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Increasing energy efficiency in manufacturing has significant environmental and cost benefits. Turning on or off a machine dynamically while considering the production rate requirements can offer substantial energy savings. In this work, we examine the optimal policies to control production and turn on and off a machine that operates in working, idle, off, and warmup modes for the case where demand inter-arrival, production, and warmup times have phase-type distributions. The optimal control problem that minimises the expected costs associated with the energy usage in different energy modes and the inventory and backlog costs is solved using a linear program associated with the underlying Markov Decision Process. We also present a matrix-geometric method to evaluate the steady-state performance of the system under a given threshold control policy. We show that when the inter-arrival time distribution is not exponential, the optimal control policy depends on both the current phase of the inter-arrival time and inventory position. The phase-dependent policy implemented by estimating the current phase based on the time elapsed since the last arrival yields a buffer- and time-based policy to control the energy mode and production. We show that policies that only use the inventory position information can be effective if the control parameters are chosen appropriately. However, the control policies that use both the inventory and time information further improve the performance. [ABSTRACT FROM AUTHOR]

The significance of evaluating the design quality of Maximum Power Point Tracking (MPPT) controllers in photovoltaic systems lies in ensuring efficient and stable operation under various environmental conditions. By assessing the controller's tracking efficiency, dynamic response speed, steady-state error, and disturbance rejection capability, the system's energy output can be optimized, equipment lifespan extended, operating costs reduced, and overall system reliability and performance improved, thereby maximizing solar energy utilization efficiency. The quality evaluation of MPPT controller design for photovoltaic systems is framed as a multiple-attribute group decision-making (MAGDM) problem. Recently, the Exponential TODIM (ExpTODIM) approach has been introduced to address this type of decision-making. Single-valued neutrosophic sets (SVNSs) are employed as a decision-making tool to represent fuzzy data during the quality evaluation process of MPPT controller design in photovoltaic systems. In this study, a method combining the single- valued neutrosophic number and Exponential TODIM (SVNN-Com-ExpTODIM), based on the SVNN Hamming distance (SVNNHD) and SVNN Euclidean distance (SVNNED), is proposed to solve the MAGDM problem under SVNSs. Finally, a numerical study is conducted to demonstrate the effectiveness of the SVNN-Com-ExpTODIM approach for the quality evaluation of MPPT controller design in photovoltaic systems, supported by comparative analysis. [ABSTRACT FROM AUTHOR]

In view of the sudden grid voltage distortions, such as voltage sags and unbalances, that may occur in photovoltaic (PV) grid-connected systems under local shading conditions, this paper proposes a control strategy integrating a linear active disturbance rejection controller (LADRC)-based virtual synchronous generator (VSG) and an active disturbance rejection controller (ADRC)-based dynamic voltage restorer (DVR). To enhance the stability and response speed of the PV inverter system, a novel LADRC-based voltage–current dual closed-loop control strategy with pre-synchronization is designed, ensuring stable operation of the inverter and load. To address the overshooting issues found in traditional PI control under local shading, the ADRC-based DVR compensates for PV system voltage fluctuations, achieving rapid voltage distortion compensation and ensuring grid-connected system safety. Simulink experiments verify the feasibility and effectiveness of the proposed control strategy in improving transient voltage quality in PV systems affected by local shading. The total harmonic distortion rates of voltage and current are both less than 0.5%, which significantly improves the performance compared to existing research. [ABSTRACT FROM AUTHOR]

To address the issue of power utilization system redundancy in methods focusing solely on either module solar-tracking or electrical maximum power point tracking (MPPT) to enhance photovoltaic (PV) generation efficiency, the integration of PV module solar-tracking with inverter maximum power tracking is proposed to streamline the system. Combined with cost–performance analyses of single-axis and dual-axis solar-tracking, the surface characteristic relationship between the single-axis azimuth change of the module and the output voltage and output power is studied, and the output characteristic surface of the PV system is constructed. In this paper, a maximum power tracking algorithm without a position sensor on the output characteristic surface is designed, requiring only the acquisition of electrical parameters from the PV modules. Experimental comparison and analysis show that the algorithm effectively combines the azimuth tracking and the electrical maximum power tracking algorithm without a position sensor, and realizes the maximum power output of a single PV module. Under the condition of simulation settings, the output power of module is increased by 14.97–21.91%, and the daily power generation is increased by 20.42% on average under varying experimental weather conditions, providing a new idea for the effective utilization of solar energy by PV modules. [ABSTRACT FROM AUTHOR]

Solar energy as a limitless, clean, and cost-effective energy source could be converted to electrical power by photovoltaic cells. In this paper, a photovoltaic power conditioner, which is connected to the low voltage grid via an LCL filter, is presented. This power conditioning system supplies high-quality current and has high stability against disturbances. Since the generated PV voltage is lower than the grid voltage, a DC-DC converter is utilized. Also, DC voltage is also converted to AC by an LCL-type inverter. To ensure the resonance elimination yielded from the LCL filter, inverter and grid side currents, a double feedback strategy is employed. By considering resonance damping, stability margin, and the grid-connected requirements, a systematic design procedure for the suggested strategy is investigated to ensure the system stability in the low-voltage network. In comparison with the conventional strategies, the suggested strategy gives suitable resonance damping capability, higher marginal stability, and better grid-connected properties. Simulation results of the PV power conditioning system illustrate the accurate performance of the control system, suitable tracking of the PV array maximum power point, and high-quality current injection into the grid. [ABSTRACT FROM AUTHOR]

Due to the high dependency of photovoltaic (PV) solar cell's output on solar irradiance and, the ambient temperature. Maximum power point tracking (MPPT) algorithms are used extensively to operate the system at its full potentials. Moreover, being installed in outdoor spaces, PV modules are inevitably subjected to partial shading conditions, where different parts of the system are receiving different amounts of solar irradiance. In case of occurrence of partial shading conditions on a PV module that is equipped with bypass diodes, the power-voltage (P-V) curve will have multiple peaks. This multi-peak curve requires using an advanced algorithm which track the global maximum power point (GMPP) instead of being deceived and trapped in a local maximum power point. In this paper, the flamingo search algorithm (FSA) is adapted for GMPP tracking for a PV system under partial shading conditions. The FSA algorithm fetch for the GMPP by reading the PV panel power and setting accordingly the duty cycle of the buck converter. To investigate model validity, simulation is performed using the MATLAB/Simulink platform and results demonstrate good tracking performance and fast response that prove the robustness of the system against rapid variations in solar irradiance levels. [ABSTRACT FROM AUTHOR]

The limits of direct current (DC) injection and output current distortion of grid-connected photovoltaic (PV) inverters are specified in the IEEE 1547-2018 standard. The standard prescribes limits of output current harmonics, but the input voltage and power at which output current distortion is measured are not specified. This manuscript presents the results of DC injection and output current distortion measurements for three commercial single-phase PV inverters, with 3 kVA, 3.3 kVA, and 6 kVA rated power. During the measurements, the inverters are powered by a programmable DC source that emulates the power voltage characteristic of a PV array, providing different input conditions. In addition to steady-state measurements at constant input voltage and power, the change in the output current spectrum over time during the maximum power point tracking (MPPT) is also measured. The results show that the output current distortion depends on the input voltage and power. Moreover, the current distortion of some of the tested inverters exceeds the limits specified by the standard in some cases. The presented results suggest that further research on the dependence of the output current distortion from PV inverters on their input power and voltage is needed. [ABSTRACT FROM AUTHOR]

