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Conversion efficiency, power production, and cost of PV panels’ energy are remarkably impacted by external factors including temperature, wind, humidity, dust aggregation, and induction characteristics of the PV system such as tilt angle, altitude, and orientation. One of the prominent elements affecting PV panel performance and capability is dust. Nonetheless, dust features including size, shape, type, etc. are geologically known. Several mitigation methods have been studied for the reduction of dust concentration on the exterior face of the PV modules. The outcomes have demonstrated that dust concentration and pollutants remarkably affect the PV panel energy production. This paper reviews the recently developed research on the outcomes of the dust effect on PV panels in different locations and meets the needs of future research on this subject. Moreover, different cleaning methods that could be advantageous for future researchers in opting for the most applicable technique for dust removal are reviewed.

Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. The goal of this systematic review is to offer a comprehensive overview of the recent advancements in AI-based methodologies for PV fault detection, consolidating the key findings from 31 research papers. An initial pool of 142 papers were identified, from which 31 were selected for in-depth review following the PRISMA guidelines. The title, objective, methods, and findings of each paper were analyzed, with a focus on machine learning (ML) and deep learning (DL) approaches. ML and DL are particularly suitable for PV fault detection because of their capacity to process and analyze large amounts of data to identify complex patterns and anomalies. This study identified several AI techniques used for fault detection in PV systems, ranging from classical ML methods like k-nearest neighbor (KNN) and random forest to more advanced deep learning models such as Convolutional Neural Networks (CNNs). Quantum circuits and infrared imagery were also explored as potential solutions. The analysis found that DL models, in general, outperformed traditional ML models in accuracy and efficiency. This study shows that AI methodologies have evolved and been increasingly applied in PV fault detection. The integration of AI in PV fault detection offers high accuracy and effectiveness. After reviewing these studies, we proposed an Artificial Neural Network (ANN)-based method for PV fault detection and classification.

Hybrid PVT systems simultaneously produce electrical energy using photovoltaic technology and thermal energy using a heat extraction method that collects induced heat from the module. The purpose of this work is to establish a PVT system based on characterization, efficiency study, and performance analysis for both an electrical and a thermal system. A mathematical analysis of the electrical, thermal, and optical model is performed to establish the proposed system. Three types of heat exchanger pipes, including stainless steel, aluminum, and copper, are considered for a heat transfer analysis of the system. The results include temperature profiling, a comparison of the PVT system’s different components, and an overall output and efficiency study for all of the mentioned pipes. Results show that the obtained electrical and thermal efficiency for stainless steel is 0.1653 and 0.237, respectively, for aluminum it is 0.16515 and 0.2401, respectively, and for copper it is 0.16564 and 0.24679, respectively. After comparison, it was found that the overall efficiency for stainless steel is 0.40234, for aluminum is 0.40526, and for copper is 0.41244. Thus, this study will enhance the opportunity to provide an effective hybrid PVT energy management system.

The worldwide air pollution situation reveals significant environmental challenges. In addition to being a major contributor to the deterioration of air quality, particulate matter (PM) is also an important factor affecting the performance of solar energy systems given its ability to decrease light transmission to solar panels. As part of our research, the present investigation involves monitoring concentrations of PM using a high-performance optical instrument, the in situ calibration protocol of which is described in detail. For the city of Rabat, observations revealed significant variations in concentrations between day and night, with peaks observed around 8 p.m. correlating with high relative humidity and low wind speeds, and the highest levels recorded in February with a monthly average value reaching 75 µm/m3. In addition, an experimental protocol was set up for an analysis of the elemental composition of particles in the same city using SEM/EDS, providing a better understanding of their morphology. To assess the impact of meteorological variables on PM concentrations in two distinct climatic environments, a database from the city of Marrakech for the year 2024 was utilized. Overall, the distribution of PM values during this period did not fluctuate significantly, with a monthly average value not exceeding 45 µm/m3. The random forest method identified the most influential variables on these concentrations, highlighting the strong influence of the type of environment. The findings provide crucial information for the modeling of solar installations’ soiling and for improving understanding of local air quality.

Nitrates can cause severe ecological imbalances in aquatic ecosystems, with considerable consequences for human health. Therefore, monitoring this inorganic form of nitrogen is essential for any water quality management structure. This research was conducted to develop a novel Nitrate Portable Measurement System (NPMS) to monitor nitrate concentrations in water samples. NPMS is a reagent-free ultraviolet system developed using low-cost electronic components. Its operation principle is based on the Beer–Lambert law for measuring nitrate concentrations in water samples through light absorption in the spectral range of 295–315 nm. The system is equipped with a ready-to-use ultraviolet sensor, light emission diode (LED), op-amp, microcontroller, liquid crystal display, quartz cuvette, temperature sensor, and battery. All the components are assembled in a 3D-printed enclosure box, which allows a very compact self-contained equipment with high portability, enabling field and near-real-time measurements. The proposed methodology and the developed instrument were used to analyze multiple nitrate standard solutions. The performance was evaluated in comparison to the Nicolet Evolution 300, a classical UV–Vis spectrophotometer. The results demonstrate a strong correlation between the retrieved measurements by both instruments within the investigated spectral band and for concentrations above 5 mg NO3−/L.

