Solar Data Research Articles

Observations of the 2022 September 5 Solar Energetic Particle Event at 15 Solar Radii

On 2022 September 5, Parker Solar Probe (Parker) observed a large solar energetic particle (SEP) event at the unprecedented distance of only 15 R S from the Sun. The observations from the Integrated Science Investigation of the Sun (IS⊙IS) obtained over the course of this event are remarkably rich, and an overview is presented here. IS⊙IS is capable of measuring ions from 20 keV to over 100 MeV nuc−1 and electrons from 30 keV to 6 MeV; here, we primarily focus on the proton and helium measurements above 80 keV. Among the surprising results are evidence of inverse velocity dispersion at energies above 1 MeV during the onset of the event, a sharp decrease in the energetic particle intensities at all energies at the interplanetary shock crossing, and repeated short durations of highly anisotropic sunward flow. Many changes in the SEP intensities, anisotropy, and spectral steepness are coincident with solar wind structure boundaries identified using the Parker solar wind magnetic field and plasma data. However, there are significant changes that are not correlated with any clearly visible solar wind variation. The observations presented here serve as an introduction to a complex event with numerous opportunities for future, more in-depth studies.

The Sun and core-collapse supernovae are leading probes of the neutrino lifetime

The large distances travelled by neutrinos emitted from the Sun and core-collapse supernovae together with the characteristic energy of such neutrinos provide ideal conditions to probe their lifetime, when the decay products evade detection. We investigate the prospects of probing invisible neutrino decay capitalising on the detection of solar and supernova neutrinos as well as the diffuse supernova neutrino background (DSNB) in the next-generation neutrino observatories Hyper-Kamiokande, DUNE, JUNO, DARWIN, and RES-NOVA.We find that future solar neutrino data will be sensitive to values of the lifetime-to-mass ratio τ 1/m 1 and τ 2/m 2 of 𝒪(10-1–10-2) s/eV. From a core-collapse supernova explosion at 10 kpc, lifetime-to-mass ratios of the three mass eigenstates of 𝒪(105) s/eV could be tested. After 20 years of data taking, the DSNB would extend the sensitivity reach of τ 1/m 1 to 108 s/eV. These results promise an improvement of about 6–15 orders of magnitude on the values of the decay parameters with respect to existing limits.

Short-term Classification of Strong Solar Energetic Particle Events Using Multivariate Time-series Classifiers

Solar energetic particle (SEP) events are one of the most crucial aspects of space weather that require continuous monitoring and forecasting. Their prediction depends on various factors, including source eruptions. In the present work, we use the Geostationary Solar Energetic Particle data set covering solar cycles 22, 23, and 24. We develop a framework using time-series-based machine-learning (ML) models with the aim of developing robust short-term forecasts by classifying SEP events. For this purpose, we introduce an ensemble learning approach that merges the results from univariate time series of three proton channels (E ≥10, 50, and 100 MeV) and the long-band X-ray flux (1–8 Å) channel from the Geostationary Operational Environmental Satellite missions and analyze their performance. We consider three models, namely, time series forest, supervised time series forest (STSF), and Bag-of-Symbolic Fourier Approximation Symbols. Our study also focuses on understanding and developing confidence in the predictive capabilities of our models. Therefore, we utilize multiple evaluation techniques and metrics. Based on that, we find STSF to perform well in all scenarios. The summary of metrics for the STSF model is as follows: the area under the ROC curve = 0.981, F 1-score = 0.960, true skill statistics = 0.919, Heidke skill score = 0.920, Gilbert skill score = 0.852, and Matthew’s correlation coefficient = 0.920. The Brier score loss of the STSF model is 0.077. This work lays the foundation for building near-real-time short-term SEP event predictions using robust ML methods.

