua variasi terbaik untuk mengoptimalkan perolehan energi. Dua variasi terbaik yang digunakan dalam skema switching adalah skema 1 yaitu sensor diletakkan di setiap sisi PV dan skema 3 yaitu sensor dibagi menjadi 2 kelompok yang masing-masing terdiri dari 2 buah sensor dan diletakkan pada sisi yang berbeda. Skema 1 memiliki error MSE, RMSE, dan MAE berturut-turut adalah 0.071; 0.266; dan 0.087. Nilai energi netto yang dihasilkan oleh panel surya skema 1, skema 2, skema 3, skema switching dan skema fixed berturut-turut adalah 1165.29 Wh; 1233.74 Wh; 1222.63 Wh; 1313.78 Wh; dan 996.43 Wh. Nilai performansi peningkatan energi netto oleh panel surya smart sensor active terhadap skema 1, skema 2, skema 3, dan skema fixed berturut-turut adalah 12.742%; 6.487%; 7.455%; dan 31.848%
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An active solar tracker is a solar tracker that detects the sun's position and follows its movement by adjusting the solar panel's position to always face the sun. The LDR (Light Dependent Resistor) sensor is an electronic component used in active solar trackers. This study examines three placement schemes for LDR sensors and optimizes their placement using an Adaptive Neuro-Fuzzy Inference System (ANFIS). The inputs to the ANFIS method include solar radiation, light intensity, panel temperature, voltage, and current generated by the solar panel, and the output is the power generated by the solar panel. MSE (Mean Squared Error), RMSE (Root Mean Squared Error), and MAE (Mean Absolute Error) are used as performance parameters for ANFIS. Based on the ANFIS optimization results, a solar tracker with a smart active sensor was designed, featuring a switching scheme between the two best variations to optimize energy acquisition. The two best variations used in the switching scheme are scheme 1, where sensors are placed on each side of the PV, and scheme 3, where sensors are divided into two groups, each consisting of two sensors placed on different sides. Scheme 1 has MSE, RMSE, and MAE errors of 0.071; 0.266; and 0.087, respectively. The energy netto output of the solar panels for scheme 1, scheme 2, scheme 3, the switching scheme, and the fixed scheme were 1165.29 Wh; 1233.74 Wh; 1222.63 Wh; 1313.78 WH; and 996.43 Wh, respectively. The performance improvement of the solar panel smart sensor active compared to scheme 1, scheme 2, scheme 3, and the fixed scheme was 12.742%; 6.487%; 7.455%; and 31.848%, respectively.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	ANFIS, optimisasi, sensor, solar tracker, switching, ANFIS, optimization, sensor, solar tracker, switching
Subjects:	Q Science > QC Physics > QC100.5 Measuring instruments (General) Q Science > QC Physics > QC271.8.C3 Calibration T Technology > TJ Mechanical engineering and machinery > TJ164 Power plants--Design and construction T Technology > TJ Mechanical engineering and machinery > TJ217.6 Predictive Control T Technology > TJ Mechanical engineering and machinery > TJ810.5 Solar energy
Divisions:	Faculty of Industrial Technology > Physics Engineering > 30201-(S1) Undergraduate Thesis
Depositing User:	Vinar Shinta Saitama Sitanggang
Date Deposited:	05 Aug 2024 02:46
Last Modified:	05 Aug 2024 02:46
URI:	http://repository.its.ac.id/id/eprint/109631

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