Utility-scale PV plants increasingly operate under partial shading, soiling, temperature swings, and rapid irradiance ramps that depress yield and challenge stability on weak grids. This critical review addresses those conditions by (i) unifying a stressor-to-method taxonomy that links field stressors to global intelligent MPPT (metaheuristics and learning-based trackers) and to advanced inverter controls (adaptive/MPC and grid-forming), (ii) standardizing metrics and reporting aligned with IEC 61724-1 and IEEE 1547/1547.1 to enable fair, reproducible comparisons, and (iii) framing MPPT and grid support as a co-design problem with a DT→HIL→Field validation pathway and seedable scenarios. We identify persistent gaps—fragmented partial-shading benchmarks, limited low-SCR testing, and scarce field-grade validation—and compile a quantitative synthesis: global soiling typically reduces annual production by ≈3%–5%, and hybrid/learning MPPT frequently report ≈99% tracking efficiency under PSC in simulation/HIL studies. To demonstrate practical relevance, we validate the framework on a seeded scenario library: DRL trackers achieve median ηMPPT ≈ 0.996 with t95 ≈ 0.19 s and Hybrid trackers ≈0.992/0.26 s, outperforming Metaheuristics (≈0.984/0.42 s); at SCR = 2.5, grid-forming control raises VRI from ~0.78 (tuned GFL) to ~0.95 while keeping THD within 2.5%–3.2%, with all stacks meeting IEEE-1547.1 Category-II ride-through. The resulting taxonomy, standards-aligned reporting, and open seeds provide a replicable basis for comparable, grid-relevant benchmarking and clear guidance for real-world design and operations.

Recent progress in metaheuristic optimization has shown that biologically inspired algorithms could be useful for solving hard numerical and engineering design issues. The Artificial Protozoa Optimizer (APO) is one of them. It uses a bio-inspired exploration technique, although it has problems with convergence efficiency, handling constraints, and scaling up in high-dimensional or confined environments. Also, current APO variations do not perform considering the balance between exploration and exploitation or supporting dynamic membrane-based parallelism. This paper suggests MAAPO-E, an Entropy-Guided, Constraint-Aware Membrane Protozoa Optimizer, to fill in these gaps. It is meant to help with large-scale numerical and constrained engineering optimization issues. MAAPO-E adds a number of important new features: (1) a self-adaptive membrane-computing framework that uses entropy-based diversity metrics to dynamically control communication between membranes; (2) an improved Roulette Fitness–Distance Balance (RFDB⁺) mechanism to keep a strong trade-off between exploration and exploitation; (3) a hybrid local search module that starts when stagnation is detected to speed up fine-tuning near optima; and (4) a multi-stage constraint-handling strategy that combines Deb’s feasibility rules with adaptive penalty functions. These changes make MAAPO-E more powerful, allowing it to reach steady convergence and stay feasible in engineering problems in both the real world and in numbers. The approach is compared to ten of the best optimizers in the CEC 2017 high-dimensional numerical test suite (D = 50, 100) and six classic restricted engineering design problems. The results reveal that MAAPO-E beats other approaches in 83

The solid oxide fuel cell (SOFC) stands as a vital clean energy technology; however, accurate parameter identification remains challenging due to its nonlinear, multi-physical characteristics. This study introduces the Artificial Satellite Search Algorithm (ASSA), a novel physics-inspired metaheuristic that mimics satellite orbital mechanics employing Medium Earth Orbit (MEO) for global exploration and Low Earth Orbit (LEO) for local exploitation to identify seven unknown parameters (E0, A, I0,c, Rohm, B, I_L, I0,a) in SOFC electrochemical models. ASSA demonstrated mean squared errors (MSE) as low as 1.6810-5 (at 1123K) and 5.510-6 (at 9 atm) as a 8299 average compared to 9 state-of-the-art competitors including SDO, SSA, WOA, and GWO, and was also validated at ten operating conditions between temperatures of 1073K and pressure of 1 atm down to 9 atm. ASSA showed standard deviation 40-85 times smaller than the competitor algorithms, and its calculations took an average of 0.113 seconds 237 times faster than the slowest competitor (SCSO) and 42 times faster than GWO. Association Statistical validation using Friedman Ranking Tests placed ASSA first in all cases (assigned average score 1.4), and convergence curves and box plot analysis verified that it would converge quickly and be robust. The parameters found gave V-I and P-V curves that corresponded to simulated values with R2 of greater than 0.999 in most running conditions, indicating that ASSA can be used to model, control and diagnostics SOFC in a real time and with high accuracy.

