Search

The deployment of 5G technologies in the agricultural sector promises to revolutionize smart farming practices by enabling unprecedented levels of connectivity, data exchange, and real-time monitoring. This paper presents a comprehensive review of the challenges, considerations, and future directions of integrating 5G technologies into smart agriculture, aligning with Sustainable Development Goals (SDGs) such as SDG 2 (Zero Hunger) and SDG 9 (Industry, Innovation, and Infrastructure).Key topics discussed include the necessity of dense network infrastructure, optimization strategies for cross-deployment of 5G and sensing networks, and the role of edge computing in 5G-enabled farming production. Additionally, the paper explores development optimization of various nodes, fault detection, self-healing mechanisms, AI application optimization, and security issues specific to 5G-enabled smart agriculture. Furthermore, the paper examines the potential impact of 5G technology on crucial agricultural tasks such as real-time monitoring, UAV operations, augmented reality (AR), virtual reality (VR) applications, virtual consultation, predictive maintenance, AI-driven robotics, and data analytics. Through a thorough analysis of these topics, the paper underscores the potential of 5G technology in enhancing productivity, reducing environmental impact, and advancing sustainable agricultural practices. The paper identifies critical areas for further research and emphasizes the importance of collaborative efforts among stakeholders to maximize the benefits of 5G-enabled smart farming, thereby contributing to global efforts to achieve SDGs related to food security, innovation in technology, and sustainable infrastructure.

This paper investigates innovative solutions to enhance the performance and lifespan of standalone photovoltaic (PV)-based microgrids, with a particular emphasis on off-grid communities. A major challenge in these systems is the limited lifespan of batteries. To overcome this issue, researchers have created hybrid energy storage systems (HESS) along with advanced power management strategies. This study introduces innovative multi-level HESS approaches and a related energy management strategy designed to alleviate the charge/discharge stress on batteries. Comprehensive Matlab Simulink models of various HESS topologies within standalone PV microgrids are utilized to evaluate system performance under diverse weather conditions and load profiles for rural site. The findings reveal that the proposed HESS significantly extends battery life expectancy compared to existing solutions. Furthermore, the paper presents a novel energy management strategy based on the Reduced Fractional Gradient Descent (RFGD) algorithm optimization, tailored for hybrid systems that include photovoltaic, fuel cell, battery, and supercapacitor components. This strategy aims to minimize hydrogen consumption of Fuel Cells (FCs), thereby supporting the production of green ammonia for local industrial use. The RFGD algorithm is selected for its minimal user-defined parameters and high convergence efficiency. The proposed method is compared with other algorithms, such as the Lyrebird Optimization Algorithm (LOA) and Osprey Optimization Algorithm (OOA). The RFGD algorithm exhibits superior accuracy in optimizing energy management, achieving a 15 % reduction in hydrogen consumption. Its efficiency is evident from the reduced computational time compared to conventional algorithms. Although minor losses in computational resources were observed, they were substantially lower than those associated with traditional optimization techniques. Overall, the RFGD algorithm offers a robust and efficient solution for enhancing the performance of hybrid energy systems.

Abstract The present power generation corporations are working on Renewable Power Systems (RPS) for supplying electrical power to the automotive power industries. There are several categories of RPSs available in the atmosphere. Among all of the RPSs, the most general power network used for Electric Vehicles (EVs) is hydrogen fuel which is available in nature. The H2 fuel is fed to the Proton Exchange Membrane Fuel Stack (PEMFS) for producing electricity for the EV stations. The advantages of this selected fuel system are more power conversion efficiency, environmentally friendly, low carbon emissions, more power density, less starting time, plus able to work at very low-temperature values. However, this fuel stack faces the issue of a nonlinear power density curve. Due to this nonlinear power supply from the fuel stack, the functioning point of the overall network changes from one position of the I–V curve to another position. So, the peak voltage extraction from the fuel stack is not possible. In this article, there are various metaheuristic optimization-based Maximum Power Point Tracking (MPPT) methodologies are studied along with the conventional methods for obtaining the Maximum Power Point (MPP) position of the PEMFS. From the simulative investigation, the Continuous Different Slope Value-based Cuckoo Search Method (CDSV with CSM) provides better efficiency with more output power. Also, for all the MPPT methods comprehensive analysis has been made by utilizing the simulation results.

Abstract The present power generation government companies focus on Renewable Power Sources (RPS) because their features are zero carbon footprint, unlimited power source, fewer greenhouse pollutants, fewer output wastages, plus creatinga very healthy atmosphere. In this work, the sunlight source is utilized for the Photovoltaic (PV) standalone network. The merits of sunlight sources are very optimal human resources needed, unlimited natural sources, plus easy operation. However, the solar power resource is nonlinear fashion. As a result, the operating point of the sunlight network fluctuates concerning sunlight intensity. So, in this article, the Modified Grey Wolf Methodology with Adaptive Fuzzy Logic Controller (MGWM-AFLC) is introduced to maintain the operating point of the sunlight system at the global power point position of the PV array. This controller traces the MPP with very low fluctuations in the PV-produced voltage. The advantages of this proposed method arefewer sensing devices required, less difficulty in development, more useful for rapid changes inthe sunlight temperatures, simpler to realize operation, greater economic growth, plus highly useful for household applications. The sunlight set-up generation voltage is lowwhich is improved by introducing the new Wide Power Rating High Voltage DC-DC Boost Converter (WPRHVBC). The features of this WPRHV converter are low voltage strain on semiconductor devices, few passive elements are enough to develop the circuit, plus easy understanding.

