Both long-distance oil and gas pipelines often pass through areas with unstable geological conditions or natural disasters. As a result, they are prone to bending, displacement, and deformation due to the action of an external environmental loading, which poses a threat to the safe operation of pipelines. The in-line inspection method that is based on the implementation of high-precision inertial measurement units (IMU) has become the main means of pipeline bending stress-strain detection technique. However, to address the problems of the inconsistent identification, low identification efficiency, and high misjudgment rate during the application of the traditional manual identification methods, a feature identification approach for the in-line inspected pipeline bending strain based on the employment of an optimized deep belief network (DBN) model is proposed in this work. In addition, our model can automatically learn features from the pipeline bending strain signals and complete clas... [more]

The study was conducted on a sample of 60 of the world’s biggest banks financing the largest fossil fuel entities. The aim is to identify the determinants of ESG ratings of these banks and to determine how relevant their actual credit and investment exposure is to this assessment. The indirect objective is also an examination of whether coal power financing affects ESG ratings. Two logistic regression models have been explored: one dedicated to the identification of high ESG risk banks and the second to predict low ESG risk, which thereafter were combined into one final model. The results indicate that an increase in the Sustainable Development Index (SDI) translates into a decline in the odds of being assigned to the high-risk ESG group relative to the probability of being qualified to the low- or medium-risk ESG group. This study is the first to analyse the impact of actual exposures of the world’s largest banks to the fossil fuels sector on their ESG ratings. The value added is the... [more]

The insulation system’s dielectric of the electric motor is very often subjected to severe electrical stress generated by the high dv/dt seen at the machine’s terminals. The electrical stress and high reflected wave transient overvoltage are even more evident in case of high-speed machines fed by high-frequency (HF) converters featuring very fast wide-bandgap devices. They are promoting the occurrence of partial discharges and consequently accelerate ageing. As this is serious issue and the main cause of the drive failure, it is important to analyse and characterise the surges at the motor terminals. Several HF models of motors have been proposed in the literature for this purpose. This article presents a survey on HF motor models, which is crucial in understanding and studying the most critical parameter identification and overvoltage mitigation techniques. Moreover, it offers a comparison of the models’ main features as well as a comparison with the experimental voltage waveform at m... [more]

In this paper, the available methods and procedures for creating equivalents for the analysis of electromagnetic transients in power systems are presented and discussed. General requirements of power system representation during simulation of electromagnetic transients are shown. The main available procedures are shown, along with an assessment of their advantages and disadvantages. Methods to search for the optimal replacement of structures in time and frequency domains are discussed. Optimization and direct methods in the frequency domain are presented. Each of these methods is discussed with respect to their possible use in determining the structure of the equivalent circuit for the study of electromagnetic phenomena. Methods to reduce a complex power system, as one of the approaches to determining the structure and parameters of the equivalent circuit, are also presented. Contraindications to the search for equivalents in the frequency domain to study electromagnetic transients are... [more]

The development of smart buildings, as well as the great need for energy demand reduction, has renewed interest in building energy demand prediction. Intelligent controllers are a solution for optimizing building energy consumption while maintaining indoor comfort. The controller efficiency on the other hand, is mainly determined by the prediction of thermal behavior from building models. Due to the development complexity of the models, these intelligent controllers are not yet implemented on an industrial scale. There are primarily three types of building models studied in the literature: white-box, black-box, and gray-box. The gray-box models are found to be robust, efficient, of low cost computationally, and of moderate modeling complexity. Furthermore, there is no standard model configuration, development method, or operation conditions. These parameters have a significant influence on the model performance accuracy. This motivates the need for this review paper, in which we examin... [more]

This study analyzes the water invasion characteristics and water encroachment of the deep sea bottom-water gas reservoir (LS17 field) in the South China Sea for the purpose of developing horizontal wells. Gas-producing profile tests and a three-dimensional (3D) water invasion simulation are used to produce a quantitative analysis of the bottom-water cresting influence factors. On this basis, we establish a suitable risk identification method for the water influx of a deep bottom-water reservoir. The results show that: (1) During the development of a bottom-water gas reservoir, the water ridging is affected by reservoir heterogeneity, production system and heel−toe effect of a horizontal wellbore, and reservoir heterogeneity is the main influencing factor; (2) the horizontal−vertical ratio of the well area determines whether the gas well productivity will be affected by the risk of water invasion. The stronger the reservoir heterogeneity, the smaller the safety limit value of the horizo... [more]

