The smart grid (SG), which has revolutionized the power grid, is being further improved by using the burgeoning cyber physical system (CPS) technology. The conceptualization of SG using CPS, which is referred to as the smart grid cyber physical system (SGCPS), has gained a momentum with the synchrophasor measurements. The edifice of the synchrophasor system is its communication network referred to as a synchrophasor communication network (SCN), which is used to communicate the synchrophasor data from the sensors known as phasor measurement units (PMUs) to the control center known as the phasor data concentrator (PDC). However, the SCN is vulnerable to hardware and software failures that introduce risk. Thus, an appropriate risk assessment framework for the SCN is needed to alleviate the risk in the protection and control of the SGCPS. In this direction, a comprehensive risk assessment framework has been proposed in this article for three types of SCNs, namely: dedicated SCN, shared SCN... [more]

This paper uncovers linkages between oil price uncertainty, total factor productivity (TFP) growth, and critical indicators of knowledge production and spillovers. It contributes to the literature by investigating the effects of oil price volatility on TFP growth, controlling for two different channels for TFP growth; benefits from the quality of the national innovation system and from adopting new technologies. We use an unbalanced panel for 28 European Union countries for the period from 1990 to 2018. We find that oil price uncertainty has a negative and statistically significant effect on TFP growth, even after we control for technological advancements and the effects of globalization. We also find that the scale of research and innovation and international trade are positive contributors to TFP growth.

Vegetation encroachment along electric power transmission lines is one of the major environmental challenges that can cause power interruption. Many technologies have been used to detect vegetation encroachment, such as light detection and ranging (LiDAR), synthetic aperture radar (SAR), and airborne photogrammetry. These methods are very effective in detecting vegetation encroachment. However, they are expensive with regard to the coverage area. Alternatively, satellite imagery can cover a wide area at a relatively lower cost. In this paper, we describe the statistical moments of the color spaces and the textural features of the satellite imagery to identify the most effective features that can increase the vegetation density classification accuracy of the support vector machine (SVM) algorithm. This method aims to distinguish between high- and low-density vegetation regions along the power line corridor right-of-way (ROW). The results of the study showed that the statistical moments... [more]

The most general quantities of interest (called “responses”) produced by the computational model of a linear physical system can depend on both the forward and adjoint state functions that describe the respective system. This work presents the Fourth-Order Comprehensive Adjoint Sensitivity Analysis Methodology (4th-CASAM) for linear systems, which enables the efficient computation of the exact expressions of the 1st-, 2nd-, 3rd- and 4th-order sensitivities of a generic system response, which can depend on both the forward and adjoint state functions, with respect to all of the parameters underlying the respective forward/adjoint systems. Among the best known such system responses are various Lagrangians, including the Schwinger and Roussopoulos functionals, for analyzing ratios of reaction rates, the Rayleigh quotient for analyzing eigenvalues and/or separation constants, etc., which require the simultaneous consideration of both the forward and adjoint systems when computing them and/... [more]

Despite the growing share of renewable energy sources, most of the world energy supply is still based on hydrocarbons and the vast majority of world transport is fuelled by oil products. Thus, the profitability of many companies may depend on the effective management of oil price risk. In this article, we analysed the effectiveness of artificial neural networks in hedging against the risk of WTI crude oil prices increase. This was reformulated from a regressive problem to a classification problem. The effectiveness of our approach, using artificial neural networks to classify observations, was verified for over ten years of WTI futures quotes, starting from 2009. The data analysis presented in this paper confirmed that the buyer of a call option was more often likely to incur a loss as a result of its purchase than make a profit after the final payoff from the call option. The results of the conducted research confirm that neural networks can be an effective form of protection against... [more]

Initiated by a case study to assess the effectiveness of the modernisation actions undertaken in a water supply system, some R&D activities were conducted to construct a global predictive model, based on the available operational failure and recovery data. The available operational data, regarding the water supply system, are the pipes’ diameter, failure modes, materials, functional conditions, seasonality, and the number of failures and time-to-recover intervals. The operational data are provided by the water company responsible of the supply system. A predictive global model is proposed based on the output of the operational data statistical assessment. It should assess the expected effectiveness of decisions taken in support of the modernisation and the extension plan.

