A Survey on Ageing Mechanisms in II and III-Generation PV Modules: Accurate Matrix-Method Based Energy Prediction Through Short-Term Performance Measures

none5siSolar energy utilization has been triggered by advances in new technology to reduce the cost of photovoltaic (PV) panels with an increase of efficiency. To improve the energy production quality, it is necessary to undergo the PV panels to characterization both in the indoor and outdoor scenarios; these latter characterizations generally require all seasons-based measurements. Therefore, it is essential to find models for characterizing PV panels in terms of energy production but also production and operating mode tolerance. The paper illustrates the findings of global research dedicated to PV panels ageing and their impact on energy production in the years. At first, an in-depth analysis of the ageing mechanisms affecting II and III generations' PV panels has been presented when exposed to atmospheric agents. Afterwards, the PV panels' characterization, conducted in a short time (i.e. a total of seven days), has been reported, performing outdoor measurements in conjunction with an electronic calibrator able to measure currents and voltages. The MPPT (Maximum Power Point Tracker) device is the core instrumentation of the employed measurement system. Obtained results are convincing since they have been compared with simultaneous measurements of PV panels located in the same place.openP. Visconti, R. de Fazio, D. Cafagna, R. Velazquez, A. Lay-EkuakilleVisconti, P.; de Fazio, R.; Cafagna, D.; Velazquez, R.; Lay-Ekuakille, A

Benchmarking study between capacitive and electronic load technic to track I-V and P-V of a solar panel

To detect defects of solar panel and understand the effect of external parameters such as fluctuations in illumination, temperature, and the effect of a type of dust on a photovoltaic (PV) panel, it is essential to plot the Ipv=f(Vpv) characteristic of the PV panel, and the simplest way to plot this I-V characteristic is to use a variable resistor. This paper presents a study of comparison and combination between two methods: capacitive and electronic loading to track I-V characteristic. The comparison was performed in terms of accuracy, response time and instrumentation cost used in each circuit, under standard temperature and illumination conditions by using polycrystalline solar panel type SX330J and monocrystalline solar panels type ET-M53630. The whole system is based on simple components, less expensive and especially widely used in laboratories. The results will be between the datasheet of the manufacturer with the experimental data, refinements and improvements concerning the number of points and the trace time have been made by combining these two methods

Index to 1984 NASA Tech Briefs, volume 9, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1984 Tech B Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Long-Term Durability of Rooftop Grid-Connected Solar Photovoltaic Systems

Compared to their initial performance, solar photovoltaic (PV) arrays show long-term performance degradation, resulting in lower like-for-like efficiencies and performance ratios. The long-term durability of polycrystalline silicon (p-Si) solar PV modules in three roof-top grid-connected arrays has been examined. Electrical output, ambient temperature, cell temperature, solar irradiance, solar irradiation, and wind speed data were collected at hourly intervals from 2017 to 2021 from three 50 kWp PV installations in Northern Ireland. The results show the extent to which higher PV temperatures associated with more intense solar radiation decrease efficiency, fill factor and maximum power output for PV arrays in a temperate climate. Long-term durability trends for grid-connected roof-top solar photovoltaic systems can be obscured by diurnal and seasonal changes in environmental conditions. To reduce the influence of variable conditions, performance ratios (PRcorr) were “corrected” using the measured annual average cell temperature (Tcell_avg). Introduction of this temperature-correction reduced the seasonal variation of the performance ratio. Using temperature-corrected performance ratios, long-term (in this case those seen after fiveyears operation) performance degradation trends become evident with high confidence after six months for one PV array and within three years for the two other arrays. If lower statistical confidence in trends is acceptable, long-term degradation rates can be identified within one year of operation for all PV arrays examined. These results have the important implication that relatively short-duration outdoor PV performance monitoring may be reliably used to estimate long-term degradation and/or to calibrate normally-conducted accelerated testing

Solar Photovoltaic Modules’ Performance Reliability and Degradation Analysis—A Review

