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With the large-scale integration of renewable energy systems, the power inertia and voltage support of the grid are low. Using the inverter with temporary storage capabilities cope with shortages and power fluctuations. This paper presents a new sensorless control of a single-phase grid-connected PV system based on a multilevel flying capacitor inverter MFCI) able to smooth out the power produced. The designed controller involves two cascade loops, an outer loop control extracts the maximum power despite climate change, and an inner loop consists of a model predictive control based on the Lyapunov theory for power factor correction and flying capacitor voltage balancing. Furthermore, an interconnected Kalman-like high-gain observer is combined with the predictive control algorithm to estimate the flying capacitor voltages accurately based on the actual grid current. The stability study of the suggested observer in the closed loop has been described. An algorithm that generates reference signals to manage power flows is also developed. The performance of the proposed controller has been verified on the MATLAB/Simulink platform, and the simulation results confirm the effectiveness of the proposed controller over different grid operating modes. • the proposed PV system is based on MFC inverter able to smooth out the grid-injected power. • The designed controller combines a Lyapunov-based model predictive control with a Kalman-like high-gain observer. • The PV conversion system optimizes energy extraction, injecting it smoothly into the grid. • The inverter, with temporary storage, copes with power fluctuations without external energy storage during grid faults. • The closed-loop stability of the controller is rigorously proved during the normal mode and grid fault conditions. [ABSTRACT FROM AUTHOR]

The integration of hybrid energy sources, such as batteries and supercapacitors, in off-grid photovoltaic installations is of crucial importance. This method promotes energy autonomy, offers operational flexibility, compensates for fluctuations in solar production, and can result in long-term economical savings. It also allows for optimized energy management through efficient storage and redistribution. This work details the design and simulation of a self-sufficient solar system that uses supercapacitors and batteries as part of a hybrid energy storage system. Recognizing the increasing significance of efficient energy systems, this study addresses the importance of such installations in delivering sustainable energy solutions. The FOPI-PSO controller optimized using the Particle Swarm Optimization (PSO) technique; demonstrates greater flexibility with a greater number of parameters, surpassing the adaptability of the conventional PI controller. By using multiple simulation scenarios that take into consideration both variations in load and irradiance, the study compares the effectiveness of both controllers in terms of synchronizing batteries and supercapacitors. The results demonstrate the PSO-based FOPI control strategy's outstanding performance, showing its optimal efficiency and robustness. [ABSTRACT FROM AUTHOR]

Overhead cranes are used to move heavy, bulky objects above the factory floor instead of along floor walkways. They are commonly used to load and unload goods in factories, outdoor warehouses and serve at stations, ports. During operation, chain hoists or cable hoists are the main equipment, plays the role of hoisting/lowering materials and moving mechanism along the main beam. Therefore, vibration cannot be avoided during the process of moving heavy objects, causing danger to people and affecting the product. In addition, the overhead crane is an uncertain nonlinear system, compared to the single pendulum type, moving two loads at the same time is much more complicated. That's why the author proposes to design a new controller that not only helps balance the cart but also the two pendulums during operation. First, the system dynamics model is built. Next, an adaptive controller based on the radial basis function neural network (RBFNN) is designed and proven to be stable according to Lyapunov theory. Simulation results of the overhead crane system on MATLAB/Simulink software have shown the effectiveness of the proposed algorithm even when the working system is affected by model uncertainty. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems have emerged as a promising energy resource that caters to the future needs of society, owing to their renewable, inexhaustible, and cost-free nature. The output power of these systems relies on solar cell radiation and temperature. In order to mitigate the dependence on atmospheric conditions and optimize power extraction from PV systems, Maximum Power Point Tracking (MPPT) algorithms are employed. To overcome limitations such as steady-state voltage oscillations and improve transient response, presented by the traditionnal Perturb and Observe (P&O) strategy, this paper deals with an improved fuzzy variable step size P&O. The developed MPPT strategy is validated by using Matlab/Simulink™. The obtained results demonstrate the good performance in terms of rapidity and accuracy tracking as well as power loss. [ABSTRACT FROM AUTHOR]

