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The generation and distribution of furfural are significantly affected by moisture content in the insulating paper. However, the diffusion mechanism of furfural under moisture effect is not clear, hence, this study investigates the effect of moisture on the diffusion characteristics of furfural by combining macro experiment and micro molecular dynamics simulation. The simulation results demonstrate that the increased moisture content in paper accelerates the diffusion of furfural from paper to oil, which is also proved by the experimental data. Then, the results of interaction energy analysis indicate that the increase of moisture enhances the van der Waals force between oil and furfural. The present findings are expected to improve the theoretical level of furfural analysis, which will facilitate the aging evaluation of the transformer insulating paper based on furfural. [ABSTRACT FROM AUTHOR]

The space charge effect of transformer insulating paper will distort its internal electric field, and its space charge effect will be more prominent under extremely complex working conditions. At present, the micromechanism of charge transport and electric field distortion characteristics of paperboard are not clear under complex working conditions. So in this article, based on the bipolar carrier transport (BCT) model and the dynamic carrier balance model, the effects of high temperature, aging degree, moisture content, and bent degree on charges accumulation in paperboard are studied under dc high voltage by numerical simulation and the measured data needed for simulation. The results show that the higher temperature strengthens the carrier migration, deepens the injection depth of the charge, and aggravates the distortion of electric field intensity in paperboard. The more extended the aging time of paperboard, the greater the internal trap density and the deeper the trap depth, which lead to charges migration difficultly. Increasing water content in the paperboard will accelerate space charges to reach a steady state, while further increasing it will slow down this process, which is related to the shallow trap getting deeper. When the critical part of paperboard is extruded, many traps are introduced, resulting in charges accumulation seriously. The research results can reference the design of paperboard and the maintenance of transformer insulation system. [ABSTRACT FROM AUTHOR]

Due to rapid temperature rise and insulation moisture, bubbles can generate in the oil-paper insulation of oil-immersed power transformers, which can reduce the dielectric strength of oil-paper insulation and even bring about the risk of insulation breakdown. In order to accurately evaluate the conditions of bubble formation, the physical process of bubble formation in oil-paper insulation is studied in this article. Considering the effects of water vaporization, gas dissolution in oil, and moisture migration on bubble formation, a bubble formation prediction model is constructed. Based on Henry’s law, the dissolution equilibrium of gas in oil is taken into consideration and the upper boundary of the initial temperature of bubble effect (ITBE) envelope is obtained in this model. Based on the moisture equilibrium in oil-paper insulation, the bubble formation process caused by the emergence and rapid evaporation of free water is analyzed, and then, the lower boundary of the ITBE envelope is obtained in this model. The model results show that the moisture content of the pressboard and the presence of free water have a great impact on ITBE. Specifically, the higher the moisture content, the lower the ITBE value. In addition, with the further increase of moisture content, the upper and lower boundaries of the ITBE envelope gradually tend to be the same. However, once free water is present on the surface of cellulose, ITBE decreases rapidly to below 120 °C. The model built in this article is of great significance to study the temperature limit of the power transformer. Hence the ITBE envelope can provide an important theoretical reference for reducing the risk of bubble formation during power transformer operation. [ABSTRACT FROM AUTHOR]

This article is aimed at quantifying the health condition of the power transformer insulation system through its frequency-domain dielectric characteristic by using the concept of the resistor and fractional calculus. In this regard, the dielectric model of the fractional Poynting–Thomson (PT) is proposed, and a mathematical model for parameter identification of the proposed model is presented. The shuffled frog leaping algorithm (SFLA) is used to realize the parameter identification and the fractional PT model parameters of the oil-paper samples under different insulation health conditions. The influence of moisture and aging on the fractional PT dielectric model parameters is experimentally studied. Results show that, with the increase in the water content within the insulation system, the parameters of the fractional PT model gradually decrease, and there is a strong correlation between the relaxation time and the water content. This facilitates the employment of the fractional PT model parameters to quantify the aging and moisture content of the power transformer insulation system. [ABSTRACT FROM AUTHOR]

Methanol concentration as an aging indicator can assess cellulose aging and estimate cellulose degree of polymerization (DP). Despite the advantages of methanol (MeOH), its major disadvantage is that its concentration in the oil decreases after some time. This point is called the MeOH peak in this article. Therefore, using this indicator when MeOH concentration is reduced leads to incorrect results. When using methanol, the main problem is that whenever the methanol concentration in a transformer is measured, it cannot be identified that the measured concentration belongs to the left or right side of the MeOH peak. If the measured concentration belongs to the right side of the MeOH peak, the estimated DP is incorrect. In this article, an empirical equation is established to estimate the DP value as a function of methanol concentration when methanol concentration belongs to the left side of the MeOH peak. Then, a new index called methanol peak detector index (MPDI) composed of a 2-furfural/MeOH ratio and normalized DP is presented to ensure the validity of the calculated DP value. If it is found that the measured concentration belongs to the left side of the MeOH peak, then the estimated DP by the presented equation is valid. Finally, the application of this method is verified by applying it to measurement data samples indicating the validity of the method. [ABSTRACT FROM AUTHOR]

