Infrared thermography technology is one of the most effective non-destructive testing techniques for predictive faults diagnosis of electrical components. Faults in electrical system show overheating of components which is a common indicator of poor connection, overloading, load imbalance or any defect. Thermographic inspection is employed for finding such heat related problems before eventual failure of the system. However, an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In the present study, statistical features and artificial neural network (ANN) with confidence level analysis are utilized for inspection of electrical components and their thermal conditions are classified into two classes namely normal and overheated. All the features extracted from images do not produce good performance. Features having low performance reduce the diagnostic performance. The study reveals the performance of each feature individually for selecting the suitable feature set. In order to find the individual feature performance, each feature of thermal image was used as input for neural network and the classification of condition types were used as output target. The multilayered perceptron network using Levenberg-Marquardt training algorithm was used as classifier. The performances were determined in terms of percentage of accuracy, specificity, sensitivity, false positive and false negative. After selecting the suitable features, the study introduces the intelligent diagnosis system using suitable features as inputs of neural network. Finally, confidence percentage and confidence level were used to find out the strength of the network outputs for condition monitoring. The experimental result shows that multilayered perceptron network produced 79.4% of testing accuracy with 43.60%, 12.60%, 21.40, 9.20% and 13.40% highest, high, moderate, low and lowest confidence level respectively.
Floating solar photovoltaic has emerged as a highly sustainable and environmentally friendly solution worldwide from the various clean energy generation technologies. However, the installation of floating solar differs from rooftop or ground-mounted solar due to the significant consideration of the availability of water bodies and suitable climatic conditions. Therefore, conducting a feasibility analysis of the suitable climate is essential for installing a floating solar plant on water bodies. These data are evaluated for the viability of installing a 6.7 MW floating solar power plant on Hatirjheel Lake in Dhaka, Bangladesh. The feasibility analysis incorporated various climatic data, such as temperature, humidity, rainfall, sunshine hours, solar radiation, and windspeed, obtained from Meteonorm 8.1 software and the archive of the Bangladesh Meteorological Department. Besides, this study gathered and analyzed the energy demands of the local grid substation operated by Dhaka Power Distribution Company, to determine the appropriate capacity and architecture of the power plant. The power plant design was conducted using the PVsyst 7.3 software, which determined the necessary equipment quantities, DC energy generation capacity, and the energy injected into the grid in MWh. The study also calculated the Levelized Cost of Energy per kilowatt-hour and the payback period for the system, which indicates the economic viability of installing the system. Furthermore, the acquired dataset possesses significant potential and can be utilized for the establishment of all sorts of solar power plants, including floating solar plants, in any location or body of water within the Dhaka Metropolitan area.