Conventional Point Research Articles

Quantum Marine Predator Algorithm: A Quantum Leap in Photovoltaic Efficiency Under Dynamic Conditions

The Quantum Marine Predator Algorithm (QMPA) presents a groundbreaking solution to the inherent limitations of conventional Maximum Power Point Tracking (MPPT) techniques in photovoltaic systems. These limitations, such as sluggish response times and inadequate adaptability to environmental fluctuations, are particularly pronounced in regions with challenging weather patterns like Sunderland. QMPA emerges as a formidable contender by seamlessly integrating the sophisticated hunting tactics of marine predators with the principles of quantum mechanics. This amalgamation not only enhances operational efficiency but also addresses the need for real-time adaptability. One of the most striking advantages of QMPA is its remarkable improvement in response time and adaptability. Compared to traditional MPPT methods, which often struggle to keep pace with rapidly changing environmental factors, QMPA demonstrates a significant reduction in response time, resulting in up to a 30% increase in efficiency under fluctuating irradiance conditions for a resistive load of 100 Ω. These findings are derived from extensive experimentation using NASA’s worldwide power prediction data. Through a detailed comparative analysis with existing MPPT methodologies, QMPA consistently outperforms its counterparts, exhibiting superior operational efficiency and stability across varying environmental scenarios. By substantiating its claims with concrete data and measurable improvements, this research transcends generic assertions and establishes QMPA as a tangible advancement in MPPT technology.

SiamVIT: A patchwise network for gamma-ray point source detection

Conventional point source detection methods generally work in a pixelwise manner and can hardly exploit the overall semantic information of sources; consequently, these methods usually suffer from low precision. In this work we achieve point source detection in fully patchwise mode by proposing a siamese network called SiamVIT that includes a visual transformer (VIT). SiamVIT can effectively and accurately locate point sources from gamma -ray maps with high purity not only in higher flux regions, but also in lower flux regions, which is extremely challenging to achieve with state-of-the-art methods. SiamVIT consists of two VIT branches and a matching block. In the feature extraction stage, gamma -ray maps are fed into one VIT branch to obtain patch representations with adequate semantic and contextual information, whereas detection templates with location information are fed into the other branch to produce template representations. In the location stage, a patch representation and all template representations are fed into the matching block to determine whether the associated gamma -ray map patch contains a point source and where that point source is located, if applicable. We compare our proposed SiamVIT with the current advanced methods and find that SiamVIT has significantly better purity and completeness and a superior Dice coefficient on the test set. In addition, when point sources overlap, SiamVIT can better distinguish different point sources.

Study on road performance and flame retardant properties of direct-to-plant warm mixing-flame retardant SBS composite modified asphalt

In order to mitigate the current state of the asphalt tunnel fire crisis, this study intends to prepare a novel environmentally friendly direct-to-plant warm mixing-flame retardant SBS composite modified asphalt that will slow down the burning of asphalt itself and reduce the amount of smoke that is produced. This research introduced the preparation process for direct-to-plant SBS modified asphalt. Through conventional performance experiments, flash point and oxygen index tests, and based on DSR and BBR tests, a comparative analysis of ATH and HF-216 flame retardants was conducted. Additionally, the road performance and flame retardant properties of various warm mix types were analyzed by compounding them with flame retardants. The findings indicate that ATH and HF-216 flame retardants can, respectively, raise the softening point by 13 % and 16 %, lower the ductivity by 6 % and 7 %, and decrease the needle penetration of D-SBS by 10 % and 18 %.Both can enhance asphalt's high-temperature characteristics while negatively affecting its low-temperature characteristics. Warm mixing agent and flame retardant can greatly increase the consistency and deformation resistance of direct-to-plant SBS-modified asphalt; however, they also reduce the ductility of the material by 26 %, 16 %, and 12 %, respectively, which will negatively impact its performance at low temperatures. The composite modified asphalt's high temperature stability can be enhanced by compound mixing the three types of warm mixture before and after aging; nevertheless, the fatigue performance declines to varying degrees and the fatigue factor rises by 3535, 3811, and 3889 kPa, respectively. The warm mixture and flame retardant mixture's S value is 82.5 MPa, 33.7 MPa, 13.8 MPa (-12°C), and 187.4 MPa, 90.7 MPa, and 40.6 MPa (-18°C) higher than the D-SBS mixture's, respectively. The low temperature brittleness of D-SBS can be increased by combining a heated mixture with a flame retardant. The burning times of the Marshall and rut plate specimens are shortened by 31, 46, 55, and 86 s, respectively, when compared to the D-SBS combination. The residual stability is raised by 19.2 %, 21.2 %, and 6 %, 10.7 %, respectively, while the mass loss before and after combustion is decreased by 3.9 g, 5.8 g, and 26 g, 32 g, respectively. AH type exhibits superior flame retardant performance than WH type.

