ABSTRACT This paper presents a critical review of parameter estimation techniques for solar photovoltaic (PV) systems, highlighting their merits and limitations. The techniques are classified into three groups based on the number of data samples used and the estimation approach adopted for the single-diode model (SDM). Further sub-classifications are included to emphasize the limitations within each category. Various objective functions and performance parameters used to evaluate the effectiveness of these methods are also discussed. In addition, a comparative analysis of major estimation approaches—Numerical, Analytical, Metaheuristic, and Hybrid—is presented to identify the most suitable method. It is observed that hybrid approaches are relatively simple, faster, and offer improved performance indices compared to other methods. Moreover, they require fewer assumptions and approximations, resulting in characteristics closer to actual PV behavior. This review compiles SDM parameter estimation techniques in one place, providing a clearer understanding of this widely used PV modeling approach.

ABSTRACT This study investigates the combined effects of rotation, Hall current, chemical reaction, and nonlinear thermal radiation on the flow and heat transfer of a rotating hybrid nanofluid (Cu–Al₂O₃/water) over a stretching surface with an internal heat source. The governing partial differential equations are transformed into nonlinear ordinary differential equations using similarity transformations and solved numerically via the shooting method. The results indicate that an increase in the Hall current parameter enhances transverse velocity and temperature while reducing longitudinal velocity. A higher chemical reaction parameter decreases nanoparticle concentration due to intensified species depletion. Variations in skin friction coefficients, Nusselt number, and Sherwood number are also analyzed. Furthermore, a neural network model is developed to predict skin friction coefficients (Cfx and Cfy). Comparative analysis shows that the Levenberg–Marquardt algorithm achieves higher accuracy, whereas the BFGS method ensures faster convergence, demonstrating the effectiveness of machine learning in modeling complex nanofluid transport phenomena.

ABSTRACT The ecological model with ratio-dependent functional response is described in this article incorporating the toxicant haphazardness and the gestational time lag. The primary objective of this study is to examine how the time delay and environmental toxins affect this proposed system. During the investigation of this considered system, some preliminary results such as positivity and boundedness are presented with the help of model parameters. We find three feasible equilibriums and deduce their stability criteria. Also, the impact of gestational time lag on the steadiness of the boundary and the positive equilibrium has been analyzed. The system experiences the Hopf bifurcation about positive equilibrium concerning the time delay as well as the transcritical bifurcation about the planer equilibrium at the crucial value of predator’s death rate in the absence of time lag, besides both bifurcations have been shown visually. Finally, under the specific parametric constraints, we determine the duration of time delay for preserving the stability and direction and stability of the Hopf bifurcation. Numerical simulations have confirmed the results of these analytical studies, and have also examined how environmental contaminants and gestational time lag affect this system’s dynamics.

ABSTRACT This study proposes a novel algorithm Converter Output Current Based Maximum Power Point Tracking (COCB-MPPT) for systems, addressing challenges in traditional MPPT methods such as slower response times and increased steady-state oscillations. This proposed method utilizes the relationship between the converter output current and duty ratio to automatically adjust step changes, thereby maximizing power output from the PV panel. As power exhibits less sensitivity to current perturbations than voltage perturbations, this approach demonstrates enhanced efficiency during steady-state operation. Furthermore, a novel adaptation scheme is introduced to accelerate the convergence speed of the MPPT which relies on the current used for battery charging through the boost converter to monitor the PV array peak power. The efficacy of the proposed technique is substantiated through hardware experiments employing both solar array simulator and actual solar panels. The proposed algorithm enhances PV system performance by optimizing energy extraction and stabilizing operation in varying conditions.

ABSTRACT In industrial applications, thermoelectric generators (TEGs) are commonly constructed using several p-n types of thermoelectric (TE) materials interconnected in series. The TEG module is placed over the heat source to extract the maximum power output from a hot surface, with the opposite face exposed to a lower temperature. However, achieving a homogeneous temperature distribution on the module’s hot side can be challenging due to many industrial heat sources’ variable and non-homogeneous nature. As a result, mismatched temperature conditions are often encountered, with some TE pairs experiencing a different T h temperature from others. This can significantly impact the overall performance of the TEG module, necessitating comprehensive modeling techniques to predict the behavior of these systems under the Dirichlet and Neumann boundary conditions. This work presents a mathematical model for evaluating thermoelectric generators that includes the effect of temperature-dependent TE properties and conditions of mismatch. The model also introduces a novel method for solving the strongly non-linear problem with low computational cost. The one-dimensional equations for temperature profile, electrical potential, and heat generation in the steady state are derived using radial basis functions and the homotopy method.

