Photovoltaic Research Articles

Enhanced MPPT Based on Zebra Optimization Algorithm for Control of a Partially Shaded Photovoltaic System

Objective: The objective of this paper is to adapt the Zebra Optimization Algorithm (ZOA) to track the Global Maximum Power Point (GMPP) of a PV system when subjected to partial shading conditions. Theoretical Framework: Photovoltaic (PV) systems are increasingly being used due to their clean nature and low maintenance cost. However, solar cells exhibit a nonlinear relationship between current and voltage, which necessitates the use of Maximum Power Point Tracking (MPPT) algorithms to ensure the system operates at its full potential. Bypass diodes are integrated into PV modules to protect solar cells from damage during partial shading, but this leads to a distorted multi-peak Power-Voltage (P-V) curve. This distortion presents a challenge for algorithms to distinguish between Global Maximum Power Point (GMPP) and Local Maximum Power Points (LMPPs). Method: The Zebra Optimization Algorithm (ZOA) is applied to track the GMPP of a PV system under partial shading conditions. Simulations were conducted using the MATLAB/Simulink platform to evaluate the algorithm’s performance. The focus was on ensuring fast and accurate tracking of the GMPPT under rapid changes in solar irradiance levels. Results and Discussion: Simulation results demonstrated that the ZOA-based MPPT algorithm effectively tracked the GMPPT of the PV system, even under rapid variations in solar irradiance. The algorithm proved its accuracy and speed in distinguishing the GMPP from LMPPs, addressing the challenges posed by the distorted P-V curve. Research Implications: The successful application of the ZOA for tracking the GMPP in partial shading conditions offers a significant improvement for PV systems. This can lead to enhanced efficiency in energy production and more reliable performance in real-world scenarios, where shading and other environmental factors frequently affect solar panels. Originality/Value: This study introduces the adaptation of the Zebra Optimization Algorithm (ZOA) in MPPT for PV systems, addressing the issue of distinguishing between GMPP and LMPPs under partial shading. The approach demonstrates both innovation and practical value in improving the efficiency of PV systems in challenging environmental conditions.

AIoT-Based Visual Anomaly Detection in Photovoltaic Sequence Data via Sequence Learning

Anomaly detection is a common analytical task aimed at identifying rare cases that differ from the majority of typical cases in a dataset. In the management of photovoltaic (PV) power generation systems, it is essential for electric power companies to effectively detect anomalies in PV sequence data, as this helps operators and experts understand and interpret anomalies within PV arrays when making response decisions. However, traditional methods that rely on manual labor and regular data collection are difficult to monitor in real time, resulting in delays in fault detection and localization. Traditional machine learning algorithms are slow and cumbersome in processing data, which affects the operational safety of PV plants. In this paper, we propose a visual analytic approach for detecting and exploring anomalous sequences in a PV sequence dataset via sequence learning. We first compare the sequences with their reconstructions through an unsupervised anomaly detection algorithm (Long Short-Term Memory) based on AutoEncoders to identify anomalies. To further enhance the accuracy of anomaly detection, we integrate the artificial intelligence of things (AIoT) technology with a strict time synchronization data collection and real-time processing algorithm. This integration ensures that data from multiple sensors are synchronized and processed in real time. Then, we analyze the characteristics of the anomalies based on the visual comparison of different PV sequences and explore the potential correlation factors to analyze the possible causes of the anomalies. Case studies based on authentic enterprise datasets demonstrate the effectiveness of our method in the anomaly detection and exploration of PV sequence data.

MIWO based MPPT of PV system for induction motor driven water pumping system

Renewable energy source-based Water Pumping Systems (WPSs) have gained popularity for drinking, agricultural, and industrial purposes. Locally situated standalone Photovoltaic (PV) powered WPS can provide a viable water supply solution, especially with induction motors (IM) due to their advantages over other motor types. However, solar PV energy output is greatly influenced by weather conditions, necessitating energy storage systems like batteries, which increase costs and require maintenance. This research proposes an efficient energy management system for a PV-powered IM-driven WPS that does not rely on batteries. The system handles Partial Shading Conditions (PSC) effectively, thanks to a sensorless speed controller combining a Modified Invasive Weed Optimization (MIWO) mechanism with the Perturb and Observe (P&O) method. This controller uses the inverter as an MPPT circuit, eliminating the need for a distinct converter to follow the MPP. The hybrid P&O-MIWO method's outcomes are compared with those of P&O hybridization with GA, PSO, and GWO to assess efficiency under various PSCs. TS-Fuzzy controllers are employed for voltage control at the DC-link and motor speed. The proposed system is analyzed across different scenarios and validated using a Simulink model in MATLAB. Results indicate that the hybrid P&O-MIWO mechanism enhances energy generation from the PV unit without relying on energy storage devices, providing a cost-effective solution for PV-based WPS implementation.

