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Early fault detection and diagnosis of grid-connected photovoltaic systems (GCPS) is imperative to improve their performance and reliability. Low-cost edge devices have emerged as innovative solutions for real-time monitoring, reducing latency, and improving response times. In this work, a lightweight Convolutional Neural Network (CNN) is designed and fine-tuned using Energy Valley Optimizer (EVO) for fault diagnosis. The CNN input consists of two-dimensional scalograms generated using Continuous Wavelet Transform (CWT). The proposed diagnosis technique demonstrated superior performance compared to benchmark architectures, namely MobileNet, NASNetMobile, and InceptionV3, achieving higher test accuracies and lower losses on binary and multi-fault classification tasks on balanced, unbalanced, and noisy datasets. Further, a quantitative comparison is conducted with similar recent studies. The obtained results indicate good performance and high reliability of the proposed fault diagnosis method. [ABSTRACT FROM AUTHOR]

This paper presents a comparative study between four techniques recently used to improve the wind energy conversion system (WECS) to water pumping systems. The WECS is a renewable energy source which has developed rapidly in recent years. The use of the WECS in the water pumping field is a free solution (economically) compared to the use of the electricity grid supply. The control of WECS, equipped with a permanent magnet synchronous generator, has the objective of carefully maximising power generation. A comparative study between the proposed Fuzzy Logic Control, optimised using a genetic algorithm and particle swarm optimisation algorithm, and the conventional Perturb and Observe MPPT method using Matlab/Simulink, is presented. The performance of the proposed system has been verified against the generated output voltage, current and power waveforms, intermediate circuit voltage waveform, and generator speed. The presented results demonstrate the effectiveness of the control strategy applied in this work. [ABSTRACT FROM AUTHOR]

In the present work, we report on theoretical studies of thermodynamic properties, structural and dynamic stabilities, dependence of unit-cell parameters and elastic constants upon hydrostatic pressure, charge carrier effective masses, electronic and optical properties, contributions of interband transitions in the Brillouin zone of the novel Tl2HgGeSe4 crystal. The theoretical calculations within the framework of the density-functional perturbation theory (DFPT) are carried out employing different approaches to gain the best correspondence to the experimental data. The present theoretical data indicate the dynamical stability of the title crystal and they reveal that, under hydrostatic pressure, it is much more compressible along the a-axis than along the c-axis. Strikingly, the charge effective mass values ( m e ∗ and m h ∗ ) vary considerably when the high symmetry direction changes indicating a relative anisotropy of the charge-carrier’s mobility. Furthermore, the Young modulus and compressibility are characterized by the maximum and minimum values ( E max and E min ) and ( β max and β min ) that are equal to (62.032 and 28.812) GPa and (13.672 and 6.7175) TPa–1, respectively. Additionally, we have performed calculations of the Raman spectra (RS) and reached a good correspondence with the experimental RS spectra of the Tl2HgGeSe4 crystal. The XPES associated to RS constitutes powerful techniques to explore the oxidized states of Se and Ge in Tl2HgGeSe4 system. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative approach to improve the performance of grid-connected photovoltaic (PV) systems operating in environments with variable atmospheric conditions. The dynamic nature of atmospheric parameters poses challenges for traditional control methods, leading to reduced PV system efficiency and reliability. To address this issue, we introduce a novel integration of fuzzy logic and sliding mode control methodologies. Fuzzy logic enables the PV system to effectively handle imprecise and uncertain atmospheric data, allowing for decision-making based on qualitative inputs and expert knowledge. Sliding mode control, known for its robustness against disturbances and uncertainties, ensures stability and responsiveness under varying atmospheric conditions. Through the integration of these methodologies, our proposed approach offers a comprehensive solution to the complexities posed by real-world atmospheric dynamics. We anticipate applications in grid-connected PV systems across various geographical locations and climates. By harnessing the synergistic benefits of fuzzy logic and sliding mode control, this approach promises to significantly enhance the performance and reliability of grid-connected PV systems in the presence of variable atmospheric conditions. On the grid side, both PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) algorithms were employed to tune the current controller of the PI (Proportional-Integral) current controller (inverter control). Simulation results, conducted using MATLAB Simulink, demonstrate the effectiveness of the proposed hybrid MPPT technique in optimizing the performance of the PV system. The technique exhibits superior tracking efficiency, achieving a convergence time of 0.06 s and an efficiency of 99.86%, and less oscillation than the classical methods. The comparison with other MPPT techniques highlights the advantages of the proposed approach, including higher tracking efficiency and faster response times. The simulation outcomes are analyzed and demonstrate the effectiveness of the proposed control strategies on both sides (the PV array and the grid side). Both PSO and GA offer effective methods for tuning the parameters of a PI current controller. According to considered IEEE standards for low-voltage networks, the total current harmonic distortion values (THD) obtained are considerably high (8.33% and 10.63%, using the PSO and GA algorithms, respectively). Comparative analyses with traditional MPPT methods demonstrate the superior performance of the hybrid approach in terms of tracking efficiency, stability, and rapid response to dynamic changes. [ABSTRACT FROM AUTHOR]