Abstract A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers. Various factors, such as voltage, current, power, weather dependence, cost, complexity, response time, periodic tuning, stability, partial shading, and accuracy, are all intended to be evaluated by the study. It is aimed to provide insight into how well each controller performs in various circumstances by carefully examining these broad parameters. The main goal is to identify and recommend the best controller based on their performance. It is notified that, conventional techniques like INC, P&O, INC-PSO, P&O-PSO, achieved accuracies of 94.3, 97.6, 98.4, 99.6 respectively while AI based techniques Fuzzy-PSO, ANN, ANFIS, ANN-PSO, PSO, and FLC achieved accuracies of 98.6, 98, 98.6, 98.8, 98.2, 98 respectively. The results of this study add significantly to our knowledge of the applicability and effectiveness of both AI and traditional MPPT controllers, which will help the solar industry make well-informed choices when implementing solar energy systems.

With the increase in reliance on doubly fed induction generator-based wind energy conversion systems (DFIG-WECSs), extracting maximum power from wind energy and enhancing fault ride-through (FRT) techniques meeting the grid code requirements is the foremost concern. This paper proposes a modified control scheme that operates in normal running conditions and during faults as a dual mode. The proposed control scheme operates in a coordinated wind speed estimation-based maximum power point tracking (WSE-MPPT) mode during normal running conditions to extract maximum power from wind energy and enhances the crowbar rotor active impedance-based FRT mode during faults. The proposed technique controls the rotor side converter (RSC) parameters during faults by limiting the transient surge in the rotor and stator currents. In this study, the transient behavior of the proposed technique is analyzed under a three-phase symmetrical fault with a severe voltage dip, and it is observed that, when the fault is over and the RSC is activated and connected to the system, a large inrush current is produced with transient oscillations; the proposed scheme suppresses this post-fault inrush current and limits the transient oscillation. During the FRT operating mode under a symmetrical fault, the simulation results of the proposed technique are validated by the conventional crowbar strategy. In contrast, during the WSE-MPPT operating mode under normal running conditions, a smooth achievement of system parameters after starting the inrush period to a steady state at fixed wind speed is observed.

Energy scarcity in various regions worldwide not only adversely affects people's quality of life but also hinders overall development. Pakistan is among the nations grappling with energy shortages, with high consumption and limited generation, resulting in a substantial energy shortfall of 2500 MW. Floating photovoltaic (FPV) systems present an attractive solution for harnessing solar energy, particularly where land availability is constrained. These systems offer benefits such as conserving water and land while delivering higher power output compared to conventional terrestrial PV systems. While the advantages of FPV systems are generally recognized, research on their specific characteristics remains limited. This paper aims to address this gap by providing a comprehensive review of FPV technology and its potential applications, particularly in Pakistan. By comparing ground-mounted and FPV systems, the study explores the economic, technological, and environmental implications of FPV adoption in regions with similar geographical profiles. The analysis highlights not only the cooling impact of water, which enhances the efficiency of FPV systems, but also other key aspects such as cost-effectiveness, site preparation, and environmental impact. Output power of FPV and land based obtained are 390 kW and 370 kW at irradiances of 180kW/m2and 170 kW/m2 at temperature from 30 to35°C respectively. The findings demonstrate the viability of FPV systems and emphasize that by leveraging the characteristics of water temperature, it becomes feasible to more accurately predict the electricity generation potential of FPV systems. This synthesis offers new insights and underscores the potential for FPV adoption in areas facing similar energy challenges.

Abstract The potential of solid waste as an energy source is clear, owing to its wide availability and renewable properties, which provide a critical answer for energy security. This can be especially effective in reducing the environmental impact of fossil fuels. Countries that rely heavily on coal should examine alternatives such as electricity from solid waste to provide a constant energy supply while also contributing to atmospheric restoration. In this regards, Low Emissions Analysis Platform (LEAP) is used for simulation the entire energy system in Pakistan and forecasted its capital cost and future CO2 emissions in relation to the use of renewable and fossil fuel resources under the different growth rates of solid waste projects like 20%, 30% and 40% for the study period 2023–2053. The results revealed that, 1402.97 TWh units of energy are generated to meet the total energy demand of 1193.93 TWh until 2053. The share of solid waste based electricity in total energy mix is increasing from a mere 0.81% in 2023 to around 9.44% by 2053 under the 20% growth rate, which then increase to 39.67% by 2053 under the 30% growth rate and further increases to 78.33% by 2053 under the 40% growth rate. It is suggested that 40% growth rate for solid waste based electricity projects is suitable for Pakistan until 2053 because under this condition, renewable sources contributes 95.2% and fossil fuels contributed 4.47% in the total energy mix of Pakistan. Hence, CO2 emissions are reduced from 148.26 million metric tons to 35.46 million metric tons until 2053 but capital cost is increased from 13.23 b$ in 2023 to 363.11 b$ by 2053.

