Ensuring a stable and efficient transformer operation is a very crucial task nowadays, especially with the integration of modern and sensitive electrical equipment and appliances down the line. However, transformer maloperation still cannot be completely avoided, particularly with the existence of inrush current that possess similar characteristics as the fault currents when a fault occurred. Thus, this paper proposes an enhanced method for inrush current identification based on a backpropagation (BP) network, optimized using genetic and simulated annealing algorithms. The proposed method has the ability to find the global optimal solution while avoiding local optima, with increased solution accuracy and low calculation complexity. Through extensive simulations, it was found that the inrush and fault currents have differences in their harmonic contents, which can be exploited for the identification of those currents using the proposed identification method. The proposed genetic simulat... [more]

Reservoir lithology identification is the basis for the exploration and development of complex lithological reservoirs. Efficient processing of well-logging data is the key to lithology identification. However, reservoir lithology identification through well-logging is still a challenge with conventional machine learning methods, such as Convolutional Neural Networks (CNN), and Long Short-term Memory (LSTM). To address this issue, a fully connected network (FCN) and LSTM were coupled for predicting reservoir lithology. The proposed algorithm (LSTM-FCN) is composed of two sections. One section uses FCN to extract the spatial properties, the other one captures feature selections by LSTM. Well-logging data from Hugoton Field is used to evaluate the performance. In this study, well-logging data, including Gamma-ray (GR), Resistivity (ILD_log10), Neutron-density porosity difference (DeltaPHI), Average neutron-density porosity(PHIND), and (Photoelectric effect) PE, are used for training and... [more]

Calculating the values of the parameters of distorted periodic signals in real-time is important for the control of many processes. In particular, this information is necessary for the proper operation of power electronics devices that cooperate with the power grid. In such cases, it is necessary to determine the phase, frequency, and amplitude of the fundamental component of the voltage in the power grid node. Also, in many cases, the control process needs a signal which is synchronised with the power grid voltage. Both processes should be realised in real-time. A number of solutions to the problem of calculating the values of the voltage parameters have been described in the literature. However, these methods generally introduce significant time delays and have several restrictions regarding the variability in the values of these parameters. They also often require the significant computational power of a unit that performs the task of identification. The algorithm presented in this... [more]

The proliferation of low-power consumer electronic appliances (LPCEAs) is on the rise in smart homes in order to save energy. On the flip side, the current harmonics induced due to these LPCEAs pollute low-voltage distribution systems’ (LVDSs’) supplies, leading to a poor power factor (PF). Further, the energy meters in an LVDS do not measure both the total harmonic distortion (THD) of the current and the PF, resulting in inaccurate billing for energy consumption. In addition, this impacts the useful lifetime of LPCEAs. A PF that takes the harmonic distortion into account is called the true power factor (TPF). It is imperative to measure it accurately. This article measures the TPF using a four-term minimal sidelobe cosine-windowed enhanced dual-spectrum line interpolated Fast Fourier Transform (FFT). The proposed method was used to measure the TPF with a National Instruments cRIO-9082 real-time (RT) system, and four different LPCEAs in a smart home were considered. The RT results exhi... [more]

Hydraulic spool valve, pilot operated with bi-state switching micro valves is a low-cost alternative to the conventional proportional and high-response valves. However, high-frequency switching causes variations in the control flow which limits achievable spool tracking error. This paper presents the design of a robust μ-controller for the spool position reference tracking synthesized with D-K iterative procedure. Furthermore, in order to reduce wind-up effects in the closed-loop, the μ-controller is decomposed to a canonical observer and state feedback components which allows explicit introduction of the saturated control signal in the controller equations. The uncertainty model required for the μ-synthesis is inferred from the nonlinear hydraulic model by identification of a Box−Jenkins model set characterized by its parameter covariance matrix. The regulator is implemented in a 32-bit programmable logic controller (PLC) and its performance is experimentally verified on a laboratory... [more]

The accuracy of multibeam echosounder bathymetric measurement depends on the accuracy of the data of the sound speed layers within the water column. This is necessary for the correct modeling of ray bending. It is assumed that the sound speed layers are horizontal and static, according to the sound speed profile traditionally used in the depth calculation. In fact, the boundaries between varying water masses can be curved and oscillate. It is difficult to assess the parameters of these movements based on the sparse sampling of sound velocity profiles (SVP) collected through a survey; thus, alternative or augmented methods are needed to obtain information about water mass stratification for the time of a particular ping or a series of pings. The process of water column data collection and analysis is presented in this paper. The proposed method updates the sound speed profile by the automated detection of varying water mass boundaries, giving the option to adjust the SVP for each beam s... [more]

