Search

Device-to-device (D2D) communication is developed as a new paradigm to enhance network performance according to LTE and WiMAX advanced standards. The D2D communication may have dedicated spectrum (overlay) or shared spectrum (underlay). However, the allocated dedicated spectrum may not be effectively used in the overlay mode, while interference between the D2D users and cellular users cause impairments in the underlay mode. Can the resource allocation of a D2D system be optimized using the cognitive approach where the D2D users opportunistically access the underutilized radio spectrum? That is the focus of this paper. In this paper, the transmission rate of the D2D users is optimized while simultaneously satisfying five sets of constraints related to power, interference, and data rate, modeling D2D users as cognitive secondary users. Furthermore, a two-stage approach is considered to allocate the radio resources efficiently. A new adaptive subcarrier allocation scheme is designed first, and then, a novel power allocation scheme is developed utilizing geometric water-filling approach that provides optimal solution with low computation complexity for this nonlinear problem. Numerical results show that the proposed approach achieved significant performance enhancement than the existing schemes. [ABSTRACT FROM PUBLISHER]

This paper investigates the impacts of typical semiconductor material properties—electron affinity, bandgap, and dielectric constant, on the electrical performance of a p-type core–shell heterojunction nanowire FET by numerical simulations. At the heterojunction, a valence band offset of 200 meV forms a sufficient energy barrier confining the holes in the quantum well, resulting in the optimal OFF-state current. A higher dielectric constant of the shell region is found to be able to decrease the leakage current of the device. The optimum conditions from the parameter analysis are demonstrated by a realistic and achievable material combination of Si/SiGe for the core–shell configuration. This paper provides physical insights into the materialwise impacts for designing the proposed transistor showing the reduced OFF-current and a better subthreshold swing for low-power applications. [ABSTRACT FROM PUBLISHER]

The variability of crosstalk due to changes in differential and ground via configurations is studied in this paper. The polynomial-chaos method and the response surface method are adopted to mathematically model the variability. One goal of the work is to exploit the obtained models to locate the optimal response that both meets the performance requirement and remains robust to geometry variations due to manufacturing tolerances. Both methods correlate well with simulations and show great capabilities in practical applications for optimization and sensitivity analysis. The other goal of the work is to compare the two methods in terms of mathematical theories, sampling schemes, postprocessing, accuracy, efficiency, and limitations. Details of the comparison is given through this paper and a summary table is included in Section V. [ABSTRACT FROM AUTHOR]

On account of complementary control, reduced size, and energy saving, the switched-capacitor (SC) based equalizer becomes promising for the energy management of energy storage system. Traditionally, the number of the bypass capacitor in the SC based equalizer equals to the number of the battery module in series or parallel connections. The amount of bypass capacitors is enormous, thus costly and bulky for electric vehicles (EVs). In this paper, the integrated energy storage is proposed to reduce cost and save space, meanwhile, the equalizations between the batteries and the ultracapacitors (UCs) are achieved by the reutilization of SC network. UCs not only perform as energy storage units, but also work as energy exchange units. Thus, the number of balanced elements can be reduced significantly. The operational principles of the storage system and the analyses of the balancing process are examined in detail. The system simulation is performed for functionality evaluations. Meanwhile, an experimental prototype is also implemented for six cells to demonstrate the balancing process. The accordance of experiment and simulation can further testify the feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

Emergency responders are typically notified immediately after a major earthquake strikes. However, a time delay, called the travel time, is usually experienced between the notification and the arrival of the responders on the scene. The reparative work necessary after the responders arrive takes an additional amount of time, called the response time, depending on the nature of the damage and the volume of resources available. In a smart city, the restoration time, which is the sum of the travel and response times, should be minimized. A new discrete-event-based simulation (DEBS) model is presented in this paper to estimate the restoration time needed to bring the situation under control after notifying the response center. The DEBS model not only relaxes restrictive assumptions on travel time made by the Markov chain models from the existing literature, but it can also quantify the impact of resource volumes on restoration times. Additionally, the DEBS model is very useful for training purposes. The DEBS model was employed on a case study from the state of Missouri (U.S.). The experiments demonstrate that numerical results with the model take a short amount of computational time and that it can be implemented on a real-time basis in a smart-city infrastructure. [ABSTRACT FROM AUTHOR]

Availability of a common, shared benchmark to provide repeatable, quantifiable, and comparable results is an added value for any scientific community. International consortia provide benchmarks in a wide range of domains, being normally used by industry, vendors, and researchers for evaluating their software products. In this regard, a benchmark of untimed Petri net models was developed to be used in a yearly software competition driven by the Petri net community. However, to the best of our knowledge there is not a similar benchmark to evaluate solution techniques for Petri nets with timing extensions. In this paper, we propose an evaluation framework for the comparative analysis of generalized stochastic Petri nets (GSPNs) simulation techniques. Although we focus on simulation techniques, our framework provides a baseline for a comparative analysis of different GSPN solvers (e.g., simulators, numerical solvers, or other techniques). The evaluation framework encompasses a set of 50 GSPN models including test cases and case studies from the literature, and a set of evaluation guidelines for the comparative analysis. In order to show the applicability of the proposed framework, we carry out a comparative analysis of steady-state simulators implemented in three academic software tools, namely, GreatSPN, PeabraiN, and TimeNET. The results allow us to validate the trustfulness of these academic software tools, as well as to point out potential problems and algorithmic optimization opportunities. [ABSTRACT FROM AUTHOR]