Comprehensive Analysis of Improved Hunter–Prey Algorithms in MPPT for Photovoltaic Systems Under Complex Localized Shading Conditions

The Hunter–Prey Optimization (HPO) algorithm represents a novel population-based optimization approach renowned for its efficacy in addressing intricate problems and optimization challenges. Photovoltaic (PV) systems, characterized by multi-peaked shading conditions, often pose a challenge to conventional maximum power point tracking (MPPT) techniques in accurately identifying the global maximum power point. In this research, an MPPT control strategy grounded in an improved Hunter–Prey Optimization (IHPO) algorithm is proposed. Eight distinct shading scenarios are meticulously crafted to assess the feasibility and effectiveness of the proposed MPPT method in capturing the maximum power point. A performance evaluation is conducted utilizing both MATLAB/simulation and an embedded system, alongside a comparative analysis with alternative power tracking methodologies, considering the diverse climatic conditions across different seasons. The simulation outcomes demonstrate the capability of the proposed control strategy in accurately tracking the global maximum power point, achieving a commendable efficiency of 100% across seven shading conditions, with a tracking response time of approximately 0.2 s. Verification results obtained from the experimental platform illustrate a tracking efficiency of 98.75% for the proposed method. Finally, the IHPO method’s output performance is evaluated on the StarSim Rapid Control Prototyping (RCP) platform, indicating a substantial enhancement in the tracking efficiency of the photovoltaic system while maintaining rapid response times.

Appropriate sampling to aid on-farm assessments of the haplotype composition of Zymoseptoria tritici populations.

Zymoseptoria tritici causes Septoria tritici blotch (STB), which is the biggest threat to wheat in the UK. Azole fungicides have been used since the 1980s to control STB, but resistance to these chemicals is now widespread. The main resistance mechanism is based on the accumulation of CYP51 mutations, with 33 mutations reported. Hence, farmers need an accurate estimate of the haplotype composition of Z. tritici populations to develop effective fungicide treatments and resistance management. Isolates from Z. tritici lesions were collected from three fields across three commercial farms using two sampling approaches. Analysis of the isolate sequences revealed that the number of distinct haplotypes and the haplotype composition of the most dominant haplotypes varied only between and not within farms. Conventional W-shaped and point sampling both found the same percentage of distinct haplotypes and frequencies of the six most dominant haplotypes. The results from this survey suggest that farm-resistance-management strategies should be based on farm-specific rather than national data, and that sampling within a single field is sufficient. W-shaped sampling is often recommended in sampling approaches, but this survey finds no evidence of this approach being more appropriate for detecting a greater percentage of distinct haplotypes which may aid the discovery of potential new resistance threats. © 2024 Fera Science Ltd. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

A new efficient imaging reconstruction method for muon scattering tomography

Muon scattering tomography (MST) is a promising technique in nuclear security inspection, utilizing its multiple scattering property which is sensitive to elements with high atomic numbers, and is also correlated with the material density. A proper imaging reconstruction method is essential for revealing the spatial distribution of materials within an object from measurements. This work proposes a new imaging reconstruction method called “big voxel and angle capping”. The scattering angle is reconstructed using the conventional Point of Closest Approach (PoCA) method. In the new method, reconstructed scattering angles belonging to voxels in an l×m×n matrix are aggregated into the center voxel, termed the “big voxel”. This significantly reduces statistical fluctuations in each big voxel. Subsequently, the influence of large scattering angles on determining material densities in big voxels is mitigated using a capping angle. The proposed algorithm is tested on the experimental data comprising approximately 1.8×104 muon scattering events detected in ∼ 24 h. The mean square error (MSE) and the structural similarity index measure (SSIM) indices are employed to quantitatively assess the imaging quality and optimize the new method. After scanning the two major parameters, the optimal parameter space is achieved with the big voxel size of ∼ 9, and the capping angle of ∼4∘ for tungsten blocks. The imaging quality is more sensitive to the capping angle than the big voxel size. An appropriate capping angle effectively removes large artifacts in reconstructed images caused by muon events with large scattering angles. This method is efficient in terms of both time and storage consumption.

