Search Results (20)

The energy generated from renewable sources has an intermittent nature since solar irradiation and wind speed vary continuously. Hence, their energy should be stored to be utilized throughout their shortage. There are various forms of energy storage systems while the most widespread technique is the battery storage system since its cost is low compared to other techniques. Therefore, batteries are employed in several applications like power systems, electric vehicles, and smart grids. Due to the merits of the lithium-ion (Li-ion) battery, it is preferred over other kinds of batteries. However, the accuracy of the Li-ion battery model is essential for estimating the state of charge (SOC). Additionally, it is essential for consistent simulation and operation throughout various loading and charging conditions. Consequently, the determination of real battery model parameters is vital. An innovative application of the red-billed blue magpie optimizer (RBMO) for determining the model parameters and the SOC of the Li-ion battery is presented in this article. The Shepherd model parameters are determined using the suggested optimization algorithm. The RBMO-based modeling approach offers excellent execution in determining the parameters of the battery model. The suggested approach is compared to other programmed algorithms, namely dandelion optimizer, spider wasp optimizer, barnacles mating optimizer, and interior search algorithm. Moreover, the suggested RBMO is statistically evaluated using Kruskal–Wallis, ANOVA tables, Friedman rank, and Wilcoxon rank tests. Additionally, the Li-ion battery model estimated via the RBMO is validated under variable loading conditions. The fetched results revealed that the suggested approach achieved the least errors between the measured and estimated voltages compared to other approaches in two studied cases with values of 1.4951 × 10⁻⁴ and 2.66176 × 10⁻⁴. Full article

The red-billed blue magpie optimizer (RBMO) is employed in this research study to address parameter extraction in polymer exchange membrane fuel cells (PEMFCs), along with three recently implemented optimizers. The sum of squared deviations (SSD) between the simulated and measured stack voltages defines the fitness function of the optimization problem under investigation subject to a set of working constraints. Three distinct PEMFCs stacks models—the Ballard Mark, Temasek 1 kW, and Horizon H-12 units—are used to illustrate the applied RBMO’s feasibility in solving this challenge in comparison to other recent algorithms. The highest percentages of biased voltage per reading for the Ballard Mark V, Temasek 1 kW, and Horizon H-12 are, respectively, +0.65%, +0.20%, and −0.14%, which are negligible errors. The primary characteristics of PEMFC stacks under changing reactant pressures and cell temperatures are used to evaluate the precision of the cropped optimized parameters. In the final phase of this endeavor, the sensitivity of the cropped parameters to the PEMFCs model’s performance is investigated using two machine learning techniques, namely, artificial neural network and Gaussian process regression models. The simulation results demonstrate that the RBMO approach extracts the PEMFCs’ appropriate parameters with high precision. Full article

Researchers tackled the challenge of finding the right parameters for a transformer-equivalent circuit. They achieved this by minimizing the difference between actual measurements (currents, powers, secondary voltage) during a transformer load test and the values predicted by the model using different parameter settings. This process considers limitations on what values the parameters can have. This research introduces the application of a new and effective optimization algorithm called the dandelion algorithm (DA) to determine these transformer parameters. Information from real-time tests (single- and three-phase transformers) is fed into a computer program that uses the DA to find the best parameters by minimizing the aforementioned difference. Tests confirm that the DA is a reliable and accurate tool for estimating the transformer parameters. It achieves excellent performance and stability in finding the optimal values that precisely reflect how a transformer behaves. The DA achieved a significantly lower best fitness function value of 0.0136101 for the three-phase transformer case, while for the single-phase case it reached 0.601764. This indicates a substantially improved match between estimated and measured electrical parameters for the three-phase transformer model. By comparing DA with six competitive algorithms to prove how well each method minimized the difference between measurements and predictions, it could be shown that the DA outperforms these other techniques. Full article