In response to the uncertainty of renewable energy output and the fluctuation of load, this paper proposes a hybrid energy storage management strategy based on the State of Charge (SOC) to smooth power fluctuations and thereby improve the power quality of photovoltaic energy storage DC microgrids. Firstly, a hybrid algorithm for power tracking control is formed by incorporating the Particle Swarm Optimization (PSO) algorithm into the variable step-size Incremental Conductance (INC) method, thereby optimizing the maximum power point tracking control system of the photovoltaic system. Then, a first-order filter is employed for the initial allocation of demand power. Taking the SOC of supercapacitors and energy storage batteries as a reference, a secondary power allocation energy management strategy based on rule-based control is proposed to ensure the service life and application safety of the hybrid energy storage system. Finally, simulation experiments are conducted in MATLAB/Simulink 23.2 (R2023b). The results indicate that the proposed energy management strategy can maintain the SOC of the hybrid energy storage system at a reasonable level and effectively smooth DC bus voltage fluctuations. [ABSTRACT FROM AUTHOR]

This manuscript aims to increase the utilization of solar energy, which is both environmentally friendly and easily accessible, to satisfy the energy needs of developing countries. In order to achieve this goal, maximum power generation should be provided from photovoltaic panels. Several maximum power point tracking (MPPT) methods are utilized for maximum power generation in photovoltaic panel systems under different weather conditions. In this paper, a novel intelligent hybrid fuzzy logic-controlled maximum power point tracking algorithm founded on the perturb and observe (PO) algorithm is presented. The proposed fuzzy logic controller algorithm and the incremental conductivity maximum power point tracking algorithm were simulated in a MATLAB(2018b version)/Simulink environment and evaluated by comparing the results. Four Sharp ND-F4Q295 solar panels, two in series and two in parallel, were used for the simulation. In this study, the voltage ripple of the proposed hybrid method was measured at 1% compared to the classical incremental conductivity method, while it was 8.6% in the IncCon method. Similarly, the current ripple was 1.08% in the proposed hybrid FLC method, while the current ripple was 9.27% in the IncCon method. It is observed that the proposed smart method stabilizes the system voltage faster, at 25 ms, in the event of sudden weather changes. [ABSTRACT FROM AUTHOR]

This paper investigates the emerging field of grid-connected wind-powered pumping kite generator system (PKGS), focusing on the challenges associated with the generator/motor speed control. Conventional use of proportional–integral (PI) controllers faces difficulties in meeting requirements for dynamic response, tracking performance, stability, and disturbance rejection encountered in this technology, notably the periodical variation in the rotational speed reference in maximum power point tracking in generation phases and the dynamic response for the step reference in transient ones. To overcome these limitations, a model-free controller (MFC) approach is introduced, also known as intelligent PID controllers. Unlike traditional methods, MFC does not rely on a control model of the system and adapts to uncertainties and disturbances through online estimation based on the system's input–output behavior. To further improve the control performances, a tracking differentiator based on a nonlinear hyperbolic tangent function is integrated in the MFC. The effectiveness of the proposed strategy is proved through simulations in MATLAB/Simulink. The results highlight the superior performances of the proposed MFC approach in terms of speed control accuracy, response time, and robustness. [ABSTRACT FROM AUTHOR]

Introduction: Photovoltaic systems offer immense potential as a future energy source, yet maximizing their efficiency presents challenges, notably in achieving optimal voltage due to their nonlinear behavior. Operating current and voltage fluctuations, driven by temperature and radiation changes, significantly impact power output. Traditional Maximum Power Point Tracking (MPPT) methods struggle to adapt accurately to these dynamic environmental conditions. While Artificial Intelligence (AI) and optimization techniques show promise, their implementation complexity and longer attainment times for Maximum Power Point (MPP) hinder widespread adoption. Method: This paper proposes a hybrid MPPT technique that integrates the Pelican Optimization algorithm (POA) with the Perturb and Observe algorithm (P&O) for a grid-connected photovoltaic system (PV). The proposed technique consists of two loops: PO as the reference point setting loop (inner loop) and POA as a fine-tuning (outer)loop. The combination of inner and outer loops minimizes oscillations by adjusting the perturbation direction and enhancing the convergence speed of the MPPT. Results and Discussion: To validate the efficacy of the proposed MPPT technique for different environmental conditions, a comprehensive comparison is conducted between the proposed hybrid pelican and perturb and observe (HPPO) technique and other MPPT algorithms. The proposed technique has optimized PV and grid outputs with an MPPT efficiency of 99%, best tracking speed, and total harmonic distortion (THD) for all conditions below 5% agree with IEEE 519 standards. [ABSTRACT FROM AUTHOR]

Health monitoring and analysis of photovoltaic (PV) systems are critical for optimizing energy efficiency, improving reliability, and extending the operational lifespan of PV power plants. Effective fault detection and monitoring are vital for ensuring the proper functioning and maintenance of these systems. PV power plants operating under fault conditions show significant deviations in current-voltage (I-V) characteristics compared to those under normal conditions. This paper introduces a diagnostic methodology for photovoltaic panels using I-V curves, enhanced by new techniques combining optimization and classification-based artificial intelligence. The research is organized into two key sections. The first section outlines the implementation of a DC/DC buck-boost converter, which is designed to extract and display real-time data from the PV system based on actual (I-V) measurements. The second section focuses on the comprehensive processing of the experimental dataset, where the Harris Hawks Optimization (HHO) algorithm is combined with machine learning methods to identify the most critical features. The HHO algorithm is combined with an advanced machine learning model, XGBoost, to accurately detect faults within the PV system. The proposed HHO-XGBoost algorithm achieves an impressive accuracy of 99.49%, outperforming other classification-based artificial intelligence methods in fault detection. In validation and comparison with previous approaches, the HHO-XGBoost model consistently outperforms established methods such as GADF-ANN, PCA-SVM, PNN, and Fuzzy Logic, achieving an overall accuracy of 98.48%. This outstanding performance confirms the model's effectiveness in accurately diagnosing PV system conditions, further validating its robustness and reliability in fault detection and classification. [ABSTRACT FROM AUTHOR]

Kω² control, also called torque control, is a popular method for maximizing wind turbine power. For hydrostatic wind turbines, torque control becomes pressure control with pc = K′ω² because pressure is proportional to torque. Inverse Kω² control is an alternative approach using rotor speed control with ωc = (p/K′)1/2. This work analyzes the dynamics of hydrostatic wind turbines using forward and inverse Kω² control with P-, proportional derivative, proportional integral, and proportional integral derivative-control for feedback. Dimensionless, linearized models are used. Analysis shows that the mechanical rotor dynamics are much slower than the hydrostatic transmission dynamics and that frictional and leakage losses are negligible. Linear perturbation of the nonlinear model reveals that the closed-loop control is not in Evan's form so that closed-loop poles and zeros both vary with the loop gain. Pole and zero root locus analysis shows how systems responses change with controller gains. Both control approaches require derivative controller action to sufficiently dampen their responses; both are also fundamentally limited in speed of response by a slow stable pole regardless of controller loop gains. Nonlinear system simulation shows that both control approaches track the maximum power point with nearly identical transient behavior and have nearly identical power losses when using suboptimal values of the control law gain, ⁠. Pressure control has a gain margin of infinity and a phase margin of 99.3 deg, while speed control has a gain margin of 22.4 dB and a phase margin of 73.8 deg showing that pressure control is more robust than speed control. Experiments using the power regenerative hydrostatic test stand at the University of Minnesota show that the control approaches have different transient responses but capture comparable power under steady and turbulent conditions. [ABSTRACT FROM AUTHOR]