Accurate identification of the lithofacies and sedimentary facies of coal-bearing series is significant in the study of peat accumulation, coal thickness variation and coal-measured unconventional gas. This research integrated core, logging and 3D seismic data to conduct a comprehensive seismic−geological study on the sedimentary evolution characteristics and peat accumulation regularity of the Shanxi Formation in the Xinjing mining area of the Qinshui Basin. Firstly, the high-resolution sequence interface was identified, and the isochronous stratigraphic framework of the coal-bearing series was constructed. Then, the temporal and spatial evolution of sedimentary filling and sedimentary facies was dynamically analyzed using waveform clustering, phase rotation, stratal slice and frequency−division amplitude fusion methods. The results show that the Shanxi Formation in the study area can be divided into one third-order sequence and two fourth-order sequences. It developed a river-dominat... [more]

Solid oxide fuel cell (SOFC) generation system is an important equipment to realize “carbon neutralization”. In SOFC system, a fault will cause changes in working conditions, which is difficult to detect early and find the reason due to the high temperature and seal environment. Therefore, the mechanistic model is a feasible way to find the reasons for the change of system working conditions. In this paper, based on the first law of thermodynamics, the system model of SOFC is built under multiple working conditions, and the influence of stack, afterburner, heat exchanger, and reformer fault is studied on the thermoelectric characteristics and efficiency of the system. The results show that with the introduction of these fault mechanistic models, the dynamic response characteristics of SOFC system under multiple working conditions can be obtained by tracking the key performance parameters qualitatively. The work of this paper is helpful for the guidance of the fault diagnosis of SOFC sy... [more]

The accurate prediction of the solar energy that can be generated using the rooftops of buildings is an essential tool for many researchers, decision makers, and investors for creating sustainable cities and societies. This study is focused on the development of an automated method to extract the useable areas of rooftops and optimize the solar PV panel layout based on the given electricity loading of a building. In this context, the authors of this article developed two crucial methods. First, a special pixel-based rooftop recognition methodology was developed to analyze detailed and complex rooftop types while avoiding the challenges associated with the nature of the particular building rooftops. Second, a multi-objective enveloped min−max optimization algorithm was developed to maximize solar energy generation and minimize energy cost in terms of payback based on the marginal price signals. This optimization algorithm facilitates the optimal integration of three controlled variables... [more]

Top coal caving is an important way of thick coal seam mining. The current coal gangue identification technology is not mature, resulting in a low degree of automation of coal caving. The current numerical simulation pays little attention to the real shape of rocks. This paper aims to reveal the vibration response of the tail beam under the action of real shape coal gangue particles. First, the real shape rock and hydraulic support model are established, and the relationship between the Rock Mixed Ratio (RMR) and the vibration signal characteristics during the impact process are studied. The influence of falling velocity and coal strength on this relationship are analyzed. Finally, the influence of motion mode on this relationship is analyzed and discussed. The numerical results show that the increase of the RMR can significantly enhance the velocity and acceleration signals of the tail beam, and the signals caused by different RMR are still different under different velocities and str... [more]

In this study, the feedback effects of a three-phase half-controlled rectifier (also known as a semiconverter) to three-phase AC power grid was analyzed. Special attention was paid to the identification of harmonic order of the phase current. As a reference point for analysis, the mathematical model of an uncontrolled rectifier was used. The harmonic order of the phase current was identified by displaying the measurement results as well as using a mathematical model that is easily applicable, although this is unusual for harmonic analysis because it is based on time domain data. For this purpose, laboratory models of uncontrolled and half-controlled rectifiers were assembled. For both converters, the results obtained by practical laboratory measurements were compared to the mathematically obtained results, with the commutation in the mathematical model being ignored. The effects of commutation were analyzed in more detail for the laboratory model. For the semiconverter, the characteris... [more]

The accurate magnetic model is mandatory for high-performance control of high anisotropy synchronous machines. This paper presents a time-efficient and accurate magnetic model identification based on triangle current injection while the machine under the test is driven at a constant speed by a prime mover. The current injection pattern allows scanning the whole range of current, reducing the identification time compared to the standard constant-speed method (CSM) with the same level of accuracy. The ohmic voltage drop and inverter nonlinearities are compensated by using the average voltage of motor and generator modes. The synchronous reluctance machine is used as a case study for validation through the comparison between the experimental results obtained by the proposed method and the CSM against finite element simulation. Moreover, the temperature variation of the machine winding is measured showing no considerable changes during the identification test.

