Search

The research objective is to enhance the power quality performance of a single-phase unified power quality conditioner (UPQC) system supplied by a combination of photovoltaic (PV) array and battery energy storage (BES) using fuzzy logic control (FLC) connected to a 220 V-50 Hz distribution system. The PV array consists of several PV panels with a maximum power of 12 kW. Solar PV is highly dependent on external environmental factors i.e. sunlight, temperature, radiation, weather and seasons where levels can change at any time. BES functions to store PV power and both provide a more stable load power supply to the load when an interruption/disconnection occurs on the source (off-grid mode). The proposed system is also more efficient because it does not require a DC-link capacitor. The FLC method with the Mamdani fuzzy inference system (FIS) is proposed to overcome the weaknesses of proportional and integral (PI) control in determining proportional constants and integral constants. The faults were selected in a total of six case studies (CS) based on the fault categories and single-phase UPQC configuration i.e. CS 1 (sinusoidal/S-Sag-non-linear load/NL-PV), CS 2 (S-Swell-NL-PV), CS 3 (S-interruption/Inter-NL-PV), CS 4 (SSag- NL-PV-BES), CS 5 (S-Swell-NL-PV-BES), and CS 6 (S-Inter-NL-PV-BES). In single-phase UPQC systems from CS 1 to CS 6, FLC is able to produce lower total harmonic distortion (THD) of load voltage and source current, as well as higher source power factor (PFS) compared to proportional-integral (PI) control. The system using singlephase UPQC on CS 1 to CS 6 with PI/FLC control is capable of producing load voltage THD and source current THD below IEEE 519 limits. The system with a single-phase UPQC configuration with PI/FLC control shows that CS 4 to CS 6 is capable produces a change in load voltage disturbance that is slightly greater than CS 1 to CS 3. The system uses single-phase UPQC with PI/FLC control on CS 3 and CS 6 is able to deliver load active power with values close to CS 1 and CS 4. [ABSTRACT FROM AUTHOR]

Introduction. With the increasing complexity of power systems and the integration of diverse energy sources, issues such as voltage sags, swells, and signal distortions have emerged as critical challenges. These power quality problems can result in equipment malfunction, production downtime, and financial losses for industries, as well as inconvenience and potential damage to electrical appliances in households. There is an urgent need for enhanced system efficiency. Methods. This objective is effectively achieved through the utilization of the newly proposed power theory, which is rooted in solar photovoltaic (PV) control, in conjunction with the Unified Power Quality Conditioner (UPQC). Purpose. The proposed method incorporates a modified synchronous reference frame scheme, coupled with a phase-locked loop mechanism. This control strategy enables the UPQC to effectively mitigate power quality issues. Novelty. PV-UPQC is utilized to uphold power integrity in the presence of diverse current and voltage distortions. This device, known as a multi-objective power conditioning apparatus, serves the purpose of maintaining power quality. PV-UPQC incorporates both a shunt and series voltage source converter, which are interconnected through a shared DC-link. Additionally, the PV system is interconnected at the DC-link of the UPQC in order to supply power to the load. Results. In this study, a novel approach is presented for controlling the UPQC, aiming to address power quality concerns such as unbalanced grid voltage and harmonic distortions and enabling us to control active and reactive power. References 16, tables 2, figures 15. [ABSTRACT FROM AUTHOR]

This article proposes a hierarchical distributed voltage control (HDVC) scheme for active distribution networks (ADNs) with high penetration of photovoltaics based on distributed model predictive control (DMPC) and alternating direction method of multipliers (ADMM). The reactive power outputs of several photovoltaic clusters (PVCs) and photovoltaic (PV) units within each PVC are optimally coordinated to keep PV terminal voltages and the voltages of all critical buses of ADNs within the feasible range and mitigate voltage fluctuations. In the ADN layer, a distributed reactive power control scheme based on DMPC is designed for the PVC, which regulates the voltages of all critical buses to be closed to the rated value and mitigates the reactive power variations. In the PVC layer, the reactive power outputs of PV units are optimized based on ADMM to minimize the voltage deviation of each PV terminal and track the reactive power reference from the PVC control. The proposed HDVC scheme requires communication only between neighboring PVC controller, while each PV controller only communicates with the corresponding PVC controller. This regulates the voltages in a completely decentralized manner and effectively reduces the computation burden of the PVC and PV controllers. A modified Finnish distribution network with 10 PVCs was used to validate the control performance of the proposed HDVC scheme. [ABSTRACT FROM AUTHOR]