In recent years, the power quality (PQ) improvements have been explored through various approaches. The employment of electronic devices with renewable energy sources has expanded the harmonics level of voltage and current. Due to harmonics, the PQ of a specific electrical system gets affected. At critical load conditions, the traditional PQ mitigation approaches fail to develop the performance of the system. Therefore, in this work, the Spider Monkey Optimization convolutional neural network (SM-CNN)-based 31-level multilevel inverter (MLI) is used. This method balances the reactive power demands and enhances real power in the grid-tied photovoltaic (PV) system. The maximum power point tracking (MPPT) algorithm depending on radial basis function neural networks (RBFNNs) is used to maximize PV power. For strengthening the voltage level of the PV and to generate higher DC voltage with a minimized switching loss, an integrated boost fly back converter (IBFC) is introduced. The presented technique is implemented in the MATLAB/Simulink platform to figure out the estimation of PQ issues. The suggested MLI lessens the total harmonic distortion (THD) value to 2.45% with an improved power factor. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach for implementing the maximum power point that could be generated from a photovoltaic (PV) panel while eliminating the need for current sensors through the application of the Hill Climbing algorithm is proposed. The active power generated by the PV panel is injected into the grid via a three-phase inverter using voltage-oriented voltage control with Spatial Vector Modulation (SVM). The developed strategy ensures minimal ripples for both active and reactive power and produces a sinusoidal alternating current waveform, even under varying lighting conditions. A comprehensive description of the adopted control strategy is provided and validated through numerical simulations conducted in MATLAB/Simulink environment. Furthermore, the performance of the proposed method is assessed by analyzing the simulation results. In an attempt to validate the effectiveness of the proposed approach, an implementation of the inverter control was conducted with the DSpace 1104 board, and the results underscored the feasibility and effectiveness of the employed approach for grid-connected PV systems. [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]

The sun serves as the primary energy source, providing our planet with the essential energy for sustaining life. To efficiently harness this energy, photovoltaic cells, commonly known as PV cells, are employed. These cells convert the solar energy they receive into electrical energy. The operational point of the solar cell, delivering maximum output power, is referred to as the maximum power point (MPP). However, as light availability and temperature fluctuate throughout the day, the MPP also varies accordingly. To maintain constant operation at the MPP, Maximum Power Point Tracking (MPPT) algorithms are employed to trace the MPP during module operation. These algorithms can be categorized into four groups: classical, intelligent, optimization, and hybrid, based on the tracking algorithm utilized. Each MPPT algorithm, existing in these categories, comes with its own set of advantages and limitations. This paper extensively reviews fifteen algorithms categorized under different groups. The review concludes with a comparative analysis of these algorithms, considering various parameters such as cost, complexity, tracking accuracy, and sensed parameters in a succinct manner. The paper focuses on elucidating the necessity of MPPT algorithms, their classification as per existing literature, and a comparative assessment of the studied MPPT algorithms. This comprehensive review aims to address advancements in this field, paving the way for further research. [ABSTRACT FROM AUTHOR]

Parameter estimation of photovoltaic (PV) solar cells and module models pays attention to researchers owing to their importance in practical considerations. The single diode model (SDM) circuit with five unknown parameters is widely used to model PV solar cells and modules. In this paper, a novel approach called alternate optimization (AO) algorithm based on a discrete search is proposed to estimate the SDM parameters. The proposed algorithm provides efficient and robust performance, considering a limited set of discrete values and increasing the convergence speed. Two practical case studies with actual measurements are considered to assess the proposed AO algorithm: the RTC France solar cell and monocrystalline PV modules with different irradiations and temperatures. The numerical findings underscore the superior performance of the proposed AO algorithm across various metrics. Notably, it achieves an exceptional Root Mean Square Error (RMSE) of 7.7426 × 10−04 for the RTC France PV cell and approximately 1 × 10−03 RMSE for monocrystalline PV modules. Additionally, the algorithm exhibits unparalleled speed, showcasing the fastest convergence with an elapsed time of 1.66 × 10−05—markedly 4.45 times quicker than the fastest method documented in the literature for SDM parameter estimation. Furthermore, the proposed AO algorithm stands out for its efficiency, requiring a maximum of five iterations for parameter estimation, a substantial improvement compared to the more than 10 iterations typically needed by algorithms in the existing literature. Its robustness is also commendable, as evidenced by the stability of final RMSE values across a variety of experiments, distinguishing it from less robust algorithms found in the literature. [ABSTRACT FROM AUTHOR]

In this paper, an improved speed sensorless control method including the maximum power point tracking (MPPT) algorithm for grid-connected squirrel-cage induction generator (SCIG) wind turbine systems using modified reduced-order generalized integrator (ROGI)–frequency-locked loop (FLL) with the DC offset compensation capability is proposed. The rotor flux linkages are estimated by the modified ROGI-FLL-based observer, of which the inputs are d-q axis rotor EMFs, and hence the position of rotor flux linkage can be obtained directly based on these estimated flux linkages using the arc tangent function. The DC offset in the estimated rotor flux linkages, which can cause oscillations in estimated rotor speed, leading to oscillations in SCIG stator active power due to power signal feedback (PSF)-MPPT algorithm, can be significantly reduced using the DC offset compensators included in modified ROGI-FLL structure. Moreover, the negative effects of high-frequency components on the performance of the rotor flux linkage estimation can be remarkably mitigated owing to the excellent high-frequency component rejection capability of ROGI. The dynamic response analysis of the modified ROGI-FLL with DC offset compensators is provided as well. The feasibility of the proposed method has been demonstrated in comparison with dual SOGI-FLL with DC offset compensator-based existing method. [ABSTRACT FROM AUTHOR]

