Search

In practical engineering applications, factors like dust adhesion and environmental changes can cause photovoltaic arrays to exhibit multiple peaks in output power. An optimization algorithm with global optimization capability is needed to track its maximum power. In this regard, this paper proposes an improved marine predator algorithm (IMPA) to extract the maximum power point of photovoltaic system under complex solar irradiation conditions. To overcome the issues in the traditional marine predator algorithm (MPA), the opposition-based learning(OBL) strategy is introduced in IMPA, and the sine cosine algorithm (SCA) is integrated into the iteration stage to enhance the search ability of the algorithm. Furthermore, the low-order converter in the traditional MPPT control system is replaced by the Zeta converter, which increases the operating voltage range. Ultimately, simulation results demonstrate that the MPPT based on IMPA has higher tracking efficiency and shorter response time.The experimental results also indicate the practical feasibility of this method, as well as its high level of stability and robustness. [ABSTRACT FROM AUTHOR]

Now, the present power generation and distribution companies are working on renewable energy systems because their features are low-level atmospheric pollution, producing less greenhouse pollutants, more reliability, good static performance, and high robustness. In this work, the sunlight Photovoltaic (PV) system is selected because of its advantages are easily available in the atmosphere, high flexibility, zero carbon footprint, easy to maintain, and less transportation cost. However, solar networks produce nonlinear I-V characteristics. Due to the non-linear nature of the solar system, the extraction of peak voltage from the PV module is a very tough task. So, in this article, a variable modified step grey wolf method is integrated with the adaptive-neuro-fuzzy-inference-system to improve the energy production of solar systems. The features of this proposed maximum power point tracking controller are fast identification of the solar system operating point, generating the less fluctuated oriented converter load power, providing more MPP tracking accuracy, less dependence on the solar system installation, and useful for all environmental bad weather conditions. Another problem of solar systems is less voltage production which is improved by introducing a wide voltage gain-boost converter circuit. The features of this converter circuit are less development cost because it does not require more power electronics switches. Here, the proposed IGWM with an AFLC-fed sunlight system is investigated by using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

The present power production companies are working on renewable energy systems because their features are more reliable for the local energy consumers, high continuity in the energy production, and less cost is required for maitainence. In this article, the proton exchange membrane fuel stack (PEMFS) renewable energy is utilized to supply energy to the automotive systems. Here, the PEMFS is selected because of its merits are high energy density, quick system response concerning the source operational temperature, and more suitable for electric vehicle application. However, the PEMFS supplied voltage is completely nonlinear which is solved by utilizing the modified particle swarm optimization with adaptive neuro-fuzzy inference system (MPSO with ANFIS) controller. This hybridization-based maximum power point tracking controller provides more accuracy, high power point identifying speed, best dynamic response at different fuel stack functioning temperature conditions, and easy maitainence. Here, the fuel stack generated current is very high which is optimized by introducing the new DC–DC converter. The advantages of this DC–DC converter are more voltage transformation ratio, low-level voltage stress appearing across the switches, and wide voltage gain. The overall system is investigated by utilizing the MATLAB/Simulink tool. [ABSTRACT FROM AUTHOR]

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

The fuel cell stack power mismatch problem limits the large-scale application of fuel cells. In order to guarantee the normal operation of the series fuel cell bank under fault condition, this paper proposes a multi-port DC converter for fuel cell distributed maximum power tracking based on the coupled inductor principle. The converter is based on the voltage equalization method and consists of a series fuel cell bank with a diode clamping circuit and a Boost converter in sequential cascade. In contrast to the degraded usage method of fuel cell stacks under fault conditions, the voltage equalization strategy does not affect the lifetime of non-faulty fuel cell units, so the overall system is more efficient. The proposed converter combines high output voltage gain and cell voltage equalization due to the use of only one switching tube. Simulation models are used to verify the feasibility and advancement of the proposed converter. [ABSTRACT FROM AUTHOR]

One of the most known and usable renewable energy is photovoltaic solar energy. Its properties such as recursivity, cleanness, limitlessness, and reliability make it the most useful source of energy. Some undesirable natural (irradiance and temperature) variation affects the photovoltaic solar modules' characteristics. However, photovoltaic PV solar power needs to be monitored accurately, effectively, and efficiently in order to keep the global maximum power point (GMPP) steady. Therefore, this paper presents a modified multi-stepped constant current based on the gray wolf optimization (MMSCC-GWO) algorithm for photovoltaic systems. The proposed technique utilizes a multi-step constant current to the pack of wolves by monitoring the inside and the outside step of the hierarchical wolves. This proposed (MMSCC-GWO) algorithm accurately tracks the maximum power point from the photovoltaic systems under different partial shading conditions (PSCs). A comparative result with other conventional methods is investigated and the simulation results carried out in the MATLAB/Simulink environment reveal that the proposed approach outperforms the existing techniques in terms of efficiency, tracking time, and accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a novel maximum power point tracking control for a stand-alone photovoltaic (PV) system based on a robust polynomial static output feedback control law subject to input saturation. In detail, a DC/DC boost converter is used to regulate the load and extract the maximum power from the photovoltaic panel. First, a polynomial fuzzy model is used to represent the photovoltaic system. Then, as this control method is based on a reference model, a regression plane is used to generate the desired trajectory representing the optimal dynamics where the PV system supplies maximum power. Then, in order to reduce the number of required sensors, a polynomial output feedback controller was developed, in which the problem of converter performance degradation resulting from duty cycle saturation was avoided by using a saturated control approach. The controller gains have been obtained by solving a sum-of-squares optimization problem, where the H ∞ performance criterion is applied to guarantee the stability of the closed-loop system while achieving an optimal rejection level of external disturbances. To evaluate the performance of the suggested controller, a number of simulations and comparisons were carried out in MATLAB/Simulink environment and under various scenarios of weather conditions. [ABSTRACT FROM AUTHOR]

Parameter identification of solar photovoltaic (PV) cells is crucial for the PV system modeling. However, finding optimal parameters of PV models is an intractable problem due to the highly nonlinear characteristics between currents and voltages in different environments. To address this problem, whale optimization algorithm (WOA)-based meta-heuristic algorithm has turned out to be a feasible and effective approach. As a highly promising optimization algorithm, different enhanced WOA variants have been proposed. Nevertheless, there has been no comparative study of WOA and its variants for parameter identification of PV models so far. To further investigate and analyze the performance of WOA in the studied problem, this work applied and compared WOA and ten enhanced WOA variants for identifying five PV model parameters. Different evaluation indices including solution accuracy, search robustness, and convergence curve were employed to reveal their performance variation. Based on the simulation results, a multi-model statistical analysis with the Friedman test at a confidence level 0.05 was conducted to rank all algorithms. EWOA that hybridizes the sorting-based differential mutation operator and the Lévy flight strategy ranked first and its performance was further verified. Besides, according to the simulation results, possible effective improvement directions for WOA in tackling this intractable problem are concluded to guide future work. [ABSTRACT FROM AUTHOR]