This work presents a potential solution for enabling the use of multicast in the 5G New Radio Release 17, called 5G NR Mixed Mode. The proposed multicast/broadcast mode follows one of the two approaches envisaged in 3GPP, which enables a dynamic and seamless switching between unicast and multicast, both in the downlink and the uplink. This paper also provides a performance evaluation of several IMT-2020 KPIs, including available data rate and spectral efficiency, user and control plane latencies, energy efficiency, and mobility, highlighting the potential advantages of this solution over unicast in relevant scenarios. Finally, other multipoint–based KPIs such as coverage or packet loss rate are also evaluated by means of system level simulations. [ABSTRACT FROM AUTHOR]

Integrated energy systems (IESs) are composed of multiple heterogeneous subsystems, i.e., electrical power system, natural gas system, and district heating system (DHS), which endow the whole system with excellent performance in overall efficiency and renewable energy utilization. The paper aims to offer a concise and analytical model for the thermal dynamic characteristics (i.e., thermal inertia) of the district heating network (DHN) and buildings to facilitate the analysis, planning, and operation of IESs. Firstly, an equivalent start network is introduced for modeling the radial DHN, and a synchronous response model is proposed for buildings to approximate the optimal response of heat load. Secondly, the thermal inertia aggregation model (TIAM) is proposed, which offers an accurate DHN and buildings model for the planning and operation of IESs. Finally, some properties of the TIAM are derived to reveal its potential in general applications such as analysis and evaluation. Simulation results of different scale systems demonstrate the performance of the proposed model and reveal its advantages in the computational efficiency and sensitive information protection of DHN. [ABSTRACT FROM AUTHOR]

In this paper, we consider requirement-driven selection of Costas arrays for the detection of closely spaced targets. We review existing order selection strategies in terms of the time–bandwidth product and treat selection of specific Costas arrays separably. Our computational analyses prioritize a clear central region in single-pulse discrete ambiguity functions (DAFs) and also address maximum frequency hops. Using finite field arithmetic, we significantly generalize results about 1s in the central square DAF regions for Welch and Lempel–Golomb arrays, and prove two general results about the nonexistence of such clear regions. We find that order 14 is very beneficial for target detection in which Costas arrays having an almost entirely clear $5 \times 5$ square central region in the single-pulse DAF exist and feature the corresponding time–bandwidth product of 196 within an example. We feature a first-principles system simulation with such an order-14 Costas array to obtain a 3-dB target detection improvement. Since radars commonly use burst waveforms, we also consider three different kinds of bursts and discuss their merits using ambiguity function analyses. We provide supporting computational database surveys on central rectangular $3 \times m$ regions. [ABSTRACT FROM AUTHOR]

This paper presents a modification of the Vienna rectifier (VR) to obtain more than three voltage levels at the DC line connection, while preserving the unity power factor at the AC grid side. The proposed unidirectional multilevel there-phase rectifier, called the “Negev rectifier”, has a reduced device count and utilizes a unique novel Time Division Multiplexed based control scheme, which makes the challenging N-level VR expansion possible. The Negev rectifier can serve as a grid interface for supplying dc voltage to multilevel inverters with 4 up to 7 voltage levels, eliminating the burden of inverter-side voltage balancing control. The Negev hardware can be constructed by two complementary topologies, making it suitable for either high current or high voltage applications. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic model with iron saturation to obtain better dynamic performance and more accurately steady-state performance than the dynamic model without iron saturation for the dual-stator brushless doubly-fed induction generator. Based on the proposed dynamic model, the MATLAB/SIMULINK model is established. The key to the simulation model considering iron saturation is that the magnetizing inductance can be updated for each simulation cycle. The magnetizing inductance used in the simulation is experimentally measured to guarantee the simulation accuracy. Dynamic simulations including the current, voltage, flux linkage, active power, and torque are performed, showing that the dynamic model considering iron saturation contributes to the merits of reducing the setting time and the oscillation amplitudes of the torque and the power in starting stage. Experimental assessment is carried out to verify the effectiveness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper presents a learning approach to predict air-to-ground communication channel strength to support the communication-relay mission using the unmanned aerial vehicle (UAV) in complex urban environments. The knowledge of the air-to-ground communication channel quality between the UAV and ground nodes is essential for optimal relay trajectory planning. However, because of the obstruction by buildings and interferences in the urban environment, modeling and predicting the communication channel strength is a challenging task. We address this issue by leveraging the Gaussian process (GP) method to learn the communication shadow fading in a given environment and then employing the optimization-based relay trajectory planning by using learned communication properties. The key advantage of this learning method over fixed communication model based approaches is that it can keep refining channel prediction and trajectory planning as more channel measurement data are obtained. Two schemes incorporating GP-based channel prediction into trajectory planning are proposed. Monte Carlo simulations demonstrate the performance gain and robustness of the proposed approaches over the existing methods. [ABSTRACT FROM AUTHOR]