Abstract Optimal power flow is a complex and highly non-linear problem in which steady-state parameters are needed to find a network’s efficient and economical operation. In addition, the difficulty of the Optimal power flow problem becomes enlarged when new constraints are added, and it is also a challenging task for the power system operator to solve the constrained Optimal power flow problems efficiently. Therefore, this paper presents a constrained composite differential evolution optimization algorithm to search for the optimum solution to Optimal power flow problems. In the last few decades, numerous evolutionary algorithm implementations have emerged due to their superiority in solving Optimal power flow problems while considering various objectives such as cost, emission, power loss, etc. evolutionary algorithms effectively explore the solution space unconstrainedly, often employing the static penalty function approach to address the constraints and find solutions for constrained Optimal power flow problems. It is a drawback that combining evolutionary algorithms and the penalty function approach requires several penalty parameters to search the feasible space and discard the infeasible solutions. The proposed a constrained composite differential evolution algorithm combines two effective constraint handling techniques, such as feasibility rule and ɛ constraint methods, to search in the feasible space. The proposed approaches are recognized on IEEE 30, 57, and 118-bus standard test systems considering 16 study events of single and multi-objective optimization functions. Ultimately, simulation results are examined and compared with the many recently published techniques of Optimal power flow solutions owing to show the usefulness and performance of the proposed a constrained composite differential evolution algorithm.

This paper addresses the challenging problem of Unit Commitment (UC), which involves the optimal scheduling of power generation units while adhering to numerous network operational constraints called security-constrained UC (SCUC). SCUC problem aims to minimize costs subject to turning on economically efficient generators and turning off expensive ones. These operational constraints include load balancing, voltage level at buses, minimum up and down time requirements, spinning reserve, and ramp up and down constraints. The SCUC problem, subject to these operational constraints, is a complex mixed-integer nonlinear problem (MINLP). There has been a growing interest in using evolutionary algorithms (EAs) to tackle large-scale multi-objective MINLP problems in recent two decades. This paper introduces a novel approach to address the SCUC problem, which is further complicated by including network constraints. They are pioneering the integration of single and multi-objective EAs to solve the SCUC problem while incorporating AC network constraints through hybrid binary and real coded operators. The development of an ensemble algorithm that combines mixed real and binary coded operators, extended by a bidirectional coevolutionary algorithm to tackle multi-objective SCUC problems. The paper implements a new formulation based on three conflicting objective functions: cost of energy supplied, startup and shutdown costs of generators, energy loss, and voltage deviation to solve the SCUC problem. Implementing a new formulation also addresses the solution of single and multi-objective SCUC problems using a combination of proposed technical and economic objective functions. The proposed algorithm is rigorously tested on a 10-unit IEEE RTS system and a 6-unit IEEE 30-bus test system, both with and without security constraints, addressing week-ahead and day-ahead SCUC scenarios. Simulation results show that the proposed algorithm finds near-global optimal solutions compared to other state-of-the-art EAs. Additionally, the research demonstrates the effectiveness of the proposed search operator by integrating it with a multi-objective coevolutionary algorithm driven by both feasible and infeasible solutions, showcasing superior performance in solving multi-objective SCUC problems. These results are compared with various recently implemented Multi-Objective Evolutionary Algorithms (MOEAs), demonstrating the superiority of

The sub-synchronous oscillation (SSO) phenomenon presents a significant risk to the overall safety and stability of power networks, as evidenced by numerous real-world incidents. This paper introduces a novel approach to mitigating SSO in series-compensated transmission lines by employing a resonant controller in conjunction with a control loop for the doubly fed induction generator (DFIG). To begin, the resonant controller is integrated into the converter controllers of the DFIG system to determine the optimal placement that yields enhanced damping characteristics. Subsequently, an analysis is conducted to explore the impact of different parameters associated with the resonant controller on system stability, with the goal of identifying the most suitable parameter configuration. The proposed control strategy is thoroughly evaluated through time-domain simulations and small signal stability analysis, considering various compensation levels, wind speeds, and sub-synchronous control interactions (SSCI). Furthermore, a comparative analysis is performed between the proposed method and a recent SSO damping technique to validate the efficacy and robustness of the proposed approach. The results of the comparison clearly demonstrate the superiority and effectiveness of the proposed control strategy in alleviating SSO. Specifically, the proposed method achieves the shortest SSO damping time across all feasible operational scenarios, thereby reinforcing its practical value and applicability.