This paper studies the possibility of connecting Wind Farms (WF) to the electric grid with the use of finite space model predictive command (FS-MPC) to manage wind farms to improve the quality of the current output from the doubly-fed induction generator (DFIG) with considering fault ride-through technique. This proposed system can generate active power and enhance the power factor. Furthermore, the reduction of harmonics resulting from the connection of non-linear loads to the electrical grid is achieved through the self-active filtering mechanism in DFIGs-WF, facilitated by the now algorithm proposed. FS-MPC technique has the ability to improve system characteristics and greatly reduce active power ripples. Therefore, MATLAB software is used to implement and verify the safety, performance, and effectiveness of this designed technique compared to the conventional strategy. The results obtained demonstrated the effectiveness of the proposed algorithm in handling the four operational modes (Maximum power point tracking, Delta, Fault, and Filtering). Additionally, the suggested technique exhibited flexibility, robustness, high accuracy, and fast dynamic response when compared to conventional strategies and some recently published scientific works. On the other hand, the THD value of the current was significantly reduced, obtaining at one test time the values 56.87% and 0.32% before and after filtering, respectively 27.50% and 0.26% at another time of testing, resulting in an estimated THD reduction percentage of 99.43% and 99.05%, respectively. These high percentages prove that the quality of the stream is excellent after applying the proposed strategy. [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]

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]

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]

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 maximum power point tracking (MPPT) mechanism is one of the important components of a solar photovoltaic system (SPS). For efficient working of the SPS, the MPPTs must be flexible to adapt to any change in irradiance level, as the output from the Solar PV system majorly depends on the level of irradiance present at a geographical location. This study deals with the scope of improvement in artificial neural network (ANN) by incorporating the metaheuristic tuning. The modified version of ANN MPPT controller has been developed with the help of a novel Horse herd optimization (HHO) algorithm that strategically optimizes the weights of the neural network. The systematic implementation and comparison of different versions of ANN MPPT controller have been performed, taking into account different scenarios like dynamically changing irradiance levels and several cases of partial shading condition. For the training of the neural network, the irradiance and temperature dataset has been collected from the NASA website. The analysis of the results has been done using parameters like fill factor, maximum power tracked, percentage mismatch power loss and efficiency, which shows that the ANN MPPT controller tuned with HHO algorithm tracks maximum power and attains maximum efficiency under various considered scenarios. Further, the EN50530 MPPT efficiency test has been performed under the fast- and slow-changing irradiance level to compare the efficacy of different algorithms. [ABSTRACT FROM AUTHOR]

The manuscript proposes the design and implementation of a photovoltaic-assisted dynamic voltage restorer with fuzzy-logic control (FLC)-based Improved Second-order Generalized Integrator (I-SOGI) scheme to improve power quality (PQ) in the power system by using an enhanced approach. The proposed method utilizes Firebug Swarm Optimization (FSO) which is motivated by the reproductive swarming behavior of Firebugs. The swarming behavior of FSO is enhanced by using two operators called mutation and crossover. Hence, it is termed as Enhanced FSO (En-FSO) method. Power quality issues considered include harmonic distortions and voltage sag/swell. The Z-source inverter (ZSI) by compensating the dynamic voltage restorer (DVR's), fed voltage into the grid through a series transformer. Long-term balanced voltage sags, swells, unbalanced sags, swells, and interruptions of voltage are all offset by the ZSI-DVR. Using the suggested control method, the total harmonic distortion (THD) of load voltage is kept below 5%. The FLC-based I-SOGI control method for generating the reference signals for voltage compensation enhances a considerable parameter tweaking. The entire analysis demonstrates that the proposed FLC-based I-SOGI control technique effectively mitigates voltage sag, swell, and harmonics while supplying solar photovoltaic (SPV) power to the grid. The simulation and experimental verification of the proposed control algorithm successfully maintains pure sinusoidal load voltage in a variety of test conditions. The results are compared with existing methods to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In recent years, extreme focus on renewable energy has intensified due to environmental concerns and the depletion of fossil fuel supplies. In a DC microgrid that includes AC grid, photovoltaic (PV), wind, and battery storage systems, there are some problems such as intermittency and variability, mismatched generation and demand, inefficient energy utilization by storage batteries, and unstable DC link voltage. To overcome these problems, an integrated strategy for better energy management and a new MPPT technique is proposed in this research. The robust model predictive control (RMPC) method is proposed to deal with uncertainties and disturbances while offering the best possible control options. A comparison of the two algorithms reveals that the RMPC performs better than the conventional model predictive control (MPC) method. To harvest the maximum solar electricity from the PV system, a sophisticated MPPT optimization technique called muddled tuna swarm optimization (MTSO) is applied. Drone squadron optimization (DSO) and slime mould optimization (SMO) are outperformed by MTSO in terms of dynamic performance, effectively tracking the maximum power point (MPP) of the PV system even under various irradiance conditions. The suggested RMPC approach and MTSO technique are effective in achieving optimal energy and battery management as well as maximum solar power extraction, according to the simulation findings. On the OPAL-RT platform, real-time simulation is used to test the control strategy. [ABSTRACT FROM AUTHOR]

This article proposed the architecture of a stand-alone photovoltaic connected system (SPVS) with energy storage. An SPVS with energy storage requires power management for various operating modes. A coordinate controller is often necessary to manage the change in control architecture depending on the operating mode. This proposed system contains a boost converter (BC) and a bidirectional switched quasi-Z-source DC-DC converter (BSQZSDC). The BC is used for two purposes. The first is extracting the maximum power from the PV. The second is to control the DC link voltage when the SPVS is not in MPPT mode; dual-loop PI controllers are used for this. The BSQZSDC is used for energy management and regulates DC link voltage when SPVS operates in MPPT mode. This paper addresses about the effective control approach that BSQZSDC used, i.e., the double-integral sliding mode control, to regulate the DC link voltage and battery current in various operating modes. Experimental results were carried out to depict that the controller reasonably achieves the control objectives and proper energy management. [ABSTRACT FROM AUTHOR]

