The commercialization of eco-friendly hydrogen vehicles has elicited attempts to expand hydrogen refueling stations in urban areas; however, safety measures to reduce the risk of jet fires have not been established. The RISKCURVES software was used to evaluate the individual and societal risks of hydrogen refueling stations in urban areas, and the F−N (Frequency−Number of fatalities) curve was used to compare whether the safety measures satisfied international standards. From the results of the analysis, it was found that there is a risk of explosion in the expansion of hydrogen refueling stations in urban areas, and safety measures should be considered. To lower the risk of hydrogen refueling stations, this study applied the passive and active independent protection layers (IPLs) of LOPA (Layer of Protection Analysis) and confirmed that these measures significantly reduced societal risk as well as individual risk and met international standards. In particular, such measures could effe... [more]

Thanks to advances in smart metering devices (SM), the electricity sector is undergoing a series of changes, among which it is worth highlighting the ability to control the response to all events that occur in the electricity grid with the intention of making it more smart. Predicting electricity consumption data is a key factor for the energy sector in order to create a completely intelligent electricity grid that optimizes consumption and forecasts future energy needs. However, it is currently not enough to give a prediction of energy consumption (EC), but it is also necessary to give the prediction as fast as possible so that the grid can operate in the shortest possible time. An approach for developing EC prediction systems is introduced here by the use of artificial neural networks (ANN). Differently from other research studies on the subject, a divide-and-conquer strategy is used so that the target system’s execution switches from one to another specialized small models that fore... [more]

The subject of this study is the chemical composition of potentially geothermal waters of the Mesozoic basement of the central part of the Carpathian Foredeep and the Outer Carpathians regions. The research objectives were: (1) to identify statistically significant differences between the chemical composition of waters from the Cretaceous, Jurassic, and Triassic aquifers, and between the waters of both regions; and (2) the discovery of zones indicating active water exchange—attractive due to the operational efficiency of wells. Knowledge of the chemical composition of water allows for the preliminary identification of areas of interest for the exploitation of water for recreational, healing, and heating purposes. The research methods used were: (1) statistical tests and (2) methods of multivariate data analysis, such as the Kruskal−Wallis test and Principal Component Analysis (PCA). The performed tests and statistical analyses allowed us to draw conclusions about significant difference... [more]

This research presents the modeling and prediction of the harmonic behavior of current in an electric power supply grid with the integration of photovoltaic power by inverters using artificial neural networks to determine if the use of the proposed neural network is capable of capturing the harmonic behavior of the photovoltaic energy integrated into the user’s electrical grids. The methodology used was based on the use of recurrent artificial neural networks of the nonlinear autoregressive with external input type. Work data were obtained from experimental sources through the use of a test bench, measurement, acquisition, and monitoring equipment. The input−output parameters for the neural network were the current values in the inverter and the supply grid, respectively. The results showed that the neural network can capture the dynamics of the analyzed system. The generated model presented flexibility in data handling, allowing to represent and predict the behavior of the harmonic ph... [more]

The objective of this research was to design a neural network (ANN) to predict the methanol flux at the outlet of a carbon dioxide dehydrogenation plant. For the development of the ANN, a database was generated, in the open-source simulation software “DWSIM”, from the validation of a process described in the literature. The sample consists of 133 data pairs with four inputs: reactor pressure and temperature, mass flow of carbon dioxide and hydrogen, and one output: flow of methanol. The ANN was designed using 12 neurons in the hidden layer and it was trained with the Levenberg−Marquardt algorithm. In the training, validation and testing phase, a global mean square (RMSE) value of 0.0085 and a global regression coefficient R of 0.9442 were obtained. The network was validated through an analysis of variance (ANOVA), where the p-value for all cases was greater than 0.05, which indicates that there are no significant differences between the observations and those predicted by the ANN. Ther... [more]

This paper investigates a use case of robust anomaly detection applied to the scenario of a photovoltaic production factory—namely, Enel Green Power’s 3SUN solar cell production plant in Catania, Italy—by considering a Monte Carlo based pre-processing technique as a valid alternative to other typically used methods. In particular, the proposed method exhibits the following advantages: (i) Outlier replacement, by contrast with traditional methods which are limited to outlier detection only, and (ii) the preservation of temporal locality with respect to the training dataset. After pre-processing, the authors trained an anomaly detection model based on principal component analysis and defined a suitable key performance indicator for each sensor in the production line based on the model errors. In this way, by running the algorithm on unseen data streams, it is possible to isolate anomalous conditions by monitoring the above-mentioned indicators and virtually trigger an alarm when exceedin... [more]

