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Given the considerable scale of distribution networks in urban and rural areas, as well as the lack of management records, adjustments of switches during the distribution system operation are poorly documented. Such deficiency results in the inaccuracy of models stored in the distribution network automation system, and thus misleads the state estimation. With the emergence of information and communication technology, a large number of the feeder and residential smart meter data are accumulated. Such data can help recognize the operation modes of distribution networks by analyzing the relationships between the on/off states of switches and the voltage correlations among buses. However, the limited quantity and quality of the sampling data restrict the implementation of data-driven recognition. In this paper, a physical-probabilistic-network (PPN) model applied for inferring overall operation mode of distribution networks is proposed. Based on which, a belief propagation-based algorithm is proposed for the inference even under situations when there are only partial bus voltages data available. Meanwhile, the required variable for inference can be reduced from the active trail analysis. Experiment results are used to compare its performance with classic methods and to prove its effectiveness and advantages.

Abstract Newer generation sources and loads are posing new challenges to the conventional power system protection schemes. Adaptive and intelligent protection methodology, based on advanced measurement techniques and intelligent fault diagnosis such as machine learning (ML), is found to be useful to meet these challenges. A large number of research works are reported on ML-based power system fault diagnosis. However, ML techniques are evolving at a very fast pace, and an inclusive, as well as state-of-the-art review on ML-based power system fault diagnosis, is not available in the literature. Given this need and growing trend towards ML, the study presented in this paper aims to provide a comprehensive review of ML-based power system fault diagnosis. At first, efforts have been made to enlist the issues present in conventional fault diagnosis which led to the popularity of ML techniques. Also, a baseline framework and workflow for ML-based fault diagnosis are presented. Next, various unsupervised and supervised learning techniques have been discussed separately which have been used by several researchers for fault diagnosis. The discussion throughout is supported with tabulated facts for fault detection, classification and localization works with techniques used, different simulation tools used, and their application system. The advantages and disadvantages of all the techniques of fault diagnosis have also been discussed which will help the readers in the selection of techniques for their research. A brief review of reinforcement learning and transfer learning is also given as they are gaining popularity in power system-related studies and have the potential to be used for fault diagnosis. Finally, the research trends, some key issues, and directions for future research have been highlighted.

Abstract In this paper, the prevention of negative technogenic impact on the environment of oil sludge by using it as a secondary resource is considered. Oil sludge from various objects of oil fields in Kazakhstan (Mangystau region) has been studied. The possibilities of using oil (after its separation from oil sludge by bioremidiation) as a partial substitute for bitumen base in the production of modified bitumen are considered. The main physical and mechanical characteristics of modified bitumen are determined. The results confirm that the modified bitumen prepared with oil sludge and oil separated by bioremiation method meets the requirements for polymer-bitumen binder to Kazakhstan standards and is suitable for the production of modified bitumen in its physico-chemical characteristics.

Abstract Wellbore pressure gradient in gas wells is significant in designing deliquification technologies and optimizing production. At present, no model has yet to be established specifically for gas wells at a wide gas flowrate range. When calculating pressure gradient in a specific gas field, engineers must evaluate these widely used models and get the best performance model at a certain range. To establish a more comprehensive model in horizontal gas wells, an experimental study was conducted to investigate the flow behavior of liquid-gas two-phase flow at different gas and liquid velocities and inclined angles in a 50 mm visual pipe. The evaluation of these widely used models against the experimental data shows that no model can predict liquid holdup at different gas velocity ranges, and huge deviations due to several reasons can be observed. After conducting a comprehensive analysis, a new liquid holdup correlation was proposed based on the Mukherjee–Brill model by correlating from the experimental results, which have parametric ranges closer to the production of gas wells. This new model adopts a new dimensionless gas velocity number to characterize flow similarities and better scale up pressure from the experiment to the gas wells. By validating against experimental data and field data, the results indicate that the new two-phase flow model has stable performance and can accurately predict pressure gradients at different ranges of pressure and gas/liquid velocities.