Solar tracker widely maximizes solar energy harvesting by maintaining a perpendicular relative position between the sun and the solar panel. Single and dual-axis solar tracker controllers are the most control mechanisms that are widely implemented. The single-axis solar tracker (SAST) is preferable between those two control mechanisms due to economic and simpler control algorithm features. Many control algorithms have been proposed to improve the performance of SAST. The conventional proportional integral derivative (PID) controller has major limitations mainly corresponding to slower response. Moreover, it cannot handle the uncertainties of the sunlight. To overcome the problem, type 2-fuzzy logic control (T2-FLC) is proposed. The single-axis solar tracker controller based on T2-FLC is applied in Arduino and implemented in the hardware environment. It was monitored that the T2-FLC provides much better responses than the conventional controllers in terms of better dynamic response and more efficiency in harvesting solar energy. [ABSTRACT FROM AUTHOR]

Direct-current (DC) power converters play an indispensable and crucial role in various systems, particularly in today's increasing electrification of global energy. The characteristics of DC power converters have garnered widespread attention in the design and practical application of energy-saving systems. General system design not only considers the output voltage and power rating of DC power converters but also factors such features as efficiency, input voltage regulation, output load regulation, dynamic load regulation, and ripple voltage characteristics as specified in the manufacturer's datasheet. The objective is to provide a stable power supply to ensure the regular operation of the system. However, the characteristics of maximum startup capacitance are often overlooked, and sometimes only numerical values from the datasheet are used as reference, limiting the insight into the startup characteristics curve. The improper assessment of the startup capacitance characteristics may lead to power startup failure or repetitive oscillations, thereby affecting the operational stability of the system. In this study, the in-depth examination of four high-power-density DC power converters is investigated. Firstly, foundational characteristics testing research is conducted. The characteristics of the maximum startup capacitance are examined in this study by constructing and analyzing the experimental environment. In this research, we explain the critical points in constructing the environment for maximum capacitor load testing and compare the differences between passive components for load and electronic load machine simulation experiments. Finally, we complete the experiment in a low-cost detection environment constructed with essential passive components and obtain the maximum capacitor load characteristic curves of the high-power-density DC power converters. This information will become a reference for system design, ensuring the stability assessment of DC power converters under maximum capacitor load conditions. Additionally, sensor integration for real-time performance monitoring further enhances the understanding of startup capacitance dynamics in high-power-density DC power converters. [ABSTRACT FROM AUTHOR]

In this paper, an improved Beluga Whale Optimization algorithm based on data mining and scheduling rules with AdaBoost(IBWO-DDR-AdaBoost) rule heuristic scheduling method for solving job-shop scheduling problems (JSP) is proposed, in which data mining-extracted dispatching rules are incorporated into the heuristic algorithm to guide the optimization process. Firstly, an AdaBoost-CART integrated learning algorithm is introduced to evolve dispatching knowledge from historical data and convert it into effective dispatching rules. Secondly, in order to address the issues of local optimality and slow convergence speed faced by the beluga whale optimization algorithm (BWO) when solving JSP, this study presents an improved beluga whale optimization algorithm (IBWO) that incorporates two enhancement mechanisms: a neighborhood search strategy based on greedy thinking and genetic operators. These enhancements aim to improve both the efficiency and quality of reconciliation in scheduling, ultimately leading to better scheduling schemes. Furthermore, the extracted scheduling rules obtained through the AdaBoost-CART integrated learning algorithm are embedded into the improved beluga optimization algorithm, enabling real-time solution updates for optimized schedules. Finally, extensive simulation tests are conducted on JSP benchmark examples of varying scales with minimizing maximum completion time as the objective function for schedule optimization. The simulation results demonstrate the significant advantages of our proposed IBWO-DDR-AdaBoost rule heuristic scheduling method in terms of accuracy, performance optimization, and convergence speed. [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]