Due to the dynamic nature of load changes, arc devices are receivers that generate disturbances to the network that affect the power quality. The main disturbance generated by these receivers are voltage fluctuations. One of the effects of voltage fluctuations is the flicker of light caused by lighting receivers. The article presents an analysis of changes indicators flicker of light measured in networks supplying arc furnaces. The propagation of voltage fluctuations to the lines supplying lighting receivers was analyzed. The network parameters influencing the amount of light flicker were estimated. The paper presents a method for calculating the increased flicker of light when several electric arc furnaces are operated in parallel. The conclusions regarding the use of the presented research in practical applications are given in the summary.

In the resilient and reliable electrical power system, the condition of high voltage insulation plays a crucial role. In the field of high voltage insulation integrity, the partial discharge (PD) inception and development trends are essential for assessment criteria in diagnostics systems. The observed trend to employ more and more sophisticated algorithms with machine learning features and artificial intelligence (AI) elements is observed everywhere. The classification and identification of features in PD images is perceived as a critical requirement for an effective high voltage insulation diagnosis. In this context, techniques allowing for anomaly detection, trends observation, and feature extraction in partial discharge patterns are important. In this paper, the application of few algorithms belonging to image processing, machine learning and optical flow is presented. The feature extraction refers to image segmentation and detection of coherent forms in the images. The anomaly det... [more]

Global research interest in the domain of maritime autonomous surface ships (MASS) is dramatically increasing. With new prototypes planned to be set to the seas where various operational modes (OMs) are claimed, the issue of the safety evaluation of an MASS, and criteria for selecting the appropriate OM for given conditions remain open questions. This paper proposes a four-step risk-informed framework to assess risk in a scenario for an MASS operating at one of three OMs: manual control (MC), remote control (RC), and autonomous control (AC). To this end, the concept of risk priority numbers (RPNs), adopted from failure mode and effects analysis (FMEA), is utilized. The required parameters to defined RPNs are obtained in the course of analyzing a model MASS accident with expert knowledge. The applicability of the proposed framework is demonstrated via a model MASS case study. Results reveal that, in the same scenario, the risk of MASS varied across the analyzed OMs. On the basis of the... [more]

Security of supply of water, which meets the quality parameters specified in applicable standards, is now the basis for the functioning of most societies. In addition to climatic, biological, chemical, and physical hazards, it is worth paying attention to consumers’ subjective perception of the quality of tap water supplied in the area of Poland. The article discusses various activities related to water resources management and analyses the results of an evaluation of selected quality parameters of tap water in Poland. A novelty on a European scale here is an examination of the evaluation of these parameters based on potential seasonal differences (spring, summer, autumn, winter). For the first time in the world literature, PROFIT analysis was used to evaluate selected parameters of tap water quality. The aim of the article was to present a model for the evaluation of the parameters of tap water supplied in different seasons of the year in Poland. Due to the complexity of the research... [more]

As the amount of information collected by wind turbines continues to grow, so too does the potential of its leveraging. The application of machine learning techniques as an advanced analytic tool has proven effective in solving tasks whose inherent complexity can outreach expert-based ability. Such is the case presented by this study, in which the dataset to be leveraged is high-dimensional (79 turbines × 7 SCADA channels) and high-frequency (1 Hz). In this paper, a series of machine learning techniques is applied to the retrospective power performance analysis of a withheld test set containing SCADA data collectively representing 2 full days worth of operation at the Horns Rev I offshore wind farm. A sequential machine-learning based methodology is thoroughly explored, refined, then applied to the power performance analysis task of identifying instances of abnormal behaviour; namely instances of wind turbine under and over-performance. The results of the final analysis suggest that a... [more]

Countries of the European Union have stated transition to carbon-neutral economy until the year of 2050. Countries with a higher share of coal-fired power generation currently have no solution to end their combustion and use clean, emission-free energy immediately. The solution to this problem in the energy industry appears to be the increased use of natural gas, which significantly reduces CO2 emissions. In this article, we investigated the possibility of using coal in situ, using UCG (underground coal gasification) technology. We focused on verified geological, hydrogeological, and tectonic information about the selected brown coal deposit in Slovakia. This information has been assessed in research projects in recent years at the Technical University. From the abovementioned information, possible adverse factors were evaluated. These factors affect the rock environment around the underground generator by UCG activity. As part of the process management, measures were proposed to elimi... [more]