Novel risk index integrating practical operation limits enhances probabilistic contingency ranking for large-scale photovoltaic plant planning

This research addresses the pressing need for heightened grid security amid increasing uncertainties in photovoltaic PV generation. The research problem lies in the limitations of conventional contingency analysis metrics, failing to adequately consider both contingency occurrences and uncertainties inherent in PV generation. In response, a comprehensive algorithm is proposed that introduces a novel severity function framework, enhancing traditional contingency ranking metrics. This approach incorporates grid remedial actions and refines line and bus voltage classification by considering available correction time, aiming to offer a more robust security assessment. Motivated by the imperative to address uncertainty in PV generation, the proposed work builds on established analysis tools. A probabilistic load flow algorithm manages PV generation uncertainties, utilizing historical data for contingency incidence uncertainty. Additionally, a probabilistic model for PV plants integrates historical solar data, deriving hourly probability density functions to meet grid code requirements, including reactive power considerations. The justification for this work lies in the algorithm's demonstrated efficacy, validated on the IEEE 14-bus network. Results highlight its ability to identify risks associated with line overloading and bus voltage breaches. Comparative evaluations underscore proper coupling buses for security, favoring distributed capacity to mitigate line overloading risks. The study's key results emphasize voltage risk amplification with reactive power omission, stressing the significance of compensation strategies. This research addresses a critical problem, presenting a comprehensive algorithmic solution to enhance grid security amidst uncertainties in PV integration. Findings offer valuable insights for strategically interaction between large scale PV plants and electrical grid, contributing to an improved grid security paradigm in a dynamic and uncertain energy model.

Probabilistic photovoltaic generation and load demand uncertainties modelling for active distribution networks hosting capacity calculations

With the increasing integration of photovoltaic (PV) systems in active distribution networks (ADNs), accurate modelling of PV power generation and the network demand has become essential, especially for system operators (SO). However, existing studies have focused on deterministic representations of hourly profiles for PV generation and load consumption, which cannot thoroughly evaluate the existing uncertainties of PV power output and load demands. In this study, uncertain parameters load demand and PV power output profile will be modelled with forecasted values, and their profile will be obtained over probability density functions (PDFs). Firstly, a vast quantity of realistic load and PV generation profiles are produced over a day with 15-minute resolution, with a scenario generation method using the Monte Carlo methodology. Afterward, the generated scenarios are reduced to a set of scenarios to represent the span of all generated scenarios. A fully local reactive power regulation strategy is used in this study to evaluate the hosting capacity of the ADN. This proposed method is tested on modified 33-bus and 69-bus distribution test systems by using practical solar generation and load data. The proposed methodology results in the hosting capacity improvement by 20% besides the existing Q-Voltage and PF-Power local voltage control methods, where it has the flexibility to be implemented to any distribution feeder.

Adaptive energy management strategy for solar energy harvesting IoT nodes by evolutionary fuzzy rules

This study explores the integration of genetic programming (GP) and fuzzy logic to enhance control strategies for Internet of Things (IoT) nodes across varied locations. It is introduced a novel methodology for designing a fuzzy-based energy management controller that autonomously determines the most suitable controller structure and inputs. This approach is evaluated using a solar harvesting IoT model that leverages historical solar irradiance data, highlighting the methodology’s potential for diverse geographical applications and compatibility with low-performance microcontrollers. The findings demonstrate that the integration of GP with designed fitness function enables the dynamic learning and adaptation of control strategies, optimizing system behavior based on historical data. The experimental model showcases an ability to efficiently use historical datasets to derive optimal control strategies, with the fitness metric indicating consistent improvement throughout the learning phase. The results indicate that useful control strategies learned at a certain location may outperform a locally-trained control strategies and can be successfully re-applied in other locations.

Design and implementation of microcontroller-based solar charge controller using modified incremental conductance MPPT algorithm

This paper presents the modeling, design, and implementation of a rapid prototyping low-power solar charge controller with maximum power point tracking (MPPT). The implemented circuit consists of a 60 W photovoltaic (PV) module, a buck converter with an MPPT controller, and a 13.5V-48Ah battery. The performance of the solar charge controller is increased by operating the PV module at the maximum power point (MPP) using a modified incremental conductance (IC) MPPT algorithm. While the traditional IC MPPT approach requires a substantial amount of code and algorithmic steps to keep the PV module at the MPP, the proposed IC achieves the same process with a reduced number of lines of code, owing to its optimized algorithmic approach. By adjusting the duty cycle of the generated Pulse Width Modulation (PWM) signal, maximum power transfer from the PV module is attained during the operation of the battery charging process. The simulation model is configured and tested in Matlab/Simulink environment under different solar data (1000 W/m2, 500 W/m2, 800 W/m2) with constant temperature (25 °C). To validate the simulations, experimental studies are conducted using the developed rapid prototype with the 32-bit embedded microcontroller in the laboratory. According to the simulation and laboratory results, the maximum power tracking performance of the traditional method was found to be 95.51%, while the proposed method achieved a ratio of 96.59% with less steady-state oscillation. The average tracking efficiency has increased by 1.13%. The proposed IC tracks the MPP more accurately and provides maximum available power for battery charging at different solar radiations compared to the traditional IC approach. For low-powered electric devices, the proposed system can be used to provide a charging infrastructure solution.