This Special Issue of Energies, “Smart Cities and Positive Energy Districts: Urban Perspectives in 2021”, introduces contemporary research on Smart Cities and on Positive Energy Districts. The present Special Issue, namely the fourth Special Issue, Smart Cities and Positive Energy Districts: Urban Perspectives in 2021, has been dedicated to tools, technologies, and system integration for Smart Cities and for Positive Energy Districts. The topic highlights the variety of research within this field, including research on: tools facilitating the evaluation of Sustainable Plus Energy neighborhoods, of enabling-technologies and procedures for Positive Energy Districts, and of multicriteria assessment for the identification of Positive Energy Districts; system integration related to optimized energy and air quality management in the COVID-19 scenario; system integration upgrading existing residential areas to the status of Positive Energy Districts; and renovation models for large scale acti... [more]

As an important part of the ubiquitous power Internet of Things, the distribution Internet of Things can further improve the automation and informatization level of the distribution network. The reliability of the measurement data of the low-voltage terminal unit, as the sensing unit of the sensing layer of the distribution Internet of Things, has a great impact on the fault processing and advanced applications of the distribution Internet of Things. The self-check and the equipment working status monitoring of the main station of the low-voltage terminal unit struggle to identify the abnormality of measurement data. Aiming at this problem, an abnormal data detection and identification recognition method of a distribution Internet of Things monitoring terminal is proposed on the basis of spatiotemporal correlation. First, using the temporal correlation of monitoring terminal data, the proposed composite temporal series similarity measurement criterion is used to calculate the distance... [more]

Power systems and their primary components, mostly the transmission and distribution of overhead lines, substations, and other power facilities, are distributed in space and are exposed to various atmospheric and meteorological conditions. These conditions carry a certain level of risk for reliable electrical power delivery. Various atmospheric hazards endanger the operation of power systems, where the most significant are thunderstorms, wildfire events, and floods which can cause various ranges of disturbances, faults, and damages to the power grid, or even negatively affect the quality of life. By utilizing a weather monitoring and early warning system, it is possible to ensure a faster reaction against different weather-caused fault detections and elimination, to ensure a faster and more adequate preparation for fighting extreme weather events, while maintaining overhead line protection and fault elimination. Moreso, it is possible to bypass overhead lines that have the highest risk... [more]

Modern textronic RFID transponders offer a lot of new possibilities for household appliances designers. Possibility to implement new functions is most evident in clothes washing and ironing techniques, where the information stored in the memory of the RFID transponder sewn into the textiles can be used to choose the most appropriate ironing program for a given type of fabric or to select the best washing program for different clothes placed in a drum of washing machine. The purpose of the work was to propose, design, and develop a laboratory stand to demonstrate usage of RFIDtex transponders in a washing machine. The developed device enabled simulation of the presence of textiles equipped with RFIDtex transponders in a washing machine drum. A set of measurements of the constructed device readout efficiency of textronic transponders placed in the drum was also performed. The device firmware, which manages multiple data readings from tags inside the drum for the performed by integrated R... [more]

The current need for power-system control-room operational tools to increase real-time situational awareness has led to various proposals on data analytics algorithms to obtain operational information from SCADA and PMU power system networks. Most of the proposals are implemented in simulation, with reduced practical significance for system operators. This work proposes a web-based simple algorithm to identify the dynamic parameters of a reduced-order model of power system frequency dynamics that is implemented with industrial hardware on a PMU network in the Chilean power system. To demonstrate the applicability of the proposed tool, the algorithm is implemented in a web page with real-time monitoring of the frequency of the Chilean power system, so that utilities and academic institutions with access to PMU measurements can apply the proposed concepts regardless of the topology of the monitored system.

The accurate identification of Very Fast Transient Overvoltage (VFTO) is the key of overvoltage control in modern smart grids. In order to accurately identify VFTO generated by the operation of a disconnector in Gas Insulated Substation (GIS), a VFTO identification method based on Wavelet Transform (WT) and Singular Value Decomposition (SVD) is proposed. The simulation model of VFTO is established in ATP-EMTP software first, and then wavelet decomposition is used in MATLAB software for VFTO simulation of the waveform from the ATP-EMTP software. Then, the feature matrix is composed of the coefficients of each frequency layer of the wavelet. The SVD is used to decompose the feature matrix, and finally the characteristic parameters of the VFTO are obtained. The simulation results in Matlab software indicate that the characteristic parameters of VFTO have an obvious difference compared with those of power frequency AC voltage, especially in the load-side, which verifies the effectiveness o... [more]

The paper presents a novel method for non-intrusive appliances identification. It can be used for energy load disaggregation in a smart grid. The approach identifies changes in the state of the particular appliance by measuring and processing the common supply current signal. Analysis of the instantaneous changes in the aggregated current on the output of the analyzed circuit in the power network is exploited here. The signal is processed using the time alignment of the current and voltage signals samples represented in the array form. The scheme includes filtering, event detection and identification, which is performed by comparing parameters of the detected event against previously determined signatures of monitored appliances. The analysis is performed in the time domain; therefore (unlike other existing methods), the information contained in the original signal is not lost. The approach was tested in the laboratory designed specifically for this purpose. All tests have been conduct... [more]

