The ability of a Photovoltaic array to generate maximum power is influenced by the Partial Shading Condition. Optimized strategies are therefore essential to improve energy harvesting capability of a Photovoltaic array. The effective interconnection of Photovoltaic modules can solve this problem. Of all the conventional configurations, the Total Cross Tied interconnection method has been proven to effectively extract maximum power from the Photovoltaic System. This research article focuses on a detailed investigation of temporary shading on a superior total cross-tied configuration. Here, four practical field shading conditions - Corner, Border, Random, and Diagonal- are considered for the study. Forty-nine 200 W 7×7 PV TCT connected Sun Earth Solar Power TPB 156×156-54-P 200W PV modules are modeled and simulated in MATLAB-Simulink environment. Performance indices were analyzed based on open circuit voltage, short circuit current, current at maximum power, voltage at maximum power, and maximum extracted power. The novelty of this research lies in identifying the behavior of the TCT configuration in maintaining the near-constant efficiency under diverse shading scenarios. Research also shows strong inter-dependency between the fill factor and the mismatch power loss. An increase in fill factor results in reduced mismatch power loss, indicating improved utilization of power and hence improved efficiency.
Performance of small wind turbines can be improved by adding an enclosure (diffuser) through velocity enhancement. Diffuser Augmented Wind Turbine uses different diffuser shapes to improve the performance. The flanged straight converging-diverging diffusers are still preferred by many researchers because of their various advantages. The major research works about these are based on parametric analysis in terms of global velocity values. However, it is important to study the detailed velocity distribution. This research paper presents the detailed velocity evaluation of nine straight converging-diverging diffusers in addition to parametric study. Computational Fluid Dynamics analysis is performed for all model (S1-S9) designed using Design of Experiments. Four variables - inlet diameter, converging section, diverging section lengths and exit diameter are considered at three levels. The throat diameter and flange height are kept as constant for all models. Flow simulations are carried out at an inlet wind velocity of 7 m/s using turbulence model using ANSYS 2022 R1. Velocity path lines, local velocity ratios, average radial velocities and radial velocity variations are evaluated. Among these nine models, model S3 is observed with better average radial velocity and less radial velocity variation. Models S7, S8 and S9 are observed with poor performance.
Short-term photovoltaic power forecasting is increasingly important for grid integration, operational planning, and renewable energy management; however, model selection remains a challenge in African systems, where historical records, meteorological inputs, and data quality are often scarce. This study developed a comparative mathematical benchmarking framework for evaluating representative short-term photovoltaic forecasting model families under data-scarce African grid conditions. Using secondary evidence from the literature, this study compares baseline, physical or weather-assisted, classical machine-learning, deep-learning, hybrid, and probabilistic forecasting families across four sparse-data scenarios: output-only operation, limited-weather operation, weather-assisted short-history operation, and utility deployment under operational constraints. A suitability score was proposed to integrate predictive accuracy, robustness to sparse or missing data, transferability, interpretability, data burden, and computational burden. The analysis showed that no single model family was universally superior across all scenarios. Classical ensemble machine-learning models appear most suitable when limited weather information is available, weather-assisted physical models are strongest when historical records are short but forecast inputs are available, and endogenous temporal models are most relevant when only output history is available. This study contributes a context-sensitive model-selection framework for photovoltaic forecasting in African grid environments.
In this investigation, two distinct wood samples Anogeissue Leiocarpa (AL) and Cordia Milleni (CM)) were converted to bio-oil using a rapid pyrolysis procedure. Chemical components of the bio-oil yields were identified by gas chromatography-mass spectrometry (GC-MS) analysis. Experimental runs are 450°C, 500°C, 550°C, 650°C and 650°C, with energy content for AL and CM at optimum Temperature 550°C was 33.20 and 33.33 MJ/kg, respectively. Results of the study further shows that the bio-oil yields from AL and CM contained low nitrogen (0.88% and 0.77%), sulphur (1.36% and 0.94%) and another organic compound. Wood samples have a modest pollutant effect, as a result of low sulphur and nitrogen content. The stidy highlight the possibility of using the bio-oil yields as energy for internal combustion engines, raw materials for the chemical processing sector and serve as alternative energy supply sources to encourage the use of renewable energy sources to reduce pollution.
This work examines the design, modeling, and control of a grid-connected PV-wind hybrid renewable energy system (HRES) based on a DC-coupled architecture comprising photovoltaic (PV) panels and wind turbines equipped with a permanent magnet synchronous generator. Mathematical models for key components such as PV arrays, wind turbines, and power converters were created in tandem with their respective control systems. To maximize energy capture and ensure system stability under changing climatic circumstances, the PV and wind systems used maximum power point tracking (MPPT) with the perturb and observe (P&O) method. The inverter connects the DC bus to the grid, controlled by a dual fuzzy logic control, to enhance system stability under variable renewable energy conditions. The system's performance was validated through simulations in the MATLAB/Simulink environment. Dynamic changes in solar radiation were modeled as follows: G = 600 W/m², G = 800 W/m², and G = 1000 W/m². Simultaneously, wind speed variations were simulated with profiles of 8 m/s, 10 m/s, and 12 m/s. The simulation results showed that the system could maintain stable voltage and current waveforms at the point of common coupling. More precisely, the fuzzy logic control successfully controlled the inverter output voltage for the given operating conditions. Additionally, the MPPT method achieved a good average efficiency in getting power from the PV arrays and wind system, which helped improve the overall system efficiency. The results also demonstrated the potential of hybrid renewable energy systems to provide reliable and clean electricity, paving the way for future flexible and carbon-neutral energy solutions.