These days, wireless communication is facing the problems of short battery lifetime and lower capacity globally for Beyond 5G (B5G) communication. These problems can be addressed by proposing the advantages of nonorthogonal multiple access (NOMA)-based cooperative green cognitive radio networks (GCRNs) utilizing green secondary users (GSUs) as relays. For this, an auction market is designed with the composition of BS (auctioneer), GSUs (sellers), and PUs (buyers). The PUs must pay for the cooperative service provided by GSUs, while GSUs sell it for revenue. Moreover, PUs can adjust their bid according to their residual energy. Thus, less energy PUs have a higher chance of winning GSUs' service to prevent energy exhaustion. Meanwhile, GSUs reduce their ask price as per the amount of energy harvesting (EH). Hence, GSUs can serve more PUs if they harvest more energy from the received NOMA signal. Three auction rules impacting GSUs (as relay) selection are also being proposed. Finally, the simulation illustrates the diverse performance of three auction rules (with their merits and demerits) in selecting GSUs. Although all of them effectively enhance system capacity and extend the battery lifetime of PUs, with 0.01% battery energy consumption noticed in each second while transmitting 10 GB of data from the BS to the PUs.

Ultra-wideband antennas with electromagnetic band-gap (EBG) structures play a crucial role in next-generation wireless and energy-efficient communication systems due to their ability to provide broad spectral coverage, high gain, and reduced interference. This paper presents an intelligent prediction framework that integrates a Generative Adversarial Network (GAN) with the Ninja Optimization Algorithm (NOA) to accurately model and predict the electromagnetic performance of ultra-wideband antenna-EBG configurations. The core innovation of the proposed framework lies in coupling adversarial learning with NOA-based optimization to enhance surrogate modeling accuracy and robustness for antenna-EBG systems. The proposed method is compared with multiple deep learning architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and Artificial Neural Network (ANN) models. Experimental results demonstrate that the GAN tuned with the NOA achieves superior predictive accuracy, yielding a mean squared error of [Formula: see text], a root mean squared error of [Formula: see text], and a coefficient of determination of [Formula: see text]. The integration of the Ninja Optimization Algorithm significantly enhances learning stability, convergence rate, and generalization performance. The framework also demonstrates competitive robustness when benchmarked against hybrid optimization strategies such as PSO-GAN, BA-GAN, and DE-GAN. Overall, the proposed NOA-enhanced GAN establishes an efficient, scalable, and high-precision modeling pathway for the design and optimization of ultra-wideband antenna EBG structures, contributing to the advancement of intelligent communication and renewable energy systems.

The fast decarbonization process demands hydrogen storage technologies that are compact, safe and can easily be combined with the proton exchange membrane fuel cells. In the current paper, a critical appraisal of graphene-based nanocomposites as solid-state electrochemical hydrogen storage in low-carbon operating conditions is provided. It gives emphasis on the synthesis of graphene materials by traditional and green methods, such as plant-extract assisted reduction method and reports on its structure-property-performance correlations. Special focus is made on graphene-based metal oxides, metal-decorated systems including ZnAl2O4-TiO2 composites and N-doped Pd-graphene hybrids, which have been shown to exhibit high electrochemical hydrogen storage characteristics in alkaline electrolytes at near-ambient temperatures (25 degrees C-30 degrees C) during galvanostatic charge discharge cycling. Reversible hydrogen storage capacity of up to an important of 7.6 wt% is of critical interest in terms of the testing conditions, cycling stability and as being dominated by electrochemical proton insertion and spillover-assisted chemisorption, as opposed to being dominated by physisorption-driven uptake [1-4].This review has summarized evidence on more than 100 recent experimental studies to identify fundamental material design levers such as defect engineering, heteroatom doping, catalytic metal decoration, and porosity control as controlling the kinetics, reversibility and cycling durability of hydrogen uptake at low pressures and moderate electrochemical potentials. The review also indicates the significance of scalable and environmentally friendly synthesis strategies in developing a viable deployment. All in all, the article places graphene-based materials as versatile platforms to ambient-condition electrochemical hydrogen storage, and specifies the outstanding challenges and future research directions.