The traditional method for transformer moisture diagnosis is to establish empirical equations between feature parameters extracted from frequency domain spectroscopy (FDS) and the transformer’s moisture content. However, the established empirical equation may not be applicable to a novel testing environment, resulting in an unreliable evaluation result. In this regard, it is acknowledged that FDS combined with machine learning is more suitable for estimating moisture content in a variety of test environments. Nonetheless, the accuracy of the estimation results obtained using the existing method is limited by the algorithm’s inability to generalize. To address this issue, we propose an AdaBoost algorithm-enhanced back-propagation neural network (BP_AdaBoost). This study creates a database by extracting feature parameters from the FDS that characterize the insulation states of the prepared samples. Then, using the BP_AdaBoost algorithm and the newly constructed database, the moisture estimation models are trained. Finally, the results of the estimation are discussed in terms of laboratory and field transformers. By comparing the proposed BP_AdaBoost algorithm to other intelligence algorithms, it is demonstrated that it not only performs better in generalization, but also maintains a high level of accuracy. [ABSTRACT FROM AUTHOR]

The service life of a transformer is determined by the condition of its oil?paper insulation. A modified Cole?Cole model was introduced to describe the frequency domain spectroscopy (FDS) curves based on the classic Debye relaxation model and thereby accurately determined the ageing state of the insulation of a power transformer using FDS. Oil?paper insulation samples with different ageing states are prepared under laboratory conditions, and a characteristic parameter is extracted to quantify their ageing states after a detailed investigation of the frequency domain dielectric properties of the samples. Results reveal that the modified Cole?Cole model, including the ? relaxation components, dc and hopping conductivity components, can be employed to effectively model the frequency dielectric characteristics of oil?paper insulation. Furthermore, the extracted characteristic parameter from this model, ??, can be utilized in the quantitative evaluation of the ageing states of transformer insulation. A case example is discussed to demonstrate the applicability of the proposed technique in the condition assessment of field power transformers. [ABSTRACT FROM AUTHOR]

This paper studies the three-dimensional (3D) trajectory optimization problem for unmanned aerial vehicle (UAV) aided wireless communication. Existing works mainly rely on the kinematic equations for UAV’s mobility modeling, while its dynamic equations are usually missing. As a result, the planned UAV trajectories are piece-wise line segments in general, which may be difficult to implement in practice. By leveraging the concept of state-space model, a control-based UAV trajectory design is proposed in this paper, which takes into account both of the UAV’s kinematic equations and the dynamic equations. Consequently, smooth trajectories that are amenable to practical implementation can be obtained. Moreover, the UAV’s controller design is achieved along with the trajectory optimization, where practical roll angle and pitch angle constraints are considered. Furthermore, a new energy consumption model is derived for quad-rotor UAVs, which is based on the voltage and current flows of the electric motors and thus captures both the consumed energy for motion and the energy conversion efficiency of the motors. Numerical results are provided to validate the derived energy consumption model and show the effectiveness of our proposed algorithms. [ABSTRACT FROM AUTHOR]

The simulation of electric field transients in HVDC cables can be a fundamental tool to support insulation system design when operation dynamics, which includes voltage polarity inversions and load variation, becomes significant during the expected cable life. The field behavior during voltage and temperature transients is associated with intrinsic and extrinsic ageing processes that can affect the cable design life. Traditional models employed for electric field simulations consider only the non–linear, steady–state dependence of conductivity on temperature and field magnitude, but they neglect the transient behavior of permittivity and conductivity, that is, polarization and charge trapping phenomena. These processes impact on the duration of the field transient and thus ageing. This paper discusses the numerical implementation of an electro–quasistatic model that can be used to perform transient simulations of electric fields in HVDC cables insulation. With respect to the model traditionally employed, the presented one is extended with Debye's equations for dielectric polarization processes. The model is implemented numerically using finite differences, or the Occhini equivalent circuit: this paper demonstrates that the two approaches are equivalent. Numerical simulations show that, during transients, polarization processes slow down the electric field dynamics and reduce its maximum intensity. [ABSTRACT FROM AUTHOR]

Chemical mechanical planarization (CMP) is vital for ensuring chip fabrication uniformity at nanometer scales. The emergence of a series of phenomenological CMP process models (Stine et al., 1997; Gbondo-Tugbawa, 2002; Xie, 2007; Vasilev, 2011) suggests that the existing model upgrade approach is largely based on a change in phenomenological model assumptions, demanding deep insights into complex process mechanisms and protracted period for accuracy improvements. To tackle this issue, this paper proposes a decoupling regression strategy for model upgrades. This strategy employs a data-driven approach to enhance the coupling relationships within the model, facilitating continuous improvement of simulation accuracy based on the existing model. It is capable of achieving improvements in model accuracy even in scenarios where modelers lack insight into complex process mechanisms. We validate our method by upgrading the Density Step Height (DSH) model to the Extend-DSH model to address poor erosion predictions at the 28nm node. Comparing model predictions with silicon data reveals that the Extend-DSH model aligns better with the measured data, reducing the root mean square error from 159.31Å to 6.89Å and increasing the coefficient of determination from -0.83561 to 0.6058, showcasing the effectiveness of the proposed chip-level CMP model upgrade method grounded in the decoupling regression strategy. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty of single-phase grounding fault line selection in a small current grounding system, a distribution network fault line selection method based on modified CEEMDAN and convolutional neural network is proposed. Firstly, the random forest and multiscale permutation entropy are used to modify the Complete Ensemble Empirical Mode Decomposition Adaptive Noise algorithm (CEEMDAN), and the zero-sequence current of each line is decomposed into a series of intrinsic mode functions through the modified CEEMDAN (MCEEMDAN) algorithm. Secondly, the intrinsic mode function is transformed into the image formation by using the signal-image conversion method, and the generated image is upgraded to a three-dimensional color image by combining pseudo-color coding technology. Finally, the color images converted from the signals of each line are fused as the input of the GoogLeNet network, and the fault line selection of the distribution network is realized in the form of probability output by the Softmax function. The experimental results show that the proposed method has not only strong feature extraction ability and high recognition accuracy but also has good anti-noise and robustness. [ABSTRACT FROM AUTHOR]