Bridging Photovoltaic (PV) energy generation with Electric Vehicle (EV) technology is an essential development in the modern quest for energy sustainability and advanced transportation methods. Consequently, this research focuses on enhancing efficient power utilization in PV-based EVs through the application of a novel Modified Boost-Luo DC-DC converter and a Chaotic Genetic Algorithm -based Maximum Power Point Tracking system. The Modified Boost-Luo converter provides a high voltage conversion ratio by incorporating a Voltage Multiplier Cell (VMC) and Three-Winding Coupled Inductor (3WCL) into its circuit configuration. The augmented PV output from Modified Boost-Luo converter is directed to the Brushless DC (BLDC) motor of the EV through a three-phase Voltage Source Inverter (3ΦVSI). Moreover, speed regulation in the BLDC motor is achieved using a Proportional–Integral (PI) controller. A distinctive feature of the proposed system involves the storage of surplus PV-generated power in a battery, interconnected to the DC-link via a bidirectional converter. In instances of PV power unavailability, the BLDC motor seamlessly taps into the stored energy in the battery for continued operation. The proposed approach is evaluated through both MATLAB simulation and laboratory prototype implementation, showcasing its potential to advance EV technology. [ABSTRACT FROM AUTHOR]

The emergence of DC microgrids has heightened interest in optimizing photovoltaic (PV) panels and storage systems to enhance energy management, minimize power losses, and bolster system stability. This study addresses a critical gap in current methodologies by proposing the Aquila algorithm, an optimization technique specifically designed for the unique characteristics of DC power distribution systems. Unlike traditional optimization methods that primarily cater to AC microgrids, the Aquila algorithm uniquely focuses on the intricate dynamics of DC microgrids, enabling it to determine optimal locations and sizes for PV panels and battery storage systems effectively. The efficiency of the proposed PV system optimization was validated using a MATLAB/Simulink model, with simulations executed under controlled settings to ensure accurate results. Notably, the system achieved a rapid settling time of just 0.2 seconds during load fluctuations, with the Aquila algorithm mitigating temporary imbalances and deviations in key parameters like grid power, battery power, and load power. Simulation results revealed that the PV system operated under a solar irradiance level of 700 W/m², generating a controlled power output of 10 kW with a corresponding PV output of 8 kW. The algorithm demonstrated a significant reduction in system oscillations and fluctuations, ensuring consistent performance even when sudden load changes occurred. At a critical moment (t = 0.025 s), the Aquila algorithm-maintained system stability despite a drop in solar generation, thus emphasizing its reliability in adapting to varying solar conditions. Overall, the findings indicate that the Aquila algorithm significantly enhances energy management within PV-connected DC microgrids, optimizing battery performance and improving overall system reliability and efficiency. This research presents a promising approach for optimizing battery-connected PV systems, ultimately contributing to the advancement of sustainable energy solutions within DC microgrid frameworks. [ABSTRACT FROM AUTHOR]

This paper presents an efficiency optimization method for laser wireless power transmission (LWPT) system, focusing on the coordination and control of multiple laser diodes. A distributed laser wireless power transmission (D-LWPT) system is proposed, which includes multiple independent and parallel power transmission chains. The system has the characteristics of power scalability, redundancy, and control flexibility. The efficiency characteristics of each key component in the LWPT system are discussed. Due to the internal losses of the laser, the transmission efficiency is also affected by the transmission power. For distributed architecture, its flexibility allows for the rational allocation of transmission power. To achieve optimal efficiency, a central scheduling controller is designed to regulate the current of LDs. A swarm intelligence-based optimization algorithm is used to determine the optimal operating current. This significantly improves the system's efficiency and ensures real-time control. Experimental results validate the effectiveness of the proposed techniques. The DC to DC efficiency of the power transmission chain can reach over 14%, and the photovoltaic array can output a maximum power of over 130 W. The impact of beam combination on the efficiency and output power of PV arrays is less than 3%, indicating that the distributed structure does not affect system performance. The experimental results show that the proposed efficiency optimization method has excellent power following performance (algorithm execution time < 10 ms) and effective efficiency optimization performance. Under light load conditions, the LDs' efficiency is optimized from 27.5% to 45.0%, and under medium load conditions, it is optimized from 41.5% to 44.5%. This distributed structure and efficiency optimization method provide a solution for improving the performance of LWPT systems. [ABSTRACT FROM AUTHOR]

Uneven power distribution, transient voltage, and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes. In response to these issues, this paper proposes a grid-connected/island switching control strategy for photovoltaic storage hybrid inverters based on the modified chimpanzee optimization algorithm. The proposed strategy incorporates coupling compensation and power differentiation elements based on the traditional droop control. Then, it combines the angular frequency and voltage amplitude adjustments provided by the phase-locked loop-free pre-synchronization control strategy. Precise pre-synchronization is achieved by regulating the virtual current to zero and aligning the photovoltaic storage hybrid inverter with the grid voltage. Additionally, two novel operators, learning and emotional behaviors are introduced to enhance the optimization precision of the chimpanzee algorithm. These operators ensure high-precision and high-reliability optimization of the droop control parameters for photovoltaic storage hybrid inverters. A Simulink model was constructed for simulation analysis, which validated the optimized control strategy's ability to evenly distribute power under load transients. This strategy effectively mitigated transient voltage and current surges during mode transitions. Consequently, seamless and efficient switching between grid-connected and island modes was achieved for the photovoltaic storage hybrid inverter. The enhanced energy utilization efficiency, in turn, offers robust technical support for grid stability. [ABSTRACT FROM AUTHOR]

Introduction. Photovoltaic (PV) systems play a crucial role in converting solar energy into electricity, but their efficiency is highly influenced by environmental factors such as irradiance and temperature. To optimize power output, Maximum Power Point Tracking (MPPT) techniques are used. This paper introduces a novel approach utilizing a Type-2 Fuzzy Logic Controller (T2FLC) for MPPT in PV systems. The novelty of the proposed work lies in the development of a T2FLC that offers enhanced adaptability by managing a higher degree of uncertainty, we introduce an original method that calculates the error between the output voltage and a dynamically derived reference voltage, which is obtained using a mathematical equation. This reference voltage adjusts in real-time based on changes in environmental conditions, allowing for more precise and stable MPPT performance. The purpose of this paper is to design and validate the effectiveness of a T2FLC-based MPPT technique for PV systems. This approach seeks to enhance power extraction efficiency in response to dynamic environmental factors such as changing irradiance and temperature. The methods used in this study involve the implementation of T2FLC to adjust the duty cycle of a DC-DC converter for continuous and precise MPPT. The system was simulated under various environmental conditions, comparing the performance of T2FLC against T1FLC. The results show that the T2FLC MPPT system significantly outperforms traditional methods in terms of tracking speed, stability, and power efficiency. T2FLC demonstrated faster convergence to the MPP, reduced oscillations, and higher accuracy in rapidly changing environmental conditions. The findings of this study confirm the practical value of T2FLC logic in improving the efficiency and stability of PV systems, making it a promising solution for enhancing renewable energy technologies. [ABSTRACT FROM AUTHOR]

This paper introduces a novel maximum power point tracking (MPPT) controller for photovoltaic (PV) systems that leverages the strengths of both metaheuristic and heuristic methods. Classical MPPT algorithms, such as incremental conductance (IC) and perturb and observe (P&O), are widely used but often struggle with instability, oscillations near the steady state, and slow convergence, particularly under fluctuating weather conditions such as static partial shading conditions (PSCs). To address these challenges, we propose a hybrid MPPT approach that combines the war strategy optimization (WSO) algorithm with the IC method, termed war strategy optimization-incremental conductance (WSO-IC). The performance of the WSO-IC algorithm is rigorously compared against traditional IC, P&O, and standalone WSO techniques. Simulation results validate that the WSO-IC approach provides superior MPPT with faster convergence and high efficiency. The results obtained in SIMULINK demonstrate that the proposed method can achieve efficiencies exceeding 99%, even under static partial shading conditions. [ABSTRACT FROM AUTHOR]