Among the electrical problems observed from the solar irradiation variability, the electrical energy quality and the energetic dispatch guarantee stand out. The great revolution in batteries technologies has fostered its usage with the installation of photovoltaic system (PVS). This work presents a proposition for voltage regulation for residential prosumers using a set of scalable power batteries in passive mode, operating as a consumer device. The mitigation strategy makes decisions acting directly on the demand, for a storage bank, and the power of the storage element is selected in consequence of the results obtained from the power flow calculation step combined with the prediction of the solar radiation calculated by a recurrent neural network Long Short-Term Memory (LSTM) type. The results from the solar radiation predictions are used as subsidies to estimate, the state of the power grid, solving the power flow and evidencing the values of the electrical voltages 1-min enabling t... [more]

Biofuels are important additives to conventional fuels in combustion engines of the transport sector, as they reduce atmospheric emissions and promote environmental-friendly production chains. The mechanical and thermal performance of a PT6A-61A engine on a test bench of the Colombian Air Force operating with blends of JETA-1 and Biodiesel up to 25% volume values of substitution is evaluated in this work. Experimental results show that blends are operationally reliable up to 15% volume content. In that range, the engine operation is not compromised in terms of response variables. Moreover, experimental properties of fuel blends show that the freezing point—which is the most critical variable, does not comply with aeronautical regulations. The system dynamics are subject to several variations in the test parameters, which mainly affected fuel flow, Inter-Turbine Temperature (ITT), and engine performance. A Principal Component Analysis (PCA) is performed over the experimental results to... [more]

In this study, based on the slope of power versus voltage, a novel maximum-power-point tracking algorithm using a neural network compensator was proposed and implemented on a TI TMS320F28335 digital signal processing chip, which can easily process the input signals conversion and the complex floating-point computation on the neural network of the proposed control scheme. Because the output power of the photovoltaic system is a function of the solar irradiation, cell temperature, and characteristics of the photovoltaic array, the analytic solution for obtaining the maximum power is difficult to obtain due to its complexity, nonlinearity, and uncertainties of parameters. The innovation of this work is to obtain the maximum power of the photovoltaic system using a neural network with the idea of transferring the maximum-power-point tracking problem into a proportional-integral current control problem despite the variation in solar irradiation, cell temperature, and the electrical load cha... [more]

Direct current microgrids are attaining attractiveness due to their simpler configuration and high-energy efficiency. Power transmission losses are also reduced since distributed energy resources (DERs) are located near the load. DERs such as solar panels and fuel cells produce the DC supply; hence, the system is more stable and reliable. DC microgrid has a higher power efficiency than AC microgrid. Energy storage systems that are easier to integrate may provide additional benefits. In this paper, the DC micro-grid consists of solar photovoltaic and fuel cell for power generation, proposes a hybrid energy storage system that includes a supercapacitor and lithium−ion battery for the better improvement of power capability in the energy storage system. The main objective of this research work has been done for the enhanced settling point and voltage stability with the help of different maximum power point tracking (MPPT) methods. Different control techniques such as fuzzy logic controller... [more]

Frequency response analysis (FRA) is a well-known method to assess the mechanical integrity of the active parts of the power transformer. The measurement procedures of FRA are standardized as described in the IEEE and IEC standards. However, the interpretation of FRA results is far from reaching an accepted and definitive methodology as there is no reliable code available in the standard. As a contribution to this necessity, this paper presents an intelligent fault detection and classification algorithm using FRA results. The algorithm is based on a multilayer, feedforward, backpropagation artificial neural network (ANN). First, the adaptive frequency division algorithm is developed and various numerical indicators are used to quantify the differences between FRA traces and obtain feature sets for ANN. Finally, the classification model of ANN is developed to detect and classify different transformer conditions, i.e., healthy windings, healthy windings with saturated core, mechanical de... [more]

Barrier machines are a key component of automatic level crossing systems ensuring safety on railroad crossings. Their failure results not only in delayed railway transportation, but also puts human life at risk. To prevent faults in this critical safety element of automatic level crossing systems, it is recommended that fault and anomaly detection algorithms be implemented. Both algorithms are important in terms of safety (information on whether a barrier boom has been lifted/lowered as required) and predictive maintenance (information about the condition of the mechanical components). Here, the authors propose fault models for barrier machine fault and anomaly detection procedures based on current waveform observation. Several algorithms were applied and then assessed such as self-organising maps (SOM), autoencoder artificial neural network, local outlier factor (LOF) and isolation forest. The advantage of the proposed solution is there is no change of hardware, which is already homol... [more]