The main purpose of this study was to research and develop maximum power point tracking (MPPT) of a photovoltaic module array (PVMA) with partial module shading and sudden changes in solar irradiance. Modified cat swarm optimization (MCSO) was adopted to track the global maximum power point (GMPP) of the PVMA. Upon a sudden changes in solar irradiance or when certain modules in the PVMA were shaded, the maximum power point (MPP) of the PVMA will change accordingly, and multiple peak values may appear on the power–voltage (P-V) characteristic curve. Therefore, if the tracking pace is constant, the time required to track the MPP might extend, and under certain circumstances, the GMPP might not be tracked, as only the local maximum power point (LMPP) can be tracked. To prevent this problem, a maximum power point tracker based on MCSO is proposed in this paper in order to adjust the tracking pace along with the slope of the P-V characteristic curve and the inertia weight of the iteration formula. The initial voltage for tracking commencement was set to 0.8 times the voltage at the maximum power point of the PVMA under standard test conditions. Firstly, MATLAB 2022a was used to construct the four-series, three-parallel PVMA model under zero shading and partial shading. The feedback of PVMA voltage and current was obtained, where the GMPP was tracked with MCSO. From the simulation results, it was proven that, under different shading percentages and sudden changes in solar irradiance for partial modules in the PVMA, the MCSO proposed in this paper provided better tracking speed, dynamic response, and steady performance compared to the conventional CSO. [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 pursuit of sustainable transportation, green energy sources (GES) are taking center stage, propelling traction vehicles like tramways and trolleybuses towards a zero-emission future. Solar is the most prominent source among the available renewable sources and also for the transport system applications. To utilize photovoltaic (PV) source effectively, model predictive controller-based PV maximum power tracking algorithm is used to identify the PV parameters. Equipped with the smart energy storage system (SESS), light traction vehicles rely on the efficiency and reliability of brushless DC (BLDC) motors for smooth operation. However, BLDC motors operate at high voltages, which requires them to be connected to high voltage microgrids. Bridging the gap between ESS/SESS and the highvoltage microgrid with traditional DC-DC boost converters incurs efficiency losses and component stress. This paper tackles high-voltage needs in microgrids through innovative, efficient DC-DC boost converter designs. An innovative finite-control-set based model-predictive control (FCS MPC) controller tackles clean energy harnessing from PV and grid stability in traction applications, enabling optimized power sharing within microgrid constraints. [ABSTRACT FROM AUTHOR]

In this paper, a novel multi-objective optimization strategy is proposed for the parallel tuning of six fractional order controllers used in regulation loops of a PMSG based wind energy conversion system connected to the electric grid. The nonlinear nature of the WECS components has made controllers design challenging. To enhance the transient response of system variables, Fractional Order Proportional Integral (FOPI) controllers are considered as they are more suitable for such nonlinear physical systems. The additional parameters introduced by FOPI are normally tuned using a single objective, multi-dimensional particle swarm optimization algorithm. However, the inter-dependence of regulation loops introduces additional complexity that is solved in this work using a multi-objective optimization strategy based on a succession of Single-Objective PSO and Multi-Objective PSO. A simulation study has been conducted in order to demonstrate the higher performance and superior tracking accuracy of the proposed multi-objective optimization strategy in variable wind speed operating conditions. [ABSTRACT FROM AUTHOR]

In this paper, a method based on an improved intelligent bat algorithm (IIBA) in cooperation with a voltage and current sensor was applied in maximum power point tracking (MPPT) for a photovoltaic module array (PVMA), where the power generation performance of a PVMA was enhanced. Due to the partial shading of the PVMA from climate changes or the surrounding environment, multiple peak values were generated on the power–voltage (P-V) curve, where the conventional MPPT technology could only track the local maximum power point (LMPP), hence the reduction in output power of PVMAs. Therefore, the IIBA-based MPPT was proposed in this paper to solve such issues and to ensure the capability of a PVMA in tracking the global maximum power point (GMPP) and utilization for enhancing the output power of a PVMA. Firstly, the Matlab/Simulink software was used to establish a boost converter model that simulated the actual 4-series–3-parallel PVMA under different shaded conditions, where the P-V curve with 1-peak, 2-peak, 3-peak and 4-peak values were generated. Subsequently, the tracking paces of the conventional bat algorithm (BA) were adjusted according to the gradient of the P-V curve for a PVMA. At the same time, 0.8 times the maximum power point (MPP) voltage Vmp under standard test conditions (STCs) for a PVMA was set as the initial tracking voltage. Lastly, the simulation results proved that under different environmental impacts, the proposed IIBA led to better performances in tracking both dynamic and steady responses. [ABSTRACT FROM AUTHOR]