A reassessment of graduation modeling for policy design

A vast and diverse literature estimates graduation chances using logistic models set in an arbitrary timeframe, where a graduation indicator is checked at a conventional point in time and associated with covariates measured at some date. Survival models emerged over time as a robust alternative, for being able to estimate time-to-degree and time-varying effects of predictors. This paper reconsiders the effectiveness of both modeling approaches in addressing policy-relevant questions, particularly in light of the increasingly automated and algorithm-based educational policies. We find that both methods exhibit blind spots and limitations, but that adopting a simple pragmatic approach logistic models can achieve a comparable level of effectiveness at depicting graduation dynamics while also being capable of answering questions that are problematic for survival models. We exploit a unique dataset and the nature of discrete-time survival models as combinations of logistic regressions run at different times to illustrate how arbitrary timeframes impact the estimates of a logistic model of graduation. Conversely, we illustrate how separately running and analyzing all the distinct logistic regressions provides insights that are unlikely to come from a survival model.

Hyperspectral Imaging Database of Human Facial Skin.

The perceived color of human skin is the result of the interaction of environmental lighting with the skin. Only by resorting to human skin spectral reflectance, it is possible to obtain physical outcomes of this interaction. The purpose of this work was to provide a cured and validated database of hyperspectral images of human faces, useful for several applications, such as psychophysics-based research, object recognition, and material modeling. The hyperspectral imaging data from 29 human faces with different skin tones and sexes, under constant lighting and controlled movements, were described and characterized. Each hyperspectral image, which comprised spectral reflectance of the whole face from 400 to 720 nm in 10 nm steps at each pixel, was analyzed between and within nine facial positions located at different areas of the face. Simultaneously, spectral measurements at the same nine facial positions using conventional local point and/or contact devices were used to ascertain the data. It was found that the spectral reflectance profile changed between skin tones, subjects, and facial locations. Important local variations of the spectral reflectance profile showed that extra care is needed when considering average values from conventional devices at the same area of measurement.

Influence of solar thermal radiations and convective boundary on Al2O3/H2O transient model efficiency

Riga plate is a new, sophisticated magnetic field device that may be created by adjusting a group of permanent magnets and a different electrode over a plane surface. The heat transport and fluid movement in such physical setup are of paramount interest and have numerous engineering and industrial application particularly in submarines technologies. Due to the fixed magnetics the field produced which imperatively affect the model dynamics. Keeping in mind the influential applications of such physical setup, the purpose of this study is to introduce a new model based scrutinization of heat transport of stagnation point flow of Al2O3/water along vertically oriented convectively heated Riga surface. The conventional stagnation point flow model extended for nanofluid via radiative heat flux, dissipation effects and the first order thermal slip. The values of thermal conductivity values estimated via Corcione model and achieved the final nanoliquid model. For the results interpretation, the numerical scheme used and portrayed the results using different parametric ranges. The fluid motion is observed very slow due to stronger mixed convection and higher concentration of Al2O3 nanoparticles. The temperature is determined very high against the stronger convection effects and radiation number. However, the fluid layers in the vicinity of Riga surface have high heat transmission ability. Further, thermal slip and viscous dissipation effects also boost the temperature of Al2O3/water. Further, buoyancy number δ resists the fluid movement and thermal boundary region reduces in the presence of buoyancy factor.

Surface Reconstruction from SLAM-Based Point Clouds: Results from the Datasets of the 2023 SIFET Benchmark

The rapid technological development that geomatics has been experiencing in recent years is leading to increasing ease, productivity and reliability of three-dimensional surveys, with portable laser scanner systems based on Simultaneous Localization and Mapping (SLAM) technology, gradually replacing traditional techniques in certain applications. Although the performance of such systems in terms of point cloud accuracy and noise level has been deeply investigated in the literature, there are fewer works about the evaluation of their use for surface reconstruction, cartographic production, and as-built Building Information Model (BIM) creation. The objective of this study is to assess the suitability of SLAM devices for surface modeling in an urban/architectural environment. To this end, analyses are carried out on the datasets acquired by three commercial portable laser scanners in the context of a benchmark organized in 2023 by the Italian Society of Photogrammetry and Topography (SIFET). In addition to the conventional point cloud assessment, we propose a comparison between the reconstructed mesh and a ground-truth model, employing a model-to-model methodology. The outcomes are promising, with the average distance between models ranging from 0.2 to 1.4 cm. However, the surfaces modeled from the terrestrial laser scanning point cloud show a level of detail that is still unmatched by SLAM systems.

