Search Results (58)

Against the global backdrop of energy transition, the precise assessment of urban rooftop photovoltaic (PV) system capacity is recognized as crucial for optimizing the energy structure and enhancing the sustainable utilization efficiency of spatial resources. Publicly available aerial imagery is characterized by non-orthorectified issues; direct utilization is known to lead to geometric distortions in rooftop PV and errors in capacity prediction. To address this, a dual-optimization framework is proposed in this study, integrating monocular vision-based 3D reconstruction with a lightweight linear model. Leveraging the orthogonal characteristics of building structures, camera self-calibration and 3D reconstruction are achieved through geometric constraints imposed by vanishing points. Scale distortion is suppressed via the incorporation of a multi-dimensional geometric constraint error control strategy. Concurrently, a linear capacity-area model is constructed, thereby simplifying the complexity inherent in traditional multi-parameter fitting. Utilizing drone oblique photography and Google Earth public imagery, 3D reconstruction was performed for 20 PV-equipped buildings in Wuhan City. Two buildings possessing high-precision field survey data were selected as typical experimental subjects for validation. The results demonstrate that the 3D reconstruction method reduced the mean absolute percentage error (MAPE)—used here as an estimator of measurement uncertainty—of PV area identification from 10.58% (achieved by the 2D method) to 3.47%, while the coefficient of determination (R²) for the capacity model reached 0.9548. These results suggest that this methodology can provide effective technical support for low-cost, high-precision urban rooftop PV resource surveys. It has the potential to significantly enhance the reliability of energy planning data, thereby contributing to the efficient development of urban spatial resources and the achievement of sustainable energy transition goals. Full article

This paper addresses the challenges encountered by grid-connected photovoltaic (PV) systems, including the stochastic behavior of the system, harmonic distortion, and variations in grid impedance. To this end, an in-depth technical and pedagogical analysis of three linear multivariable current control strategies is performed: proportional-integral (PI), proportional-resonant (PR), and deadbeat (DB). The study contributes to theoretical formulations, detailed system modeling, and controller tuning procedures, promoting a comprehensive understanding of their structures and performance. The strategies are investigated and compared in both the rotating ($dq$) and stationary ($\alpha \beta$) reference frames, offering a broad perspective on system behavior under various operating conditions. Additionally, an in-depth analysis of the PR controller is presented, highlighting its potential to regulate both positive- and negative-sequence components. This enables the development of more effective and robust tuning methodologies for steady-state and dynamic scenarios. The evaluation is conducted under three main conditions: steady-state operation, transient response to input power variations, and robustness analysis in the presence of grid parameter changes. The study examines the impact of each controller on the total harmonic distortion (THD) of the injected current, as well as on system stability margins and dynamic performance. Practical aspects that are often overlooked are also addressed, such as the modeling of the inverter and photovoltaic generator, the implementation of space vector pulse-width modulation (SVPWM), and the influence of the output LC filter capacitor. The control structures under analysis are validated through numerical simulations performed in MatLab^® software (R2021b) using dedicated computational routines, enabling the identification of strategies that enhance performance and ensure compliance of grid-connected photovoltaic systems. Full article

This study proposes an improved Black-Winged Kite Algorithm (SRQ-BKA) for accurate parameter identification of the photovoltaic (PV) double diode model (DDM). The proposed method integrates three key mechanisms: specular reflection learning (SRL) to improve initial population diversity, preventing premature convergence and enabling a more comprehensive exploration of the solution space for optimal parameters; soft rime search (SRS) to balance global exploration and local exploitation, ensuring efficient identification by dynamically adjusting the search focus; and quadratic interpolation (QI) to accelerate convergence by fine-tuning the search toward optimal parameters, enhancing accuracy and speeding up the identification process. The root mean square error (RMSE) is employed as the objective function to minimize the error between the measured and predicted I-V characteristics of the PV module. Experimental results demonstrate that the SRQ-BKA outperforms other algorithms, achieving a minimum RMSE of 0.00262 A for the DDM and exhibiting strong stability, as evidenced by an average RMSE of 0.00278 A across 1000 runs. The method also demonstrates excellent parameter identification accuracy for both the single diode model (SDM) and triple diode model (TDM), further validating its robustness and practical applicability. Full article

