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Maximizing the efficiency of photovoltaic (PV) systems relies heavily on employing efficient maximum power point tracking (MPPT) algorithms. This research focuses on the advancement of enhanced MPPT algorithms capable of achieving the maximum power point (MPP) under different climatic profiles. This paper proposes an adapted perturb and observe-based model predictive control (APO-MPC) strategy to validate the effectiveness of PV systems under three climatic situations. The APO algorithm incorporates variable step sizes to compute reference currents to reduce oscillations while maintaining a steady state in output power. The APO-MPC efficiently tracks and stabilizes output power by predicting future states using reference current and minimizing the cost function. This eliminated the necessity for expensive sensing and communication equipment and networks designed for directly measuring variations in solar irradiation. The computational burden of an algorithm is reduced using a simplified mathematical model of a boost converter and a one-step prediction approach. The PV panel and boost converter are modeled to get appropriate parameters for implementing the proposed algorithm. The system undergoes simulations using the MATLAB/Simulink environment, and multiple test cases are conducted under constant, rapid, and linearly changing irradiances. The outcomes demonstrate that the proposed APO-MPC MPPT algorithm outperforms APO, Kalman filter-based MPC (KMF-MPC), and other existing strategies in terms of stability, transient response time, overshoots, steady-state oscillations, and follow of reference trajectory under dynamic weather conditions. [ABSTRACT FROM AUTHOR]

Due to the complex-shading and ununiform-corrosion problems caused by the oceanic climate, the working conditions of photovoltaic (PV) system in port are poor. In this study, some effective PV topologies are developed and compared under typical oceanic conditions. In order to improve the output of port PV system, a novel maximum power point tracking (MPPT) method is developed, in which the convolutional neural network (CNN) and bidirectional long short time memory network (BiLSTM) are introduced to forecast the real-time optimal operation point. Finally, the developed topology and MPPT method are verified by a comprehensive set of case studies. Experimental results show that the total-cross-tied (TCT) and gradient (GT) topologies can achieve the best performance. However, by considering the complex structure and higher failure rate due to the large number of joints in TCT topology, the PV arrays installed on the roofs, in the free space of green belt, freight yard and near the seaside are suggested to be connected in GT topology to balance operation efficiency, failure rate and maintenance costs. Moreover, the developed CNN-BiLSTM-based MPPT method and PV topology can effectively work together, and the average forecasting error is just 3.482%, which outperforms the compared state-of-the-art methods significantly. [ABSTRACT FROM AUTHOR]

In addressing the critical challenge of developing sustainable energy solutions for electric vehicle (EV) battery charging, this study introduces an innovative direct current (DC) microgrid system optimized for areas with high solar irradiance, such as Ain El Ibel, Djelfa. The research confronts two primary difficulties: maximizing solar energy utilization in the microgrid system and ensuring system stability and response accuracy for reliable EV charging. To tackle these challenges, the study presents two original achievements. Firstly, it develops a neural network-enhanced Maximum Power Point Tracking (MPPT) controller, which is further optimized with Particle Swarm Optimization (PSO) to increase the efficiency of solar energy capture. Secondly, it refines the system's reliability through the advanced calibration of a Fractional Order Proportional-Integral (FOPI) controller using the Grey Wolf Optimization (GWO) technique, marking a notable improvement in microgrid system stability and response accuracy. The integration of a solar panel array, battery storage, and a supercapacitor, coupled with these advanced optimization techniques, exemplifies a significant leap forward in enhancing efficiency and reliability of EV battery charging through renewable energy sources. Comprehensive simulation and evaluation of the system underscore its superiority over conventional methods, demonstrating the effectiveness of combining neural network-based optimization with PSO and GWO. This breakthrough not only advances the field of renewable energy, particularly for solar-powered EV charging stations, but also aligns with global efforts towards sustainable transportation solutions. [ABSTRACT FROM AUTHOR]

Effective energy management in grid-connected renewable energy systems is essential for achieving cost-efficiency and reliability. This work presents a versatile control technique to tackle power system difficulties in grid-connected and grid-failure modes. The concept employs a digital iterative approach to optimize the operation of a DC-DC converter, guaranteeing the attainment of maximum power output and stability. During a grid breakdown, the system injects carefully adjusted active power into the grid to ensure that voltage and current levels are maintained at appropriate levels, hence preventing overloading. The proposed strategy exhibits a 99% enhancement in system responsiveness and a 96% decrease in overshoot when compared to conventional methods, thus substantiating its efficacy in practical scenarios. [ABSTRACT FROM AUTHOR]

The control strategy of a distributed photovoltaic (PV) power generation system within a microgrid consists of an inner-loop controller and an outer-loop controller. The inner-loop controller is divided into two types, namely, the maximum power point tracking (MPPT) control strategy and DC bus voltage support strategy. Switching between these two control strategies results in issues such as DC bus overvoltage, system oscillations, or even PV system failure. An improved droop control strategy with a novel inner-loop controller is proposed, incorporating an output power derivative regulator. The control system unifies MPPT and DC bus voltage support strategy without switching the controller structure. A simulation model is built to validate the effectiveness of the proposed control strategy, and the results show that the ripple of DC bus voltage decreases by more than 60%. [ABSTRACT FROM AUTHOR]

This work presents a system design for extracting maximum power using the modified maximum power point tracking (MPPT) technique and a novel high-gain DC-DC converter, which was then used to supply a microgrid system with a conventional buck converter. We present a novel structure comprising the MPPT, voltage boosting, and voltage regulating components for a DC microgrid in a single system. The most important features of a photovoltaic (PV) system include a high-gain converter and maximum PV power extraction; considering these, we present a high-gain DC-DC converter that boosts the output voltage to ten times the input voltage. Furthermore, the MPPT technique extracts maximum power from the PV panel based on model predictive control through its better transient response than the conventional incremental conductance method. The MPPT approach was tested with both fixed-and variable-step operations, and the results were compared for load variations. Considering the economics of the system, the proposed approach attempts cost reduction by optimizing the number of sensors to two instead of three. Simulations were conducted under different environmental conditions using MATLAB-Simulink, and the performance differences between the conventional incremental conductance and proposed MPPT-based methods are shown. Next, DC voltage regulation was implemented for the proposed PV and existing systems by considering different load and irradiation conditions while maintaining constant temperature. The simulation results showed the latter system had better performance than the former under different environmental conditions, with persistent results for voltage regulation at different load and irradiation conditions. [ABSTRACT FROM AUTHOR]