A Model-Free Deep Reinforcement Learning-Based Approach for Assessment of Real-Time PV Hosting Capacity

Assessments of the hosting capacity of electricity distribution networks are of paramount importance, as they facilitate the seamless integration of rooftop photovoltaic systems into the grid, accelerating the transition towards a more carbon neutral and sustainable system. This paper employs a deep reinforcement learning-based approach to evaluate the real-time hosting capacity of low voltage distribution networks in a model-free manner. The proposed approach only requires real-time customer voltage data and solar irradiation data to provide a fast and accurate estimate of real-time hosting capacity at each customer connection point. This study addresses the imperative for accurate electrical models, which are frequently unavailable, in evaluating the hosting capacity of electricity distribution networks. To meet this challenge, the proposed approach utilizes a deep neural network-based, data-driven model of a low-voltage electricity distribution network. This proposed methodology incorporates model-free elements, enhancing its adaptability and robustness. In addition, a comparative analysis between model-based and model-free hosting capacity assessment methods is presented, highlighting their respective strengths and weaknesses. The utilization of the proposed hosting capacity estimation model enables distribution network service providers to make well-informed decisions regarding grid planning, leading to cost minimization.

ENSEMBLE MODELS FOR FORECASTING FLOODS IN GREAT BRITAIN BASED ON SOLAR ACTIVITY

This study aims to uncover hidden dependencies between streams of solar particles emanating from the Sun and flood events caused by precipitation in the UK. The uniqueness of this work lies in applying solar activity data as input parameters for machine learning and correlating them with real data on precipitation intensity and floods in the UK, which serve as output data. The analysis covered 20 significant floods from October 2001 to December 2019. The analysis was performed on a daily basis, taking into account the time shift between solar emissions and their impact on terrestrial weather conditions, which ranged from 0 to 9 days. The study employs correlation analysis to determine the degree of interrelation between time series of solar activity and floods, laying the foundation for a deeper understanding of possible cause-and-effect relationships. Subsequently, using predictive modeling methods, including decision tree algorithms and ensemble classification models, the potential connection between changes in solar activity and terrestrial floods was explored. An ensemble of models was created using the hard voting method, which takes into account various indicators such as proton density, differential proton flux, and ion temperature, with a time shift of up to 9 days. The analysis showed that these key parameters could effectively predict the occurrence of floods with up to 92% accuracy for the specified period. The results underscore the importance of integrating space weather and solar activity into the models for predicting terrestrial weather conditions, particularly in the context of floods in England. This not only allows for a better understanding of the causes of natural disasters but also enhances the effectiveness of emergency planning and reduces potential damages from them. Thus, this work makes a significant contribution to the field of space weather research and its impact on Earth, particularly in the areas of hydrometeorology and flood risk management.

An uncertainty methodology for solar occultation flux measurements: ammonia emissions from livestock production

Abstract. Ammonia (NH3) emissions can negatively affect ecosystems and human health, so they should be monitored and mitigated. This study presents methodology for the estimation of uncertainties in NH3 emissions measurements using the solar occultation flux (SOF) method. The reactive nature of NH3 makes its measurement challenging, but SOF offers a reliable open-path passive method which utilizes solar spectrum data, thereby avoiding gas adsorption within the instrument. To compute NH3 gas fluxes, horizontal and vertical wind speed profiles, as well as plume height estimates and spatially resolved column measurements, are integrated. A unique aspect of this work is the first-time description of plume height estimations derived from ground and column NH3 concentration measurements aimed at uncertainty reduction. Initial validation tests indicated measurement errors between −31 % and +14 % on average, which was slightly larger than the estimated expanded uncertainty ranging from ± 12 % to ± 17 %. Application of the methodology to assess emission rates from farms of various sizes showed uncertainties between ± 21 % and ± 37 %, generally influenced by systematic wind uncertainties and random errors. The method demonstrates the capacity to measure NH3 emissions from both small (∼ 0.5–1 kg h−1) and large (∼ 100 kg h−1) sources in high-density farming areas. Generally, the SOF method provided an expanded uncertainty below 30 % in measuring NH3 emissions from livestock production, which could be further improved by adhering to best application practices. This paper's findings offer the potential for broader applications, such as measuring NH3 fluxes from fertilized fields and in the oil and gas sector. However, these applications would require further research to adapt and refine the methodologies for these specific contexts.