As is known, cable plays a significant role in electric power transmission of the ship. From the safety perspective, this paper aims to predict the ship cable life more accurately and analyze its reliability under small sample conditions. To achieve this goal, based on the non-equidistant grey model (NEGM (1, 1)), an optimized equal-dimension dynamic NEGM (1, 1) is proposed in this paper. In the improved model, the raw data smoothness and the background value are optimized to apply to grey model parameters. Equal-dimension dynamic prediction algorithm (EDDPA) is adopted to predict the lifetime of ship CXF type cable. Compared with the aging model from literature and NEGM (1, 1), the prediction effectiveness and reliability of the proposed model are more considerable. Interestingly, with the increase of recurrence times, the numerical results show this model has higher prediction performance. Finally, the reliability and confidence interval of the life evaluation of ship CXF cable are given to provide more valuable information for the cable management. [ABSTRACT FROM AUTHOR]

This paper is devoted to electric arc furnace (EAF) modeling using a random differential equation based on the power balance equation. The proposed approach broadens and improves the model through the introduction of stochastic processes in place of existing coefficients. The paper presents a method which enables the estimation of EAF model coefficients with the help of measurement data - voltage and current waveforms recorded during the melting stage of an EAF work cycle. The estimation process is conducted with a Monte Carlo method and genetic algorithm, which is applied iteratively to each of the defined frames of the input signal. The estimated coefficients have been analyzed with respect to their time variability as well as the probability distributions of their values and increments. The results have been extensively visualized. Next, the identification of the stochastic processes representing the model coefficients has been carried out. Based on the previous results and autocorrelation functions, the density functions and parameters of discrete-time stochastic processes were identified. The paper presents solutions validated with statistical tests. [ABSTRACT FROM AUTHOR]

This paper refers to the bubble effect in transformer bushings. The bubble effect consists in releasing water from moistened cellulose insulation after exceeding a critical temperature. The most dangerous result is a pressure increase that leads to an explosion and fire. Research was conducted using laboratory models of OIP (Oil- Impregnated Paper) and RBP (Resin-Bonded Paper) types of bushings immersed in mineral oil and synthetic ester. For all of the investigated systems we determined the dynamics of water adsorption upon contact of the insulating material with air and the temperature characteristics of bubble effect initiation depending on water content in cellulose. In the experiment we investigated the dynamics of changes of water content in dielectric liquid, water content in air over the surface of the dielectric liquid, and air pressure over the surface of the dielectric liquid. In the future, the technical possibilities will be analyzed of using the dynamic changes of the three quantities investigated here to detect the bubble effect in bushings. [ABSTRACT FROM AUTHOR]

Public Bike Systems (PBSs) offer the popular service for the short distance in daily life. The battery powered bike is an interesting and feasible method to extend the bike trip length, which can promote the PBS service but faces the challenges caused by the limited budget for the battery cabinet deployment and user demand. Thus, the realistic problem is how to deploy the cabinets near a part of public bike stations by considering the challenges. This paper is the first to study the novel problem, Cabinet Deployment Problem (CDP) in PBS, based on the features extracted from the real dataset of PBS in Hangzhou China, and proposes our strategies in the case of the Euclidean space and Manhattan model. In the Euclidean space, CDP can be specified as the ${e}$ lectric-bike Set Cover problem (e-SC), and this paper proposes a Greedy Station Coverage algorithm (GSC). Its distributed version, called the Localized Greedy Selection algorithm (LGS), is also presented because of the large amount of bike stations. In many cities, the roads have Manhattan-type directions, i.e., either east-west or south-north. In order to close to the realistic scenario, this paper develops a Genetic Algorithm based Cabinet Search algorithm (GAS) to determine the locations for the cabinet deployment in the Manhattan model. The extensive numerical experiment is conducted for our strategies, which are compared to a straightforward method, the Random Placement Strategy (RPS) under the diverse parameter settings. [ABSTRACT FROM AUTHOR]

Direct-drive wave energy converter (WEC) and buoy control algorithms have shown great potential for renewable wave energy extraction in ideal conditions. However the actual power take-off (PTO) impacts are barely considered in the WEC design. This paper highlights the demands of designing the WEC wave-to-wire control from a global point of view by studying the actual PTO impacts. A permanent magnet linear electrical machine (LEM) PTO unit is simulated and controlled to fulfill the WEC buoy control requirements. Several state of the art control algorithms, which include singular-arc (SA) control, shape-based (SB) control, model predictive control (MPC), and proportional-derivative (PD) control, are applied to maximize the wave energy production (mechanical energy). Multiple types of electrical PTOs, including ideal PTO, unlimited PTO and limited PTO, are all implemented to evaluate WEC wave-to-wire performances. Further, the PTO copper loss model and the PTO actual efficiency maps are introduced and studied to improve the electrical PTO operation efficiency. To further assess the control schemes in various wave conditions, one-year PacWave ground-truth data is applied as well. Numerical simulations are conducted using MATLAB/Simulink and the Simscape toolbox. The electrical PTO unit is composed of a LEM, an ideal inverter, and an ideal energy storage system. The results show that the actual PTO will impact the constrained controls (MPC and SB) less comapring to unconstrained controls (SA and PD). Although SB can produce the maximum energy with the limited PTOs, it is not robust for all wave conditions. At the end of the paper, the possible solutions for improving the WEC wave-to-wire performances are also provided. [ABSTRACT FROM AUTHOR]