A viable strategy for promoting sustainable development and a cleaner environment is the reuse of demolition-related ceramic waste and ceramic manufacturing byproducts in the production of concrete. The purpose of this study is to assess the possibilities for using ceramic waste in the production of concrete as a fine aggregate and cementitious material. The effectiveness of concrete mixtures incorporating 20–100% ceramic waste fine (CWF) as a replacement for natural fine aggregate and 10–30% ceramic waste powder (CWP) in place of cement was evaluated. Their influence was assessed with respect to workability, mechanical performance, durability, and elevated temperature resistance. The results were analyzed via energy dispersive x-ray (EDX) and scanning electron microscopy (SEM). The findings illustrated that the increase in the replacement levels of CWP and CWF decreases the concrete workability. The mechanical performance of concrete mixtures is enhanced under compression and flexural tests as the replacement ratios of CWF and CWP increase up to 50% and 10% as replacements of sand and cement, respectively. The increases in compressive and flexural strength were 5.33% and 8.14%, respectively, at age 28 days. The concrete water permeability significantly increases as the CWF replacement ratio increases, and the incorporation of CWP reduces this negative impact. After exposure to 200, 400, 600, and 800 °C, the residual compressive strengths of concrete mixtures incorporating CWF and CWP were up to 95.02%, 89.66%, 74.33%, and 51.34%, respectively, compared to control mixtures, which achieved 84.25%, 76.03%, 59.36%, and 35.84% of their initial strength. Microstructure analysis revealed that combining CWP and CWF significantly improves cement hydration when compared to the reference mixture. Thus, the use of CWF and CWP in the production of masonry mortar might be an economical alternative that would aid in raising the recycling rate of demolition and construction debris and supporting sustainable growth in the building sector. Full article

Subversive environmental impacts and limited amounts of conventional forms of energy necessitate the utilization of renewable energies (REs). Unfortunately, REs such as solar and wind energies are intermittent, so they should be stored in other forms to be used during their absence. One of the finest storage techniques for REs is based on hydrogen generation via an electrolyzer during abundance, then electricity generation by fuel cell (FC) during their absence. With reference to the advantages of the proton exchange membrane fuel cell (PEM-FC), this is preferred over other kinds of FCs. The output power of the PEM-FC is not constant, since it depends on hydrogen pressure, cell temperature, and electric load. Therefore, a maximum power point tracking (MPPT) system should be utilized with PEM-FC. The techniques previously utilized have some disadvantages, such as slowness of response and largeness of each oscillation, overshoot and undershoot, so this article addresses an innovative MPPT for PEM-FC using a consecutive controller made up of proportional-integral (PI) and proportional-derivative (PD) controllers whose gains are tuned via the golden jackal optimization algorithm (GJOA). Simulation results when applying the GJOA-PI-PD controller for MPPT of PEM-FC reveal its advantages over other approaches according to quickness of response, smallness of oscillations, and tininess of overshoot and undershoot. The overshoot resulting using the GJOA-PI-PD controller for MPPT of PEM-FC is smaller than that of perturb and observe, GJOA-PID, and GJOA-FOPID controllers by 98.26%, 86.30%, and 89.07%, respectively. Additionally, the fitness function resulting when using the GJOA-PI-PD controller for MPPT of PEM-FC is smaller than that of the aforementioned approaches by 93.95%, 87.17%, and 87.97%, respectively. Full article

Photovoltaic systems have become more attractive alternatives to be integrated into electrical power systems. Therefore, PV cells have gained immense interest in studies related to their operation. A photovoltaic module’s performance can be optimized by identifying the parameters of a photovoltaic cell to understand its behavior and simulate its characteristics from a given mathematical model. This work aims to extract and identify the parameters of photovoltaic cells using a novel metaheuristic algorithm named Modified Social Group Optimization (MSGO). First, a comparative study was carried out based on various technologies and models of photovoltaic modules. Then, the proposed MSGO algorithm was tested on a monocrystalline type of panel with its single-diode and double-diode models. Then, it was tested on an amorphous type of photovoltaic cell (hydrogenated amorphous silicon (a-Si: H)). Finally, an experimental validation was carried out to test the proposed MSGO algorithm and identify the parameters of the polycrystalline type of panel. All obtained results were compared to previous research findings. The present study showed that the MSGO is highly competitive and demonstrates better efficiency in parameter identification compared to other optimization algorithms. The Individual Absolute Error (IAE) obtained by the MSGO is better than the other errors for most measurement values in the case of single- and double-diode models. Relatedly, the average fitness function obtained by the MSGO algorithm has the fastest convergence rate. Full article