In voltage-source converter systems, utilization of filter inductors with deep saturation characteristics is often advantageous because of the improved size, cost, and efficiency. However, with the use of conventional current regulation methods, the inductor saturation results in significant dynamic performance loss and poor steady-state current waveform quality. This paper proposes the saturation compensation with resistive decoupling (SCRD) method to overcome these performance issues. The method converts the nonlinear inductor of the plant to a fictitious linear inductor such that linear current regulators can perform satisfactorily. The steady-state and dynamic performances of SCRD are investigated in detail in comparison with other methods and advantageous performance attributes of SCRD are demonstrated on a 1.5-kW grid-connected system via simulations and laboratory experiments. In addition, identification of L–R parameters is performed, parameter sensitivity issues are addressed, and satisfactory performance is demonstrated. [ABSTRACT FROM AUTHOR]

Many signal processing applications require estimation of time-varying sparse signals, potentially with the knowledge of an imperfect dynamics model. In this paper, we propose an algorithm for dynamic filtering of time-varying sparse signals based on the sparse Bayesian learning (SBL) framework. The key idea underlying the algorithm, termed SBL-DF, is the incorporation of a signal prediction generated from a dynamics model and estimates of previous time steps into the hyperpriors of the SBL probability model. The proposed algorithm is online, robust to imperfect dynamics models (due to the propagation of dynamics information through higher-order statistics), robust to certain undesirable dictionary properties such as coherence (due to properties of the SBL framework), allows the use of arbitrary dynamics models, and requires the tuning of fewer parameters than many other dynamic filtering algorithms do. We also extend the fast marginal likelihood SBL inference procedure to the informative hyperprior setting to create a particularly efficient version of the SBL-DF algorithm. Numerical simulations show that SBL-DF converges much faster and to more accurate solutions than standard SBL and other dynamical filtering algorithms. In particular, we show that SBL-DF outperforms state of the art algorithms when the dictionary contains the challenging coherence and column scaling structure found in many practical applications. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is regarded as a promising technology for the next-generation wireless communication system. Introducing NOMA into the fog radio access networks (F-RANs) is able to provide simultaneous transmissions to multiple users and significantly enhance F-RAN performance. However, due to the increasing number of users and the constraint of caching storage capacity, there exists a tradeoff between NOMA transmission performance and fronthaul saving. In this paper, a hierarchical game framework is presented to solve the joint optimization problem of user access mode selection and content popularity prediction in NOMA based F-RANs. More specifically, the access mode selection problem is formulated as an evolutionary game. The proposals’ evolutionary payoff expressions are derived by stochastic geometry tool, and the cost functions are related to the fog access point (F-AP) content placement profile as well as the fronthaul constraint. Moreover, the problem of what contents the F-AP should cache is modeled as a content popularity prediction problem, and based on both local and global user request states, a machine learning algorithm is presented to solve it. Simulation results validate the accuracy of analytical results and demonstrate our proposed algorithms can further improve the performance of NOMA based F-RANs. [ABSTRACT FROM AUTHOR]

Accurate and stable short-term traffic flow prediction is an indispensable part in current intelligent transportation systems. In this paper, a novel short-term traffic flow forecasting model termed as EnLSTM-WPEO is proposed based on ensemble learning of long short term memory neural network (LSTM), no negative constraint theory (NNCT) weight integration and population extremal optimization (PEO) algorithm. In the first stage, a cluster of LSTMs is constructed to separately forecast with different time lag, which is a significant element to affect the prediction performance. In the second stage, the PEO-based NNCT weight integration strategy is introduced to determine the weight coefficients of the ensemble model. The simulation results for six different datasets from highways of Seattle have testified the superiority of the proposed EnLSTM-WPEO to other six popular traffic flow forecasting models in terms of two commonly used performance indices and three statistical tests. [ABSTRACT FROM AUTHOR]

Transportation electrification is increasing and recently more focus has been directed on heavy vehicles and especially on city buses. Battery electric buses are inherently more energy efficient than diesel buses and the efficiency can be further increased by different methods. This paper evaluates the energy consumption reductions that are achievable with an aluminum chassis, low-drag body, low-rolling-resistance class C tires, heat pump, and predictive driving. A simulation model of a generic electric bus was developed in the Simulink software. Simulations were carried out on various types of driving cycles in cold (−10 °C) and warm conditions (20 °C). A novel nonlinear model predictive control problem formulation was created for minimizing the energy consumption of an electric bus. Using a heat pump instead of an electric heater provided the highest energy savings in the cold conditions with an average consumption reduction of 12.7%. The results indicated that a heat pump is particularly effective on low-speed bus routes. However, the class C tires and aluminum chassis provided higher energy savings than the heat pump in the warm conditions. The low-rolling-resistance tires achieved the most robust energy savings. The aluminum chassis reduced the energy consumption more than the class C tires, but the benefit of the lighter chassis was shown to also correlate strongly with the aggressiveness of the driving. The results showed that a low-drag body is a potential method for consumption reduction on high-speed bus routes. Predictive driving was found to reduce the average consumption by 9.5% at −10 °C when using 10-second prediction and control horizons. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to comprehensively predict the passive intermodulation (PIM) distortion in loose contact coaxial connectors by the time-domain reflectometry (TDR) test. The proposed approach treats the insulating film current and the electrothermal (ET) coupling as PIM sources. An ET coupling model is introduced to analyze the generating process of the PIM caused by the ET coupling in the loose contact coaxial connectors. The simulation and measurement results show good agreement. The proposed method is a cost-effective approach for the evaluation of the PIM distortion, because it uses a common TDR method to replace professional PIM test instruments. The proposed method can also be extended to all the loose metallic contact cases. [ABSTRACT FROM AUTHOR]