This paper demonstrates the potential of inverter-based loads to support grid reliability during power system transients thereby enabling reliable integration of renewable energy in power systems. Such loads are referred to in this paper as grid-supportive loads (GSLs). A new GSL model is developed that simulates the transient response capabilities that can be programmed in electronic loads. The model’s design enables it to be easily integrated in widely used commercial power system transient analysis software. Theoretical expressions are derived that explain the workings of the GSL model. The performance, numerical stability, and impact of the GSL model is validated on 9-bus and 2000-bus synthetic power system models using generator tripping and bus fault disturbances. Results on the 2000 bus system show that in the absence of frequency support from wind/solar generation resources, just 20% of loads with grid-supportive capabilities can improve frequency response by up to 2000 MW/0.1 Hz and reduce deviation in frequency at nadir by up to 60% compared to the situation when GSLs are absent. Power system reliability also improves under fault events. It is further shown that GSLs can aid in integrating more renewable generation without degrading the overall transient response of the power system. [ABSTRACT FROM AUTHOR]

Registration between terrestrial LiDAR and optical imagery plays a crucial role in information fusion. However, it is difficult to find reliable correlations among the different feature information of optical imagery and LiDAR point clouds. Therefore, in order to achieve high-precision registration of heterogeneous sensors, a method based on spherical epipolar line and spherical absolute orientation is proposed in this paper. The method firstly projects the LiDAR point clouds into spherical images based on the spherical imaging model and derives the spherical epipolar line equation. Then the relative and absolute orientations of the spherical LiDAR images and the optical images are performed based on manually selected control points. Finally, based on Harris corner extraction, combined with the geometric constraints of the spherical epipolar line and absolute orientation, dense matching between optical and LIDAR images are achieved, and all matching points are used as control points for registration to improve the accuracy of manually selected points registration. Multiple sets of test data are acquired outdoors using a FARO Focus S laser scanner, a Z + F IMAGER 5010C laser scanner, and a Ladybug5+ panoramic camera. The experimental results show that the method in this paper is practical and improves the accuracy of manual points selection registration, and the degree of improvement is related to the number of successfully matched corner points. [ABSTRACT FROM AUTHOR]

Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers. [ABSTRACT FROM AUTHOR]

This paper presents the results of mathematical simulation of processes in argon-hydrogen inductively coupled plasma/radio frequency induction plasma torch for plasma chemical reactions. This first part starts a series of papers for simulation and study of plasma properties for a variety of gases and mixtures, which could be used for a number of prospective technologies. The results of mathematical simulation allow further research on the mechanisms of chemical reactions in argon-hydrogen plasma to increase effectiveness of obtaining the target product. [ABSTRACT FROM AUTHOR]

Though Global Navigation Satellite System (GNSS) has been pretty mature and widely used in our daily life, the positioning of the indoor environment cannot be realized due to the shielding of concretes or glasses by GNSS. Many positioning methods have been proposed to solve that problem, and Bluetooth Low Energy (BLE) is one of the most widely used wireless technologies. This paper takes BLE Angle of Arrival (AOA) as the research object and proposes a method based on signal fitting and Propagator Direct Data Acquisition (PDDA) angle estimation algorithm. This method can be applied to both linear antenna array and rectangular antenna array and has good positioning performance. The method proposed in this paper does not need to calculate the phase differences between antennas, making it unnecessary to calculate the intersection of cones during spatial positioning. The angle estimation is divided into two steps. Firstly, the frequency deviation of Constant Tone Extension (CTE) is calculated based on the IQ samples captured during the reference period, and the received signal matrix of all antennas is fitted based on frequency deviation. Secondly, the antenna received signal model is established based on hardware antenna arrangement, and the arrival angle is estimated by the PDDA algorithm. In order to verify the effectiveness of the proposed method, the plane and spatial positioning experiments are carried out using the BLE devices of Nordic Company, which shows a good positioning accuracy. In spatial positioning, the average positioning accuracy of BLE in channel 37, channel 38, and channel 39 is 0.59m, 0.74m, and 0.77m, respectively, which can meet the basic indoor positioning requirements and cover a large area by one receiver. Compared with the classical MUSIC algorithm, the PDDA algorithm has the same positioning accuracy but lower computational complexity, so it has certain advantages. [ABSTRACT FROM AUTHOR]

As the need for processing long contexts in large language models (LLMs) increases, attention-based LLMs face significant challenges due to their high computation and memory requirements. To overcome this challenge, there have been several recent works that seek to alleviate attention's system-level bottlenecks. An approach that has been receiving a lot of attraction lately is state space models (SSMs) thanks to their ability to substantially reduce computational complexity and memory footprint. Despite the excitement around SSMs, there is a lack of an in-depth characterization and analysis on this important model architecture. In this paper, we delve into a representative SSM named Hungry Hungry Hippos (H3), examining its advantages as well as its current limitations. We also discuss future research directions on improving the efficiency of SSMs via hardware architectural support. [ABSTRACT FROM AUTHOR]