This article presents a promising self-learning-based robust control for output voltage tracking in DC–DC buck power converters, particularly for applications demanding high precision performance in face of large load uncertainties. The design involves a computationally simple online single hidden layer neural network, to rapidly estimate the unanticipated load changes and exogenous disturbances over a wide range. The controller is designed within a backstepping framework and utilizes the learnt uncertainty from the neural network for subsequent compensation, to eventually ensure an asymptotic stability of the tracking error dynamics. The results obtained feature a significant improvement of dynamic and steady-state performance concurrently for both output voltage and inductor current in contrast to other competent control strategies lately proposed in the literature for similar applications. Extensive numerical simulations and experimentation on a developed laboratory prototype are carried out to justify the practical applicability and feasibility of the proposed controller. Experimental results substantiate the claims of fast dynamic performance in terms of 94% reduction in the settling time, besides an accurate steady-state tracking for both output voltage and inductor current. Moreover, the close resemblance between computational and experimental results is noteworthy and unveils the immense potential of the proposed control system for technology transfer. [ABSTRACT FROM AUTHOR]

The current work analyzes the on-orbit performance degradation and physical properties of triple-junction InGaP2/InGaAs/Ge solar cells based on the Gaofen-3 (GF-3) satellite in low-earth orbit (LEO) with long-term shading and standard conditions. From a long-term perspective of 7 years, the temperature range of the solar array with shading is −60.9°C to +119.9°C, while the temperature range of the solar array with the same design and without shading is +48.5°C to +102.3°C. After calibrating the illumination angle, operating temperature, and sun–earth distance, the performance degradation of the solar cell array under long-term shading and standard conditions is comparatively analyzed. The annual degradation coefficients calculated for the solar array under shading and standard conditions are 0.980251 and 0.998199, respectively, which means that at the end of the satellite's 8-year life, the current degradation of the solar array with shading and standard working conditions is 14.75% and 1.43%, respectively. If the mission time is extended to 15 years, the on-orbit performance degradation of the solar array under shading and standard conditions will be 25.86% and 2.67%, respectively. This work thus provides a reference for the design of subsequent satellite solar arrays. [ABSTRACT FROM AUTHOR]

Efficient photovoltaic devices must be efficient light emitters to reach the thermodynamic efficiency limit. Here, we present a promising prospect of perovskite photovoltaics as bright emitters by harnessing the significant benefits of photon recycling, which can be practically achieved by suppressing interfacial quenching. We have achieved radiative and stable perovskite photovoltaic devices by the design of a multiple quantum well structure with long (∼3 nm) organic spacers with oleylammonium molecules at perovskite top interfaces. Our L-site exchange process (L: barrier molecule cation) enables the formation of stable interfacial structures with moderate conductivity despite the thick barriers. Compared to popular short (∼1 nm) Ls, our approach results in enhanced radiation efficiency through the recursive process of photon recycling. This leads to the realization of radiative perovskite photovoltaics with both high photovoltaic efficiency (in-lab 26.0%, certified to 25.2%) and electroluminescence quantum efficiency (19.7 % at peak, 17.8% at 1-sun equivalent condition). Furthermore, the stable crystallinity of oleylammonium-based quantum wells enables our devices to maintain high efficiencies for over 1000 h of operation and >2 years of storage. Efficient radiation is essential to reach thermodynamic limit of photovoltaic efficiency. Here, authors design thick quantum barriers to suppress interfacial quenching and boost photon recycling in perovskite cells, achieving high radiation and photovoltaic efficiencies and long device stability. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV)-based power generation systems are becoming increasingly popular as a due to its high performance and cleanliness. Several factors influence the performance of a PV system, including shadowing effects. PV systems employ MPPT methodologies to obtain the power from PV array. Conventional MPPTs works well under normal conditions when there is no shadow effects or partial shading. The presence of partial shading affects the system performance and generates several power peaks. This complicates the process of finding out of the global peak (GMPP) with improved tracking efficiency and reduced settling time including conversion efficiency. This work proposes three hybrid MPPT techniques: Water Cycle Optimisation-Perturb and Observe (WCO-PO), Artificial Neural Network Supported Adaptable Stepped-Scaled Perturb and Observe (ANN-ASSPO), Grey Wolf Optimisation-Modified Fast Terminal Sliding Mode Controller (GWO-MFTSMC), and two conventional MPPT techniques WCO and P&O have been implemented. The proposed system utilizes interleaved boost converter with three phase. The performances of proposed hybrid MPPTs strategies were compared in terms of output voltage, output current and extracted power. The comparison also includes conversion efficiency and average settling time. To analyse the performances, four different cases have been used to test the efficacy of hybrid MPPTs under changing climatic conditions. The MATLAB/Simulink tool has been used to analyze the PV system performances. In the three hybrid MPPT techniques, WCO-PO has performed better when compared to other two hybrid MPPTs in terms of conversion efficiency (99.56%) and settling time (1.4 m). [ABSTRACT FROM AUTHOR]

This paper focuses on enhancing the energy extraction efficiency of photovoltaic (PV) modules through the use of a straightforward power converter and control algorithm. This research delves into the electrical characteristics of PV modules, explaining the concepts of global maximum power point, and local maximum power points. By integrating maximum power point tracking algorithms and differential power processing technology, an innovative scheme for power equalization and optimization of PV modules is introduced. The scheme is based on a single-switch multi-winding forward-flyback converter. Using the STP-340-72-Vfh-type PV module as a case study, a simulation model is developed with PLECS simulation software. The simulations cover 30 different irradiance scenarios. The findings illustrate the effectiveness of the proposed PV module power optimization system in achieving maximum power output under different irradiance conditions, achieving an average efficiency of 94.61%. This efficiency rate is 13.95% greater than that of existing global maximum power tracking schemes. [ABSTRACT FROM AUTHOR]

Global electricity demand is increasing with the rising population and rapid urbanization. Building Integrated photovoltaic (BIPV) system is a new method of renewable energy generation where solar photovoltaic (PV) modules are integrated into the building surfaces such as façade, shades, windows, roofs, and tiles. BIPV systems reduce the urban energy demand. Utilization of vertical surfaces makes the BIPV system a preferable choice where land scarcity affects the implementation of large PV systems. The economic viability depends on the maximum power generated by the BIPV array. In urban environments, the BIPV arrays experience severe partial shading conditions (PSCs). The PSCs cause mismatch losses, reducing the global maximum power of the BIPV array and efficiency. Fixed array configurations such as series-parallel (SP), total-cross-tied (TCT), triple-tied (TT), and quarter-tied (QT) are designed to solve this issue. The cross ties across the rows of the BIPV array improve the performance at the expense of more wiring. Researchers proposed various optimal array configurations with different shading patterns. This research attempts to generalize the design of the BIPV array configurations by considering the trade-off between wiring requirements and shading losses. The performance of SP, TT, QT, and TCT configurations under four different shading conditions is simulated with the proposed BIPV array design algorithm. A 9 × 8 BIPV array of 3.6 kW is considered. QT configuration reduces the wiring requirement by 10.45% compared to TCT and improves up to 8.43% maximum power than SP. The fill factor is improved to 48.49%, and the mismatch loss is limited to 34.10%. Therefore, QT and TCT are considered favorable configurations for BIPV array design. [ABSTRACT FROM AUTHOR]