The study applies artificial intelligence algorithms to the field of photovoltaic power generation. After exploring the construction of a photovoltaic power generation system, the simulation system of the photovoltaic array is constructed in the Matlab/Simulink platform, the MPPT algorithm is utilized to ensure the maximum output power state of the photovoltaic system, the step-up Boost circuit is added, and the maximum power point is tracked by selecting the perturbation observation method. Through simulation experiments, the output characteristics of the PV cells and the maximum power point tracking performance of the PV power generation system constructed in this paper were detected. Then, the maximum power tracking MPPT algorithm is optimized by the particle swarm algorithm (PSO) to construct the optimal control strategy for PV power generation using the particle swarm algorithm. The PSO algorithm is compared with the PSO-MPPT algorithm to maximize power extraction and track performance, in order to check the optimization effect. When the intensity of sunlight increases, the short-circuit current of the PV cell increases and the maximum power of the cell increases. When the external temperature increases, the short-circuit current of the PV cell decreases very little, and the maximum power of the cell decreases. The extraction efficiency of the PSO-MPPT algorithm in the four modes is 99.78%, 99.60%, 99.59%, and 99.54%, which is significantly better than that of the PSO algorithm, and the PSO-MPPT algorithm has a better tracking ability. [ABSTRACT FROM AUTHOR]

As a representative of multi-functional engineering machinery, the excavator is irreplaceable in the field of engineering construction. To autonomously control the excavator bucket, it is essential to control the position of the bucket hydraulic cylinder. As a consequence of the problem of position tracking control spawned from external disturbance and other factors in the self-mining servo system of excavators, a strategy of sliding mode backstepping control based on the particle swarm optimization algorithm and neural network disturbance observer (PSO-NNDO-SMBC) was recommended accordingly. Meanwhile, the complex disturbance was estimated online and compensated for by the system control input by the universal approximation property of the neural network disturbance observer (NNDO). Afterwards, the uncertainty of control parameters was optimized by the particle swarm optimization algorithm (PSO) and was fed back to the controller parameter input end. Afterwards, a co-simulation model of MATLAB/Simulink (MATLAB2023b) and AMESim (Simcenter Amesim 2304) was established for simulation analysis, and a test bench was set up for comparison and verification. As proven by the experimental results, PSO-NNDO-SMBC possessed strong anti-interference ability. In contrast to the sliding mode backstepping control based on the particle swarm optimization algorithm (PSO-SMBC), the maximum displacement tracking error was lowered by 50.5%. Furthermore, in comparison with the Proportional-Integral-Derivative (PID), the maximum displacement tracking error was decreased by 75.2%, which tremendously optimized the control accuracy of excavator bucket displacement tracking. [ABSTRACT FROM AUTHOR]

Abstract The dependency of photovoltaic (PV) systems‐based generation systems on constantly varying temperatures and incident sunlight affect the non‐linear behaviour of PV panels. Therefore, tracking the maximum power output of the PV panels for efficient utilization becomes a challenge, resulting in power losses and the creation of intense heating spots in the shaded areas of the PV modules when the PV modules receive varying degrees of insolation, that is, under partial shading conditions (PSCs). The inclusion of bypass diodes in parallel to each PV module mitigates this problem to an extent but leads to the formation of several peaks in the P‐V characteristics. As a result, maximum power point tracking (MPPT) to deliver maximum power at the load becomes a limitation for conventional optimization algorithms as they are normally based on hill climbing algorithm and get stuck at local maxima of the P‐V curve. Therefore, metaheuristic algorithms are used thereby eliminating the possibility of getting trapped at the local optima. However, particle swarm optimization (PSO) suffers from delayed convergence, more iterations to reach the optimal point, and random parameter selection. Hence, this study employs an improved version of PSO called Phasor‐PSO (P‐PSO) in an MPPT controller. The proposed algorithm is parameter‐less which results in reduced computational complexity and thus provides quick decision in achieving the maximum power point (MPP). The hardware‐in‐loop real‐time analysis demonstrates the supremacy of P‐PSO over PSO in faster convergence, higher efficiency, and reduced power losses during tracking under various PSCs. The P‐PSO based MPPT method will find application in grid connected and stand‐alone solar PV system with better efficiency of power transfer.

Abstract This study presents a new Maximum Power Point Tracking (MPPT) approach for solar photovoltaic (PV) systems, combining the Super‐Twisting Algorithm (STA) and Grey Wolf Optimizer (GWO). The STA‐GWO‐MPPT method improves efficiency in dynamic conditions by using STA for control and GWO for parameter optimization, enhancing stability and robustness. Performance evaluation is conducted through MATLAB/Simulink simulations and experimental validation on a small‐scale test bench. Various quantitative metrics, including rise time, settling time, power production, efficiency, root mean square error (RMSE), and standard deviation (STD), are employed for assessment. Results indicate significantly faster convergence speeds for the proposed method compared to conventional MPPT techniques. Specifically, the rise time for the proposed method is 0.0129 seconds, outperforming Fuzzy Logic Control (FLC) (0.2638 seconds) and Grey Wolf Optimizer with Sliding Mode Control (GWO‐SMC) (0.0181 seconds). Additionally, the proposed method exhibits superior tracking efficiency, with an average efficiency of 99.33%, surpassing FLC (96.93%) and GWO‐SMC (99.19%). Moreover, it reduces power fluctuations, with an RMSE of 7.819% and STD of 6.547%, compared to FLC (RMSE: 13.471%, STD: 4.519%) and GWO‐SMC (RMSE: 8.507%, STD: 6.108%). Overall, this study contributes valuable insights into enhancing MPPT efficiency in solar PV systems, with implications for both research and practical applications.

Abstract To mitigate the challenges posed by the non-linear multi-peak power-voltage output characteristics of photovoltaic (PV) systems operating under partial shading conditions, which often lead to suboptimal performance of conventional Maximum Power Point Tracking (MPPT) algorithms, a novel approach was introduced. We introduce the LGWGCA-P&O method, which synergistically combines an modified Great Wall Construction Algorithm (LGWGCA) with the Perturbation and Observation (P&O) technique. The LGWGCA is refined with a positional update mechanism inspired by the Grey Wolf Optimization (GWO) algorithm, optimizing the distribution of solution agents, while a Levy flight strategy is employed to reduce excessive randomness during agent replacement and recombination, thereby accelerating the tracking process. As the algorithm approaches the maximum power point, it seamlessly transitions to the P&O method, leveraging its rapid convergence to ensure precise and efficient power point identification. Experimental results demonstrate that the LGWGCA-P&O method achieves a PV conversion efficiency exceeding 99.96%, significantly minimizing power losses during MPPT. Furthermore, the method enhances convergence speed by approximately 40% compared to traditional GWO-P&O approaches, and it consistently outperforms Particle Swarm Optimization and Cuckoo Search Optimization algorithms under extreme conditions, demonstrating superior computational efficiency and robustness.

Abstract Marine current power is attracting more attention as a renewable energy option. Similar to wind power, marine current power often requires a maximum power point tracking (MPPT) method to optimize power extraction from the free‐flowing water. Research into MPPT methods for marine current power remains limited. Therefore, this paper presents a comprehensive investigation of MPPT methods for marine current power, building upon similar research in wind power. Three methods, namely the optimal tip speed ratio (OTSR), optimal torque (OT), and two variants of the perturb and observe (P&O) method, are explored. Using a simulation model developed for a specific marine current energy converter, where hydrodynamic calculations are coupled with electrical simulations, the study demonstrates that the OTSR method achieves MPPT with a comparably fast convergence time. After a change in water speed, the OTSR method achieves optimal operation within two turbine rotations. Additionally, the P&O methods are shown to achieve MPPT, albeit with a significantly longer convergence time. However, the P&O methods can be more convenient since no model of the system is required, and no water speed measurements are necessary. The proposed implementation of the OT method underperforms but positions the system close to the optimal operational point.