Recently, issues related to the effects (benefit or harm) of processing nuclear waste and its further use as fuel have been increasingly often raised in the scientific discussion. In this regard, the research aims to investigate issues related to the assessment of the economic potential of nuclear waste use, as well as the cooperation between states in the context of the reduction of risks associated with nuclear waste storage and processing. The research methodology is based on an integrated approach, including statistical, factor analysis, and the proposed system of performance indicators for managing spent nuclear fuel use. The research was carried out on the basis of materials from Russia and the EU countries. In the course of the study, a model of cooperation between states has been developed (based on the example of technologies and methods of processing nuclear waste used in the EU and Russia) according to the nuclear waste (spent nuclear fuel) management algorithm. The model co... [more]

The monitoring of the performance of heat treatment equipment has been the subject of a number of studies. This paper proposes and explores a new study on the models—and the monitoring thereof—for predicting the energy intensity of low-pressure carburisation processes using the DeepCaseMaster Evolution soaking furnace. For research purposes, 18 carburising experiments were performed with different carbon layers, at different input parameters, such as the number of cycles, time, temperature and average carburising pressure. Based on the research experiments conducted and statistical analysis, the influence of individual parameters on the energy consumption of the pump and heating systems was determined. Moreover, the models were verified on real data of low-pressure carburising processes. The innovativeness of the proposed solution is a combination of two areas: (1) defining and measurement of the parameters of the low-pressure carburising process; and (2) predicting the energy consumpt... [more]

Fault interpretation is an important part of seismic structural interpretation and reservoir characterization. In the conventional approach, faults are detected as reflection discontinuity or abruption and are manually tracked in post-stack seismic data, which is time-consuming. In order to improve efficiency, a variety of automatic fault detection methods have been proposed, among which widespread attention has been given to deep learning-based methods. However, deep learning techniques require a large amount of marked seismic samples as a training dataset. Although the amount of synthetic seismic data can be guaranteed and the labels are accurate, the difference between synthetic data and real data still exists. To overcome this drawback, we apply a transfer learning strategy to improve the performance of automatic fault detection by deep learning methods. We first pre-train a deep neural network with synthetic seismic data. Then we retrain the network with real seismic samples. We u... [more]

As the effects of climate change are becoming severe, countries need to substantially reduce carbon emissions. Small hydropower (SHP) can be a useful renewable energy source with a high energy density for the reduction of carbon emission. Therefore, it is necessary to revitalize the development of SHP to expand the use of renewable energy. To efficiently plan and utilize this energy source, there is a need to assess the future SHP potential based on an accurate runoff prediction. In this study, the future SHP potential was predicted using a climate change scenario and an artificial neural network model. The runoff was simulated accurately, and the applicability of an artificial neural network to the runoff prediction was confirmed. The results showed that the total amount of SHP potential in the future will generally a decrease compared to the past. This result is applicable as base data for planning future energy supplies and carbon emission reductions.

Reservoir modeling to predict shale reservoir productivity is considerably uncertain and time consuming. Since we need to simulate the physical phenomenon of multi-stage hydraulic fracturing. To overcome these limitations, this paper presents an alternative proxy model based on data-driven deep learning model. Furthermore, this study not only proposes the development process of a proxy model, but also verifies using field data for 1239 horizontal wells from the Montney shale formation in Alberta, Canada. A deep neural network (DNN) based on multi-layer perceptron was applied to predict the cumulative gas production as the dependent variable. The independent variable is largely divided into four types: well information, completion and hydraulic fracturing and production data. It was found that the prediction performance was better when using a principal component with a cumulative contribution of 85% using principal component analysis that extracts important information from multivariat... [more]