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

To overcome the limitations of conventional solar simulators, which suffer from inadequate irradiance adjustment range, poor adjustment accuracy, serious arc displacement of the xenon lamp at the low power supply, and insufficient consideration of irradiance uniformity, we have designed an electrically controlled irradiance continuous adjustment device. Our approach involves a theoretical analysis of the radiation distribution on the attenuation diaphragm using the annular aperture method of a condenser, enabling modulation of the radiation distribution of the converging light path using a sector-shaped mesh structure with carefully calculated and simulated structural parameters.We then determined the structural scheme of the electrically controlled irradiance continuous adjustment device, which incorporates an attenuation diaphragm, a dimming dial, rolling balls, a cage, pinions, a motor fixation plate, a triangular bracket, and an electric control system. Key components were designed and optimized using finite element analysis, and the control system was also studied.In the final step, we tested the performance of the electrically controlled irradiance continuous adjustment device. Experimental results demonstrated that the irradiance of the solar simulator can be adjusted within the range of 23.7–1315.5 W/m2, with a maximum relative error of the attenuation ratio within ± 0.2% and irradiance non-uniformity within ± 3%. Our device has achieved electrically controlled irradiance continuous adjustment, thereby expanding the application range of the solar simulator and improving its overall performance. [ABSTRACT FROM AUTHOR]

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

Background: With the increasingly serious environmental pollution and natural environment damage, renewable energy such as solar cells have gradually become the key to change this situation. Therefore, the local abnormal diagnosis of the charge and discharge of solar cell capacitors is particularly important. Objective: To extend the life of ultracapacitors by resolving the issue of their low detection rate and enhancing the capacity to recognize fault diagnosis factors. A novel approach to charging and discharging, as well as the diagnosis of local anomalies, is put forth, utilizing switching networks. Methods: By controlling the capacitors of multiple solar cells and supercapacitors to work together, it is possible to compensate for the shortage of photovoltaic power. The performance of fault diagnosis is optimized by combining principal component analysis and binary K-means clustering, which completes the fault diagnosis of capacitors. Results: The experimental results show that the research method can increase the maximum output power of photovoltaic by 32.9% under multi-layer shadows. In the charging state of the training set, the number of abnormal capacitors is 6, and the number of normal capacitors is 12, and both of them are in accordance with the preset value. The number of abnormal capacitors and normal capacitors in the discharge state is the same as that in the charging state, which is also 6 and 12. Conclusion: The research method can effectively address the issue of unbalanced energy storage battery packs and minimize the impact of local shadows on photovoltaic systems. In comparison to fuzzy C-means clustering, this method requires fewer iterations, enables faster fault diagnosis, and produces more accurate clustering results. It can provide technical support for diagnosing local abnormalities in the charging and discharging of solar cell capacitors. [ABSTRACT FROM AUTHOR]

Traditionally, isolated and non-isolated boost converters are used for solar photovoltaic systems (SPV). These converters have limitations such as low voltage gain, less voltage ripples, temperature dependence, high voltage stress across the switches, and being bulky in size. Besides, the solar PV system also has non-linear characteristics between I–V and P–V, and the energy yield potential is affected by partial shading phenomena. Therefore, maximum power point tracking (MPPT) is being added to the SPV system to get the maximum output power under steady and dynamic climate conditions. Although the conventional MPPT has drawbacks such as less accuracy in predicting the MPP under partial shading conditions, low tracking speed, and more ripples, Hence, the research proposes a stackable single switch boost converter (SSBC) with a Cuckoo search MPPT controller for the SPV system. The efficiency of the proposed circuit topology has been compared with conventional boost converters with various MPPTs. Subsequently, the accuracy of tracking true MPPT by CSO is compared with that of PSO and FPNA. The results show, that the CMPPT with CBC has produced more ripples, whereas the BMPPT with SSBC produces ripple-free power under steady conditions. It is also observed that SSBC with BMPPT produces more power than SSBC with TMPPT. The efficiency of SSBC with BMPPT is better than other combinations. Finally, a prototype model has been developed and verified. [ABSTRACT FROM AUTHOR]

Currently, the majority of the country has moved to renewable energy sources for electricity generation, and power companies are concentrating their efforts on renewable resources. Solar, wind, hydropower, and biomass are examples of renewable resources; of these, due to a lack of non-renewable resources, the solar industry is expanding. All year long, solar electricity is available, and it creates a calm, quiet atmosphere. The majority of large and small companies, as well as individual consumers, have shifted to PV solar cells for electricity generation. A trustworthy and precise simulation design of a photovoltaic system prior to installation is required to predict a photovoltaic system's performance. The current research aims to build models for solar PV systems with one, two, and three diodes and determine which model is most appropriate for each environmental circumstance to forecast performance accurately. By contrasting the experimental data of solar panel with simulated results of single-, double-, and triple-diode models, this study examines the accuracy of each model. These models' comparative performance study has been done using the MATLAB/Simulink, taking into account the influence of changing model parameters and the performance of the models under varying climatic circumstances. These models, despite their simplicity, are quite sensitive and react to even a little change in temperature and irradiance. Under conditions of low solar irradiance or shading conditions, three-diode photovoltaic models are shown to be more accurate. We can forecast the power output of solar photovoltaic systems under changeable input circumstances by understanding the I-V curves with the help of the performance assessment of the models used in this work. [ABSTRACT FROM AUTHOR]

The growing demand for solar energy conversion underscores the need for precise parameter extraction methods in photovoltaic (PV) plants. This study focuses on enhancing accuracy in PV system parameter extraction, essential for optimizing PV models under diverse environmental conditions. Utilizing primary PV models (single diode, double diode, and three diode) and PV module models, the research emphasizes the importance of accurate parameter identification. In response to the limitations of existing metaheuristic algorithms, the study introduces the enhanced prairie dog optimizer (En-PDO). This novel algorithm integrates the strengths of the prairie dog optimizer (PDO) with random learning and logarithmic spiral search mechanisms. Evaluation against the PDO, and a comprehensive comparison with eighteen recent algorithms, spanning diverse optimization techniques, highlight En-PDO's exceptional performance across different solar cell models and CEC2020 functions. Application of En-PDO to single diode, double diode, three diode, and PV module models, using experimental datasets (R.T.C. France silicon and Photowatt-PWP201 solar cells) and CEC2020 test functions, demonstrates its consistent superiority. En-PDO achieves competitive or superior root mean square error values, showcasing its efficacy in accurately modeling the behavior of diverse solar cells and performing optimally on CEC2020 test functions. These findings position En-PDO as a robust and reliable approach for precise parameter estimation in solar cell models, emphasizing its potential and advancements compared to existing algorithms. [ABSTRACT FROM AUTHOR]

