Although photovoltaic (PV) systems play an essential role in distributed generation systems, they also suffer from serious safety concerns due to DC series arc faults. This paper proposes a lightweight convolutional neural network-based method for detecting DC series arc fault in PV systems to solve this issue. An experimental platform according to UL1699B is built, and current data ranging from 3 A to 25 A is collected. Moreover, test conditions, including PV inverter startup and irradiance mutation, are also considered to evaluate the robustness of the proposed method. Before fault detection, the current data is preprocessed with power spectrum estimation. The lightweight convolutional neural network has a lower computational burden for its fewer parameters, which can be ready for embedded microprocessor-based edge applications. Compared to similar lightweight convolutional network models such as Efficientnet-B0, B2, and B3, the Efficientnet-B1 model shows the highest accuracy of 96.... [more]

This paper presents a method to invert the two-dimensional distribution of a temperature and volume concentration of soot particles from color images. By using numerical simulation, the temperature field and particle volume-concentration field of a non-uniform soot flame are simultaneously reconstructed using the wide-response spectrum of a color CCD camera without adding monochromatic filters. The influence of number of cameras, error of camera position angle, measurement noise and different reconstruction algorithms on measurement accuracy are analyzed. The numerical-simulation results demonstrate that camera-position angle errors play a crucial role in the reconstruction accuracy. In addition, increasing the number of cameras can improve the reconstruction result accuracy. Compared with the least squares algorithm, the Tikhonov-regularization algorithm has a stronger anti-noise ability and can resist 39 dB of noise. The conclusions obtained in this paper are helpful to guide followi... [more]

There has been growing interest in fuel supply chains regarding transport safety and LNG reloading. This is due to the increasing consumption of this gas in the economy to create sustainable transport systems. Poland is in the phase of energy transformation, which increases the demand for this type of alternative fuel. For this reason, the number of logistic operations carried out by Polish sea terminals handling LNG deliveries is increasing. This article aims to present a method for assessing the risk of adverse events occurring during the logistic handling of LNG deliveries at a port terminal and its implementation for a selected LNG terminal in Poland. Fuzzy logic methodology was used to assess the risk due to the lack of access to specific historical data on identified events. The conducted analysis considers the guidelines applicable at the LNG terminal, described in the Terminal Operation Manual, and the specific reloading conditions occurring in the tested Polish gas terminal. B... [more]

This work aims to analyse different injection configurations for the analysis of the emulsification process in a Y-junction staggered horizontal pipeline. The case study comprises a multiphase analysis between two liquids, one with high and the other with low viscosity. Through numerical simulations, it is intended to explain the behaviour and describe the mechanism that produces the water−glycerol emulsification process with three supply zones for both fluids. According to the phase injection scheme, six input scenarios or combinations were analysed. Through strain rate and shear velocity analyses, it was possible to describe the early stages of the emulsification process before a flow pattern is constituted. The results show significant variations concerning the high viscosity fluid, mainly because it presents a partial pipe flooding, even in the injection zone of the low viscosity fluid. The fluid ratio varies according to the input position of the phases. Additionally, a smooth ble... [more]

In recent decades, steel frames infilled with precast load-bearing walls have been successfully employed as lateral load-resisting structural systems in high-rise buildings. This is due to their structural efficiency as outer and major inner facades and to the higher construction speed of the building. This paper presents a detailed experimental investigation of a sustainable, prefabricated, composite structural wall system, using a representative test model named the Precast Concrete Steel Panel-Infilled Steel Frame (PCSP-ISF) in full-scale dimensions and subjected to in-plane cyclic loading. A series of experiments was conducted on critical structural specimens, including three-point bending, concentric axial compression, and diagonal compression, together with additional cycling loading tests on steel connection joint specimens, with the aim of validating the reliability and the structural response of the connections. The resulting test data and the observed failure mechanisms are d... [more]

This article focuses on developing both statistical and machine learning approaches for forecasting hourly electricity demand in Ontario. The novelties of this study include (i) identifying essential factors that have a significant effect on electricity consumption, (ii) the execution of a Bayesian optimization algorithm (BOA) to optimize the model hyperparameters, (iii) hybridizing the BOA with the seasonal autoregressive integrated moving average with exogenous inputs (SARIMAX) and nonlinear autoregressive networks with exogenous input (NARX) for modeling separately short-term electricity demand for the first time, (iv) comparing the model’s performance using several performance indicators and computing efficiency, and (v) validation of the model performance using unseen data. Six features (viz., snow depth, cloud cover, precipitation, temperature, irradiance toa, and irradiance surface) were found to be significant. The Mean Absolute Percentage Error (MAPE) of five consecutive weekd... [more]

