Battery models have gained great importance in recent years, thanks to the increasingly massive penetration of electric vehicles in the transport market. Accurate battery models are needed to evaluate battery performances and design an efficient battery management system. Different modeling approaches are available in literature, each one with its own advantages and disadvantages. In general, more complex models give accurate results, at the cost of higher computational efforts and time-consuming and costly laboratory testing for parametrization. For these reasons, for early stage evaluation and design of battery management systems, models with simple parameter identification procedures are the most appropriate and feasible solutions. In this article, three different battery modeling approaches are considered, and their parameters’ identification are described. Two of the chosen models require no laboratory tests for parametrization, and most of the information are derived from the man... [more]

The increase in the role of companion robots in everyday life is inevitable, and their safe communication with the infrastructure is one of the fundamental challenges faced by designers. There are many challenges in the robot’s communication with the environment, widely described in the literature on the subject. The threats that scientists believe have the most significant impact on the robot’s communication include denial-of-service (DoS) attacks, satellite signal spoofing, external eavesdropping, spamming, broadcast tampering, and man-in-the-middle attacks. In this article, the authors attempted to identify communication threats in the new robot-to-infrastructure (R2I) model based on available solutions used in transport, e.g., vehicle-to-infrastructure (V2I), taking into account the threats already known affecting the robot’s sensory systems. For this purpose, all threats that may occur in the robot’s communication with the environment were analyzed. Then the risk analysis was carr... [more]

The improvement of the supercapacitor model redundancy is a significant method to guarantee the reliability of the power system in electric vehicle application. In order to enhance the accuracy of the supercapacitor model, eight conventional supercapacitor models were selected for parameter identification by genetic algorithm, and the model accuracies based on standard diving cycle are further discussed. Then, three fusion modeling approaches including Bayesian fusion, residual normalization fusion, and state of charge (SOC) fragment fusion are presented and compared. In order to further improve the accuracy of these models, a two-layer fusion model based on SOC fragments is proposed in this paper. Compared with other fusion models, the root mean square error (RMSE), maximum error, and mean error of the two-layer fusion model can be reduced by at least 23.04%, 8.70%, and 30.13%, respectively. Moreover, the two-layer fusion model is further verified at 10, 25, and 40 °C, and the RMSE ca... [more]

Accurately determined acoustic emission (AE) locations provide significant information on fracture systems, such as the orientation of fractures in a geothermal reservoir. To determine the relative source locations among a group of seismic events, similar AE waveforms must be detected and the relative arrival times of the P and S waves must be determined. In this paper, a method to identify similar AE waveforms is proposed, in which wavelet transform scalograms are used to determine the phase-only correlation function. The proposed method was applied to arbitrarily selected seismic waveforms, and its feasibility was evaluated by comparing the results with those obtained when the phase-only correlation function was obtained by using Fourier transform results.

This paper presents an event identification process in complementary feature extractions via convolutional neural network (CNN)-based event classification. The CNN is a suitable deep learning technique for addressing the two-dimensional power system data as it directly derives information from a measurement signal database instead of modeling transient phenomena, where the measured synchrophasor data in the power systems are allocated by time and space domains. The dynamic signatures in phasor measurement unit (PMU) signals are analyzed based on the starting point of the subtransient signals, as well as the fluctuation signature in the transient signal. For fast decision and protective operations, the use of narrow band time window is recommended to reduce the acquisition delay, where a wide time window provides high accuracy due to the use of large amounts of data. In this study, two separate data preprocessing methods and multichannel CNN structures are constructed to provide validat... [more]

The study presented in the paper is an innovative research approach. It is the result of linking the concept of supply chain management and global changes, which at present are clearly visible on a global scale, with research methodology based on the systematic literature review, knowledge visualization and an expert method that makes use of knowledge, experience and opinions of experts in a given field. This research is about a Delphi study that was conducted in the context of the development of trends of supply chain and global changes, based on the findings of a systematic literature review. The qualitative study was conducted with 30 Delphi experts in the field of the supply chain. This progressive approach to the research topic allowed us to discover key global trends and modern supply chain development directions in the context of global changes, as well as their assessment and projection of the developmental potential of these trends.