Photo-driven flexoelectric actuators combine the advantages of wireless photovoltaic generator and flexoelectric precision actuation, and thus can realize non-contact precision actuation and vibration control of structures. This study presents a novel hybrid photovoltaic/flexoelectric actuation mechanism by using PbLaZrTi (PLZT) as a photovoltaic generator to drive a flexoelectric actuator consisting of an atomic force microscope (AFM) probe and a flexoelectric layer covered with an electrode layer. Spatial-time distributions of the opto-flexoelectric-induced control moments are calculated. Static deflection control and dynamic vibration control of cantilever beams are investigated, and independent modal control effects are evaluated. Analysis results demonstrate that this photovoltaic/flexoelectric actuation mechanism can effectively accelerate the beam damping process. The control effects by the Lyapunov controls are better than those via the proportional feedback and velocity feedback controls. The customization of modal control forces can be realized via a non-contact strategy by adjusting the light intensity and pre-illumination time. Design issues related to the optimal position of AFM probe and actuator size are also addressed. [ABSTRACT FROM AUTHOR]

The primary reason for the decrease in power output from the PV array is partial shading. During partial shading conditions (PSCs) PV modules experience a mismatch in current and voltage generated. Also, the power voltage (P–V) characteristics of the PV array possess multiple peaks under PSCs. To mitigate the effects caused by PSCs, the configuration of the PV array is the finest solution. This paper proposes a novel Reduced Cross Tied (RCT) configuration to enhance maximum power output from a symmetrical PV array under PSCs. The RCT configuration has fewer cross-ties compared to the TCT configuration and is applicable to a square array of any size. The proposed RCT configuration along with conventional configurations such as Total Cross Tied (TCT), Series–Parallel, Bridge-Link, and Honey-Comb are validated in simulation and hardware for 7 × 7 and 8 × 8 symmetrical PV arrays under Short and Narrow (SN), Short and Wide (SW), Long and Narrow (LN) and Long and Wide (LW) shading patterns. A comparative analysis is done between the proposed configuration and conventional configurations in terms of mismatch loss (ML), fill factor, efficiency (η ), Wiring Loss, and Global Maximum PowerPoint (GMPP). The proposed RCT configuration is able to achieve GMPP equal to the TCT configuration under the LN shading pattern and on par with the TCT configuration under SN, SW, and LW shading patterns. RCT-configured PV array possesses a minimum reduction of 25% in wiring loss for any array size. RCT configuration applies to a square array of any size.The results replicate the efficacy of the proposed RCT configuration compared to existing configurations. [ABSTRACT FROM AUTHOR]

Light guide plate (LGP) is a key component of liquid crystal display (LCD) display systems, so its quality directly affects the display effect of LCD. However, LGPs have complex background texture, low contrast, varying defect size and numerous defect types, which makes realizing efficient and accuracy-satisfactory surface defect automatic detection of LGPS still a big challenge. Therefore, combining its optical properties, dot distribution, defect imaging characteristics and detection requirements, a surface defect detection algorithm based on LGP-YOLO for practical industrial applications is proposed in this paper. To enhance the feature extraction ability of the network without dimensionality reduction, expand the effective receptive field and reduce the interference of invalid targets, we built the receptive field module (RFM) by combining the effective channel attention network (ECA-Net) and reviewing large kernel design in CNNs (RepLKNet). For the purpose of optimizing the performance of the network in downstream tasks, enhance the network's expression ability and improve the network's ability of detecting multi-scale targets, we construct the small detection module (SDM) by combining space-to-depth non-strided convolution (SPDConv) and omini-dimensional dynamic convolution (ODConv). Finally, an LGP defect dataset is constructed using a set of images collected from industrial sites, and a multi-round experiment is carried out to test the proposed method on the LGP detect dataset. The experimental results show that the proposed LGP-YOLO network can achieve high performance, with mAP and F1-score reaching 99.08% and 97.45% respectively, and inference speed reaching 81.15 FPS. This demonstrates that LGP-YOLO can strike a good balance between detection accuracy and inference speed, capable of meeting the requirements of high-precision and high-efficiency LGP defect detection in LGP manufacturing factories. [ABSTRACT FROM AUTHOR]

Due to the many attractions of solar energy, a lot of research is being done in research centers to improve the usefulness of photovoltaic systems (PVs). Despite the widespread use of PVs in different societies, one of the biggest challenges of these sources is to obtain the maximum possible output power. Up to now, several investigations are performed to achieve the maximum power point (MPP) in PV systems. In this paper, an incremental conductance (INC) in the combination of farmland fertility optimization (IFFO) based on the adaptive neuro-fuzzy inference system (ANFIS) is applied to implement the MPPT controller. The proposed MPPT method applies hybrid IFFO in different temperatures and radiation situations to attain optimal voltages. Then, the ANFIS and the INC algorithm try to find the MPP as the ANFIS cannot find the precise point alone. The main advantage of the ANFIS and INC is providing less number of samples for training. Simulations are performed to confirm the advantage of the technique over the conventional approach. Achieving the global optimum point, lower oscillations around MPP and fast tracking are approved using the simulations with the suggested IFFO-ANFIS-INC method. [ABSTRACT FROM AUTHOR]

In this study, a frequency regulation is proposed that adjusts the release of rotational kinetic energy to ensure that a wind turbine remains near the maximum power point in the event of persistent under-frequency. The proposed scheme calculates wind turbine power by multiplying the frequency deviation with the variable control gain and then adding the resulting value to the torque controller power. Considering the operational characteristics of the wind turbine, separate control gains are applied in both the over-frequency section (OFS) and the under-frequency section (UFS). When the rotor speed approaches the maximum power point (MPP), the UFS control gain decreases, causing the wind turbine to gradually transition to maximum power point tracking (MPPT). This overcomes the limitations of conventional frequency regulation, enhancing both the power output of the wind turbine and the average grid frequency. To validate the proposed scheme, an IEEE 14 bus system, including a wind power plant and a photovoltaic plant, was simulated using the EMTP-RV simulator. The simulation results demonstrate that the proposed scheme effectively performs frequency regulation in the power grid regardless of the wind penetration. [ABSTRACT FROM AUTHOR]