This paper introduces an innovative, adaptive Fractional Open-Circuit Voltage (FOCV) algorithm combined with a robust Improved Model Reference Adaptive Controller (IMRAC) for Maximum Power Point Tracking (MPPT) in standalone photovoltaic (PV) systems. The proposed two-stage control strategy enhances energy efficiency, simplifies system operation, and addresses limitations in conventional MPPT methods, such as slow convergence, high oscillations, and susceptibility to environmental fluctuations. The first stage dynamically estimates the Maximum Power Point (MPP) voltage using a novel adaptive FOCV method, which eliminates the need for irradiance sensors or physical disconnection of PV modules. This stage incorporates a real-time adjustment of the kv factor based on variations in PV power, ensuring precise voltage estimation. In the second stage, the IMRAC controller ensures accurate tracking of the MPP by adapting swiftly to changes in irradiance and temperature, while minimizing ripple and power loss. Validation of the proposed system was carried out using Processor-in-the-Loop (PIL) testing on an Arduino Due microcontroller, showcasing real-world applicability. Comparative analysis with state-of-the-art MPPT controllers, including P&O-PI, InC-SMC, FLC, and VS P&O Backstepping, demonstrates superior tracking efficiency exceeding 99.49% under EN 50,530 standard test conditions. The system also maintains exceptional performance with minimal efficiency loss across a wide range of temperature and irradiance variations. By combining simplicity, robustness, and sustainability, this work establishes a cutting-edge solution for standalone PV systems, paving the way for more efficient and reliable renewable energy applications. [ABSTRACT FROM AUTHOR]

With the rapid advancement of the new energy transformation process, the stability of photovoltaic microgrid output is particularly important. However, current photovoltaic microgrids suffer from unstable output and power fluctuations. To improve the stability and system controllability of photovoltaic microgrid output, this study constructs an optimized grey wolf optimization algorithm. Using the idea of small step perturbation, it is applied to the maximum power point tracking solar controller to construct a maximum power point controller algorithm based on the improved algorithm. Secondly, the algorithm is combined with photovoltaic arrays to construct a maximum tracking point control system for photovoltaic arrays based on the algorithm. Finally, the system is combined with low-pass filtering power allocation and secondary power allocation strategies, as well as a hybrid storage system, to construct a photovoltaic microgrid control model. In the performance comparison analysis of the research algorithm, the average accuracy and average loss value of the algorithm were 98.2% and 0.15, respectively, which were significantly better than the compared algorithms. The performance analysis of the photovoltaic microgrid control model showed that the model could effectively regulate and control the output power of the microgrid under two operating conditions, demonstrating its effectiveness. The above results indicate that The proposed algorithm and the improved algorithm of the PV microgrid control model can not only improve the steady-state tracking accuracy, but also have better dynamic performance and improve the tracking speed. The control strategy can maintain the operational stability of the microgrid system and realize the smooth switching control of each mode, meeting the stability and flexibility requirements of the PV microgrid system. The novelty of this study is that the improved Grey Wolf optimization algorithm enhances the global search ability by introducing the random jump mechanism of Levy flight algorithm and the combination of particle swarm optimization algorithm and Grey Wolf optimization algorithm to avoid falling into the local optimal. The randomness and ergodicity of Levy flight algorithm enable the hybrid algorithm to quickly adapt to the changes of light intensity and environmental conditions, and maintain the efficient operation of MPPT. Moreover, particle swarm optimization has strong local search ability, and gray Wolf optimization improves local search accuracy. The combination of the two improves local search accuracy. By combining the characteristics of Levy flight algorithm, the parameters of PSO and GWO algorithm, such as inertia weight and convergence factor, are dynamically adjusted to adapt to different working conditions of MPPT. The optimal solution is output as the optimal strategy of MPPT through collaboration. The potential practical impact is that the improved MPPT control strategy can track the maximum power point more effectively, improve the efficiency and stability of the photovoltaic power generation system, reduce energy waste by improving the tracking accuracy and convergence speed of the photovoltaic system, and improve the robustness of the system. [ABSTRACT FROM AUTHOR]

Unmanned driving technology for agricultural vehicles is pivotal in advancing modern agriculture towards precision, intelligence, and sustainability. Among agricultural machinery, autonomous driving technology for agricultural tractor-trailer vehicles (ATTVs) has garnered significant attention in recent years. ATTVs comprise large implements connected to tractors through hitch points and are extensively utilized in agricultural production. The primary objective of current research focus on autonomous driving technology for tractor-trailers is to enable the tractor to follow a reference path while adhering to constraints imposed by the trailer, which may not always align with agronomic requirements. To address the challenge of path tracking for ATTVs, this paper proposes a fuzzy back-stepping path tracking controller based on the kinematic model of ATTVs. Initially, the path tracking kinematic error model was established with the trailer as the positioning center in the Frenet coordinate system using the velocity decomposition method. Then, the path tracking controller was designed using the back-stepping algorithm to calculate the target front wheel steering angle of the tractor. The gain coefficient was adaptively adjusted through a fuzzy algorithm. Co-simulation and experiments were conducted using MATLAB/Simulink/CarSim and a physical platform, respectively. Simulation results indicated that the proposed controller reduced the trailer's online time by 36.33%. When following a curved path, the trailer's tracking error was significantly lower than that of the Stanley controller designed for a single tractor. In actual experiments, while tracking a U-turn path, the proposed controller reduced the average absolute value of the trailer's path tracking lateral error by 65.27% and the maximum lateral error by 87.54%. The mean absolute error (MAE) values for lateral error and heading error were 0.010 and 0.016, respectively, while the integral of absolute error (IAE) values were 1.989 and 2.916, respectively. The proposed fuzzy back-stepping path tracking controller effectively addresses the practical challenges of ATTV path tracking. By prioritizing the path tracking performance of the trailer, the quality and efficiency of ATTVs during field operations are enhanced. The significant reduction in tracking errors and online time demonstrates the effectiveness of the proposed controller in improving the accuracy and efficiency of ATTVs. [ABSTRACT FROM AUTHOR]

This paper presents an inductor current-based maximum power point tracking (IC-MPPT) strategy and a single-inductor multi-input single-output (SI-MISO) structure with energy storage battery for distributed photovoltaic (PV) systems. In this study framework, the duty cycle of each PV channel can be controlled independently based on the presented IC-MPPT strategy, and the components/sensors costs are reduced through the presented SI-MISO PV system structure. In addition, a model predictive control (MPC) method is presented to regulate DC bus voltage, by controlling the bidirectional converter in the battery circuit. The presented control strategies have been rigorously derived and experimentally validated, and the experimental results demonstrate that each PV module can rapidly and efficiently track to the maximum power point in less than 0.016 s, while the bus voltage is stabilized near the set value, with an overshoot of less than 2.6%. [ABSTRACT FROM AUTHOR]