ABSTRACT High-performance solar photovoltaic models rely on a precise understanding of solar photovoltaic (PV) cell parameters. This necessity arises from the importance of comprehending and optimizing the performance of photovoltaic systems to ensure reliable and efficient energy production. Nonetheless, due to the intrinsic nonlinear nature of solar photovoltaic systems, employing a reliable algorithm is essential for accurate modeling. In this article, an enhanced algorithm inspired by the behavior of the Tasmanian Devil, named Improved Tasmanian Devil Optimization (ITDO), is proposed to enhance the performance of the original TDO. Our algorithm includes improvements to the exploitation phase, increasing the frequency of prey detection and attacks in the target zone. The proposed method retains the original steps, with the exploration stage unchanged. However, the second step has been enhanced with an adaptation mechanism, and the final step has been improved to efficiently select the global optimum. These modifications do not impact the method’s complexity. To assess ITDO’s effectiveness, experiments were conducted using single, double, and PV module models. Thorough comparison with seven other algorithms revealed ITDO’s superior solution accuracy. Additionally, statistical analyses using Wilcoxon rank-sum and Friedman tests confirmed ITDO’s superiority as the most robust and efficient algorithm for parameter estimation in PV systems.

ABSTRACT In this work, a hybrid PID controller, combining Zeigler–Nichols (ZN) theory and the dominant pole placement, has been developed for the automatic voltage regulator (AVR) systems. Furthermore, the reduced order models (ROMs) of AVR systems, namely, AVR-1, AVR-2, and AVR-3, have been established using Particle Swarm Optimization (PSO), Big Bang Big Crunch (BBBC), and Pade’s approximation, respectively. Then, the proposed PID controller has been applied to both the original and developed lower order models of AVR to check the efficacy and robustness of the developed hybrid controller. It is shown that the proposed hybrid PID controller provides a very small value of the percentage peak overshoot (%M p ) and settling time (t s ). Also, the gain margin (GM) and phase margin (PM) are found to be in the desired range. Finally, the sensitivity analysis has also been performed to check the robustness of the proposed controller. The sensitivity function, complementary sensitivity function, and various performance indices have been evaluated for the proposed controller based on Bode’s Integral (BI), Tyreus–Lyuben (T–L), and Internal Model Control (IMC) approaches. The comparative studies of the results show that the proposed controller has smaller values of performance indices and sensitivity function.

ABSTRACT Photovoltaic system performance depends on factors like bypass diode topology, array size, arrangement, and shading intensity. Partial shading causes mismatch losses and reduced power production. Though many methods address this, they may not fully optimize power. Reconfiguring PV modules offers a promising solution to minimize power loss. In this paper, major Static and Dynamic reconfiguration strategies have been discussed. The methodology, benefits, and limitations of each approach are compared and evaluated. The research set out to compare and contrast the effectiveness of static and dynamic reconfiguration approaches with regards to energy output, shading losses, and mismatch losses. The results showed that dynamic reconfiguration was more effective in reducing shading losses and increasing energy yield compared to static reconfiguration. This article aims to help researchers and readers in gaining an understanding of the available reconfiguration strategies and factors that affect their selection. Overall, this article is a valuable resource for researchers, engineers, and professionals working in the field of renewable energy, particularly in the area of PV systems.

ABSTRACT Indoor environmental quality is crucial for human health and comfort, necessitating precise and efficient computational methods to optimise indoor climate parameters. Recent advancements in machine learning (ML) and computational fluid dynamics (CFD) are promising. However, applying ML to complex building airflow presents challenges. This research aims to investigate the integration of ML with CFD in the context of built environment applications using a systematic review approach. It highlights a critical knowledge gap: the need to synthesise innovative approaches that address the limitations of indoor modelling using data-driven ML methods. The review examines contemporary literature, identifying current developments and suggesting potential future directions. It delves into the innovations in combining ML with CFD to predict thermal comfort and indoor air quality, uncovering key limitations such as the lack of high-quality experimental data for training and validation, the computational complexity of detailed CFD simulations, and the interpretability issues of ‘black-box’ ML models. The emergence of data-driven techniques in fluid mechanics offers promising prospects for modelling in the built environment. Future research should focus on incorporating physics-based rules in ML models, adapting turbulence closure models for indoor flows, and enhancing model validation using real-world datasets. The research emphasises the synergistic relationship between ML and CFD; it proposes pathways to overcome current limitations, aiming to enhance the precision and efficiency of indoor environment modelling through their integration.

ABSTRACT To optimize radial distribution systems, this study suggests the utilization of the Grey Wolf Optimizer (GWO), a hybrid metaheuristic optimization method, combined with the Sine Cosine method (SCA). The primary objective of this work is to enhance the distribution system by determining the most efficient network reconfiguration, sizing, and placement of various distributed energy sources in distribution system. The energy sources considered include capacitors, solar cells, wind turbines, biomass-based distributed generation units, and battery storage units. To achieve this goal, the proposed strategy incorporates the power loss sensitivity technique, which assists in identifying suitable candidate buses and accelerates the resolution process. Moreover, the model considers fluctuations in solar irradiance and wind speed using Weibull and Beta probability distribution functions, compensating for the intermittent nature of renewable energy sources and the variability in demand. To address power fluctuations, voltage surges, significant losses, and inadequate voltage stability challenges, battery energy storage, diesel generators, and dispatchable biomass DGs are employed to mitigate variability and enhance supply continuity. The proposed approach is evaluated and validated by comparing it to existing optimization strategies using IEEE 69-bus and 84-bus RDSs. The results demonstrate that the suggested technique achieves faster convergence to near-optimal solutions. The proposed methodology yields a significant reduction of up to 80% in power losses in the 69-bus system and a 35% reduction in the 84-bus system, signifying higher performance than existing methods.