The current geometric increase in the global deployment of solar photovoltaic (PV) modules, both at utility-scale and residential roof-top systems, is majorly attributed to its affordability, scalability, long-term warranty and, most importantly, the continuous reduction in the levelized cost of electricity (LCOE) of solar PV in numerous countries. In addition, PV deployment is expected to continue this growth trend as energy portfolio globally shifts towards cleaner energy technologies. However, irrespective of the PV module type/material and component technology, the modules are exposed to a wide range of environmental conditions during outdoor deployment. Oftentimes, these environmental conditions are extreme for the modules and subject them to harsh chemical, photo-chemical and thermo-mechanical stress. Asides from manufacturing defects, these conditions contribute immensely to PV module’s aging rate, defects and degradation. Therefore, in recent times, there has been various investigations into PV reliability and degradation mechanisms. These studies do not only provide insight on how PV module’s performance degrades over time, but more importantly, they serve as meaningful input information for future developments in PV technologies, as well as performance prediction for better financial modelling. In view of this, prompt and efficient detection and classification of degradation modes and mechanisms due to manufacturing imperfections and field conditions are of great importance towards minimizing potential failure and associated risks. In the literature, several methods, ranging from visual inspection, electrical parameter measurements (EPM), imaging methods, and most recently data-driven techniques have been proposed and utilized to measure or characterize PV module degradation signatures and mechanisms/pathways. In this paper, we present a critical review of recent studies whereby solar PV systems performance reliability and degradation were analyzed. The aim is to make cogent contributions to the state-of-the-art, identify various critical issues and propose thoughtful ideas for future studies particularly in the area of data-driven analytics. In contrast with statistical and visual inspection approaches that tend to be time consuming and require huge human expertise, data-driven analytic methods including machine learning (ML) and deep learning (DL) models have impressive computational capacities to process voluminous data, with vast features, with reduced computation time. Thus, they can be deployed for assessing module performance in laboratories, manufacturing, and field deployments. With the huge size of PV modules’ installations especially in utility scale systems, coupled with the voluminous datasets generated in terms of EPM and imaging data features, ML and DL can learn irregular patterns and make conclusions in the prediction, diagnosis and classification of PV degradation signatures, with reduced computation time. Analysis and comparison of different models proposed for solar PV degradation are critically reviewed, in terms of the methodologies, characterization techniques, datasets, feature extraction mechanisms, accelerated testing procedures and classification procedures. Finally, we briefly highlight research gaps and summarize some recommendations for the future studies

PV System Design and Performance

Photovoltaic solar energy technology (PV) has been developing rapidly in the past decades, leading to a multi-billion-dollar global market. It is of paramount importance that PV systems function properly, which requires the generation of expected energy both for small-scale systems that consist of a few solar modules and for very large-scale systems containing millions of modules. This book increases the understanding of the issues relevant to PV system design and correlated performance; moreover, it contains research from scholars across the globe in the fields of data analysis and data mapping for the optimal performance of PV systems, faults analysis, various causes for energy loss, and design and integration issues. The chapters in this book demonstrate the importance of designing and properly monitoring photovoltaic systems in the field in order to ensure continued good performance

Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids

This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader

Electro-thermal modelling of large PV array degradation for thermography and peak power conditioning monitoring

Photovoltaic (PV) panels started their long technological development journey at the hands of legendary pioneers such as Edmond Bequerel. He discovered the key solar energy principles in 1839 and following this Heinrich Hertz was credited with the discovery of the photoelectric effect in 1887. Nikolas Tesla developed key patents in 1901 and Albert Einstein published a paper in 1905. This work in 1954 lead to Bell Laboratories producing the first commercial PV cell and since then PV cells have advanced to astronomical levels. This project aimed to model the effects of degradation of photovoltaic panels. The goal was to observe the effects that PV cell failure has on the cells internal resistance, and then determine what effect this had on the performance of the panel’s output. Field trials were also undertaken to detect this heating using an infrared thermograph and to also relate the temperatures to the simulated results. Results showed that any increase in panel temperature above 25°C caused the panel’s output to reduce up to 63% at 90°C. The physical detection of heating or hot spots was successful with six out of the thirty-six arrays having cells with increased temperatures. Additionally, the maximum cell temperature scanned was 61°C which was a 24°C increase from the nominal of the rest of the PV array

Application of artificial intelligence in fault detection and classification of solar power plants and prediction of power generation of combined cycled power plants

Solar energy is one of the most dependable renewable energy technologies, as it is feasible almost everywhere globally and is environmentally friendly. Photovoltaic-based renewable energy systems are highly susceptible to power grid transients. Their operation may suffer drastically during faults in the solar arrays, power electronics, and the inverter. Thus, it is vital to develop an intelligent mechanism to detect any type of fault or abnormalities within the shortest possible time that will increase reliability and decrease the maintenance cost of the solar system. To accomplish that, in this research, different artificial intelligence (AI) techniques are utilized to develop classification, fault detection, and optimization algorithms for solar photovoltaic (PV) panels. Initially, a convolutional neural network (CNN) model was designed to detect and classify PV modules based on the images taken from the solar panels. It is found that the proposed CNN model can identify the fault with an accuracy of 91.1% for binary (i.e., healthy and faulty) and 88.6% for multi-classification (i.e. cracked, shadowy, dusty and normal). However, sometimes the fault in the solar panel may not be detectable from the images of the solar panels. That is why an adaptive neuro-fuzzy inference system (ANFIS) model is developed to detect and classify the defects of PV systems based on the signals collected from the solar panels. The performance of the developed defect detection and classification algorithms was tested using real-life solar farm datasets. The performance of the proposed ANFIS-based fault detection scheme has been compared with different machine learning algorithms. It is found from the comparative results that the proposed ANFIS-based fault detection technique is robust and straightforward. Thus, the developed ANFISbased intelligent technique will enhance the reliability of the PV system by minimizing the maintenance cost and saving energy. Finally, another ANFIS model is developed to predict the power generation in a combined cycle power plant. The codes were written in MATLAB, and their validity is confirmed with the available ANFIS toolboxes in MATLAB. The proposed ANFIS is found capable of successfully predicting power generation with extremely high accuracy and being much faster than the built-in ANFIS of MATLAB Toolbox. Thus, the developed ANFIS model could be utilized as a promising tool for energy generation applications