Abstract Solar photovoltaic energy is a vital renewable resource because it is clean, endless, and pollution‐free. Due to the fast growth of the semiconductor and power electronics sectors, photovoltaic (PV) technologies are climbing significant attention in modern electrical power applications. Operating PV energy conversion systems at the maximum power point is essential for getting the maximum power output and raising efficiency. This paper proposes a regression‐based Perturb and Observe method to quickly find a global maximum power point, avoiding being stuck in local maxima, likewise analytical and metaheuristic methods. The improved control focuses on the narrowed search areas by linear and non‐linear regression analyses using the generated PV model on a flexible Python environment. Furthermore, the method's accuracy is validated in real time under variable temperatures, irradiations, and loads. This method was proven with a hardware implementation. The proposed method is more than 98% accurate and can withstand long‐term modelling. The suggested regression‐based perturbation and observation method provided a short learning time and easy implementation. Additionally, the dynamic recorded results can be visualized for researchers to utilize efficiently.

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]

Programmable matter refers to material that can be programmed to alter its physical properties, including its shape. Such matter can be built as a lattice of attached robotic modules, each seen as an autonomous agent with communication and motion capabilities. Self-reconfiguration consists in changing the initial arrangement of modules to form a desired goal shape, and is known to be a complex problem due to its algorithmic complexity and motion constraints. In this paper, we propose to use a max-flow algorithm as a centralized global planner to determine the concurrent paths to be traversed by modules through a porous structure composed of 3D Catoms meta-modules with the aim of increasing the parallelism of motions, and hence decreasing the self-reconfiguration time. We implement a traffic light system as a distributed asynchronous local planning algorithm to control the motions to avoid collisions. We evaluated our algorithm using VisibleSim simulator on different self-reconfiguration scenarios and compared the performance with an existing fully distributed synchronous self-reconfiguration algorithm for similar structures. The results show that the new method provides a significant gain in self-reconfiguration time and energy efficiency. [ABSTRACT FROM AUTHOR]

We present an algorithm for evaluating analytic nuclear energy gradients of the state-averaged density matrix renormalization group complete-active-space self-consistent field (SA-DMRG-CASSCF) theory based on the newly derived coupled-perturbed (CP) DMRG-CASSCF equations. The Lagrangian for the conventional SA-CASSCF analytic gradient theory is extended to the SA-DMRG-CASSCF variant that can fully consider a whole set of constraints on the parameters of multi-root canonical matrix product states formed at all the DMRG block configurations. An efficient algorithm to solve the CP-DMRG-CASSCF equations for determining the multipliers was developed. The complexity of the resultant analytic gradient algorithm is overall the same as that of the unperturbed SA-DMRG-CASSCF algorithm. In addition, a reduced-scaling approach was developed to directly compute the SA reduced density matrices (SA-RDMs) and their perturbed ones without calculating separate state-specific RDMs. As part of our implementation scheme, we neglect the term associated with the constraint on the active orbitals in terms of the active–active rotation in the Lagrangian. Thus, errors from the true analytic gradients may be caused in this scheme. The proposed gradient algorithm was tested with the spin-adapted implementation by checking how accurately the computed analytic energy gradients reproduce numerical gradients of the SA-DMRG-CASSCF energies using a common number of renormalized bases. The illustrative applications show that the errors are sufficiently small when using a typical number of the renormalized bases, which is required to attain adequate accuracy in DMRG's total energies. [ABSTRACT FROM AUTHOR]

This paper compares different maximum power point tracking (MPPT) control strategies in microgrid dynamics, focussing on perturb and observe (P&O), adaptive neuro-fuzzy inference system (ANFIS), particle swarm optimisation (PSO), and PSO sliding mode controller techniques. The study investigates their performance under varying microgrid conditions, considering factors like weather and load variations. The simulation results provide a detailed comparative analysis of the power at the point of common coupling (PCC) for MPPT techniques at different time intervals. Both the P&O and PSO sliding mode recorded a power output of 287 kW, while PSO-ANFIS achieved a slightly higher power output of 294 kW. At 2.5 seconds, the P&O method recorded a power output of 712 kW, while the PSO sliding mode and the PSO-ANFIS techniques achieved 717 kW and 738 kW, respectively. Overall, the PSO-ANFIS technique consistently outperformed the other methods in terms of power output, demonstrating its effectiveness in maximising energy extraction and adaptability to dynamic conditions. These findings provide valuable insights for designing and implementing MPPT controllers in microgrid systems, emphasising the efficiency of the hybrid PSO-ANFIS technique in enhancing the overall performance and stability of renewable energy systems.