Geospatial optimisation of wind and solar energy generation: A framework for optimised resource allocation

Wind and solar power generation profiles exhibit a high degree of power output variability and uncertainty compared to conventional fossil fuel generation plants. However, it is generally accepted that this variability and uncertainty can be mitigated by geographic siting and dispersion of the renewable energy plants, particularly with the view to leverage aggregation effects. In this context, this paper proposes a framework for the geospatial optimisation of wind and solar PV generation for scenarios with a high penetration of wind and solar renewable energy. The proposed optimisation framework represents a probabilistic risk-based approach that seeks to minimise the number of events where high residual load values require ancillary service interventions to maintain power balance. In this approach, the wind and solar resources are categorised in terms of the statistical properties of the associated spatiotemporal wind and solar power profiles for a given set of daily and seasonal Time of Use periods. It is thereby recognised that the resource characteristics and grid impact of wind and solar generation profiles can be interpreted with reference to the daily and seasonal cycles exhibited by the demand profiles, wherein some Time of Use periods are more important than others. The proposed framework is implemented for a number of baseline case studies and optimisation case studies, using wind and solar resource data sets covering South Africa. Overall, it is concluded that this approach can leverage geospatial site optimisation to substantially reduce the risks associated with high residual load events. Furthermore, the proposed framework is highly flexible in that the formulation of the minimum and maximum allocation constraints allows application for real-world scenarios whereby stringent capacity allocation constraints can be applied.

Adaptive solar power generation forecasting using enhanced neural network with weather modulation

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices.

Numerical assessment and optimization of photovoltaic-based hydrogen-oxygen Co-production energy system: A machine learning and multi-objective strategy

Green hydrogen production technology based on photovoltaic (PV), battery energy storage system (BESS) and proton exchange membrane (PEM) water electrolysis plays a crucial part in the transition process of energy to zero carbon technology. But few studies have focused on achieving all-day continuous hydrogen production, which is the key to large-scale hydrogen utilization. In this study, we developed an off-grid PV-BESS-PEM system for hydrogen and oxygen production. Firstly, the PV, BESS and PEM water electrolysis components were modeled and model validation was performed. Secondly, an energy management strategy (EMS) is proposed to achieve the goal of continuous and stable hydrogen production. Then, a surrogate model between design variables and optimization objectives is established based on machine learning methods, and a multi-objective optimization algorithm is used to drive the optimization process. Finally, a case study of the PV-BESS-PEM system is presented based on solar irradiation data in Lhasa, where there is sufficient solar resources and oxygen demand. The results demonstrate that the EMS can satisfy the purpose of all-day continuous and stable hydrogen production. The number of cells in the electrolyzer and the water temperature have a significant effect on the hydrogen production and the electrolysis efficiency. And the optimal system has increased the hydrogen production by 16.80 % and the electrolysis efficiency of PEM by 12.08 %, respectively. Herein, the methodological framework is expected to provide theoretical guidance for large-scale water electrolysis hydrogen production applications based on solar energy.

An integrated energy system day-ahead scheduling method based on an improved dynamic time warping algorithm

With the construction and development of the new energy system, the integrated energy system (IES) has garnered significant attention as a crucial energy carrier in recent years. Therefore, to address the scheduling challenges of IES influenced by uncertainty in source load and mitigate the conservatism of scheduling schemes while enhancing clustering accuracy, a method for day-ahead top-note scheduling of IES is proposed. First, by improving dynamic time warping (DTW) for hierarchical clustering of wind, solar, and load data in IES, typical scenarios of IES are derived. Second, using the interval method to model wind, solar, and load data in IES along with their coupled devices and considering the conservatism issue of interval optimization, the established IES interval model undergoes affine processing. Finally, with the goal of minimizing the operating costs of IES, a day-ahead interval affine scheduling model is established, which is solved using the CPLEX Solver and INTLAB toolbox, and scheduling schemes for all typical scenarios are provided. Through comparative analysis of calculation examples, it is found that the method proposed in this paper can enhance clustering accuracy and reduce the conservatism of system scheduling schemes.