This article presents a low‐cost maximum power point tracking (MPPT) charge controller designed to efficiently charge solar batteries using a simple and robust MPPT method, while providing protection against over‐currents and over‐voltages. The proposed MPPT approach utilizes a modified Incremental Conductance (INC) algorithm adapted to low‐cost embedded controllers, as it avoids all the mathematical division calculations typically used in the conventional INC algorithm. It also introduces a modified step size based only on photovoltaic (PV) power change to enhance tracking accuracy and convergence speed. Furthermore, to reduce the stress and switching losses of DC–DC converter, a feedback control with two proportional and integral (PI) compensators is also used to control both the PV voltage and to protect the battery from over‐currents using PV power limitation technique. Simulation results, carried out under MATLAB/Simulink, demonstrate the good performance of the proposed control scheme regarding any variations in atmospheric conditions, both for the MPPT control and for the battery charge control. Finally, a processor‐in‐the‐loop (PIL) test using a low‐cost Arduino MKR ZERO board is also performed to verify the hardware implementation of the proposed method. PIL results are in totally accordance with MATLAB/Simulink simulation results, which further proves the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira. [ABSTRACT FROM AUTHOR]

This article is an editorial that discusses the potential of solar photovoltaic (PV) systems in contributing to the achievement of the United Nations' Sustainable Development Goals (SDGs). The authors highlight the importance of renewable energy systems, particularly solar PV systems, in addressing global disparities and improving living standards. They emphasize the need for research and innovation to enhance the efficiency and cost-effectiveness of solar PV systems. The article also provides summaries of various studies that explore different aspects of solar PV systems, such as performance assessment, power quality improvement, PV array layout, and environmental impact. The authors conclude by advocating for the transition to sustainable energy sources like solar PV to achieve carbon neutrality. [Extracted from the article]

Using a particle swarm optimization algorithm (a population-based stochastic optimization technique) combined with 3D finite-difference time-domain simulations, we inverse design periodic arrays of metallic nanoparticles on indium-tin-oxide electrodes and nanoholes in metallic thin films working as electrodes in P3HT (Poly(3-hexylthiophene-2,5-diyl)):PCBM ([6,6]-Phenyl C61 butyric acid methyl ester) organic solar cells to achieve the maximum short-circuit currents (J s c ). Nanohole-array electrodes have large optical losses, leading to a net reduction of J s c compared to a reference solar cell. On the other hand, nanoparticle arrays can lead to a significant enhancement of J s c of up to 20%. Detailed simulations show that this enhancement is caused by the grating coupling of the incident light to surface plasmon polaritons at the interface of the metal electrode and the hole transport layer, leading to the enhancement of the electromagnetic field in the organic blend. [ABSTRACT FROM AUTHOR]

Introduction. In recent times, there has been a growing popularity of photovoltaic (PV) systems, primarily due to their numerous advantages in the field of renewable energy. One crucial and challenging task in PV systems is tracking the maximum power point (MPP), which is essential for enhancing their efficiency. Aim. PV systems face two main challenges. Firstly, they exhibit low efficiency in generating electric power, particularly in situations of low irradiation. Secondly, there is a strong connection between the power output of solar arrays and the constantly changing weather conditions. This interdependence can lead to load mismatch, where the maximum power is not effectively extracted and delivered to the load. This problem is commonly referred to as the maximum power point tracking (MPPT) problem various control methods for MPPT have been suggested to optimize the peak power output and overall generation efficiency of PV systems. Methodology. This article presents a novel approach to maximize the efficiency of solar PV systems by tracking the MPP and dynamic response of the system is investigated. Originality. The technique involves a multivariate linear regression (MLR) machine learning algorithm to predict the MPP for any value of irradiance level and temperature, based on data collected from the solar PV generator specifications. This information is then used to calculate the duty ratio for the boost converter. Results. MATLAB/Simulink simulations and experimental results demonstrate that this approach consistently achieves a mean efficiency of over 96 % in the steady-state operation of the PV system, even under variable irradiance level and temperature. Practical value. The improved efficiency of 96 % of the proposed MLR based MPP in the steady-state operation extracting maximum from PV system, adds more value. The same is evidently proved by the hardware results.