In this research work, three maximum power point tracking (MPPT) control algorithms based on power feedback are applied to two types of converters, namely Zeta and buck-boost, in the photovoltaic (PV) system. The PV system is intended to guarantee the power supply to a remote smart greenhouse. The control algorithms investigated are perturb and observe (P&O), incremental conductance (IncCond), and fuzzy logic (FLC) methods. Their performance are investigated and compared for each converter. It is found that the maximum power is always achieved, even during abrupt changes in irradiation or/and in temperature. The three methods have shown to have good performance; fast response time and very low steady-state error, with minor preference of the P&O method where the output voltage followed the input with high efficiency. The comparative study revealed that the power response time of the PV generator under stable conditions (constant irradiance and constant temperature) for P&O and IncCond was longer in the buck-boost converter than in the Zeta. On the other hand, the ripple level was better for the buck-boost. For the FLC, the maximum power was reached in a shorter time (short response time) with the smallest ripple. As for operation under variable environmental conditions, the Zeta outperformed the buck-boost for each control technique.

Abstract The article introduces a new model of a Reference Signal Tracking (RST) controller for the fractional order quantities. The controller is applied to an indirect field‐oriented control (IFOC) system used for the speed control of an asynchronous motor powered by a photovoltaic (PV) generator with a maximum power point tracking (MPPT) algorithm. This model utilizes two fractional order controllers: the fractional order proportional‐integral (FOPI) regulator and the fractional‐order integral‐proportional (FOIP) regulator. These controllers are used with the generalized predictive control (GPC) technique. The first step in the approach is to derive the equivalent digital RST controller's model from the FOPI and FOIP controller's transfer functions. The GPC technique converts the continuous‐time FOPI (and FOIP) controller into a discrete‐time version. This conversion ensures a fast response and effective disturbance rejection. Simulation tests are conducted to analyze the rotor speed and stator current ripples to evaluate the performance of the proposed method. The results demonstrate the effectiveness of the introduced scheme in achieving improved control performance in terms of response speed and disturbance rejection. The article presents a modified RST controller model based on the fractional order approach applied to an IFOC system for motor speed control driven by a photovoltaic generator. Using FOPI, FOIP controllers, and GPC contributes to enhanced control performance, as evidenced by the simulation results.

Abstract This study elucidates the use of optimization algorithms to identify the controller parameters employed in adjusting the current and voltage values of loads powered by solar energy systems and battery groups. Parameters for these controllers were independently derived using a combination of ant colony optimization with Levy flight, hybrid firefly‐particle swarm optimization, hybrid gravitation search algorithm‐particle swarm optimization, alongside the implementation of Jaya and whale optimization algorithms. The results from each method were juxtaposed for thorough analysis. In addition, three distinct Maximum Power Point Tracker (MPPT) algorithms were employed in the system: perturbation and observation, open circuit voltage, and incremental conductance (IC). To assess the system’s adaptability to real‐world conditions, it was tested against varying temperatures and sunlight levels. Moreover, potential changes in the loads were considered by varying the load. The efficacy of the controllers was examined by altering both the environment and load. The effectiveness of the controllers was examined by referring to the integral of time‐weighted absolute error value. The system was simulated using MATLAB/Simulink software. This study demonstrates that the fractional‐order PID controller achieves the most effective results, the Jaya algorithm provides the best controller parameters, and the IC technique exhibits the highest performance in MPPT.

