Search Results (2)

Increasing silicon solar cell efficiency plays a vital role in improving the dominant market share of photo-voltaic systems in the renewable energy sector. The performance of the solar cells can be evaluated by making a profound analysis on various effective parameters, such as the sheet resistance, doping concentration, thickness of the solar cell, arbitrary dopant profile, etc., using software simulation tools, such as $PC1D$. In this paper, we present the observations obtained from the evaluation carried out on the impact of sheet resistance on the solar cell’s parameters using $PC1D$ software. After which, the $EDNA2$ simulation tool was used to analyse the emitter saturation current density for the chosen arbitrary dopant profile. Results indicated that the diffusion profile with low surface concentration and shallow junction depth can improve the blue response at the frontal side of the solar cell. The emitter saturation current density decreases from 66.52 to 36.82 fA/cm${}^2$ for the subsequent increase in sheet resistance. The blue response also increased from $89.6\%$ to $97.5\%$ with rise in sheet resistance. In addition, the short circuit density and open circuit voltage was also observed to be improved by 0.6 mA/cm${}^2$ and 3 mV for the sheet resistance value of 130 $\mathsf{\Omega }$/sq, which resulted in achieving the highest efficiency of $20.6\%$. Full article

The central problems of some of the existing Non-Intrusive Load Monitoring (NILM) algorithms are indicated as: (1) higher required electrical device identification accuracy; (2) the fact that they enable training over a larger device count; and (3) their ability to be trained faster, limiting them from usage in industrial premises and external grids due to their sensitivity to various device types found in residential premises. The algorithm accuracy is higher compared to previous work and is capable of training over at least thirteen electrical devices collaboratively, a number that could be much higher if such a dataset is generated. The algorithm trains the data around $1.8\times {10}^8$ faster due to a higher sampling rate. These improvements potentially enable the algorithm to be suitable for future “grids and industrial premises load identification” systems. The algorithm builds on new principles: an electro-spectral features preprocessor, a faster waveform sampling sensor, a shorter required duration for the recorded data set, and the use of current waveforms vs. energy load profile, as was the case in previous NILM algorithms. Since the algorithm is intended for operation in any industrial premises or grid location, fast training is required. Known classification algorithms are comparatively trained using the proposed preprocessor over residential datasets, and in addition, the algorithm is compared to five known low-sampling NILM rate algorithms. The proposed spectral algorithm achieved 98% accuracy in terms of device identification over two international datasets, which is higher than the usual success of NILM algorithms. Full article