Historically, multi-junction solar cells have evolved to capture a broader spectrum of sunlight, significantly enhancing efficiency beyond conventional solar technologies. In this study, we utilized Silvaco TCAD tools to optimize a five-junction solar cell composed of AlInP, AlGaInP, AlGaInAs, GaInP, GaAs, InGaAs, and Ge, drawing on advancements documented in the literature. Our research focused on optimizing these cells through sophisticated statistical modeling and material innovation, particularly examining the relationship between layer thickness and electrical yield under one sun illumination. Employing III-V tandem solar cells, renowned for their superior efficiency in converting sunlight to electricity, we applied advanced statistical models to a reference solar cell configured with predefined layer thicknesses. Our analysis revealed significant positive correlations between layer thickness and electrical performance, with correlation coefficients (R² values) impressively ranging from 0.86 to 0.96 across different regions. This detailed statistical insight led to an improvement in overall cell efficiency to 44.2. A key innovation in our approach was replacing the traditional germanium (Ge) substrate with Copper Indium Gallium Selenide (CIGS), known for its adjustable bandgap and superior absorption of long-wavelength photons. This strategic modification not only broadened the absorption spectrum but also elevated the overall cell efficiency to 47%. Additionally, the optimization process involved simulations using predictive profilers and Silvaco Atlas tools, which systematically assessed various configurations for their spectral absorption and current–voltage characteristics, further enhancing the cell’s performance. These findings underscore the critical role of precise material engineering and sophisticated statistical analyses in advancing solar cell technology, setting new efficiency benchmarks, and driving further developments in the field.

In the context of the escalating global climate crisis and the urgent need for sustainable energy solutions, this study explores the integration of wind energy as a supplementary source to solar photovoltaic energy in Naama, Algeria. The research utilizes a decade-long anemometric dataset, along with concurrent solar radiation data, to investigate the potential of harnessing wind energy, particularly during periods of low solar irradiance. Employing advanced statistical methods, including the Weibull distribution, the study assesses the wind power generation potential of a 2 kW/day turbine. The research highlights an average evening increase in wind speeds, which inversely correlates with the diminished solar energy production after sunset. This seasonal pattern is further substantiated by a significant negative correlation between wind speed and solar radiation for most of the year (January to May and September to December), with Pearson coefficients ranging from −0.713 to −0.524 (p < 0.05). However, the study also notes an absence of a notable correlation during the summer months (June to August) attributed to seasonal wind variations and the peak of solar irradiance. These findings confirm Naama as an ideal location for integrated renewable energy systems, thereby demonstrating the natural synergy between solar and wind energy. This synergy is particularly effective in mitigating the intermittency of solar power, thus highlighting the potential of wind energy during periods of low solar activity.

Wind energy has emerged as a pivotal renewable energy source, offering a sustainable solution to the growing global energy demands and environmental challenges. This work presents the design, optimization, and experimental vali- dation of a mini horizontal-axis wind turbine equipped with a Permanent Magnet Synchronous Generator (PMSG). A comprehensive approach was adopted, beginning with a 3D design of the turbine’s structural components using Tinkercad software, followed by mathematical modeling and simulation of the PMSG using Python. The mathematical model incorporates advanced techniques, including the Park transformation and equations of electromagnetic and mechanical dynamics, to ensure accurate predictions of the generator’s performance. The turbine prototype was fabricated, and a custom-designed wattmeter utilizing Arduino technology was developed to facilitate performance characterization. Experimental tests were conducted under controlled conditions to evaluate the electrical output of the wind turbine. The results were compared with the theoretical simulations, revealing a satisfactory correlation. However, minor deviations were identified, attributed to losses and limitations in the prototype’s physical components. This study underscores the feasibility and effectiveness of employing small-scale wind turbines with PMSGs for sustainable energy applications. The insights derived from this work provide a foundation for further opti- mization in terms of design, material selection, and integration with energy storage systems. Future prospects include enhancing the magnetic flux density of the generator and exploring hybrid renewable systems for improved efficiency.

Accurate performance prediction for large offshore wind turbines requires a principled treatment of uncertainty in both the wind resource and the rotor design parameters. In the present work, we develop a surrogate-based, multi-level uncertainty quantification (UQ) framework coupling a physics-based Blade Element Momentum (BEM) solver with a spectral Polynomial Chaos Expansion (PCE) surrogate that replaces the expensive Monte Carlo loop and apply it to the IEA 15 MW offshore reference wind turbine. The framework is completed by Sobol variance-based global sensitivity analysis. The contribution is methodological rather than algorithmic: although each individual ingredient (PCE, Sobol, BEM, and Jensen) is well established, their joint deployment in a single, internally consistent, end-to-end probabilistic workflow that simultaneously delivers (i) aerodynamic–structural UQ with analytical Sobol ranking, (ii) a like-for-like cross-comparison of three reference turbines, (iii) a quantitative leading-edge icing degradation study, and (iv) a farm-level wake-steering optimization on the same IEA 15 MW reference rotor yields a unified probabilistic envelope from which manufacturing tolerances, cold-climate investment thresholds, and farm-layout/control trade-offs can be read off consistently. Five input parameters are treated as random variables: hub-height wind speed (Weibull, k = 2.2, c = 9.8 m/s), air density, blade chord length, twist angle, and rotor speed. A degree-4 sparse PCE is built by non-intrusive spectral projection using N = 5000 Sobol quasi-random realizations, which allows the Sobol indices to be recovered analytically from the expansion coefficients at essentially no extra cost. Three parallel engineering studies complement the core UQ analysis: (A) a head-to-head comparison of the NREL 5 MW, DTU 10 MW, and IEA 15 MW reference turbines; (B) a quantitative assessment of leading-edge ice accretion at four severity levels; and (C) a Jensen-based wake optimization for a 25-turbine offshore array with static wake steering. The main results are as follows: the turbine reaches C_p,max = 0.480 at λ_opt = 8.51, and an annual energy production (AEP) of 71,261 MWh/year (PCE: 70,840 ± 2,140 MWh/year, 95% CI). Wind speed emerges as the dominant driver of C_p variance (S₁ = 0.412), followed by blade twist (0.198) and chord (0.143). Severe icing (30 kg/m) reduces C_p by 18.2% and increases the blade-root Damage Equivalent Load (DEL) by 18.5%. For the array, the optimal spacing (s_x = 8D, s_y = 6D) gives a farm efficiency of 89.6% and 1296 GWh/year, and a 15° wake-steering offset adds a further +3.2% to farm AEP. Compared with plain Monte Carlo, the sparse PCE delivers the same statistics with about 36% fewer model evaluations and a relative error below 0.8%.

In the last thirty years, low power field programmable gate arrays (FPGAs) becoming more commonly used to implement a countless of applications in different electronics industry domains. Due to their flexible design, strong compatibility, parallel computing, and compared to the CPU architecture, FPGA accentuate computing efficiency and con sidered as one of the devices with the lowest application risk and the shortest development cycle among the variety of available programmable circuits families. This article details the design and implementation of a direct digital synthesis (DDS) signal generator using the Spartan-6 FPGA, focusing on high-quality sine wave generation. The system utilizes look-up tables (LUTs) and Block RAM (BRAM) for efficient storage and retrieval of sine wave data, while an 8-bit DAC0808 digital-to-analog converter (DAC) ensures precise waveform output. The FPGA's reconfigurable architecture allows real-time adjustments of frequency and phase, making the design suitable for various signal processing applications and modulation techniques like binary phase shift keying (BPSK).