Nontechnical losses (NTL) are irregularities in the consumption of electricity and mainly caused by theft and fraud. NTLs can be characterized as outliers in historical data series. The use of computational tools to identify outliers is the subject of research around the world, and in this context, artificial neural networks (ANN) are applicable. ANNs are machine learning models that learn through experience, and their performance is associated with the quality of the training data together with the optimization of the model’s architecture and hyperparameters. This article proposes a complete solution (end-to-end) using the ANN multilayer perceptron (MLP) model with supervised classification learning. For this, data mining concepts are applied to exogenous data, specifically the ambient temperature, and endogenous data from energy companies. The association of these data results in the improvement of the model’s input data that impact the identification of consumer units with NTLs. The... [more]

The vessel speed reduction program (VSRP) was first introduced in the Port of Los Angeles in 2001 to improve air quality. In this study, an algorithm was developed to calculate ship emissions with a bottom-up approach based on ship activity using automatic identification system (AIS) data. The target vessel applied to the emission calculation was a vessel participating in Korea’s VSRP. Factors considered for the calculation of emissions were ship type, speed, gross tonnage, engine power, load, sulfur content of fuel, and fuel consumption rate by engine age. The algorithm is designed to calculate the reduction amount by VSRP by simultaneously calculating the emission amount of the actual speed and the emission amount of the cruise speed when not participating in VSRP. The emission results of ships that participated in the VSRP in 2020 revealed that their speed was reduced by 47% and carbon dioxide emissions by 71.9%. These results were verified through comparison with the fuel consumpti... [more]

The quantitative identification of water sources is an important prerequisite for objectively evaluating the degree of aquifer interference and predicting the production potential of coalbed methane (CBM) wells. However, this issue has not been solved yet, and water sources are far from being completely understood. Stable water isotopes are important carriers of water source information, which can be used to identify the water sources for CBM wells. Taking the Zhijin block in the Western Guizhou Province as an example, the produced water samples were collected from CBM wells. The relationships between the stable isotopic compositions of the produced water samples and the production data were quantitatively analyzed. The following main conclusions were obtained. (1) The δD and δ18O values of the produced water samples were between −73.37‱ and −27.56‱ (average −56.30‱) and between −11.04‱ and −5.93‱ (average −9.23‱), respectively. The water samples have D-drift characteristics, showing t... [more]

Calcite cement is a common type of cementation in tight sandstone reservoirs of the Upper Triassic Yanchang Formation Chang 8 Member in the Zhijing-Ansai area of the Ordos Basin, which has significant influence on reservoir densification and heterogeneity. Calcite cements affect the quality of the reservoir conspicuously. The characteristics and origins of calcite were investigated using a series of approaches from the perspective of petrography and geochemistry, including thin section observation and identification, cathode luminescence, scanning electron microscopy, AMICS analysis, LA-ICP-MS elements analysis, and carbon and oxygen isotopes analysis. The results of all analytical tests indicated that calcite cements can be divided into two types according to their occurrence and origins. Type-I calcite cements mainly occur in sandstone reservoirs near the sandstone−mudstone interface or the sandstone layers adjacent to mudstone. Generally, there is no chlorite coating around them, an... [more]

The microgrid (MG) is a popular concept to handle the high penetration of distributed energy resources, such as renewable and energy storage systems, into electric grids. However, the integration of inverter-interfaced distributed generation units (IIDGs) imposes control and protection challenges. Fault identification, classification and isolation are major concerns with IIDGs-based active MGs where IIDGs reveal arbitrary impedance and thus different fault characteristics. Moreover, bidirectional complex power flow creates extra difficulties for fault analysis. This makes the conventional methods inefficient, and a new paradigm in protection schemes is needed for IIDGs-dominated MGs. In this paper, a machine-learning (ML)-based protection technique is developed for IIDG-based AC MGs by extracting unique and novel features for detecting and classifying symmetrical and unsymmetrical faults. Different signals, namely, 400 samples, for wide variations in operating conditions of an MG are o... [more]