Maximum power point tracking (MPPT) algorithms are commonly used in photovoltaic (PV) systems to optimize the power output from the solar panels. Among the various MPPT algorithms, the perturb and observe (P&O) algorithm is a popular choice due to its simplicity and effectiveness. However, the basic P&O algorithm has some limitations, such as oscillations and steadystate error under rapidly changing irradiance conditions. The enhanced algorithm includes a modified perturbation step and a dynamic step size adjustment scheme. This reduces the oscillations and improves the tracking accuracy. In the dynamic step size adjustment scheme, the step size is adjusted based on the rate of change of the PV output power. This improves the tracking performance under rapidly changing irradiance conditions. In order to prove the performance of the designed control algorithm, we will test it under simple climatic conditions of fixed temperature (30°C) and variable irradiation in the form of steps (500W/m2 and 2000w/m2) and see the system response. The performance of the enhanced P&O algorithm has been evaluated using MATLAB simulations. [ABSTRACT FROM AUTHOR]

As electricity demand increases, especially in transportation, renewable sources such as solar energy become more important. The direct integration of solar energy in rail transportation mostly involves utilizing station roofs and track side spaces. This paper proposes a novel approach by proposing the integration of photovoltaic systems directly on the roofs of trains to generate clean electricity and reduce dependence on the main grid. Installing solar photovoltaic (PV) systems on train rooftops can reduce energy costs and emissions and develop a more sustainable and ecological rail transport system. This research focuses on the Milan Cadorna-Saronno railway line, examining the feasibility of installing PV panels onto train rooftops to generate power for the train's internal consumption, including lighting and air conditioning. In addition, it is a solution to reduce the power absorbed by the train from the main supply. Simulations conducted using PVSOL software 2023 (R7) indicate that equipping a train roof with PV panels could supply up to almost 10% of the train's auxiliary power needs, equating to over 600 MWh annually. Implementing the suggested system may also result in a decrease of more than 27 tons of CO2 emissions per year for one train. To optimize the performance of PV systems and maximize power output, the gravitational search algorithm (GSA) as an evolutionary-based method is proposed alongside a DC/DC boost converter and its performance is compared with two other main maximum power point tracking (MPPT) methods of perturb and observe (PO), and incremental conductance (INC). The accuracy of the suggested algorithm was confirmed utilizing MATLAB SIMULINK R2023b, and the results were compared with those of the PO and INC algorithms. The findings indicate that the GSA performs better in terms of accuracy, while the PO and INC algorithms demonstrate greater robustness and dynamic response. [ABSTRACT FROM AUTHOR]

Summary As wind energy is sustainable, pollution‐free, easily available, and free of cost, it has become an efficient source of renewable energy for electricity generation. But, the problem with wind energy is that it varies with time, seasons, and location. This makes the Wind Energy Conversion System (WECS) unstable as it frequently needs to match the load demands. The balance in power generation by wind energy is essential since it has to be connected to various grids. So, this unbalanced energy production can affect the stability of the associated power grids as well. It also results in expensive regulatory measures, storage options, and load shedding. So, the stable operation of the WECS is highly essential to adapt it as a trustable source of electricity production. The stable operation of the WECS requires a robust and advanced system for control. Better control of the wind power extracting model is achieved by controlling the Maximum Power Point Tracking (MPPT) and blade pitch. So, an Adaptive MPPT and Blade Pitch Controller (BPC) for the WECS have been developed in this article, with the support of a hybrid optimization algorithm. In order to enhance the working principles of this controller, two effective algorithms such as Sand Cat Swarm Optimization (SCSO) and Galactic Swarm Optimization (GSO) are integrated and named Hybrid Sand Cat Galactic Swarm Optimization (HSC‐GSO). With the help of the recommended HSC‐GSO, the functionality of the controller is enhanced and also at the same time this algorithm helps to optimize the three gains in the Proportional Integral Differential (PID) controller of both MPPT and BPC, respectively. Moreover, with the support of the proposed HSC‐GSO the damping oscillations in the WECS output power and voltage are minimized. In the end, the numerical analysis is conducted for the presented system by comparing it with the traditional techniques. From the overall result analysis, the stability of the recommended adaptive WECS is 97, which is higher than the conventional algorithms such as DHOA, SCSO, GSO, and DA. Thus, it has been proved that the proposed HSC‐GSO algorithm for the parameters optimization in the PID controller of MPPT and the PID controller of BPC attains high robustness, increased steady‐state stability, and efficient transient response than the traditional techniques. [ABSTRACT FROM AUTHOR]

This article presents a proportional-integral sliding mode control (PI-SMC) approach for a two-mass variable speed wind turbine (VSWT) system. Most studies on wind turbines typically focus mainly on the electromagnetic part of the generators, or even on the high-speed part, considering the shaft stiffness as negligible. However, the generator torque is actually driven by the aerodynamic torque, and a two-mass system like the one studied here plays the role of a transmission element for this power. To address this challenge, the problem of low power generation resulting from wind speed variability is tackled by designing a PI-SMC control law, capable of controlling the mechanical turbine model that optimizes power and torque by tracking the maximum power point (MPPT) for rotational speed and aerodynamic power. To validate the developed theoretical results, an application of the wind turbine system is simulated in Matlab/Simulink, for a particular case. The control used is capable of satisfying the dynamic performance of the systems. [ABSTRACT FROM AUTHOR]

This study presents a new approach for Maximum Power Point Tracking (MPPT) by combining the honey badger algorithm (HBA) with a Genetic Algorithm (GA). The integration aims to optimize photovoltaic (PV) system performance in partial shading conditions (PSCs). Initially, the HBA is utilized to explore extensively and identify potential solutions while avoiding local optima. If necessary, the GA is then employed to escape local optima through selection, crossover, and mutation operations. On average, this proposed method has a 40% improvement in tracking time and 0.77% in efficiency compared with the HBA. In a dynamic case, the proposed method achieves a 4.81% improvement compared to HBA. [ABSTRACT FROM AUTHOR]

In recent years, grid-connected multifunctional photovoltaic (PV) systems have proven to be highly efficient. This system integrates PV panels with a DC–DC boost converter (DC–DC–BC) and the electrical distribution grid (DEG). Linking the PV to the AC-DEG is accomplished through a three-level multifunctional voltage source inverter (MVSI). The DC–DC–BC component in this study is engineered to perform maximum power point tracking (MPPT) irrespective of normal or abnormal conditions. The conventional MPPT technique poses several challenges and constraints on system usage. Hence, the suggestion is to adopt synergetic control (SC) and sliding mode control (SMC) to enhance the MPPT technique's performance within the proposed system framework. Moreover, predictive direct power control is applied to the MVSI-based shunt active power filter, utilizing a phase-locked loop technique to achieve multiple objectives: minimizing energy fluctuations, injecting active power, correcting power factors, compensating reactive power, and mitigating harmonic currents. To implement the proposed system, the MATLAB is used for this purpose, with several tests used to study the behavior of the controls proposed in this work. Numerical results indicate significant reductions in active and reactive power fluctuations, with estimated rates of 38.46% and 15.30%, respectively, compared to traditional strategies. Moreover, the total harmonic distortion (THD) of the source current after filtering is reduced by 31.88% under solar irradiation of G = 1000 Wm2. Before filtering, the THD of current experiences a reduction estimated at 97.65%. These findings underscore the superior performance of the proposed control technique across all evaluated aspects. [ABSTRACT FROM AUTHOR]