The universal filtered multicarrier (UFMC) modulation is among the most considered solutions for the realization of beyond orthogonal frequency division multiplexing (OFDM) air interfaces for future wireless networks. This paper focuses on the design and analysis of a UFMC transceiver equipped with multiple antennas and operating at millimeter-wave carrier frequencies. This paper provides the full mathematical model of an MIMO-UFMC transceiver, taking into account the presence of hybrid analog/digital beamformers at both ends of the communication links. Then, several detection structures are proposed, both for the case of single-packet isolated transmission and for the case of multiple-packet continuous transmission. In the latter situation, this paper also considers the case in which no guard time among adjacent packets is inserted, trading off an increased level of interference with higher values of spectral efficiency. At the analysis stage, several considered detection structures and transmission schemes are compared in terms of bit-error rate, root-mean-square error, and system throughput. The numerical results show that the proposed transceiver algorithms are effective and that the linear minimum mean-square error (MMSE) data detector is capable of well managing the increased interference brought by the removal of guard times among consecutive packets, thus yielding throughput gains of about 10%–13%. The effect of phase noise at the receiver is also numerically assessed, and it is shown that the recursive implementation of the linear MMSE exhibits some degree of robustness against this disturbance. [ABSTRACT FROM AUTHOR]

This paper investigates the deployment issue of an emergency medical service (EMS) system to maintain the preferred service coverages under different considerations. Specifically, two coverage levels are introduced to reflect the requirements under the regular situation and the situation with ambulance unavailable. We propose the two-stage robust optimization (RO) models to design a reliable ambulance system subject to unavailability of the ambulances, with and without the ambulance relocation. For the RO problem with mixed-integer recourse for relocation, we customize the column and constraint generation method with an approximation strategy to handle the computational challenge. Our numerical study: 1) demonstrates that our RO formulations have a strong modeling capacity on designing the EMS system; 2) shows that our approximation algorithm performs very well; and 3) provides a quantitative evaluation of, including, relocation operations on the system performance. Note to Practitioners—This paper presents the novel optimization models to help ambulance deployment. Due to ambulance unavailability and relocation operations, traditional optimization formulations might not be sufficient for modeling or might be hard for computation. In this paper, we provide an uncertainty set-based approach to capture ambulance unavailability and to build the robust optimization models (with relocation recourse decisions). Also, efficient algorithms are designed to support practical instances. Numerical results are very supportive to our new models and computational methods. [ABSTRACT FROM AUTHOR]

Building clock trees for tight skew constraints of clock delivery networks is standard in the industry. Tight slew constraints of high-performance designs require post-processing techniques to satisfy slew constraints after clock tree synthesis (CTS). Post-processing adversely impacts the power dissipation. This paper proposes slew merging region CTS (SMRcts); a novel algorithm to satisfy bounded slew and skew constraints simultaneously during synthesis. Experimental results performed on International Symposium on Physical Design (ISPD) 2010 benchmarks using a 20-nm FinFET technology show an average reduction of 15% power over a bounded skew approach. Comparison to the ISPD 2010 CTS contest solutions in the literature shows SMRcts producing a 51% improvement in a utility metric. Scalability of SMRcts is demonstrated on ISPD 2013 benchmarks with up to 100k sinks. [ABSTRACT FROM AUTHOR]

Fast and accurate power-flow methods are of great importance, especially in near real-time optimal operation of power systems. This importance will be even more highlighted in the presence of more repetitions of power-flow calculations, which cause more computational complexities in optimization problems. As a solution, in this paper, a novel fast and accurate approach of linear power-flow formulation is proposed. Principles of the proposed approach are based on dividing power-flow calculations into base and variable parts. To this aim, at first, system modeling of base and variable parts are presented. For the base-part modeling, utilizing a nonlinear power-flow, an accurate base power-flow (BPF) is extracted. Afterwards, by linearizing the power system around the BPF, variable-part model which is the result of a linear fitting process, is obtained. Then, it is shown that the variable-part of the operating point is always a function of the obtained base-part and variable-part models. In this paper, by focusing on the stochastic application of the proposed approach, different uncertainties in a distribution system are considered. Finally, numerical results carried out in the Matlab environment, for a IEEE 34-bus standard distribution system and then a 1486-bus case study, verify the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a model-free distributed multi-sensor extended Kalman filter (DMSEKF) full state estimation algorithm to provide long-term convergent flight parameters for small fixed-wing unmanned aerial vehicles (UAVs). The full state has the attitude, velocity, position, airspeed, and 2D horizontal wind speed. The airspeed and wind speed are estimated in wind disturbance to provide more robust perception information. The model-free estimator has a low-cost standard sensor suite, including an IMU, a magnetometer, a barometer, a GPS module, and an airspeed tube, rather than the aerodynamic model of the UAVs to increase the multi-sensor fusion algorithm versatility in various UAVs. Then, the full state integrated navigation model is established based on the onboard sensor suite fused by the distributed tightly-coupled EKF. In addition, a consistent multiple sensors data processing method is designed to synchronize the time node of all onboard sensors. Finally, the proposed algorithm is verified through the experimental flight sensor data. The results demonstrate that the proposed algorithm can provide a reliable full state vector and achieve an effective solution performance during the UAVs flight application. [ABSTRACT FROM AUTHOR]

For a DC microgrid with constant power loads (CPLs), the existence of a feasible power-flow solution, which is usually difficult to analyze, is a necessary condition for the correct operation of systems. In this paper, the solvability of power-flow equation of DC microgrid with CPLs is analyzed, where a majority of distributed generations (DGs) are under MPPT control while other DGs are under droop control. At first, this paper builds a power-flow mathematical model of the DC microgrid. Secondly, three solvability conditions are proposed with the first one being applied to ensure the power-flow equation has a solution, while the other two applying to ensure the solution within the given voltage deviation. The first analytical condition () is obtained by applying Brouwer fixed-point theorem. Compared with the existing results, this obtained sufficient condition is less conservative. Furthermore, the other two sufficient solvability conditions (and) can guarantee the equilibrium not only exists, but also the voltage deviation is within an acceptable range. Finally, case studies verify the correctness of the proposed theorems. The obtained conditions provide a guidance for establishing a dependable DC microgrid. [ABSTRACT FROM AUTHOR]