Corrugated cardboard is an ecological material, mainly because, in addition to virgin cellulose fibers also the fibers recovered during recycling process are used in its production. However, the use of recycled fibers causes slight deterioration of the mechanical properties of the corrugated board. In addition, converting processes such as printing, die-cutting, lamination, etc. cause micro-damage in the corrugated cardboard layers. In this work, the focus is precisely on the crushing of corrugated cardboard. A series of laboratory experiments were conducted, in which the different types of single-walled corrugated cardboards were pressed in a fully controlled manner to check the impact of the crush on the basic material parameters. The amount of crushing (with a precision of 10 micrometers) was controlled by a precise FEMat device, for crushing the corrugated board in the range from 10 to 70% of its original thickness. In this study, the influence of crushing on bending, twisting and... [more]

Wind is a physical phenomenon with uncertainties in several temporal scales, in addition, measured wind time series have noise superimposed on them. These time series are the basis for forecasting methods. This paper studied the application of the wavelet transform to three forecasting methods, namely, stochastic, neural network, and fuzzy, and six wavelet families. Wind speed time series were first filtered to eliminate the high-frequency component using wavelet filters and then the different forecasting methods were applied to the filtered time series. All methods showed important improvements when the wavelet filter was applied. It is important to note that the application of the wavelet technique requires a deep study of the time series in order to select the appropriate family and filter level. The best results were obtained with an optimal filtering level and improper selection may significantly affect the accuracy of the results.

The drive to increase the efficiency of processes based on two-phase flow demands the better precision and selection of boundary conditions in the process’ control. The two-phase flow pattern affects the phenomena of momentum, heat, and mass transfer. It becomes necessary to shift from its qualitative to quantitative evaluation. The description of the stationary structure has long been used in structural studies applied to metals and alloys. The description of a gas−liquid two-phase mixture is difficult because it changes in time and space. This paper presents a study of the precise determination of two-phase flow patterns based on stereological parameters analysis. The research area is shown against the flow map proposed by other researchers. The experiment was taken in the thin clear channel with dimensions of W = 50 × H = 1200 × T = 5 mm. The test method is based on the visualization of a two-phase air−water adiabatic flow pattern in the rectangular channel where superficial air vel... [more]

The COVID-19 pandemic obliged the Romanian government to take drastic measures to contain the virus. More than this, they imposed the heaviest restrictions in the EU. For more than a month, during the lockdown period, everything stopped: schools and universities had only online classes, national and international flights and gatherings were forbidden, and many restrictions for travel were imposed. This paper analyzes the changes that occurred in electricity consumption linked with economic growth, during the pandemic, in Romania. For a better understanding of the correlations between gross domestic product (GDP) and electricity consumption (EC) in different economic contexts, the period 2008−2020 was divided into three series: the 2008−2012 financial crisis and the post-crisis recovery period, the 2013−2019 period of economic growth, and the Q1−Q3 2020 pandemic period. Using correlation coefficients and regression analysis, the authors found that the GDP decoupled from EC in the first... [more]

Production forecasting is the basis for decision making in the oil and gas industry, and can be quite challenging, especially in terms of complex geological modeling of the subsurface. To help solve this problem, assisted history matching built on ensemble-based analysis such as the ensemble smoother and ensemble Kalman filter is useful in estimating models that preserve geological realism and have predictive capabilities. These methods tend, however, to be computationally demanding, as they require a large ensemble size for stable convergence. In this paper, we propose a novel method of uncertainty quantification and reservoir model calibration with much-reduced computation time. This approach is based on a sequential combination of nonlinear dimensionality reduction techniques: t-distributed stochastic neighbor embedding or the Gaussian process latent variable model and clustering K-means, along with the data assimilation method ensemble smoother with multiple data assimilation. The... [more]

Wireless sensor networks (WSNs) are among the most popular wireless technologies for sensor communication purposes nowadays. Usually, WSNs are developed for specific applications, either monitoring purposes or tracking purposes, for indoor or outdoor environments, where limited battery power is a main challenge. To overcome this problem, many routing protocols have been proposed through the last few years. Nevertheless, the extension of the network lifetime in consideration of the sensors capacities remains an open issue. In this paper, to achieve more efficient and reliable protocols according to current application scenarios, two well-known energy efficient protocols, i.e., Low-Energy Adaptive Clustering hierarchy (LEACH) and Energy−Efficient Sensor Routing (EESR), are redesigned considering neural networks. Specifically, to improve results in terms of energy efficiency, a Levenberg−Marquardt neural network (LMNN) is integrated. Furthermore, in order to improve the performance, a sub... [more]