Spatial-temporal characteristics analysis of solar irradiance forecast errors in Europe and North America

Accurate photovoltaic (PV) power forecast is crucial to power systems. Solar irradiance forecast is the fundamentals of PV power forecast. Current researches merely focus on improving the forecast accuracy. There hasn't been comprehensive study on the spatial-temporal characteristics of solar irradiance forecast errors. Thus, this paper analyzed the error characteristics of solar irradiance forecast provided by European Centre for Medium-Range Weather Forecasts (ECMWF), one of the most accurate numerical weather prediction (NWP) products. ECMWF's forecasts were compared with ERA5 solar irradiance reanalysis data to reveal the error distribution characteristics. Four-year (2017–2020) data from the geographical region bounded by 63°N, −126°W, 21°S, and 36°E (covering most parts of Europe and North America) were studied. Experiments show that solar irradiance forecast errors peak at noon of the local time. Furthermore, correlations between solar irradiance forecast errors and other weather variables were also revealed. Experiments show that solar irradiance forecast errors have negative correlation with low cloud cover forecast errors and positive correlation with air temperature forecast errors. The possible reasons for the correlation relationship were also analyzed in detail. This paper systematically reveals the spatial-temporal characteristics of solar irradiance forecast errors and provides a useful guideline for solar PV system operation.

Exploring the Feasibility of Solar Photovoltaic Generation for Residential Applications in Hubballi, Karnataka: A Simulation and Analysis Approach

This research paper explores the potential of solar photovoltaic (PV) generation in Hubballi, Karnataka, India, focusing on a small residential home premises. The objective is to assess the solar PV potential at this location using PVsyst simulation software and analyze the results. The paper begins with an introduction to solar PV technology and its significance in meeting energy demands. A literature review examines previous studies on solar PV potential in Karnataka, highlighting the factors influencing solar PV generation efficiency. The methodology section describes the study area, data collection methods, and the PVsyst simulation setup. The data analysis section presents the collected solar irradiation and temperature data and discusses the simulation results for solar PV generation at the selected premises. The performance evaluation of the PV system is based on the simulation outcomes. The discussion section compares the simulated results with expected solar PV potential, identifies factors affecting solar PV generation efficiency in Hubballi, and discusses the implications of the findings for residential installations. The conclusion summarizes the research findings, emphasizes the solar PV potential in Hubballi, and provides recommendations for future research and practical applications. This research contributes to the understanding of solar PV generation at the specific location of Hubballi and offers valuable insights for promoting renewable energy in residential settings

Comparison of Solar Multifrequency Microwave Data with Other Solar Indices for Understanding Solar and Stellar Microwave Data

Thermal microwave emissions detected from stellar atmospheres contain information on stellar activity. However, even for the Sun, the relationship between multifrequency microwave data and other activity indices remains unclear. We investigated the relationships among the thermal microwave fluxes with 1, 2, 3.75, and 9.4 GHz, their circular polarizations, and several activity indices recorded during recent solar cycles and observed that these relationships can be categorized into two groups. In the first group, the relationship between the microwave fluxes and solar indices, which are strongly related to the active regions, can be well-fitted by using a linear function. In the second group, the fitting function is dependent on frequency. Specifically, the microwave fluxes at 1 and 2 GHz can be well-fitted to the total unsigned magnetic and extreme ultraviolet fluxes by employing a power-law function. The trend changes around 3.75 GHz, and the trend for the 9.4 GHz fluxes can be fitted by using a linear function. For the first time, we present the relationship between circular polarization and solar indices. Moreover, we extrapolated these relationships of the solar microwave fluxes to higher values and compared them with the solar-type stars. We found that ϵ Eri, whose microwave emission originates from thermal plasma, follows the extrapolated relationship. However, to date, only one star’s emission at 1–10 GHz has been confirmed as thermal emission. More solar-type stars should be observed with future radio interferometers to confirm that relationships based on solar data can be applied to stellar microwave data.