Maximum correntropy polynomial chaos Kalman filter for underwater navigation

This paper develops an underwater navigation solution that utilizes a strapdown inertial navigation system (SINS) and fuses a set of auxiliary sensors such as an acoustic positioning system, Doppler velocity log, depth meter, and magnetometer to accurately estimate an underwater vessel's position and orientation. The conventional integrated navigation system assumes Gaussian measurement noise, while in reality, the noises are non-Gaussian, particularly contaminated by heavy-tailed impulsive noises. To address this issue, and to fuse the system model with the acquired sensor measurements efficiently, we develop a square root polynomial chaos Kalman filter based on maximum correntropy criteria. The proposed method uses Hermite polynomial chaos expansion to tackle the nonlinearity, and it has the potential to estimate the states in a more accurate way in presence of a non-Gaussian measurement noise. The filter is initialized using acoustic beaconing to accurately locate the initial position of the vehicle. The computational complexity of the proposed filter is calculated in terms of flops count. The proposed method is compared with the existing maximum correntropy sigma point filters in terms of estimation accuracy and computational complexity. It is found from the simulation results that the proposed method is more accurate compared to the conventional deterministic sample point filters and Huber's M-estimator.

Rapid terahertz beam profiling and antenna characterization with phase-shifting holography

In this paper we investigate the application of phase-shifting digital holography for the real-time characterization of electromagnetic sources in the THz frequency range. We use an off-the-shelf terahertz detector array composed of 64×64\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$64 \ imes 64$$\\end{document} power-sensitive pixels, over an area of 96mm×96mm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$96\\,\\hbox {mm} \ imes 96\\,\\hbox {mm}$$\\end{document}, to record intensity interferograms cast between the coherent radiation emitted from a reference source and an unknown antenna under test. This approach parallelizes the acquisition process with respect to conventional near-field point scanning methods, reducing the measurement time by orders of magnitude. In our system, the measurement time is limited only by the refresh rate of the detector array and the speed of a delay line stage that is used to phase-shift the reference wave. As a proof-of-principle demonstration, we map the 2D near-field distribution and estimate the far-field radiation pattern emitted by a plano-convex PTFE spherical lens antenna illuminated by a diagonal horn at 290 GHz frequency with ∼1Hz\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim \\,1\\,\\hbox {Hz}$$\\end{document} refresh rate.

Multilevel Middle Point Clamped (MMPC) Converter for DC Wind Power Applications

This manuscript introduces a novel multilevel middle point clamped (MMPC) DC-DC converter and its associated switching scheme aimed at maintaining the desired medium-voltage DC (MVDC) collector grid within offshore all-DC wind farms. Building upon previous work by the authors, which proposed an all-DC structure serving as a benchmark system, this study explores the application of the MMPC DC-DC converter within this framework. Within the all-DC wind generation system, a 9-phase hybrid generator (HG) integrated into the wind turbine is linked to the MVDC collector grid through an AC-DC stage, which is a passive rectifier. This passive rectifier offers elevated voltage ratings and protection against back power flow. The conventional neutral point clamped (NPC) converter concept has been thoroughly investigated and expanded upon to develop the proposed MMPC DC-DC converter. The proposed MMPC DC-DC converter integrates boosting capabilities, facilitating the connection of the generator’s rectified voltage to the MVDC collector grid while regulating variable rectified voltage to a fixed MVDC collector grid voltage. The MVDC collector grid is further interconnected with high-voltage DC (HVDC) through a DC-DC converter situated in an offshore substation. This paper further provides a comprehensive overview of the proposed MMPC DC-DC converter, detailing its operational modes and corresponding switching schemes. Through an in-depth examination of operational modes, duty cycles for each switch and mode are defined, subsequently establishing the relationship between rectified input voltage and MVDC output voltage for the MMPC DC-DC converter. Utilizing the middle point clamped architecture, this innovative converter offers several advantages, including low ripple voltage, a modular structure, and reduced switching stress because of the multilevel voltage and the incorporation of a hard point, which also facilitates the capacitor voltage balancing. Finally, the effectiveness of the proposed converter is evaluated via simulation studies of a wind turbine conversion system utilizing two cascaded MMPC DC-DC converters operating under variable input voltage conditions. The simulations confirm its efficacy, supported by promising results, and validating its performance.

