Three-phase motors are commonly adopted in several industrial contexts and their failures can result in costly downtime causing undesired service outages; therefore, motor diagnostics is an issue that assumes great importance. To prevent their failures and face the considered service outages in a timely manner, a non-invasive method to identify electrical and mechanical faults in three-phase asynchronous electric motors is proposed in the paper. In particular, a measurement strategy along with a machine learning algorithm based on an artificial neural network is exploited to properly classify failures. In particular, digitized current samples of each motor phase are first processed by means of FFT and PSD in order to estimate the associated spectrum. Suitable features (in terms of frequency and amplitude of the spectral components) are then singled out to either train or feed a neural network acting as a classifier. The method is preliminarily validated on a set of 28 electric motors,... [more]

Accurate estimation of lithium-ion battery state-of-health (SOH) is important for the safe operation of electric vehicles; however, in practical applications, the accuracy of SOH estimation is affected by uncertainty factors, including human operation, working conditions, etc. To accurately estimate the battery SOH, a hybrid neural network based on the dilated convolutional neural network and the bidirectional gated recurrent unit, namely dilated CNN-BiGRU, is proposed in this paper. The proposed data-driven method uses the voltage distribution and capacity changes in the extracted battery discharge curve to learn the serial data time dependence and correlation. This method can obtain more accurate temporal and spatial features of the original battery data, resulting higher accuracy and robustness. The effectiveness of dilated CNN-BiGRU for SOH estimation is verified on two publicly lithium-ion battery datasets, the NASA Battery Aging Dataset and Oxford Battery Degradation Dataset. The... [more]

The rate of technical innovation, system interconnection, and advanced communications undoubtedly boost distributed energy networks’ efficiency. However, when an additional attack surface is made available, the possibility of an increase in attacks is an unavoidable result. The energy ecosystem’s significant variety draws attackers with various goals, making any critical infrastructure a threat, regardless of scale. Outdated technology and other antiquated countermeasures that worked years ago cannot address the complexity of current threats. As a result, robust artificial intelligence cyber-defense solutions are more important than ever. Based on the above challenge, this paper proposes an ensemble transfer learning spiking immune system for adaptive smart grid protection. It is an innovative Artificial Immune System (AIS) that uses a swarm of Evolving Izhikevich Neural Networks (EINN) in an Ensemble architecture, which optimally integrates Transfer Learning methodologies. The effecti... [more]

The current train model of the train control system is unable to accurately reflect the influence of nonlinear running resistance, line conditions, the mutative train mass value, and external environment changes on the model in train dynamics, resulting in a defect of poor train model performance. The train basic model and additional resistances are discussed in this paper, a novel neural network online learning method of the time-varying dynamic train model is proposed, combined with the characteristics of rail transit lines, and a neural network learning algorithm is designed by categories and steps. This method can identify the train mass value that changes continuously with passengers during running. The energy savings resulting from using the actual varying train mass in the train control system are calculated. The results show that, when compared to the traditional model’s invariant approximate empirical parameters, the time-varying parameter model can follow changes in the train... [more]

Over 30% of the world’s land area is covered by forests. Approximately 761 million m3 of wood is harvested annually in Europe (2017). The aim of the paper is to assess the amount of wood (biomass) produced in Europe per year, as it determines the amount of carbon dioxide released from wood because of combustion for heating and energy purposes. The circular bioeconomy was applied as the theoretical framework for this study. The study employs official statistics on material flows and also uses a technology assessment, which allows for more precise estimations. It can be estimated that 110 million tons of harvested woody biomass are converted into energy every year. This constitutes nearly 69% of processed wood, with burned wood treated as zero-emission. From the analysis of the compiled results, it can be concluded that, in Europe, more than 50% of the mass of raw wood material harvested per year is used for energy in the first stage of processing by manufacturing industries. These proce... [more]

To better forecast solar variability, spatio-temporal methods exploit spatially distributed solar time series, seeking to improve forecasting accuracy by including neighboring solar information. This review work is, to the authors’ understanding, the first to offer a compendium of references published since 2011 on such approaches for global horizontal irradiance and photovoltaic generation. The identified bibliography was categorized according to different parameters (method, data sources, baselines, performance metrics, forecasting horizon), and associated statistics were explored. Lastly, general findings are outlined, and suggestions for future research are provided based on the identification of less explored methods and data sources.