Optimal control provides a systematic approach to control robots. However, computing optimal controllers for hardware-in-the-loop control is sensitively affected by modeling assumptions, computationally expensive in online implementation, and time-consuming in practical application. This makes the theoretical appeal of optimization challenging to exploit in real-world implementation. In this paper, we present a novel online optimal control formulation that aims to address the above-mentioned limitations. The formulation combines a model with measured state information to efficiently find near-optimal feedback controllers. The idea to combine a model with measurements from the actual motion is similar to what is used in model predictive control formulations, with the difference that here the model is not used for future prediction, the optimization is performed along the measured trajectory of the system, and the online computation is reduced to a minimum; it requires a small-scale, one time step, static optimization, instead of a large-scale, finite time horizon, dynamic optimization. The formulation can be used to solve optimal control problems defined with nonlinear cost, nonlinear dynamics, and box-constrained control inputs. Numerical simulations and hardware-in-the-loop experiments demonstrate the effectiveness of the proposed hardware-in-the-loop optimal control approach. [ABSTRACT FROM AUTHOR]

Users’ load aggregation, control of smart appliances, and exploitation of thermal inertia of buildings are among key elements to improve the energy demand of buildings, toward the goals of near zero energy buildings. In this paper, we investigated a case study of an existing residential and commercial building with a microgrid and advanced technical and control systems. We have developed a model to simulate loads and the effects of control systems. The results show how increasing the extent of the microgrid and control strategies improves the load profile and energy consumption, also preserving the users’ habits and comfort. Via these elements, the concept of near zero energy building can further advance toward a “near zero power building” or “zero-kilometer energy building.” [ABSTRACT FROM AUTHOR]

Predicting channel state information (CSI) is a fundamental element in wireless communications systems. An accurate CSI estimation and prediction is critical to the system performance. This paper introduces a recurrent neural network (RNN) based approach for real-time prediction in real-world non-stationary channels. It uses the recent history data for online training, followed by prediction employing the trained model, in order to adapt to the changing channel and obtain a more accurate CSI prediction compared to conventional methods. Furthermore, the proposed method needs no additional knowledge, such as the internal properties of the channel itself, or the external features that affect the channel propagation, greatly facilitating its use in practical systems. Simulation results show that the proposed adaptive and parameter free recurrent neural structure (APF-RNS) outperforms the existing methods under a dynamically changing non-stationary environment. Therefore, the proposed online training based RNN approach is a promising method for channel prediction in wireless communications. [ABSTRACT FROM AUTHOR]

Heterogeneous networks (HetNets) as a combination of macro cells and small cells are used to increase the cellular network's capacity, and present a perfect solution for high-speed communications. Increasing area spectrum efficiency and capacity of HetNets largely depends on the high speed of backhaul links. One effective way that is currently utilized in HetNets is the use of full-duplex (FD) technology that potentially doubles the spectral efficiency without the need for additional spectrum. On the other hand, one of the most critical network design requirements is delay, which is a key representation of the quality of service in modern cellular networks. In this paper, by utilizing tools from the stochastic geometry, we analyze the local delay for downlink channel, which is typically defined as the mean number of required time slots for a successful communication. Given imperfect self-interference cancellation in practical FD communications, we utilize duplex mode (half-duplex (HD) or FD) for each user based on the distance from its serving base station. Further, we aim to investigate the energy efficiency for both duplexing modes, i.e., HD and FD, by considering local delay. We conduct extensive simulations to validate system performance in terms of local delay versus different system key parameters. [ABSTRACT FROM AUTHOR]

In modern microprocessor and SoC designs, supply noise margin has been significantly reduced due to the continuously decreasing supply voltage level. On the other hand, with increasing current density, chips may see larger supply noise variations on various spots and from time to time. As a result, chip robustness and reliability are inevitably deteriorated with more frequent supply noise emergencies. It is therefore crucial to have an efficient supply noise prediction method to enhance design robustness. The state-of-art solutions either try to build a spatial noise estimation framework at the layout-level using the limited distributed physical noise sensors or attempt to develop emergency predictors at the architecture-level thus ignore back-end power delivery details. In this paper, we propose a cross-layer framework for temporal supply noise prediction. Our method not only accounts for the temporal characteristics of workload execution at micro-architecture-level but also incorporates the power delivery model at the circuit-level into such system-level prediction. In order to enable the capability of on-the-fly noise prediction, we first bridge the gap between system-level workload and micro-architectural-level power by employing an ordinary least square-based power estimation model and an adaptive auto-regressive integrated moving average model (ARIMA)-based power prediction model. Then a layout-level supply noise model is developed to explore the correlations between micro-architectural-level power and layout-level supply noise. Compared with existing methods, the proposed ARIMA-based power model improves the prediction performance by up to 37.5%/63.0% in X86/ARM. Moreover, compared with SPICE simulation, our framework is able to estimate present supply noise with an average error of 0.005% and predict future supply noise with an average error of 1.58%/1.17% for X86/ARM architecture. [ABSTRACT FROM AUTHOR]