Long-Term Optimal Scheduling of Hydro-Photovoltaic Hybrid Systems Considering Short-Term Operation Performance

Integrating photovoltaic power stations into large-capacity hydropower stations is an efficient and promising method for regulating large-scale photovoltaic power generation. However, constrained by the time step length, traditional long-term scheduling of hydro-PV hybrid systems does not adequately consider short-term operational performance indicators, resulting in sub-optimal scheduling plans that fail to coordinate the consumption of photovoltaic power and the utilization of water resources in the basin. To address this, this study established a long-term optimal scheduling model for hydro-PV hybrid systems. This model overcomes the limitation of the time step length in long-term scheduling by incorporating long-term power generation goals and short-term operation performance targets into the long-term optimal scheduling process based on scheduling auxiliary functions. In case studies, the optimised model balanced the long-term power-generation goals and short-term operational performance targets by redistributing energy across different periods. Compared to optimization models that did not consider short-term operation performance, in a typical normal year, the model effectively reduced the electricity curtailment volume (28.54%) and power shortage volume (10.91%) of the hybrid system while increasing on-grid electricity (0.03%). Similar improvements were observed in wet and dry years. These findings provide decision support for hydropower scheduling in the context of large-scale photovoltaic power integration.

Life cycle assessment of recycling waste crystalline silicon photovoltaic modules: A comparison between traditional and green solvent recycling processes

Crystalline silicon photovoltaic (PV) modules that have reached the end of their service life, if not effectively recycled, result in the loss of valuable resources such as silicon, silver, aluminum, and others. Moreover, improper disposal can lead to long-term environmental pollution. Therefore, the development of efficient and green recycling technologies for waste PV modules has become a key focus of current research. In this study, a life cycle assessment (LCA) of green solvent recycling (GSR) and traditional recycling technologies was conducted using the ReCiPe 2016 Mid-point evaluation method. The energy and resources consumed, along with the environmental impacts generated, were comparatively analysed, and their resource recovery benefits were calculated. Results show that GSR performs well in terms of environmental impact and even better in terms of resource recovery benefits. Leaching with 1,3-dimethyl-2-imidazolidinone (DMI) solvents reduces global warming potential by 487 kg CO2 eq compared to nitric acid used in chemical recycling. Deep eutectic solvents (DES) achieve higher leaching rates of up to 99 %, compared to metals recovered via mechanical separation, and reduce terrestrial ecotoxicity by 742 kg of 1,4-DCB. Of all energy and resource inputs, electricity consumption contributes the most to environmental impacts. In summary, this study offers a more environmentally friendly and effective recycling solution for the PV industry, supporting its green transformation and sustainable development.

Multi-objective optimization of non-fossil energy structure in China towards the carbon peaking and carbon neutrality goals

Numerous nations and regions are earnestly dedicated to propelling energy transition. China accounts for 31.78 % of the total global carbon emissions, and China's energy structure holds paramount significance in the broader context of global initiatives. China has officially proposed to achieve the target of carbon peak by 2030 and carbon neutrality by 2060, imposing substantial pressure on the transformation of the energy structure. In this study, we propose a multi-objective optimization model for the non-fossil energy structure, encompassing three primary optimization objectives: maximizing power generation, minimizing economic costs, and reducing CO₂ emissions. The optimization involves determining the proportion of seven key non-fossil energy sources—hydropower, onshore wind, offshore wind, photovoltaic (PV), bioenergy, geothermal, and nuclear power—while adhering to constraints related to power demand, nuclear safety, and other factors. The analysis explores the installed capacity, power generation, power generation costs, and CO₂ emission reductions of non-fossil energy under distinct scenarios in 2025, 2030, and 2060. The findings reveal that by 2030, the optimal share of power generation should be sequenced as onshore wind, hydropower, nuclear power, offshore wind, PV, bioenergy, and geothermal. Subsequently, by 2060, the top three developed power should be onshore wind, nuclear power, and hydropower.