Photovoltaic (PV) systems are the core technology for implementing net-zero carbon emissions by 2050. The performance of PV systems is strongly influenced by environmental factors, including irradiance, temperature, and shading, which makes it difficult to characterize the nonlinear and multi-coupling behavior of the systems. Accurate modeling is essential for reliable performance prediction and lifespan estimation. To address this challenge, a novel metaheuristic algorithm called shuffled puma optimizer (SPO) is deployed to perform parameter extraction and optimal configuration identification across four PV models. The robustness and stability of SPO are comprehensively evaluated through comparisons with advanced algorithms based on best fitness, mean fitness, and standard deviation. The root mean square error (RMSE) obtained by SPO for parameter extraction are 8.8180 × 10⁻⁴, 8.5513 × 10⁻⁴, 8.4900 × 10⁻⁴, and 2.3941 × 10⁻³ for the single diode model (SDM), double diode model (DDM), triple diode model (TDM), and photovoltaic module model (PMM), respectively. A one-factor-at-a-time (OFAT) sensitivity analysis is employed to assess the relative importance of undetermined parameters within each PV model. The SPO-based modeling framework enables high-accuracy PV performance prediction, and its application to sensitivity analysis can accurately identify key factors that lead to reduced computational cost and improved adaptability for integration with energy management systems and intelligent electric grids. Full article

Photovoltaic (PV) models are hard to optimize due to their intrinsic complexity and changing operation conditions. Root mean square error (RMSE) is often given precedence in classic single-objective optimization methods, limiting them to address the intricate nature of PV model calibration. To bypass these limitations, this research proposes a novel multi-objective optimization framework balancing accuracy and robustness by considering both maximum error and the L2 norm as significant objective functions. Along with that, we introduce the Random Search Around Bests (RSAB) algorithm, which is a parameterless metaheuristic designed to be effective at exploring the solution space. The primary contributions of this work are as follows: (1) an extensive performance evaluation of the proposed framework; (2) an adaptable function to adjust dynamically the trade-off between robustness and error minimization; and (3) the elimination of manual tuning of the RSAB parameters. Rigorous testing across three PV models demonstrates RSAB’s superiority over 17 state-of-the-art algorithms. By overcoming significant issues such as premature convergence and local minima entrapment, the proposed procedure provides practitioners with a reliable tool to optimize PV systems. Hence, this research supports the overarching goals of sustainable energy technology advancements by offering an organized and flexible solution enhancing the accuracy and efficiency of PV modeling, furthering research in renewable energy. Full article

Photovoltaic (PV) technology is essential for achieving net-zero emissions by 2050. PV system efficiency is highly sensitive to irradiance, temperature, and shading. However, accurate parameter identification is critical for modeling, as PV models often exhibit multi-modal and strongly coupled characteristics. In addition, commercial datasheets typically lack sufficient parameter information, making precise parameter extraction difficult and limiting the accuracy of maximum power point predictions. To address these challenges, this research employs a novel metaheuristic algorithm called Puma Optimizer (PO) to optimize the parameters of multiple PV models. The PO’s performance is benchmarked against four advanced metaheuristic algorithms using convergence curves, error bars, and boxplots to evaluate its robustness. Results show that PO demonstrates strong adaptability and reliable performance in PV parameter optimization. Lastly, the research analyzes parameter sensitivity to help reduce computational resource usage. Visual analysis confirms that the PO parameter optimization approach provides an effective and practical solution for enhanced energy management and stable grid integration as solar adoption continues to expand. Full article