This review explores the potential of floating waterwheel power generation systems as a sustainable source of energy. With increasing concerns about environmental degradation and the need for renewable energy sources, the utilization of flowing water for power generation presents an attractive solution. By analyzing existing literature and case studies, this review assesses the feasibility and effectiveness of floating waterwheel systems in harnessing energy from rivers and streams. Key metrics, including the total potential of floating hydro generation systems and their contribution to renewable energy, are evaluated to provide insights into the scalability and impact of this technology. Furthermore, the review identifies the most viable types of generating systems and highlights the environmental benefits they offer. This paper outlines the significant potential of floating hydro generation systems, with an estimated contribution of up to 10% of global renewable energy production. Among these systems, tidal barrage technology stands out as the most viable option, offering predictability and high energy density. By harnessing the power of moving rivers and tides, this technology addresses the pressing demand for clean energy while minimizing environmental impact compared to traditional dams. By addressing these aspects, this study aims to contribute to the advancement of eco-friendly energy solutions and provide valuable insights for policymakers, researchers, and industry professionals working in the field of renewable energy.

This research paper conducts an extensive exploration of onshore, offshore, and floating offshore wind turbines, pivotal components in the landscape of sustainable energy generation. The study thoroughly investigates various wind energy systems, shedding light on their distinct attributes, advantages, and limitations. The examination of onshore wind turbines encompasses aspects such as installation procedures, size considerations, wind dynamics, accessibility challenges, and visual impact assessments. Similarly, the analysis of offshore wind turbines covers considerations such as location selection, installation complexities, size variations, wind conditions, environmental impacts, and visual aesthetics. The paper also delves into the emerging technology of floating offshore wind turbines, highlighting their unique features, advantages, and technological advancements. A thorough comparative examination is conducted to assess the economic, environmental, and social aspects of onshore, offshore, and floating offshore wind turbines. The research paper concludes by offering insights into policy considerations, addressing research gaps, and outlining prospects for the seamless integration and advancement of these diverse wind energy systems. This comprehensive review aspires to contribute to the existing knowledge base in sustainable energy generation, fostering informed decision-making for a greener and more sustainable future.

This paper explores the synergistic role of Distributed Resources (DR), including Distributed Generation (DG) and Battery Energy Storage Systems (BESS), in enhancing modern power systems’ sustainability, reliability, and flexibility. It addresses the gap in concurrent distribution network reconfiguration and DR allocation, especially under the variability of renewable energy. The study aims to minimize energy costs, losses, and voltage deviations by integrating wind and solar PV-type DGs with BESS. A novel multi-objective function and an improved bi-directional coevolutionary (I-BiCo) Algorithm are employed to find the optimal RES and BESS placement and sizing, showing marked improvements over existing methods. Furthermore, statistical comparisons using hypervolume, objective function values (diversity), and near-global solutions (convergence) underscore the proposed algorithm’s superiority over existing MOEAs. The final non-dominated solution, obtained through fuzzy set theory, highlights simulation results that minimize power loss, achieve substantial energy savings, and smooth demand, particularly with the integration of BESS devices. Moreover, optimal network reconfiguration (ONR) is a key strategy for balancing load demand. Simulation results affirm that minimizing bi-objective and tri-objective functions, coupled with optimal feeder reconfiguration, significantly reduces power loss and enhances voltage profiles, approaching unity across all buses. The proposed ONR formulation, in conjunction with DGs and BESS, maximizes the overall performance of power distribution networks. Furthermore, the paper addresses various time-dependent constraints of BESS, DG, and ONR, formulating and efficiently solving these constraints by integrating different constraint-handling techniques with the proposed multi-objective evolutionary algorithm. The study contributes to academic discourse and provides practical insights for designing more efficient and sustainable power systems in the face of evolving energy landscapes.

Anomaly detection in power systems is crucial for operational reliability and safety, often addressed through binary classification in existing research. However, a research gap exists in multiclass classification for enhanced reliability. To bridge this gap, this study employs four machine learning (ML) classifiers: Random Forest (RF), Decision Tree, Naive Bayes (NB), and Support Vector Machine (SVM) using comprehensive testing on a dataset comprising sixteen indices and their pair combinations (totaling 136 pairs). These classifiers, trained on a dataset derived from simulating a test system with hybrid DGs, exhibit superior anomaly detection, especially with the $\frac {dv}{dq}\& \frac {dv}{dp}$ pair. Among them, RF and DT classifier achieves precision, recall, and F score of unity and outperforming NB and SVM. The performance of the proposed RF and DT classifiers with $\frac {dv}{dq}\& \frac {dv}{dp}$ pair is compared with existing research papers in terms of accuracy and data division. The comparison shows that the proposed RF and DT classifiers with $\frac {dv}{dq}\& \frac {dv}{dp}$ pair achieve 100% accuracy even with 50% data division, whereas other techniques fail to achieve it even at 20% for testing and 80% for training. The study underscores the critical role of pair selection and classifier combinations in effective anomaly detection, facilitating the implementation of robust mitigating strategies for power system stability.

