21 research outputs found
Deep Learning and Support Vector Machine Algorithms Applied for Fault Detection in Electrical Power Transmission Network
No full textIn this paper, an interesting application of machine learning algorithms is presented. The main idea consists of applying both deep-learning and support vector machine supervised machine learning approaches to improve the quality and to guarantee the stability and the reliability of an electric power transmission system. These techniques are used mainly to detect, classify, and consequently locate faults in the electric power transmission network. To test the performance of the proposed techniques, the standard IEEE 14-bus power system is used. The fault free, the one fault and the multiple fault cases are investigated. Faults are applied to the IEEE 14-bus system and simulated using SimPowerSystems toolbox of Matlab. The accuracy score is used to compare the proposed techniques performances. Different results proved that studied machine learning methods made correct predictions. Nevertheless, the deep learning algorithm performances are proved while classifying all types of faults. Simulation results demonstrate that the deep learning technique can achieve an accuracy of 100% compared to the support vector machine which had an accuracy of 87%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.udemauteur: Azeddine Kaddour
Nonparametric Bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data
No full textHigh resolution synthetic irradiance is of interest for theoretical studies such as grid integration of solar PV and battery storage analysis. Access to site-specific data is often limited to inadequate temporal resolutions for such application. A new model for producing synthetic solar global horizontal irradiance (GHI) time-series at up to 1-min resolution is presented as derived from >10-min input data. Briefly, it is a clustered-based method for daily clearness index distributions using Dirichlet process Gaussian mixture model (DPGMM). DPGMM is a nonparametric Bayesian (NPB) model indexed with an infinite-dimensional space of parameters. The key benefit of the NPB paradigm is the automatic adaptation to the correct complexity level and model size, suggesting a local adaptation of the model to all climatic conditions. A posterior inference using Markov chain Monte Carlo algorithm (namely Gibbs sampling) is applied. The model only requires a valid number of intraday data to construct daily distributions, then it can be applied worldwide. The synthetic GHI time series are validated against observed 1-min GHI data for four locations distributed throughout the world with different climatic conditions and significant geographic separation. Moreover, the presented method can generate data based on similar climatic conditions. A good fit between real and generated data is observed. We present an nRMSE ≤ 4% and nMBE < ±4% between generated and measured means at both daily and monthly scales for all sites. The agreement between the real and generated cumulative density distributions of six comparative variability metrics (defined in text) at four different sites is measured using the overlapping and the Kullback-Leibler coefficients, which are ≥ 75% and ≤ 10% respectively, in all cases. To ensure the reproducibility of the research presented in this paper, the methodology is freely available as an R-package downloadable from SolarClusGnr
Identifying small decentralized solar systems in aerial images using deep learning
No full textStatistics on installed solar energy systems (SES) play a crucial role in the solar energy industry, providing valuable information for a wide range of stakeholders, such as policy makers, authorities, and financial evaluators. For example, grid operators rely on accurate data on photovoltaic penetration levels to ensure the quality and stability of the power supply. In this research, we present an automatic approach helping generate these statistics using deep learning and image processing techniques. Our proposed model is a machine learning approach that utilizes a specific architecture of convolutional neural networks (CNN) called the "U-net'' to detect SES from aerial images. We experimented different network settings to enhance the SES identification performance.In this study, the model was evaluated using two datasets from different locations, one from Sweden and one from Germany. Additionally, the model was trained and tested on a combination of both datasets. The impact of image resolution was also examined. The experimental results show that this architecture performs better than many recent CNN models that have been proposed in the literature for the task of SES identification from aerial images. To make it easy for others to replicate our findings, We have shared all the scripts, software, and dependencies required for running the model in this paper, along with instructions on how to use it in Appendix A
The effect of wetting on the relative permeability behavior and oil recovery