Air conditioning loads (ACLs) are potential flexible resources that can provide various grid services to the power system. Recent studies have attempted to represent their flexibility using a virtual battery (VB) model for quantification, but the modeling process requires information on thermal parameters and heat disturbances (e.g., solar irradiation and internal heat load) that are difficult to measure. In this paper, we present a new method that models a VB without prior knowledge of such information. First, we construct a thermal dynamic model of an individual ACL using historical input-output data. The linear regression model parameters are identified without using the measurements of disturbances. Second, we derive a VB model from the linear regression parameters using a change of variable technique. We show that the VB can be directly modeled from the regression model of thermal dynamics without estimating the exact thermal parameters and heat disturbances. Third, aggregation of... [more]

This paper proposes a parameter identification method for a PEM electrolyzer electrical equivalent circuit for pulse electrolysis. Since general water electrolysis mainly uses DC currents, identifying equivalent circuit parameters using electrical characteristics mostly ignores the operation frequency and unnecessarily adheres to the secondary RC model. However, looking at the Nyquist plot of the PEM electrolyzer, it can be confirmed that identifying the operational frequency is necessary, and the secondary RC model correction is essential. Therefore, the proposed method confirms the necessity of reconstructing an electrical equivalent circuit with a primary RC model by analyzing the transient cell voltage using step current inputs and calculating an appropriate operating frequency by identifying the parameters of the reconstructed equivalent circuit. To verify the proposed parameter identification method, a simulation was constructed from the raw test data of a 400 W commercial PEM el... [more]

Recently, various detection approaches that identify anomalous events (e.g., discoloration, contamination) by analyzing data collected from smart meters (so-called structured data) have been developed for many water distribution systems (WDSs). However, although some of them have showed promising results, meters often fail to collect/transmit the data (i.e., missing data) thus meaning that these methods may frequently not work for anomaly identification. Thus, the clear next step is to combine structured data with another type of data, unstructured data, that has no structural format (e.g., textual content, images, and colors) and can often be expressed through various social media platforms. However, no previous work has been carried out in this regard. This study proposes a framework that combines structured and unstructured data to identify WDS water quality events by collecting turbidity data (structured data) and text data uploaded to social networking services (SNSs) (unstructure... [more]

To identify the parameters of variable flux reluctance machines (VFRMs), the input voltage value is usually obtained from the reference voltage indirectly. However, the reference and actual voltages may be different due to the nonlinearity of the open winding inverter when using zero-sequence current, leading to the inaccuracy of parameter identification. To solve this problem, this paper proposes an equivalent nonlinearity voltage error model of the open winding inverter to compensate for the input voltage of VFRM during the online parameter identification. Since in the proposed method the phase current direction is not required, the calculation time can be reduced. Moreover, in the developed parameter identification model of VFRM the derived equivalent nonlinear voltage error is used to correct the input of the identification model so as to improve the accuracy of parameters. Finally, the experimental results on the prototype VFRM are presented for verification.

The types of fractured-vuggy reservoirs are diverse, with dissolution holes and fractures of different scales as the main reservoir spaces. Clarifying the connectivity between wells is crucial for improving the recovery rate of fractured-vuggy reservoirs and avoiding problems of poor water- flooding balance and serious water channeling. A traditional dynamic connected model hardly describes the geological characteristics of multiple media, such as karst caves and fractures, which cause multiple solutions from the calculation. The static analysis is the basis for connectivity evaluation. In this study, we designed an intelligent search strategy based on an improved A* algorithm to automatically find a large-scale fractured-vuggy connected path by seismic multi-attribute analysis. The algorithm automatically evaluates the interwell-connected mode and clarifies the relationship between the static connected channel and the fractured-vuggy space configuration. Restricted by various factors,... [more]