As the significance and complexity of solar panel performance, particularly at their maximum power point (MPP), continue to grow, there is a demand for improved monitoring systems. The presence of variable weather conditions in Maroua, including potential partial shadowing caused by cloud cover or urban buildings, poses challenges to the efficiency of solar systems. This study introduces a new approach to tracking the Global Maximum Power Point (GMPP) in photovoltaic systems within the context of solar research conducted in Cameroon. The system utilizes Genetic Algorithm (GA) and Backstepping Controller (BSC) methodologies. The Backstepping Controller (BSC) dynamically adjusts the duty cycle of the Single Ended Primary Inductor Converter (SEPIC) to align with the reference voltage of the Genetic Algorithm (GA) in Maroua's dynamic environment. This environment, characterized by intermittent sunlight and the impact of local factors and urban shadowing, affects the production of energy. The Genetic Algorithm is employed to enhance the efficiency of BSC gains in Maroua's solar environment. This optimization technique expedites the tracking process and minimizes oscillations in the GMPP. The adaptability of the learning algorithm to specific conditions improves energy generation, even in the challenging environment of Maroua. This study introduces a novel approach to enhance the efficiency of photovoltaic systems in Maroua, Cameroon, by tailoring them to the specific solar dynamics of the region. In terms of performance, our approach surpasses the INC-BSC, P&O-BSC, GA-BSC, and PSO-BSC methodologies. In practice, the stabilization period following shadowing typically requires fewer than three iterations. Additionally, our Maximum Power Point Tracking (MPPT) technology is based on the Global Maximum Power Point (GMPP) methodology, contrasting with alternative technologies that prioritize the Local Maximum Power Point (LMPP). This differentiation is particularly relevant in areas with partial shading, such as Maroua, where the use of LMPP-based technologies can result in power losses. The proposed method demonstrates significant performance by achieving a minimum 33% reduction in power losses. [ABSTRACT FROM AUTHOR]

The present study used the wind turbine as a model to focus on combining sliding mode and fractional-order theory for maximum power point tracking (MPPT) enhancement. The combination of sliding mode and fractional-order theory was implemented considering the nonlinearity of the studied model for studying the system's response. The response of the wind turbine was evaluated after introducing disturbance to the output of the regulator. The results showed the excellent ability of the system to track the reference, regardless of any disturbances. There was no impact of any disturbance on the system or the system's good follow-up. Moreover, the control scheme showed robustness as regards rejecting the disturbances. [ABSTRACT FROM AUTHOR]

This research article presents a novel approach to Maximum Power Point Tracking (MPPT) for photovoltaic systems, employing a modified bacterial foraging algorithm with dynamically adjustable mutation rates. This method is specifically tailored to address the challenges presented by partial shading conditions, ensuring efficient and rapid tracking of the MPP while preventing local optima entrapment. To evaluate the performance of this innovative technique, a comparative analysis is conducted against the original bacterial foraging algorithm and the grey wolf optimization algorithm, both commonly employed in MPPT applications. The modified algorithm incorporates a unique strategy that dynamically adapts mutation rates based on the algorithm's convergence behavior, enhancing the tracking accuracy from 81.31% to 89.39%. To validate the effectiveness of the proposed technique, extensive simulations are carried out using MATLAB Simulink, considering various partial shading scenarios commonly encountered in practical photovoltaic applications. It's worth noting that the shading scenario data were extracted from the NASA Worldwide Prediction of Energy website, specifically from the city of Ain El Ibel Djelfa irradiance records. The simulation results unequivocally demonstrate the superiority of the modified bacterial foraging MPPT technique over both algorithms in terms of tracking efficiency (0.4s to 0.9s) and robustness under partial shading conditions. The findings of this research offer valuable insights into the potential advantages of employing a modified bacterial foraging approach for MPPT applications. This innovative techniques with its ability significantly enhance its performance in real-world scenarios involving partial shading, positioning it as a promising choice for optimizing photovoltaic system efficiency and power output. [ABSTRACT FROM AUTHOR]

Electricity production from photovoltaic (PV) panels is maximized when the operating point is located at the maximum power point thanks to dedicated controllers. These controllers are driven to track the maximum power by using various algorithms within distributed or centralized architectures accounting for factors such as partial irradiation and temperature changes. The effect of irradiance on the optimal panel voltage is weak or even negligible, while it is strong and quasi-linear-dependent on temperature. Based on this observation, this article introduces a straightforward method for tracking the maximum power of a PV panel by using an optimizer, focusing solely on its temperature response as an input variable. The proposed approach hinges on linearizing the relationship between panel temperature and operating voltage. This relationship enables the estimation of the maximum power point through temperature measurement alone. Thus, after determination of the linear temperature coefficient of the voltage requiring only the knowledge of two optimal voltages at different temperatures, for example from the datasheet of the panel, the power tracking involves only one temperature sensor placed on the panel alongside a voltage sensor for regulation. The principle, modelling, and validation post-panel ageing of the method are detailed in this paper. Simulation, conducted using real experimental irradiation and temperature data, attests to the effectiveness of the control. Results indicate an average effectiveness of the method of >99.1% in tracking the maximum power, with the panel generating 2.33 kWh out of a possible 2.35 kWh. This performance is comparable to that of tracking devices employing more complex algorithms. The simplicity and efficiency of the method make it a promising option for maximizing the power production at low cost from PV systems in small or residential, on- or off-grid connected applications. [ABSTRACT FROM AUTHOR]

Integrating photovoltaic systems (PV) into the grid has garnered significant attention due to increasing interest in renewable energy sources. Maximizing the PV systems output power is crucial for improving energy efficiency and reducing operating costs. This paper presents a comparative analysis of two different techniques of maximum power point tracking (MPPT): perturb and observe (P&O) and artificial neural network (ANN) MPPT, focusing on their application in grid-connected PV systems. The paper evaluates their performance under various operating conditions, including changes in irradiance and temperature, that are discussed in three cases. The comparative analysis includes metrics such as voltage regulation and power loss. MATLAB Simulink is utilized to implement P&O and ANN MPPT methods, which include a PV cell connected to an MPPT-controlled boost converter. The simulation demonstrates the power loss of the PV model as well as the voltage regulation in the three cases for the two methods. The results obtained in simulation and implementations show that the ANN method outperforms the P&O in the three cases discussed in terms of power loss, voltage regulation, and efficiency. The results also show that the change in output power from PV is noticeable when compared to changes in radiation, while the change is slight when temperatures change. [ABSTRACT FROM AUTHOR]