We describe the relation of the carrier lifetime of a light-absorber material determined with pulse-excitation time-resolved techniques to the steady-state carrier density and lifetime in a solar cell under continuous-wave excitation. Our approach constitutes a simple experimental examination of the excitation-fluence-dependent carrier lifetime of absorber materials. It provides the steady-state carrier density and lifetime under 1-sun solar illumination for metal halide perovskite solar cells. The determination of the steady-state carrier responses allows the clarification of optical and photovoltaic properties under 1-sun illumination and thus the identification of loss mechanisms in device performance. Model calculations are also provided to show how the carrier lifetime governs the luminescence quantum yields and open-circuit voltages. The calculations quantify a scaling law between a monomolecular recombination lifetime and an open-circuit voltage as a result of a combination of two density-dependent effects. [ABSTRACT FROM AUTHOR]

Solar energy generation requires photovoltaic (PV) systems to be optimised, regulated, and simulated with efficiency. The performance of PV systems is greatly impacted by the fluctuation and occasionally restricted accessibility of model parameters, which makes it difficult to identify these characteristics over time. To extract the features of solar modules and build highly accurate models for PV system modelling, control, and optimisation, current-voltage data collecting is essential. To overcome these difficulties, the modified particle swarm optimization rat search algorithm is presented in this manuscript. The modified rat search algorithm is incorporated to increase the PSO algorithm's accuracy and efficiency, which leads to better outcomes. The RSA mechanism increases both the population's diversity and the quality of exploration. For triple diode model of both monocrystalline and polycrystalline, PSORSA has showed exceptional performance in comparison to other algorithm i.e. RMSE for monocrystalline is 3.21E-11 and for polycrystalline is 1.86E-11. Similar performance can be observed from the PSORSA for four diode model i.e. RMSE for monocrystalline is 4.14E-09 and for polycrystalline is 4.72E-09. The findings show that PSORSA outperforms the most advanced techniques in terms of output, accuracy, and dependability. As a result, PSORSA proves to be a trustworthy instrument for assessing solar cell and PV module data. [ABSTRACT FROM AUTHOR]

In the Bamenda Municipality of Cameroon households are adopting Solar Photovoltaic Systems (SPVS). The penetration of SPVS in this Municipality depends on their technical performance. The study aimed to evaluate the technical installation of SPVS within the Municipality. A field inspection and administration of a questionnaire was conducted. The field inspection evaluated the respect of technical installation norms for SPVS. The questionnaire captured data on the technical situation of the SPVS. The SPVS installed included PV and grid to power separate loads, and PV and grid to power same loads. The installed loads were a mix of AC and DC loads of capacity from 360 W to 10000 W. The load powered by the installed SPVS varied from 300 W to 7000 W. The PV array varied from 200 W to 3200 W and battery bank capacity of 100 Ah to 800 Ah. The PV arrays were mostly installed on roof tops. Only 5% of the SPVS were installed by certified personnel. More than 50% of the installed SPVS operated below designed operation time. Failures in installed systems were related to inverters (36 %) and battery banks (36 %). Most of the PV arrays were installed on rooftops at tilt angles between 20° and 50°. More than 50 % of the PV arrays were oriented to directions other than South. Protective devices were installed in only 14 % of the installed systems. Some of the SPVS were not properly dimensioned. It may be concluded that most of the installed SPVS do not respect the technical installation norms and were not dimensioned according to users' needs. The survival and penetration of SPVS technology in the Bamenda Municipality, Cameroon, and other sub-Saharan communities requires awareness and capacity building, policies, and regulations in the design and installation of this technology. [ABSTRACT FROM AUTHOR]

Renewable energy sources are being increasingly deployed to achieve carbon neutrality, thereby boosting photovoltaic (PV) system adoptions. Accordingly, vertical PV systems designed for specific installations have been developed. We propose a strategy to enhance the PV hosting capacity of a connected distribution line (DL) by combining vertical installations with modules facing east-west and conventional PV systems with modules facing south at an installation angle of approximately 30°. The data were obtained from a real testbed located in South Korea, which is situated in a mid-latitude region. We analyzed the generation patterns of vertical and combined PV systems (vertical and conventional) to enhance the hosting capacity. The results showed that vertical PV combination ratios of 40–60% effectively flattened the peak generation curve. Additionally, the DL hosting capacity improved by 40% under real-world conditions. In an actual industrial scenario, the system feasibility was validated to be within the voltage maintenance range and thermal capacity of lines in South Korea, indicating that this approach can mitigate the need for additional line installations and renewable energy curtailments. Furthermore, the issue of Duck Curves in the power grid can be addressed by smoothing the power production of the PV systems, particularly during low-demand periods. [ABSTRACT FROM AUTHOR]