The health monitoring technology of transmission towers based on vibration data had become a research hotspot. At present, vibration data mainly relied on sensors installed on the tower, which was time-consuming and laborious. Nevertheless, the ROI computer vision method could achieve long-distance, multi-point, and non-contact monitoring, which offers a new possibility for the structure-safety identification of power transmission towers. However, transmission towers are generally located in the field environment, and the background is complicated, resulting in the ROI key point method for vibration data acquisition encountering various types of noise. Thus, the key point in practice was clearing the noise and reducing the impact of noise on identification accuracy. The subpixel corner method was used to detect a minor error with the research object of pixel sets. The dilation + erosion method could reduce image noise. Under white noise with a variance of 0.05, the dilation + erosion c... [more]

In this paper, a modified bald eagle search optimization algorithm was applied for the first time to determine the parameters of the triple diode model (TDM) of perovskite solar cells (PSCs). Two experimental datasets are considered; the first is measured I−V points for a PSC at standard conditions. The second consists of the measured I−V points for a modified PSC. In contrast, the cost function to be minimized is the root mean square error (RMSE) between the experimental dataset and the calculated one. To prove the superiority of modified bald eagle search optimization (mBES), a comparison with the original bald eagle search optimization (BES), particle swarm optimizer (PSO), Hunger games search (HGS), and recent Coronavirus Disease Optimization Algorithm (COVIDOA) was implemented. Furthermore, statistical analysis of ANOVA and Tukey tests was performed. The results demonstrate the lead of the recommended mBES in identifying the parameters of the TDM for PSCs, where the RMSE achieved... [more]

Due to the advantages of high convergence accuracy, fast training speed, and good generalization performance, the extreme learning machine is widely used in model identification. However, a gas turbine is a complex nonlinear system, and its sampling data are often time-sensitive and have measurement noise. This article proposes an online sequential regularization extreme learning machine algorithm based on the forgetting factor (FOS_RELM) to improve gas turbine identification performance. The proposed FOS_RELM not only retains the advantages of the extreme learning machine algorithm but also enhances the learning effect by rapidly discarding obsolete data during the learning process and improves the anti-interference performance by using the regularization principle. A detailed performance comparison of the FOS_RELM with the extreme learning machine algorithm and regularized extreme learning machine algorithm is carried out in the model identification of a gas turbine. The results show... [more]

In order to explore the hydrogen source term characteristics under severe accidents of marine pressurized water reactors (PWR) and effectively assess the hydrogen risk, the best estimation program SCDAP/RELAP5/MOD3.2 is used to establish the marine reactor severe accident analysis model. Based on the Latin Hypercube sampling (LHS) method and the Wilks sampling theory, a set of methods for the uncertainty analysis of severe accidents is developed. This method can be applied to the uncertainty and sensitivity analysis of different target parameters. The phenomenon identification and ranking table (PIRT) under the severe accident induced by the break are established, and 14 uncertain parameters are selected as input variables. The established PIRT fills the gap in the uncertainty and sensitivity analysis of severe accidents of marine reactors and provides a reference for subsequent research. The quantitative uncertainty analysis of the calculation results is carried out, and the uncertain... [more]

The health monitoring of a system remains a major issue for its lifetime preservation. In this paper, a novel fault diagnosis algorithm is proposed. The proposed diagnosis approach is based on a unique variable measurement in the time domain and manages to extract the system behavior evolution. The developed tool aims to be generic to several physical systems with low or high dynamic behavior. The algorithm is depicted in the present paper and two different applications are considered. The performance of the novel proposed approach is experimentally evaluated on a fan considering two different faulty conditions and on a proton exchange membrane fuel cell. The experimental results demonstrated the high efficiency of the proposed diagnosis tool. Indeed, the algorithm can discriminate the two faulty operation modes of the fan from a normal condition and also manages to identify the current system state of health. Regarding the fuel cell state of health, only two conditions are tested and... [more]