The aim of the article is to present key mechanisms for shaping the safety culture of high reliability organizations through digital transformation, which is now a key challenge for the entire global economy. It is particularly important in processes conducted by so-called high reliability organizations. From this cognitive perspective, it is important to define the place and role of digital transformation in shaping the safety culture of high reliability organizations. The comparison of the issues of the safety culture and digital transformation of high reliability organizations seems to be an important cognitive aspect resulting from technological progress in the area of the digital economy. The socio-technological system in which high reliability organizations exist is organized in such a way that all technical, operational and organizational aspects, including the participation of many entities involved in the operation of this complex system, are coherent. This coherence can be in... [more]

In the International Thermonuclear Experimental Reactor, plasma is magnetically confined with Superconductive Magnets (SMs) that must be maintained at the cryogenic temperature of 4.5 K by one or more Superconducting Magnet Cryogenic Cooling Circuits (SMCCC). To guarantee cooling, Loss-of-Flow Accidents (LOFAs) in the SMCCC are to be avoided. In this work, we develop a three-step methodology for the prompt detection of LOFA precursors (i.e., those combinations of component failures causing a LOFA). First, we randomly generate accident scenarios by Monte Carlo sampling of the failures of typical SMCCC components and simulate the corresponding transient system response by a deterministic thermal-hydraulic code. In this phase, we also employ quick-running Proper Orthogonal Decomposition (POD)-based Kriging metamodels, adaptively trained to reproduce the output of the long-running code, to decrease the computational time. Second, we group the generated scenarios by a Spectral Clustering (S... [more]

This work proposes a data-driven methodology for identifying critical components in Complex Technical Infrastructures (CTIs), for which the functional logic and/or the system structure functions are not known due the CTI’s complexity and evolving nature. The methodology uses large amounts of CTI monitoring data acquired over long periods of time and under different operating conditions. The critical components are identified as those for which the condition monitoring signals permit the optimal classification of the CTI functioning or failed state. The methodology includes two stages: in the first stage, a feature selection filter method based on the Relief technique is used to rank the monitoring signals according to their importance with respect to the CTI functioning or failed state; the second stage identifies the subset of signals among those highlighted by the Relief technique that are most informative with respect to the CTI state. This identification is performed on the basis o... [more]

In order to ensure continuous energy supply, Distribution System Operators (DSOs) have to monitor and analyze the condition of the power grid, especially checking for random events, such as breakdowns or other disturbances. Still, relatively little information is available on the operation of the Low Voltage (LV) grid. This can be improved thanks to digital tools, offering online processing of data, which ultimately increases effectiveness of the power grid. Among those tools, the use of the Advanced Metering Infrastructure (AMI) is especially conducive for improving reliability. AMI is one of the elements of the system Supervisory Control and Data Acquisition (SCADA) for the LV grid. Exact knowledge of the reliability conditions of a power grid is also indispensable for optimizing investment. AMI is also key in providing operational capacity for carrying out energy balance in virtual power plants (VPPs). This paper deals with methodology of identification and location of faults in the... [more]

In recent years, the need for generation mixes that consider the inertial constraints in unit commitment (UC) has increased because the inertia of these systems has decreased with the increased use of renewable energy. In these circumstances, single-machine models can calculate the minimum frequency and rate of change of frequency (RoCoF) at a high speed in terms of the characteristics of the changes in the generation mix, in order to identify the generation mixes that can satisfy inertial constraints. This study proposed methods to determine the parameters of the reduced frequency response (RFR) model, which is a single-machine model that considers the nonlinearity caused by restrictions on the generator’s output power, in order to apply inertial constraints to UC. The RFR models can include various forms of governor models and consider the nonlinear response characteristics of restrictions on the generator’s output power that change according to the scales of contingencies, system in... [more]

The aim of this study was to present a relay shifting method for relay feedback identification of dynamical systems suitable for PID controller tuning. The proposed technique uses a biased relay to determine frequency response points from a single experiment without any assumptions about a model transfer function. The method is applicable for open-loop stable, unstable, and integration processes, even with a delay, and regardless of whether they are oscillating or non-oscillating. The core of this technique was formed by the so-called relay shifting filter. In this study, the method was applied to a parameter estimation of a second-order time-delayed (SOTD) model that can describe, with acceptable accuracy, the dynamics of most processes (even with a transport delay) near the operating point. Simultaneously, a parameter setting for the PID controller was derived based on the model parameters. The applicability of the proposed method was demonstrated on various simulated processes and t... [more]

Variable moment of inertia systems are common, and a popular laboratory system of this type is the “ball-and-beam”. Such systems are, however, nonlinear and often unstable. Efficient control requires full state information (or at least partial velocities), which are generally difficult to measure. That is why the design of state observers is a relevant problem. In this paper, a new design of an observer is proposed. This new nonlinear observer uses partial output injection and the circle criterion to ensure semiglobal stability. Moreover, we present a complete modeling of the system and systematic testing of the observer in comparison to a baseline in the form of a linear observer. The results show that the designed observer outperforms its linear counterpart and does not impede control.