The reliability analysis and lifetime prediction for SiC-based power modules is crucial in order to fulfill the design specifications for next-generation power converters. This paper presents a fast mission-profile-based simulation strategy for a commercial 1.2-kV all-SiC power module used in a photovoltaic inverter topology. The approach relies on a fast condition-mapping simulation structure and the detailed electro-thermal modeling of the module topology and devices. Both parasitic electrical elements and thermal impedance network are extracted from the finite-element analysis of the module geometry. The use of operating conditions mapping and look-up tables enables the simulation of very long timescales in only a few minutes, preserving at the same time the accuracy of circuit-based simulations. The accumulated damage related to thermo-mechanical stress on the module is determined analytically, and a simple consumed lifetime calculation is performed for two different mission profiles and compared in different operating conditions. [ABSTRACT FROM AUTHOR]

The paper discusses the modeling and the simulation of the limiters of proportional integral (PI) controllers included in voltage-sourced converters. Seven PI controller models are considered. These include windup and anti-windup limiters, back calculation with different gains and delayed feedback and the model recommended by the IEEE standard 421.5-2016. The dynamic behavior and numerical issues arising for each PI implementation are thoroughly compared by means of a detailed all-island dynamic model of the Irish system including the HVDC East–West Interconnector and a simplified dynamic model of the Great Britain system; and the Nordic system with inclusion of a STATCOM device. Recommendations on the computer-based implementation of each model are provided in the concluding remarks. [ABSTRACT FROM AUTHOR]

This paper considers the optimal dispatch of energy-constrained heterogeneous storage units to maximize security of supply. A policy, requiring no knowledge of the future, is presented and shown to minimize unserved energy during supply-shortfall events, regardless of the supply and demand profiles. It is accompanied by a graphical means to rapidly determine unavoidable energy shortfalls, which can then be used to compare different device fleets. The policy is well suited for use within the framework of system adequacy assessment; for this purpose, a discrete time optimal policy is conceived, in both analytic and algorithmic forms, such that these results can be applied to discrete time systems and simulation studies. This is exemplified via a generation adequacy study of the British system. [ABSTRACT FROM AUTHOR]

The efficiency of multiterminal dc (MTDC) grid simulation decreases with an expansion of its scale and the inclusion of accurate component models. Thus, the variable time-stepping scheme is proposed in this paper to expedite the electromagnetic transient computation. A number of criteria are proposed to evaluate the time-step and regulate it dynamically during simulation. Meanwhile, as the accuracy of results is heavily reliant on the switch model in the modular multilevel converter, the nonlinear behavioral model with a greater accuracy is proposed in addition to the classic ideal model, and their corresponding variable time-stepping schemes are analyzed. Circuit partitioning is effective in accelerating the MTDC grid simulation via fine-grained separation of nonlinearities. A subsequent large number of identical circuits enabled a massively parallel implementation on the graphics processing unit, which achieved a remarkable speedup over the CPU-based implementation. The inclusion of variable time-stepping schemes eventually makes the simulation of MTDC grid with highly detailed nonlinear switch models feasible. The results are validated by commercial device-level and system-level simulation tools. [ABSTRACT FROM AUTHOR]

The nonlinear state-space dynamical model of the drive train of large-scale wind turbine systems (WTSs) and its scaling for small-scale real-time WTS emulation in the laboratory are discussed in this paper. Shaft elasticity and the nonlinear, aerodynamical turbine torque are considered. To allow for a physically reasonable emulation of the dynamical behavior of multimegawatt wind turbine drive trains at a small-scale laboratory test bench, a general scaling method is presented, which is based on the ratios of physical SI-units. Furthermore, inertia emulation, elasticity emulation, and friction compensation are addressed and possible implementations are proposed. Simulation results of the full-scale WTS model and measurement results of the small-scale real-time WTS emulation are compared to illustrate the effectiveness of the proposed scaling. [ABSTRACT FROM AUTHOR]

In this paper, a new Lyapunov-based model predictive controller is proposed for a 7-level packed U-cell (PUC7) grid-connected inverter. A cost function of the proposed model predictive controller is designed from the system stability point of view, inspired by the Lyapunov control theory. The proposed controller eliminates the need for gains that are associated with cost function coefficients, as seen in classical model predictive control based controllers. Therefore, the control design problem is significantly simplified. In addition, the average switching frequency is also reduced, as shown in both simulation and experimental results, leading to a reduction in switching losses. Simulation and experimental tests, on a PUC7 lab prototype, demonstrate the excellent performance of the proposed control system, in terms of high disturbance rejection, robustness to parameter mismatches, and fast dynamic response. Such features qualify the proposed control strategy as a good candidate for grid-tied applications. [ABSTRACT FROM AUTHOR]

The lack of dc fault clearance capability is a major obstacle that restricts the development of high-voltage direct current (HVdc) systems. Employing an active mechanical dc circuit breaker (M-DCCB) is an effective method to handle the dc fault. However, the existing active M-DCCBs can interrupt the fault current once but have problems in making the second interruption instantly in case of reclosing a permanent dc fault. In this paper, a novel topology of the active M-DCCB is proposed by inserting a precharged capacitor into a bridge formed by four spark gap switches. The operating strategy for the proposed M-DCCB is discussed to handle the dc faults. Its parameters, including the oscillation inductor and the oscillation capacitor, are determined by inequations derived from the requirements of dc fault interruption. The selection standard for the spark gap switch is set based on its breakdown characteristics. Finally, simulations are conducted to investigate the performance of the proposed M-DCCB. Results show that the capacitor can accumulate a sufficiently large reverse charge voltage automatically charged by the fault current. The proposed active M-DCCB topology and the dc fault handling strategy can interrupt the dc fault in HVdc power systems consecutively. [ABSTRACT FROM AUTHOR]