Accurate predictions of fuel spray behavior and mixture formation in simulations of direct-injection spark-ignition (DISI) engines are fundamental to ensure proper description of all subsequent processes including ignition, combustion, and emissions. In this work, the spray evolution in a single-cylinder optical DISI engine was studied experimentally and numerically with the goal of enabling predictive computational fluid dynamics (CFD) modeling of in-cylinder sprays. The authors explored a wide range of operating conditions characterized by several fuel injection temperatures and engine speeds, using a well-characterized nine-component gasoline surrogate known as PACE-20. The effect of flash boiling and intake crossflow on the spray is discussed, with a focus on evaluating the ability of the spray models to capture highly transient spray behavior. In the experiments, the fuel temperature was varied between 20°C and 80°C, allowing for non-flash- to flash-boiling transition to emerge with enhanced flashing intensity at the highest temperatures. Spray collapse resulted in vapor-rich regions, owing to the locally lower inertia of the fluid. Varying the engine speed from 650 to 1950 rpm promoted increasingly more turbulent in-cylinder crossflow which interacted with the spray during the injection event and resulted in enhanced spray dispersion. The CFD model was able to capture the spray morphology transition at different fuel temperatures and engine speeds adequately. It is shown that the spray breakup model could capture the transitional spray behavior induced by flash boiling atomization and intake flow via proper initialization of the spray cone angle and calibration of the spray models’ constants.

Abstract Fractures along interfaces between host rock and wellbore cement have long been identified as potential CO2 leakage pathways from subsurface CO2 storage sites. As a consequence, cement alteration due to exposure to CO2 has been studied extensively to assess wellbore integrity. Previous studies have focused on the changes to either chemical or mechanical properties of cement upon exposure to CO2-enriched brine, but not on the effects of loading conditions. This paper aims to correct this deficit by considering the combined effects of the fracture pathway and changing effective stress on chemical and mechanical degradation at conditions relevant to geologic carbon storage. Flow-through experiments on fractured cores composed of cement and tight sandstone caprock halves were conducted to study the alteration of cement due to exposure to CO2-enriched brine at 3, 7, 9, and 12 MPa effective stress. We characterized relevant reactions via solution chemistry; fracture permeability via changes to differential pressure; mechanical changes via micro-hardness testing, and pore structure changes via x-ray tomography. This study showed that the nature and the rates of the chemical reactions between cement and CO2 were not affected by the effective stress. The differences in the permeability responses of the fractures were attributed to interactions among the geometry of the flow path, the porosity increase of the reacted cement, and the mechanical deformation of reacted asperities. The suite of observed chemical reactions contributed to change in cement mechanical properties. Compared to the unreacted cement, the average hardness of the amorphous silica and depleted layers was decreased while the hardness of the calcite layer was increased. Tomographic imaging showed that preferential flow paths formed in some of the core-flood experiments, which had a significant impact on the permeability response of the fractured samples. We interpreted the observed permeability responses in terms of competition between dissolution of cement phases (leading to enhanced permeability) and mechanical deformation of reacted regions (leading to reduced permeability).

Abstract This paper uses research-quality, ground measurements of irradiance and temperature that are accurate to ±2% to estimate the electric energy yield of fixed solar modules for utility-scale solar power plants at 18 sites in Saudi Arabia. The calculation is performed for a range of tilt and azimuth angles and the orientation that gives the optimum annual energy yield is determined. A detailed analysis is presented for Riyadh including the impact of non-optimal tilt and azimuth angles on annual energy yield. It is also found that energy yield in March and October are higher than in April and September, due to milder operating temperatures of the modules. A similar optimization of tilt and azimuth is performed each month separately. Adjusting the orientation each month increases energy yield by 4.01% compared to the annual optimum, but requires considerable labour cost. Further analysis shows that an increase in energy yield of 3.63% can be obtained by adjusting the orientation at five selected times during the year, thus significantly reducing the labour requirement. The optimal orientation and corresponding energy yield for all 18 sites is combined with a site suitability analysis taking into account climate, topography and proximity to roads, transmission lines and protected areas. Six sites are selected as having high suitability and high energy yield: Albaha, Arar, Hail, Riyadh, Tabuk and Taif. For these cities the optimal tilt is only slightly higher than the latitude, however the optimum azimuth is from 20° to 53° west of south due to an asymmetrical daily irradiance profile.

Abstract Building energy performance is often inadequate given design goals. While different types of assessment methods exist, they either do not consider design goals and/or are not general enough to integrate new and innovative energy concepts. Furthermore, existing assessment methods focus mostly on the building and system level while ignoring more detailed data. With the availability and affordability of more detailed measured data, the increased number of measured data points requires a structure to organize these data. This paper presents the Energy Performance Comparison Methodology (EPCM), which enables the identification of performance problems based on a comparison of measured data and simulated data representing design goals. The EPCM is based on an interlinked building object hierarchy that structures the detailed performance data from a spatial and mechanical perspective. This research is developed and tested on multiple case studies that provide real-life context and more generality compared to single case studies.