The paper presents an investigation of the performance of the proton exchange membrane (PEM) electrolyser directly connected with large-scale PV systems under faulty, degradation and partial shading conditions. With the direct coupled system, the power loss and external price on auxiliary devices are avoided. However, the adaptability of the direct coupled system to fluctuate solar irradiance and ambient temperature is reduced significantly compared to systems with DC-DC converters. To maximise the system efficiency, the optimal electrolyser (ELY) cell configuration is identified using an optimal sizing approach or operating strategy. This paper applied the Particle Swarm Optimisation (PSO) algorithm to find the optimal configuration for the two approaches, namely the Sizing technique and Operating strategy under different PV systems: faults, degradation and partial shading conditions. Additionally, a large-scale system is simulated to describe the characteristics of a large-scale direct coupled PV-electrolyser system with actual operating faults and phenomena. Simulations of the large-scale direct coupling system were conducted using measured irradiance and temperature data for a typical day period. Based on the collected output, it is found that compared to the optimal sizing approach, the optimal operating strategy allows the direct coupled system to increase its coupling efficiency from 0.17% to 4.47%. In contrast, the amount of hydrogen production when applying the two methods witnessed different trends, depending on the type of faults. The differences are even more significantly presented during multiple short-circuit fault scenarios. • Modelling of a large-scale direct coupling PV-electrolyser system up to 1 MW. • An analysis of typical PV system faults, partial shading, and degradation phenomenon. • A comparison of the optimal sizing approach and operating strategy. • Proposed optimal operating strategy allows 99.99% coupling factor in normal operation. [ABSTRACT FROM AUTHOR]

The world depends heavily on electrical energy for accessing technologies. For the generation of electricity, technology can utilize renewable energy sources like solar energy and wind energy. Solar photovoltaic (PV) systems occupy space among consumers due to their feasibility, flexibility, cost, and simple implementation procedures. The solar PV system experiences many factors causing power loss like partial shading, hotspots, and diode failure. In this work, a new static PV array configuration, named Renzoku puzzle patternbased array configuration, is proposed. This proposed configuration technique was designed to overcome the drawbacks of the previously proposed array configurations in terms of power generation, fewer mismatch losses, a high shade-dispersion rate, and consistent performance under any level of partial shading. The proposed array configuration has been validated using both simulation and hardware. The simulation is carried out in a 9 x 9 PV array in MATLAB/Simulink®. The performance analysis, results, and corresponding characteristic curves are presented in this manuscript. [ABSTRACT FROM AUTHOR]

Localization in hospitals can be valuable in improving different services in medical environments. In this sense, an accurate location system in this environment requires adequately enabling communication technology. However, widely adopted technologies such as Wireless Fidelity (WiFi), Bluetooth, and Radio Frequency Identification (RFID) are considered poorly suited to enable hospital localization due to their inherent drawbacks, including high implementation costs, poor signal strength, imprecise estimates, and potential interference with medical devices. The increasing expenses associated with the implementation and maintenance of these technologies, along with their limited accuracy in dynamic hospital environments, underscore the pressing need for alternative solutions. In this context, it becomes imperative to explore and present novel approaches that not only avoid these challenges but also offer more cost effective, accurate, and interference-resistant connectivity to achieve precise localization within the complex and sensitive hospital environment. In the quest to achieve adequate localization accuracy, this article strategically focuses on leveraging Visible Light Communication (VLC) as a fundamental technology to address the specific demands of hospital environments to achieve the precise localization and tracking of life-saving equipment. The proposed system leverages existing lighting infrastructure and utilizes three transmitting LEDs with different wavelengths. The Received Signal Strength (RSS) is used at the receiver, and a trilateration algorithm is employed to determine the distances between the receiver and each LED to achieve precise localization. The accuracy of the localization is further enhanced by integrating a trilateration algorithm with the sophisticated Particle Swarm Optimization (PSO) algorithm. The proposed method improves the localization accuracy, for example, at a height of 1 m, from a 11.7 cm error without PSO to 0.5 cm with the PSO algorithm. This enhanced accuracy is very important to meet the need for precise equipment location in dynamic and challenging hospital environments to meet the demand for life-saving equipment. Furthermore, the performance of the proposed localization algorithm is compared with conventional positioning methods, which denotes improvements in terms of the localization error and position estimation. [ABSTRACT FROM AUTHOR]

This paper introduces a novel five-port, three-input, dual-output isolated bidirectional dc-dc converter (FPIBC) topology with an effective controller for power-sharing and voltage-balancing in bipolar dc microgrids (BPDCMGs). The proposed converter acts as the interface for the integration of a hybrid generation system comprising a solid oxide fuel cell (SOFC), a photovoltaic (PV) system, and a battery into BPDCMGs. It employs a reduced number of circuit elements compared with similar multiport converter topologies suggested for BPDCMG applications. Symmetrical bipolar output voltages are ensured by a voltage-balancing circuit composed of a fully controlled switch and four diodes. The FPIBC is equipped with different controllers for output voltage regulation and balancing, power sharing, maximum power point tracking of the PV, the optimum operating region of the SOFC, and constant-current, constant-voltage charging of the battery. To verify the viability and effectiveness of the proposed system, a simulation model was developed with a 4.2 kW SOFC, a 3.7 kW PV, and a 140 V 10.8 Ah battery in MATLAB/Simulink. The performance of the FPIBC was evaluated through extensive case studies with different operational modes, including battery charge/discharge states and SOFC and PV parameter changes under varying load conditions. In addition, the proposed system was examined using a daily dynamic load profile. According to the simulation results, a peak efficiency of 97.28% is achieved and the voltage imbalance between the output ports is maintained below 0.5%. It is shown that the FPIBC has advantages over previous converters in terms of the number of ports, number of circuit elements, bipolar output voltage, bidirectional power flow, and efficiency. [ABSTRACT FROM AUTHOR]