Analysis and control of grid-interactive PV-fed BLDC water pumping system with optimized MPPT for DC-DC converter

In this study, a novel water pumping module fed by grid interactive Photo-Voltaic with a bidirectional Power Flow Control was proposed. In addition to improving the pumping system’s reliability, a water pump is powered by a brushless DC motor drive. This method enables the pump to work at its maximum capacity for the entirety of that day, regardless of the weather. The entire system becomes more reliable as a result of the motor’s increased use of photovoltaic (PV) generated power for pumping applications. Maximum Power Point Tracking (MPPT) controller incorporating Machine Learning algorithm drives bridgeless greater static gain DCDC converter to achieve higher power generation point and increment PV efficiency. The PV array’s operation would be managed using the ML back propagation technology to capture the most electricity under any ecological circumstance. A BLDC motor is fed by a Voltage Source Inverter (VSI) that includes a DC bus controlled in both directions by a unit vector template (UVT) approach incorporated in a single-phase voltage source converter (VSC). Additionally, utilizing a PI controller to manage the DC capacitor voltage in the UVT controller at a particular level is not appropriate for the increased PQ capabilities. However, due to tuning problems with the current controller, this controller is unpopular. The aforementioned problems are resolved by employing a unique intelligent-based fuzzy logic controller that achieves good performance features. In this technique, the function of a PV array at its Maximum Power Point (MPP), as well as power quality enhancements and a decrease in Total Harmonic Distortion (THD) of the grid are accomplished. The proposed PI controller attains a significant voltage THD of 3.736. The PI controller, on the other hand, managed to achieve a load voltage THD of 2.629%. The ANFIS method, whose value is 1.739%, is discovered to have a lower THD than all remotes with improved features, it lessens abrupt swings while maintaining steady DC-link voltage.

Determination of I-V curves for photovoltaic generator by an analytical method; Akbaba model

In order to maximize the amount of electrical energy generated and ensure effective use, the conventional I-V or V-I characteristics derived from the diode model of Photovoltaic (PV) Solar Panels are important. Unfortunately, these features cannot be extracted using linear equations. It follows that the answer requires extremely drawn-out, time-consuming procedures. Many studies focus on calculating maximum power extraction. The analytical solutions to these equations are also nonexistent. Programming on computers can assist in solving the equations. The Akbaba Model was created specifically for the PVG to address these challenges. The Akbaba Model relies heavily on coefficients A, B, and C in this equation. The diode model serves as the true model in this case, and the coefficients A, B, and C are calculated. Another advantage of this model is that, because its qualities have partially deteriorated with time, the genuine I-V characteristics of the PV Panels can be recovered by measuring again.

Powering the circular future: Climate change and economic perspectives on second‐life batteries in the Belgian context

AbstractThis paper calculates the future levelized cost of storage (LCOS) and conducts a prospective life cycle assessment (PLCA) for second‐life batteries (SLB) in Flanders, Belgium. A cradle‐to‐grave approach is chosen for climate change (CC) and economic impacts of SLB. Impacts of processes related to the first and the second life are allocated according to the delivered electricity. Furthermore, impacts are determined according to their temporal occurrence. For LCOS, activities are time adjusted by discounting. For PLCA, new background databases are generated and changed based on the activities occurrence in time. Additionally, future CC impact of three Belgian energy paths is modeled, introducing user‐defined scenarios of PLCA. To conceptualize impacts, three use cases are defined: (a) residential, (b) industrial and (c) utility use case (UTI). The residential and industrial use cases (INDs) represent photovoltaic (PV) installations with battery storage, the UTI is a large‐scale battery participating in the Belgian secondary reserve market. Lowest LCOS of the SLB in 2050 are found in the IND, namely 39.66 €/MWh, and are below the benchmark batteries. CC impact of SLB in the residential is 58.7 /kWh and below the benchmark batteries. The CC impact of SLB is 75.2 and 78.5 /kWh in the industrial and UTI and thus higher than the benchmark batteries. Crucial for both assessments are increased dismantling and repurposing facility throughput, fair charging tariffs, the manufacturing, the charging electricity generated by PV installations, and power electronics. On the contrary, changing the background does not lead to major changes in CC.

