The identification of parameters in solar cell models is still a major challenge in photovoltaic (PV) system simulation and design. Because of its more basic ideas, efficiency, adaptability, swarm and evolutionary optimization algorithms, as well as simple procedural frameworks, have been generally used in industry with real-world problems. However, due to the nonlinearity and complication of the PV parameter identification, the obtained solutions from swarm and evolutionary optimizers were immature. An efficient metaheuristic approach for identifying PV model parameters based on the salp swarm algorithm (SSA) is presented in this paper. In the suggested modified salp swarm optimization (MSSA), the leaders and followers will be updated based on the new formulas. The algorithm’s exploration potential is increased by this modification while also preventing it from converge prematurely. The behavior of the suggested technique is verified using benchmark functions, and the outcomes are contrasted with those of SSA and other successful optimization approaches. The suggested MSSA detects numerous characteristics in the PV model include single diode, double diode, and PV modules, in the most efficient way possible. According to the simulation results, MSSA outperforms the competition and may produce better optimal solutions. The findings demonstrate that the best value of RMSE obtained by MSSA is up to 69 percent lower than other methods and is nearly 5.6 percent lower than that assessed by SSA.

This article presents a systematic approach for modeling and analysis of machining characteristics of Micro-Wire Electric Discharge Machining (Micro-WEDM) process using the Response Surface Methodology (RSM). The effect of various input parameters, such as voltage, capacitance, and feed rate on machining, performance of material removal rate (MRR), kerf width (KW), and surface roughness (SR) was investigated. Experimental work was carried out on Aluminum Matrix Composite (A413-9% B₄C) with zinc coated copper wire using RSM with central composite design (CCD). The second order mathematical modeling and analysis of variance (ANOVA) were performed to optimize the machining parameters. It has been found out that main and interaction effects are significant on the machining performance. The optimized values of MMR, KW, and SR parameters were found to be 0.259943 mm³/min, 87 µm, and 0.97 µm, respectively.

Micro-machining technology is effectively used in modern manufacturing industries. This paper investigates the influence of three different input parameters such as voltage, capacitance and feed rate of micro-wire electrical discharge machining (micro-WEDM) performances of material removal rate (MRR), Kerf width (KW) and surface roughness (SR) using response surface methodology with central composite design (CCD). The experiments are carried out on titanium alloy (Ti–6Al–4V). The machining characteristics are significantly influenced by the electrical and non-electrical parameters in micro-WEDM process. Analysis of variance (ANOVA) was performed to find out the significant influence of each factor. The model developed can use a genetic algorithm (GA) to determine the optimal machining conditions using multi-objective optimization technique. The optimal machining performance of material removal rate, Kerf width and surface roughness are 0.01802 mm3/min, 101.5 μm and 0.789 μm, respectively, using this optimal machining conditions viz. voltage 100 V, capacitance 10 nF and feed rate 15 μm/s

This paper proposes an improved salp swarm algorithm (ISSA) as an effective metaheuristic method for tackling global optimization issues and damping power system oscillations. In the suggested ISSA, new equations are introduced to update the location of the leader and followers. This modification improves the method’s exploration possibilities while also preventing it from converging prematurely. Benchmark test functions are used to confirm the proposed algorithm’s performance, and the results are compared to SSA and other effective optimization algorithms. According to the extensive comparisons, the enhanced ISSA algorithm has higher convergence accuracy and stability than the original SSA and other researched algorithms. Furthermore, the feasibility and efficiency of the proposed method were demonstrated by the simultaneous coordinated design of UPFC based damping controllers. For the two-area, four-machine system, the experimental findings are provided. Simulation experiments reveal that ISSA designed controllers outperform those created using other methods.

Edge detection determines and localizes unique breaks or continuity breaks in a picture. This may be accomplished by recognizing and localizing different interv

Opportunities for the fabrication of plant fiber hybrids using thermoplastics and thermosets may be found in a variety of industries, including automobiles and agriculture. This can lessen reliance on crude oil, which contributes to a number of sustainability problems. In the current study, calotropis gigantea fiber (CGF) and nanosilicon dioxide (SiO₂)-derived hybridized materials’ mechanical, dynamic mechanical, and water absorption properties were examined. Utilizing varying weight proportions of nanoSiO₂ (0, 1.5, 3, and 4.5 wt%) and 30 wt% of CGF, we manufactured the composite using the hand lay-up method. The moisture absorption of the manufactured composites was measured during periods of 500, 1000, and 2000 h. For composite materials containing 1.5 wt% SiO₂, the highest interlaminar shear strength (ILSS) failure point was 12.52 MPa for 500 h, which is 12.32% lower than the breaking strength for dried products (14.28 MPa). In comparison to the dry specimens, the bending strength of hybrids with 1.5% SiO₂ that were immersed in water for 500, 1000, and 2000 h decreased by 2.56%, 5.21%, and 9.65%, respectively. The storage modulus of the damp hybrids with 3% and 4.5 wt% SiO₂ was higher than that of the dry samples in terms of their dynamic mechanical properties. While the inclusion of nano-SiO₂ significantly reduced water absorption and moisture diffusion, especially for hybrid materials with 4.5 weight percent SiO₂, the water-absorption behaviour of hybrid natural fiber materials followed the Fickian law. With prolonged exposure time, the mechanical properties of the nanocomposite, both with and without nano-SiO₂, such as ILSS and bending strength, declined. Due to the effective distribution of filler in the matrices, the samples with 4.5 weight percent SiO₂ exhibited the smallest drop in strengths for both the flexural and interlaminar examinations, although all of them remained stronger than the CGF blends. The outcomes of the study point to potential applications in areas such as automobile manufacture, agriculture, construction, and general manufacturing.