In general coding theory, we often assume that error is observed in transferring or storing encoded symbols, while the process of encoding itself is error-free. Motivated by recent applications of coding theory, in this paper, we consider the case where the process of encoding is distributed and prone to error. We introduce the problem of distributed encoding, comprised of a set of $K \in \mathbb {N}$ isolated source nodes and $N \in \mathbb {N}$ encoding nodes. Each source node has one symbol from a finite field, which is sent to each of the encoding nodes. Each encoding node stores an encoded symbol from the same field, as a function of the received symbols. However, some of the source nodes are controlled by the adversary and may send different symbols to different encoding nodes. Depending on the number of the adversarial nodes, denoted by $\beta \in \mathbb {N}$ , and the cardinality of the set of symbols that each one generates, denoted by $v \in \mathbb {N}$ , the process of decoding from the encoded symbols could be impossible. Assume that a decoder connects to an arbitrary subset of $t \in \mathbb {N}$ encoding nodes and wants to decode the symbols of the honest nodes correctly, without necessarily identifying the sets of honest and adversarial nodes. An important characteristic of a distributed encoding system is $t^{*} \in \mathbb {N}$ , the minimum of such $t$ , which is a function of $K$ , $N$ , $\beta $ , and $v$. In this paper, we study the distributed linear encoding system, i.e. one in which the encoding nodes use linear coding. We show that $t^{*}_{\textrm {Linear}}=K+2\beta (v-1)$ , if $N\ge K+2\beta (v-1)$ , and $t^{*}_{\textrm {Linear}}=N$ , if $N\le K+2\beta (v-1)$. In order to achieve $t^{*}_{\textrm {Linear}}$ , we use random linear coding and show that in any feasible solution that the decoder finds, the messages of the honest nodes are decoded correctly. In order to prove the converse of the fundamental limit, we show that when the adversary behaves in a particular way, it can always confuse the decoder between two feasible solutions that differ in the message of at least one honest node. [ABSTRACT FROM AUTHOR]

This paper introduces a possible mechanism to explain the changes in neurons following extremely low frequency magnetic fields (ELF-MFs). The model in this paper contains two parts: the physical part and the bio-trigger part. The first part is an electric current equation, which is a generalization of the Nernst–Plank equation with the exposure of ELF-MF, and the second bio-part is a phenomenological model, which is a simple two-state channel with two steady state open (O) state and closed state (C). The two parts are complementary. The first part is utilized to explain changes about synaptic plasticity following ELF-MF exposure at embryonic and postnatal age. The second part is used to simulate the process of three different types of neurons reported by previous papers. According to the results, the present model is feasible for the simulation process. The concept of ion mobility or permeability is emphasized in this paper in part I, and channel state transition probabilities are paid much attention to in part II. In this paper, the ion mobility or permeability is supposed to have close relationships with ion channel transition probabilities, which connects the two parts of the model together. [ABSTRACT FROM PUBLISHER]

Existing non-intrusive load identification methods face with the difficulty of being applied to industrial users whose operation and ON/OFF status are hard to be acquired. Focusing on the above challenge, this paper proposes a non-intrusive identification method of harmonic source loads for industrial users. Utilizing Clarke transform, an equation with respect to voltage and current is formulated to calculate the integrated equivalent impedance, which is then decomposed with the improved complex local mean decomposition (CLMD) and complex fast independent component analysis (C-FastICA) achieving non-intrusive separation of the equivalent impedance signals of individual branches. The separated impedance signals will be used as the basis for load type identification. The proposed method is validated by numerical simulations as well as actual three-phase voltage and current monitoring data. The case study shows that it can effectively identify the types of harmonic source loads while being resistant to noise and impacts of sampling accuracy and diverse load capacities, which obviously demonstrates its practical applicability. [ABSTRACT FROM AUTHOR]

The opening up of the railway to competition in Europe has been accompanied by many technical challenges never before seen in the railway field. One of these challenges concerns precise energy metering. As European railways have been a monopoly until now, the current systems can only measure the energy consumption of a single operator. In accordance with European law, the installation of an energy meter has been mandatory in new or refurbished rolling stocks since 2014. Nevertheless, these energy meters are still not fully reliable, with misreadings and missing readings being common occurrences. To address the problem of energy metering data reliability, this paper presents a method for validating the reading measurement based on a physical model and validates it in two different case studies. For the issue of energy metering data availability, different estimators are developed, and their performance are shown with the aim of calculating the missing consumptions for both case studies. Finally, the paper suggests how these methods may be implemented in other types of trains. [ABSTRACT FROM AUTHOR]

This paper presents an approach for the time domain statistical simulation of field-to-wire coupling model with uncertain wire position in traverse direction. The parameters of governing equations of field-to-wire coupling model about the wire position are expanded to the augmented governing equations by using the generalized polynomial chaos technique with normalized orthogonal basis functions. And the coefficients of the polynomial chaos expansions are solved by combining with the stochastic Galerkin method. However, the solution of augmented governing equations is a complex problem. Therefore, a new finite-difference time-domain (FDTD) algorithm based on the implicit Wendroff difference format is proposed to solve the governing equations, and the general iterative equations are given in this paper. The proposed methods are verified by a field-to-wire coupling model. The effectiveness and advantages of the new FDTD algorithm and the uncertainty analysis method based on generalized polynomial chaos technique are verified, which are achieved by comparing with the conventional leapfrog fashion FDTD algorithm and the Monte Carlo technique, respectively. [ABSTRACT FROM AUTHOR]