Research into renewable energies is expanding quickly, especially photovoltaic (PV) systems. PV systems are employed extensively in several renewable energy applications. The primary challenge with PV systems is maximizing electricity output. Consequently, a significant amount of research into modeling PV continues to focus on maximizing the generated power. Maximum power point tracking (MPPT) refers to the optimization of PV power generation. Accordingly, an effective MPPT approach deploying a converged barnacles mating optimizer (CBMO)-based adaptive neuro-fuzzy inference system (ANFIS) is introduced in this paper. The mentioned strategy is utilized to detect and track the maximum power point (MPP) in two phases. At the initial stage, ideal voltages are determined using the CBMO algorithm in various temperatures and irradiances in the offline mode. After being trained, the ANFIS calculates the ideal voltage depending on the radiation conditions on solar panels. It then enters the tracking cycle and attempts to identify the MPP. To evaluate the behavior of the suggested technique, a Matlab/Simulink-based MPPT model is created. The proposed approach is evaluated under various weather conditions. The results demonstrate that the suggested methodology for tracking is efficacious under all environmental circumstances. Simulation of the suggested technique is carried out, and the results demonstrate that the introduced MPPT algorithm will effectively give the global maximum under a variety of climatic circumstances. Additionally, this approach is exceedingly effective, rapid, and stable. The findings demonstrate that the suggested technique properly identifies the optimum MPP at 99.3% efficiency. [ABSTRACT FROM AUTHOR]

Due to environment concerns, Electric Vehicles (EV) are becoming more and more common in the automobile industry. Since rechargeable batteries of EV manage power, it is crucial to have a battery charger that is dependable, efficient, and affordable in order to provide the battery of the specific EV with the stable required output. Due to the expanding market for renewable energy combined with EV, converters have received a lot of attention recently. Because it reduces system losses and transformer winding losses, non-isolated DC/DC converters are suitable for EV applications. Other problems with the non-isolated DC/DC converter include inadequate voltage gain, a high duty cycle ratio, and the requirement for additional circuitry for better performance. To achieve reliable control of converters, a combination of an optimization technique with Tilt Integral Derivative (TID) controller is used in the Non-Isolated High Step-Up (NIHSU) DC/DC converter for Solar Photo Voltaic (SPV) integrated with EV Applications are proposed. The proposed system TID tuned by Dung Beetle Optimizer are executed in the MATLAB platform and compared with various approaches in terms of voltage regulation, efficiency. The proposed methodology improves closed-loop systems functionality and achieves significant voltage increase, greater power density and higher efficiency of 97.8%. [ABSTRACT FROM AUTHOR]

Due to its abundant natural supply and environmentally friendly features, solar photovoltaic (PV) production based on renewable energy is the ideal substitute for conventional energy sources. The efficiency of solar power generation under partial shading conditions (PSCs) is significantly increased by maximizing power extraction from the PV system. The maximum power point tracking (MPPT) method is to track maximum PowerPoint (MPP). This research proposes a photovoltaic MPPT control in partial shading conditions using Loxo-Canis (LOXOCAN) optimization algorithm. The ultimate goal of the novel method is to track the solar photovoltaic system's maximum power point under conditions of partial shading using the LOXOCAN algorithm. The proposed LOXOCAN algorithm is a combination of Elephant-herd optimization (EHO) and Coyote Optimization Algorithm (COA). The K p , K i , and K d parameters of the PID controller of the MPPT controller will be tuned to their optimum values using the proposed optimization strategy. Higher MPPT performance and a quick convergence at the global maxima are shown in the proposed Loxo-Canis approach. Also, the recommended hybrid Loxo-Canis MPPT approach offers faster MPPT, less computational work, and higher efficiency. [ABSTRACT FROM AUTHOR]

High utility itemset mining is a crucial research area that focuses on identifying combinations of itemsets from databases that possess a utility value higher than a user-specified threshold. However, most existing algorithms assume that the databases are static, which is not realistic for real-life datasets that are continuously growing with new data. Furthermore, existing algorithms only rely on the utility value to identify relevant itemsets, leading to even the earliest occurring combinations being produced as output. Although some mining algorithms adopt a support-based approach to account for itemset frequency, they do not consider the temporal nature of itemsets. To address these challenges, this paper proposes the Scented Utility Miner (SUM) algorithm that uses a reinduction strategy to track the recency of itemset occurrence and mine itemsets from incremental databases. The paper provides a novel approach for mining high utility itemsets from dynamic databases and presents several experiments that demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

A sustainable, affordable, and eco-friendly solution has been proposed to address water heating, electricity generation, space cooling, and photovoltaic (PV) cooling requirements in scorching climates. The photovoltaic thermal system (PV/T) and the direct expansion PV/T heat pump (PV/T DXHP) were numerically studied using MATLAB. A butterfly serpentine flow collector (BSFC) and phase change material (PCM) were assimilated in the PV system and MATLAB model was developed to evaluate the economic and enviroeconomic performance of the PV/T water system (PV/T-W), PV/T PCM water system (PV/T PCM-W), the PV/T DXHP system, and the PV/T PCM heat pump system (PV/T-PCM-DXHP). In this study, annual energy production, socioeconomic factors, enviro-economic indicators, and environmental characteristics are assessed and compared. Also, an economic, environmental, and enviro-economic analysis was conducted to assess the commercial viability of the suggested system. The PV/T PCM-DXHP demonstrated the highest electrical performance of 53.69%, which is comparatively higher than the other three configurations. The discounted levelized cost of energy (DLCOE) and payback period (DPP) of the PV/T PCM-DXHP were ₹2.87 per kW-h and 3–4 years, respectively, resulting in a total savings of ₹67,7403 over its lifetime. Furthermore, installing this system mitigated 280.72 tonnes of CO2 emissions and saved the mitigation cost by ₹329,700 throughout its operational lifecycle. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) technology exhibits significant potential as a form of energy due to its green characteristics. Nevertheless, the modeling of PV modules is subject to unpredictability due to the fluctuating levels of heat and irradiance. To optimize efficiency and reduce the return on investment of a PV system, it is necessary to keep the PV panels at the highest possible power point (MPP). The application of fuzzy logic control-based intelligent maximum power point tracking (MPPT) approach is found to be more appropriate than traditional techniques in PV systems for addressing the issue of unpredictability. The study presents an investigation of an isolated PV system that utilizes a push–pull converter in conjunction with a fuzzy logic-based MPPT technique. To address the previously mentioned issues, this research proposes the utilization of a fuzzy logic controller (FLC) that is centered on a modified sparrow search algorithm (MSSA) as a means to enhance the performance of the current MPPT controllers. The findings demonstrated the efficacy of the recommended approach in precisely following the global maximum power point (GMPP) with improved convergence rates and reduced power fluctuations when compared to alternative methods. The PV system, which incorporates the MSSA-MPPT algorithm, has been developed with MATLAB/SIMULINK program. The mathematical evaluation of the proposed approach, which increases the tracking effectiveness of PV systems to 99.7%, is well validated by the system findings. In instances where the solar array fails to generate sufficient energy to satisfy the required demand, the Battery Energy Storage (BES) system is activated to ensure continuous power within the test system. [ABSTRACT FROM AUTHOR]

