PERANCANGAN SEPEDA LISTRIK DENGAN PANEL SURYA TIPE J-FEATHER SEBAGAI SUMBER ENERGI

Indonesia sebagai negara yang terletak di wilayah garis Khatulistiwa memiliki potensi penyinaran 12 jam setiap hari sepanjang tahun. Semakin menipisnya cadangan minyak bumi membuat manusia harus mencari energi yang terbaharukan. Matahari  merupakan  sumber  energi  yang  potensial  bagi    kebutuhan    manusia,  dimana energi  tersebut  bisa  didapat  dari  panas  yang  merambat  sampai  permukaan  bumi,  atau  cahaya yang  jatuh  sampai  permukaan  bumi. Salah satu ilmuwan yang pernah merancang penelitian terkait sebelumnya ialah Lorenzo Eduardo yang menjelaskan tentang sel  surya  dapat  beroperasi  secara  maksimum  jika  temperatur  sel  tetap  normal  (pada  25 °C),    kenaikan  temperatur    lebih    tinggi    dari    temperatur    normal    pada sel    akan  menurunkan    nilai    tegangan    (Voc).    Setiap  kenaikan    temperatur sel    surya    10  Celsius  (dari  25 °C) akan  berkurang  sekitar  0,4  %  pada  total  tenaga  yang dihasilkan  atau  akan  melemah  dua  kali  (2x)  lipat  untuk  kenaikan  temperatur  Sel  per  10 °C. Teknik analisis yang dilakukan secara kuantitatif (manual) dengan perancangan yang mensimulasikan beberapa jenis sel  surya dan ditarik kesimpulan yang cukup efisien dalam  konsumsi energinya. Perhitungan massa total melibatkan massa pengendara, baterai dsb.  Lalu  perhitungan gaya total dengan meliputi gaya aerodinamik dan gaya menggelinding yang nantinya akan digunakan untuk menghitung torsi. Setelah didapat nilai torsi, maka mulai perhitungan daya dan energi yang dikonsumsi dan efisiensi storage sebagai pemasok listrik. untuk setiap 1 kWh pada panel surya tipe J-Feather dapat menempuh jarak 134,66 km, pada panel surya tipe J-Leaf dapat menempuh jarak 134,06 km dan pada panel surya tipe konvensional dapat menempuh jarak 132,57 km. Meskipun cukup efisien dalam menghasilkan daya, persentase tipe J-Feather cukup kecil dibandingkan tipe lainnya

G-ID: identifying 3D Prints using slicing parameters

We present G-ID, a method that utilizes the subtle patterns left by the 3D printing process to distinguish and identify objects that otherwise look similar to the human eye. The key idea is to mark different instances of a 3D model by varying slicing parameters that do not change the model geometry but can be detected as machine-readable differences in the print. As a result, G-ID does not add anything to the object but exploits the patterns appearing as a byproduct of slicing, an essential step of the 3D printing pipeline. We introduce the G-ID slicing & labeling interface that varies the settings for each instance, and the G-ID mobile app, which uses image processing techniques to retrieve the parameters and their associated labels from a photo of the 3D printed object. Finally, we evaluate our method’s accuracy under different lighting conditions, when objects were printed with different filaments and printers, and with pictures taken from various positions and angles

Machine learning for advanced characterisation of silicon solar cells

Improving the efficiency, reliability, and durability of photovoltaic cells and modules is key to accelerating the transition towards a carbon-free society. With tens of millions of solar cells manufactured every day, this thesis aims to leverage the available characterisation data to identify defects in solar cells using powerful machine learning techniques. Firstly, it explores temperature and injection dependent lifetime data to characterise bulk defects in silicon solar cells. Machine learning algorithms were trained to model the recombination statistics’ inverse function and predict the defect parameters. The proposed image representation of lifetime data and access to powerful deep learning techniques surpasses traditional defect parameter extraction techniques and enables the extraction of temperature dependent defect parameters. Secondly, it makes use of end-of-line current-voltage measurements and luminescence images to demonstrate how luminescence imaging can satisfy the needs of end-of-line binning. By introducing a deep learning framework, the cell efficiency is correlated to the luminescence image and shows that a luminescence-based binning does not impact the mismatch losses of the fabricated modules while having a greater capability of detecting defects in solar cells. The framework is shown in multiple transfer learning and fine-tuning applications such as half-cut and shingled cells. The method is then extended for automated efficiency-loss analysis, where a new deep learning framework identifies the defective regions in the luminescence image and their impact on the overall cell efficiency. Finally, it presents a machine learning algorithm to model the relationship between input process parameters and output efficiency to identify the recipe for achieving the highest solar cell efficiency with the help of a genetic algorithm optimiser. The development of machine learning-powered characterisation truly unlocks new insight and brings the photovoltaic industry to the next level, making the most of the available data to accelerate the rate of improvement of solar cell and module efficiency while identifying the potential defects impacting their reliability and durability