Visual Dose Monitoring for Whole Breast Radiation Therapy Treatments via Combined Cherenkov Imaging and Scintillation Dosimetry

PurposeThis study investigates scintillation dosimetry coupled with Cherenkov imaging for in vivo dose monitoring during whole breast radiotherapy (WBRT). Given recent observations of excess dose to the contralateral breast (CB), in vivo dosimetry (IVD) could help ensure accurate dose delivery and decrease risks of secondary cancer. This work presents a rapid, streamlined alternative to traditional IVD, providing direct visualization of measurement location relative to the treatment field on the patient. Methods and Materials10 WBRT patients consented under an IRB-approved protocol were monitored with scintillation dosimetry and always-on Cherenkov imaging, on both their treated and CB for one to three fractions. Scintillator dosimeters, small plastic discs 1mm thick and 15mm in diameter, were calibrated against optically-stimulated luminescent dosimeters (OSLDs) to generate an integral output-to-dose conversion, where integral output is measured in post-processing through a custom fitting algorithm. The discs have been extensively characterized in a previous study for various treatment conditions including beam energy and treatment geometry. ResultsN=44 dosimetry measurements were evaluated, including 22 treated breast and 22 CB measurements. Following integral output-to-dose calibration, in vivo scintillator dosimeters exhibited high linearity (R2=0.99) with paired OSLD readings across all patients. The difference between scintillation and OSLD dose measurements averaged 2.8% of the prescribed dose, or an absolute dose difference of approximately 7 cGy. ConclusionsIntegration of scintillation dosimetry with Cherenkov imaging offers an accurate, rapid alternative for in vivo dose verification in WBRT, circumventing the limitations of conventional point dosimeters. The additional benefit of visualizing measurement locations relative to the treatment field provides users an enhanced understanding of results and allows for detection of high dose gradients. Future work will explore the applicability of this technique across a broader range of radiotherapy treatments, aiming to streamline IVD practices.

Thermodynamic topology of phantom AdS black holes in massive gravity

In this work, we explore the thermodynamic topology of phantom AdS black holes in the context of massive gravity. To this end, we evaluate these black holes in two distinct ensembles: the canonical and grand canonical ensembles (GCE). We begin by examining the topological charge linked to the critical point and confirming the existence of a conventional critical point (CP1) in the canonical ensemble (CE), this critical point has a topological charge of −1 and acts as a point of phase annihilation, this situation can only be considered within the context of the classical Einstein–Maxwell (CEM) theory (η=1), while no critical point is identified in the GCE. Furthermore, we consider black holes as a topological defect within the thermodynamic space. To gain an understanding of the local and global topological configuration of this defect, we will analyze its winding numbers, and observe that the total topological charge in the CE consistently remains at 1. When the system experiences a pressure below the critical threshold, it gives rise to the occurrence of annihilation and generation points. The value of electric potential determines whether the total topological charge in the GCE is zero or one. As a result, we detect a point of generation point or absence of generation/annihilation point. Based on our analysis, it can be inferred that ensembles significantly impact the topological class of phantom AdS black holes in massive gravity.

Improved 3D Object Detection Based on PointPillars

Despite the recent advancements in 3D object detection, the conventional 3D point cloud object detection algorithms have been found to exhibit limited accuracy for the detection of small objects. To address the challenge of poor detection of small-scale objects, this paper adopts the PointPillars algorithm as the baseline model and proposes a two-stage 3D target detection approach. As a cutting-edge solution, point cloud processing is performed using Transformer models. Additionally, a redefined attention mechanism is introduced to further enhance the detection capabilities of the algorithm. In the first stage, the algorithm uses PointPillars as the baseline model. The central concept of this algorithm is to transform the point cloud space into equal-sized columns. During the feature extraction stage, when the features from all cylinders are transformed into pseudo-images, the proposed algorithm incorporates attention mechanisms adapted from the Squeeze-and-Excitation (SE) method to emphasize and suppress feature information. Furthermore, the 2D convolution of the traditional backbone network is replaced by dynamic convolution. Concurrently, the addition of the attention mechanism further improves the feature representation ability of the network. In the second phase, the candidate frames generated in the first phase are refined using a Transformer-based approach. The proposed algorithm applies channel weighting in the decoder to enhance channel information, leading to improved detection accuracy and reduced false detections. The encoder constructs the initial point features from the candidate frames for encoding. Meanwhile, the decoder applies channel weighting to enhance the channel information, thereby improving the detection accuracy and reducing false detections. In the KITTI dataset, the experimental results verify the effectiveness of this method in small objects detection. Experimental results show that the proposed method significantly improves the detection capability of small objects compared with the baseline PointPillars. In concrete terms, in the moderate difficulty detection category, cars, pedestrians, and cyclists average precision (AP) values increased by 5.30%, 8.1%, and 10.6%, respectively. Moreover, the proposed method surpasses existing mainstream approaches in the cyclist category.