The accuracy of wind power prediction is crucial for the economic operation of a wind power dispatching management system. Wind power generation is closely related to the meteorological conditions around wind plants; a small variation in wind speed could lead to a large fluctuation in the extracted power and is difficult to predict accurately, causing difficulties in grid connection and generating large economic losses. In this study, a wind power prediction model based on a long short-term memory network with a two-stage attention mechanism is established. An attention mechanism is used to measure the input data characteristics and trend characteristics of the wind power and reduce the initial data preparation process. The model effectively alleviates the intermittence and fluctuation of meteorological conditions and improves prediction accuracy significantly. In addition, the modified particle swarm optimization algorithm is introduced to optimize the hyperparameters of the LSTM netw... [more]

Gas-fire-suppression systems are currently applied to some specific buildings in Norway, as sprinkler systems may not provide sufficient protection in some cases. The application of inert-gas-fire-suppression systems for hazard class 6 buildings needs further intensive validation by experimental and numerical study. Due to the presence of cracks and ventilation systems, it becomes doubtful whether inert-gas agents can extinguish a deep-seated fire located in a leaky enclosure. In this study, tests and numerical simulations were both conducted to investigate the extinguishing effectiveness of inert-gas agents for a closet fire in a leaky apartment. The results show that the location of cracks plays a nonnegligible role in determining the oxygen level in the leaky apartment. The tests and simulations demonstrated that the gas-fire-suppression system successfully extinguished the closet fire even if the activation of the gas-fire-suppression system was postponed or the path available for... [more]

The nonstandard finite-difference time-domain (NS-FDTD) method is implemented in the differential form on orthogonal grids, hence the benefit of opting for very fine resolutions in order to accurately treat curved surfaces in real-world applications, which indisputably increases the overall computational burden. In particular, these issues can hinder the electromagnetic design of structures with electrically-large size, such as aircrafts. To alleviate this shortcoming, a nonstandard path integral (PI) model for the NS-FDTD method is proposed in this paper, based on the fact that the PI form of Maxwell’s equations is fairly more suitable to treat objects with smooth surfaces than the differential form. The proposed concept uses a pair of basic and complementary path integrals for H-node calculations. Moreover, to attain the desired accuracy level, compared to the NS-FDTD method on square grids, the two path integrals are combined via a set of optimization parameters, determined from the... [more]

An ultracapacitor State-of-Charge (SOC) fusion estimation method for electric vehicles under variable temperature environment is proposed in this paper. Firstly, Thevenin model is selected as the ultracapacitor model. Then, genetic algorithm (GA) is adopted to identify the ultracapacitor model parameters at different temperatures (−10 °C, 10 °C, 25 °C and 40 °C). Secondly, a variable temperature model is established by using polynomial fitting the temperatures and parameters, which is applied to promote the ultracapacitor model applicability. Next, the off-line experimental data is iterated by adaptive extended Kalman filter (AEKF) to train the Nonlinear Auto-Regressive Model with Exogenous Inputs (NARX) neural network. Thirdly, the output of the NARX is employed to compensate the AEKF estimation and thereby realize the ultracapacitor SOC fusion estimation. Finally, the variable temperature model and robustness of the proposed SOC fusion estimation method are verified by experiments. T... [more]

Well planning for every drilling project includes cost estimation. Maximizing the rate of penetration (ROP) reduces the time required for drilling, resulting in reducing the expenses required for the drilling budget. The empirical formulas developed to predict ROP have limited field applications. Since real-time drilling data acquisition and computing technologies have improved over the years, we implemented the data-driven approach for this purpose. We investigated the potential of machine learning and deep learning algorithms to predict the nonlinear behavior of the ROP. The well was drilled to confirm the geothermal reservoir characteristics for the FORGE site. After cleaning and preprocessing the data, we selected two models and optimized their hyperparameters. According to our findings, the random forest regressor and the artificial neural network predicted the behavior of our field ROP with a maximum absolute mean error of 3.98, corresponding to 19% of the ROP’s standard deviatio... [more]

In existing proton exchange membrane fuel cell (PEMFC) applications, improper membrane water management will cause PEMFC performance decay, which restricts the reliability and durability of PEMFC systems. Therefore, diagnosing improper water content in the PEMFC membrane is the key to taking appropriate mitigations to guarantee its operating safety. This paper proposes a novel approach for diagnosing improper PEMFC water content using a two-dimensional convolutional neural network (2D-CNN). In the analysis, the collected PEMFC voltage signal is transformed into 2D image data, which is then used to train the 2D-CNN. Data enhancement and pre-processing techniques are applied to PEMFC voltage data before the training. Results demonstrate that with the trained model, the diagnostic accuracy for PEMFC membrane improper water content can reach 97.5%. Moreover, by comparing it with a one-dimensional convolutional neural network (1D-CNN), the noise robustness of the proposed method can be bett... [more]

The main aim of this article, carried out in relation to ash−cement mortars, is to determine the effect of complex additives of polyfunctional modifiers, including, in addition to superplasticizers, air-entraining and water-retaining additives, at different values of water−cement ratios. With the use of experimental−statistical models, the complex effect on the adhesive strength of cement−ash mortars of water−cement and ash−cement ratios, as well as complex additives of polyfunctional modifiers, including air-entraining and water-retaining additives, is considered. The extreme nature of the water−cement and ash−cement ratios on the adhesive strength of ash−cement mortars are established. Their optimal values are in the ranges of 0.7−0.75 and 0.35−0.4, respectively. The addition of a naphthalene-formaldehyde superplasticizer makes it possible to increase the adhesive strength of mortars by up to 40%. A positive effect is achieved along with the addition of a superplasticizer by introduc... [more]