In this paper, the performance drift of digital closed-loop quartz flexible accelerometer (QFA) in start-up period is attributed to the heating of the feedback circuit and coil. Compared to the drift compensation by temperature model which is built by statical rule, the gain stabilization of closed-loop channel is an essential and direct method to restrain the drift. In this paper, a high accurate current detection circuit is designed to evaluate the parameters drift and to stabilize the gain of the feedback channel. A stability experiment is conducted to prove the efficiency of the proposed stabilization strategy. The result shows that the stability of output in start-up period improves by 37.6% at most. Therefore, the proposed stabilization strategy can effectively improve the start- up performance of QFA, which could be promoted to other sensors whose performance suffer from the drift of the channel. [ABSTRACT FROM AUTHOR]

This paper proposes a trajectory optimization approach for high-speed trains to reduce traction energy consumption and increase riding comfort. Besides, the proposed approach can also achieve energy-saving effects by optimizing the operation time between stations. First, an optimization model is developed by defining the objective function as a trade-off function of the traction energy consumption and riding comfort. In addition to constraints in the classic optimal train control model, three new factors–the discrete throttle settings, neutral zones, and sectionalized tunnel resistance–are considered. Then, the model is discretized and turned into a multi-step decision optimization problem. All the nonlinear constraints are approximated using piecewise affine (PWA) functions, and the trajectory optimization problem is turned into a mixed integer linear programming (MILP) problem which can be solved by existing solvers CPLEX and YALMIP. Finally, some case studies with real-world data sets are conducted to present the effectiveness of the proposed approach. The simulation results are compared with the practical running data of trains, which shows that the proposed model and the optimization approach save energy and improve the riding comfort. [ABSTRACT FROM AUTHOR]

This paper proposes a way to represent the anisotropic nonlinear superconducting properties using a discrete model for magnetization with the corresponding critical parameters, equivalent to the Power Law model for currents. This simplified model makes it possible to analyze magnetic systems with superconducting elements in a stationary mode, which reduces the calculation time. Using of proposed model is demonstrated by the calculation of force interactions between high-temperature superconducting (HTS) samples and permanent magnet in zero-field-cooling (ZFC) and field-cooling (FC) modes. HTS bulks in the form of a prism and stack made of HTS tape are considered. The calculation of the electromagnetic field was carried out using the method of integral equations for field sources. The data of calculations are compared with the results of experimental studies. [ABSTRACT FROM AUTHOR]

Factor graph optimization (FGO) is a well-known approach in the robotics field. Due to multiple iterations and application of large amounts of data, it usually could superior performance than the Kalman Filter (KF) under complex scenarios. However, it is rarely used in the underwater environment, especially for applications based on fiber-optic inertial navigation. Taking the ultra-short baseline system (USBL) as the application background, a novel inertial navigation system (INS)/USBL integrated navigation scheme based on FGO is presented in this paper. The traditional pre-integration factor simplifies the earth rotation and coordinate system, which limits its application in high-precision sensors. This paper proposed an improved pre-integration model using the high precision measurements of fiber optics gyroscope in FGO. Simulation and field tests proved that, with the FGO method, improved accuracy of INS/USBL integration was obtained compared to the traditional KF method. It confirms the potential of FGO in the applications of underwater navigation and positioning. [ABSTRACT FROM AUTHOR]

The energy transition is aimed to take advantage of the operational flexibility of hydropower to extend the integration of intermittent renewable sources. Consequently, the hydrogenerators will have to operate in regimes far away from their designed best-point operation. In order to accurately assess the impact, this paper presents a useful approach to determine the overall operating efficiency of synchronous generators under intermittent operation. An accumulated average efficiency (AAE) model is proposed and compared against an alternative approach; the weighted average efficiency (WAE) model. It is found that the WAE approach produces unrealistic low efficiencies when the generator operates in synchronous condenser mode (SCM) for long periods. In general, the AAE supersedes the WAE for all the different load distributions that were investigated. This was further illustrated by a worked example and by constructing more complex load distributions. A load distribution dominated by SCM yields a difference as high as $33.18 \,\%$ , while an even distribution deviates $1.43 \,\%$ in their respective efficiencies. Finally, a yearly on-site measurement of our studied $103 \,\mathrm{MVA}$ generator’s concentrated load distribution revealed a discrepancy of $0.67 \,\%$ , which is a significant deviation considering what the operating regime would mean in terms of economic implications. [ABSTRACT FROM AUTHOR]

Imaging at a long distance often requires advanced image restoration algorithms to compensate for the distortions caused by atmospheric turbulence. However, unlike many standard restoration problems such as deconvolution, the forward image formation model of the atmospheric turbulence does not have a simple expression. Thanks to the Zernike representation of the phase, one can show that the forward model is a combination of tilt (pixel shifting due to the linear phase terms) and blur (image smoothing due to the high order aberrations). Confusions then arise between the ordering of the two operators. Should the model be tilt-then-blur, or blur-then-tilt? Some papers in the literature say that the model is tilt-then-blur, whereas more papers say that it is blur-then-tilt. This paper clarifies the differences between the two and discusses why the tilt-then-blur is the correct model. Recommendations are given to the research community. [ABSTRACT FROM AUTHOR]