Solar electricity has become one of the most important renewable power sources due to rapid developments in the manufacturing of photovoltaic (PV) cells and power electronic techniques as well as the consciousness of environmental protection. In general, PV panels are connected to DC-DC converters and/or DC-AC inverters to implement the maximum power point tracking algorithm and to fulfill the load requirements. Thus, power conversion efficiency and power density need to be taken into consideration when designing PV systems. Three-port and partial power conversion technologies are proposed to improve the efficiency of a whole PV system and its power density. In this paper, three types of three-port converters (TPCs), including fully isolated, partly isolated, and non-isolated TPCs, are studied with detailed discussions of advantages, disadvantages, and comparisons. In addition, based on partial power conversion technologies, partial power two-port and three-port topologies are analyzed in detail. Their efficiency and power density can be further improved by the combination of three-port and partial power conversion technologies. Moreover, comparisons among seven different types of distributed PV systems are presented with their advantages and disadvantages. Compared to distributed PV systems without energy storage, distributed PV systems with hybridization of energy storage and with partial power regulation can use solar energy in a more efficient way. [ABSTRACT FROM AUTHOR]

This paper proposes a robust maximum power point tracking algorithm based on a super twisting sliding modes controller. The underlying idea is solving the classical trajectory tracking control problem where the maximum power point defines the reference path. This trajectory is determined through two approaches: a) using the simplest linear and multiple regression models that can be constructed from the solar irradiance and temperature, and b) considering optimum operating parameters derived from the photovoltaic system's characteristics. The proposal is compared with the classical methods Perturbation and Observation and Incremental Conductance, as well as with two recently reported hybrid algorithm based on Artificial Neural Networks: one uses the Levenberg-Marquardt algorithm and the other applies Bayesian regularization to generate current and voltage references, respectively. Both use a Proportional-Integral-Derivative controller to solve the maximum power point tracking problem. Numerical simulations confirm the effectiveness of the method proposed in this work regarding convergence time, power efficiency, and amplitude of oscillations. Furthermore, it has been shown that, although no significant differences in the system response are observed with respect to the Artificial Neural Networks-based methods, the proposed algorithm with a reference generated through a linear regression constitutes a low-complexity solution that does not require a temperature sensor to efficiently solve the maximum power point tracking problem. [ABSTRACT FROM AUTHOR]

The dependency of photovoltaic (PV) systems‐based generation systems on constantly varying temperatures and incident sunlight affect the non‐linear behaviour of PV panels. Therefore, tracking the maximum power output of the PV panels for efficient utilization becomes a challenge, resulting in power losses and the creation of intense heating spots in the shaded areas of the PV modules when the PV modules receive varying degrees of insolation, that is, under partial shading conditions (PSCs). The inclusion of bypass diodes in parallel to each PV module mitigates this problem to an extent but leads to the formation of several peaks in the P‐V characteristics. As a result, maximum power point tracking (MPPT) to deliver maximum power at the load becomes a limitation for conventional optimization algorithms as they are normally based on hill climbing algorithm and get stuck at local maxima of the P‐V curve. Therefore, metaheuristic algorithms are used thereby eliminating the possibility of getting trapped at the local optima. However, particle swarm optimization (PSO) suffers from delayed convergence, more iterations to reach the optimal point, and random parameter selection. Hence, this study employs an improved version of PSO called Phasor‐PSO (P‐PSO) in an MPPT controller. The proposed algorithm is parameter‐less which results in reduced computational complexity and thus provides quick decision in achieving the maximum power point (MPP). The hardware‐in‐loop real‐time analysis demonstrates the supremacy of P‐PSO over PSO in faster convergence, higher efficiency, and reduced power losses during tracking under various PSCs. The P‐PSO based MPPT method will find application in grid connected and stand‐alone solar PV system with better efficiency of power transfer. [ABSTRACT FROM AUTHOR]

The maximum power point tracker (MPPT) is essential for optimizing the performance of PV systems, especially off-grid systems in remote areas. Although incremental conductance (InC) is the most commonly used of MPPT strategies, it has drawbacks, including trade-off between fluctuation and convergence speed, as well as the occurrence of drift phenomenon. To address these issues, this paper proposes an enhanced InC MPPT strategy. The latter utilizes a new adaptive step magnitude based on an absolute value of the variation in power, voltage, and current of the PV array to automatically adjust the perturbation step with respect to the weather variation circumstances. In addition, it includes a drift avoidance mechanism to further increase the robustness of the tracking. To investigate the performance of the amended InC MPPT strategy, it is integrated into an off-grid PV system, which feeds batteries as loads. First, it is examined by simulation using MATLAB/Simulink software under sudden and ramp changes in solar intensity and ambient temperature. Then it is performed using an experimental test bench using a dSPACE DS1104 card. The results obtained during simulation and practical investigations verify the highest effectiveness of the amended InC strategy compared to the original one in terms of tracking precision, fluctuation, converging velocity, power loss, and tracking efficiency. Additionally, it shows robust and superior performance compared to recently proposed MPPT techniques, with the lowest tracking time (less than 0.012 s) and the highest average tracking efficiency (99.84%), leading to effective optimization of PV energy. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive centralized energy management algorithm is developed for an islanded bipolar DC microgrid (BDCMG). The proposed microgrid consists of a solar photovoltaic (SPV) system, a wind energy conversion system (WECS), and a battery energy storage system (BESS). The SPV and WECS are operated in maximum power point tracking mode to extract maximum power. The adaptive control algorithm focus on power sharing at a bipolar DC bus based on variations in wind speed, solar irradiance, battery state of charge (SoC), and load. Furthermore, this algorithm maintains voltage balance and voltage regulation on a bipolar DC bus without the use of a communication link and provides effective energy management through coordinated control of renewable energy sources and BESS. The efficacy of the proposed bipolar DC microgrid is simulated and validated on a lab scale prototype to verify its feasibility, and it is observed that the results are found to be satisfactory. [ABSTRACT FROM AUTHOR]

Generalized predictive control (GPC) became one of the most popular and useful control strategies for academic and industry applications. An augmented model is applied to predict the future plant responses. This augmented model can be designed to embed the model of the plant reference, allowing its tracking by the controller according to the internal model principle (IMP). On the other hand, the performance of many controllers can be improved by adding zeros and poles in their structures (e.g., lead and lag compensators). However, according to the authors' research, adding arbitrary poles or zeros to the GPC augmented model has not been explored yet. This paper presents a simple methodology to add arbitrary zeros and poles in the GPC augmented model. A new augmented model state variable is defined. The control law of the proposed approach embeds zeros and poles when zero-pole cancellation is avoided. Simulation results (considering a LCL filter controlled by a single-phase inverter of 500 W and a polynomial reference tracking controller) and experimental tests (using a third-order linear plant controlled by a resonant controller) prove that the proposed approach improves the transient response of different kinds of predictive tracking controllers applied to control different plants (including power electronics applications), without affecting the steady-state tracking capabilities of the control systems. [ABSTRACT FROM AUTHOR]

The central inverter topology presents some advantages such as simplicity, low cost and high conversion efficiency, being the first option for interfacing photovoltaic mini-generation, whose shading and panel orientation studies are evaluated in the project planning phase. When it uses only one power converter, its control structures must ensure synchronization with the grid, tracking the maximum power generation point, appropriate power quality indices, and control of the active and reactive power injected into the grid. This work develops and contributes to mathematical models, the principles of formation of control structures, the decoupling process of the control loops, the treatment of nonlinearities, and the tuning of the controllers of a single-stage photovoltaic system that is integrated into the electrical grid through a three-phase voltage source inverter. Using the parameters and configurations of an actual inverter installed at the power plant CRESP (Reference Center for Solar Energy of Petrolina), mathematical modeling, implementation, and computational simulations were conducted in the time domain using MatLab® software (R2021b). The results of the currents injected into the grid, voltages, active powers, and power factor at the connection point with the grid are presented, analyzed, and compared with real measurement data during one day of operation. [ABSTRACT FROM AUTHOR]