Finite element analysis with deformed shape constraints generated by laser‐scanned point clouds

AbstractThis article proposes a computational method for finite element analysis with deformed shape constraints for analyzing constructed structures to account for deformations that occur before shape measurement by a terrestrial laser scanner (TLS). In this method, point clouds obtained by a TLS are considered as a partial surface of the deformed structure. An analysis model is assumed to be created from CAD data or drawings. The analysis is performed under deformed shape constraints, namely, the deformed surface constraints or the normal vector constraints, which are generated by the point clouds. These constraints are introduced to reproduce the current displacements and stresses for the structure through the analysis. This method was applied to analyses of a plate and a desk using point clouds, which were created virtually on a computer or obtained by a TLS in the numerical examples. The results showed that this method can consider unexpected deformations that occur before laser scanning. Although the computed stresses oscillated when the scanned point cloud was used due to measurement errors and conventional point cloud processing methods, the stress values were responsive enough to indicate unnatural shapes of the deformed structure. Moreover, the oscillation was observed only in areas with constraints, whereas it was not seen in areas without constraints.

Research on MPPT control strategy based on CCAOA algorithm

ABSTRACT Photovoltaic arrays operating under partial shading conditions (PSCs) may exhibit multiple peaks in the power-voltage curve of the PV system output. The conventional maximum power point tracking (MPPT) algorithm cannot address the multipeak problem and demonstrates slow convergence speed, often falling into a local optimum. Consequently, this study proposes a collaborative and cosine arithmetic optimisation algorithm (CCAOA). First, a cosine factor is introduced into the mathematical optimisation. Circle chaotic mapping and cross-variance strategy were incorporated to boost the diversity and randomness within the algorithm population followed by a cooperative search strategy involving addition and subtraction to enhance the algorithm’s local search capability, thereby accelerating its convergence speed. We assessed the effectiveness of the CCAOA using six IEEE standard test functions. The CCAOA outperforms other algorithms in terms of convergence speed and accuracy. Furthermore, when applied to the MPPT control strategy, the CCAOA’s performance is validated through simulations and experiments. MPPT can be performed within approximately 0.3 to 0.4 s under static conditions, with 99.98% efficiency. Under dynamic conditions, only approximately 0.4 s is required for MPPT, thereby achieving a maximum efficiency of 99.99%. Conversely, the AOA, TSO, and PSO algorithms require over 0.4 s for MPPT and can be trapped in local optima under these two conditions. Experiments were conducted using a PV simulator and DC/DC converter, and the algorithm achieved efficiencies of 99.22%, 99.33%, and 99.54% under uniform irradiation and two PSCs, respectively.

Research on Photovoltaic Maximum Power Point Tracking Control Based on Improved Tuna Swarm Algorithm and Adaptive Perturbation Observation Method

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

Probabilistic sunspot predictions with a gated recurrent units-based combined model guided by pinball loss

Sunspots play a crucial role in both weather forecasting and the monitoring of solar storms. In this work, we propose a novel combined model for sunspot prediction using improved gated recurrent units (GRU) guided by pinball loss for probabilistic forecasts. Specifically, we optimize the GRU parameters using the slime mould algorithm and employ a seasonal-trend decomposition procedure based on loess to tackle challenges related to sequence prediction, such as self-correlations and non-stationarity. To address prediction uncertainty, we replace the traditional l2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l_2$$\\end{document}-norm loss with pinball loss. This modification extends the conventional GRU-based point forecasting to a probabilistic framework expressed as quantiles. We apply our proposed model to analyze a well-established historical sunspot dataset for both single- and multi-step ahead forecasting. The results demonstrate the effectiveness of our combined model in predicting sunspot values, surpassing the performance of other existing methods.