Efficiency losses resulting from electrical mismatching in densely packed photovoltaic arrays present a significant challenge, particularly exacerbated in nonuniformly illuminated receivers and under varying temperatures. Serial configurations are particularly susceptible to radiation nonuniformities, while parallel systems are negatively affected by temperature variations. Various authors have recommended the incorporation of electrical voltage and current sources to mitigate these losses. This study explores different electrical connection configurations utilizing concentrated photovoltaic (CPV) cells and DC-DC electrical current converters. A self-adaptive microfluidic cell matrix cooling system is employed to mitigate thermal dispersion caused by the highly nonuniform illumination profile. The obtained results for each configuration are compared with the total electrical power produced by individual cells, operating under identical radiation and temperature conditions to those of the entire array. The results reveal a noteworthy increase in production across all studied configurations, with the parallel–series arrangement demonstrating the most promising practical utility. This configuration exhibited a remarkable 50.75% increase in power production compared with the standard series connection. [ABSTRACT FROM AUTHOR]

The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research into renewable energy sources. The accurate prediction of temperature and solar irradiance is essential for optimizing the performance and grid integration of PV systems. Machine learning (ML) has become an effective tool for improving the accuracy of these predictions. This comprehensive review explores the pioneer techniques and methodologies employed in the field of ML-based forecasting of temperature and solar irradiance for PV systems. This article presents a comparative study between various algorithms and techniques commonly used for temperature and solar radiation forecasting. These include regression models such as decision trees, random forest, XGBoost, and support vector machines (SVM). The beginning of this article highlights the importance of accurate weather forecasts for the operation of PV systems and the challenges associated with traditional meteorological models. Next, fundamental concepts of machine learning are explored, highlighting the benefits of improved accuracy in estimating the PV power generation for grid integration. [ABSTRACT FROM AUTHOR]

The maximum power point tracking (MPPT) system ensures that the solar cell operates at its maximum power point, which is the point at which it produces the most power for a given set of conditions. This study uses an artificial bee colony (ABC) algorithm and particle swarm optimization (PSO) algorithms to optimize the boost converter. The ABC and PSO algorithms help to optimize the boost converter by finding the best possible duty cycle and other parameters that maximize efficiency. The resulting duty cycle of 20% produced the highest efficiency of 78.26% obtained from the boost converter. A system-wide test was conducted between 9:00 and 11:10 WIB. In MPPT systems using boost converters with the PSO algorithm, the average power is 16.80 W, while in MPPT systems using boost converters with the ABC algorithm, the average power is 14.53 W. The novelty of the study discusses a system designed to optimize power by using a boost converter in order to increase power output from solar panels. The algorithms PSO and ABC are used to analyze the data from the PV and adjust the converter parameters to optimize the output in order to increase the efficiency of the system. [ABSTRACT FROM AUTHOR]

The position of MPP of a solar panel changes with changes in weather conditions such as radiation, temperature and partial Shadow. In such a situation, several local MPPs and one GMPP are created in the P-V curve, and the common methods of MPPT(P&O, IC,...) have converged due to the failure to recognise the local MPP. They cause a lot of losses to the photovoltaic system. Therefore, in this article, it has been tried to provide a new method of particle swarm optimisation with variable coefficients, which in combination with the perturb and observe method can reach the GMPP in the fastest possible time. In this proposed method, we first reach the convergence of the GMPP using the VCPSO method, and after converging to it, we go to the P&O method and use P&O until significant changes are made in the system, and if observed Variations To re-examine the GMPP, we return to the VCPSO algorithm. The results of the simulation of the efficiency of the proposed method show that this method was able to reach the GMPP in the shortest time, and the efficiency of the photovoltaic system increases and the effects of partial shading are reduced. [ABSTRACT FROM AUTHOR]