With different structures and control strategies, inverter-interfaced renewable energy generators (IIREGs) have different fault characteristics from synchronous generators. This makes the conventional distance protection used in networks with synchronous generators not applicable for transmission lines emanating from IIREGs. Therefore, supported by the fault current analysis, operating performances of distance relays on both sides of the transmission line are unveiled. It reveals the problem that the conventional distance relay on the IIREG side has a high risk of malfunction or refusing to operate. To cope with this adaptability problem, an improved scheme based on time delay and zero-sequence impedance is proposed in this paper. To validate the operating performances of the scheme, a detailed IIREG model is built in a real-time digital simulator, and simulation tests are carried out. Apart from these, a field short-circuit test is performed in a real wind power plant to examine the practical feasibility of the proposed scheme. Both the simulation results and the field test confirm the problem of the conventional distance protection and verify the reliability of the improved scheme. [ABSTRACT FROM AUTHOR]

High-dimensional and large-scale time series processing has aroused considerable research interests during decades. It is difficult for traditional methods to reveal the evolution state in dynamical systems and discover the relationship among variables automatically. In this paper, we propose a unified framework for nonuniform embedding, dynamical system revealing, and time series prediction, termed as Structured Manifold Broad Learning System (SM-BLS). The structured manifold learning is introduced for nonuniform embedding and unsupervised manifold learning simultaneously. Graph embedding and feature selection are both considered to depict the intrinsic structure connections between chaotic time series and its low-dimensional manifold. Compared with traditional methods, the proposed framework could discover potential deterministic evolution information of dynamical systems and make the modeling more interpretable. It provides us a homogeneous way to recover the chaotic attractor from multivariate and heterogeneous time series. Simulation analysis and results show that SM-BLS has advantages in dynamic discovery and feature extraction of large-scale chaotic time series prediction. [ABSTRACT FROM AUTHOR]

For some practical industrial systems or components, such as blast furnaces and Li-ion batteries, there are two important factors to model the degradation processes. One is the long-range dependence, which can reflect the non-Markovian nature of the degradation processes. The other factor is the existence of multiple modes, because the operating conditions and external environments inevitably change during the whole lifetime of these systems. In this paper, we first propose a fractional Brownian motion (FBM) based degradation model with long-range dependence and multiple modes, and then consider the prediction of remaining useful life. To identify the multiple modes in the degradation process, we propose a two-step method, including change-points detection and linear segments clustering. In each degradation mode, the degradation rate is assumed to be normally distributed. The means and variances of these distributions can be obtained by the maximum likelihood estimation. To describe the switching between different modes, the continuous-time Markov chain is applied, and its transition rate matrix can be estimated by the historical switching time. An approximation of the first passage time with a predefined threshold can be obtained by a weak convergence theorem and a time-space transformation. A numerical simulation and a practical case of a blast furnace wall are provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With pancake-coil electromagnetic launcher as a research object, the experimental platform of the pancake-coil electromagnetic launch intercepting system with 50-kV pulse capacitors as energy storage was built and tested for discharge to study the electromagnetic launch intercepting system. The oscillation problem of the current flowing through freewheeling loop was solved by connecting the reverse-cut diode stack in series with trigger vacuum switch (TVS). However, due to limiting power component, the experimental system can work normally at 9.4-kV capacitor voltage. A proportional 3-D model of the pancake-coil electromagnetic launcher and an external excitation circuit model were established based on the structural and circuit parameters of the experimental system to perform simulation. The accuracy of the simulation model was proved by comparing the simulated and measured currents obtained under same capacitor parameters. Based on the established finite element model, maximum launching capacity and energy efficiency of the experimental system platform were estimated through simulation, and the space–time distribution characteristics of the magnetic field were analyzed during the launching process. In addition, the electromagnetic-structure coupling simulation was carried out to study the deformation problems of components of launcher by sequential coupling. All the work done in this paper provides guidance for a detail research, and we want to try to improve our electromagnetic launch experimental system. [ABSTRACT FROM AUTHOR]