In this article, a maximum Power Point Tracking (MPPT) controller is designed for photovoltaic (PV) applications. This controller has been implemented with Fuzzy Gain Scheduling of Proportional–Integral–Derivative (PID) type controller (FGS-PID). To implement this controller, scaling factors (SF) for the input signals of FGS are applied. The recommended adaptive two-level controller has all the welfare of fuzzy logic system (FLC) and PID control. Zieglere–Nichols technique is used to fine-tune the initial PID's gains. The PID's gains are updated with FGS-PID in transient and steady-state conditions to cope with fluctuations, minimize settling time and ensure stability. FLC is used for gain factors to deal with tuning of conditioned input signals of the FGS-PID. Moreover, FLC and an improved shuffled frog leaping algorithm (ISFLA) are applied to tune the member functions (MFs) of FGS. The use of this algorithm can lead to automatic regulation of the triangular MFs. Simulations are done to confirm the edge of this approach over conventional methods. It is very fast and accurate in tracking the maximum power. It provides minimum oscillations and improved dynamic response than other approaches. The speed of the tracking is also improved with acceptable accuracy. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems-based harvest energy becomes huge support for the electric grids in recent days and also becomes the sustainable replacement for conventional energy sources. But the efficacy of PV-based electricity is degraded due to the effect of partial shading conditions. The reconfiguration technique is one effective strategy to diminish the impact of shadow on the photovoltaic (PV) array. This entails rearranging the PV array's switching matrix by the temperature and shading levels. A Seagull Optimization Algorithm (SOA) is proposed in this work to identify the optimal pattern for rearranging the matrix and optimising power output from the PV array. A 9 × 9 PV array operating under four common shadow patterns—Short Wide (SW), Long Wide (LW), Short Narrow (SN), and Long Narrow (LN)—is subjected to the algorithm. Mismatch power, fill factor, percentage power loss, and percentage power enhancement are used to assess the algorithm's effectiveness. Comparisons are made between the results from the proposed SOA and those from the Total-Cross-Tied (TCT), Competence Square, Butterfly Optimisation Algorithm, and Harris Hawks Optimization. The highest increase in Global Maximum Power has been attained by the proposed SOA corresponding to the TCT array is 36% and 30% in the SW and LW shadow arrangement, while the least increase of 7% occurs in the SN and LN arrangements. The evaluated results show the proposed SOA's competence and effective in optimising the shaded array reconfiguration. [ABSTRACT FROM AUTHOR]

The fast-growing influence of grid-interfaced photovoltaic (PV) networks makes it necessary to adhere to grid-code (GC) regulations. These regulations mandate that PV systems inject active power both during and after the grid fault occurrence, as well as provide reactive current to the grid during voltage dips, in order to prevent power system stability concerns. In this context, two novel control schemes have been proposed in this paper for grid-connected photovoltaic networks that can support low-voltage ride-through (LVRT). The proposed control techniques have been demonstrated to be simple and efficient in the event of severe voltage dips, i.e., work up to a voltage drop of 90% of the nominal grid voltage. Our first approach involves an enhanced reactive current injection strategy that provides reactive power support to the power grid during voltage dips without any additional devices. The control structure is comprised of a voltage level estimator and a reactive current controller. The voltage level estimator obtains power grid voltage, while the reactive current controller computes active−reactive current commands. It is particularly noteworthy that the proposed scheme generates active current as much as possible according to the voltage dips and tracks the active current reference correctly, where conventional strategies fail to cope. The second control method for LVRT presented in this research work involves using hardware, specifically a crowbar. The use of a crowbar is the most straightforward method to provide support to the power system, bypassing over-current during voltage dips and keeping the current within the normal converter rating. The control strategies proposed in this paper are validated in MATLAB/Simulink under symmetrical and unsymmetrical severe voltage dips as well as grid voltage harmonics, and a detailed comparison study with a state-of-the-art PQ-based fault-ride-through controller has been conducted. [ABSTRACT FROM AUTHOR]

The generation of electricity from renewable sources has made considerable strides in today's industrialized nations. The primary objective of this research is to examine the grid integration of solar systems within permissible total harmonic distortion (THD) limits, with the help of a proposed cascaded feedforward neural network (CFNN) controller fed advanced power conversion devices such as symmetrical hybrid (SH), switched inductor (SL), and switched capacitor (SC) based boost converter and reduced switch cascade multilevel inverter (CMLI). The SH–SL–SC converter for power conversion features a high voltage gain and reduced switch CMLI features minimum number of power electronic switches and low harmonic content in both the voltage and current profiles. When producing a control signal for the recommended SH–SL–SC boost converter, the proposed CFNN-MPPT controller performs better under different weather scenarios where it needs significant data to operate effectively. Consequently, the CFNN-MPPT controller fed SH–SC–SC boost converter-based PV system is integrated into the grid through reduced switch CMLI where it is controlled by proposed CFNN controller. The CMLI can be operated on asynchronous input voltage sources and making it ideal for PV systems when integrated to the grid with improved power quality by reducing total harmonic distortion (THD) to less than 5% at point of common coupling. With reference to the standards outlined in IEEE 1547 and 519, the simulation's outcomes were reviewed. Finally, a symmetric hybrid SL–SC boost converter and modified cascaded nine level inverter have been conceived and produced as a laboratory prototype model and its output waveforms are presented in this article. The proposed intelligent controller fed converter exhibits enhancements in the converter voltage gain and power quality by lowering the THD level at PCC in the grid integration with the PV power systems. [ABSTRACT FROM AUTHOR]