Satellite altimetry has enhanced the understanding of ocean dynamics through high-rate sampling and global coverage. However, land contamination and bad reflection effects limit its accuracy. We present a geometrical method for retracking altimetry waveforms in coastal areas. Our method follows a geometrical assumption related to the symmetrical reciprocal motion of the radar pulse. Based on this assumption, the altimetry waveform is modelled as a continuous and differentiable third-order spline function, and the symmetry point of this function is considered as the retracking gate. The spline retracking algorithm is validated against the tide gauges at Onsala, Halmstad, and Muscat stations in Sweden and Oman, and its performance is compared with existing retracking algorithms. Our results showed a remarkable reduction of 50-91% in the unbiased-Root-Mean-Squared-Error (ubRMSE) and an increase of at least 13% in correlation coefficients when compared with other algorithms in Swedish coast. This algorithm presented equivalent results with the threshold and improved threshold retrackings in Muscat station, based on Jason-2 measurements. However, along the Jason-3 pass, our spline method showed a considerable reduction of 80% in ubRMSE and the minimum increase of 42% in correlation coefficients than the empirical algorithms. This method also outperformed the ALES algorithm in most cases. [ABSTRACT FROM AUTHOR]

Accurately measuring the combustion and destruction removal efficiency of flaring is important when accounting for methane emissions from oil and gas production. Despite this, the amount of experimental data from full-size flares is limited, especially for flares built without air or steam assistance. The use of a single destruction value of 98% is commonly applied. In this paper, we present new empirical measurements of flare efficiency using three common flare designs employed in upstream applications. Combustion products were analyzed using an extractive sampling method. The results demonstrate that whilst destruction efficiencies in excess of 98% are achievable, if the gas composition falls below a critical heating value of ~300 BTU/scf, the efficiency deteriorates leading to elevated methane emissions. This is further complicated by accurately measuring the flow of combustible gas and the impact of crosswinds. In an operational setting, continuous tracking of flare conditions is therefore a key resource in reducing methane emissions but further work is required to standardize how continuous performance tracking is evaluated if such measurements are to attain full traceability. [ABSTRACT FROM AUTHOR]

Summary: The demand for green energy sources has led to the exploration of photovoltaic (PV) systems as a sustainable and cost‐effective solution. However, PV systems have an uncertain nature that makes the tracking of global maximum power point a challenge. To get the optimal benefits from the PV source, the extraction of power is required in a precise manner, especially in the case of irregular complex partial shading conditions (CPSCs). In the case of CPSCs, the global maximum point tracking (GMPP) is a bit difficult to manage by the conventional algorithms presented so far. As the power peaks increase with CPSCs, the conventional algorithms are usually stuck in the local maxima. To avoid sticking at all the local maxima points and to find GMPP with better efficiency, stretching is used here along with the repulsion technique. Stretching and repulsion techniques are merged with the chimp optimization algorithm (SRCA). The tuning parameters of SRCA are so effective that remove all local points and lead to finding the GMPP accurately. The proposed algorithm is applied to the PV array to extract optimal power off‐grid and on‐grid as well. The average tracking time and output power efficiency in the case of off‐grid is observed as 0.4 s and 99.87% while in the case of on‐grid as 0.09 s and 98.88%, respectively. [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]

A generalized closed-form equation for the shaded collector fraction in solar arrays on rolling or undulating terrain is provided for single-axis tracking and fixed-tilt systems. The equation accounts for different rotation angles between the shaded and shading trackers, cross-axis slope between the two trackers, and offset between the collector plane and axis of rotation. The validity of the equation is demonstrated through comparison with numerical ray-tracing simulations and remaining minor sources of error are quantified. Additionally, a simple procedure to determine backtracking rotations for each row in an array installed on the rolling terrain (varying in the direction perpendicular to the tracker axes) is provided. The backtracking equation accounts for a desired shaded fraction (including complete shade avoidance) as well as an axis-collector offset. Test cases are provided to facilitate implementation of these equations. [ABSTRACT FROM AUTHOR]

Traditional methods for estimating output property of the photovoltaic (PV) modules are strongly influenced by the selection of reference condition and transforming equations, which determine the calculated physical parameters under real operating conditions. The differences in the carrier transport properties of PV cells under varying operating conditions, such as the number and velocity of minority carriers at the junction edge and their recombination speed, lead to large deviations in the estimation of the output characteristics, especially under low irradiance conditions. To enhance the accuracy of performance estimation, we propose an improved method that is independent of reference condition. This method eliminates the impact of reference conditions and improves the transformation equations under all irradiance levels. Transformation equations of single diode model are established in different irradiance intervals based on the dependence of physical parameter on irradiance and temperature. Especially in the low irradiance range, all effects of irradiance and temperature are considered for each physical parameter in improved transformation equations. To optimize the unknown parameters in the transformation equations, the artificial hummingbird algorithm is used to fit experimental I–V data. The experimental results of six different types PV modules under a wide range of operating conditions are used to verify the effectiveness of the proposed method. The proposed method offers immediate benefits, including independence from reference condition and a more precise relationship between physical parameters and environmental factors in the estimation of PV output properties. Comparing the results to the traditional method by Laudani, the proposed method demonstrates superior capability in estimating I–V characteristics and accurately identifies the maximum power point under various operating conditions, which is of significant value for engineering applications. [ABSTRACT FROM AUTHOR]