Background and Objectives: Psoriasis vulgaris (PV) is a chronic inflammatory disease associated with oxidative stress. It has been reported that oxidative stress caused by disruption of redox signaling can cause molecular damage, activate dendritic cells, lymphocytes, and keratinocytes, and lead to angiogenesis, inflammation, cell necrosis, and apoptosis by increasing the levels of lipid peroxidation products. In this study, serum levels of asymmetric dimethylarginine (ADMA), malondialdehyde (MDA), and reduced glutathione (GSH) were analyzed to gain insight into the oxidative balance in patients with PV. Materials and Methods: This prospective study included 59 PV patients and 40 healthy volunteers as the healthy control group. Age, gender, body mass index (BMI), waist circumference, routine hematologic parameters [fasting blood glucose, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), blood lipid levels, hemogram parameters], disease duration, and disease severity were recorded on data forms. The levels of ADMA, MDA, and GSH were analyzed using the high-performance liquid chromatography (HPLC) method. Results: When analyzed in terms of demographic characteristics, no statistically significant difference was observed between the patient and control groups. When examined in terms of biochemical variables, white blood cell (WBC) values were found to be significantly higher in the patient group (t: 2.825; p < 0.05). Although waist circumference, BMI, glucose, CRP, ESR, lipids, platelet count, and systolic and diastolic blood pressure were higher in the patient group, this difference was not statistically significant (p > 0.05). ADMA (t: 4.532; p < 0.05) and MDA (t: 9.598; p < 0.05) values were found to be higher and GSH (t: −4.717; p < 0.05) values were found to be lower in the patient group compared to the control group. When correlation analysis was performed between the parameters, a significant relationship was found only between GSH values and ADMA values (r: −0.256; p < 0.05). Accordingly, as the patients’ GSH values increased, ADMA values decreased. Conclusions: Increased WBC, ADMA, and MDA levels, and decreased GSH levels in PV patients reveal the critical role of oxidative stress and inflammation in the disease process. Evaluation of these biomarkers may contribute to the identification of new targets for the treatment of PV and the development of more effective management strategies. Full article

This study presents a comparative analysis of various optimization algorithms—Differential Evolution (DE), Particle Swarm Optimization (PSO), Arithmetic Optimization Algorithm (AOA), and Hippopotamus Optimization Algorithm (HOA)—for parameter identification in photovoltaic (PV) models. By utilizing RTC France solar cell data, we demonstrate that accurate parameter estimation is crucial for enhancing the efficiency of PV systems, ultimately supporting sustainable energy solutions. Our results indicate that DE achieves the lowest root mean square error (RMSE) of 0.0001 for the double-diode model (DDM), outperforming other methods in terms of accuracy and convergence speed. Both the HOA and PSO also show competitive RMSE values, underscoring their effectiveness in optimizing parameters for PV models. This research not only contributes to improved PV model precision but also aids in the broader effort to advance renewable energy technologies, thereby fostering a more sustainable future. Full article

Precise statistics on the spatial distribution of photovoltaics (PV) are essential for advancing the PV industry, and integrating remote sensing with artificial intelligence technologies offers a robust solution for accurate identification. Currently, numerous studies focus on the detection of single-type PV installations through aerial or satellite imagery. However, due to the variability in scale and shape of PV installations in complex environments, the detection results often fail to capture detailed information and struggle to scale for multi-scale PV systems. To tackle these challenges, a detection method known as Dynamic Spatial-Frequency Attention SwinNet (DSFA-SwinNet) for multi-scale PV areas is proposed. First, this study proposes the Dynamic Spatial-Frequency Attention (DSFA) mechanism, the Pyramid Attention Refinement (PAR) bottleneck structure, and optimizes the feature propagation method to achieve dynamic decoupling of the spatial and frequency domains in multi-scale representation learning. Secondly, a hybrid loss function has been developed with weights optimized employing the Bayesian Optimization algorithm to provide a strategic method for parameter tuning in similar research. Lastly, the fixed window size of Swin-Transformer is dynamically adjusted to enhance computational efficiency and maintain accuracy. The results on two PV datasets demonstrate that DSFA-SwinNet significantly enhances detection accuracy and scalability for multi-scale PV areas. Full article