In this work, a newly developed self-contained, portable, and compact iron measurement system (IMS) based on spectroscopy absorption for determination of Fe2+ in water is presented. One of the main goals of the IMS is to operate the device in the field as opposed to instruments commonly used exclusively in the laboratory. In addition, the system has been tuned to quantify iron concentrations in accordance with the values proposed by the regulations for human consumption. The instrument uses the phenanthroline standard method for iron determination in water samples. This device is equipped with an optical sensing system consisting of a light-emitting diode paired with a photodiode to measure absorption radiation through ferroin complex medium. To assess the sensor response, four series of Fe2+ standard samples were prepared with different iron concentrations in various water matrices. Furthermore, a new solid reagent prepared in-house was investigated, which is intended as a “ready-to-use” sample pre-treatment that optimizes work in the field. The IMS showed better analytical performance compared with the state-of-the-art instrument. The sensitivity of the instrument was found to be 2.5 µg Fe2+/L for the measurement range established by the regulations. The linear response of the photodiode was determined for concentrations between 25 and 1000 µg Fe2+/L, making this device suitable for assessing iron in water bodies.

Recently, the estimation of photovoltaic parameters has drawn the attention of researchers, and most of them propose new optimization methods to solve this problem. However, the process of photovoltaic parameters estimation can be affected by other aspects. In a real experimental setup, the I–V characteristic is obtained with IV tracers. Depending on their technical specifications, these instruments can influence the quality of the I–V characteristic, which in turn is inevitably linked to the estimation of photovoltaic parameters. Besides the uncertainties that accompany the measurement process, a major effect on parameters estimation is the size of the measurement interval of current and voltage, where some instruments are limited to measure a small portion of the characteristic or cannot reach their extremum regions. In this paper, three case studies are presented to analyse this phenomenon: different characteristic measurement starting points and different measurement intervals. In the simulation study the parameters are extracted from 1000 trial runs of the simulated I-V curve. The results are then validated using an experimental study where an IV tracer was built to measure the I–V characteristic. Both simulation and experimental studies concluded that starting the measurements at the open circuit voltage and having an interval spanning a minimum of half of the I–V curve results in an optimal estimation of photovoltaic parameters.

Solar photovoltaic technology is a major contender in the race for renewable, sustainable and green energy. This paper introduces the characteristics of different PV cell equivalent circuit and its output behaviour. It describes and implements the proposed characterization method by using a selected model. It generates I-V and P-V curve using iterative method. Noise analysis and observation of curve fitting are briefly described here. The white noise effect and its related output characteristics are explained too. To introduce and implement the generalized method, a photovoltaic electrical equivalent circuit is used here. The fundamental equation of a PV cell is used to study the model and to analyze the best fit of observed data. The values of ideal parameters are used to study the model’s behaviour. The main objective is to measure the noise in data approximation and on the polynomial curve fitting method for both the I-V and P-V curve.

We consider deterministic and probabilistic cellular automata to study and describe certain types of patterns in idealized material blocks. We have particular interest in patterns similar to fractures. The internal structure of these material blocks is assumed to be unknown and probabilistic cellular automata are used to obtain distributions for the referred internal structure. We consider the 1D case. Certain deterministic elementary rules are identified as elementary ideal fracture rules and the probabilistic rules are introduced as probabilistic interpolation of these elementary rules. The initial conditions are obtained from the visible borders of the surface (2D block). Therefore, each visible edge is giving additional information and a probabilistic fracture type pattern. Different methods to combine these patterns, into a final one, are discussed. Moreover, we introduce refinement techniques of the CA rules to improve the probabilities distributions. This refinement process may consider prescribed behaviour or empirical data, and, therefore, the CA rules behaviour becomes adjustable.

Nowadays, the inner shape and economic viability of a stone block is dependent on the skill and experience of the “expert” that makes predictions based on external observations. This actual procedure is an extremely high empirical method, and when it fails, substantial work, time, and money is wasted. At present, researchers are committed to developing models to predict the stone block internal structure based on non-destructive tests. Ultrasonic tomography and electrical resistivity tomography are the tests that best fit these objectives. Trying to improve the existing procedures for collecting stone information and data exporting, a novel approach to perform both tomographies is proposed in this paper. This novel approach presents sound advantages regarding the current manual procedure: namely, (i) high accuracy due to a new automatic positioning system; (ii) no need for highly skilled operators to process measurements; (iii) measurements are much easier to derive, and results are quickly delivered. A comparison between the new automatic process and the current manual procedure shows that the manual procedure has a very low accuracy when compared to the new developed automatic system. The automatic measurements show extremely significant time savings, which is a relevant issue for the future competitiveness of the stone sector.