Hybrid sine cosine and spotted Hyena based chimp optimization for PI controller tuning in microgrids

In this paper, a novel hybrid sine-cosine and spotted Hyena-based chimp optimization algorithm (hybrid SSC) is adopted for the precise tuning of proportional-integral (PI) controllers in a microgrid system. The microgrid integrates multiple renewable energy sources, including photovoltaic (PV) panels, wind turbines, a fuel cell, and a battery storage system, all connected to a common DC bus. This DC bus interfaces with the main grid through a voltage source converter (VSC). The microgrid comprises a total of eight PI controllers distributed across various components: the boost converter in the wind system, the fuel cell system, the battery energy storage device, and the VSC controller. The hybrid SSC optimization algorithm effectively combines the exploration capabilities of the sine-cosine algorithm (SCA) with the exploitation strengths of the spotted Hyena optimizer (SHO) and Chimp optimization algorithm (ChOA), aiming to achieve optimal tuning of the PI controllers. This hybrid approach ensures an enhanced dynamic response and overall system performance by minimizing the integral of the time-weighted squared error (ITSE) for each controller. The simulation results, directed in a MATLAB/SIMULINK environment, demonstrate the efficacy of the hybrid SSC algorithm in improving the stability, response time and efficacy of the microgrid. The proposed technique significantly outperforms traditional tuning techniques, ensuring robust operation and seamless addition of renewable energy sources with the main grid. This study contributes to the advancement of intelligent control strategies for modern microgrids, emphasizing the importance of hybrid optimization algorithms in achieving optimal performance in complex energy systems.

Selected social impact indicators influenced by materials for green energy technologies

The social risks of green energy transition are underexplored. One of the important questions is which materials used in green energy technologies offer the greatest social benefits, such as ensuring decent living conditions, and which pose the most social risks. To address this issue, we develop a dynamic material-energy flow model integrating system dynamics, social life cycle assessment, and geometallurgical approaches. The analysis focuses on critical materials: Rare Earth Elements, Nickel, Silicon, Graphite, Magnesium, Gallium, Germanium, Indium, Aluminum, Cobalt, Lithium, Zinc, and Tellurium used in wind turbines, electric vehicles, lithium-ion batteries and solar photovoltaic panels. We assess their social impact on work safety, gender equality, informal employment, labor income share, employment rate, and child labor—key issues addressed by Sustainable Development Goals 1, 5, and 8. Here we show that Aluminum production for electric vehicles, wind turbines and solar photovoltaic panels generates the most jobs and income opportunities, while extraction of Cobalt, Lithium, Silicon, and Zinc carry the highest social risks.

Evaluation of self-cleaning mechanisms for improving performance of roof-mounted solar PV panels: A comparative study.

Solar panel installation is generally exposed to dust. Therefore, soiling on the surface of the solar panels significantly reduces the effectiveness of solar panels. Accumulation of dust also shortens their lifespan and reduces efficiency by about 15% to 20%. A significant reduction in the efficiency of solar photovoltaic panels has been observed due to inadequate insulation and dust deposition or shading. To harness maximum solar energy from solar panels up to their rated capacity, they need to be cleaned periodically. Therefore, the current study focuses on the comparative performance analysis of two distinct types of self-cleaning mechanisms, namely self-cleaning wiper (SCW) and nano-coating method. These methods are economical and sustainable for the standard atmospheric conditions of Pakistan. Solar panels (reference, nano-coated, and self-cleaning wiper mechanism) were placed on the roof of the Mechanical Engineering Department MUST Mirpur AJK for five weeks. Solar irradiance, dust density & performance parameters of these three panels were recorded on weekly basis. It was observed that an increase in the rate of dust deposition negatively affects the conversion efficiency of solar panels. When dust density was increased from 7.5 to 18.15 (g/m2), the percentile drops in rated power (50W) for reference, nano-coated, and self-cleaning wiper mechanisms are 37%, 33% and 23%, respectively. Moreover, the payback period of nano-coated and SCW is 1.07 years and 2.79 years, respectively.