The operation of photovoltaic (PV) panels in partial shade (PS) conditions (PSCs) lowers the quantity of energy generated due to the hot spots that increase the power loss. In this scenario, a maximum power point tracker (MPPT) is necessary to monitor the power–voltage curve, which features multiple local peaks in addition to one global peak (GP). The GP could not be extracted from the panel at PSCs using traditional MPPT techniques such as hill climbing (HC), perturb and observe (P&O), and incremental conductance (IC). Therefore, this paper proposes a new construction of MPPT simulated via a Raspberry Pi 4 controller programed through a recent bald eagle search (BES) algorithm. Because of its substantially enhanced diversification, which aids in avoiding local peaks, the suggested BES can capture the global power of PV during PSCs. The Raspberry Pi 4‐BES MPPT in the recommended hardware adjusts the direct current (DC)–DC boost converter's duty cycle based on its inputs, which are the voltage and current from the PV panel output. Eight shade patterns are studied, and the fetched results through the proposed BES are compared to other programed approaches of skill optimization algorithm (SOA), gray wolf optimizer (GWO), and particle swarm optimization (PSO). Additionally, the proposed tracker's Simulink model is created and evaluated in relation to the experimental hardware. Moreover, the power errors and efficiencies of all considered approaches are calculated in both simulation and experimental analyses. The results revealed that the least errors are 0.0018% and 0.296% obtained through the proposed Raspberry Pi 4‐BES MPPT in simulation and experimental, respectively. Also, in the simulation and experimental setting, the recommended tracker yielded the highest efficiencies of 99.99% and 99.92%, respectively. The suggested Raspberry Pi 4‐BES MPPT is praised for being a useful tracker for monitoring the GP of PV panels at various PSCs. [ABSTRACT FROM AUTHOR]

This article presents a reconfiguration strategy, named the Cheetah Optimization Algorithm (COA), for maximizing power generation in partially shaded solar photovoltaic (PSPV) arrays. The proposed method's originality aims to mitigate the negative impacts of partial shading, improve the output power of the PV system, and prevent thermal failure of shaded solar panels. The strategy combines efficient maximum power point tracking techniques with suitable photovoltaic system design topologies. The COA is done on the MATLAB platform and is analyzed with the existing methods such as the Wild Horse Optimizer (WHO), Salp Swarm Algorithm and Heap-based Optimizer. The outcome shows the superior performance of the COA in achieving the extraction of maximum power compared to the other methods. Additionally, by comparing the two patterns of partial shading, it is shown that the COA extracts more power than the existing methods and gets to the global power value faster. The COA is compared with existing methods, including WHO, SSA, and HBO. In terms of accuracy, the proposed system achieves the highest value of 0.93, surpassing the accuracy of HBO, WHO, and SSA, which stand at 0.8, 0.9, and 0.9, respectively. The specificity, precision and recall are also reflect the superiority of the proposed system with the values of 0.87, 0.9, and 0.86, respectively, outperforming the existing algorithms in most categories. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) system transforms sunlight into electricity. PV cells have non-linear I-V relationships with one point producing the maximum power output from the PV cells. The best efficiency is obtained at the maximum power point (MPP) of PV system. This paper proposes a hybrid MPP control strategy that incorporates both fuzzy logic controller (FLC) and the Perturbation and Observation (P&O) algorithm under various cases of whether conditions. The proposed algorithm is continuously searching for maximum power point and modify it as needed under rapidly changing weather conditions (irradiance and temperature) and be able to perform successful tracking of the MPP under partially shaded conditions. The performance and the power output of the system will be evaluated using simulation under specific weather conditions. The developed controller is implemented in two stages to overcome the drawbacks of the conventional P&O algorithm; the first stage uses fuzzy controller to provide an initial guess for P&O where it combines the speed of FLC approximation with the accuracy of P&O. The second stage uses another fuzzy controller to find a proper step size of P&O to enhance the transient response and reduce the steady-state oscillations. The results show that the efficiency of the proposed remains high under various scenarios; Uniform irradiation, sudden irradiation, partial shading (weak, moderate, and strong). Furthermore, results demonstrated that the proposed hybrid FLC-P&O can effectively improve the accuracy of the conventional P&O algorithm. [ABSTRACT FROM AUTHOR]

Amidst the burgeoning need for sustainable energy solutions worldwide, this article delves into a novel approach that amalgamates a photovoltaic (PV) system with a high-gain interleaved super boost converter, featuring innovative coupled inductors. The overarching goal is to amplify the voltage output of PV system, thereby optimizing power extraction and bolstering overall efficiency. Implementation of an adaptive incremental conductance maximum power point tracking algorithm ensures precise power tracking, while subsequent phases involve the conversion of DC to AC through a 1φ VSI combined with Proportional-Integral controller. This strategic integration aims to achieve seamless grid synchronization and mitigate harmonic distortions. Validation of proposed system is methodically undertaken using MATLAB simulations, revealing commendable performance metrics. With an impressive converter efficiency of 98.83% and a markedly reduced total harmonic distortion of 1.95%, this pioneering amalgamation not only meets the exigency for sustainable energy solutions but also emerges as a promising avenue for advancing dependability and efficacy of PV systems in the sphere of applications related to renewable energy sources. [ABSTRACT FROM AUTHOR]

In this study, the combination of crossover algorithm and particle swarm optimization—crossover algorithm-particle swarm optimization (CS-PSO) algorithm—to optimize photovoltaic hybrid energy storage scheduling, improving global search and convergence speed, is discussed. The new method reduces energy storage costs and energy losses, ensures supply–demand balance and interaction power constraints, and maintains population diversity through cross-search. The CS-PSO algorithm introduces battery state of charge optimization for energy storage scheduling, improving global search and convergence speed, and obtaining accurate multi-objective scheduling results. The experiment shows that the optimal configuration for photovoltaic energy storage is 10 045 batteries + 687 244 supercapacitors, with a cost of 3.452 × 105 yuan and an energy loss of less than 5%. CS-PSO has similar costs but lower losses and faster convergence compared to traditional methods. [ABSTRACT FROM AUTHOR]

The Maximum Power Point Tracking (MPPT) technique in the solar energy field optimises the performance of solar panels in different atmospheric conditions and variable loads. In this study, we present a new method that uses unsupervised learning (K-means clustering) to identify the atmospheric clusters of solar irradiance and cell temperature (G, T) and delimit homogeneous atmospheric zones or clusters to reduce the search space of the optimal parameters (${V_{mp}}$ V mp , ${I_{mp}}$ I mp , and ${P_{mp}}$ P mp ). The data collected for one year is segregated into 12 clusters; in every cluster, 04 regions are defined based on every cluster's centroid (${G_c}$ G c , ${T_c}$ T c ). A local search of the reference voltage/Duty cycle per cluster region is initiated for every sensed (G, T). Variable atmospheric conditions and resistive loads are tested. The results show that the efficiency of the DC/DC converter is 97.5% with a settling time (4.013 ms/5.577 ms) compared to the Perturb and Observe (P&O) the conventional tracking method and the Particle Swarm Optimisation (PSO), both applied locally inside a cluster and a deviation of 2% from the global maximum. [ABSTRACT FROM AUTHOR]