Partial shading (PS) is a common issue in photovoltaic systems (PVs), and it can significantly reduce the system's output power. This paper presents the advanced particle swarm optimization (APSO) algorithm. APSO is designed to alleviate the challenges posed by PS in PVs in from where of effectiveness and stability speed so that it works to achieve and maintain the global maximum power point (GMPP) under PS conditions. It leverages persistent variables to store and track system states and iterations; it also includes checks to ensure that the duty cycle remains within specified bounds facilitating more effective optimization. Additionally, APSO optimizes solar panel duty cycles and velocities to converge toward an optimal solution to improve overall power generation efficiency and settling time. The results evaluation involves testing the performance of photovoltaic panels under three different shading scenarios and comparative analysis against recent Heuristic-optimization-based GMPP techniques, this study and comparative analyses demonstrate APSO's effectiveness and superiority in terms of high efficiency that reaches 99.85% and fast settling time of GMPP at less than 0.01 second across all test cases. APSO presents a promising solution for maximizing PV power output in the presence of partial shading. [ABSTRACT FROM AUTHOR]

Abstract In recent years, grid-connected multifunctional photovoltaic (PV) systems have proven to be highly efficient. This system integrates PV panels with a DC–DC boost converter (DC–DC–BC) and the electrical distribution grid (DEG). Linking the PV to the AC-DEG is accomplished through a three-level multifunctional voltage source inverter (MVSI). The DC–DC–BC component in this study is engineered to perform maximum power point tracking (MPPT) irrespective of normal or abnormal conditions. The conventional MPPT technique poses several challenges and constraints on system usage. Hence, the suggestion is to adopt synergetic control (SC) and sliding mode control (SMC) to enhance the MPPT technique's performance within the proposed system framework. Moreover, predictive direct power control is applied to the MVSI-based shunt active power filter, utilizing a phase-locked loop technique to achieve multiple objectives: minimizing energy fluctuations, injecting active power, correcting power factors, compensating reactive power, and mitigating harmonic currents. To implement the proposed system, the MATLAB is used for this purpose, with several tests used to study the behavior of the controls proposed in this work. Numerical results indicate significant reductions in active and reactive power fluctuations, with estimated rates of 38.46% and 15.30%, respectively, compared to traditional strategies. Moreover, the total harmonic distortion (THD) of the source current after filtering is reduced by 31.88% under solar irradiation of G = 1000 Wm2. Before filtering, the THD of current experiences a reduction estimated at 97.65%. These findings underscore the superior performance of the proposed control technique across all evaluated aspects.

This paper proposes a dual-output DC–DC power conversion system based on Photovoltaic (PV) technology. PV panels are connected to a series compensated Buck-Boost Converter (SCBBC) to harvest solar energy, while a sliding mode controller (SMC) ensures maximum power point tracking (MPPT). During the intermediate phase, a synchronized Buck-Boost Converter (SBBC) topology is used to ensure effective charging and discharging of batteries. Additionally, a PI-SMC hybrid control strategy is applied at the back end to the Super Lift Luo Converter (SLLC) to maintain the load voltage at a desired value. A Gravitational Search Algorithm (GSA)-based PI controller controls the input current, while the output voltage is controlled by the outer loop (SMC). We use a single-loop SMC approach to validate the performance of the proposed dual-loop control scheme. According to the presented results, the dual-loop control scheme demonstrated higher dynamic performance in controlling input current and output voltage. [ABSTRACT FROM AUTHOR]

The charge transfer coefficient is a dimensionless coefficient used in the kinetics of chemical reactions. In this paper, the effect of the charge transfer coefficient on hydrogen fuel cell characteristics such as polarization curve and power diagram in terms of current density and losses is investigated. The charge transfer coefficient affects the activation losses of the fuel cell and therefore affects the performance of the fuel cell. For this investigation, a basic sample is selected and the changes of charge transfer coefficient are studied on its characteristics. The obtained results show that with an increase in this factor, the activation loss decreases. In addition, increasing the charge transfer coefficient increases the maximum power point. The increase in the power of this point is more visible in lower values of the charge transfer coefficient and when this coefficient exceeds the value of 0.5, this effect becomes very small. Also, the appropriate value of this coefficient is determined to maintain the balance of the chemical reaction. The activity of the fuel cell is disrupted due to an excess amount of the coefficient.

This study proposes a machine learning-based maximum power point tracking method for fuel cell systems. Initially, a Matlab model was developed to accurately represent the behaviour of a proton exchange membrane fuel cell. A dataset for training of machine learning methods was collected from fuel cells operating under different conditions. To demonstrate the effectiveness of the proposed method, comparison studies were conducted using classical methods, namely "perturb and observe" and "incremental conductance" under varying temperature and pressure conditions. Notably, under the condition of temperature and pressure variation, the output powers obtained from the system were 2005.2 W for support vector machine and 2018.5 W for linear regression at 4.5 s, while the output powers of 'incremental conductance' and 'perturb and observe' were 1989.6 W and 1986.3 W, respectively. The results demonstrate that the proposed method contributes to a more efficient operating condition and faster dynamic responses to changes. • An MPPT method based on machine learning is proposed. • The utilization of a machine learning-based MPPT technique enhances the efficiency of PEMFC. • By utilizing the machine learning, the PEMFC system adapts to varying operating conditions. • The proposed method provides an efficient solution for maximizing power extraction from PEMFC. [ABSTRACT FROM AUTHOR]

The occurrence of partial shading in solar power systems presents a substantial challenge with widespread implications, sparking extensive research, notably in the field of maximum power point tracking (MPPT). This study emphasizes the critical process of accurately tracking the maximum power points with the characteristic curves of photovoltaic (PV) modules under real-time, diverse partial shading patterns. It explores the various stages of the tracking process and the methodologies employed for optimization. While conventional methods show effectiveness, they often fall short in swiftly and accurately tracking maximum power points with minimal errors. To address this limitation, this research introduces a novel machine learning approach known as adaptive reinforcement learning with neural network architecture (ARL-NNA) for MPPT. The results obtained from ARL-NNA are compared with existing algorithms using the same experimental data. Furthermore, the outcomes are validated through different factors and processing time measurements. The findings conclusively demonstrate the efficacy and superiority of the proposed algorithm in effectively tracking maximum power points in PV characteristic curves, providing a promising solution for optimizing solar energy generation in partial shading patterns. This study significantly impacts various realms of electrical engineering including power engineering, power electronics, industrial electronics, solid-state electronics, energy technology, and other related field of engineering and technology. [ABSTRACT FROM AUTHOR]