Summary A nonlinear control strategy for a solar photovoltaic (SPV) integrated three‐phase multifunctional converter (3PMFC) is proposed in this study. The proposed control approach integrates a Lyapunov function controller (LFC) for the voltage control loop and a model predictive controller (MPC) for the current tracking control loop. The required 3PMFC reference currents are calculated using a variable conductance factor (VCF) technique. The key highlights of the proposed control strategy are as follows: (1) LFC with only one control gain provides less steady‐state error than the proportional integrator (PI) with two control gains, (2) MPC generates the optimal switching signals for the associated converters in the system without using modulation stages, and (3) The VCF approach simplifies the mathematical formulation to derive the required reference current. The effectiveness of the proposed control technique was evaluated using different solar irradiation, DC loads, and AC loads (linear balanced, linear unbalanced, and nonlinear loads). The performance of the proposed control technique has been evaluated and validated in a dynamic operation scenario utilizing MATLAB/Simulink and a real‐time simulator (OPAL‐RT OP4510). The simulation results demonstrate that the proposed control approach outperforms the conventional control strategy in terms of grid current total harmonic distortion (THD) improvement, which is in accordance with the IEEE‐519 standard. The performance evaluated through simulation studies indicates that the 3PMFC can perfectly perform all of its grid‐supported multifunctional services, such as current harmonic compensation, reactive power support, and source current balance, along with injecting active power generated by the SPV into the utility system. [ABSTRACT FROM AUTHOR]

This paper recommends new design for non-isolated semi-quadratic buck/boost converter with two similar structure that includes the following features: (a) the continuous input current has made it reasonable for PV solar applications and reduced the value of the capacitors in the input filter reducing the input ripple as well as EMI problems; (b) the topology is simple, and consists of a few numbers of components; (c) the semiconductor-based components have lower current/voltage stresses in comparison with the recently recommended designs; (d) semi-quadratic voltage gain is D (2 − D) / (1 − D)2; (e) 94.6 percent from the theoretical relations and 91.8 percent from the experimental for the output power of 72W, the duty of 54.2 percent, and output voltage of 72 V are the efficiency values in boost mode; (f) 89.3 percent from the theoretical relations and 87.2 percent from the experimental for the output power of 15W, the duty of 25.8 percent, and output voltage of 15 V are the efficiency values in buck mode. One structure is the continuous output current and negative output polarity, and other structure is positive output polarity. The recommended topologies have been studied in both ideal and non-ideal modes. The continuous current mode (CCM) is the suggested mode for the proposed converters. Moreover, the requirements of CCM have been discussed. The various kinds of comparisons have been held for voltage gain, efficiency, and structural details, and the advantages of the suggested design have been presented. A small-signal analysis has been completed, and the suitable compensator has been planned. Finally, PLECS simulation results have been associated with the design considerations. [ABSTRACT FROM AUTHOR]

Solar cells are playing a significant role in aerospace equipment. In view of the surface defect characteristics in the manufacturing process of solar cells, the common surface defects are divided into three categories, which include difficult-detecting defects (mismatch), general defects (bubble, glass-crack and cell-crack) and easy-detecting defects (glass-upside-down). Corresponding to different types of defects, the deep learning model with different optimization methods and a classification detection method based on multi-models fusion are proposed in the paper. In the proposed model, in order to solve the mismatch problem between the default anchor boxes size of YOLOv5s model and the extreme scale of the battery mismatch defect label boxes, the K-means algorithm was adopted to re-cluster the dedicated anchor boxes for the mismatch defect label boxes. In order to improve the comprehensive detection accuracy of YOLOv5s model for the general defects, the YOLOv5s model was also improved by the methods of image preprocessing, anchor box improving and detection head replacing. In order to ensure the recognition accuracy and improve the detection speed for easy-detecting defects, the lightweight classification network MobileNetV2 was also used to classify the cells with glass-upside-down defects. The experimental results show that the proposed optimization model and classification detection method can significantly improve the defect detection precision. Respectively, the detection precision for mismatch, bubble, glass-crack and cell-crack defects are up to 95.64%, 91.8%, 93.1% and 98.0%. By using lightweight model to train the glass-upside-down defect dataset, the average classification accuracy reaches 100% and the detection speed reaches 13.29 frames per second. The comparison experiments show that the proposed model has a great improvement in detection accuracy compared with the original model, and the defect detection speed of lightweight classification network is improved more obviously, which confirms the effectiveness of the proposed optimization model and the multi-defect classification detection method for solar cells defect detection. [ABSTRACT FROM AUTHOR]