In historic centers, construction works consist of complex activities that must balance the operative requirements and lower the impacts on a delicate and sensible environment. In this urban system, especially regarding relevant reconstruction processes such as post-natural disaster scenarios, construction operations are performed through the traditional construction processes, using fuel-based generators and vehicles with limited efficiency and with relevant impacts in terms of the consumed energy, noise and vibrations. In the global transition of the construction sectors towards a zero-emission and fuel-free future, construction sites in historic centers represent a particular opportunity where the application of fuel-free strategies is particularly feasible and can provide additional value in terms of the environmental impact, productivity and health and safety. This work addresses the need for a framework to provide the basis for the application of fuel-free principles in construct... [more]

As the water injection oilfield enters into the high water cut stage, a large number of water flooding advantage seepage channels are formed in the local reservoir dynamically changing with the water injection process, which seriously affects the water injection development effect. In oilfield production, water injection and fluid production profile test data are direct evidence to identify advantage seepage channels. In recent years, some scholars have carried out research related to the identification of advantage seepage channels based on machine learning methods; however, the insufficient profile test data limit the quantity and quality of learning samples, leading to problems such as low prediction accuracy of learning models. Therefore, the author proposes a new method of advantage seepage channel identification based on meta-learning techniques, using the MAML algorithm to optimize the neural network model so that the model can still perform well in the face of training tasks wi... [more]

The precise characterization of geological bodies in fracture-vuggy carbonates is challenging due to their high complexity and heterogeneous distribution. This study aims to present the hybrid of Visual Geometry Group 16 (VGG-16) pre-trained by Gradient-Boosting Decision Tree (GBDT) models as a novel approach for predicting and generating karst cavities with high accuracy on various scales based on uncertainty assessment from a small dataset. Seismic wave impedance images were used as input data. Their manual interpretation was used to build GBDT classifiers for Light Gradient-Boosting Machine (LightGBM) and Unbiased Boosting with Categorical Features (CatBoost) for predicting the karst cavities and unconformities. The results show that the LightGBM was the best GBDT classifier, which performed excellently in karst cavity interpretation, giving an F1-score between 0.87 and 0.94 and a micro-G-Mean ranging from 0.92 to 0.96. Furthermore, the LightGBM performed better in cave prediction t... [more]

Faulted phase identification is one of the segments of conventional system protection that is severely vulnerable in the presence of inverter-based resources (IBR) such as Type IV wind and solar PV power plants. The work presented in this paper investigates the effect of IBRs on the conventional phasor-based faulted phase identification methods widely implemented in contemporary commercial protection relays using theoretical analysis and simulation results. Moreover, this premise is further validated by testing commercial line protection relays using hardware-in-the-loop simulations. This paper also evaluates the applicability of recently proposed transients/incremental quantities-based techniques to overcome the deficiencies of conventional methods to correctly identify the faulted phase in systems with IBRs through real-time and control hardware-in-the-loop simulations. Comparisons with commercial relays show that transient/incremental quantities-based methods are more suitable for s... [more]

In order to identify the degree of water flooding in a reservoir and to discover any remaining oil-enriched areas, in this paper, a systematic study on the water flooding of cores in obturated coring wells is carried out. With observations and testing data of the cores, based on the notion of sedimentary facies, the water-flooding degrees of 4−7 sand groups in member one of the Paleogene Sanduo Formation (E2s14−7) of the Zhenwu area in the Gaoyou Sag are determined. Overall, the results show that the study area is formed under the background of lake regression, with various sedimentary systems, mainly including delta facies, braided fluvial facies, and meandering fluvial facies. The degree of water flooding is determined using a point-by-point drip experiment of the core. Combined with the testing results of the core, the water-flooding degrees of the different sedimentary facies are quantitatively determined. Identification standards for the water-flooding degree of delta facies, brai... [more]