The life expectancy of power transformers is primarily determined by the integrity of the insulating oil and cellulose paper between the conductor turns, phases and phase to earth. During the course of their in-service lifetime, the solid insulating system of windings is contingent on long-standing ageing and decomposition. The decomposition of the cellulose paper insulation is strikingly grievous, as it reduces the tensile strength of the cellulose paper and can trigger premature failure. The latter can trigger premature failure, and to realize at which point during the operational life this may occur is a daunting task. Various methods of estimating the DP have been proposed in the literature; however, these methods yield different results, making it difficult to accurately estimate a reliable DP. In this work, a novel approach based on the Feedforward Backpropagation Artificial Neural Network has been proposed to predict the amount of DP in transformer cellulose insulation. Presentl... [more]

Lately, adequate protection strategies need to be developed when Microgrids (MGs) are connected to smart grids to prevent undesirable tripping. Conventional relay settings need to be adapted to changes in Distributed Generator (DG) penetrations or grid reconfigurations, which is a complicated task that can be solved efficiently using Artificial Intelligence (AI)-based protection. This paper compares and validates the difference between conventional protection (overcurrent and differential) strategies and a new strategy based on Artificial Neural Networks (ANNs), which have been shown as adequate protection, especially with reconfigurable smart grids. In addition, the limitations of the conventional protections are discussed. The AI protection is employed through the communication between all Protective Devices (PDs) in the grid, and a backup strategy that employs the communication among the PDs in the same line. This paper goes a step further to validate the protection strategies based... [more]

Space charge measurement accuracy is crucial when assessing the suitability of cables for high-voltage direct current (DC) systems. This study assembled state-of-the-art analysis technologies, including time-domain deconvolution, to mark electric field estimation accuracy, which the present techniques achieve. The pulse electroacoustic method was applied to a 66 kV-class extruded cable, and waveforms were obtained and analyzed to reproduce the electric field distribution. The DC voltage was set to be sufficiently low so that the analysis results can be compared with Laplace’s equation. The statistical analysis of 81 waveforms under a DC voltage of 30 kV showed that the estimation accuracy was −0.3% ± 19.9% with a 95.4% confidence interval, even with the deconvolution parameter optimized. The estimated accuracy using the “reference” waveform is applied to waveforms at higher voltages since similar estimation accuracies were confirmed for waveforms obtained under a DC voltage of 45 kV.

Primary fuels, i.e., crude oil, natural gas, and power coal, dominate the total global demand for primary energy. Among them, crude oil plays a particularly important role due to the universality of applications and the practical lack of substitutes in transport. Crude oil is also one of the main sources of primary energy in Poland and accounts for around 30% of the energy consumed. Poland covers only 3% of its needs from domestic deposits. The rest is imported from Russia, Saudi Arabia, Nigeria, Great Britain, Kazakhstan, and Norway. Due to such a high import of raw material, Poland must anticipate future demand. On the one hand, this article aims to analyze the current (2020) and future (2040) crude oil consumption on the Polish market. The study analyzes the geopolitical and economic foundations of the functioning of the energy raw-materials market, the crude oil supply, the structure of Poland’s energy mix, and assumptions about the energy policy until 2040. On the other hand, conc... [more]

In predicting energy consumption, classic econometric and statistical models are used to forecast energy consumption. These models may have limitations in an increasingly fast-changing energy market, which requires big data analysis of energy consumption patterns and relevant variables using complex mathematical tools. In current literature, there are minimal comparison studies reviewing machine learning algorithms to predict energy consumption in Korea. To bridge this gap, this paper compared three different machine learning algorithms, namely the Random Forest (RF) model, XGBoost (XGB) model, and Long Short-Term Memory (LSTM) model. These algorithms were applied in Period 1 (prior to the onset of the COVID-19 pandemic) and Period 2 (after the onset of the COVID-19 pandemic). Period 1 was characterized by an upward trend in energy consumption, while Period 2 showed a reduction in energy consumption. LSTM performed best in its prediction power specifically in Period 1, and RF outperfor... [more]

The marine environment may change the force on the fluid and inevitably influence bubble behavior and the two-phase flow in the reactor core, which are vital to the safety margin of a nuclear reactor. To explore the effect of the marine motion on the flow and heat transfer features of subcooled flow boiling in the reactor core, the volume of fluid (VOF) method is employed to reveal the interaction between the interface structure and two-phase flow in the subchannel under rolling motion. The variations of several physical parameters are obtained, including the transverse flow, the vapor volume fraction, the vapor adhesion ratio, and the phase distribution of boiling two-phase flow with time. Sensitivity analyses of the amplitude and the period of the rolling motion were performed to demonstrate the mechanisms of the influence of the rolling motion. We found that the transverse flow in the subchannel was mainly affected by the Euler force under the rolling motion. In contrast to the two-... [more]