A metaheuristic algorithm named the Crystal Structure Algorithm (CrSA), which is inspired by the symmetric arrangement of atoms, molecules, or ions in crystalline minerals, has been used for the accurate modeling of Mono Passivated Emitter and Rear Cell (PERC) WSMD-545 and CS7L-590 MS solar photovoltaic (PV) modules. The suggested algorithm is a concise and parameter-free approach that does not need the identification of any intrinsic parameter during the optimization stage. It is based on crystal structure generation by combining the basis and lattice point. The proposed algorithm is adopted to minimize the sum of the squares of the errors at the maximum power point, as well as the short circuit and open circuit points. Several runs are carried out to examine the V-I characteristics of the PV panels under consideration and the nature of the derived parameters. The parameters generated by the proposed technique offer the lowest error over several executions, indicating that it should be implemented in the present scenario. To validate the performance of the proposed approach, convergence curves of Mono PERC WSMD-545 and CS7L-590 MS PV modules obtained using the CrSA are compared with the convergence curves obtained using the recent optimization algorithms (OAs) in the literature. It has been observed that the proposed approach exhibited the fastest rate of convergence on each of the PV panels. Full article

As the LCOE for photovoltaics has decreased several times, it is once again gaining popularity. The intensification of the development of PV installations is contributing to the duck curve phenomenon in an increasing number of countries and, consequently, affecting current electricity prices. Decisions on new investments in large-scale PV sources are driven by potential economic and environmental effects, and these, in turn, are subject to locational considerations, both as to the country and its region. In calculating the economic impact of locating a 1 MWp PV farm, it was assumed that the electricity generated by the farm would be fed into the national grid, and that the life of the PV farm would be 20 years. Poland was considered as an example country for the placement of a photovoltaic farm. The authors of this paper proposed that the main verification parameter is the availability of connection capacities to feed the produced electricity into the country’s electricity grid. The methodology proposed by the authors for the selection of the location of a PV farm consists of four steps: step (i) identification and selection of the administrative division of a given country; step (ii) verification of available connection capacities; step (iii) (two stages) verification of other factors related to the location of the PV farm (e.g., information on land availability and the distance of the land from the substation), and analysis of productivity at each potential location and electricity prices achieved on the power exchange; step (iv) economic analysis of the investment—analyses of PV farm energy productivity in monetary terms on an annual basis, cost analysis (CAPEX, OPEX) and evaluation of economic efficiency (DPP, NPV, IRR). The greatest impact on the economic efficiency of a PV project is shown by the value of land (as part of CAPEX), which is specific to a given location, and revenues from energy sales, which are pretty similar for all locations. Full article