A two-stage circuit configuration with 3-phase utility grid assisted solar power generation system is designed. In order to track the solar PV arrays maximum peak power (MPP), a DC boost converter with Composite Incremental Conductance (InC) MPP technique is employed in first stage. In Second stage, a grid-tied four-leg DC-AC converter, is employed to provide the solar power that was extracted into the utility grid and to enhancing power quality. An adaptive hybrid Multi-Second Order Generalized Integrator-Quadrature (MSOGI-Q) control algorithm, in conjunction with Neuro Fuzzy system (NFS) is proposed for controlling this DC-AC converter. MSOGI-Q reference current generation strategy is designed to mitigate current harmonics by retrieving the fundamental constituents from non-linear load. The Neuro Fuzzy system along with PID controller, by adjusting the voltage from dc link keeps consistent power in between AC and DC sides for varying isolation condition. To get the peak power out of a PV panel even in different environmental conditions, the composite incremental conductance algorithm is used. To upgrade the dynamic performance of voltage variation at Point of Common Coupling (PCC) a feedforward descriptor for Photovoltaic contributions has been implemented. In addition, a comparison between the proposed NFS-MSOGIQ control technique and the existing adaptive control technique is done to assess the effectiveness of the proposed adaptive control technique. The proposed adaptive control technique for the distributed generation system is designed and stimulated in MATLAB/SIMULINK and the simulation outcomes verify the effectiveness of the proposed control technique under various transition circumstances. A laboratory prototype model of the proposed system is developed for theoretical analysis and its results are found to be compliant with IEEE standards. Furthermore, THD values of voltage and current are found to be within standard limits. [ABSTRACT FROM AUTHOR]

Summary This paper presents the symmetric differential Buck–Boost converter, obtained from the differential connection between two Buck–Boost converters. The topology is characterized by presenting low‐voltage ripple, partial power processing, ability to provide intermediate voltage gains (1 < M < 10) and design simplicity. In this configuration, the output voltage is composed of the sum of the input voltage and the output voltages of both converters differentially connected. The topology description along with its main equations and waveforms in continuous conduction mode with phase‐shift modulation are presented in this paper. Moreover, a mathematical analysis that demonstrates its capability of performing with partial power processing and a comparative analysis with similar topologies are also presented. In addition, experimental results extracted from a prototype are presented and discussed. Because the proposed converter is applied to photovoltaic energy processing, it is validated in open loop and as maximum power point tracker, in which the obtained CEC efficiency is 97.8%. [ABSTRACT FROM AUTHOR]

Summary: An adaptive optimal controller is proposed in this paper to maximize the captured power of a variable speed wind power system. The proposed controller is a combination of optimal and adaptive control components. The Adaptive Dynamic Programming technique is used to design the optimal control component to overcome the nonlinear problem of system dynamics and ensure stability. While the neural network is used to approximate unknown disturbances and system uncertainties. After that, the adaptive control component fully compensates for the effects of these unknown elements. Neither optimal nor adaptive control components necessitate prior knowledge of system dynamics. Furthermore, the approximation network updates only the weight matrix norm rather than the weight matrix of the neural network in each interval time, which significantly reduces computation. The stability analysis of the closed‐loop system is obtained using Lyapunov stability theory. The correctness and robustness of the control scheme are validated in two different scenarios using MATLAB/Simulink. The presented robust adaptive optimal controller is also compared to other existing controllers to demonstrate its benefits. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid COA‐QNN approach for an interleaved KY converter with closed‐loop control for a single‐phase grid‐connected photovoltaic (PV) system. The proposed strategy combines both Cheetah Optimizer algorithm (COA) and Quantum Neural Network (QNN), and it is commonly named the COA‐QNN technique. The interleaved KY converter is connected in the converter side. The primary goal of the COA‐QNN technique is to enhance PQ while maximizing PV electricity being transferred to the grid. The proposed COA is utilized to identify the optimal closed‐loop controller enhancements for on‐grid solar photovoltaic systems. The QNN is used to predict the optimal control parameter. The PV‐interleaved KY converter is managed by a predictive control mechanism to carry out both tasks of PQ enhancement. By then the COA‐QNN technique is implemented on the MATLAB platform and compared with existing methods. Finally, the proposed method shows better results in all methods, such as GWO, FF optimization and combined GWO‐FF. [ABSTRACT FROM AUTHOR]

This paper proposes a Symmetric High Voltage-Gain (SHVG) boost converter control for photovoltaic system applications. The concept is based on a multilevel boost converter configuration, which presents an advantage compared to a classic boost converter such as the ability to transfer a high amount of power with less stress on the power electronics components in the high voltage-gain conditions. This advantage allows the power losses in the converter to be reduced. A mathematical-based voltage model of the PV system using variable series resistance depending on solar irradiance and the temperature is proposed. This model is connected to an SHVG boost converter to supply the load's power. A control strategy of the DC-bus voltage with maximum power point tracking (MPPT) from the PV system using PI controllers is developed. The contributions of the paper are focused on the SHVG operating analysis with the passive components' sizing, and the DC-bus voltage control with maximum power point tracking of the PV systems in dynamic operating conditions. The performances of the proposed control are evaluated through simulations, where the results are interesting for high-power photovoltaic applications. [ABSTRACT FROM AUTHOR]

A thermoelectric generator (TEG) is a semiconductor-based device that can exhibit a flow of electric current through the external circuit if the sides of the TEG are subjected to a temperature difference caused by a heat source. The terminal voltage and the TEG’s internal resistance are altered depending on the temperature difference. Therefore, a suitable power electronic converter with a maximum power point tracking (MPPT) feature is required to harvest maximum power from the TEG and store the harvested energy in a battery-based energy storage system. This paper investigates a novel energy harvesting methodology from TEG using a series compensated buck-boost converter (SCBBC). MPPT has also been implemented using a sliding mode controller (SMC). A mathematical model of a TEG source has been developed in MATLAB-Simulink and tested with generic boost converter (GBC) and SCBBC for their dynamic performances during battery charging. The efficiency of conversion is found to be higher for SCBBC, at 97.87%. An experimental prototype has been developed with SCBBC and tested for two configurations, viz high voltage and low current configuration. The former configuration exhibits an efficiency of 86.3%, while the latter has an efficiency of 83%. The results indicate that SCBBC, along with SMC, opens up new efficient prospects for harvesting waste heat energy using TEGs. [ABSTRACT FROM AUTHOR]

In situations where photovoltaic (PV) systems are exposed to varying light intensities, the conventional maximum power point tracking (MPPT) control algorithm may become trapped in a local optimal state. In order to address this issue, a two-step MPPT control strategy is suggested utilizing an improved tuna swarm optimization (ITSO) algorithm along with an adaptive perturbation and observation (AP&O) technique. For the sake of enhancing population diversity, the ITSO algorithm is initialized by the SPM chaos mapping population. In addition, it also uses the parameters of the spiral feeding strategy of nonlinear processing and the Levy flight strategy adjustment of the weight coefficient to enhance global search ability. In the two-stage MPPT algorithm, the ITSO is applied first to track the vicinity of the global maximum power point (MPP), and then it switches to the AP&O method. The AP&O method's exceptional local search capability enables the global MPP to be tracked with remarkable speed and precision. To confirm the effectiveness of the suggested algorithm, it is evaluated against fuzzy logic control (FLC), standard tuna swarm optimization (TSO), grey wolf optimization (GWO), particle swarm optimization (PSO), and AP&O. Finally, the proposed MPPT strategy is verified by the MATLAB R2022b and RT-LAB experimental platform. The findings indicate that the suggested method exhibits improved precision and velocity in tracking, efficiently following the global MPP under different shading conditions. [ABSTRACT FROM AUTHOR]

