Time-varying Power Research Articles

Random Characteristics of Vehicle-Bridge System Vibration by an Optimized Pseudo Excitation Method

The pseudo excitation method (PEM) is improved for its efficiency by incorporating the self-adaptive Gauss integration (SGI) technology as a new combining integration. The PEM can transform the random rail irregularities into some pseudo harmonic excitation, which is a mature approach to deal with the random excitation for vehicle–bridge systems. The SGI was used to distinguish the significant from the insignificant parts of an integral section for the random excitation frequency on the stochastic response of the system, thereby reducing the computational effort required for the random vibration analysis of the system. Also, the SGI can intelligently handle the recognized integral section, by subdividing the important sections into several necessary frequency points, making rough decomposition, and allowing the unimportant regions to be eliminated. Based on selected frequency points, the deterministic pseudo harmonic excitations were generated, and then the standard deviation (SD) of the time history for the system was calculated by the PEM. The vehicle subsystem was simulated as a 23-degree of freedom model, and the bridge subsystem as a three-dimensional finite element model. The time-varying power spectral density (PSD) plots of the system were presented. Besides, the cumulative distribution function (CDF) of the response was calculated using Poisson’s crossing assumption. The random characteristics for the vehicle–bridge vibrations for different speeds and rail irregularities were calculated.

Attenuation of receiver ghosts in variable-depth streamer high-resolution seismic reflection data

Receiver ghosts attenuate marine seismic reflection data at harmonic frequencies that depend on the propagation angle and the streamer depth below the sea surface. The resulting loss of bandwidth is one of the major factors hampering seismic resolution. In near-surface and legacy multi-channel data, receivers depth is often unknown and may vary significantly both along the streamer length and during the survey, making frequency–slowness deghosting techniques unsuitable. In this work, we present a method for the attenuation of receiver ghost reflections in data with an arbitrary streamer depth profile varying during the survey. For each trace, a different deghosting operator is estimated and applied at different two-way-time windows, in order to account for depth-dependent changes in reflection angles. The ghost null frequencies are picked on the time-varying power spectrum via an automatic algorithm, guided by a user-dependent a priori function, and optimised to respect the harmonics’ periodicity. The power of the inverse filter is adjusted by adaptively damping abnormal amplitudes in the deghosted spectra. The algorithm is applied to high-resolution (GI-gun, 20–400 Hz) and ultra-high-resolution (Sparker, 0.2–3.0 kHz) multi-channel datasets, yielding an excellent bandwidth recovery and gain in resolution in the final stacks. Limited computing time and straightforward application make the method widely applicable and cost-effective.

The COST IRACON Geometry-Based Stochastic Channel Model for Vehicle-to-Vehicle Communication in Intersections

Vehicle-to-vehicle (V2V) wireless communications can improve traffic safety at road intersections and enable congestion avoidance. However, detailed knowledge about the wireless propagation channel is needed for the development and realistic assessment of V2V communication systems. We present a novel geometry-based stochastic MIMO channel model with support for frequencies in the band of 5.2–6.2 GHz. The model is based on extensive high-resolution measurements at different road intersections in the city of Berlin, Germany. We extend existing models, by including the effects of various obstructions, higher order interactions, and by introducing an angular gain function for the scatterers. Scatterer locations have been identified and mapped to measured multi-path trajectories using a measurement-based ray tracing method and a subsequent RANSAC algorithm. The developed model is parameterized, and using the measured propagation paths that have been mapped to scatterer locations, model parameters are estimated. The time variant power fading of individual multi-path components is found to be best modeled by a Gamma process with an exponential autocorrelation. The path coherence distance is estimated to be in the range of 0–2 m. The model is also validated against measurement data, showing that the developed model accurately captures the behavior of the measured channel gain, Doppler spread, and delay spread. This is also the case for intersections that have not been used when estimating model parameters.

Use of Discrete Orthonormal S-Transform to Simulate Earthquake Ground Motions

ABSTRACTThe S-transform and discrete orthonormal S-transform (DOST) produce time–frequency representations, which is in contrast to the wavelet transformations. Similar in the Fourier transform, the use of the S-transform and DOST provides frequency-dependent resolution with absolutely referenced phase information. Although the decomposed signal using DOST is expressed as a sum of orthonormal basis function, this is not the case if the S-transform is used. In the present study, a procedure to simulate nonstationary ground motions based on DOST is proposed based on a seed record or given a target amplitude of DOST coefficients. It is shown that the model has zero mean, and its variance equals the assigned target. Using five real records, each from a larger earthquake, the application of the DOST and S-transform to the records is carried out. Although the time–frequency resolution obtained from DOST is coarse as compared to that obtained using the S-transform, its use identifies clearly time–frequency characteristics. Samples of ground motions are simulated using the proposed method based on the amplitude of the DOST coefficients of a seed record or on the average amplitude of the DOST coefficients of a set of actual records. The comparison of the time–frequency resolution, Fourier spectrum, time-varying power distribution, and response spectrum of the simulated and seed records indicates that the proposed simulation model is a useful and practical tool to simulate nonstationary ground motions.

