Monitoring centers in the smart grid exchange the collected data by sensors and smart meters to monitor the current conditions and performance of electric power components. Distribution Power Transformers (DPTs) have a key role in maintaining the integrity of power flow in the smart grid. Online monitoring of DPTs to detect possible faults can potentially increase the reliability of modern power systems. Mechanical defects of DPTs are the major issues in their proper operation that must be detected in their early stage of occurrence. One of the most effective solutions for diagnosing mechanical defects in DPTs is Frequency Response Analysis (FRA). In this study, an appropriate condition monitoring scheme for DPTs is developed to identify even minor winding defects. Disk-Space Variation (DSV), a common DPT windings fault, is applied to the 20 kV-winding of a 1.6 MVA DPT in various locations and with different severity. Their corresponding frequency responses are then computed, and all f... [more]

The article aims to identify the relationship between energy efficiency and particular indicators of energy losses in Europe. The results of the bibliographic analysis showed a knowledge gap in energy losses in Europe regarding the new challenges of energy security. For the analysis, annual panel data from 32 European countries were collected from 1990 to 2019. The authors used the Jarque−Bera test to assess the normality of the residuals, utilized the Breush−Pagan test for heteroskedasticity check, and applied regression analysis to determine the relationship between energy efficiency and energy loss rates in Europe. To assess the effects of energy losses, the authors performed OLS modeling using the stats model’s package in Python. According to the modeling results, an increase in distribution losses (% of available energy from all sources) by 1% in Europe leads to an increase in energy consumption by 17.16% under other constant conditions. There is significant heterogeneity between... [more]

The forecasting of electricity sales is directly related to the power generation planning of power enterprises and the progress of the generation tasks. Aiming at the problem that traditional forecasting methods cannot properly deal with the actual data offset caused by external factors, such as the weather, season, and spatial attributes, this paper proposes a method of electricity sales forecasting based on a deep spatio-temporal residual network (ST-ResNet). The method not only relies on the temporal correlation of electricity sales data but also introduces the influence of external factors and spatial correlation, which greatly enhances the fitting degree of each parameter of the model. Moreover, the residual module and the convolution module are fused to effectively reduce the damage of the deep convolutional process to the training effectiveness. Finally, the three comparison experiments of the ultra-short term, short term and medium term show that the MAPE forecasted by the ST-R... [more]

Counting objects in video images has been an active area of computer vision for decades. For precise counting, it is necessary to detect objects and follow them through consecutive frames. Deep neural networks have allowed great improvements in this area. Nonetheless, this task is still a challenge for edge computing, especially when low-power edge AI devices must be used. The present work describes an application where an edge device is used to run a YOLO network and V-IOU tracker to count people and bicycles in real time. A selective frame-downsampling algorithm is used to allow a larger frame rate when necessary while optimizing memory usage and energy consumption. In the experiments, the system was able to detect and count the objects with 18 counting errors in 525 objects and a mean inference time of 112.82 ms per frame. With the selective downsampling algorithm, it was also capable of recovering and reduce memory usage while maintaining its precision.

Power generation forecasts for wind farms, especially with a short-term horizon, have been extensively researched due to the growing share of wind farms in total power generation. Detailed forecasts are necessary for the optimization of power systems of various sizes. This review and analytical paper is largely focused on a statistical analysis of forecasting errors based on more than one hundred papers on wind generation forecasts. Factors affecting the magnitude of forecasting errors are presented and discussed. Normalized root mean squared error (nRMSE) and normalized mean absolute error (nMAE) have been selected as the main error metrics considered here. A new and unique error dispersion factor (EDF) is proposed, being the ratio of nRMSE to nMAE. The variability of EDF depending on selected factors (size of wind farm, forecasting horizons, and class of forecasting method) has been examined. This is unique and original research, a novelty in studies on errors of power generation for... [more]

State of charge (SOC) is one of the most important parameters in battery management systems, and the accurate and stable estimation of battery SOC for real-world electric vehicles remains a great challenge. This paper proposes a long short-term memory network based on grid search and cross-validation optimisation to estimate the SOC of real-world battery systems. The real-world electric vehicle data are divided into parking charging, travel charging, and finish charging cases. Meanwhile, the parameters associated with the SOC estimation under each operating condition are extracted by the Pearson correlation analysis. Moreover, the hyperparameters of the long short-term memory network are optimised by grid search and cross-validation to improve the accuracy of the model estimation. Moreover, the gaussian noise algorithm is used for data augmentation to improve the accuracy and robustness of SOC estimation under the working conditions of the small dataset. The results indicate that the a... [more]