No full textOil is one of the major contributors to energy consumption. Oil reserves are expressed as the total amount of economically and technically producible oil. Total oil consumption is increasing (per capita it remains more or less the same) but it has no unambiguous influence on the remaining reserves due to new discoveries. However, increasingly sophisticated recovery methods are used to produce oil i.e. enhanced oil recovery methods. A recently proposed enhanced oil recovery method is by injection of low-salinity water in completely oil-wet reservoirs, which leads to more water-wet behavior and “consequently” to improved oil recovery.This report will focus on the effect of wettability on the recovery efficiency. Based on the papers by Lomeland, Ebeltoft and Thomas we derive so-called LET relative permeability curves that only depend on irreducible water saturation. This is possible by using the irreducible water saturation dependence of the residual oil saturation, end point relative water permeability and the sketched behavior (Lomeland, Ebeltoft and Thomas) of the other six parameters. Admittedly this is a gross simplification, but it grasps the essence of the relative permeability behavior and makes it possible to study the recovery in terms of two parameters viz. the irreducible water saturation and viscosity ratio as opposed to eight relative permeability parameters and the viscosity ratio. High irreducible water saturation is both indicative of pore size heterogeneity and water-wet behavior. We use the theory of Buckley-Leverett to construct recovery curves for 1D and 2D displacement and various mobility (M = displacing fluid mobility / displaced fluid mobility) ratios. We solve the 1-D equations both analytically (using fractional flow theory) and numerically. For the numerical simulations in 1D and 2D we use COMSOL 5.2©. The simulations show that water-wet behavior is conducive to stable displacement, however, low recovery at breakthrough, whereas intermediate oil-wet behavior is more unstable but conducive to high ultimate recoveries. Completely oil-wet behavior leads to less stable displacement and low ultimate recoveries.Applied Earth Science
Drag Reduction in Turbulent Flows by Polymers and Surfactants: An Experimental Study Into the Mechanisms of Drag Reduction by Additives
No full textIn 1949, Toms (Toms B.A., (1949, 1977)) observed that small amounts of a drag reducing agent (DRA) could cause a considerable drag reduction in turbulent pipe flow. In application of polymer enhanced oil recovery, degradation of polymers in the supply lead could cause clogging. It was, however observed that surfactants at sufficiently high concentration also showed drag reduction without the problem clogging. A DRA reduces the energy loss by friction and unstable flow, thus improving injection throughput with the same pressure pump and thereby reducing the exergetic pumping costs. This study investigates experimentally the drag reducing capacity of surfactants and compares it to the drag reducing capacity of polymers.For the experiment, a set-up consisting of a pump, a coiled test tube with a length of 1.48 m and an inner diameter of 0.5 mm and pressure gauges is built. The diameter of the coil is 12.5 cm. We use a pump capable of injection up to 200 ml/min. The pressure drop is measured between the entrance and end of the tube. The injection rate is varied between 1 and 200 ml/min, roughly corresponding to Reynolds numbers between 50 and 10,000. The additives are dissolved in brine with a 33,000 ppm salt concentration. The viscosity of the solution is dependent on the concentration of the DRA. The ratio of the measured pressure drop with only brine and the pressure drop with the DRA solution was used to calculate the drag reduction (DR) factor, as from a technical point of view we are only interested whether adding DRA reduces the drag with respect to the original brine solution. From an academic point of view, we remark that for low concentrations the viscosity enhancement due to the presence of the DRA is negligible. As polymers we use xanthan (a biopolymer), and a synthetic emulsion polymer based on polyacrylamide. Maximum DR factors are 23% for xanthan at 90ppm and 32% at 90ppm for the synthetic emulsion polymer. DR only occurs at turbulent conditions.Three types of surfactants, each from a different branch of surfactants are used in this study. The surfactants used are AOS {훼-Olefin Sulfonate}, CTAB {hexadeCylTrimethylAmmonium Bromide} and APG {Alkyl PolyGlucoside} which are a cationic, anionic and a nonionic surfactant respectively. The surfactants did not show any DR at (for DRA applications) high concentrations up to 20.000ppm. Addition of Sodium Salicylate (NaSaL) to CTAB with a 1:1 ratio led to a maximum DR of 33% at 2500 ppm concentration.Several pressure gauges have been installed along the test tube in order to observe how the pressure drops along the tube, how the DRAs affect these pressure drops and at what location of the test tube the DR factor is the highest. It is found that xanthan has the same DR factor at each location of the test tube, the emulsion polymer has a decreasing DR factor as the distance from the inlet of the test tube increases and the CTAB+NaSaL DRA has an increasing DR factor as the distance from the inlet increases.The DRAs are sheared using a constriction in the flow loop while the degradation is monitored. It is observed that xanthan is less susceptible to degradation in comparison to the emulsion polymer due to its more rigid chemical structure. But xanthan and the emulsion polymer would be inefficient to use in looped flow systems as they are affected by degradation. The CTAB+NaSaL DRA on the other hand shows no degradation meaning that the micellar rod-like structures that give the DR effect are being repaired when the shear force is being removed. However, for surfactants higher concentrations (1000-2500 ppm) are required.Petroleum Engineering and Geo-science
Experimental and Modeling Dynamic Study of the Indirect Solar Water Heater: Application to Rabat Morocco
No full textThe Indirect Solar Water Heater System (SWHS) with Forced Circulation is modeled by proposing a theoretical dynamic multi-node model. The SWHS, which works with a 1,91 m2 PFC and 300 L storage tank, and it is equipped with available forced circulation scale system fitted with an automated sub-system that controlled hot water, is what the experimental setup consisted of. The system, which 100% heated water by only using solar energy.
The experimental weather conditions are measured every one minute.