The key goal of this effort is to develop an efficient control system for a three-phase cascaded H-bridge multilevel inverter powered by the photovoltaic (PV) system. The power for the system is generated through the use of PV modules, which serve as DC inputs for the cascaded H-bridge multilevel inverter. The authors aim to achieve a nearly sinusoidal signal at the voltage level and are specifically focused on minimizing the total harmonic distortion (THD) to the smallest possible value. Hence, an advanced N-level space vector modulation (SVM) is developed to ensure an appropriate control for the cascaded inverter. The aim is to design an effective control strategy to increase inverter efficacy and, thus, supply the best output quality. In addition, a robust approach to the maximum power point (MPP) tracking (MPPT) technique is developed based on an adaptive perturb and observe (P&O) algorithm to ensure superior tracking of the MPP. The developed algorithm eliminates 90% of the power curve area in the search space process and only maintains 10% of the area that includes the MPP. Each PV system employs its own improved MPPT control. The numerical results confirm that the enhanced P&O algorithm attains a precise response with superior efficiency and a fast response under the fast alteration of environmental conditions. Hence, the energy loss is reduced. The simulation results validate the effectiveness of this study, highlighting the high efficiency of the control strategy and the enhanced performance of the proposed scheme with lesser THD values. Full article

The low bandgap antimony selenide (Sb₂Se₃) and wide bandgap organic solar cell (OSC) can be considered suitable bottom and top subcells for use in tandem solar cells. Some properties of these complementary candidates are their non-toxicity and cost-affordability. In this current simulation study, a two-terminal organic/Sb₂Se₃ thin-film tandem is proposed and designed through TCAD device simulations. To validate the device simulator platform, two solar cells were selected for tandem design, and their experimental data were chosen for calibrating the models and parameters utilized in the simulations. The initial OSC has an active blend layer, whose optical bandgap is 1.72 eV, while the initial Sb₂Se₃ cell has a bandgap energy of 1.23 eV. The structures of the initial standalone top and bottom cells are ITO/PEDOT:PSS/DR3TSBDT:PC₇₁BM/PFN/Al, and FTO/CdS/Sb₂Se₃/Spiro-OMeTAD/Au, while the recorded efficiencies of these individual cells are about 9.45% and 7.89%, respectively. The selected OSC employs polymer-based carrier transport layers, specifically PEDOT:PSS, an inherently conductive polymer, as an HTL, and PFN, a semiconducting polymer, as an ETL. The simulation is performed on the connected initial cells for two cases. The first case is for inverted (p-i-n)/(p-i-n) cells and the second is for the conventional (n-i-p)/(n-i-p) configuration. Both tandems are investigated in terms of the most important layer materials and parameters. After designing the current matching condition, the tandem PCEs are boosted to 21.52% and 19.14% for the inverted and conventional tandem cells, respectively. All TCAD device simulations are made by employing the Atlas device simulator given an illumination of AM1.5G (100 mW/cm²). This present study can offer design principles and valuable suggestions for eco-friendly solar cells made entirely of thin films, which can achieve flexibility for prospective use in wearable electronics. Full article

Wound healing has grown to be a significant problem at a global scale. The lack of multifunctionality in most wound dressing-based biopolymers prevents them from meeting all clinical requirements. Therefore, a multifunctional biopolymer-based tri-layered hierarchically nanofibrous scaffold in wound dressing can contribute to skin regeneration. In this study, a multifunctional antibacterial biopolymer-based tri-layered hierarchically nanofibrous scaffold comprising three layers was constructed. The bottom and the top layers contain hydrophilic silk fibroin (SF) and fish skin collagen (COL), respectively, for accelerated healing, interspersed with a middle layer of hydrophobic poly-3-hydroxybutyrate (PHB) containing amoxicillin (AMX) as an antibacterial drug. The advantageous physicochemical properties of the nanofibrous scaffold were estimated by SEM, FTIR, fluid uptake, contact angle, porosity, and mechanical properties. Moreover, the in vitro cytotoxicity and cell healing were assessed by MTT assay and the cell scratching method, respectively, and revealed excellent biocompatibility. The nanofibrous scaffold exhibited significant antimicrobial activity against multiple pathogenic bacteria. Furthermore, the in vivo wound healing and histological studies demonstrated complete wound healing in wounded rats on day 14, along with an increase in the expression level of the transforming growth factor-β1 (TGF-β1) and a decrease in the expression level of interleukin-6 (IL-6). The results revealed that the fabricated nanofibrous scaffold is a potent wound dressing scaffold, and significantly accelerates full-thickness wound healing in a rat model. Full article

