Around the world, countries are integrating photovoltaic generating systems to the grid to support climate change initiatives. However, solar power generation is highly uncertain due to variations in solar irradiance level during different hours of the day. Inaccurate modelling of this variability can lead to non-optimal dispatch of system resources. Therefore, accurate characterization of solar irradiance patterns is essential for effective management of renewable energy resources in an electrical power grid. In this paper, the Weibull distribution based probabilistic model is presented for characterization of solar irradiance patterns. Firstly, Weibull distribution is utilized to model inter-temporal variations associated with reference solar irradiance data through moving window averaging technique, and then the proposed model is used for irradiance pattern generation. To achieve continuity of discrete Weibull distribution parameters calculated at different steps of moving window, Generalized Regression Neural Network (GRNN) is employed. Goodness of Fit (GOF) techniques are used to calculate the error between mean and standard deviation of generated and reference patterns. The comparison of GOF results with the literature shows that the proposed model has improved performance. The presented model can be used for power system planning studies where the uncertainty of different resources such as generation, load, network, etc., needs to be considered for their better management.

In this paper, the coupled system of Whitham–Broer–Kaup equations of the Caputo fractional derivative (CFD) is studied using the Sumudu decomposition method (SDM). Using different dispersion relations, these equations are needed to describe the properties of waves in shallow water. The current investigation for the future scheme includes convergence and error analysis. We use two examples to demonstrate the leverage and effectiveness of the proposed scheme, and the error analysis is discussed to ensure its accuracy. The numerical simulation is carried out to ensure the accuracy of the future technique. The obtained numerical and graphical results are presented, and the proposed scheme is computationally very accurate and simple to study and solve fractionally coupled nonlinear complex phenomena encountered in science and technology.

In this study, the numerical scheme for the Caputo fractional derivative (NCFD) method and the He–Laplace method (H-LM) are two powerful methods used for analyzing fractional-order systems. These two approaches are used in the study of the complex dynamics of the fractional-order inverted Rössler system, particularly for the detection of chaotic behavior. The enhanced NCFD method is used for reliable and accurate numerical simulations by capturing the intricate dynamics of chaotic systems. Further, analytical solutions are obtained using the H-LM for the fractional-order inverted Rössler system. This method is popular due to its simplicity, numerical stability, and ability to handle most initial values, yielding very accurate results. Combining analytical insights from the H-LM with the robust numerical accuracy of the NCFD approach yields a comprehensive understanding of this system’s dynamics. The advantages of the NCFD method include its high numerical accuracy and ability to capture complex chaotic dynamics. The H-LM offers simplicity and stability. The proposed methods prove to be capable of detecting chaotic attractors, estimating their behavior correctly, and finding accurate solutions. These findings confirm that NCFD- and H-LM-based approaches are promising methods for the modeling and solution of complex systems. Since these results provide improved numerical simulations and solutions for a broad class of fractional-order models, they will thus be of greatest use in forthcoming applications in engineering and science.

This paper investigates the dynamical properties of the fractional-order Arneodo system using a Grünwald–Letnikov-based numerical discretization. Fractional-order operators introduce memory and hereditary effects, enabling a more realistic description than classical integer-order models. The local stability of equilibrium points is examined through eigenvalue analysis of the Jacobian matrix, along with dissipativity conditions and the emergence of complex attractors. A comprehensive dynamical investigation is presented through phase portraits, time series, Lyapunov exponents, and bifurcation diagrams for varying fractional orders. Numerical findings demonstrate the emergence of new chaotic and hyperchaotic attractors. The results confirm that the fractional order strongly influences the system’s stability, sensitivity, and complexity. Our results confirm the relevance of fractional-order modeling in applications, such as secure communication, random number generation, and complex system analysis.

In industrial devices like heat recovery systems, heat pumps, as well as symmetric and complex engineering systems, a nano fluid mixture is used. Regarding the nature of the energy sources (thermal or thermal and electrical), many physical systems could represent possible applications in manufactural activities. The presence of nanoparticles inside a solvent is of great interest in order to optimize the efficacy of the nano-technology systems. The present work deals with heat and mass transfer through a vertical channel where an alumina/water film mixture flows on one of its plates. For simulation, we use a numerical method under mixed convection during water/alumina nano fluid evaporation. We heat the flown plate uniformly while the other is dry and exchange heat with a constant coefficient. The gas mixture enters channel with a constant profile. Results show that an augmentation of the volume rate of the nanoparticle disadvantages evaporation if the heating is absent. Otherwise, if the heating exists, an increasing volume rate of the nanoparticle advantages evaporation. We found also that the film velocity behavior when the volume rate of the nanoparticle varies, independent of the heating.

The Lyapunov method is one of the most effective methods to analyze the stability of stochastic differential equations (SDEs). Different authors analyzed the stability of SDEs based on Lyapunov techniques when the origin can be considered as an equilibrium point. When the origin is not necessarily an equilibrium point, it is still possible to analyze the asymptotic stability of solutions concerning a small neighborhood of the origin. The purpose is to study the asymptotic stability of a system whose solution behavior is a small ball of state space or close to it. Thus, all state trajectories are bounded and close to a sufficiently small neighborhood of the origin. In this sense, the limited boundedness of solutions of random systems, or the chance of convergence of solutions needs to be analyzed on a ball centered on the origin. This is the so called “Practical Stability”. In this article, we mainly investigate the practical uniform exponential stability in the mean square of stochastic linear time–invariant systems. In addition, we are developing the problem of stabilization of certain classes of perturbed stochastic systems. Our crucial techniques include Lyapunov techniques and generalized Gronwall inequalities. Lastly, we provide a numerical example to illustrate our theoretical findings.