Solid oxide fuel cells (SOFCs) offer high energy conversion efficiency alongside excellent fuel flexibility. However, their complex underlying electrochemical processes require successful parameter identification so that modeling and control can be achieved. Traditional optimization algorithms on the other hand often face limitations in this problem, such as premature convergence to suboptimal solutions, or an inability to explore the entire solution space. In this manuscript a new metaheuristic based on vulture foraging is derived i.e., Griffon Vulture Optimization Algorithm (GVOA). It is superior in terms of accuracy, stability and computational speed in case of complicated optimization, such as SOFC parameter identification. GVOA’s guided convergence and controlled diversity provide a theoretically sound and practical solution. Because of its efficiency, it can be used for real-time modeling and adaptive control. The GVOA algorithm was successfully used to estimate seven nonlinear model parameters of a simplified SOFC model in ten different operating conditions (varying temperature and pressure), based on simulated V-I data. In comparison with 9 well-known metaheuristic algorithms, such as BKA, AO, LSO, and PEOA, GVOA always gave the lowest minimum mean squared error (MSE) results under any temperature and pressure conditions. Furthermore, it occupied the highest rank in the Friedman test in all situations and had the quickest convergence behaviour. These results validate GVOA’s superior accuracy, stability and speed with respect to the challenging task of identification of SOFC parameters. Theoretically, this is evidence in support of the efficacy of its combination of guided convergence and controlled diversity, and, practically, is a reliable method for modeling and adaptive control of fuel cell systems. Upcoming research will be spread to investigational validation and application to other electrochemical systems.

Accurate state-of-health (SOH) estimation is essential for improving the safety, reliability, and maintenance planning of lithium-ion battery systems. This study develops an interpretable hybrid framework in which Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are combined with metaheuristic optimization for battery SOH prediction. Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), and Differential Evolution (DE) are used to tune the ANFIS premise parameters, while the consequent parameters are estimated through least-squares learning. The framework is evaluated on the public NASA PCoE lithium-ion battery degradation benchmark using a leave-one-cell-out cross-cell protocol involving Batteries B0005, B0006, and B0007. In each fold, two battery trajectories are used for model training and parameter selection, while the excluded battery is reserved for independent testing. Among the evaluated variants, GWO-ANFIS achieves the most favorable average cross-cell performance, with mean RMSE of 0.039 +/- 0.006, mean MAPE of 1.94%, and mean R2 of 0.968 across the three held-out trajectories, while maintaining inference latency below 50 ms under MATLAB/Simulink and processor-in-the-loop assessment. These results suggest that metaheuristic tuning improves ANFIS-based SOH prediction while preserving interpretability and computational feasibility. At the same time, the study remains limited to a controlled public benchmark, and broader validation on larger external datasets and physical embedded BMS platforms is identified as a necessary future step. Executive summary: Lithium-ion battery SOH prediction is essential for improving the safety, reliability, and maintenance planning of electric vehicles and other energy-storage systems. This study investigates an interpretable hybrid ANFIS-based framework in which GWO, PSO, and DE are used to tune the ANFIS premise parameters for SOH estimation on the NASA battery degradation dataset. Among the tested models, GWO-ANFIS achieved the best predictive performance, with RMSE of 0.037, MAPE of 1.82%, and R2 of 0.973, outperforming the baseline ANFIS while maintaining inference latency below 50 ms. These results indicate that metaheuristic tuning can improve ANFIS accuracy while preserving computational feasibility for real-time inference assessment. Overall, the study highlights the value of the proposed framework as a balanced solution that combines predictive accuracy, interpretability, and practical implementation potential for battery management applications.