The experiments validation steps were performed for two periods, the first one concern the cloudy days in December, the second for the sunny days in May; the average deviations between the predicted and the experimental values is 2 %, 5 % for the water temperature output and for the useful energy are 4 %, 9 % respectively for the both typical days, which is very satisfied. The thermal efficiency was determined experimentally and theoretically and shown to agree well with the EN12975 standard for the flow rate between 0,02 kg/s and 0,2kg/s
Biological pre-hydrolysis and thermal pretreatment applied for anaerobic digestion improvement : Kinetic study and statistical variable selection
No full textIn the present study, two pretreatment methods (thermal pretreatment and biological pre-hydrolysis) were suggested for food waste (FW) with the aim to enhance biomass conversion and biogas production by anaerobic. The effects of thermal pretreatment (TP), including TP at 60°C and 80°C for 60 min, and TP at 100°C, 120°C and 140°C for 30 min, well as biological pre-hydrolysis (BPH) at 37°C, 55°C, 37°C followed by 55°C and 55°C followed by 37°C for 40 hour on anaerobic digestion performance of FW were evaluated in batch tests. Results were compared with untreated FW. The BPH and TP caused an increase in the soluble chemical oxygen demand and hydrolysis efficiency. The methane yield (MY) increased from 371.17 ml CH4/g VS for untreated FW to 471.95 ml CH4/ g VS. The maximal MY was recorded for BPH at 37°C for 20 h followed by 55°C for 20 h. The pretreatments increased the biogas production rate and reduced the lag phase. The most influential variables on the methane yield were investigated using three statistical methods: Principal component analysis, Mutual Information and R-squared. The results allowed a good modeling of the methane yield and minimized the overfitting effect. For reproduction and solid contribution to the field, we have attached to our article all the necessary material to reproduce the same statistical work as in the paper body
Nonparametric Bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data
No full textHigh resolution synthetic irradiance is of interest for theoretical studies such as grid integration of solar PV and battery storage analysis. Access to site-specific data is often limited to inadequate temporal resolutions for such application. A new model for producing synthetic solar global horizontal irradiance (GHI) time-series at up to 1-min resolution is presented as derived from >10-min input data. Briefly, it is a clustered-based method for daily clearness index distributions using Dirichlet process Gaussian mixture model (DPGMM). DPGMM is a nonparametric Bayesian (NPB) model indexed with an infinite-dimensional space of parameters. The key benefit of the NPB paradigm is the automatic adaptation to the correct complexity level and model size, suggesting a local adaptation of the model to all climatic conditions. A posterior inference using Markov chain Monte Carlo algorithm (namely Gibbs sampling) is applied. The model only requires a valid number of intraday data to construct daily distributions, then it can be applied worldwide. The synthetic GHI time series are validated against observed 1-min GHI data for four locations distributed throughout the world with different climatic conditions and significant geographic separation. Moreover, the presented method can generate data based on similar climatic conditions. A good fit between real and generated data is observed. We present an nRMSE ≤ 4% and nMBE < ±4% between generated and measured means at both daily and monthly scales for all sites. The agreement between the real and generated cumulative density distributions of six comparative variability metrics (defined in text) at four different sites is measured using the overlapping and the Kullback-Leibler coefficients, which are ≥ 75% and ≤ 10% respectively, in all cases. To ensure the reproducibility of the research presented in this paper, the methodology is freely available as an R-package downloadable from SolarClusGnr
Transnationality, Mobile Identity, and Cultural Dislocation in Rabih Alameddine’s I, the Divine (2002)
No full textInspired by diasporic philosophy, conception, and avidity, Anglophone diasporic authors—such as Rabih Alameddine, a prolific Arab American author recognized for his bold yet creative narratives—have foregrounded heterogeneity, post-nationality, and cross-pollination, as approaches to contest essentialist national identifications and reductionist ethnic ideologies. Equally, diaspora literary criticism emphasizes the importance of border crossings and transnational movements, exemplified in diasporic narratives, prompting a re-evaluation of understandings and mindsets. Drawing on this theoretical premise, this article explores themes of traveling identity and transnational belonging, by meticulously analyzing instances from Rabih Alameddine’s I, the Divine (2002). It also unearths personal and cultural dislocation embodied in the protagonist’s disjointed life narrative, the lack of a central plot, and the uncertainty of claiming an irrevocable belief in belonging to a fixed abode. It concludes that the approach of belonging, the novel advocates, aligns with the postmodernist diasporic view, based on revisiting outdated assumptions of cultural identity and welcoming, instead, hybridity and post-ethnicity, which complicates the fixity of home and the pre-givenness of identity
Bilateral Central Core and an External Envelope and its Impact on the Thermal Behaviour of Individual Self-construction Housing in the City of Biskra
No full textAbstractThe essence of architectural design rests upon in kind of manipulation between dualism central core and external envelope of any architectural composition; there are some compositions that are concerned with the external envelope,while other compositions the outer shell is result of the interned division. Besides there are other compositions that blend the central core and the external envelope in a harmonious dialogue. This combination between central core and the external envelope touch this diversity in houses of Biskra city through different periods of time to create a comfortable thermal environment.The dry areas, which are distributed on a large scale over the space of Algeria, characterized by climate is hot and dry. We found Morphological diversity in houses of this region that reflects primarily adaptation to climatic conditions, social and economic through different periods.In our research, we depend on the experimental method through digital simulation technology program ECOTECT to calculate data, for various thermal models. in addition in the selection of network studied models we adopted to the variables morphology of both core and the external envelope of the dwelling which are: 1) the oceanic layer, 2) type of the core 3) type of the external envelope. Then we calculated the temperature of various houses layers to make comparisons between various layers and various models.The results of this study came to show the laws that control heat in the atmosphere and that is affected by alphabet elements of local architecture of Biskra region. These laws allow the architect to manipulate to these elements to search for improved thermal yield of the building and control of energy consumption in the range of what is available to him