Effect of soiling loss in solar photovoltaic modules and relation with particulate matter concentration in nearby mining and industrial sites

Effect of soiling loss in solar photovoltaic modules and relation with particulate matter concentration in nearby mining and industrial sites

Enhancement of PV and concentered PV panels using evaporative cooling during summer and winter seasons: A case study in Egypt

Numerous efforts are made to comprehend the performance evolution of photovoltaic (PV) panels. The elevated surface temperature causes a deterioration in PV panels' performance, hindering the expected increase in harvested energy associated with integrating solar reflectors. In this context, the year-round efficacy of cooling PV panels supported by reflective mirrors is investigated to assess the effectiveness of solar reflector implementation. A conventional PV panel, a CPV (cooled PV panel), and a CCPV (concentrated cooled PV panel) were compared side-by-side to determine their relative performance and energy productivity. Evaporative cooling pads are utilized to chill the two modified PV systems by varying the coolant flow rate during the summer and winter seasons of 2022 and 2023, respectively, in the environmental conditions of the Tenth of Ramadan City, Egypt. The findings indicate that the average daily power increment ratio (PIR) for CPV ranges from 4.7 % up to 7.4 %, while CCPV ranges from 6.7 % to 12 % for different climate conditions year-round. Finally, changing the setup configuration from CPV to CCPV increased the (PIR) 1.5 times during the summer tests and 1.7 times during the winter tests.

Ultra 40 % optical and electrical performance enhancement of silver nanosphere array-structured radioluminescent nuclear batteries

Weak performance output is currently the major factor limiting the further development of radioluminescent nuclear batteries for sustainable and stable applications. This work proposed a method for enhancing the radiofluorescence absorption to boost the electrical output of these batteries with metal nanosphere array structures for photovoltaic modules. The radiofluorescence absorption enhancement of photovoltaic modules, the electrical output enhancement of batteries, and the fundamental enhancement mechanism of metal nanosphere array structures were investigated from multiple perspectives. Silver nanosphere array structure on the surface of the photovoltaic module could enhance the forward scattering probability of radiofluorescence generated by the radioluminescent material inside the photovoltaic module, thereby achieving a radiofluorescence absorption gain of up to 43.66 % for the photovoltaic module and an electrical output gain of up to 44.91 % for the radioluminescent nuclear battery.

A Short-term PV Power Prediction and Uncertainty Analysis Model Based on CEEMDAN and AHA-BP

The stochastic and intermittent nature of photovoltaic (PV) generation brings a series of scheduling problems to the power system. An effective prediction of PV power is essential to minimize the impact of uncertainty. Therefore, this paper presents an integrated prediction model with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the artificial hummingbird algorithm (AHA), and the BP neural network (BPNN) for predicting power generation from PV power plants, and a methodology for uncertainty analysis by using the nonparametric kernel density estimation (NPKDE). First, one month of PV power is decomposed into an array of components using CEEMDAN. Then, the weights and thresholds of the BPNN are optimized by using AHA. These components are trained using the BPNN. Finally, the final prediction results are obtained by superimposing the components, and NPKDE is employed to compute the probability density and confidence interval of the prediction error. The proposed prediction method demonstrates superior predictive performance in comparison with other models. Also, the NPKDE approach better describes the accuracy of the probability density distribution.

Evaluating the energy and economic performance of hybrid photovoltaic thermal system integrated with multiwalled carbon nanotubes enhanced phase change material

Photovoltaic thermal (PVT) systems represent an advanced evolution of traditional photovoltaic (PV) modules designed to generate electrical and thermal energy simultaneously. However, achieving optimal and commercially viable performance from these systems remains challenging. To overcome this issue, in this research, multiwalled carbon nanotube (MWCNT) enhanced phase change materials (PCMs) integrated with PVT system to enhance electrical and thermal performance has been studied. An experimental investigation with three different configurations, PVT, PCM integrated PVT (PVTPCM), and MWCNT enhanced PCM integrated PVT (PVTNePCM) systems, was carried out under varying solar radiations and a water flow rate of 0.013–0.016 kg/s compared to conventional PV system. A two-step technique was employed to formulate the nanocomposites, and the energy performance of both PV and PVT systems assessed experimentally. The performance of PVTPCM and PVTNePCM systems was evaluated using the TRNSYS simulation technique. The formulated nanocomposite exhibited a 71.43% enhancement in thermal conductivity, a significant reduction in transmittance up to 92% and remained chemically and thermally stable. Integration of NePCM in the PVT system resulted in a notable decrease in panel temperature and a 25.03% increase in electrical efficiency compared to the conventional PV system. The highest performance ratio and overall efficiency for PVTNePCM were 0.55 and 81.62%, respectively, at a flow rate of 0.013 kg/s. The energy payback periods of PVTNePCM, PVTPCM, and PVT setup were 4.7, 4.8 and 5.6 years, respectively. Additionally, a significant improvement in thermal efficiency were observed for PVTPCM and PVTNePCM systems compared to water-based PVT systems, due to the energy stored in the thermal energy storage material.