Electrical Vehicles have the potential to transform the energy sector, bringing with them advantages for sustainability and new chances for grid optimization. However, the stability, reliability, and effectiveness of distribution networks are also put at risk by this transformation. Therefore, it is crucial to perform thorough analysis of how EV integration may affect distribution networks. The vital importance of impact analyses for EV integration into distribution networks is highlighted in this paper. These evaluations are essential for comprehending and controlling the complex impacts of EV charging on the distribution system. The paper also highlights how impact evaluations help with effective infrastructure planning, improving the integration of renewable energy according to regulatory requirements, and making the most use of already existing assets. This paper examines how the EVs adoption affects distribution network based on three important parameters: voltage instability, power losses and reliability. The IEEE 33 bus test system for ten different scenarios is used for the whole investigation. Additionally, a method for placing EV charging stations on distribution networks based on the analysed grid parameters is proposed. The outcomes show that the VRP (Voltage, Reliability and Power loss) is effective to decide the optimize location for charging station.

Photovoltaic (PV) systems in the field operate under complex, uncertain conditions rapid irradiance ramps, partial shading, temperature swings, surface soiling, and weak-grid disturbances including off-nominal frequency and voltage distortion that degrade energy yield and power quality. We propose a drift-aware, power-quality-constrained MPPT framework that co-optimizes MPPT, PLL, and current-loop gains under stochastic frequency drift, while enforcing IEEE-519 limits (per-order Ih/IL and TDD) during optimization. Unlike energy-only or THD-only methods, the design target integrates PQ constraints into the objective and is validated across calibrated drift scenarios with explicit per-order and TDD reporting. Operating scenarios are calibrated to Cameroon’s Southern Interconnected Grid and city-specific profiles (Douala/Yaoundé), combining measured-style irradiance/temperature traces, partial-shading patterns, and stochastic frequency drift up to ±0.8 Hz with synthetic contingencies. Across a 30-scenario campaign, the proposed controller achieves ηMPPT = 99.3%–99.6% (vs. 98.6% Incremental Conductance and 97.8% Perturb-and-Observe), lowers DC-link ripple by 35%–48%, reduces oscillatory PCC power by ≈41%, maintains THD ≤ 2.5% (5% limit) and PF ≥ 0.99, and shortens irradiance-step settling from 85–110 ms to 50–65 ms. Sensitivity to PLL bandwidth shows a broad optimum (≈60–90 Hz) with minimum THD/ripple, and ablations confirm that explicit drift weighting is pivotal to ripple and THD suppression without sacrificing yield. The approach is controller-agnostic, firmware-deployable, and generalizes to other converter-interfaced renewables; we outline a short hardware-/HIL-validation path for adoption in Sub-Saharan grids.

A non-invasive bio-impedance technique provides a quick response to small changes in the electrical impedance of a phantom or object, making it suitable for agriculture-based applications. This method generates high-frequency, low-current signals that vary with impedance changes in the phantom (e.g., papaya) detected through paired electrodes. The electrodes, positioned at either end of the cylindrical phantom, measure electrical impedance based on voltage changes in response to constant current insertion. Reconstruction algorithms designed in MATLAB generate electrical impedance images using initial conductivity and measured potentials. Electrical Impedance Tomography applies forward and inverse solutions to estimate conductivity distribution within an object, leveraging finite element meshes with triangular elements for computational accuracy. The forward problem involves determining current magnitude in a homogeneous conducting medium. This study developed a GUI-based reconstruction algorithm for the agricultural phantom model using MATLAB. Data acquisition integrates Internet of Things technology, connecting sensors to the GUI system and further to remote monitoring systems. This enables real-time parameter monitoring for agricultural phantom applications. The IoT-based approach demonstrates versatility for agriculture and medical applications, offering efficient, remote-access monitoring of critical parameters.

