Although different materials and designs have been tried in search of the ideal as well as ultra-wideband light absorber, achieving ultra-broadband and robust unpolarized light absorption over a wide angular range has proven to be a major issue. Light-field regulation capabilities provided by optical metamaterials are a potential new technique for perfect absorbers. It is our goal to design and demonstrate an ultra-wideband solar absorber for the ultraviolet to a mid-infrared region that has an absorptivity of TE/TM light of 96.2% on average. In the visible, NIR, and MIR bands of the solar spectrum, the absorbed energy is determined to be over 97.9%, above 96.1%, and over 95%, respectively under solar radiation according to the Air Mass Index 1.5 (AM1.5) spectrum investigation. In order to achieve this wideband absorption, the TiN material ground layer is followed by the SiO2 layer, and on top of that, a Cr layer with patterned Ti-based resonators of circular and rectangular multiple patterns. More applications in integrated optoelectronic devices could benefit from the ideal solar absorber's strong absorption, large angular responses, and scalable construction.
This article aims to provide a technique for identifying and categorizing interturn insulation problems in variable-speed motor drives by combining Salp Swarm Optimization (SSO) with Recurrent Neural Network (RNN). The goal of the proposed technique is to detect and classify Asynchronous Motor faults at their early stages, under both normal and abnormal operating conditions. The proposed technique uses a recurrent neural network in two phases to identify and label interturn insulation concerns, with the first phase being utilised to establish whether or not the motors are healthy. In the second step, it discovers and categorises potentially dangerous interturn errors. The SSO approach is used in the second phase of the recurrent neural network learning procedure, with the goal function of minimizing error in mind. The proposed CSSRN technique simplifies the system for detecting and categorizing the interturn insulation issue, resulting in increased system precision. In addition, the proposed model is implemented in the MATLAB/Simulink, where metrics such as accuracy, precision, recall, and specificity may be analysed. Similarly, existing methods such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), and Salp Swarm Algorithm Artificial Neural Network (SSAANN) are utilised to evaluate metrics such as Root mean squared error (RMSE), Mean bias error (MBE), Mean absolute percentage error (MAPE), consumption, and execution time for comparative analysis.
In an expedition for green-energy generation and to lower the cost per watt of solar energy, environmentally friendly biotic colorants were separated from Tectona grandis seeds. The prime colorant in the extract is pelargonidin which sensitizes titanium dioxide (TiO2)-based photo anodes. The pelargonidin-sensitized TiO2 nanomaterials endured structural, photosensitive, spectral and current-voltage interpretations. Frontier molecular orbital analysis, physicochemical and electronic parameter computation, UV–visible and DOS spectral analysis, van der Waals prediction and molecular electrostatic potential map were performed theoretically with Gaussian tools, and IR symmetry response was computed using the crystal maker software package. The pelargonidin-sensitized TiO2-created dye-sensitized solar cells which exhibited capable solar light energy to photon conversion proficiency. For comparative purposes, the commercial P25 Degussa TiO2-based DSSC was also fabricated and its proficiency was analyzed. The commercial TiO2 exhibited 57 % higher proficiency in comparison to the sol-gel-derived TiO2-based DSSC.
The increased need for renewable energy systems to generate power, store energy, and connect energy storage devices with applications has become a major challenge. Energy storage using batteries is most appropriate for energy sources like solar, wind, etc. A non-isolated three-port DC–DC-converter energy conversion unit is implemented feeding the brushless DCmotor drive. In this paper, a non-isolated three-port converter is designed and simulated for battery energy storage , interfaced with an output drive. Based on the requirements, the power extracted from the solar panel during the daytime is used to charge the batteries through the three-port converter. The proposed three-port converter is analyzed in terms of operating principles and power flow. An FPGA-based NI LabView PXI with SbRio interface is used to develop the suggested approach’s control hardware, and prototype model results are obtained to test the proposed three-port converter control system’s effectiveness and practicality. The overall efficiency of the converter’s output improves as a result. The success rate is 96.5 percent while charging an ESS, 98.1 percent when discharging an ESS, and 95.7 percent overall.
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