Based on Deep Learning Model and Flink Streaming Computing Short Term Photovoltaic Power Generation Prediction for Suburban Distribution Network

With the advancement of global energy internet construction, accurate prediction of new energy generation power such as photovoltaic is an important foundation for ensuring the safety and economic working of new power systems. A short-term photovoltaic power generation prediction method for suburban distribution networks based on deep learning model fusion and Flink flow calculation is proposed to address the challenges of complex power grids, diversified disturbance factors, and isolated monitoring points. This method uses Bi directional Long Short Term Memory(BiLSTM) to extract cross sequential nonlinear characteristic of photovoltaic power generation time series data. Compared with standard LSTM, BiLSTM can consider both historical and future information simultaneously, thus extracting richer extracted features from power generation time series data. This method also integrates attention mechanism to capture the importance distribution of historical temporal features for power generation prediction, effectively solving the problem of long-term temporal dependence in standard LSTM models. The Flink streaming computing framework embeds a trained BiLSTM-Attention photovoltaic power generation prediction model, enabling real-time prediction and monitoring analysis of photovoltaic power generation at various monitoring points in the suburban distribution network. This article uses a dataset of a suburban photovoltaic power station for validation, and trains the model with historical power generation data, meteorological factors, weather types, seasons, and other information as inputs. The BiLSTM-Attention fusion model studys the temporal characteristics of power generation, and has high accuracy in predicting short-term photovoltaic power generation in different scenarios. The Flink streaming computing platform can not only process high throughput predicted power data, but also has low time delay.

Black Jamun Fruit Peel Extract Modified Gum Acacia Biopolymer Suitable for Energy Device Applications via Charge Transfer Enhancement

The visible light absorption capabilities, along with its electrical characteristics, of a low-cost, plant-based, biopolymer, gum acacia, have been chemically modified by tailoring its defect concentrations via pH modification, where the acid solvent of fruit (peel) extracts of jamun was used to influence the chemical structure by reactive modification. Ultraviolet-visible and photoluminescence spectroscopy of the gum acacia biopolymer treated with the extracted anthocyanin from jamun peel showed a prominent, characteristic red shift in the visible spectrum range. Impedance spectroscopy analysis showed the occurrence of localized ionic conduction. The bulk conductivity of the modified specimens increased due to the profound release of conducting ions in the water-swollen network. The reactively modified biopolymer could be used in multiple fields of material science, specifically in energy device applications, viz., photovoltaics (PV), by utilizing its cost effectiveness and photolytic effectiveness. 2nd, as a polymer electrolyte and dye-sensitised solar cells (DSSCs) material by utilizing its capacity of gel formation. In our research described in this article, the underlying charge transfer mechanism for such responses was examined after crosslinking in the organic dyes extracted from the peel of jamun fruit with gum acacia.

Quantitative analysis of high-frequency radiometric databases. Application to the case of Seville

AbstractThe short-term variability of solar resource is one of the main challenges faced by the large-scale implementation of photovoltaic (PV) systems today; this will increase as the installed power of solar PV increases. To evaluate its influence on electrical grid operation, it is essential to analyse the variability of solar resource based on high-frequency data, 30 s and shorter. In this study, the high-frequency measurements of Global Horizontal Irradiation (GHI) and Direct Normal Irradiation (DNI) recorded every 5 s at the radiometric station of the University of Seville for 20 years are analysed and characterised. For this purpose, a corrected database for GHI and DNI was obtained from the application of a correction methodology. To understand the reliability of this database it is required to know its uncertainty. This work proposes a methodology to quantify the uncertainty of high-frequency radiometric databases whose wrong data have been corrected. Applying it, the daily uncertainty is 2.69% for GHI and 4.07% for DNI. To characterize the distribution of the database, the distribution of high frequency kt and kb indices are modelled providing the fitting parameters. To characterize the variability of high-frequency measurements, transition matrices are proposed, which allow identifying both the magnitude and frequency of jumps. The results obtained show that jumps of up to 800 W/m2 occur in both GHI and DNI. In the case of GHI, the percentage of jumps equal to or greater than 500 W/m2 is 1.36%, about 43,000 jumps in a year in measurements every 5 s.