Many kinds of defects are present in AlGaN/GaN-on-Si based power electronics devices. Their identification is the first step to understand and improve device performance. Electron traps are investigated in AlGaN/GaN-on-Si power diodes using deep level transient Fourier spectroscopy (DLTFS) at different bias conditions for two Schottky contact’s etching recipes. This study reveals seven different traps corresponding to point defects. Their energy level ET ranged from 0.4 eV to 0.57 eV below the conduction band. Among them, two new traps are reported and are etching-related: D3 (ET = 0.47−0.48 eV; σ ≈ 10−15 cm2) and D7 (ET = 0.57 eV; σ = 4.45 × 10−12 cm2). The possible origin of the other traps are discussed with respect to the GaN literature. They are proposed to be related to carbon and nitrogen vacancies or to carbon, such as CN-CGa. Some others are likely due to crystal surface recombination, native defects or a related complex, or to the nitrogen antisite: NGa.

Advancements in energy technologies created a new application for gas turbine generators, which are used to balance load. This usage also brought new challenges for maintenance because of harsh operating conditions that make turbines more susceptible to random failures. At the same time, reliability requirements for energy equipment are high. Reliability-centered maintenance based on forecasting the remaining useful life (RUL) of energy equipment, offers improvements to maintenance scheduling. It requires accurate forecasting methods to be effective. Defining stages in energy equipment operation allows for the improvement of quality of data used for training. At least two stages can be defined: normal operation and degradation process. A new method named Head move—Head move is proposed to robustly identify the degradation process by detecting its starting point. The method is based on two partially overlapping sliding windows moving from the start of operation to the end of life of the... [more]

This paper proposes a new efficient two-step method for parametrizing control-oriented zero-dimensional physical polymer electrolyte membrane fuel cell (PEMFC) models with measured stack data. Parametrizations of these models are computationally intensive due to the numerous unknown parameters and the typically nonlinear, stiff model properties. This work reduces an existing model to decrease its stiffness for accelerated numerical simulations. Subdividing the parametrization into two consecutive subproblems (thermodynamic and electrochemical ones) reduces the solution space significantly. A parameter sensitivity analysis further reduces each sub-solution space by excluding non-significant parameters. The method results in an efficient parametrization process. The two-step approach minimizes each sub-solution space’s dimension by two-thirds, respectively three-fourths, compared to the global one. An achieved R2 value between simulation and measurement of 91% on average provides the req... [more]

The digitization of manufacturing processes has led to an increase in the availability of process data, which has enabled the use of data-driven models to predict the outcomes of these manufacturing processes. Data-driven models are instantaneous in simulate and can provide real-time predictions but lack any governing physics within their framework. When process data deviates from original conditions, the predictions from these models may not agree with physical boundaries. In such cases, the use of first-principle-based models to predict process outcomes have proven to be effective but computationally inefficient and cannot be solved in real time. Thus, there remains a need to develop efficient data-driven models with a physical understanding about the process. In this work, we have demonstrate the addition of physics-based boundary conditions constraints to a neural network to improve its predictability for granule density and granule size distribution (GSD) for a high shear granulat... [more]

Since December 2019, the world has been facing the outbreak of the SARS-CoV-2 pandemic that has infected more than 149 million and killed 3.1 million people by 27 April 2021, according to WHO statistics. Safety measures and precautions taken by many countries seem insufficient, especially with no specific approved drugs against the virus. This has created an urgent need to fast track the development of new medication against the virus in order to alleviate the problem and meet public expectations. The SARS-CoV-2 3CL main protease (Mpro) is one of the most attractive targets in the virus life cycle, which is responsible for the processing of the viral polyprotein and is a key for the ribosomal translation of the SARS-CoV-2 genome. In this work, we targeted this enzyme through a structure-based drug design (SBDD) protocol, which aimed at the design of a new potential inhibitor for Mpro. The protocol involves three major steps: fragment-based drug design (FBDD), covalent docking and molec... [more]