On the Origin of the Sudden Heliospheric Open Magnetic Flux Enhancement During the 2014 Pole Reversal

Coronal holes are recognized as the primary sources of heliospheric open magnetic flux (OMF). However, a noticeable gap exists between in situ measured OMF and that derived from remote-sensing observations of the Sun. In this study, we investigate the OMF evolution and its connection to solar structures throughout 2014, with special emphasis on the period from September to October, where a sudden and significant OMF increase was reported. By deriving the OMF evolution at 1 au, modeling it at the source surface, and analyzing solar photospheric data, we provide a comprehensive analysis of the observed phenomenon. First, we establish a strong correlation between the OMF increase and the solar magnetic field derived from a potential-field source-surface model (cc Pearson = 0.94). Moreover, we find a good correlation between the OMF and the open flux derived from solar coronal holes (cc Pearson = 0.88), although the coronal holes only contain 14%–32% of the Sun’s total open flux. However, we note that while the OMF evolution correlates with coronal hole open flux, there is no correlation with the coronal hole area evolution (cc Pearson = 0.0). The temporal increase in OMF correlates with the vanishing remnant magnetic field at the southern pole, caused by poleward flux circulations from the decay of numerous active regions months earlier. Additionally, our analysis suggests a potential link between the OMF enhancement and the concurrent emergence of the largest active region in solar cycle 24. In conclusion, our study provides insights into the strong increase in OMF observed during 2014 September–October.

An Experimental Study on EUV‐To‐Magnetogram Image Translation Using Conditional Generative Adversarial Networks

AbstractDeep generative models have recently become popular in heliophysics for their capacity to fill in gaps in solar observational data sets, thereby helping mitigating the data scarcity issue faced in space weather forecasting. A particular type of deep generative models, called conditional Generative Adversarial Networks (cGAN), has been used since a few years in the context of image‐to‐image (I2I) translation on solar observations. These algorithms have however hyperparameters whose values might influence the quality of the synthetic image. In this work, we use magnetograms produced by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) and EUV images from the Atmospheric Imaging Assembly (AIA) for the problem of generating Artificial Intelligence (AI) synthetic magnetograms from multiple SDO/AIA channels using cGAN, and more precisely the Pix2PixCC algorithm. We perform a systematic study of the most important hyperparameters to investigate which hyperparameter might generate magnetograms of highest quality with respect to the Structural Similarity Index. We propose a structured way to perform training with various hyperparameter values, and provide diagnostic and visualization tools of the generated versus targeted image. Our results shows that when using a larger number of filters in the convolution blocks of the cGAN, the fine details in the generated magnetogram are better reconstructed. Adding several input channels besides the 304 Å channel does not improve the quality of generated magnetogram, but the hyperparameters controlling the relative importance of the different loss functions in the optimization process have an influence on the quality of the results.

Imaging a Large Coronal Loop Using Type U Solar Radio Burst Interferometry

Solar radio U-bursts are generated by electron beams traveling along closed magnetic loops in the solar corona. Low-frequency (<100 MHz) U-bursts serve as powerful diagnostic tools for studying large-sized coronal loops that extend into the middle corona. However, the positive frequency drift component (descending leg) of U-bursts has received less attention in previous studies, as the descending radio flux is weak. In this study, we utilized LOFAR interferometric solar imaging data from a U-burst that has a significant descending leg component, observed between 10 and 90 MHz on 2020 June 5th. By analyzing the radio source centroid positions, we determined the beam velocities and physical parameters of a large coronal magnetic loop that reached just about 1.3 R ⊙ in altitude. At this altitude, we found the plasma temperature to be around 1.1 MK, the plasma pressure around 0.20 mdyn, cm−2, and the minimum magnetic field strength around 0.07 G. The similarity in physical properties determined from the image suggests a symmetric loop. The average electron beam velocity on the ascending leg was found to be 0.21c, while it was 0.14c on the descending leg. This apparent deceleration is attributed to a decrease in the range of electron energies that resonate with Langmuir waves, likely due to the positive background plasma density gradient along the downward loop leg.