Recently, ammonia is emerging as a potential source of energy in power generation and industrial sectors. One of the main concerns with ammonia combustion is the large amount of NO emission. Air staging is a conventional method of reducing NO emission which is similar to the Rich-Burn, Quick-Mix, Lean-Burn (RQL) concept. In air-staged combustion, a major reduction of NO emission is based on the near zero NO emission at fuel-rich combustion of NH3/Air mixture. A secondary air stream is injected for the oxidation of unburned hydrogen and NHx. On the other hand, in fuel-staged combustion, NO emission is reduced by splitting NH3 injection, which promotes the thermal DeNOx process. In this study, NOx emission characteristics of air-staged and fuel-staged combustion of partially cracked ammonia mixture are numerically investigated. First, the combustion system is modeled by a chemical reactor network of a perfectly stirred reactor and plug flow reactor with a detailed chemistry mechanism. Th... [more]

The flow ripple in an internal gear pump was measured by means of a new instantaneous high-pressure flowmeter. The flowmeter consists of two pressure sensors mounted on a piece of the straight steel pump delivery line, and a variable-diameter orifice was installed along such a line, downstream of the flowmeter, to generate a variable load. Three distinct configurations of the high-pressure flowmeter, characterized by a different distance between the pressure transducers, were analyzed. Furthermore, a comprehensive fluid dynamic 3D model of the pump and of its high-pressure delivery line was developed and validated in terms of both the delivery pressure and the flow ripple for different pump working conditions. For the three examined configurations of the flowmeter, the measured flowrate time histories matched the corresponding numerical distributions at the various operating points. Finally, the validated 3D model was applied to predict the incomplete filling working of the interteeth... [more]

The aim of this work is to adapt the methods of optical Hilbert diagnostics for the visualization and study of inverse diffusion H2/O2 flame. The diagnostic complex is implemented on the basis of the IAB-451 device with modified optical filtering. Visualization of phase perturbations induced by the studied medium in a probing multiwave light field is performed via polychromatic Hilbert and Foucault-Hilbert transformations in combination with registration and RGB-per-pixel processing of the dynamic structure of the images. From solution to the inverse problem of Hilbert optics using a physically justified initial approximation of the problem under consideration, the temperature field of the flame is reconstructed and the value of the H2, H2O, O2 and N2 concentrations may be restored.

This study characterises materials that belong to the group of refuse-derived fuels (RDF). This group of materials regarded as an alternative fuel is derived from industrial, municipal solid and commercial wastes. The aim of this study is to evaluate the quality of waste composition, demonstrate statistically different values and the energy efficiency of the fuel derived from waste. Data on incinerated waste were collected from two different sources. The basic physical and chemical parameters of waste include density and water content. The lower heating value (LHV) of waste, chlorine concentration and ash content of two groups of incinerated waste were also evaluated and compared for a given period of time (one year, with monthly breakdown). Statistical analysis indicated the differences in the combustion of waste groups, visualized by box plots and other diagrams to show the distribution of the results. An analysis of exhaust gas parameters was carried out, both in terms of chemical c... [more]

This paper concerns the application of the gamma radiation absorption method in the measurements of dispersed phase velocity in two-phase flows: liquid−gas flow in a horizontal pipeline and liquid−solid particles in a vertical pipe. Radiometric sets containing two linear 241Am gamma radiation sources and two NaI(Tl) scintillation detectors were used in the research. Due to the stochastic nature of the signals obtained from the scintillation probes, statistical methods were used for their analysis. The linear average velocity of the dispersed phase transportation was calculated using the phase of the cross-spectral density function of the signals registered by the scintillation detectors. It is shown that in the presented cases, the phase method can be more accurate than the most commonly used classical cross-correlation one.

Industrial revolution 4.0 has enabled the advent of new technological advancements, including the introduction of information technology with physical devices. The implementation of information technology in industrial applications has helped streamline industrial processes and make them more cost-efficient. This combination of information technology and physical devices gave birth to smart devices, which opened up a new research area known as the Internet of Things (IoT). This has enabled researchers to help reduce downtime and maintenance costs by applying condition monitoring on electrical machines utilizing machine learning algorithms. Although the industry is trying to move from scheduled maintenance towards predictive maintenance, there is a significant lack of algorithms related to fault prediction of electrical machines. There is quite a lot of research going on in this area, but it is still underdeveloped and needs a lot more work. This paper presents a signal spectrum-based m... [more]

Extensive research demonstrates the importance of user practices in understanding variations in residential heating demand. Whereas previous studies have investigated variations in aggregated data, e.g., yearly heating consumption, the recent deployment of smart heat meters enables the analysis of households’ energy use with a higher temporal resolution. Such analysis might provide knowledge crucial for managing peak demand in district heating systems with decentralized production units and increased shares of intermittent energy sources, such as wind and solar. This study exploits smart meter heating consumption data from a district heating network combined with socio-economic information for 803 Danish households. To perform this study, a multiple regression analysis was employed to understand the correlations between heat consumption and socio-economical characteristics. Furthermore, this study analyzed the various households’ daily profiles to quantify the differences between the g... [more]