Photovoltaic inverters are widely utilized in microgrid systems working as the key equipment for converting solar energy into usable electricity. This paper presents a fuzzy sliding mode control (FSMC) method for the photovoltaic inverter in a microgrid. The inverter module uses voltage control to achieve stable AC output voltage. Moreover, to deal with system uncertainty and non-linearity in the photovoltaic inverter, a fuzzy controller was designed to realize real-time adaptation of the gains of the constant term and the reaching term in the sliding mode control law, which serves as a compensating controller for traditional sliding mode control. The gains of the exponential reaching law can be adjusted according to the system state instead of fixed gains, which can effectively reduce the chattering phenomenon and improve the robustness of the photovoltaic microgrid. Finally, the Lyapunov stability theory was used to ensure the stability of the entire control system, achieving a high-performance independent power supply for loads in a microgrid. Simulation results show that the designed control system is more robust to load disturbances and has superior dynamic performance. [ABSTRACT FROM AUTHOR]

The transition to a low-carbon economy is one of the main challenges of our time. In this context, solar energy, along with many other technologies, has been developed to optimize performance. For example, solar trackers follow the sun's path to increase the generation capacity of photovoltaic plants. However, several factors need consideration to further optimize this process. Important variables include the distance between panels, surface reflectivity, bifacial panels, and climate variations throughout the day. Thus, this paper proposes an artificial intelligence-based algorithm for solar trackers that takes all these factors into account—mainly weather variations and the distance between solar panels. The methodology can be replicated anywhere in the world, and its effectiveness has been validated in a real solar plant with bifacial panels located in northeastern Brazil. The algorithm achieved gains of up to 7.83% on a cloudy day and obtained an average energy gain of approximately 1.2% when compared to a commercial solar tracker algorithm. [ABSTRACT FROM AUTHOR]

The increase in the production efficiency of photovoltaic technology depends on its alignment in relation to the solar position. Solar tracking systems perform the tracking action by implementing control algorithms that help the reduction of tracking errors. However, conventional algorithms can reduce the life of actuators and mechanisms due to control action, significantly reducing operation times and profitability. In this article, an unconventional control scheme is developed to address the mentioned challenges, presenting the design and implementation of an extremum-seeking control to perform maximum power point tracking for a two-axis solar tracker instrumented with a solar module. The proposed controller is governed by the dynamics of a classic proportional-integral scheme and assisted by sensorless feedback. Also, it has an anti-wind-up-type configuration for the integral component and counts with a variable amplitude for the dither signal. The proposal is validated experimentally by comparison between a fixed system and a two-axis system in azimuth-elevation configuration. In addition, two performance indices are defined and analyzed, system energy production and tracking error. The results show that the proposal allows producing up to 27.75% more than a fixed system, considering the tracker energy consumption due to the tracking action and a pointing accuracy with ±1.8° deviation. Finally, an analysis and discussion are provided based on the results, concluding that the proposed algorithm is a viable alternative to increase the performance of tracked photovoltaic systems. [ABSTRACT FROM AUTHOR]

We present a design strategy for fabricating ultrastable phase-pure films of formamidinium lead iodide (FAPbI3) by lattice templating using specific two-dimensional (2D) perovskites with FA as the cage cation. When a pure FAPbI3 precursor solution is brought in contact with the 2D perovskite, the black phase forms preferentially at 100°C, much lower than the standard FAPbI3 annealing temperature of 150°C. X-ray diffraction and optical spectroscopy suggest that the resulting FAPbI3 film compresses slightly to acquire the (011) interplanar distances of the 2D perovskite seed. The 2D-templated bulk FAPbI3 films exhibited an efficiency of 24.1% in a p-i-n architecture with 0.5-square centimeter active area and an exceptional durability, retaining 97% of their initial efficiency after 1000 hours under 85°C and maximum power point tracking. [ABSTRACT FROM AUTHOR]