This paper proposes an accurate physical layer technique to uniquely identify the links of a power line communication network. First, the power line communications (PLC) multipath channel characterization is presented and detailed. Then, a multipath channel delay detection technique is introduced to provide an accurate physical layer identification (PL ID) for the considered PLC links. The accuracy and efficiency are tested by evaluating the successful path detection probability (SPDP) in a simulated scenario under both perfect and imperfect channel state information conditions. The results confirm the advantages of the proposed scheme. Indeed, for a common PLC noise power around 90 dBuV, the provided accuracy reaches ≈90%, while for a noise power below 80 dBuV, the accuracy plateaus at 100%. Overall, the low complexity of the proposed approach and its staggering performance results pave the way for further possible applications in both the PLC and the security domain.

In the acoustics-based power transformer fault diagnosis, a transformer acoustic signal collected by an acoustic sensor is generally mixed with a large number of interference signals. In order to separate transformer acoustic signals from mixed acoustic signals obtained by a small number of sensors, a blind source separation (BSS) method of transformer acoustic signal based on sparse component analysis (SCA) is proposed in this paper. Firstly, the mixed acoustic signals are transformed from time domain to time−frequency (TF) domain, and single source points (SSPs) in the TF plane are extracted by identifying the phase angle differences of the TF points. Then, the mixing matrix is estimated by clustering SSPs with a density clustering algorithm. Finally, the transformer acoustic signal is separated from the mixed acoustic signals based on the compressed sensing theory. The results of the simulation and experiment show that the proposed method can separate the transformer acoustic signal... [more]

Terrestrial tight oil has extremely strong diagenesis heterogeneity, so a large number of rock thin slices are needed to reveal the real microscopic pore-throat structure characteristics. In addition, difficult identification, high cost, long time, strong subjectivity and other problems exist in the identification of tight oil rock thin slices, and it is difficult to meet the needs of fine description and quantitative characterization of the reservoir. In this paper, a method for identifying the characteristics of rock thin slices in tight oil reservoirs based on the deep learning technique was proposed. The present work has the following steps: first, the image preprocessing technique was studied. The original image noise was removed by filtering, and the image pixel size was unified by a normalization technique to ensure the quality of samples; second, the self-labeling image data augmentation technique was constructed to solve the problem of sparse samples; third, the Mask R-CNN alg... [more]

A solution to the inverse heat transfer problem (IHP) occurring in steam pipelines is presented in the paper. The transient steam temperature at the pipeline inlet was determined from the steam temperature measured at the pipeline outlet. Temporary changes of steam temperature at the turbine inlet are set by the turbine manufacturer and result from the conditions of safe starting of the turbine and maintaining high durability of its components. The boiler start-up should be carried out so that the time-temperature changes at the boiler outlet equal the time-temperature changes determined using the inverse problem. In this paper, the inverse problem of heat transfer in the pipeline was solved by the finite volume method using data smoothing, future times steps, and Tikhonov regularization that stabilized the solution of the inverse problem. The determined transient steam temperature at the pipeline inlet was compared with the measured temperatures. The steam temperature at the inlet to... [more]

The Vapor Compression Refrigeration System (VCRS) is one of the most critical systems in buildings typically used in Heating, Ventilation, and Air Conditioning (HVAC) systems in residential and industrial sections. Therefore, identifying their faults and evaluating their reliability are essential to ensure the required operations and performance in these systems. Various components and subsystems are included in the VCRS, which need to be analyzed for system reliability. This research’s objective is conducting a comprehensive system reliability analysis on the VCRS by focusing on fault identification and determining the fault impacts on these systems. A typical VCRS in an office building is selected for this research regarding this objective. The corresponding reliability data, including the probability distributions and parameters, are collected from references to perform the reliability evaluation on the components and subsystems of the VCRS. Then the optimum distribution parameters... [more]

The present study aimed to investigate the effect of Oenothera biennis sprout extract (OBS-E) on skin-function improvement in an in vitro system and to identify its pharmaceutically active components. OBS-E showed antioxidant ability in radical scavenging and reducing power assays, significantly inhibited matrix metalloproteinases-1 and -2, and increased the production of type I collagen, indicating its anti-wrinkle activity. Furthermore, OBS-E significantly increased the level of hyaluronic acid (HA) and the expression of moisturizing genes, such as hyaluronic acid synthase 2 (HAS2) and aquaporin 3 (AQP3), indicating it is effective in enhancing skin hydration. High-performance liquid chromatography (HPLC) and mass spectrometry (MS) analyses showed that OBS-E contained high levels of polyphenolic acids, such as gallic acid and ellagic acid, in addition to flavonoid glycosides, such as luteolin 7-glucuronide and quercetin 3-glucuronide. Our results suggest that these major phytochemica... [more]