A recent research paper by Wei et al. proposes a differential privacy algorithm in the context of Federated Learning and provides its performance analysis, mainly focusing on proving a convergence bound for the loss function. In this paper, we show that some of the mathematical derivations given in Wei et al. are not valid. Thus the bounds they prove in the paper do not hold for all loss functions. In this work, we give the correct derivation of the best possible local sensitivity bound, which is valid for all loss functions. We also state the modifications in the bounds for global sensitivity and the standard deviation of the Gaussian noise added, both before and after aggregation. [ABSTRACT FROM AUTHOR]

The paper presents an experimental validation of a method to locate partial discharges (PDs) on power distribution and transmission networks. The method is based on electromagnetic time reversal (EMTR) theory, and it uses a Transmission Line Matrix (TLM) model to describe the propagation of the PD signals in the reversed time. Since PDs are regarded as a symptom of insulation degradation, on-line PD location is considered an important approach to monitoring the integrity of a power distribution network, with the aim of detecting and preventing faults and improving network reliability. In this paper, the EMTR-based method is described and its effectiveness in PD localization using only one measurement point is demonstrated in three real 33 kV power lines. Its effectiveness is proved with and without an on-line electromagnetically noisy environment, and its accuracy is evaluated with respect to different signal-to-noise ratio (SNR) levels of the networks. The validation shows that the method is able to locate PDs with an error of 0.14% with respect to the total length of the line in the absence of noise, and with an error that is always lower than 0.5% for an SNR down to −7 dB. [ABSTRACT FROM AUTHOR]

Ramp metering offers great potential to mitigate traffic congestion and improve freeway management efficiency under traffic congestion conditions. This paper proposes an optimization program for freeway dynamic ramp metering based on Cell Transmission Model (CTM). This problem has been formulated as a discrete time optimal control problem with smooth state equations and constraints to meter traffic inflow from on-ramps. In the proposed model, the ‘min’ operators in the primal CTM are non-differentiable and thus, the corresponding optimal control problem cannot be solved directly using conventional gradient based methods. In this paper, we introduce a smooth approximation to approximate the ‘min’ operators and then a unified computational approach is developed to solve the problem. Theoretical analysis is carried out, showing that the optimal solution obtained from the approximated problem converges to the optimal solution of the primal CTM. Compared to the classical inequality relaxation method, our method can resolve the flow holding-back problem and reduce under fundamental diagram phenomenon. Compared with the Big-M method, our method has better efficiency. To achieve the desired traffic response control in real application, a series of online optimal control problems are solved using Model Predictive Control (MPC). Simulation studies show that our method can significantly improve freeway traffic management efficiency. [ABSTRACT FROM AUTHOR]

It is known that there exist mathematical problems of practical relevance which cannot be computed on a Turing machine. An important example is the calculation of the first derivative of continuously differentiable functions. This paper precisely classifies the non-computability of the first derivative, and of the maximum-norm of the first derivative in the Zheng-Weihrauch hierarchy. Based on this classification, the paper investigates whether it is possible that a Turing machine detects this non-computability of the first derivative by observing the data of the problem, and whether it is possible to detect upper bounds for the peak value of the first derivative of continuously differentiable functions. So from a practical point of view, the question is whether it is possible to implement an exit-flag functionality for observing non-computability of the first derivative. This paper even studies two different types of exit-flag functionality. A strong one, where the Turing machine always has to stop, and a weak one, where the Turing machine stops if and only if the input lies within the corresponding set of interest. It will be shown that non-computability of the first derivative is not detectable by a Turing machine for two concrete examples, namely for the problem of computing the input–output behavior of simple analog circuits and for solutions of the three-dimensional wave equation. In addition, it is shown that it is even impossible to detect an upper bound for the maximum norm of the first derivative. In particular, it is shown that all three problems are not even semidecidable. Finally, we briefly discuss implications of these results for analog and quantum computing. [ABSTRACT FROM AUTHOR]

A high proportion of wind power systems with correct as set weak network connections may suffer from cascading trip faults due to the lack of conventional power plants and voltage/var support, which brings about great challenges to secure wind farm operations. To accurately and efficiently evaluate the dynamic var margin for an online field application within large-scale integrated wind areas, the transient dynamic voltage processes of hundreds or thousands of wind turbines must all be considered due to the dramatic interior voltage fluctuation effect of wind units, which brings a heavy computational burden for solving the TSCOPF problem. Therefore, to address these large-scale challenges, this paper proposes an objective-oriented approximation dimensionality reduction equivalent (OOA-DRE) approach, which accurately transforms the high-dimensional differential system to a low-dimensional system with few differential equations and low-dimensional algebraic equations for the wind unit with the maximum electrical distance value. Meanwhile, this paper uses the Levenberg-Marquardt method to obtain the global pre-evaluation error, so that the evaluation error reaches the minimum value. Finally, an actual wind power system verifies that the calculation time is reduced by approximately 2/3 compared with the original model on the premise of ensuring accuracy for large-scale systems. [ABSTRACT FROM AUTHOR]

This paper introduces a deep learning modular network for side-channel analysis. Our approach features a deep learning architecture with the capability to exchange parts (modules) with other neural networks. We aim to introduce reusable trained modules into side-channel analysis instead of building architectures from scratch for each evaluation, reducing the body of work. Our experiments demonstrate that our architecture feasibly assesses a side-channel evaluation, suggesting that learning transferability is possible using the architecture we propose in this paper. [ABSTRACT FROM AUTHOR]