This paper discusses the impact of the feature input vector on the performance of dissolved gas analysis-based intelligent power transformer fault diagnosis methods. For this purpose, 22 feature vectors from traditional diagnostic methods were used as feature input vectors for four tree-based ensemble algorithms, namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree. To build the proposed diagnostics models, 407 samples were used for training and testing. For validation and comparison with the existing methods of literature, 89 samples were used. Based on the results obtained on the training and testing datasets, the best performance was achieved with feature vector 16, which consists of the gas ratios of Rogers' four ratios method and the three ratios technique. The test accuracies based on these vectors are 98.37, 96.75, 95.93, and 97.56% for the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree algorithms, respectively. Furthermore, the performance of the methods based on best input feature was evaluated and compared with other methods of literature such as Duval triangle, modified Rogers' four ratios method, combined technique, three ratios technique, Gouda triangle, IEC 60599, NBR 7274, the clustering method, and key gases with gas ratio methods. These methods suffer from unreliability, and this is the motivation behind the current work to develop a new technique that enhances the diagnostic accuracy of transformer faults to avoid unwanted faults and outages from the network. On validating dataset, diagnostic accuracies of 92.13, 91.01, 89.89, and 91.01% were achieved by the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree models, respectively. These diagnostic accuracies are higher than 83.15% for the clustering method, 82.02% for the combined technique, 80.90% for the modified IEC 60599, and 79.78% for key gases with gas ratios, which are the best existing methods. Even if the performance of dissolved gas analysis-based intelligent methods depends strongly on the shape of the feature vector used, this study provides scholars with a tool for choosing the feature vector to use when implementing these methods. [ABSTRACT FROM AUTHOR]

The lifetime of power transformers is closely related to the insulating oil performance. This latter can degrade according to overheating, electric arcs, low or high energy discharges, etc. Such degradation can lead to transformer failures or breakdowns. Early detection of these problems is one of the most important steps to avoid such failures. More efficient diagnostic systems, such as artificial intelligence techniques, are recommended to overcome the limitations of the classical methods. This work deals with diagnosing the power transformer insulating oil by analysis of dissolved gases using new techniques. For this, we have proposed intelligent techniques based on Multilayer artificial neural networks (ANN). Thus, a multi-layer ANN-based model for fault detection is presented. To improve its classification rate, this one was optimized by a meta-heuristic technique as the particle swarm optimization (PSO) technique. Optimized ANNs have never been used in transformer insulating oil diagnostics so far. The robustness and effectiveness of the proposed model is demonstrated, and high accuracy is obtained. [ABSTRACT FROM AUTHOR]

In this paper, a critical issue related to power management control in autonomous hybrid systems is presented. Specifically, challenges in optimizing the performance of energy sources and backup systems are proposed, especially under conditions of heavy loads or low renewable energy output. The problem lies in the need for an efficient control mechanism that can enhance power availability while protecting and extending the lifespan of the various power sources in the system. Furthermore, it is necessary to adapt the system's operations to variations in climatic conditions for sustained effectiveness. To address the identified problem. It is proposed the use of an intelligent power management control (IPMC) system employing fuzzy logic control (FLC). The IPMC is designed to optimize the performance of energy sources and backup systems. It aims to predict and adjust the system's operating processes based on variations in climatic conditions, providing a dynamic and adaptive control strategy. The integration of FLC is specifically emphasized for its effectiveness in balancing multiple power sources and ensuring a steady and secure operation of the system. The proposed IPMC with FLC offers several advantages over existing strategies. Firstly, it showcases enhanced power availability, particularly under challenging conditions such as heavy loads or low renewable energy output. Secondly, the system protects and extends the lifespan of the power sources, contributing to long-term sustainability. The dynamic adaptation to climatic variations adds a layer of resilience to the system, making it well-suited for diverse geographical and climatic conditions. The use of realistic data and simulations in MATLAB/Simulink, along with real-time findings from the RT-LAB simulator, indicates the reliability and practical applicability of the proposed IPMC strategy. Efficient load supply and preserved batteries further underscore the benefits of the fuzzy logic-based control strategy in achieving a well-balanced and secure system operation. [ABSTRACT FROM AUTHOR]