High-impedance faults (HIF) are difficult to detect because of their low current amplitude and highly diverse characteristics. In recent years, machine learning (ML) has been gaining popularity in HIF detection because ML techniques learn patterns from data and successfully detect HIFs. However, as these methods are based on supervised learning, they fail to reliably detect any scenario, fault or non-fault, not present in the training data. Consequently, this paper takes advantage of unsupervised learning and proposes a convolutional autoencoder framework for HIF detection (CAE-HIFD). Contrary to the conventional autoencoders that learn from normal behavior, the convolutional autoencoder (CAE) in CAE-HIFD learns only from the HIF signals eliminating the need for presence of diverse non-HIF scenarios in the CAE training. CAE distinguishes HIFs from non-HIF operating conditions by employing cross-correlation. To discriminate HIFs from transient disturbances such as capacitor or load swit... [more]

This paper presents load frequency control of the 2021 Egyptian power system, which consists of multi-source electrical power generation, namely, a gas and steam combined cycle, and hydro, wind and photovoltaic power stations. The simulation model includes five generating units considering physical constraints such as generation rate constraints (GRC) and the speed governor dead band. It is assumed that a centralized controller is located at the national control center to regulate the frequency of the grid. Four controllers are applied in this research: PID, fractional-order PID (FOPID), non-linear PID (NPID) and non-linear fractional-order PID (NFOPID), to control the system frequency. The design of each controller is conducted based on the novel tunicate swarm algorithm at each operating condition. The novel method is compared to other widely used optimization techniques. The results show that the tunicate swarm NFOPID controller leads the Egyptian power system to a better performanc... [more]

Reservoirs in Taiwan often provide hydroelectric power, irrigation water, municipal water, and flood control for the whole year. Taiwan has the climatic characteristics of concentrated rainy seasons, instantaneous heavy rains due to typhoons and rainy seasons. In addition, steep rivers in mountainous areas flow fast and furiously. Under such circumstances, reservoirs have to face sudden heavy rainfall and surges in water levels within a short period of time, which often causes the water level to continue to rise to the full level even though hydroelectric units are operating at full capacity, and as reservoirs can only drain the flood water, this results in the waste of hydropower resources. In recent years, the impact of climate change has caused extreme weather events to occur more frequently, increasing the need for flood control, and the reservoir operation has faced severe challenges in order to fulfil its multipurpose requirements. Therefore, in order to avoid the waste of hydrop... [more]

A cost-effective and efficient wind energy production trend leads to larger wind turbine generators and drive for more advanced forecast models to increase their accuracy. This paper proposes a combined forecasting model that consists of empirical mode decomposition, fuzzy group method of data handling neural network, and grey wolf optimization algorithm. A combined K-means and identifying density-based local outliers is applied to detect and clean the outliers of the raw supervisory control and data acquisition data in the proposed forecasting model. Moreover, the empirical mode decomposition is employed to decompose signals and pre-processing data. The fuzzy GMDH neural network is a forecaster engine to estimate the future amount of wind turbines energy production, where the grey wolf optimization is used to optimize the fuzzy GMDH neural network parameters in order to achieve a lower forecasting error. Moreover, the model has been applied using actual data from a pilot onshore wind... [more]

Photogrammetry using unmanned aerial vehicles has become very popular and is already commonly used. The most frequent photogrammetry products are an orthoimage, digital terrain model and a 3D object model. When executing measurement flights, it may happen that there are unsuitable lighting conditions, and the flight itself is fast and not very stable. As a result, noise and blur appear on the images, and the images themselves can have too low of a resolution to satisfy the quality requirements for a photogrammetric product. In such cases, the obtained images are useless or will significantly reduce the quality of the end-product of low-level photogrammetry. A new polymodal method of improving measurement image quality has been proposed to avoid such issues. The method discussed in this article removes degrading factors from the images and, as a consequence, improves the geometric and interpretative quality of a photogrammetric product. The author analyzed 17 various image degradation c... [more]

Forecasting future driving conditions such as acceleration, velocity, and driver behaviors can greatly contribute to safety, mobility, and sustainability issues in the development of new energy vehicles (NEVs). In this brief, a review of existing velocity prediction techniques is studied from the perspective of traffic flow and vehicle lateral dynamics for the first time. A classification framework for velocity prediction in NEVs is presented where various state-of-the-art approaches are put forward. Firstly, we investigate road traffic flow models, under which a driving-scenario-based assessment is introduced. Secondly, vehicle speed prediction methods for NEVs are given where an extensive discussion on traffic flow model classification based on traffic big data and artificial intelligence is carried out. Thirdly, the influence of vehicle lateral dynamics and correlation control methods for vehicle speed prediction are reviewed. Suitable applications of each approach are presented acc... [more]

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.