In CO2 sequestration projects, assessing caprock structural stability is crucial to assure the success and reliability of the CO2 injection. However, since caprock experimental data are sparse, we applied a Monte Carlo (MC) algorithm to generate stochastic data from the given mean and standard deviation values. The generated data sets were introduced to a neural network (NN), including four hidden layers for classification purposes. The model was then used to evaluate organic-rich Draupne caprock shale failure in the Alpha structure, northern North Sea. The train and test were carried out with 75% and 25% of the input data, respectively. Following that, validation is accomplished with unseen data, yielding promising classification scores. The results show that introducing larger input data sizes to the established NN provides better convergence conditions and higher classification scores. Although the NN can predicts the failure states with a classification score of 97%, the structural... [more]

Coal seam impact risk assessment is the premise of coal mine safety, which can reduce the occurrence of underground impact pressure accidents and directly affect the safety, coal production, economic and social benefits of coal mining enterprises. In order to evaluate the impact risk of coal seams more reasonably and comprehensively, and consider the weights of different influencing factors on the impact risk of coal seams, the neural network model is proposed to evaluate the impact risk of coal seams. Mining depth, impact tendency, geological structure and mining technology are selected as the influencing factors of coal seam impact risk. Each influencing factor contains different evaluation indices, a total of 18. The 18 evaluation indices and the impact risk level are normalized and quantified. The BP neural network model for evaluating coal seam impact risk level is established, and the impact risk of 2-1 coal seams in a mine in Inner Mongolia is comprehensively evaluated and analy... [more]

This paper presents a strategy for a thermal-structural test with quartz lamp heaters (TSTQLH), combined with an ultra-local model, a closed-loop controller, a linear extended state observer (LESO), and an auxiliary controller. The TSTQLH is a real time ground simulation of aerodynamic heating for hypersonic vehicles to optimize their thermal protection systems (TPS). However, lack of a system dynamic model for the TSTQLH results in inaccurate tracking of aerodynamic heating. In addition, during the control process, the TSTQLH has internal uncertainties of resistance and external disturbances. Therefore, it is necessary to establish a mathematical model between controllable α(t) and measurable T1(t). An ultra-local model of model-free control plays a crucial role in simplifying system complexity and reducing high-order terms due to high nonlinearities and strong couplings in the system dynamic model, and a global nonsingular fast terminal sliding mode control (GNFTSMC) is added to an u... [more]

Island-type petrochemical parks have gradually become the ‘trend’ in establishing new parks because of the security advantages brought by their unique geographical locations. However, due to the frequent occurrence of natural disasters and difficulties in rescue in island-type parks, an early warning model is urgently needed to provide a basis for risk management. Previous research on early warning models of island-type parks seldom considered the particularity. In this study, the early warning indicator system is used as the input parameter to construct the early warning model of an island-type petrochemical park based on the back propagation (BP) neural network, and an actual island-type petrochemical park was used as a case to illustrate the model. Firstly, the safety influencing factors were screened by designing questionnaires and then an early warning indicator system was established. Secondly, particle swarm optimization (PSO) was introduced into the improved BP neural network t... [more]

Background: The purpose of the paper is to propose different arrangements of neural networks for short-time 24-h load forecasting in Power Systems. Methods: The study discusses and compares different techniques of data processing, applying the feedforward and recurrent neural structures. They include such networks as multilayer perceptron, radial basis function, support vector machine, self-organizing Kohonen networks, deep autoencoder, and recurrent deep LSTM structures. The important point in getting high-quality results is the composition of many solutions in the common ensemble and their fusion to create the final forecast of time series. The paper considers and compares different methods of fusing the individual results into the final forecast, including the averaging, application of independent component analysis, dynamic integration, and wavelet transformation. Results: The numerical experiments have shown a high advantage of using many individual predictors integrated into the... [more]

The indicated mean effective pressure (IMEP) is a key parameter for measuring the power output of an internal combustion engine (ICE). This indicator can be used to locate the high efficiency regions of engines. Therefore, it makes sense to predict the IMEP based on the machine learning (ML) approaches. However, different ML models are applicable to different scenarios, so it is important to choose the right model for prediction. The objective of this paper was to compare three ML models’ (ANN, SVR, RF) predictive performance in forecasting IMEP indicator with the input parameters spark timing (ST), speed and load. A validated one-dimensional (1D) computational fluid dynamics (CFD) model was employed to provide 756 sets of data for the training, validation, and testing of the model. The results indicated that the random forest (RF) model had the worst prediction performance, and support vector regression (SVR) had a slightly better prediction performance than the artificial neural netw... [more]

Electric power grids, which form an essential part of the critical infrastructure, are evolving into highly distributed, dynamic networks in order to address the climate change. This fundamental transition relies on extensive automation solutions based on communications and information technologies. Thus, it also gives rise to new attack points for malicious actors and consequently, increases the vulnerability of the electric energy system. This study presents a qualitative assessment of power grid cybersecurity through expert interviews across countries in Europe and the U.S. to gain understanding of the latest developments and trends in the cybersecurity of future electric energy systems. The horizon of the assessment is 10 years spanning until the early 2030s. Thereafter, the study identifies how and to which extent the risks identified to be most significant are understood and addressed in the latest research and industry publications aiming at identifying areas deserving specific... [more]