The increasing expansion of the industrial and domestic sectors, combined with the integration of renewable energy sources, has led to an overload of the existing interconnected power systems, which in turn has led to significant bottlenecks. This confluence of factors manifests itself in severe frequency, voltage and tie-line power issues, highlighting the urgent need for intelligent control mechanisms. Automatic voltage regulation (AVR) and load frequency control (LFC) are key components that ensure the supply of high-quality energy to consumers while maintaining the nominal frequency, voltage and power deviations in the grid. These measures are essential to ensure the stability and safety of the IPS under these challenging conditions. This research addresses the control strategies for a four-area sophisticated IPS. The complexity of the system, which includes five generation units in each area, including gas, thermal reheating, hydropower and two renewable energy sources (wind and photovoltaic), requires a careful study of the control methods. In particular, the inclusion of various non-linear factors, such as the governor dead band (GDB), generation rate constraint (GRC) and boiler dynamics (BD) in each of the four areas, increases the realism of the study. In this context, a recently introduced meta-heuristic algorithm, the One-to-One-based Optimizer (OOBO), was used to determine the optimal parameters of the proportional-integral-proportional-derivative (PI-PD) controller. The tuning process involves using the integral of time multiplied by the squared error value (ITSE) as the error criterion for evaluating the fitness function. In the study, the voltage, frequency and power performance of the OOBO PI-PD controller is evaluated and compared with alternative control methods, namely Proportional-Integral-Derivative (PID), Integral-Proportional (I-P) and Integral-Proportional-Derivative (I-PD) controllers, all of which are tuned with OOBO. Under the influence of a 5% load step load perturbation (SLP), comprehensive comparisons show the superior performance of the OOBO PI-PD controller, which shows better responses in all areas. Furthermore, the reliability and effectiveness of the OOBO PI-PD method are validated by a sensitivity analysis that considers simultaneous variations of the turbine time constant and speed control within a range of ±25%. The results highlight the robustness of the OOBO-PI-PD control

Nowadays, electricity theft is a major issue in many countries and poses a significant financial loss for global power utilities. Conventional Electricity Theft Detection (ETD) models face challenges such as the curse of dimensionality and highly imbalanced electricity consumption data distribution. To overcome these problems, a hybrid system Multi-Layer Perceptron (MLP) approach with Gated Recurrent Units (GRU) is proposed in this work. The proposed hybrid system is applied to analyze and solve electricity theft using data from the Chinese National Grid Corporation (CNGC). In the proposed hybrid system, first, preprocess the data; second, balance the data using the k-means Synthetic Minority Oversampling Technique (SMOTE) technique; third, apply the GTU model to the extracted purified data; fourth, apply the MLP model to the extracted purified data; and finally, evaluate the performance of the proposed system using different performance measures such as graphical analysis and a statistical test. To verify the consistency of our proposed hybrid system, we use three different ratios for training and testing the dataset. The outcomes show that the proposed hybrid system for ETD is highly accurate and efficient compared to the other models like Alexnet, GRU, Bidirectional Gated Recurrent Unit (BGRU) and Recurrent Neural Network (RNN).

In today's modern era, chronic kidney disease stands as a significantly grave ailment that detrimentally impacts human life. This issue is progressively escalating in both developed and developing nations. Precise and timely identification of chronic kidney disease is imperative for the prevention and management of kidney failure. Historical methods of diagnosing chronic kidney disease have often been deemed unreliable on several fronts. To distinguish between healthy individuals and those afflicted by chronic kidney disease, dependable and effective non-invasive techniques such as machine learning models have been adopted. In our ongoing research, we employ various machine learning models, encompassing logistic regression, random forest, decision tree, k-nearest neighbor, and support vector machine utilizing four kernel functions (linear, Laplacian, Bessel, and radial basis kernels), to forecast chronic kidney disease. The dataset used constitutes records from a case-control study involving chronic kidney disease patients in district Buner, Khyber Pakhtunkhwa, Pakistan. For comparative evaluation of the models in terms of classification and accuracy, diverse performance metrics, including accuracy, Brier score, sensitivity, Youden's index, and F1 score, were computed.

Conventional energy networks produce energy with less efficiency. Also, these source’s development costs and size are more. So, the world is focusing on renewable energy networks for energy production to the consumer. In this work, a proton exchange membrane fuel stack (PEMFS) technology is selected for energy feeding to the hydrogen vehicle. The merits of this stack are more abundant, faster fuel stack operational response, and more efficient for electrical automotive networks. However, the fuel stack’s energy production is nonlinear and its operational point varies concerning the fuel stack device operating temperature. The particle swarm optimized adaptive network-based fuzzy inference system (PSO-ANFIS) is proposed in this work to find the operational point of the fuel cell network. The features of this hybrid methodology are the low number of iteration values required, low convergence time, low-level dependence on the fuel stack, and high compliance for the quick deviations of the fuel system temperature. The operating efficiency and tracking time of the proposed maximum power point tracking (MPPT) controller are 95.60% and 0.1089 s. Another issue of the fuel cell is high output current generation and less voltage production. This condition is happening in the fuel cell because of its chemical reaction dynamics, internal resistance of the cell, and electrochemical potential. Due to this excess current flow in the fuel cell, the direct fuel stack-fed electrical networks face the issue of high power conduction losses. To reduce the power conduction losses of the system, a single-switch power circuit is used to reduce fuel source current, thereby optimizing the excessive power losses of the system. The whole fuel stack energy production network is analyzed by selecting the MATLAB Window.