Fuel cells are key elements in the transition to clean energy thanks to their neutral carbon footprint, as well as their great capacity for the generation of electrical energy by oxidizing hydrogen. However, these cells operate under straining conditions of temperature and humidity that favor degradation processes. Furthermore, the presence of hydrogen—a highly flammable gas—renders the assessment of their degradations and failures crucial to the safety of their use. This paper deals with the combination of physical knowledge and data analysis for the identification of health indices (HIs) that carry information on the degradation process of fuel cells. Then, a failure prognosis method is achieved through the trend modeling of the identified HI using a data-driven and updatable state model. Finally, the remaining useful life is predicted through the calculation of the times of crossing of the predicted HI and the failure threshold. The trend model is updated when the estimation error b... [more]

Non-intrusive load monitoring (NILM) is a fast developing technique for appliances operation recognition in power system monitoring. At present, most NILM algorithms rely on the assumption that all fluctuations in the data stream are triggered by identified appliances. Therefore, NILM of identifying unidentified appliances is still an open challenge. To pursue a scalable solution to energy monitoring for contemporary unidentified appliances, we propose a voltage-current (V-I) trajectory enabled deep pairwise-supervised hashing (DPSH) method for NILM. DPSH performs simultaneous feature learning and hash-code learning with deep neural networks, which shows higher identification accuracy than a benchmark method. DPSH can generate different hash codes to distinguish identified appliances. For unidentified appliances, it generates completely new codes that are different from codes of multiple identified appliances to distinguish them. Experiments on public datasets show that our method can... [more]

The development of a bio-based economy has its foundations in the development of efficient processes to optimize biomass potential. In this context there are a multitude of molecules that can be either synthetized or recovered from biomass, among those the so-called 12 building-blocks reported by the US Department of Energy. Even if their identification and importance is clearly defined, research efforts concerning the purification or separation of these platform molecules are limited. To fill this gap, different configurations for the purification of levulinic acid recovered from lignocellulosic biomass are examined and compared in this work. In particular, hybrid configurations obtained by the combination of liquid-liquid extraction and distillation have been considered. It was demonstrated how a deep understanding of the subspace including all extraction-assisted simple column distillation configurations represents a fundamental step in the synthesis of different process alternative... [more]

Clustering analysis, a key step for many data mining problems, can be applied to various fields. However, no matter what kind of clustering method, noise points have always been an important factor affecting the clustering effect. In addition, in spectral clustering, the construction of affinity matrix affects the formation of new samples, which in turn affects the final clustering results. Therefore, this study proposes a noise cutting and natural neighbors spectral clustering method based on coupling P system (NCNNSC-CP) to solve the above problems. The whole algorithm process is carried out in the coupled P system. We propose a natural neighbors searching method without parameters, which can quickly determine the natural neighbors and natural characteristic value of data points. Then, based on it, the critical density and reverse density are obtained, and noise identification and cutting are performed. The affinity matrix constructed using core natural neighbors greatly improve the... [more]

The study presents the experimental and numeric heat transfer investigations in flow boiling of water through an asymmetrically heated, rectangular and horizontal minichannel, with transparent side walls. A dedicated system was designed to record images of two-phase flow structures using a high-speed video camera with a synchronous movement system. The images were analyzed with Matlab 2019a scripts for determination of the void fraction for each pattern of two-phase flow structures observed. The experimental data measured during the experimental runs included inlet and outlet temperature, temperature at three internal points of the heater body, volume flux of the flowing water, inlet pressure, pressure drop, current and the voltage drop in the heater power supply. The flows were investigated at Reynolds number characteristic of laminar flow. The mathematical model assumed the heat transfer process in the measurement module to be steady-state with temperature independent thermal propert... [more]

The asymmetric faults often cause the power grid current imbalance and power grid oscillation, which brings great instability risk to the power grid. To address this problem, this paper presented a modeling and parameter optimization method of grid-connected photovoltaic (PV) systems, considering the low voltage ride-through (LVRT) control. The harmonics of the grid current under asymmetric faults were analyzed based on the negative-sequence voltage feedforward control method. The notch filter was added to the voltage loop to filter out the harmonic components of the DC bus voltage and reduce the harmonic contents of the given grid current value. The proportional resonant (PR) controller was added to the current loop. The combination of these two components could reduce the 3rd, 5th, and 7th harmonics of the grid current and the output power fluctuation. Then, the parameters of the inverter controller were identified by the adaptive differential evolution (ADE) algorithm based on the s... [more]