The estimation of non-stationary random medium parameters of petrophysical parameters is the key to the application of random medium theory in fine seismic exploration. We proposed a method for estimating non-stationary random medium parameters of petrophysical parameters using seismic data. Based on the linear petrophysical model, the relationship between seismic data and porosity, clay volume, and water saturation in the random medium was described, and the principle and method of estimating the autocorrelation parameters of the petrophysical parameter random medium were introduced in this study. Subsequently, the specific steps of applying the power spectrum method, for parameter estimation in non-stationary random media with petrophysical parameters, were explained. The feasibility and correctness of the method were verified through the estimation test of the two-dimensional theoretical model. Eventually, the estimation test of non-stationary random medium parameters of petrophysic... [more]

A cyberattack detection model based on supervised deep neural network is proposed to identify random false data injection (FDI) on the tank’s level measurements of a water distribution system. The architecture of the neural network, as well as various hyper-parameters, is modified and tuned to acquire the highest detection performance using the smallest size of training data set. The efficacy of the proposed detection model against various activation functions including sigmoid, rectified linear unit, and softmax is examined. Regularization and momentum techniques are applied to update the weights and prohibit overfitting. Moreover, statistical metrics are presented to evaluate the performance and effectiveness of the proposed model in the presence of a range of measurement noise levels. The proposed model is tested for three attack scenarios composed for the battle of the attack detection algorithms. Results confirm that the size of the data sets required to train the neural network (... [more]

Renewable energy communities (REC) are bound to play a crucial role in the energy transition, as their role, activities, and legal forms become clearer, and their dissemination becomes larger. Even though their mass grid integration, is regarded with high expectations, their diffusion, however, has not been an easy task. Its legal form and success, entail responsibilities, prospects, trust, and synergies to be explored between its members, whose collective dynamics should aim for optimal operation. In this regard, the pairing methodology of potential participants ahead of asset dimensioning seems to have been overlooked. This article presents a methodology for pairing consumers, based on their georeferenced load consumptions. A case study in an area of Porto (Asprela) was used to test the methodology. QGIS is used as a geo-representation tool and its PlanHeat plugin for district characterization support. A supervised statistical learning approach is used to identify the feature importa... [more]

Hybrid vehicles are gaining increasing global prominence, especially in the military, where unexpected breakdowns or even power deficits are not only associated with greater expense but can also cost the lives of military personnel. In some cases, it is extremely important that all battery cells and modules deliver the specified amount of capacity. Therefore, it is recommended to introduce a new measurement line of rapid diagnostics before deployment, in addition to the usual procedures. Using the results of rapid testing, we recommend the introduction of a hierarchical three-step diagnostics and assessment procedure. In this procedure, the key factor is the building up of a hierarchical tree-structured fuzzy signature that expresses the partial interdependence or redundancy of the uncertain descriptors obtained from the rapid tests. The fuzzy signature structure has two main important components: the tree structure itself, and the aggregations assigned to the internal nodes. The fuzzy... [more]

Solar photovoltaic (PV) power generation is prone to drastic changes due to cloud cover. The power is easily affected within a very short period of time. Thus, the accuracy of grasping cloud distribution is important for PV power forecasting. This study proposes a novel sky image method to obtain the cloud coverage rate used for short-term PV power forecasting. The authors developed an image analysis algorithm from the sky images obtained by an on-site whole sky imager (WSI). To verify the effectiveness of cloud coverage rate as the parameter for PV power forecast, four different combinations of weather features were used to compare the accuracy of short-term PV power forecasting. In addition to the artificial neural network (ANN) model, long short-term memory (LSTM) and the gated recurrent unit (GRU) were also introduced to compare their applicability conditions. After a comprehensive analysis, the coverage rate is the key weather feature, which can improve the accuracy by about 2% co... [more]

One of the bottlenecks of autonomous systems is to identify and/or design models and tools that are not too resource demanding. This paper presents the concept and design process of a moving platform structure−electric vehicle. The objective is to use artificial intelligence methods to control the model’s operation in a resource scarce computation environment. Neural approaches are used for data analysis, path planning, speed control and implementation of the vision system for road sign recognition. For this purpose, multilayer perceptron neural networks and deep learning models are used. In addition to the neural algorithms and several applications, the hardware implementation is described. Simulation results of systems are gathered, data gathered from real platform tests are analyzed. Experimental results show that low-cost hardware may be used to develop an effective working platform capable of autonomous operation in defined conditions.