Now, the present electric vehicle industry is focusing on the fuel cell technology because its features are high flexibility, continuous power supply, less atmospheric pollution, fast startup, and rapid response. However, the fuel cell gives nonlinear power versus current characteristics. Due to this nonlinear behavior, the maximum power extraction from the fuel stack is quite difficult. So, in this work, an adaptive genetic algorithm with an adaptive neuro-fuzzy inference system (ACS with ANFIS) MPPT controller is introduced for finding the MPP of the fuel stack system thereby extracting the peak power from the fuel stack. The proposed hybrid maximum power point tracking (MPPT) controller is compared with the other MPPT controllers which are enhanced incremental conductance-fuzzy logic controller (EIC with FLC), improved hill climb with fuzzy logic controller (IHC with FLC), adaptive beta with FLC, enhanced differential evolutionary with FLC (EDE with FLC), and marine predators optimization with FLC (MPO with FLC). Here, these hybrid controllers’ comprehensive investigations have been carried out in terms of tracking speed of the MPP, oscillations across the MPP, settling time of the converter voltage, maximum power extraction from the fuel stack, and working efficiency of the MPPT controller. The fuel stack generates a very low output voltage which is improved by using the boost DC-DC converter, and the overall fuel stack-fed boost converter system is designed by utilizing the MATLAB/Simulink tool. From the simulation results, the AGA with ANFIS MPPT controller gives high MPP tracking efficiency when compared to the other hybrid controller.

With urban and rural infrastructure development, the power system is being forced to operate at or near its full capacity. This paper proposes four new methodologies to find the solution to the optimal reactive power dispatch (ORPD) problem, considering the capabilities of modern DFIG-based WTs and VSI-based solar PV. The proposed formulation considers the techno-economic objective functions, specifically the minimization of the active and reactive power cost and the maximization of reactive power reserve. This leads to an effective solution to the probabilistic multi-objective ORPD (PMO-ORPD) problem, especially in the context of modern wind farms (WFs) and solar PV. The proposed formulations are necessary for effectively managing power systems with renewable energy sources and contribute to developing efficient and sustainable power systems. Additionally, this study employs probabilistic mathematical modeling that incorporates Weibull, lognormal, and normal probability distribution functions (PDFs) to represent uncertainties in the wind, solar, and load demand. Monte-Carlo simulation (MCS) is employed to generate probabilistic scenarios, allowing for a comprehensive analysis of the PMO-ORPD problem. A new two-phase (ToP) multi-objective evolutionary algorithm is proposed, which incorporates the superiority of feasibility constraints to effectively solve the probabilistic multi-objective optimal reactive power dispatch (PMO-ORPD) problem. From the analysis and comparison of simulation results, it has been observed that the proposed algorithm effectively solves the deterministic and PMO-ORPD problems.

Numerous green computing applications employ sustainable energy sources to abate redundant energy consumption. Renewable energy sources are vital to improving energy efficiency and should be used optimally. This paper introduces the Energy Proficient Computing Framework (EPCF) in the resource-centric cloud environment. The main objective of the EPCF is to improve the shared efficiency of energy distribution in the computing systems. Renewable energy is distributed among computers according to their running status and the number of calculations available. Traditional k-means clustering separates the states and computations when making this determination. This mapping procedure is repeated throughout the computation until the energy is dispersed without waste. Energy is conserved for the later use if the sources of the leak can be located in advance. As a result, we can conserve and use energy more effectively. In addition, it speeds up calculations and decreases service allocation waiting times. The proposed framework achieves 14.69% less energy cost for the different service al-location rates, 6.34% less energy drain, and 14.4% high efficiency.

The increasing demand for solar renewable energy resources, driven by the global energy crisis and the depletion of conventional energy sources, has underscored the importance of harnessing solar energy. Solar photovoltaic (PV) systems, however, exhibit nonlinear output power due to their weather-dependent nature, impacting overall system efficiency. This study focuses on the development and comparative analysis of three intelligent Maximum Power Point Tracking (MPPT) controllers using the MATLAB Simulink. The controllers employ distinct methodologies, namely, Artificial Neural Networks (ANN), Adaptive Neural and Fuzzy Inference System (ANFIS), and Fuzzy Logic Controller (FLC). The results demonstrate that ANFIS achieved the highest accuracy at 99.50%, surpassing ANN and FLC with accuracies of 97.04% and 98.50%, respectively, thus establishing ANFIS as the superior MPPT controller. Additionally, the positives and negatives of all three MPPT-based algorithms are also compared in this work.