For ensuring a greener and low carbon future; renewable energies sources such as solar energy stands out as a prominent solution for generating sustainable and clean electricity due to its accessibility, abundance and numerous benefits. The use of solar panel also called as photovoltaic systems has more importance in the world for their ability to convert sun irradiation into electricity while they have significant drawbacks like the nonlinearity of Pv panel. The efficiency and performance of Photovoltaic (PV) systems can be influnced by various factors, like climate fluctuations during the day. Therefore, it is so important to optimize the power capturing from PV panels. To optimize the energy created by photovoltaic modules, it is necessary to carefully select a DC-DC converter with MPPT control. This guarantees that the maximum power is extracted from the solar power plant and sent to the demand side in less time and with greater effectiveness. This paper introduces a super twisting sliding mode control technique for achieving maximum power point tracking (MPPT) in a photovoltaic (PV) system. The Single Ended Primary Inductor Converter (SEPIC) is proposed as a superior alternative to the conventional boost dc-dc converter, as it enables the extraction of the highest possible power from the photovoltaic panels array. Upon doing a thorough comparison of the suggested control with the P&O algorithm in various scenarios using the MATLAB/SIMULINK tool, it was found that the provided STC (Synchronous Tracking Control) for the SEPIC converter demonstrates greater efficiency and reduced oscillation around the Maximum Power Point (MPP). [ABSTRACT FROM AUTHOR]

The demand for renewable energy in power systems is rapidly increasing, and various algorithms can efficiently and accurately track the maximum power point (MPP). This paper compares and analyses multiple control techniques for maximum power point tracking (MPPT) in photovoltaic systems under changing irradiance, temperature, and load characteristics, utilizing three algorithms: incremental conductance (INC), artificial neural network (ANN), and Hybrid INC-ANN. Three MPPT algorithms were developed for a standalone photovoltaic system with a high-gain cubic boost converter (HG-CBC) to optimize the solar panel's MPP. The performance of these controllers is evaluated using MATLAB/SIMULINK under varying irradiance and temperature conditions. The statement indicates that at 1000 W/m², the power output efficiency of INC is lowest at 83.79%. In comparison, the hybrid ANN-INC efficiency and ANN algorithms registered at 97.48% and 88.94%, respectively. The simulation shows that the INC algorithm has the lowest performance regarding time response, while the Hybrid INC-ANN and ANN algorithms perform better than the INC. [ABSTRACT FROM AUTHOR]

The increasing global need for renewable energy sources, driven by environmental concerns and the limited availability of traditional energy, highlights the significance of solar energy. However, weather fluctuations challenge the efficiency of solar systems, making maximum power point tracking (MPPT) systems crucial for optimal energy harvesting. This study compares ten MPPT approaches, including both conventional and artificial intelligence (AI)‐based techniques. These controllers were designed and implemented using MATLAB Simulink, and their performance was evaluated under real environmental conditions with fluctuating irradiance and temperature. The results demonstrate that conventional techniques, such as incremental conductance (INC), Perturb and Observe (P&O), Incremental conductance and Particle Swam Optimization (INC‐PSO), Fuzzy Logic Control and Particle Swam Optimization (FLC‐PSO), and Perturb and Observe and Particle Swam Optimization (P&O‐PSO), achieved accuracies of 94%, 97.6%, 98.9%, 98.7%, and 99.3% respectively. In contrast, AI‐based intelligent techniques, including Artificial Neural Network (ANN), Artificial Neural Fuzzy Interference System (ANFIS), Fuzzy Logic Control (FLC), Particle Swam Optimization (PSO), and Artificial Neural Network and Particle Swam Optimization (ANN‐PSO), outperform achieving higher accuracies of 97.8%, 99.9%, 98.9%, 99.2%, and 99%, respectively. Compared to available research, which often reports lower accuracies for conventional techniques, our study highlights the enhanced performance of AI‐based methods. This study provides a comprehensive comparative analysis, delivering critical analysis and practical guidance for engineers and researchers in selecting the most effective MPPT controller optimized to specific environmental conditions. By improving the efficiency and reliability of solar power systems, our research supports the advancement of sustainable energy solutions. [ABSTRACT FROM AUTHOR]

This paper investigates the challenge of controlling hybrid renewable energy systems (HRES), specifically those combining wind energy and photovoltaic sources, under varying environmental conditions such as fluctuating wind speeds and partial shading. The primary objective is to develop a robust backstepping control strategy that enhances the system's stability and energy efficiency while ensuring seamless grid integration through the use of dual-fed induction generators. The study uses advanced modeling techniques, including maximum power point tracking for wind turbines and particle swarm optimization for photovoltaic systems, to optimize energy capture. A detailed simulation framework was designed to validate the effectiveness of the control strategy under different climatic scenarios. Quantitative results show that the wind turbine achieved over 95% power recovery, the DC link voltage remained stable within 0.5% of the reference, and photovoltaic energy extraction was optimized with 98% accuracy, even under partial shading. These findings indicate that the proposed control strategy significantly enhances the performance, reliability, and adaptability of the HRES. This work offers a promising contribution to the integration of renewable energy sources into the electrical grid, supporting a more sustainable energy future. [ABSTRACT FROM AUTHOR]

This study investigates the impact of solar charge controller (SCC) type on battery charging in solar-powered electric scooters (e-scooters). The research compared maximum power point tracking (MPPT) and pulse width modulation (PWM) controllers by monitoring average output power, current, and voltage every 10 minutes. Results showed that under stationary conditions, MPPT controllers delivered higher efficiency, generating 5.87 W of power compared to PWM's 5.05 W. This advantage persisted even during scooter operation, with MPPT controllers producing 4.91 W versus PWM's 4.31 W. Overall, the findings demonstrate that MPPT SCCs offer a more efficient solution for charging e-scooter batteries. [ABSTRACT FROM AUTHOR]

Because of the increasing demand for non-conventional energy sources, photovoltaic system integration into the electrical grid is becoming more crucial. It is advised to employ a double-stage grid-connected PV system. This can be employed with the main parts boost converter, which helps to boost the DC output from the PV channels. And next one is the space vector pulse width modulation (SVPWM) inverter which can provide better harmonic performance and higher efficiency compared to traditional PWM methods. The SVPWM technique is employed to modulate the inverter output, generating a sinusoidal AC waveform that matches the grid frequency and voltage. By adjusting the pulse width to achieve effective and trustworthy grid integration. To maximize power extraction takes place by using maximum power point tracking (MPPT). The SVPWM inverter is crucial in converting DC power to AC power and enabling grid connection. Utilizing SVPWM allows exact control of voltage magnitude, frequency, and phase angle to provide high-quality sinusoidal AC output voltage. The power quality of the electricity pumped into the grid is improved by this precise management, which also ensures adherence to grid standards and reduces harmonic interference. [ABSTRACT FROM AUTHOR]

Purpose: Owing to the severity of variation in mechanical excitations of piezoelectric energy harvester, the output energy generated by the system is highly distorted. Hence to maximize the output power a Maximum Power Point Tracking (MPPT) methods are used, which suffers from high output impedance instead of large mechanical impedance. This results in a very low current relative to the high voltages Methods: Hence to state this here a perturb and observe (P& O) based MPPT controller is used with piezoelectric energy harvester. To ensure optimal power generation under diverse operating situations, the maximum power point tracking algorithm (MPPT) was developed. The energy valley optimized perturb and observe algorithm (EV-PO) based MPPT is proposed in this work. The optimum duty cycle is selected with EV and fed to PO as the initial duty cycle to optimize the MPP. Additionally, this work includes a hybrid flyback zeta converter to regulate the rectified output voltage. EV-PO based MPPT algorithm provides the optimal pulses for a DC voltage regulation converter. Results: By this method, voltage is regulated optimally, and the efficiency is increased to 97.63%. This converter is compared with boost, buck-boost, and DC-to-DC converters to show its effectiveness. Along with that the proposed method has resulted in maximum output voltage comparatively with other methods. Conclusion: Overall, the proposed method has improved the efficacy to 7.81% with a DC-DC full bridge converter, 20.1% with buck boost converter, and 21.13% with a boost converter. Thus the adopted method is superior than other existing methods. [ABSTRACT FROM AUTHOR]