This paper investigates an adaptive dynamic programming (ADP)-based energy management control strategy for a series-parallel hybrid electric vehicle (HEV). This strategy can further minimize the equivalent fuel consumption while satisfying the battery level constraints and vehicle power demand. Dual heuristic dynamic programming (DHP) is one of the basic structures of ADP, combining reinforcement learning, dynamic programming (DP) optimization principle, and neural network approximation function, which has higher accuracy with a slightly more complex structure. In this regard, the DHP energy management strategy (EMS) is designed by the backpropagation neural network (BPNN) as an Action network and two Critic networks approximating the control policy and the gradient of value function concerning the state variable. By comparing with the existing results such as HDP-based and rule-based control strategies, the equivalent consumption minimum strategy (ECMS), and reinforcement learning (RL... [more]

One of the major challenges in implementing the equivalent fuel consumption minimisation strategy in hybrid electric vehicles is the adaptation of the equivalence factor to real-world driving. In this paper, a novel adaptive equivalent fuel consumption minimisation strategy (A-ECMS) has been developed for a hybrid two-wheeler to further improve fuel savings by predicting the drive cycles and thereby estimating and adapting the equivalence factor online for the ECMS energy management control. A learning vector quantitative neural network (LVQNN)-based classifier was first proposed to recognise the real-world driving cycle based on a fixed time window of past driving information. Along with standardised drive cycles, real-world driving data were used in the learning process to increase the robustness of the learning. The A-ECMS is then capable of regulating its equivalence factors online based on the LVQNN controller output. Numerical simulation results indicated that there was considera... [more]

Risk assessment and management are some of the major tasks of urban power-grid management. The growing amount of data from, e.g., prediction systems, sensors, and satellites has enabled access to numerous datasets originating from a diversity of heterogeneous data sources. While these advancements are of great importance for more accurate and trustable risk analyses, there is no guidance on selecting the best information available for power-grid risk analysis. This paper addresses this gap on the basis of existing standards in risk assessment. The key contributions of this research are twofold. First, it proposes a method for reinforcing data-related risk analysis steps. The use of this method ensures that risk analysts will methodically identify and assess the available data for informing the risk analysis key parameters. Second, it develops a method (named the three-phases method) based on metrology for selecting the best datasets according to their informative potential. The method,... [more]

Short-term load forecasting plays a significant role in the operation of power systems. Recently, deep learning has been generally employed in short-term load forecasting, primarily in the extraction of the characteristics of digital information in a single dimension without taking into account of the impact of external variables, particularly non-digital elements on load characteristics. In this paper, we propose a joint MFF-SAM-GCN to realize short-term load forecasting. First, we utilize a Bi-directional Long Short-Term Memory (Bi-LSTM) network and One-Dimensional Convolutional Neural Network (1D-CNN) in parallel connection to form a multi-feature fusion (MFF) framework, which can extract spatiotemporal correlation features of the load data. In addition, we introduce a Self-Attention Mechanism (SAM) to further enhance the feature extraction capability of the 1D-CNN network. Then with the deployment of a Graph Convolutional Network (GCN), the external non-digital features such as wea... [more]

In the present study, continuous measurements of solar global (G) and ultraviolet-B (UVB) radiation are taken in Kuwait for 2014−2019 for all weather conditions. Hourly curves show a sinusoidal behavior for both G and UVB radiation. Statistical analysis indicates that there is a good agreement between hourly G and hourly UVB as the coefficients of determination (R2) for all years are larger than 0.91 and the root-mean-square error (RMSE) and mean bias error (MBE) are very small. The hourly percentage ratio (UVB/G) is found to decrease with G due to cloudy sky conditions. In addition, the ratio (UVB/G) tends to decrease with global clearness index (KT), indicating that a higher ratio of (UVB/G) can be obtained for a cloudier atmosphere. Another interesting finding is that KT and the UVB index (KTUVB) are directly proportional, and a third-order polynomial fit gives an acceptable formula (R2 = 0.859). Daily G and UVB values are very well correlated as R2 is very close to unity for all ye... [more]

To detect the aging of power cables in the TOKAMAK power supply systems, this paper proposed a deep neural network diagnosis model and algorithm for power cable aging, based on logistic regression according to the characteristics of different high-order harmonics generated by different aging parts of the power cable. The experimental results showed that the model has high diagnostic accuracy, and the average error is only 2.35%. The method proposed in this paper has certain application potential in the CFETR power cable auxiliary monitoring system.