Electricity theft (ET), which endangers public safety, creates a problem with the regular operation of grid infrastructure and increases revenue losses. Numerous machine learning, deep learning, and mathematical-based algorithms are available to find ET. Still, these models do not produce the best results due to problems like the dimensionality curse, class imbalance, improper hyper-parameter tuning of machine learning and deep learning models, etc. We present a hybrid deep learning model for effectively detecting electricity thieves in smart grids while considering the abovementioned concerns. Pre-processing techniques are first employed to clean up the data from the smart meters. Then, the feature extraction technique, like AlexNet, addresses the curse of dimensionality. The effectiveness of the proposed method is evaluated through simulations using a real dataset of Chinese intelligent meters. To conduct a comparative analysis, various benchmark models are implemented as well. Our proposed model achieves accuracy, precision, recall, and F1, up to 86%, 89%, 86%, and 84%, respectively.

The present work aims to develop a 3D numerical simulation that describes fresh tomatoes' heat- and mass-transfer characteristics using software COMSOL Multiphysics. Besides, a numerical study of the effect of drying parameters (drying temperature, Velocity of air drying and thickness of the slice) on the moisture content elimination from the tomato slice was realized. Also, an experimental study of the effect of the continuity and the discontinuity of the drying process on the water content vaporization is carried out. In contrast, the numerical simulation developed is validated by comparing the experimental data with the numerical results of the temperature distribution and mass transfer inside the tomato slice. The results show that the numerical simulation is agrees with the experimental tests. In addition, the results show that bacteria contamination appears in the tomato slice when the drying temperature decreases under 50 °C. The discontinuity of the drying process harms the quality of the dried product. Furthermore, the results show that the water evaporation rate in the thin slice was faster compared to the thick slice. Consequently, the drying time was reduced by 39 h when the thickness of the product was decreased from 33 mm to 10 mm. Also, the numerical simulation shows that moisture elimination is faster at higher drying air temperatures and increasing the drying air velocity leads to decrease drying time. The results show that the numerical simulation developed in this paper can accurately simulate the drying process characteristics of the agro-food product, especially Tomato, and that it provides a high spatial resolution of the distribution of water content and temperature inside the slice of tomatoes during the drying process.

The growing popularity of electric vehicles presents a significant challenge to current electric grids since the rising number of these vehicles places additional strain on power systems inside distribution networks. A proposed paradigm is presented for electric vehicles (EVs), which is subsequently partitioned into three distinct dispatching areas to assess its practicality. This study is structured around two primary objectives. The first objective focuses on EV owners aiming to minimize their electricity consumption costs while also receiving compensation for providing services. The second objective involves using an aggregator to establish distinct tariffs for each dispatching area. Additionally, the aggregator aims to shift the charging load demand from peak to off-peak hours and distribute the charging demand to each agent. The authors of this study propose the utilization of a charging and discharging coordination method, specifically the Multi-Agents Charging and Discharging (MACD) algorithm, as a means to successfully tackle the issue of charging demand during peak hours. The objective of the proposed algorithm is to effectively handle the increased charging requirements during peak periods by using vehicle-to-grid (V2G) technologies within the context of Smart Grid systems and electric vehicle (EV) batteries. Importantly, this reduction is achieved without compromising the performance of electric vehicles (EVs) or the convenience experienced by EV owners. The algorithm under consideration demonstrates reduced power charging costs for various sectors. Specifically, it achieves a decrease of 15% for households, 14.6% for corporate buildings, and 14.5% for Industrial Park EV aggregators.

To reduce the Carbon footprint and reduce emissions from the globe, the world has kicked-off to leave reliance of fossil fuels and generate electrical energy from renewable energy sources. The MOOPF problem is becoming more complex, and the number of decision variables is increasing, with the introduction of power electronics-based Flexible AC Transmission Systems (FACTS) devices. These power system components can all be used to increase controllability, effectiveness, stability, and sustainability. The added uncertainty and variability that FACTS devices and wind generation provide to the power system makes it challenging to find the right solution to MOOPF issues. In order to determine the best combination of control and state variables for the MOOPF problem, this paper develops three cases of competing objective functions. These cases include minimizing the total cost of power produced as well as over- and underestimating the cost of wind generation, emission rate, and the cost of power loss caused by transmission lines. In the case studies, power system optimization is done while dealing with both fixed and variable load scenarios. The proposed algorithm was tested on three different cases with different objective functions. The algorithm achieved an expected cost of $833.014/h and an emission rate of conventional thermal generators of 0.665 t/h in the case 1. In Case 2, the algorithm obtained a minimum cost of $731.419/h for active power generation and a cost of power loss is 124.498 $/h for energy loss. In Case 3, three objective functions were minimized simultaneously, leading to costs of $806.6/h for emissions, 0.647 t/h, and $214.9/h for power loss.

Multi-level inverters are widely employed to generate new energy because of their huge capacity and benefits in sound control performance. One of the critical areas of study for multi-level inverters is control strategy research. In this study, the control strategy for a multi-level inverter—which is frequently employed in HVDC and FACTS systems—is designed. An asymmetrical D.C. voltage source is supplied to create the appropriate output voltage waveform with fewer total harmonic distortions (THDs) at the output voltage and current waveforms. In this work, the pulse width modulation techniques of POD (phase opposition disposition) and APOD (alternative phase opposition disposition) MC PWM are applied to a multi-level inverter to generate the seven-level output voltage waveform. This study presents an enhanced variable carrier frequency APOD control approach that can successfully lower the overall harmonic distortion rate. The design and completion of the phase-shifting POD and APOD control strategies are followed by an analysis and comparison of the THD situation under various switching frequencies and a simulation and verification of the control strategy using MATLAB simulation. The TI DSP-based control approach has been programmed. The APOD technique increases the output voltage’s THD to 18.27%, while the output current waveform’s THD is reduced to 15.67% by utilizing the APOD PWM technique. Using the POD PWM approach increases the total harmonic distortion (THD) of the voltage waveform by 18.06% and the output current waveform’s THD by 15.45%.