Background: This investigation is both a study of potential non-invasive diagnostic approaches for the bladder cancer biomarker UBC^® Rapid test and a study including novel comparative methods for bioassay evaluation and comparison that uses bladder cancer as a useful example. The objective of the paper is not to investigate specific data. It is used only for demonstration, partially to compare ROC methodologies and also to show how both sensitivity/specificity and predictive values can be used in clinical diagnostics and decision making. This study includes ROC curves with integrated cut-off distribution curves for a comparison of sensitivity/specificity (SS) and positive/negative predictive values (PPV/NPV or PV), as well as SS-J index/PV-PSI index–ROC curves and SS-J/PV-PSI index cut-off diagrams (J = Youden, PSI = Predictive Summary Index) for the unified direct comparison of SS-J/PV results achieved via quantitative and/or qualitative bioassays and an identification of optimal separate or unified index cut-off points. Patients and Methods: According to the routine diagnostics, there were 91 patients with confirmed bladder cancer and 1152 patients with no evidence of bladder cancer, leading to a prevalence value of 0.073. This study performed a quantitative investigation of used-up test cassettes from the visual UBC^® Rapid qualitative point-of-care assay, which had already been applied in routine diagnostics. Using a photometric reader, quantitative data could also be obtained from the test line of the used cassettes. Interrelations between SS and PV values were evaluated using cumulative distribution analysis (CAD), SS/PV–ROC curves, SS-J/PV-PSI index–ROC curves, and the SS-J/PV-PSI index cut-off diagram. The maximum unified SS-J/PV-PSI index value and its corresponding cut-off value were determined and calculated with the SS-J/PV-PSI index cut-off diagram. Results: The use of SS/PV–ROC curves with integrated cut-off concentration distribution curves provides improved diagnostic information compared to “traditional” ROC curves. The threshold distributions integrated as curves into SS/PV–ROC curves and SS-J/PV-PSI index–ROC curves run in opposite directions. In contrast to the SS–ROC curves, the PV–ROC and the novel PV-PSI index–ROC curves had neither an area under the curve (AUC) nor a range from 0% to 100%. The cut-off level of the qualitative assay was 7.5 µg/L, with a sensitivity of 65.9% and a specificity of 63.3%, and the PPV was 12.4% and the NPV was 95.9%, at a threshold value of 12.5 µg/L. Based on these set concentrations, the reader-based evaluation revealed a graphically estimated 5% increase in sensitivity and a 13% increase in specificity, as compared to the visual qualitative POC test. In the case of predictive values, there was a gain of 8% for PPV and 10% for NPV. The index values and cut-offs were as follows: visual SS-J index, 0.328 and 35 µg/L; visual PV-PSI index, 0.083 and 5.4 µg/L; maximal reader Youden index, 0.0558 and 250 µg/L; and maximal PV-PSI index, 0.459 and 250 µg/L, respectively. The maximum unified SS-J/PV-PSI index value was 0.32, and the cut-off was 43 µg/L. The reciprocal SS-J index correctly detected one out of three patients, while the reciprocal PV-PSI index gave one out of twelve patients a correct diagnosis. Conclusions: ROC curves including cut-off distribution curves supplement the information lost in “traditionally plotted” ROC curves. The novel sets of ROC and index–ROC curves and the new SS/PV index cut-off diagrams enable the simultaneous comparison of sensitivity/specificity and predictive value profiles of diagnostic tools and the identification of optimal cut-off values at maximal index values, even in a unifying SS/PV approach. Because the curves within an SS-J/PV-PSI index cut-off diagram are distributed over the complete cut-off range of a quantitative assay, this field is open for special clinical considerations, with the need to vary the mentioned clinical diagnostic parameters. Complete or partial areas over the x-axis (AOX) can be calculated for summarized quantitative or qualitative effectivity evaluations with respect to single and/or unified SS-J and PV-PSI indices and with respect to single, several, or several unified assays. The SS-J/PV-PSI index-AOX approach is a new tool providing additional joint clinical information, and the reciprocal SS-J indices can predict the number of patients with a correct diagnosis and the number of persons who need to be examined in order to correctly predict a diagnosis of the disease. These methods could be used in applications like medical or plant epidemiology, machine learning algorithms, and neural networks. Full article