Estimating the quantity of seepage through the foundation and body of a dam using proper health and safety monitoring is critical to the effective management of disaster risk in a reservoir downstream of the dam. In this study, a deep learning model was constructed to predict the extent of seepage through Pakistan’s Tarbela dam, the world’s second largest clay and rock dam. The dataset included hydro-climatological, geophysical, and engineering characteristics for peak-to-peak water inflows into the dam from 2014 to 2020. In addition, the data are time series, recurring neural networks (RNN), and long short-term memory (LSTM) as time series algorithms. The RNN−LSTM model has an average mean square error of 0.12, and a model performance of 0.9451, with minimal losses and high accuracy, resulting in the best-predicted dam seepage result. Damage was projected using a deep learning system that addressed the limitations of the model, the difficulties of calculating human activity schedules,... [more]

The exhaust air glass unit (EAGU) can be treated as an integration of multilayer glazing unit and heat recovery device to utilize the exhaust air from conditioned space with a fresh air ventilation system to improve the thermal performance of window system. However, compared with the conventionally used mechanical ventilation with a heat recovery (MVHR) system, whether the use of EAGU is energy-efficient or not has not been estimated. In this paper, a numerical model, validated by experimental measurement, was used to calculate the hourly cooling and heating loads and annual energy demand of EAGU. This study compared the annual energy performance of EAGU and MVHR under various conditions, and further discusses the applicability of EAGU for different climates. The results indicate that the energy saving potential of EAGU ranges from 26.8% to 38.2% for different climate conditions. In the cooling season, the energy saving potential of EAGU performed much better than that of the commonly... [more]

With the increasing proportion of Li-ion batteries in energy structures, studies on the estimation of the state of charge (SOC) of Li-ion batteries, which can effectively ensure the safety and stability of Li-ion batteries, have gained much attention. In this paper, a new data-driven method named the probabilistic threshold compensation fuzzy neural network (PTCFNN) is proposed to estimate the SOC of Li-ion batteries. Compared with other traditional methods that need to build complex battery models, the PTCFNN only needs data learning to obtain nonlinear mapping relationships inside Li-ion batteries. In order to avoid the local optimal value problem of traditional BP neural networks and the fixed reasoning mechanism of traditional fuzzy neural networks, the PTCFNN combines the advantages of a probabilistic fuzzy neural network and a compensation fuzzy neural network so as to improve the learning convergence speed and optimize the fuzzy reasoning mechanism. Finally, in order to verify t... [more]

To understand the effect of hydrogen addition on the laminar burning velocity (LBV) of ethanol−air mixtures, experiments were conducted in a constant volume combustion chamber with the high-speed schlieren photography technique. The experiments were carried out under the equivalence ratios (ERs) of 0.7−1.4, an initial temperature of 400 K, an initial pressure of 0.1 MPa, and hydrogen fractions of 30% and 90% by volume, respectively. The effects of ER, initial temperature, initial pressure, and hydrogen fractions on the LBV were investigated. Moreover, adiabatic flame temperature (AFT), heat release rate (HRR), flow rate sensitivity analysis, and ROP (rate of production) analysis were also performed. Results showed that LBV increased with increasing hydrogen addition and temperature but decreased with increasing pressure. The hydrogen addition significantly increased the HRR of ethanol−hydrogen−air flames. The sensitivity analysis showed that R5 (O2 + H = O + OH) significantly influence... [more]

To address the safety displacement-constrained control problem of maglev trains during operation, this study applied the radial-based neural network control displacement-constrained method to maglev trains based on the multi-mass-point model, and strictly limited the output of maglev train displacement and speed values to keep the overshoot within a given range. Firstly, the dynamics and kinematics of the maglev train were modeled from the perspective of multi-mass modeling. Secondly, the basic structure of the radial-based neural network was determined according to the displacement-limited constraints of the maglev train during operation, and the stability was proven by applying the control rate and output-limited priming according to the limitations. Finally, based on the displacement-limited operation control of maglev trains, the system of the radial-based neural network was simulated. The simulation results show that this method can make the displacement and velocity signals of th... [more]

This article involves forecasting daily electricity consumption in Thailand. Electricity consumption data are provided by the Electricity Generating Authority of Thailand, the leading power utility state enterprise under the Ministry of Energy. Five forecasting techniques, including multiple linear regression, artificial neural network (ANN), support vector machine, hybrid models, and ensemble models, are implemented. The article proposes a hyperparameter tuning technique, called sequential grid search, which is based on the widely used grid search, for ANN and hybrid models. Auxiliary variables and indicator variables that can improve the models’ forecasting performance are included. From the computational experiment, the hybrid model of a multiple regression model to forecast the expected daily consumption and ANNs from the sequential grid search to forecast the error term, along with additional indicator variables for some national holidays, provides the best mean absolution percent... [more]