This article dealt the application of three-phase all-pass filter integrated with phase-locked loop (3Ø-APF-PLL) control approach in islanded electrical supply system for enhanced power quality (PQ). It also covers the effectiveness of proposed control under various operating conditions such as intermittent wind speed, variable solar irradiance, and unbalanced nonlinear load. An islanded electrical supply system includes wind energy generation, solar photovoltaic (SPV), and battery energy storage (BES). The proposed control technique provides a coordinated and balanced operation under a dynamical load. A 3Ø-APL-PLL-based control approach is proposed to extract the maximum value of fundamental component of load current and phase angle. The proposed control approach is utilized to operate voltage source converter (VSC) that performs multi-functions, such as reducing source current harmonics, power compensation (real & reactive), balancing of loads, and improving the overall power quality of the islanded SPV-wind-BES electrical supply system. The SPV system is developed using an incremental conductance (IC)-based maximum power point tracking (MPPT) technique to extract maximum power during the intermittent conditions of solar irradiance. A bidirectional controller (BDC) is designed to operate the charging/discharging modes of a BES. Generally, an islanded electrical supply system suffers from poor voltage & frequency regulation due to the intermittency of power generation sources, which can be solved by utilizing a BES with BDC control to manage the power leveling between the power generation sources and the unbalanced load. A 3Ø-APF-PLL control approach is compared to existing controllers like a least mean square (LMS) (Singh et al. in IET Power Electron 13:2767–2774, 2020) and second-order generalized integrator-based frequency locked loop (SOGI-FLL) (Golestan et al. in IEEE Trans Power Electron 34:213–224, 2018). Numerous parameters, such as the evaluation of the maximum value of the fundamental component, stability, steady-state error, and harmonic compensation capabilities, are taken into account when comparing 3Ø-APF-PLL to the control approaches described above. The dynamic parameters of the islanded electrical supply system are justified under the IEEE-519 standard. [ABSTRACT FROM AUTHOR]

At present, fossil fuel-based power generation systems are reducing drastically because of their less availability in nature. In addition, it produces hazardous gasses and high environmental pollution. So, in this work, the solar natural source is selected for generating the electricity. Due to the nonlinear behavior of PV, achieving maximum voltage from the Photovoltaic (PV) system is a more tough job. In this work, various hybrid optimization controllers are studied for tracing the working power point of the PV under different Partial Shading Conditions. The studied hybrid optimization MPPT methods are equated in terms of oscillations across MPP, output power extraction, settling time of the MPP, dependency on the PV modeling, operating duty value of the converter, error finding accuracy of MPPT, algorithm complexity, tracking speed, periodic tuning required, and the number of sensing parameters utilized. Based on the simulative comparison results, it has been observed that the modified Grey Wolf Optimization based ANFIS hybrid MPPT method provides good results when equated with the other power point tracking techniques. Here, the conventional converter helps increase the PV source voltage from one level to another level. The proposed system is investigated by using the MATLAB/Simulink tool. [ABSTRACT FROM AUTHOR]

It is noted that the traditional direct filed-oriented control (DFOC) is widely used in the field of electric power generation from wind due to its fast response dynamic, ease of implementation and simplicity, but this strategy is characterized by the presence of large ripples at the level of both active and reactive powers. This work presents a new algorithm for DFOC strategy of an asynchronous generator (AG) in a wind power (WP) system, which is based on the use of a new nonlinear controller called fractional-order synergetic control–fractional-order proportional-integral (FOSC–FOPI) controller, where the proposed technique parameters are calculated using the particle swarm optimization (PSO) strategy. It has been observed that the DFOC–FOSC–FOPI–PSO strategy is robust and works well in case of changing generator parameters. Three tests were performed to study the behavior of the designed technique under different working conditions, where the behavior of the DFOC–FOSC–FOPI–PSO strategy was compared with the behavior of the traditional DFOC technique in terms of power ripple ratio, overshoot, steady-state error, response time, tracking reference, and current quality. The simulation of the designed technique based on the FOSC–FOPI–PSO strategy of the AG–WP system is carried out using Matlab software, where the simulation results showed that the suggested technique is better than the classical technique (with PI controller) in terms of improving response time of active power (33.33%) and reactive power (10%) in second test, reduction of the steady-state error of reactive power (96.95%) and active power (97.14) in first test, minimization of harmonic distortion of current (96.57%) in third test and significant minimization of ripples of active power (99.67%, 44.69%, and 98.95%) and reactive power (99.25%, 53.65%, and 70.50%) in the three tests. The effectiveness of the DFOC–FOSC–FOPI–PSO strategy is very high, so it can be a reliable solution for controlling various generators. [ABSTRACT FROM AUTHOR]

For decentralized electrification in remote areas, small-sized wind energy systems (WESs) are considered sustainable and affordable solution when employing an efficient, small-sized component converter integrated with a less-sophisticated, cost-effective MPPT controller. Unfortunately, using a conventional buck DC/DC converter as a MPP tracker suffer from input current discontinuity. The latter results in high ripples in the tracked rectified wind power which reduces the captured power and affects system operation especially in standalone applications which are self-sufficient and independent of grid support. Furthermore, these ripples propagate to the machine side causing vibration and torque stress which impacts turbine performance and safety. To solve this issue, a large electrolytic capacitor is placed at the buck converter input to buffer these ripples, yet at the cost of larger size, losses and reduced reliability. Oppositely, the developed C1, D4 and D6 buck converters have the merit of continuous input current at small component-size. In this paper, dynamic modelling of these three converters is developed to select the one with the least input current ripples to replace the traditional buck converter in the considered WES system. Consequently, fluctuations in the tracked power are minimized and the large buffer capacitor is eliminated. This enhances system lifetime, reduces its cost and increases tracking efficiency. Moreover, mechanical power and torque fluctuations are minimized, thus maintaining machine protection. Furthermore, a sensorless MPPT algorithm, based on converter averaged state-space model, is proposed. Being dependent on variable-step P&O algorithm, the proposed approach features simple structure, ease of control and a compromise between tracking time and accuracy besides reduced cost due to the eliminated current sensor. Simulation results verified the effectiveness of the selected converter applying the proposed MPPT approach to efficiently track the wind power under wind variations with cost-effective realization. [ABSTRACT FROM AUTHOR]

As solar technology has matured, irrigation using photovoltaic pumping systems (PVPSs) has gained popularity in developing markets as an effective means to alleviate poverty and increase food security. Yet, there remains a barrier to adoption; the upfront costs of PVPSs pose a financial burden for many low-income farmers. In a PVPS, the capital cost of the solar array contributes a large portion of upfront system costs. The solar pump is the largest energy consumer in the system, thus its efficiency directly impacts the size and cost of the solar array. There is a limited quantitative understanding of how solar pump efficiency affects the capital cost of the solar array. This study presents a technoeconomic framework to directly quantify the impact of solar pump efficiency on the cost of the solar array in a PVPS, for a range of hydraulic operating conditions. New empirical efficiency scaling laws were created by characterizing the efficiencies of 4-inch multistage centrifugal borehole pumps and induction motors. The utility of the technoeconomic framework is demonstrated through a case study comparing solar pump architectures with motors of different efficiencies. Results indicate that, despite the increased motor cost, the use of high-efficiency motors in solar pumps may lead to an overall cost reduction in a PVPS. Counter to the conventional capital cost-driven process, this work demonstrates that an efficiency-driven design process could improve low-cost, solar-powered system design. Engineers and system designers can leverage the presented framework during the design process to make informed decisions to achieve more cost-effective PVPSs. [ABSTRACT FROM AUTHOR]