The emerging tribovoltaic nanogenerators (TVNGs) has garnered considerable attention in recent years owing to its higher power density, but it is still limited by unstable output, mismatched impedance, and lower charging speed. Herein, a power‐managed GaN‐based TVNG is first proposed and a self‐powered wireless temperature monitoring system (SWTMS) is developed. The energy backflow from transition capacitor to TVNG is effectively suppressed by applying a high‐voltage‐resistant diode. Benefiting from the combination of the maximum power point tracking (MPPT and the buck converter, an autonomous power management unit (PMU) is designed. The internal impedance of TVNG with PMU decreases by ten times and the charging speed of capacitor increases by 7.18 times compared to those without PMU. Moreover, 100 LEDs and six parallel thermohygrometers can be constantly powered. The wireless temperature monitoring in industrial environments without external power supply is demonstrated through the developed SWTMS. This work proposes an effective power management strategy for TVNG with high output voltage, which exhibits great potential to promote the development of self‐powered sensors and Industrial Internet of Things (IIoT). [ABSTRACT FROM AUTHOR]

Solar energy will always be available, but harnessing its power may be difficult since it is intermittent. Therefore, a technology that integrates systems is necessary to satisfy the load requirement. The integration of the system is referred to as a converter, and it has an algorithm that can be educated according to the requirements of the final product. The perturb and observe (P&O) technique is used in this work due to the fact that it is simple to implement and has gained widespread recognition in the industry. This study's objective is to construct a step voltage controller and a step-duty P&O maximum point tracking controller-based DC-to-DC boost converter, respectively, and then make a comparison between the two. In this study, a step voltage controller and step-duty controller-based DC-to-DC boost converter are used to develop and evaluate the performance of the various components under a variety of different climatic circumstances. In order to assess the efficiency of the system, it is also important to make a comparison between the oscillations in load power and the amount of time it takes for the circumstances to settle. MATLAB Simulink and coding are used in order to evaluate the system while taking into consideration the circumstances of India's changing temperature and the intensity of the sun's radiation. [ABSTRACT FROM AUTHOR]

Due to its simplicity, the one-diode model is commonly used for modeling the operation of photovoltaic (PV) modules at standard test conditions (STC). However, its inherent implicit nature often presents challenges in modeling PV energy production. In this paper, the innovative explicit one-diode model developed by us over time is adapted for estimating PV power production under real weather conditions. Simple yet accurate equations for calculating the energy output of a PV generator equipped with a maximum power point tracking (MPPT) system are proposed. The model's performance is assessed under various normal and harsh operating conditions against measured data collected from the experimental setup located at the Solar Platform at West University of Timisoara, Romania. As an application of the new equation for maximum power, this paper presents a case study where the energy loss in the absence of an MPPT system is evaluated based on atmospheric and sky conditions. [ABSTRACT FROM AUTHOR]

As energy demand increases, the investment cost of photovoltaic (PV) systems decreases due to mass production. That results the usage of PV panels to become widespread. Changes in environmental conditions result in a change in the power produced by PV panels. Maximum power point tracking (MPPT) is proposed to handle this problem. In addition to changing environmental conditions, the current ripple of the PV panel is another significant problem for the power produced. High current ripples affect the dynamic response of the PV panels and make it difficult to maintain the system to operate at maximum power point. Using an interleaved boost converter (IBC) and driving the converters in critical conduction mode (CRM) is a well-known solution for reducing high current ripple and switching losses in power supplies. In this study, an IBC operating in CRM with MPPT and ensuring low current ripple from the panels is proposed. To verify the theoretical approach, experimental and simulation studies were conducted. A 30 W prototype is designed and tested under various conditions. The MPPT efficiency of the prototype circuit is measured as 98.1%. It has been seen that the real-time and simulation results are in accordance with the theoretical ones. [ABSTRACT FROM AUTHOR]

This paper offers a unique and novel approach for electric vehicle battery charging by incorporating a solar PV-integrated dc to dc boost converter. Traditional power electronic converters used in such applications often face limitations including low voltage gain, low conversion efficiency, high ripple content, and inadequate controller performance. To overcome these challenges, the study introduces the Improved A-Source Boost Converter (IASBC) along with an interval type-2 fuzzy logic controller (IT2FLC) and MPPT technique for continuous switching with optimal design. The IASBC, equipped with a coupled inductor, enhances load side performance by achieving high conversion efficiency and voltage gain while minimizing the impact of network parasitic. Additionally, the proposed controller technique effectively reduces output side dc ripple content through gain parameter optimization. The system is optimized for charging a 48 V rechargeable battery within a solar photovoltaic voltage range of 25–75 V DC. Extensive simulations using MATLAB validate the efficacy of the proposed approach. The software analysis reveals an efficiency of 90.82% in the open-loop system and 98.57% in the closed-loop system, while the hardware system achieves an efficiency of approximately 98.55%. Furthermore, a 100 W experimental design is developed and compared with the simulation results, providing validation for the technical significance of the suggested converter. This novel approach not only addresses the limitations of conventional converters but also offers improved performance in terms of efficiency, voltage gain, and ripple content. The proposed system holds promise for efficient electric vehicle battery charging and can contribute to the advancement of sustainable energy solutions. [ABSTRACT FROM AUTHOR]

The fast expansion of renewable energy sources, particularly photovoltaic (PV) panels, has become critical in meeting the world's rising energy needs while also addressing environmental issues. An effective control technique is a factor worth consideration to achieve enough power from PV panels. This abstract summarizes in detail the use of maximum power point tracking (MPPT) and Artificial Intelligence-based technologies for achieving better performances of PV systems Firstly we will analyze the techniques of MPPT and artificial neural network (ANN) that are the best choices for high-quality power and considerations on the discussion will be put forward. The precision of determining the current, voltage, and power gradients has been accentuated by artificial intelligence to predict solar radiation within a close range of the true value. With the in-crease in the radiations, the corresponding current values go up too. In turn, voltage values increase as well as the power values. The AI system is the better watchdog as op-posed to traditional methods to temperature ambient. Now temperature change begins to adversely affect these factors. The given chart shows that current values start to decrease drastically from 25℃ of temperature. Then at 65℃ one of the power value drops to 175V and then to 16000W. The optimum power value is 16800W at 65℃. AI is also smart to determine the best possible current, voltage. [ABSTRACT FROM AUTHOR]