This paper presents the identification and classification of steel cord failures in the conveyor belt core based on an analysis of a two-dimensional image of magnetic field changes recorded using the Diagbelt system around scanned failures in the test belt. The obtained set of identified changes in images, obtained for numerous parameters settings of the device, were the base for statistical analysis. This analysis makes it possible to determine the Pearson’s linear correlation coefficient between the parameters being changed and the image of the failures. In the second stage of the research, artificial intelligence methods were applied to construct a multilayer neural network (MLP) and to teach it appropriate identification of damage. In both methods, the same data sets were used, which made it possible to compare methods.

Stable operation of fuel cell air compressions is constrained by rotating surge in low flowrate conditions. In this paper, a diagnosis criterion based on wavelet transform to solve the surge fault is proposed. First of all, the Fourier transform was used to analyze the spectral characteristics of the outlet flowrate. Before wavelet transform was used, the data are standardized. This step eliminated the influence of the flowrate’s absolute value. Then, the wavelet coefficients under characteristic frequencies were extracted. Finally, the diagnosis criterion’s threshold, which indicates the surge occurrence, was defined from the perspective of safety margin. The criterion threshold alerted a surge only 1 s after it occurred. The analysis results show that the criterion meets with the expectation, and it can be used for the control of anti-surge valve.

The energy consumption and emissions in the urban transportation are influenced not only by technical efficiency in the mobility operations but also by the citizens’ mobility behaviors including mode choices and modal shift among sustainable and unsustainable mobility modes. Information and Communication Technologies (ICTs) can play an important role in the mobility behaviors of citizens, and it is necessary to study whether ICTs support sustainable mode choices like public transport and nonmotorized modes, which increase the total energy efficiency in the urban mobility and reduce traffic congestion and related emissions. This paper focuses on the two most popular ICT services in the urban transport, which are ATIS (Advanced Traveler Information Systems), and ridesourcing services. This study used the New York Citywide Mobility Survey (CMS) findings with a sample of 3346 participants. The associations between using these two ICT services and the mobility behaviors (mode choice with AT... [more]

Increasing emissions from mining areas and a high global warming potential of methane have caused gas management to become a vital challenge. At the same time, it provides the opportunity to obtain economic benefits. In addition, the use of combined heat and power (CHP) in the case of coalbed methane combustion enables much more efficient use of this fuel. The article analyses the possibility of electricity production using gas engines fueled with methane captured from the Budryk coal mine in Poland. The basic issue concerning the energy production from coalbed methane is the continuity of supply, which is to ensure the required amount and concentration of the gas mixture for combustion. Hence, the reliability of supply for electricity production is of key importance. The analysis included the basic characterization of both the daily and annual methane capture by the mine’s methane drainage system, as well as the development of predictive models to determine electricity production base... [more]

Transportation data in a smart city environment is increasingly becoming available. This data availability allows building smart solutions that are viewed as meaningful by both city residents and city management authorities. Our research work was based on Lisbon mobility data available through the local municipality, where we integrated and cleaned different data sources and applied a CRISP-DM approach using Python. We focused on mobility problems and interdependence and cascading-effect solutions for the city of Lisbon. We developed data-driven approaches using artificial intelligence and visualization methods to understand traffic and accident problems, providing a big picture to competent authorities and supporting the city in being more prepared, adaptable, and responsive, and better able to recover from such events.

Digital technologies enable companies to build cyber-physical systems (CPS) in Industry 4.0. In the increasingly popular concept of Industry 4.0, an important research topic is the application of digital technology in industry, and in particular in specific industry sectors. The aim of this paper is to present the tools used in the steel industry in Poland on its way to the full digitalisation that is needed for the development of Industry 4.0. The paper consists of two parts: a literature review and a practical analysis. The paper provides the background information about digitalisation using digital tools in the steel industry in Poland. The paper was prepared based on secondary information and statistical data. The object of the research is the Polish steel sector. This study assumes that digitalisation is the main area of innovation in the steel industry. The digitalisation determines the creation of new or modified products, processes, techniques and expansion of the company’s inf... [more]

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations... [more]