Microwave backscattering models play key roles in surface scattering modeling and soil moisture inversion in active microwave remote sensing. However, numerous evaluations indicate that significant discrepancies between the model simulations and radar observations remain, and these discrepancies are regarded to be attributed to inaccuracies in the models. What do such discrepancies originate from is unclear and has not been comprehensively analyzed. To this end, this paper presents an uncertainty analysis to explore the intrinsic reason for the discrepancies between the backscattering model simulations and radar observations. The probability distribution function and the corresponding statistical characteristics are introduced to describe the uncertainty in the model outputs. We find that the scale dependence of the key model inputs leads to significant uncertainties in the model inputs, and the uncertainties are transferred into the model outputs. Thus, the discrepancies between the model simulations and radar observations are intrinsically caused by the spatial scaling and related uncertainties of key model inputs. In short, the scale mismatch between the model inputs and remote sensing pixels is an intrinsic factor that causes the discrepancies between the model simulations and radar observations. This finding suggests that the scaling effect of model inputs should be carefully considered when using the backscattering models at the pixel scale, and equivalent inputs matched at the corresponding scales should be developed for remote sensing applications. Thus, this analysis insights into the scale dependence of inputs for backscattering models and suggests to provide scale-matched inputs where the models are applied at different scales. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach for the detection of stator faults in inverter-fed induction motor under closed-loop control. These faults, generally, start with interturn short circuit and can evolve to phase-to-phase or phase-to-ground faults, leading to stator currents unbalance. In the considered application, the stator windings are supplied by a static converter for the control of the speed and the rotor flux of the motor. The unbalance fault has been artificially created through additional resistance in series with one phase of the stator windings. The proposed diagnosis approach is based on the computation of the symmetrical components of the stator currents. The supply fundamental frequency and the three-phase phasors are estimated based on the maximum likelihood estimator. Then, the generalized likelihood ratio test is applied for unbalance fault detection. Simulation and experimental results on a 1.5-kW induction motor illustrate the effectiveness of the proposed approach, leading to an effective diagnosis procedure for stator faults in inverter-fed induction motor under steady state and closed-loop operation. [ABSTRACT FROM AUTHOR]

The modular multilevel converter (MMC) has attracted extensive research in recent years. An appropriate model is necessary to analyze stability or to design MMC controllers. Several published MMC models have been derived in single-phase form to simplify the modeling mathematics. However, little attention is given to the zero-sequence voltage, which introduces coupling in the single-phase model and leads to significant error. In this paper, after revealing the mechanism behind the zero-sequence voltage, a more accurate generalized single-phase MMC model is proposed, which eliminates the zero-sequence voltage coupling effect and is linearized based on harmonic state space (HSS) theory to precisely characterize the internal harmonic features of MMC. A systematic HSS modeling process is presented for both open-loop and closed-loop conditions. And the proposed model is generalized as it can incorporate different control strategy by controller transfer function substitution. Hence it is valuable to analyze MMC stability and dynamics. Model effectiveness is verified by simulation results. [ABSTRACT FROM AUTHOR]

Cascaded H-bridge (CHB) is a promising topology for medium-voltage high-power applications. CHB consists of low-power H-bridge modules in each phase, and these modules are connected in a cascade manner to achieve multilevel operation. However, CHB requires a phase-shifting transformer with multiple secondary windings to generate an isolated dc source for each module, and this requirement increases the overall system cost. In this paper, a series connection of low-power modules is employed to realize a new power converter called a series-connected multilevel converter. Each phase of the proposed topology requires a single isolated dc source. Hence, the required number of secondary windings is considerably reduced. This approach also minimizes the complexity and cost of the overall system. The operation of the proposed topology is presented by using three-level neutral point clamped modules in each phase. Model predictive control (MPC) is employed to control the dc-bus capacitor voltage and output currents of the proposed topology. A discrete-time mathematical model of the proposed topology is also developed to predict the future behavior of control variables. The performance of the proposed topology with MPC is verified through MATLAB simulations and a scaled-down laboratory prototype. [ABSTRACT FROM AUTHOR]

A novel method for controlling direct voltage regulated active dc-link capacitance reduction circuits (ACRC) is proposed in this paper. Instead of forcing the dc-link voltage of the power conversion system to follow a constant reference, the ACRC imposes dc-bus voltage in proportion to the integral of its input current. Consequently, it resembles an adjustable value capacitor from the dc-link side. Therefore, as opposed to existing control strategies, the proposed method simultaneously allows the dc-link voltage to reflect the power balance of the system and the pulsating power component to be diverted into significantly reduced auxiliary capacitance via a bidirectional dc–dc converter. As a result, replacing bulk dc-link capacitance by the proposed circuitry does not require any modification of existing grid-connected power conversion system controllers, thus allowing plug-and-play operation. The proposed approach is validated by application to a commercial power factor correction front end by both simulations and experiments. [ABSTRACT FROM AUTHOR]

To avoid failures in circuit breakers (CBs) and to extend the lifetime of these critical components, condition-based-maintenance has been increasingly requested by utilities to enable them to efficiently manage their assets. The origin of the most failures in CBs is the operating mechanism. Travel curve (TC) could effectively reveal the condition of the operating mechanism. However, the measurement of a TC profile is not simple in all CBs. This paper presents the impacts of common failure modes of CBs on TC profiles, and proposes a new model-aided approach to simulate the behavior of the operating mechanism with coupling the model-based and rule-based approaches. The simulation results along with experiments conducted on 72.5 kV SF6 CBs are organized into a fuzzy-probabilistic approach through maximum likelihood and interacting multiple models (IMM) to precisely predict the condition of CB and to detect intelligently the cause of the failure. For this purpose, the CB condition has been categorized into three modes based on its operating speed: normal (about 3 m/s), faulty-1 (less than the normal range), and faulty-2 (more than the normal range). The mode variations of the CB have been estimated via IMM in each operation. The proposed approach in prediction of the failures and cause(s) prior to their occurrence has been verified against experiments. [ABSTRACT FROM AUTHOR]