In terms of energy efficiency, photovoltaic thermal (PVT) systems have great potential to produce simultaneously heat and electricity. PVT solar air collectors are often used in various applications due to their simple structure and low installation cost. While the overall performance of PVT is closely correlated with its design. In this paper, one proposes to design a novel prototype of PVT air collector, in order to improve the electrical and thermal performances. The presented numerical and Experimental studies are carried out to evaluate the impact of the designed prototype on the PVT efficiency. The essential goal of this work is to identify the optimal geometrical and operational PVT air collector parameters with square tube channel. To achieve this goal, numerical simulation using COMSOL Multiphysics is combined with experimental validation of the PVT system. The objective of the optimization consists on improving the output temperature suitable for drying applications firstly, and cooling PV modules by reducing their temperature in order to improve the electrical characteristics such as power and voltage and thereby the efficiency of the photovoltaic modules. Therfore, the PV panel temperature dropped from 53.37 °C to 42.5 °C when the design varies from PVT-1 to PVT-6. Nevertheless, PVT-5 was retained because it has the most appropriate design which gives the best overall efficiency 58.48%. Finally, it should be remembered that the tests were carried out under the same operating conditions namely, the mass flow rate Qm = 0.0235 kg/s at the inlet of the PVT, the inlet temperature (Tint), the ambient temperature (Ta) and the solar irradiation (G).

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.

Renewable energy systems have been extensively developed and they are attractive to become widespread in the future because they can deliver energy at a competitive price and generally do not cause environmental pollution. However, stand-alone energy systems may not be practical for satisfying the electric load demands, especially in places having unsteady wind speeds with high unpredictability. Hybrid energy systems seem to be a more economically feasible alternative to satisfy the energy demands of several isolated clients worldwide. The combination of these systems makes it possible to guarantee the power stability, efficiency, and reliability. The aim of this paper is to present a comprehensive analysis and to propose a technical solution to integrate a self-excited induction generator in a low power multisource system. Therefore, to avoid the voltage collapsing and the machine demagnetization, the various parameters have to be identified. This procedure allows for the limitation of a safe operating area where the best stability of the machine can be obtained. Hence, the load variation interval is determined. An improvement of the induction generator stability will be analyzed. Simulation results will be validated through experimental tests.

This paper presents a non-invasive procedure to detect inter-turn short circuit faults in the stator windings of AC electrical machines. It proposes the use of the stray external magnetic field measured in the vicinity of the machine to determine stator faults. The originality introduced by this procedure is the analysis method presented in the paper, which when compared to usual diagnosis methods, does not require any data on the healthy state of the machine. The procedure uses the magnetic unbalance created by the rotor poles and the load variation in faulty cases. The presented method can be applied to induction and synchronous machines used as a motor or generator. It is based on the variation of sensitive spectral lines obtained from the external magnetic field when the load changes. Analytical relationships are developed in the paper to justify the proposed method and to explain the physical phenomenon. To illustrate these theoretical considerations, practical experiments are also presented.

This paper proposes a self-excited induction generator model with saturation effect for power generating mode in a remote site. The model is led through the space vector mathematical formalism and allows one to analyze the steady and dynamic states. It is developed for a squirrel cage induction machine. This model provides magnetizing inductance variation able to influence the build-up and the stabilization of voltage generation when the load changes. The final result is a realistic approach model which takes into con- sideration the dependency of the magnetizing inductance versus magnetizing current. This novel model is validated through experimental measurements to demonstrate its validity and practicability.

Wind generation system is becoming increasingly important as renewable energy sources due to its advantages such as low maintenance requirement and mainly it does not cause environmental contamination. This paper presents the improvement procedure of the transient state and the regulation of the output frequency by adjusting the terminal capacitor. The aim is to provide frequency control of a self-excited induction generator in remote site using different strategies which are based on the adjustment of the reactive power at the outputs of a three-phase self-excited induction generator. A thyristor controlled reactor and a switched resistive load will be used to control reactive power. The proposed particle swarm optimization algorithm technique, location of the thyristor controlled reactor device, and parameter value are optimized simultaneously. The results obtained by this strategy will be compared with those provided by the use of Fuzzy Logic Controller. This study will be conducted through the analysis of the frequency in the steady state and transient case using a developed induction generator numerical model built using MATLAB/Simulink. Simulation and experimental results will be exposed and analyzed considering a resistive inductive load on a laboratory test bench. [ABSTRACT FROM AUTHOR]