Publications

Among different routes of the generation of electrical power, solar photovoltaic is one of the most promising and trusted technology. Organic, organic-inorganic hybrid lead-based perovskite compounds are becoming commercially appealing as absorber materials in the majority of perovskite solar cells due to high PCE (power conversion efficiency), low cost, availability of materials, and ease of production. However, as mentioned above, lead-based organic perovskite solar cells are unstable in open space. Nowadays, Sn-based inorganic perovskites are becoming more popular than lead-based perovskites as Pb-based perovskite solar cells are toxic and not environment-friendly. In this study, the thickness, doping density, defect density, energy bandgap of the absorber, and ETL (electron transport layer) and HTL (hole transport layer) materials of the cell are optimized using SCAPS-1D simulator. The final structure is FTO/ZnO/CsSnGeI3/NiO/Cu2O, where FTO works as TCO (transparent conducting oxide) layer. The optimized structure offered an open-circuit voltage (V oc ) of 1.245V, a short-circuit current (J sc ) of 28.189 mA/cm 2 , a Fill Factor (FF) of 89.97%, and an overall PCE of 31.57%. According to the literature, this is the highest PCE for the CsSnGeI3 perovskite solar cell with all-inorganic layers. The proposed optimized structure will pave the path for the fabrication of low-cost, environment friendly and stable perovskite solar cell. 

High voltage alternating current (HVAC) transmission lines are the only way to transmit electrical energy from the power plant to the utilities. There are several types of transmission line faults that can interrupt the power supply, sometimes even resulting in national blackout. To ensure a highly reliable power supply system, detecting the faults in the quickest of time is crucial. This research used machine learning and neural network algorithm to find five different types of faults using data from a real power transmission line in Bangladesh. Apart from the accuracy of the methods, the focus was given on the response time in identifying the faults. The two boosting methods CatBoost and XGBoost stood out as the most precise among all the algorithms, achieving excellent accuracy and detection time in between 20 ms to 30 ms. The response time of all the methods was found to be in millisecond range, making them perfect for not only long term faults, but also to be implied in transient fault analysis.