A new integrated single‐diode solar cell model for photovoltaic power prediction with experimental validation under real outdoor conditions

The output power prediction by a photovoltaic (PV) system is an important research area for which different techniques have been used. Solar cell modeling is one of the most used methods for power prediction, the accuracy of which strongly depends on the selection of cell parameters. In this study, a new integrated single‐diode solar cell model based on three, four, and five solar cell parameters is developed for the prediction of PV power generation. The experimental validation of the predicted results is done under outdoor climatic conditions for an Indian location. The predicted power by three models is found close to measured values within 4.29% to 4.76% accuracy range. The comparative power estimation analysis by these models shows that the three‐parameter model gives higher accuracy for low solar irradiance values <150 W/m², the four‐parameter model in the range of 150 to 500 W/m², and the five‐parameter model for >500 W/m². The present model is also compared with other models in literature and is found to be more accurate with less percentage error. The overall results also show that the power produced depends on temperature and solar radiation levels at a particular location. Thus, single solar cell model developed can be used with sufficient accuracy for power forecast of PV systems for any location worldwide. The follow‐up research areas are also identified.     

Enhanced hydrogen generation by water electrolysis employing carbon nano-structure composites

The present study describes the hydrogen generation through electrolysis by using graphene-carbon nanotube (GC) nano-composite electrode. Synthesis of GC nano-composites of various compositions utilizing solution admixing approach has been done. Structural, morphological, microstructural and analysis of quality of various carbon nano-composites have been investigated by using XRD, SEM, TEM, Raman and FTIR techniques. To determine the electrochemical catalytic performance of GC composites, these have been used as working electrode (anode) for electrolysis of water in an alkaline medium (1 M NaOH). The results reveal that the GC73 (70 wt% graphene and 30 wt% CNT) nano-composite is an optimum anode material for hydrogen production. The highest hydrogen production rate of 487 l/h-m² has been observed for the composite GC 73. Based on Tafel plot and FTIR characterizations, a feasible mechanism for this high hydrogen yield has been put forward.     

Cobalt based nanostructured alloys: Versatile high performance robust hydrogen evolution reaction electro-catalysts for electrolytic and photo-electrochemical water splitting

Engineering reduced noble metal containing electro-catalysts exhibiting superior electrochemical performance is important for efficient and economic production of hydrogen from electrolytic and photoelectrochemical (PEC) water splitting reactions. In this study, nanostructured Co–Ir solid solution alloys, Co_1?x(Ir_x) (x = 0.2, 0.3, 0.4) have been studied as electro-catalysts for hydrogen evolution reaction (HER). Co_1?x(Ir_x) (x = 0.3, 0.4) exhibit similar onset over-potential to Pt/C and lower over-potential required for Co_1?x(Ir_x) (x = 0.3, 0.4) than Pt/C in acidic, neutral as well as basic media, suggesting excellent electrochemical activity of Co–Ir alloys, further studied using theoretical first principles density functional theory calculations. Co_1?x(Ir_x) exhibit excellent electrochemical stability in acidic media similar to that of Pt/C. The applied bias photon-to-current efficiency obtained using Co_1?x(Ir_x) (x = 0.3, 0.4) electro-catalysts and (Sn_0.95Nb_0.05)O₂:N-600 NTs as photoanode in H-type cell is ～5.74% and ～7.92%, respectively which is ～40% and ～93% higher than Pt/C (～4.1%) indicating considerable promise of the system.     

An improved sparsity-aware normalized least-mean-square scheme for underwater communication

Underwater communication (UWC) is widely used in coastal surveillance and early warning systems. Precise channel estimation is vital for efficient and reliable UWC. The sparse direct-adaptive filtering algorithms have become popular in UWC. Herein, we present an improved adaptive convex-combination method for the identification of sparse structures using a reweighted normalized least-mean-square (RNLMS) algorithm. Moreover, to make RNLMS algorithm independent of the reweighted $l_1$-norm parameter, a modified sparsity-aware adaptive zero-attracting RNLMS (AZA-RNLMS) algorithm is introduced to ensure accurate modeling. In addition, we present a quantitative analysis of this algorithm to evaluate the convergence speed and accuracy. Furthermore, we derive an excess mean-square-error expression that proves that the AZA-RNLMS algorithm performs better for the harsh underwater channel. The measured data from the experimental channel of SPACE08 is used for simulation, and results are presented to verify the performance of the proposed algorithm. The simulation results confirm that the proposed algorithm for underwater channel estimation performs better than the earlier schemes.