The characterization of competent intermediates of metal complexes, involved in catalytic transformations for the activation of small molecules, is an important target for mechanistic comprehension and catalyst design. Iron complexes deserve particular attention, due to the rich chemistry of iron that allows their application both in oxidation and reduction processes. In particular, iron complexes with tetradentate Schiff base ligands show the possibility to electrochemically generate FeI intermediates, capable of reacting with carbon dioxide. In this work, we investigate the electronic and spectroscopic features of FeI intermediates in five Fe(LN2O2) complexes, and evaluate the electrocatalytic reduction of CO2 in the presence of phenol (PhOH) or trifluoroethanol (TFE) as proton donors. The main findings include: (i) a correlation of the potentials of the FeII/I couples with the electronic character of the LN2O2 ligand and the energy of the metal-to-ligand charge transfer absorption o... [more]

Fast and accurate identification of short-circuit faults is important for post-fault service restoration and maintenance in DC distribution grids. Yet multiple power sources and complex system topologies complicate the fault identification in multi-terminal DC distribution grids. To address this challenge, this paper introduces an approach that achieves fast online identification of both the location and the severity of faults in multi-terminal DC distribution grids. First, a generic model describing the dynamic response of DC lines to both pole-to-ground and pole-to-pole faults with fault currents injected from both line ends is developed. On this basis, a Kalman filter is adopted to estimate both the fault location and resistance. In the real-time simulation of various fault scenarios in a three-terminal DC distribution grid model with Opal-RT platform, the proposed method is proved to be effective with a short response time of less than 1 ms.

This paper presents improved methods to detect cracks and thermal leakage in building envelopes using unmanned aerial vehicles (UAV) (i.e., drones) with video camcorders and/or infrared cameras. Three widely used contour detectors of Sobel, Laplacian, and Canny algorithms were compared to find a better solution with low computational overhead. Furthermore, a scheme using frame-based location identification was developed to effectively utilize the existing approach by finding the current location of the drone-assisted image frame. The results showed a simplified drone-assisted scheme along with automation, higher accuracy, and better speed while using lower battery energy. Furthermore, this paper found that the cost-effective drone with the attached equipment generated accurate results without using an expensive drone. The new scheme of this paper will contribute to automated anomaly detection, energy auditing, and commissioning for sustainably built environments.

It is generally difficult to identify fluid types in low-porosity and low-permeability reservoirs, and the Chang 8 Member in the Ordos Basin is a typical example. In the Chang 8 Member of Yanchang Formation in the Zhenyuan area of Ordos Basin, affected by lithology and physical properties, the resistivity of the oil layer and water layer are close, which brings great difficulties to fluid type identification. In this paper, we first analyzed the geological and petrophysical characteristics of the study area, and found that high clay content is one of the reasons for the low-resistivity oil pay layer. Then, the formation water types and characteristics of formation water salinity were studied. The water type was mainly CaCl2, and formation water salinity had a great difference in the study area ranging from 7510 ppm to 72,590 ppm, which is the main cause of the low-resistivity oil pay layer. According to the reservoir fluid logging response characteristics, the water saturation boundary... [more]

Electric forklifts are extremely important for the world’s logistics and industry. Lead acid batteries are the most common energy storage system for electric forklifts; however, to ensure more energy efficiency and less environmental pollution, they are starting to use lithium batteries. All lithium batteries need a battery management system (BMS) for safety, long life cycle and better efficiency. This system is capable to estimate the battery state of charge, state of health and state of function, but those cannot be measured directly and must be estimated indirectly using battery models. Consequently, accurate battery models are essential for implementation of advance BMS and enhance its accuracy. This work presents a comparison between four different models, four different types of optimizers algorithms and seven different experiment designs. The purpose is defining the best model, with the best optimizer, and the best experiment design for battery parameter estimation. This best mo... [more]

This paper discusses energy behaviors in hydraulic cylinder dynamics, which are important for model-based control of agriculture scale excavators. First, we review hydraulic cylinder dynamics and update our physical parameter identification method to agriculture scale experimental excavators in order to construct a nominal numerical simulator. Second, we analyze the energy behaviors from the port-Hamiltonian point of view which provides many links to model-based control at laboratory scale at least. At agriculture scale, even though the nominal numerical simulator is much simpler than an experimental excavator, the analytical, experimental, and numerical energy behaviors are very close to each other. This implies that the port-Hamiltonian point of view will be applicable in agriculture scale against modeling errors.