Stator faults are the most critical faults in induction motors as they develop quickly hence requiring fast online diagnostic methods. A number of online condition monitoring techniques are proposed in the literature to respond to such faults, including the signature analysis of the stator current, vibration monitoring, flux leakage monitoring, negative sequence components of voltage and current and sequence components monitoring based on the identification of asymmetrical behavior in a machine. Negative sequence components of voltage and current and sequence components monitoring are commonly considered for the identification of asymmetrical behavior of induction motors. Negative sequence current analysis is a sensitive technique for the detection of shorted turns as it directly measures the asymmetry produced by the fault. However, the technique is sensitive to other asymmetrical faults and disturbances, which can also produce negative sequence currents. These disturbances include se... [more]

The Qiongdongnan (QDN) basin in the northwestern South China Sea has been shown to be rich in petroleum resources and has significant exploration potential for deepwater oil and gas resources. Therefore, it is of great significance to clarify the gas accumulation process in the deepwater area of the QDN basin, specifically the Y8 area. In this study, fluid inclusion analysis, including the identification and localization of fluid inclusions under a microscope, laser Raman spectrum analysis and homogenization temperature (Th) measurement, reconstruction of the thermal evolution burial history, geochemical analysis, and typical seismic section analysis were comprehensively used to interpret the gas accumulation process of the Y8 area. The laser Raman spectrum analysis results of fluid inclusions verified the existence of methane and confirmed the hydrocarbon charging event in this area. However, the Th of fluid inclusions in the reservoir, the mixed compositions of hydrothermal compositi... [more]

The Lower Paleozoic carbonate reservoir in the Tarim Basin is a hotspot area for deep oil and gas exploration in China. Although the Lower Cambrian of the Bachu Uplift has not encountered industrial oil flow, rich bitumen has been found there. As the most direct trace of petroleum migration, the effective identification of bitumen is the key to studying the hydrocarbon transportation path. In this study, the Adobe Photoshop quantification (PSQ) method is used to identify the bitumen content in the Xiaoerblak Formation in Well Shutan 1, and, combined with the bitumen characteristics of the Shihuiyao section, a high-quality petroleum transverse transport layer is determined. The results indicate the following: (1) In Well Shutan 1, bitumen is mainly concentrated in the middle and upper parts of the Xiaoerblak Formation with high porosity and high permeability. (2) The shale of the Yuertus Formation in the Shihuiyao section has low hydrocarbon generation potential. However, the overlying... [more]

It is necessary to develop new drilling and breaking technology for hard rock construction. However, the process of high-voltage electro-pulse (HVEP) rock-breaking is complex, and the selection of electro-pulse boring (EPB) process parameters lacks a theoretical basis. Firstly, the RLC model, TV-RLC model, and TV-CRLC model are established based on the characteristics of the HVEP circuit to predict the EPB dynamic discharge curve. Secondly, the parameters are identified by the Particle Swarm Optimization Genetic Algorithm (PSO-GA). Finally, due to the nonlinear and complex time-varying characteristics of the discharge circuit, the discharge circuit prediction models based on Bayesian fusion and current residual normalization fusion method are proposed, and the optimal weight of these three models is determined. Compared with the single models for EPB current prediction, the average relative error reduction rates based on Bayesian fusion and current residual normalization fusion methods... [more]

Several dynamic projects and fault diagnosis of mechanical structures require the knowledge of the acting external forces. However, the measurement of such forces is often difficult or even impossible; in such cases, an inverse problem must be solved. This paper proposes a force identification method that uses the response surface methodology (RSM) based on central composite design (CCD) in conjunction with a random forest regression algorithm. The procedure initially required the finite element modal model of the forced structure. Harmonic analyses were then performed with varied parameters of forces, and RSM generated a dataset containing the values of amplitude, frequency, location of forces, and vibration acceleration at several points of the structure. The dataset was used for training and testing a random forest regression model for the prediction of any location, amplitude, and frequency of the force to be identified with information on only the vibration acquisition at certain... [more]

The identification of underground formation lithology is fundamental in reservoir characterization during petroleum exploration. With the increasing availability and diversity of well-logging data, automated interpretation of well-logging data is in great demand for more efficient and reliable decision making for geologists and geophysicists. This study benchmarked the performances of an array of machine learning models, from linear and nonlinear individual classifiers to ensemble methods, on the task of lithology identification. Cross-validation and Bayesian optimization were utilized to optimize the hyperparameters of different models and performances were evaluated based on the metrics of accuracy—the area under the receiver operating characteristic curve (AUC), precision, recall, and F1-score. The dataset of the study consists of well-logging data acquired from the Baikouquan formation in the Mahu Sag of the Junggar Basin, China, including 4156 labeled data points with 9 well-loggi... [more]