Groundwater quality in arid areas is an issue that requires attention, as often the only source of irrigation and drinking. In this manuscript, a research area of 4590.2 Km2, which depicts important problems of water scarcity and water-poor quality in an arid environment, was assessed. Therefore, GIS-based mapping of the region was carried out to examine the adequacy of groundwater quality. Forty-three (43) groundwater samples were gathered in the fieldwork in March 2022, and various physicochemical limits were analysed within Gibbs and Piper diagrams. Likewise, spatial distribution maps of twelve (12) main water quality parameters were performed using the Inverse Distance Weighted interpolation method (IDW) in the ArcGIS tool. The Water Quality Index (WQI) has been calculated considering Algerian drinking water quality standards and to know the suitability of water for human consumption. A Pearson's test was carried out to explore the correlations among the different variables. Major results of the study showed that, among anions and cations, sulphates ( S O 4 - 2 ) and calcium (Ca) were the most common ions. The WQI showed that 30.23% of the samples have excellent water quality, 62.79% belong to the good water category, and 6.97% fall into the poor water quality category. Thus, the correlation study between many physicochemical properties also showed significant positive relationships between WQI, TDS, EC, TH, SO 4 - 2 , Ca + + , and Mg + + . In agreement with the Gibbs diagram, most of the samples fall into the field of rock-water interaction dominance. Hence, this research shows that using GIS and WQI as joint approaches could be useful tools for the decision-making process of local and international authorities and water resources management. [ABSTRACT FROM AUTHOR]

This paper reports the computational investigation of two thin-film organic solar cell (TFOSC) structures which are based on two different species, i.e., fullerene-based material (PTB7:PCBM) and non-fullerene-based material (PIF8BT:PDI). Computational investigations are performed on the optimization of thickness for the active absorber layers because the major portion of the sunlight is absorbed in this area. The electrical parameters include the open-circuit voltage (Voc), short-circuit current density (Jsc), fill factor (FF), and power conversion efficiency (η). The extracted modeling results indicate that 200 nm is sufficient to cover the visible spectrum range of photons within the investigation range of 150–400 nm. Moreover, the present study also highlights the optical analysis of the architecture, which shows the absorption of photons in the active region with various thicknesses. After the comparative analysis of the two structures, the results suggest that the structure based on fullerene is 0.97% more efficient than the non-fullerene-based structure. The highest conversion efficiency achieved with the fullerene-based scheme (FTO/PEDOT:PSS/PTB7:PCBM/PFN/Ag) was 4.79%, whereas the highest efficiency rate delivered by the non-fullerene scheme (FTO/PEDOT:PSS/PIF8BT:PDI/PFN/Ag) was 3.82%. The recorded results are in good agreement with the theoretical models and standard action. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1038/s41598-024-59024-4.Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-024-59024-4, published online 08 April 2024The original version of this Article contained an error in the spelling of the author Messaouda Khennane Benbitour which was incorrectly given as Messaouda Khennane Benbiotur.The original Article has been corrected.By Layachi Zaghba; Abdelhalim Borni; Messaouda Khennane Benbitour; Amor Fezzani; Abdullah Alwabli; Mohit Bajaj; Shir Ahmad Dost Mohammadi and Sherif S. M. GhoneimReported by Author; Author; Author; Author; Author; Author; Author; Author [Extracted from the article]

Thin-film photovoltaic cells are attracting increasing attention due to their remarkable properties of thin size and low cost. However, to enable the wider use of solar cells to replace conventional carbon-based methods of electricity production, the low performance parameters in thin films need to be improved. In this study, amorphous silicon (a-Si) is used as an active layer, for which the optimization of the back surface field (BSF) layer is performed. Different materials for BSF, including gallium arsenide (GaAs), zinc telluride (ZnTe), and zinc selenium (ZnSe), are tested, and ZnSe appears to be more efficient than GaAs and ZnTe. Moreover, to capture photons in the active region more efficiently, a distributed Bragg reflector (DBR) is utilized, which is composed of silicon/silicon nitride (Si/SiN). The encapsulated DBR pair appears to be beneficial for the proposed design and provides excellent electrical parameters as compared to a structure without DBR. Under Standard Test Conditions (STC) at 300 K, the structure delivers Voc = 0.832 V, Jsc = 14.68 mA/cm2, FF = 76.53%, and η = 9.34%. The present study also highlights thermal stability analysis of the proposed structure at different temperatures, and the results show that as the temperature of the device increases, the performance of the cell follows a downtrend toward decay. [ABSTRACT FROM AUTHOR]