In this study, two different parameter estimation algorithms are studied and compared. Iterated EKF and a nonlinear optimization algorithm based on on-line search methods are implemented to estimate parameters of a given permanent magnet synchronous motor whose dynamics are assumed to be known and nonlinear. In addition to parameters, initial conditions of the dynamical system are also considered to be unknown, and that comprises one of the differences of those two algorithms. The implementation of those algorithms for the problem and adaptations of the methods are detailed for some other variations of the problem that are reported in the literature. As for the computational aspect of the study, a convexity study is conducted to obtain the spherical neighborhood of the unknown terms around their correct values in the space. To obtain such a range is important to determine convexity properties of the optimization problem given in the estimation problem. In this study, an EKF-based param... [more]

This study aims to assess the potential risks of setting up a hydrogen infrastructure in the Netherlands. An integrated risk assessment framework, capable of analyzing projects, identifying risks and comparing projects, is used to identify and analyze the main risks in the upcoming Dutch hydrogen infrastructure project. A time multiplier is added to the framework to develop parameters. The impact of the different risk categories provided by the integrated framework is calculated using the discounted cash flow (DCF) model. Despite resource risks having the highest impact, scope risks are shown to be the most prominent in the hydrogen infrastructure project. To present the DCF model results, a risk assessment matrix is constructed. Compared to the conventional Risk Assessment Matrix (RAM) used to present project risks, this matrix presents additional information in terms of the internal rate of return and risk specifics.

Classic circuit modeling for supercapacitors is limited in representing the strongly non-linear behavior of the hybrid supercapacitor technology. In this work, two novel modeling techniques suitable to represent the time-domain electrical behavior of a hybrid supercapacitor are presented. The first technique enhances a well-affirmed circuit model by introducing specific non-linearities. The second technique models the device through a black-box approach with a neural network. Both the modeling techniques are validated experimentally using a workbench to acquire data from a real hybrid supercapacitor. The proposed models, suitable for different supercapacitor technologies, achieve higher accuracy and generalization capabilities compared to those already presented in the literature. Both modeling techniques allow for an accurate representation of both short-time domain and steady-state simulations, providing a valuable asset in electrical designs featuring supercapacitors.

During the last decades, a wide variety of methods to estimate permanent magnet synchronous motor (PMSM) performance have been developed. These methodologies have several advantages over conventional procedures, saving time and economic costs. This paper presents a new methodology to estimate the PMSM torque-speed-efficiency map based on the blocked rotor test using a single-phase voltage source. The methodology identifies the stator flux linkage depending on the current magnitude and angle while providing a detailed estimation of the iron losses. The torque-speed-efficiency map provides detailed information of the motor efficiency along its operating region, including the nominal conditions and the maximum power envelope. The proposed methodology does not require knowing the geometry of the machine to perform any load test, and it also avoids using expensive measurement devices and a complex experimental setup. Moreover, the proposed method allows the PMSM performance to be reproduced... [more]

Properties of state observers depend on proper gains selection. Each method of state estimation may require the implementation of specific techniques of finding those gains. The aim of this study is to propose a universal method of automatic gains selection and perform its verification on an induction machine speed observer. The method utilizes a genetic algorithm with fitness function which is directly based on the impulse response of the observer. System identification using least-squares estimation is implemented to determine the dynamic properties of the observer based on the estimation error signal. The influence of sampling time as well as signal length on the system identification has been studied. The results of gains selection using the proposed method have been compared with results obtained using the approach based on the placement of the poles of linearized estimation error equations. The introduced method delivers results comparable with analytical methods and does not req... [more]

Sensors in the built environment ensure safety and comfort by tracking contaminants in the occupied space. In the event of contaminant release, it is important to use the limited sensor data to rapidly and accurately identify the release location of the contaminant. Identification of the release location will enable subsequent remediation as well as evacuation decision-making. In previous work, we used an operator theoretic approach—based on the Perron−Frobenius (PF) operator—to estimate the contaminant concentration distribution in the domain given a finite amount of streaming sensor data. In the current work, the approach is extended to identify the most probable contaminant release location. The release location identification is framed as a Bayesian inference problem. The Bayesian inference approach requires considering multiple release location scenarios, which is done efficiently using the discrete PF operator. The discrete PF operator provides a fast, effective and accurate mode... [more]