Solar power plants are desirable because of their low environmental impact and operating costs. However, the efficiency of solar photovoltaic (PV) systems remains challenging due to their nonlinear characteristics. This research proposes novel techniques for tracking the maximal hybrid power point with the goal of improving PV system efficiency. Two well-known shortcomings of traditional maximum power point tracking (MPPT) algorithms are their volatility around the maximum power point (MPP) and their failure accuracy under frequently changing isolation. In order to get over these problems, we put out a theory that uses the Proportional Integration (PI) controller to combine beluga whale optimization (BWO) with perturb and observe (P&O). The effectiveness, intricacy, and simplicity of use of this innovative technique, created by the BWO algorithm and utilizing a Proportional Integration (PI) controller, are compared with traditional P&O and incremental conductance (IC) algorithms in this study. The suggested hybrid PI-BWO approach provides a voltage gain of 2.341 and an average tracking efficiency of about 96.8%, according to MATLAB simulation testing. The P&O theory yielded an efficiency of 93.2% and a voltage gain of 2.25, but the IC technique provided an average efficiency of 95.7% and a voltage gain of 2.28. This method is characterized by its durability, stability, and convenience of use. [ABSTRACT FROM AUTHOR]

The solar photovoltaic (PV)-based Maximum Power Point Tracking (MPPT) systems have gained popularity in recent times. This work proposes the improvement and implementation of a newly introduced optimization technique, the Improved Mountain Gazelle Optimization (IMGO) algorithm, for tuning the Fractional Order Proportional-Integral-Derivative (FOPID) and Proportional-Integral-Derivative (PID) controllers for the MPPT control strategy. The performances of the controllers were evaluated with reference to error criteria and settling time of the response. The performance parameters mentioned above are compared with those of PID and FOPID controllers tuned using Genetic Algorithm (GA) and Grey-Wolf Optimization (GWO) algorithms. The simulation study was carried out in the MATLAB/SIMULINK environment. The analysis found that the FOPID controller tuned using the Improved Mountain Gazelle Optimization algorithm provides better results in terms of settling time and error when compared to the PID controller. [ABSTRACT FROM AUTHOR]

The development of a robust optimization control technique, which can handle numerous nonlinear system problems, is one of the most challenging aspects of science production. In this study, hybrid intelligent Maximum Power Point Tracking (MPPT) control approach based on the Imperialist Competitive Algorithm (ICA), and an adaptive Artificial Neural Network (ANN) model is proposed to solve the efficiency optimization problem of Photovoltaic (PV) systems, which are considered as one of the most demanded source energy in the world. Consequently, a comparison between various metaheuristic algorithms based ANN including, Particle Swarm Optimization, Grey Wolf Optimization, and Whale Optimization Algorithm, is made for four distinct PV panel architectures to prove the effectiveness of the suggested approach in the optimization process based ICA technique, and in the training phase based three training algorithms namely, Bayesian Regularization (BR), Levenberg Marquardt (LM), and Scaled Conjugate Gradient (SCG). Accordingly, the obtained outcomes have proven that the ICA–ANN approach-based BR algorithm outperformed in three of four cases the other techniques by reaching an accuracy that can go up to 99.9994%. In the second part of this study, the evaluation of the obtained findings confirmed that our proposed model was able to track the Maximum Power Point (MPP) faster with a response time between 1.9 and 9.6 ms, and efficiency higher than 99.9652%, which is can up to 99.9984%, and it has shown excellent remarkable stability compared to the Perturb & Observe, Incremental Conductance, and the most applied metaheuristic-based MPPT techniques that we have used to conduct the optimization performance comparison. [ABSTRACT FROM AUTHOR]

Technological advancement and economic progress have made power consumption a big issue. Concern is growing as traditional energy sources dwindle. In the future, numerous fossil fuels will be insufficient to satisfy human requirements. This motivates research into the feasibility of using renewable energy sources. Renewable energy sources offer a multitude of advantages, including their cost-effectiveness, lack of environmental impact, and sustainable nature. Sunlight is currently the most prevalent source of energy because it is both free and readily accessible. Consequently, photovoltaic (PV) energy is gaining importance in the field of electricity generation. Tracking the maximum power point (MPP) in a solar PV system is challenging due to varying meteorological conditions (irradiance and temperature). To maximise the efficiency of a solar power installation, it is essential to monitor the PV array's optimum power point. This analysis compares the perturb and observe (P&O), fuzzy logic (FL), and suggested artificial neural network (ANN)-fuzzy strategy for determining the MPP of a PV system with minimal radiation exposure. Simulation results show that at low irradiation levels, the proposed ANN-fuzzy maximum power point tracking (MPPT) unit controller is superior to the FL and P&O MPPT controllers in terms of tracking maximum power. [ABSTRACT FROM AUTHOR]

This research aims to enhance the performance of photovoltaic (PV) systems on a 2-fold basis. Firstly, it introduces an advanced deep artificial neural network algorithm for accurate and fast maximum power point tracking, ensuring optimal extraction of electrical power from PV arrays. Secondly, it proposes the use of 96-V, 2.98-kW direct-current (DC) water pumps for farm irrigation, aiming to improve efficiency, reduce cost and complexity, and overcome challenges associated with connecting faraway farm irrigation systems to the utility grid. In this study, it has been demonstrated that the use of DC pumps greatly improves system performance and efficiency by eliminating the need for isolation transformers, power passive filters and inverters, therefore simplifying the architecture of the system. The efficacy of the proposed methodology is confirmed by MATLAB®/Simulink® simulation results, whereby the proposed algorithm attains a mean squared error of 6.5705 × 10–5 and a system efficiency approaching 99.8%, ensuring a steady voltage under varying load conditions. [ABSTRACT FROM AUTHOR]

Photovoltaic systems have emerged as a promising energy resource that caters to the future needs of society, owing to their renewable, inexhaustible, and cost-free nature. The power output of these systems relies on solar cell radiation and temperature. In order to mitigate the dependence on atmospheric conditions and enhance power tracking, a conventional approach has been improved by integrating various methods. To optimize the generation of electricity from solar systems, the maximum power point tracking (MPPT) technique is employed. To overcome limitations such as steady-state voltage oscillations and improve transient response, two traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb and observe (P&O), have been modified. This research paper aims to simulate and validate the step size of the proposed modified P&O and FLC techniques within the MPPT algorithm using MATLAB/Simulink for efficient power tracking in photovoltaic systems. [ABSTRACT FROM AUTHOR]