A novel integrated DC-DC converter is proposed for the first stage of two-stage grid connected photovoltaic (PV) systems with energy storage systems. The proposed three-port converter (TPC) consists of a buck–boost converter, interposed between the battery storage system and the DC-AC inverter, in series with PV modules. The buck–boost converter in the proposed TPC is utilized for maximum power point tracking by regulating two power switches. The output power of the proposed converter is regulated by controlling the DC-AC converter. During the battery-charging mode, partial power regulation is employed with a direct power flow path (the series-connection of the PV panel, the battery and the output). As resistances in this path are almost negligible, the power conversion efficiency is higher than existing topologies. During battery-discharging mode, the power conversion is processed through a buck–boost converter with only two active power switches and one inductor. With fewer components, higher power conversion efficiency is also achieved. The circuit operation and analysis are presented in detail. To illustrate the simplicity of the converter control, the performance of the converter is tested with a straightforward maximum power point tracking on a PV system with battery cells. Simulation and experimental tests are carried out to demonstrate circuit operation and power conversion efficiency. [ABSTRACT FROM AUTHOR]

This article investigates an inventive methodology for precisely and efficiently controlling photovoltaic emulating (PVE) prototypes, which are employed in the assessment of solar systems. A modification to the Shift controller (SC), which is regarded as a leading PVE controller, is proposed. In addition to efficiency and accuracy, the novel controller places a high emphasis on improving transient performance. The novel piecewise linear-logarithmic adaptation utilized by the Modified-Shift controller (M-SC) enables the controller to linearly adapt to the load burden within a specified operating range. At reduced load resistances, the transient sped of the PVE can be increased through the implementation of this scheme. An exceedingly short settling time of the PVE is ensured by a logarithmic modification of the control action beyond the critical point. In order to analyze the M-SC in the context of PVE control, numerical investigations implemented in MATLAB/Simulink (Version: Simulink 10.4, URL: https://in.mathworks.com/products/simulink.html) were utilized. To assess the effectiveness of the suggested PVE, three benchmarking profiles are presented: eight scenarios involving irradiance/PVE load, continuously varying irradiance/temperature, and rapidly changing loads. These profiles include metrics such as settling time, efficiency, Integral of Absolute Error (IAE), and percentage error (epve). As suggested, the M-SC attains an approximate twofold increase in speed over the conventional SC, according to the findings. This is substantiated by an efficiency increase of 2.2%, an expeditiousness enhancement of 5.65%, and an IAE rise of 5.65%. Based on the results of this research, the new M-SC enables the PVE to experience perpetual dynamic operation enhancement, making it highly suitable for evaluating solar systems in ever-changing environments. [ABSTRACT FROM AUTHOR]

Currently, the primary factor indicating the necessity of an operation for an abdominal aortic aneurysm (AAA) is the diameter at its widest part. However, in practice, a large number of aneurysm ruptures occur before reaching a critical size. This means that the mechanics of aneurysm growth and remodeling have not been fully elucidated. This study presents a novel method for assessing the elastic properties of an aneurysm using an ultrasound technique based on tracking the oscillations of the vascular wall as well as the inner border of the thrombus. Twenty nine patients with AAA and eighteen healthy volunteers were considered. The study presents the stratification of a group of patients according to the elastic properties of the aneurysm, depending on the relative volume of intraluminal thrombus masses. Additionally, the neural network analysis of CT angiography images of these patients shows direct (r = 0.664271) correlation with thrombus volume according to ultrasound data, the reliability of the Spearman correlation is p = 0.000215. The use of finite element numerical analysis made it possible to reveal the mechanism of the negative impact on the AAA integrity of an asymmetrically located intraluminal thrombus. The aneurysm itself is considered as a complex structure consisting of a wall, intraluminal thrombus masses, and areas of calcification. When the thrombus occupies > 70% of the lumen of the aneurysm, the deformations of the outer and inner surfaces of the thrombus have different rates, leading to tensile stresses in the thrombus. This poses a risk of its detachment and subsequent thromboembolism or the rupture of the aneurysm wall. This study is the first to provide a mechanistic explanation for the effects of an asymmetrical intraluminal thrombus in an abdominal aortic aneurysm. The obtained results will help develop more accurate risk criteria for AAA rupture using non-invasive conventional diagnostic methods. [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]

This article compares traditional and intelligent Maximum Power Point Tracking (MPPT) algorithms for photovoltaic (PV) powered DC Shunt Motors. The study evaluates five MPPT methods under varying DC loads and finds that intelligent algorithms perform well under rapidly changing solar radiation conditions. However, they struggle to track the maximum power point when the temperature changes due to inadequate data for training. The analysis focuses on the time required by each method to reach peak power under different conditions, and the advantages and disadvantages of each algorithm are discussed. The article provides valuable insights into the behavior of MPPT algorithms in real-world scenarios and aims to contribute to the advancement of PV system optimization and control. [Extracted from the article]