This article provides a comprehensive review of optimal relay coordination (ORC) in distribution networks (DNs) that include distributed generators (DGs). The integration of DGs into DNs has become a real challenge for power system protection, as the power flow changes from unidirectional to bidirectional, which complicates the relay settings. The introduction of DGs in DNs requires changes and modifications in the protective schemes to maintain proper operation, reliability, stability, and security of the system. This paper focuses on the impacts of DGs penetration into DNs, including the effects on protective scheme coordination. Various expressions for characterizing the overcurrent (OC) coordination problem, as well as related solution attempts, are discussed. Several optimization strategies and techniques are suggested by scientists to deal with coordination optimization problems aiming to achieve less computation time and better accuracy. All these efforts ultimately aim to define optimal relay settings to achieve ORC by generating the optimal setting of cascading relative OC relays. This comprehensive review provides a broad overview of the contributions of scholars in recent publications in this field, with more than 210 articles reviewed and analyzed. It is a valuable resource for other researchers in the same field who aim to tackle ORC problems in their future endeavors. Full article

Modeling of solar generating systems (SGSs) is necessary for recognizing their performance under various conditions of solar irradiance, temperature, and loading. There are nine unbeknown parameters (UPs) in the three-diode model (3-DM); if they are accurately determined, it can exactly identify the real characteristics of SGSs. Parametrization of the 3-DM of SGSs is a nonlinear problem that can be solved via optimization due to its effectivity in determining the optimal parameters to a variety of symmetrical and asymmetrical problems with nonlinearity. Root-mean-squared errors amongst measured and extracted electric current points are the fitness functions to be minimized. The main contributions of this article are the innovative utilization of the barnacles mating optimization algorithm (BMOA) for precise parametrizing of the 3-DM of SGSs and the experimental validation of the SGS. The optimization procedure is based on real measurements of I/V at specific circumstances, in which BMOA is employed to identify the nine UPs of 3-DM of SGSs. Two SGSs are under study, the first of which (Kyocera KC200GT) is widely utilized in the literature for performing comparisons, and the second (Copex P-120) is experimentally set up during different sun irradiances and temperatures. The results of BMOA emphasize its preference over other optimizers for identifying the nine UPs of 3-DM of SGSs. Full article

This study presents an innovative strategy for load frequency control (LFC) using a combination structure of tilt-derivative and tilt-integral gains to form a TD-TI controller. Furthermore, a new improved optimization technique, namely the quantum chaos game optimizer (QCGO) is applied to tune the gains of the proposed combination TD-TI controller in two-area interconnected hybrid power systems, while the effectiveness of the proposed QCGO is validated via a comparison of its performance with the traditional CGO and other optimizers when considering 23 bench functions. Correspondingly, the effectiveness of the proposed controller is validated by comparing its performance with other controllers, such as the proportional-integral-derivative (PID) controller based on different optimizers, the tilt-integral-derivative (TID) controller based on a CGO algorithm, and the TID controller based on a QCGO algorithm, where the effectiveness of the proposed TD-TI controller based on the QCGO algorithm is ensured using different load patterns (i.e., step load perturbation (SLP), series SLP, and random load variation (RLV)). Furthermore, the challenges of renewable energy penetration and communication time delay are considered to test the robustness of the proposed controller in achieving more system stability. In addition, the integration of electric vehicles as dispersed energy storage units in both areas has been considered to test their effectiveness in achieving power grid stability. The simulation results elucidate that the proposed TD-TI controller based on the QCGO controller can achieve more system stability under the different aforementioned challenges. Full article