In this paper, electro-hydraulic braking (EHB) force allocation for electric vehicles (EVs) is modeled as a constrained nonlinear optimization problem (NOP). Recurrent neural networks (RNNs) are advantageous in many folds for solving NOPs, yet existing RNNs’ convergence usually requires convexity with calculation of second-order partial derivatives. In this paper, a recurrent neural network-based NOP solver (RNN-NOPS) is developed. It is seen that the RNN-NOPS is designed to drive all state variables to asymptotically converge to the feasible region, with loose requirement on the NOP’s first-order partial derivative. In addition, the RNN-NOPS’s equilibria are proved to meet Karush−Kuhn−Tucker (KKT) conditions, and the RNN-NOPS behaves with a strong robustness against the violation of the constraints. The comparative studies are conducted to show RNN-NOPS’s advantages for solving the EHB force allocation problem, it is reported that the overall regenerative energy of RNN-NOPS is 15.39%... [more]

In order to ensure the security and stability of oilfield gathering and transportation stations and to improve the risk assessment method, this paper proposes an evaluation method that can fully and quantitatively calculate the impact range of process equipment and pipelines in the event of fire and explosion accidents based on API 581-2016 Quantitative Risk Assessment Technology. It mainly analyzes and calculates the leakage type, leakage rate and total leakage amount, combined with the occurrence probability of various failure situations, the casualty area and the fact that equipment damage could be finally determined. In addition, PHAST Software is used to verify this method. The average deviation of the calculation results is very small, which shows that the method is completely feasible and accurate. In order to further correct the error, specific correction methods and formulas are also proposed. This theoretical calculation method greatly improves the quantitative evaluation met... [more]

Currently, emerging technologies support many problems arising in the energy industry. The “cognitive enterprise” concept, introduced by the IBM company, assumes that emerging technologies are used together with cognitive workflows to increase enterprise intelligence. The pursuit of enterprises from the energy sector to obtain the status of a cognitive enterprise requires the use of many emerging technologies, including cognitive technologies. Thus, the aim of the paper was to present the current state of research and identify the core components of the cognitive enterprise. To analyze the trends and challenges in scientific research development, the bibliometric approach was used. The analysis was made by means of the Web of Science and Scopus platforms; 70,177 records were retrieved. The results comprise the geographic distribution of research and the time analysis as well as the author and affiliation analysis. Additionally, descriptive statistics are provided. Consequently, the res... [more]

In this paper, a modeling method for estimating the remaining useful life (RUL) of aged underground cable systems is proposed that uses statistical health index (HI) and operating factor (OF) data of retired systems. The HI is an indicator which identifies the condition of an underground cable system and its components and is calculated from testing and inspection results. The OF takes actual operating conditions and technical data from the system into consideration. Both factors are then combined to determine the overall health index (OHI) of each system. For RUL estimation of underground cable systems, normal distribution and Weibull distribution analyses are first applied to determine a health index curve and an aging line. The relationship between these two curves gives an estimate of the system’s apparent age. The RUL of the system is then calculated in terms of the difference between its apparent age and its actual chronological age. In this study, thirteen retired systems and te... [more]

Gas condensate reservoirs exhibit complex thermodynamic behaviors when the reservoir pressure is below the dew point pressure, leading to a condensate bank being created inside the reservoir, including gas and oil condensation. Due to natural fractures and multi-phase flows in fractured gas condensate reservoirs, there can be an erroneous interpretation of pressure-transient data using traditional multi-phase models or a fractured model alone. This paper establishes an analytical model for a well test analysis in a gas condensate reservoir with natural fractures. A three-region composite model was employed to characterize the multi-phase flow of retrograde condensation, and the fractured formation was described by a dual-porosity medium. In the first region, both the gas and condensate phases were mobile. In the second region, the gas was mobile whereas the condensates were immobile. In the third region, the only moving phase was the gas phase. The analytical solution was solved by a L... [more]

Solar radiation is by nature intermittent and influenced by many factors such as latitude, season and atmospheric conditions. As a consequence, the growing penetration of Photovoltaic (PV) systems into the electricity network implies significant problems of stability, reliability and scheduling of power grid operation. Concerning the very short-term PV power production, the power fluctuations are primarily related to the interaction between solar irradiance and cloud cover. In small-scale systems such as microgrids, the adoption of a forecasting tool is a brilliant solution to minimize PV power curtailment and limit the installed energy storage capacity. In the present work, two different nowcasting methods are applied to classify the solar attenuation due to clouds presence on five different forecast horizons, from 1 to 5 min: a Pattern Recognition Neural Network and a Random Forest model. The proposed methods are tested and compared on a real case study: available data consists of hi... [more]