The Internet of Things (IoT) is data from seamless remote connectivity and monitoring of the physical world through the Internet. This concept applied to residential settings improves home functionality, security, and automation. This project focuses on developing a smart home automation and security system that integrates features such as password-protected gate access, motion detection with alarm functionality, and intelligent appliance and ventilation control. In case of trespassing, the system raises an alert through Wi-Fi, notifying the homeowner instantly. The same infrastructure is leveraged for home automation, providing control of appliances via a Wi-Fi-enabled microcontroller. Unlike traditional systems, this solution enables real-time monitoring and control from any location, irrespective of the mobile device's connection to the local Wi-Fi network. In addition, the system utilizes the Node MCU (ESP8266), which is a microcontroller having onboard Wi-Fi for processing inputs and management of appliances by ensuring a responsive and secure operation. It's an innovative approach for efficiently, cost-effectively, and userfriendly integration into residential settings.

The parameter of a direct methanol fuel cell (DMFC) can be identified using optimization techniques to determine the optimal unknown parameter values that are needed for creating an accurate fuel cell performance prediction model. This research is motivated by the fact that parameter identification process is necessary since manufacturers' datasheets may not always provide these parameters to users. To address this, the study investigates five optimization techniques with the proposed algorithm for estimating these parameters in DMFCs. A number of optimization techniques have been adopted: Particle Swarm Optimization (PSO), Dragonfly Algorithm (DA), Harris Hawk Optimizer (HHO), Rhinoceros Search Algorithm (RSA), Artificial Hummingbird Algorithm (AHA), and its enhanced version, Enhanced artificial hummingbird algorithm (EAHA). The objective of each approach is to minimize the error between the predicted and measured voltages of the cell and the six unknown parameters. The numerical results support the improvement in the performance and robustness of the proposed approach over the existing methods and the state-of-the-art optimizers. The study shows that the EAHA significantly outperforms other optimization algorithms with a maximum estimation error of 6.34 x 10- 10. This demonstrates superior performance in finding the global minimum, consistent results with limited variability, and the ability to consistently provide near-optimal solutions. EAHA also exhibits the least standard deviation (2.10 x 10-10), highlighting its reliability and predictability due to EAHA's efficient balance of exploration and exploitation phases, enabling it to effectively locate and converge on optimal solutions.

Parameter identification in a Proton Exchange Membrane Fuel Cell (PEMFC) entails the application of optimization algorithms to ascertain the optimal unknown variables essential for crafting an accurate model that predicts fuel-cell performance. These parameters are typically not included in the manufacturer’s datasheet and must be identified to ensure precise modeling and forecasting of fuel cell behavior. This paper introduces a recently developed hybrid algorithm (Aquila Optimizer Arithmetic Algorithm Optimization (AOAAO)) that enhances the AO and AAO algorithm’s efficiency through a novel mutation strategy, aimed at determining seven unknown parameters of a PEMFC during the optimization process. These parameters function as decision variables, and the objective function aimed for minimization is the sum square error (SSE) between the predicted and actual measured cell voltages. AOAAO demonstrated superior performance across various metrics, achieving an SSE minimum in comparison to other compared algorithm. AOAAO’s robustness was validated through extensive testing with six commercially available PEMFCs, including BCS 500 W-PEM, 500 W SR-12PEM, Nedstack PS6 PEM, H-12 PEM, HORIZON 500 W PEM, and a 250 W-stack, across twelve case studies derived from various operational conditions detailed in manufacturers’ datasheets. For each datasheet, both Current–Voltage (I/V) and Power–Voltage (P/V) characteristics of the PEMFCs scenarios closely aligned with those observed in experimental data, affirming AOAAO’s superior accuracy, robustness, and time efficiency for real-time fuel cell modeling. In terms of computational efficiency, AOAAO runtime is significantly faster than all compared algorithms, demonstrating an efficiency improvement of approximately 98%.