Developing accurate models for photovoltaic (PV) systems has a significant impact on the evaluation of the accuracy and testing of PV systems. Artificial intelligence (AI) is the science of developing machine jobs to be more intelligent, similar to the human brain. Involving AI techniques in modeling has a significant modification in the accuracy of the developed models. In this paper, a novel dynamic PV model based on AI is proposed. The proposed dynamic PV model was designed based on an adaptive neuro-fuzzy inference system (ANFIS). ANFIS is a combination of a neural network and a fuzzy system; thus, it has the advantages of both techniques. The design process is well discussed. Several types of membership functions, different numbers of training, and different numbers of membership functions are tested via MATLAB simulations until the AI requirements of the ANFIS model are satisfied. The obtained model is evaluated by comparing the model accuracy with the classical dynamic models proposed in the literature. The root mean square error (RMSE) of the real PV system output current is compared with the output current of the proposed PV model. The ANFIS model is trained based on input–output data captured from a real PV system under specified irradiance and temperature conditions. The proposed model is compared with classical dynamic PV models such as the integral-order model (IOM) and fractional-order model (FOM), which have been proposed in the literature. The use of ANFIS to model dynamic PV systems achieves an accurate dynamic PV model in comparison with the classical dynamic IOM and FOM. Full article

This present study describes a novel manta ray foraging optimization approach based non-dominated sorting strategy, namely (NSMRFO), for solving the multi-objective optimization problems (MOPs). The proposed powerful optimizer can efficiently achieve good convergence and distribution in both the search and objective spaces. In the NSMRFO algorithm, the elitist non-dominated sorting mechanism is followed. Afterwards, a crowding distance with a non-dominated ranking method is integrated for the purpose of archiving the Pareto front and improving the optimal solutions coverage. To judge the NSMRFO performances, a bunch of test functions are carried out including classical unconstrained and constrained functions, a recent benchmark suite known as the completions on evolutionary computation 2020 (CEC2020) that contains twenty-four multimodal optimization problems (MMOPs), some engineering design problems, and also the modified real-world issue known as IEEE 30-bus optimal power flow involving the wind/solar/small-hydro power generations. Comparison findings with multimodal multi-objective evolutionary algorithms (MMMOEAs) and other existing multi-objective approaches with respect to performance indicators reveal the NSMRFO ability to balance between the coverage and convergence towards the true Pareto front (PF) and Pareto optimal sets (PSs). Thus, the competing algorithms fail in providing better solutions while the proposed NSMRFO optimizer is able to attain almost all the Pareto optimal solutions. Full article

In this work, an enhanced slime mould algorithm (ESMA) based on neighborhood dimension learning (NDL) search strategy is proposed for solving the optimal power flow (OPF) problem. Before using the proposed ESMA for solving the OPF problem, its validity is verified by an experiment using 23 benchmark functions and compared with the original SMA, and three other recent optimization algorithms. Consequently, the ESMA is used to solve a modified power flow model including both conventional energy, represented by thermal power generators (TPGs), and renewable energy represented by wind power generators (WPGs) and solar photovoltaic generators (SPGs). Despite the important role of WPGs and SPGs in reducing CO₂ emissions, they represent a big challenge for the OPF problem due to their intermittent output powers. To forecast the intermittent output powers from SPGs and WPGs, Lognormal and Weibull probability density functions (PDFs) are used, respectively. The objective function of the OPF has two extra costs, penalty cost and reserve cost. The penalty cost is added to formulate the underestimation of the produced power from the WPGs and SPGs, while the reserve cost is added to formulate the case of overestimation. Moreover, to decrease CO₂ emissions from TPGs, a direct carbon tax is added to the objective function in some cases. The uncertainty of load demand represents also another challenge for the OPF that must be taken into consideration while solving it. In this study, the uncertainty of load demand is represented by the normal PDF. Simulation results of ESMA for solving the OPF are compared with the results of the conventional SMA and two further optimization methods. The simulation results obtained in this research show that ESMA is more effective in finding the optimal solution of the OPF problem with regard to minimizing the total power cost and the convergence of solution. Full article