A reliable and cost-effective electricity system transition requires both the identification of optimal target states and the definition of political and regulatory frameworks that enable these target states to be achieved. Fundamental optimization models are frequently used for the determination of cost-optimal system configurations. They represent a normative approach and typically assume markets with perfect competition. However, it is well known that real systems do not behave in such an optimal way, as decision-makers do not have perfect information at their disposal and real market actors do not take decisions in a purely rational way. These deficiencies lead to increased costs or missed targets, often referred to as an “efficiency gap”. For making rational political decisions, it might be valuable to know which factors influence this efficiency gap and to what extent. In this paper, we identify and quantify this gap by soft-linking a fundamental electricity market model and an a... [more]

This paper presents an effective technique based on an artificial neural network algorithm utilized for circuit parameter identification in lightning impulse generation for low inductance loads such as low voltage windings of a power transformer, a large distribution transformer and an air core reactor. The limitation of the combination between Glaninger’s circuit and the circuit parameter selection from Feser’s suggestions in term of producing an impulse waveform to be compliant with standard requirements when working with a low inductance load is discussed. In Feser’s approach, the circuit parameters of the generation circuit need to be further adjusted to obtain the waveform compliant with the standard requirement. In this process, trial and error approaches based on test engineers’ experience are employed in the circuit parameter selection. To avoid the unintentional damage from electrical field stress during the voltage waveform adjustment process, circuit simulators, such as Pspi... [more]

The identification of underground formation lithology can serve as a basis for petroleum exploration and development. This study integrates Extreme Gradient Boosting (XGBoost) with Bayesian Optimization (BO) for formation lithology identification and comprehensively evaluated the performance of the proposed classifier based on the metrics of the confusion matrix, precision, recall, F1-score and the area under the receiver operating characteristic curve (AUC). The data of this study are derived from Daniudui gas field and the Hangjinqi gas field, which includes 2153 samples with known lithology facies class with each sample having seven measured properties (well log curves), and corresponding depth. The results show that BO significantly improves parameter optimization efficiency. The AUC values of the test sets of the two gas fields are 0.968 and 0.987, respectively, indicating that the proposed method has very high generalization performance. Additionally, we compare the proposed algo... [more]

The aim and contribution of this paper is to identify with Duval Pentagon 2 the stray gassing (SG) patterns of inhibited and uninhibited mineral oils in transformers in service and in the well-established laboratory SG tests of CIGRE and ASTM, so that SG in transformers can be easily distinguished from the other types of faults occurring in them. The SG test of IEC 60296-2020 is inadequate or much less effective for that purpose.

This paper presents an experience in the design and implementation of the condition-monitoring system for the synchronous generators whose primary purpose is to record data for the identification of the capability limits of the P−Q diagram of three generators in hydro power-plant. Paper presents details about the monitoring system, the underlying theory of the identification of the synchronous generator model with a focus on the calculation of the capability limits in the P−Q diagram. Furthermore, a computationally efficient method for the representation of capability limits suitable for the implementation within the industrial automation and control system of the power-plant is described in detail. Finally, the capability diagrams for three generators were implemented in the power-plant supervisory control and data acquisition system (SCADA) system.

The lithium-ion battery is a complicated non-linear system with multi electrochemical processes including mass and charge conservations as well as electrochemical kinetics. The calculation process of the electrochemical model depends on an in-depth understanding of the physicochemical characteristics and parameters, which can be costly and time-consuming. We investigated the electrochemical modeling, reduction, and identification methods of the lithium-ion battery from the electrode-level to the system-level. A reduced 9th order linear model was proposed using electrode-level physicochemical modeling and the cell-level mathematical reduction method. The data-driven predictor-based subspace identification algorithm was presented for the estimation of lithium-ion battery model in the system-level. The effectiveness of the proposed modeling and identification methods was validated in an experimental study based on LiFePO4 cells. The accuracy and dynamic characteristics of the identified m... [more]

State-of-the-art methods for determining thermal impedance networks for IGBT (Insulated Gate Bipolar Transistor) modules usually involves the establishment of the relationship between the measured transistor or diode voltage and temperature under homogenous temperature distribution across the IGBT module. The junction temperature is recomputed from the established voltage−temperature relationship and used in determining the thermal impedance network. This method requires accurate measurement of voltage drop across the transistors and diodes under specific designed heating and cooling profiles. Validation of the junction temperature is usually done using infrared camera or sensors placed close to the transistors or diodes (in some cases and open IGBT module) so that the measured temperature is as close to the junction as possible. In this paper, we propose an alternative method for determining the IGBT thermal impedance network using the principles of least squares. This method uses mea... [more]