Bacterial blight (BB) caused by Xanthomonas oryzae pv. Oryzae (Xoo) is a serious disease of rice worldwide that can reduce crop yield and affect food insecurity. A rice resistance variety is an alternate way to solve this problem. The broad-spectrum resistance (BSR) of ULR207 is important for durable resistance to several of the Xoo isolates. However, the inheritance of this resistance gene in ULR207 must be known before it can be utilized. Thus, this study aimed to survey the BB resistance gene with reference to the BB resistance gene for identification of non-analogous or analogous genes and confirmation of a broad-spectrum resistance, to investigate the gene effect, the number of genes, and the heritability of the BB resistance gene in the ULR207 variety. Six populations of two crosses (Maled Phai × ULR207 and RD6 × ULR207), i.e., ULR207 (Donor parent), Maled Phai and RD6 (Recurrent parent), F₁, F₂, BC₁P₁, and BC₁P₂ were constructed. These materials were evaluated for BB resistance by clipping methods under greenhouse conditions using a virulence isolate of a pathogen in Thailand. The results showed that ULR207 exhibited the strongest against BB with 0.8 of BSR with low area under the disease progress curve (AUDPC). Molecular screening for surveying of the BB resistance gene in ULR207 revealed a non-analogous resistance gene with resistance check varieties. The phenotype of the disease lesion length of F₂ and BC₁P₂ populations exhibited a ratio of 1:3 and 1:1 (resistant: susceptible), respectively, revealing a single recessive gene in both crosses. The scaling test parameters A, B, and C were non-significant (p < 0.01), indicating that variation in data was sufficiently explained by additive (d) and dominance (h) components. The gene action of ULR207 was controlled by additive gene action. Heritability of the two crosses, Maled Phai x ULR207 and RD6 x ULR207, exhibited high values with 0.817 and 0.716, whereas the numbers of the genes were 1.4 and 1.2, respectively. The result indicated that the breeding strategy could be employed in early generations when using ULR207 as a new source of bacterial blight resistance. Full article

Walnut (Juglans regia L.) is a monoecious species and although it exhibits self-compatibility, it presents incomplete overlap of pollen shed and female receptivity. Thus, cross-pollination is prerequisite for optimal fruit production. Cross-pollination can occur naturally by wind, insects, artificially, or by hand. Pollen has been recognized as one possible pathway for Xanthomonas arboricola pv. juglandis infection, a pathogenic bacterium responsible for walnut blight disease. Other than the well-known cultural and chemical control practices, artificial pollination technologies with the use of drones could be a successful tool for walnut blight disease management in orchards. Drones may carry pollen and release it over crops or mimic the actions of bees and other pollinators. Although this new pollination technology could be regarded as a promising tool, pollen germination and knowledge of pollen as a potential pathway for the dissemination of bacterial diseases remain crucial information for the development and production of aerial pollinator robots for walnut trees. Thus, our purpose was to describe a pollination model with fundamental components, including the identification of the “core” pollen microbiota, the use of drones for artificial pollination as a successful tool for managing walnut blight disease, specifying an appropriate flower pollination algorithm, design of an autonomous precision pollination robot, and minimizing the average errors of flower pollination algorithm parameters through machine learning and meta-heuristic algorithms. Full article

The installed capacity of photovoltaic systems has been rising quickly lately. Deploying photovoltaic systems to generate power, however, is a substantial problem given their reliance on weather and environmental circumstances. The various environmental factors that must be taken into account are temperature, wind direction, speed, as well as irradiation. The solar system’s standard test condition is never precisely attained outside. Because of this, it is necessary to take into account the seasonal influences to increase solar system performance in a real-time context. In the context of the Indian subcontinent, this research is especially important due to seasonal fluctuations in spectrum-related characteristics. The findings demonstrate that the multi-crystalline technology efficiency and output power evaluated for sites conform to the efficiency as well as output power anticipated using the temperature of the module. Under normal testing conditions, the solar PV module’s parameters are taken from the manufacturer’s datasheet. The accurate modeling of solar systems is necessary to address a variety of PV system problems. We may characterize a solar module’s electrical properties using this precise modeling technique to provide an accurate analysis of cell behavior under any operating situation. Three main stages must be taken into account while modeling a PV cell: the right selection of analogous models, the mathematical formulation of the model, and the precise identification of parameter values in the models. Therefore, in order to mimic the characteristics of solar modules, it is crucial to analyze and design relevant models, as well as use the right modeling technique. The root-mean-square error parameter is considered for the linear regression method. Full article