Lifetime reliability management of miniaturized CMOS devices continuously gets more importance with the shrinking of technology size. Neither of existing design-time solutions (like guard-banding) and runtime methods (like reactive monitoring) does efficiently address this issue; rather, proactive approaches, which use runtime aging prediction, are getting more promising to provide resiliency. Among various reliability threatening mechanisms in recent technologies, negative bias temperature instability is the dominant factor; it depends on multiple time-varying operational parameters, including temperature, supply voltage, and stress. This paper proposes an efficient instruction-level stress estimation model; accordingly, it introduces a runtime aging prediction approach for embedded processors, taking simultaneous impacts of the temperature, supply voltage, and stress variations. We propose instruction degradation factor and architecture degradation factor metrics, respectively, for fine-grained stress estimation and recurring runtime aging prediction. We also provide a simulation environment for model validation. Simulation results of several benchmarks show that the proposed stress estimation model has an accuracy of about 92%, indicating that the method is accurate enough, yet simple for runtime usage. [ABSTRACT FROM AUTHOR]

This paper presents an interactive feedback scheme of spatial resolution enhancement and spectral unmixing in hyperspectral imaging. Traditionally spatial resolution enhancement and spectral unmixing operations have been carried out separately, often in series. In such sequential processing, spatially enhanced hyperspectral images (HSIs) may introduce distortion in spectral fidelity making spectral unmixing results unreliable, or vice versa. Since both high- and low-resolution HSIs have the same endmembers, the deviation in spectral unmixing between targets and estimated high-resolution HSIs can be used as feedback to control spatial resolution enhancement. The spatial difference before and after unmixing can also be used as feedback to enhance spectral unmixing. Therefore, spectral unmixing is utilized as a constraint to spatial resolution enhancement, while spatial resolution enhancement helps improve spectral unmixing results. The performance of spatial resolution enhancement and spectral unmixing can be improved since one behaves like a prior to the other. Experimental results on both simulated and real HSI data sets demonstrate that the proposed interactive feedback scheme simultaneously achieved spatial resolution enhancement and spectral unmixing fidelity. This paper is an extended version of the previous work. [ABSTRACT FROM PUBLISHER]

There are few of transient control models are adapted to asynchronous induction coil launcher (AICL) with air core coil. Thus, many papers about AICL were written with the goal of increasing muzzle velocity of projectile. This paper concentrate on how to control the projectile speed and thrust accurately by using improved current filament (CF) method and variable voltage variable frequency (VVVF) scheme. The improved CF method contains mathematical model of three-phase inverter based on switch function, including conventional two level converter and cascaded multilevel converter models. The whole launch process is programmed with MATLAB. Simulation results show that this scheme have a smoother thrust fluctuation on projectile than other driving models. The motor parameters are changing with the temperature and frequency in launching process. Hence, the constant voltage frequency ratio (V/f) in this paper is more simple and reliable than vector control. To verify the control model and simulation results, a nine stages coil launcher prototype is constructed. The measured force and muzzle velocity are basically consistent with simulation results. It is proved that the improved CF method and VVVF strategy is feasible, and it is characterized by its small calculating amount that make it a valuable tool for design of AICL. The simple and excellent control of the thrust during launch process avoids load peaks, which means that the payload is handled more carefully compared to the classical launchers. [ABSTRACT FROM PUBLISHER]

Nowadays, active-front-end rectifiers are increasingly used as grid connected devices. Their efficiency and performances have become relevant for reducing the overall grid losses. This paper thus introduces a predictive hybrid pulse-width-modulation (PH-PWM) technique, adaptable to any space-vector PWM (SVPWM) controller. As opposed to hybrid PWM techniques based on sector partitioning, the PH-PWM technique selects the appropriate PWM sequence online with the help of a predictive algorithm. This selection is based on a cost-function where users can define a tradeoff between various criteria, such as the reduction of current distortion and switching losses, as presented in this paper. The use of the PH-PWM technique increases performances in comparison with the conventional PWM techniques. The simulation and experimental results are presented to confirm the effectiveness of the proposed PH-PWM technique. Aside from the tradeoff capacity of the proposed PH-PWM technique, the simulation results validate a reduction of the switching losses for the converter up to 23$\%$ as compared with the conventional SVPWM technique. [ABSTRACT FROM AUTHOR]

Condition monitoring of induction motors is important for their efficient and reliable operation. Most of the existing electrical techniques of condition monitoring and fault diagnosis are based on current, voltage, vibration, or/and flux measurements external to the motor. This paper proposes an online fault diagnostic system, which is based on measurements of internal main air gap flux density of an induction motor by an array of Hall effect flux sensors. Such an instrumentation is justified for large specialized induction motors with particularly high-reliability requirements. Fundamental theory behind the air gap flux density as a function of time and space, and its distortions introduced by various faults, is reviewed in this paper. Based on this theory, this paper proposes a comprehensive condition monitoring approach to diagnose induction motor faults including stator turn-to-turn shorts, rotor bar damage, and static and dynamic eccentricity, which may be present individually or at the same time. Moreover, the proposed approach not only detects each fault at its early stage but also determines its exact location and severity. The principles proposed in this paper are illustrated by extensive simulations and are experimentally validated on a prototype online condition monitoring system based on National Instruments real-time platform. [ABSTRACT FROM PUBLISHER]