In this paper, a full nonlinear control of a variable-pitch wind system (VPWS) based on the doubly fed induction generator (DFIG) fed by a direct matrix converter (DMC) has been presented. In this context, The MPPT has been implemented using the third order sliding mode control (TOSMC) in order to ensure maximum power provided by the wind turbine on the one side, on the other side the pitch control has been implemented in order to limit the power extracted to its nominal value. Moreover, a TOSMC has been incorporated into the direct flied-oriented control (DFOC) to ensure high-performance control of the active and reactive power of DFIG. To examine the performance of the TOSMC, a comparative study was performed between this last type and the first and second order sliding mode controllers. The obtained results affirmed the high performance provided by the TOSMC compared to lower order sliding mode controllers. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of maximum power point tracking of photovoltaic (PV) systems in the presence of model uncertainty as well as varying load and atmospheric conditions using techniques based on a Takagi–Sugeno fuzzy model. The proposed approach relies on the linear matrix inequality tool, Lambert W function, and the Newton–Raphson method. First, adopting a quadratic Lyapunov function, an active observer-based fuzzy non-parallel distributed compensation (non-PDC) controller is designed for asymptotic tracking of the desired reference input. Next, to subdue the impact of uncertainty on the PV system, the closed-loop nominal system is regarded as a reference model, and then the main control law is developed using an online lumped uncertainty estimator and keeping the nominal control law within the staple controller. This control law does not require that the bounds on uncertainties be known. The reference voltage is determined by a novel maximum power point-seeking algorithm that is organized based on the one-diode model of PV panel, Lambert W function, and Newton–Raphson method. Finally, simulations are performed for three scenarios to point out the merits and effectiveness of the proposed methodology in the presence of system uncertainties, environmental changes, and load variations. [ABSTRACT FROM AUTHOR]

Under partial shading conditions (PSCs), photovoltaic arrays exhibit power–voltage curves with multiple peaks. This phenomenon complicates the task of traditional maximum power point tracking (MPPT) algorithms, since they often converge to local maximum power points. To tackle this challenge, a novel MPPT control strategy, termed the slime mold golden sine algorithm (SMGSA), was introduced in this paper. This strategy adeptly identifies and tracks the global maximum power point. The efficacy of the SMGSA algorithm was assessed through six test functions from IEEE CEC 2020. A comparative analysis underscored its superior performance in both convergence speed and accuracy. A simulation model for MPPT was developed in MATLAB/Simulink. Within this model, various algorithms such as particle swarm optimization (PSO), tuna swarm optimization (TSO), slime mold algorithm (SMA), and SMGSA were examined. Comparative simulations revealed that the SMGSA-based MPPT strategy showcased expedited convergence speed and heightened accuracy under scenarios of uniform irradiance intensity, partial static shading, and dynamic shading. Consequently, the implementation of an SMGSA-based MPPT system can notably enhance the power generation efficiency of photovoltaic arrays under PSCs. [ABSTRACT FROM AUTHOR]

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The design of an effective fuzzy maximum power point tracking controller plays a crucial aspect in enhancing the photovoltaic system’s efficiency. This article aims to design and compare the performance of symmetric and asymmetric types of fuzzy controllers’ maximum power point tracking algorithms. Depending on the BP SX150S module’ power-voltage attributes at standard technical conditions, the input membership function parameters are derived. Moreover, the effect of fuzzy memberships’ quantity is also examined in this article. Where Five and seven triangular memberships are used. For the simulation, MATLAB is used to assess the effectiveness of the fuzzy controllers. Simulation results show that the asymmetric controller outperforms the symmetric type in terms of transient and steady-state tracking for different numbers of membership functions. Specifically, when employed with 5-triangle memberships, the asymmetric controller outperforms the symmetrical controller in terms of rise time, tracking precision, and energy output, respectively, by 83%, 0.06%, and 14.14%. While, the rise time, tracking precision, and energy yield of 7-triangle memberships are all improved by 86.7%, 0.04%, and 14.78%, respectively. Using asymmetric type, 7-triangle memberships enhance the rise time and harvested energy by around 18.2% and 0.082%, respectively. Overall, the most effective tracking technique for enhancing the photovoltaic system’s efficiency is the asymmetric type, independent of the quantity of memberships. [ABSTRACT FROM AUTHOR]

Water pumping systems are crucial for extracting water from deep wells. However, electricity shortages and high fuel prices significantly impact the efficiency and reliability of these systems. Therefore, renewable energy sources have gained more attention as alternatives to fossil fuels. Photovoltaic (PV) energy-based pumping systems, in particular, are becoming popular, especially in rural areas where grid connections are often unavailable. Several factors influence the performance of photovoltaic water pumping systems (PVWPS), including solar irradiance, temperature, system design, maintenance, and pumping load. To ensure optimal performance under these varying conditions, two controllers are crucial. The first controller is the Maximum Power Point Tracking (MPPT) controller, designed to maximize power extraction from the PV panels under varying environmental conditions (in particular, solar radiation and temperature). The second controller regulates the speed and torque of the induction motor (IM) which drives the pump responsible for water extraction. Therefore, to improve the performance of these controllers under different conditions. This review paper first examines widely used soft computing methods, providing a detailed description of each. These methods are then applied to both the MPPT and the IM controllers, offering valuable insights for researchers looking to develop advanced PVWPS control configurations for future applications.

This paper focuses on modeling, control and power quality improvement of a microgrid connected system. This last was designed as a multi-converter system with Wind Turbine driven Permanent Magnet Synchronous Generator, and lithium ion Battery Storage Energy System. These sources are connected by a continuous bus to a nonlinear load through a DC /AC converter and three-phases multi-functional voltage source inverter. Where, wind systems are considered as primary power resource, and the grid is used for the effect of consumption of the overage power available from sources, when the battery has been fully charged. Multi Functional Voltage Source Inverter is used to ameliorate the performance of the proposed system, guaranteeing both reactive power and harmonic compensation. Moreover an intelligent control by fuzzy logic control algorithm are managed In order to extract the maximum power from wind turbine, to guarantee an effective storage management, this about the DC side. For the AC side a direct power control strategy with fuzzy logic algorithm have been used. The simulation of the system solution is carried out in Matlab/Simulink. The obtained simulation results show that the technique of fuzzy logic furnish the best solution in term of robustness, optimization performance, low THD and fast dynamic response.

Today, renewable energy systems like photovoltaic system are widely used in various applications. Among the different types of microgrids, hybrid microgrids are the most used type, therefore, inverters should be used to exchange power between DC and AC sides. According to the existing economic issues, extracting the maximum possible power from these systems are an important issue. This paper presents a new neuro-fuzzy controller for achieving maximum power point tracking (MPPT) in a grid-connected PV system under partially shaded conditions. This controller uses the Gravity Search Algorithm (GSA) to track the global maximum power point (GMPP) of the presented grid-connected PV system. The method controls the grid-connected inverter at the desired voltage to achieve maximum power after receiving its required specifications from the system. The Matlab/Simulink software is used to evaluate the performance of the proposed method. The results show that the proposed method can track the maximum power point under uniform and partial shading conditions with high speed and accuracy. Specifically, the proposed algorithm improves the tracking speed and increases the power output compared to traditional methods. The neuro-fuzzy controller's adaptive capabilities allow it to respond efficiently to dynamic changes in shading, ensuring stable and optimal power output. These advantages make the proposed method a significant improvement over existing MPPT techniques.