A special experimental setup with a three-electrode discharge gap was used to study the dynamic characteristics of the ultra-high- and super-high-frequency (UHF-SHF) electromagnetic radiation of a high-voltage discharge having the streamer form with reference to the dynamics of individual streamers at the nanosecond time resolution. We performed synchronous detection of the radiation waveforms using a wideband horn antenna, on the one hand, and high-speed photography of the discharge development in the discharge gap using an ICCD camera, on the other hand. It was found that the high-voltage discharge is a source of radiation in the frequency band up to 10 GHz, which is a series of individual ultrawideband (UWB) bursts having durations of less than 1 ns and leading fronts less than 100 ps long and appears when the streamers moving from the discharge anode (thin wire) meet the discharge cathode (plane). By the order of magnitude, the number of radiation bursts corresponds to the number o... [more]

The main valve spool structure of the pilot-operated proportional directional valve is diverse and has a direct impact on the flow field. To improve the valve’s performance, this work studied the characteristics of four types of spool structures with the following throttling groove arrangements: no throttling groove, a U-shaped groove, a triangle groove, and a combined groove. This study analyzed the flow field simulation of four spool structures under the same opening degree and different pressures to study the flow field cavitation characteristics and pressure distribution in the valve. According to the simulation results, the necessity of opening throttling grooves for the pilot proportional directional valve and the advantages of combined grooves over U-shaped and triangular grooves were verified. Then, the proportional valve with a combined groove structure was simulated and analyzed to study its throttling characteristics, steady flow characteristics, and flow and load differenti... [more]

Thermal spray is an effective process for the fabrication of a metal matrix composite (MMC), where a zirconium diboride reinforcement is embedded in a molybdenum matrix to enable the combining of favorable properties in a new composite. The combination of two leading materials in the category of ultra-high-temperature ceramics (UHTCs) is due to a very high melting point (Mo: 2623 °C and ZrB2: 3245 °C), high thermal conductivity (Mo: 139 W/m°C and ZrB2: 24 W/m°C), good thermal shock resistance, low coefficient of thermal expansion (Mo: 5.35 µm/m°C and ZrB2: 5.9 × 10−6 K−1), retention of strength at elevated temperatures and stability in extreme environments. Thermal spraying of the Mo/ZrB2 composite possesses a non-linear behavior that is influenced by many coating variables. This characteristic makes finding the optimal factor combination difficult. Therefore, an effective and strategic statistical approach incorporating systematic experimental data is needed to optimize the process. I... [more]

In this paper, the analytical determination of volumic eddy-current losses in rectangular-shaped conductive massive parts is presented with experimental validations. Eddy currents, as well as the resulting volumic losses, are generated by a sinusoidal spatially uniform applied magnetic field. A U-shaped electromagnetic device with a flat mobile armature (or adjustable air gap) is used to measure the eddy-current losses. The experimental device, its instrumentation, and the conductive massive parts are presented in detail in the paper. Thereafter, the magnetic field distribution applied on the conductive massive parts, which is the mean input data for the eddy-current loss model, is studied. A two-dimensional (2D) numerical model, under the FEMM software, for the magnetic field calculation was also developed. A comparative analysis between the experimental measurements and the numerical results allowed the distribution of the applied magnetic field to be accurately validated. In the fin... [more]

Monitoring the condition of industrial equipment is fundamental to avoid failures and maximize uptime. The present work used supervised and unsupervised learning methods to create models for predicting the condition of an industrial machine. The main objective was to determine when the asset was either in its nominal operation or working outside this zone, thus being at risk of failure or sub-optimal operation. The results showed that it is possible to classify the machine state using artificial neural networks. K-means clustering and PCA methods showed that three states, chosen through the Elbow Method, cover almost all the variance of the data under study. Knowing the importance that the quality of the lubricants has in the functioning and classification of the state of machines, a lubricant classification algorithm was developed using Neural Networks. The lubricant classifier results were 98% accurate compared to human expert classifications. The main gap identified in the research... [more]

The comparison of the results obtained using two methods for measuring the radio disturbance emissions produced by compact lighting drivers in the frequency range of 30 to 300 MHz has been presented in this paper. Any electrical and electronic equipment used within the EU must comply with Directive 2014/30/EC and harmonised standards. For lighting equipment, the dedicated standard is EN-IEC 55015:2019-11E. In this standard, for tests in the frequency range of 30 to 300 MHz, two equivalent test methods are allowed for lighting drivers, i.e., the traditional method in which disturbance emissions are measured in a semianechoic chamber SAC and the alternative CDNE (Coupling Decoupling Network Emission) method. Each method is characterised by a different measurement technique. For this reason, this paper aims to compare the results obtained by the two methods and to find out whether the CDNE and SAC methods, despite the difference in measurement technique, can be considered equivalent. The... [more]