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]

The power generated by the photovoltaic (PV) array reduces drastically due to irregular solar irradiation. Shading pattern, shaded area, and array configuration are the main causes of reduction in power generation. The electrical characteristics of the modules in the array possess several peaks due to the fact that they produce distinct row currents and have various peaks in their electrical characteristics. Therefore, an appropriate technique is needed to minimize the row current difference that leads to maximum power generation at a higher level. This can be achieved by array reconfiguration; therefore, an Innovative Competence Square technique is proposed in this paper. The proposed technique disperses the shade effectively over the entire array by displacing the modules. The MATLAB simulation was run under three distinct shading conditions to validate the proposed technique's performance, and it has been compared to the Competence Square technique and TCT interconnection. The technique's benefits can be seen in terms of increased power generation, FF, and reduced mismatch losses. [ABSTRACT FROM AUTHOR]

Due to the nonlinear relation between the generated power and voltage of photovoltaic (PV) arrays, there is a need to stimulate PV arrays to operate at maximum possible power. Maximum power can be tracked using the maximum power point tracker (MPPT). Due to the presence of several peaks on the power–voltage (P–V) characteristics of the shaded PV array, conventional MPPT such as hill climbing may show premature convergence, which can significantly reduce the generated power. Metaheuristic optimization algorithms (MOAs) have been used to avoid this problem. The main shortcomings of MOAs are the low convergence speed and the high ripples in the waveforms. Several strategies have been introduced to shorten the convergence time (CT) and improve the accuracy of convergence. The proposed technique sequentially uses a recent optimization algorithm called Mexican Axolotl Optimization (MAO) to capture the vicinity of the global peak of the P–V characteristics and move the control to a fuzzy logic controller (FLC) to accurately track the maximum power point. The proposed strategy extracts both the benefits of the MAO and FLC and avoids their limitations with the use of the high exploration involved in the MOA at the beginning of optimization and uses the fine accuracy of the FLC to fine-track the MPP. The results obtained from the proposed strategy show a substantial reduction in the CT and the highest accuracy of the global peak, which easily proves its superiority compared to other MPPT algorithms. [ABSTRACT FROM AUTHOR]

The two-stage power converter is currently the most widespread topology for three-phase grid-connected photovoltaic (PV) systems. The PV power is injected to the grid through the regulation of the dc-link voltage. This method is commonly called the voltage control method, whereby the current is controlled indirectly. This paper focuses on the modelling of power converters and the parameter design of proportional-integral (PI) controllers, based on the bandwidth of the inner current loop and the outer voltage loop. It is assumed that the inner loop has a bandwidth that is ten-fold bigger than that of the outer loop, in order to fully decouple the dual loops. The PI parameters are thus determined through this relationship, and two sets of simulations are run to validate the methodology used. Results show that the controllers give highly satisfactory performance even in the presence of transients introduced by rapidly varying irradiance model. [ABSTRACT FROM AUTHOR]

Summary: This article presents an improved least mean square (LMS) algorithm‐based control approach for a three‐phase grid interfaced solar photovoltaic (GISPV) system for smart building applications. The SPV is primarily utilized to supply household loads, and the absence or intermittency of SPV is curbed by utilizing power from the grid. The majority of household loads are nonlinear loads that affect the grid power quality (PQ). However, the effects of both linear and nonlinear loads on grid PQ have been analyzed in this article. This article proposes a Gaussian Kernel function‐based normalized LMS (GKNLMS) algorithm to calculate the active peak weight component of the load current, thereby utilizing it to obtain a sinusoidal reference current. Considering a higher step size and utilizing the Gaussian Kernel function for reduced mean square error (MSE) improves both the convergence rate and accuracy of estimation in the conventional LMS. The proposed control technique effectively addresses system anomalies caused by dynamic solar irradiance, load variation, and unbalanced loading conditions. Furthermore, the system response with the GKNLMS algorithm is assessed in MATLAB/Simulink and validated by developing a laboratory setup of the system for experimentation. [ABSTRACT FROM AUTHOR]