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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 FROM AUTHOR]

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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The increasing use of wind energy generators in mountainous areas is an important necessity in establishing and enhancing renewable energy. This procedure has created a need for reliable and efficient methods for detecting anomalies remotely basing on computational sensor nodes optimization.These methods are useful for detecting deviations in the generator's behavior that may indicate a potential failure, allowing for prompt maintenance, minimizing the intervention costs, and ultimately reducing downtime and improving the generator's overall reliability compared to previous techniques. In this context, this paper presents the principal smart anomaly detection methods applied to wind energy generators. It covers different techniques, including machine learning algorithms, statistical approaches, and deep learning models. Furthermore, this paper aims to perform the optimization of computational sensor nodes (CSN) as new strategy, instead of basic wireless sensor network, in smart fault detection. This paradigm is growing very rapidly in varied fields. One of the promising application fields of CSN is wind energy generators, where this technology offers important support. This technology will allow for precise resource management. The experimental results show that the proposed approach can effectively detect anomalies in wind turbine data, and it outperforms traditional statistical methods with 99 % recognition rate against 93 % in the best way.. [ABSTRACT FROM AUTHOR]

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 consideration the dependency of the magnetizing inductance versus magnetizing current. This novel model is validated through experimental measurements to demonstrate its validity and practicability. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper concerns a three-phase self-excited induction generator used for autonomous power generation. It presents a robust control strategy which makes it possible to maintain the frequency quasi constant during the voltage regulation without any control loop on this variable. This strategy, which also prevents the machine disengagement, uses as power converter a simple dimmer. The obtained theoretical and/or numerical results are validated on a laboratory test bench that allows the analysis of this control law effectiveness. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recognizing camouflaged items in large-scale images or movies is a complex challenge requiring close attention to detail present here. These items have no discernible edges and are blended with the surrounding pixels. This solution makes use of the high variability and low contrast. The Minimum Boundary Contrast Detection (MBCD) technique is presented to determine the pixel distribution patterns across a picture's x and y axes. In recurrent learning, textures, and bounds are confirmed in n*n regions across the x and y axes after being derived from the pixels. As texture-based boundary changes are discovered, the feature extraction algorithm adjusts accordingly. Therefore, within the established limits and their min–max variations, learning occurs iteratively until the greatest precision in object detection is obtained via boundary detection. Object detection is halted, and complexity and time needs are reduced if the boundary closure arises within any part of the distribution. Features in texture and pixels at the distribution's start and end are used to determine where the boundary occurs. The deep recurrent learning uses the lowest and maximum differences in texture to determine the variations. This variation identification is repeated until a saturating or least resolution is found. Object detection may be limited to a small, specified region with the help of region segregation. As a result, this study examines the region where distribution stopped in more detail, searching for combinations of comparable pixels from the training set. The proposed technique successfully overcomes the difficulty of detecting camouflaged objects by applying boundary detection and leveraging the characteristics of low contrast and high variance. It reduces complexity by 12.49 % and saves time by 11.52 % using a recurrent learning method that accounts for shifting boundaries. The process detects the camouflage with high accuracy by 9.17 %. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

After avoiding the Standard Test Condition (STC) related to the manufacturer of PV cells, accurate solar system modeling is considered the most important task to study this system. Several mathematical developments based-approaches or algorithms were considered valuable tools to parameterize the electrical circuit of the photovoltaic models. Thus, these parameters are well determined using optimization algorithms. To achieve this goal, an experimental database or manufacturer's datasheet is requested. This paper proposes a novel Improved Arithmetic Optimization Algorithm (IAOA) to extract the solar cell/panel parameters. Also, practical tests are presented for obtaining the measured illustration of the I-V and P-V characteristics. Thus, the Double Diode Model (DDM) of the electrical circuit of PV panel is studied considering various statistical analyzes, to know the Minimum (Min Fobj,DDM), the Maximum (Max Fobj,DDM), the Average (Ave Fobj,DDM), and the Standard Deviation (SD Fobj,DDM). The estimated I-V and P-V curves using IAOA is compared with eight other recently published algorithms. In addition, the evaluation of the proposed IAOA proves that this proposal has a satisfactory performance than all tested algorithms. The obtained results show that the IAOA is considered first in terms of effectiveness and accuracy by 9.537e−06 of standard deviation. Moreover, considering the variability of operating conditions, the proposed IAOA shows its high superiority to optimize the electrical parameters of the solar cell TDM Where it has the lowest errors by 3.623e−05 of RMSE, 4.183e−06 of MAE, and 1.397e−05 of RMSD. [ABSTRACT FROM AUTHOR]