In this paper, maintenance process for on-load tap changer (OLTC) would be enhanced using nanofluid that developed by adding nanoparticles (titanium dioxide, TiO2) to the insulating oil. The occurrence of the internal arcs inside OLTC resulted in the formation of carbon atoms on the inner bonding points during its operation. Over time, these points became unusable since the contact resistance value was high. To increase the efficiency of the maintenance process, infrared radiation was used to excite the additive TiO2 atoms to attract carbon atoms. Electrical tests were performed before and after the proposed maintenance process according to IEC standards. The results showed significant differences in the maintenance process with and without nanoparticles. A prediction model to identify the OLTC state was constructed based on support vector machine classifier. The results revealed the ability of the predicted model to predict the OLTC state with accuracy 100%. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal grounding grid design using artificial intelligence techniques based on the empirical formula for grounding resistance (Rg), touch and step voltages (Et and Es), which are addressed in IEEE Std. 80-2013/Cor. 1-2015. The objective function is formulated based on the grid conductors' material cost and the installation cost. Particle swarm optimization (PSO) and genetic algorithm (GA) are individually utilized to search for and confirm the global best solution of minimizing the grounding system cost with considering all operation constraints including the safety criteria. A modified objective function is constructed based on self-adaptive penalization utilized to account for constraint violations during the optimization process. The selection strategy for modifying the local best positions of particles and the global best position of the swarm is proposed to enhance the ability of PSO for fast convergence. Results proved that either PSO or GA settles on the global best solution, successfully. To perform the space domain investigation, the optimal grounding grid design is implemented using a finite element method, where the COMSOL Multiphysics program is selected to verify the settled optimal global solution. Using COMSOL, the grounding resistance and safety criteria are evaluated over the grid diagonally. The COMSOL results ensure the operating constraints satisfaction of the optimal grounding grid design. The performance evaluation reveals that the grounding potential rise limit, the available grid area, and the number of vertical rods greatly affect the optimization of the grounding system design. The results also illustrate a great significant effect of the fault current and upper surface resistivity on the optimal grid design. [ABSTRACT FROM AUTHOR]

In this paper, the dielectric characteristics of the power transformer oil are enhanced by dispersing newly synthesized nanoparticles. Two nanocrystalline powders, barium titanate (BaTiO3) and nickel ferrite (NiFe2O4), are synthesized via the tartaric acid precursor method. The change in the crystal structure, crystalline size, and microstructure of the synthesized nanoparticles as a function of annealing temperatures are characterized using X-ray diffraction and transmission electron microscope. These nanoparticles are mixed with the transformer oil considering different concentrations such as 25, 50, and 75 mg/l where the sonicator is used to disperse the nanoparticles in the oil samples. The dielectric breakdown strength is tested based on IEC standard. The results confirm the efficiency of the NiFe2O4 than BaTiO3 for enhancing the dielectric characteristics of the power transformer oils. [ABSTRACT FROM AUTHOR]

The insulating oil performance could be enhanced in high-voltage apparatus using barriers. The importance of the barrier in increasing the dielectric strength of the insulating oils in order to reduce the oil failure stresses had not been sufficiently studied. In this paper, the effects of the barrier variables on the insulation performance of the transformer oil for point-plate and plate-plate gaps were demonstrated. These variables are: gap space (d), the barrier location relative to the high-voltage electrode (a / d) %, barrier diameter (D), barrier thickness (e), electrode configurations (EC), the presence of contaminating particles, the weight of the contaminating particles (W) and the temperature of the insulating oil (T). The statistical t test was used to explain whether the effect of these parameters was significant or not. Furthermore, the above-mentioned variables were used as training variables to construct the prediction model of oil breakdown voltage considering barrier effect based on the artificial neural networks (ANN). The ANN model was developed based on the results from experimental works. Therefore, 784 samples were used as training data set and other 25 samples were taken as testing and validating samples. The results explained that the prediction ANN model had a high ability to expect the breakdown voltage for other different experiment cases. The average errors of the training and testing samples were 1.6%, and 2.66%, respectively. Therefore, the prediction accuracy could be considered as 98.4% for training and 97.34% for testing. [ABSTRACT FROM AUTHOR]