Time-varying power spectra and coherences of non-stationary typhoon winds

Typhoon winds near its external eye wall are strongly non-stationary and disastrous, requiring a deep understanding. This paper first presents an S-transform-based method for obtaining the time-varying power spectra and coherences of a multivariate non-stationary process. The accuracy of the proposed S-transform-based method is examined through a comparison with currently-used two methods. The analytical expressions of time-varying power spectra and coherences of non-stationary typhoon winds are then proposed by introducing time-varying parameters into the stationary Von Karman wind spectra and the stationary Krenk wind coherence functions respectively. Finally, the S-transform-based method is applied to the wind data recorded by the multiple anemometers installed in the Stonecutters Bridge in Hong Kong during Typhoon Hato, and the resulting time-varying wind spectra and coherences are fitted by the analytical expressions of time-varying Von Karman wind spectra and Krenk wind coherence functions respectively. The results show that the typhoon winds recorded during Typhoon Hato are clearly non-stationary and that the time-varying Von Karman wind spectra and Krenk wind coherence functions could well fit the wind data recorded during Typhoon Hato.

Robust energy disaggregation using appliance-specific temporal contextual information

An extension of the baseline non-intrusive load monitoring approach for energy disaggregation using temporal contextual information is presented in this paper. In detail, the proposed approach uses a two-stage disaggregation methodology with appliance-specific temporal contextual information in order to capture time-varying power consumption patterns in low-frequency datasets. The proposed methodology was evaluated using datasets of different sampling frequency, number and type of appliances. When employing appliance-specific temporal contextual information, an improvement of 1.5% up to 7.3% was observed. With the two-stage disaggregation architecture and using appliance-specific temporal contextual information, the overall energy disaggregation accuracy was further improved across all evaluated datasets with the maximum observed improvement, in terms of absolute increase of accuracy, being equal to 6.8%, thus resulting in a maximum total energy disaggregation accuracy improvement equal to 10.0%.

Adaptive State-Feedback Stabilization of Stochastic High-Order Nonlinear Systems With Time-Varying Powers and Stochastic Inverse Dynamics

This article investigates adaptive state-feedback stabilization problem for a class of stochastic high-order nonlinear systems with unknown time-varying powers and stochastic inverse dynamics for the first time. The existence of stochastic inverse dynamics, unknown parameters, and time-varying powers makes stochastic high-order nonlinear systems essentially different from the related papers, which brings a series of obstacles to achieve the control objective. By virtue of the parameter separation principle, adaptive technique and some flexible algebraic methods, a novel adaptive state-feedback controller is designed to guarantee that the equilibrium of the closed-loop system is globally stable in probability. Finally, a simulation is provided to demonstrate the effectiveness of the control scheme.

Removing Feasibility Conditions on Tracking Control of Full-State Constrained Nonlinear Systems With Time-Varying Powers

This article discusses the tracking control problem of full-state constrained nonlinear systems with unknown time-varying powers. With the help of a nonlinear state-dependent transformation, by introducing lower and higher powers in control design and skillfully combining the adding a power integrator method with the dynamic gain method, a full-state constrained tracking control design is developed without frequently used feasibility conditions. It is rigorously proved that all the signals of the closed-loop system are bounded, full-state constraints are not violated, and the tracking error converges to a compact set around the origin in a finite-time.

A Day-Ahead Scheduling of Large-Scale Thermostatically Controlled Loads Model Considering Second-Order Equivalent Thermal Parameters Model

Among various kinds of flexible loads resources in power system, the thermostatically controlled loads (TCLs) are considered as the excellent candidates for the demand response (DR) programs due to the heat/cool storage characteristics. Effective operation and coordination of TCLs in DR programs requires an accurate model to capture the aggregate power. One appealing approach is the equivalent energy storage (EES) method, which describes the change of aggregate power offered by the TCLs as the dynamic of an energy storage model with limits on the output power and the energy capacity. In this paper, an EES model with the second-order equivalent thermal parameters (second-order ETP) model is established for interpretation of TCLs dynamic characteristics. Considering the internal and external heat exchange of the TCLs, the relationships between the heat exchange power and energy storage of the TCLs EES model are redefined. In order to exert the actual potential of TCLs in day-ahead scheduling, the time-varying charging-discharging power and energy storage constraints of the TCLs EES model are developed. By this way, more feasible scheduling results can be obtained. The performance of the proposed method is verified by the testing results.

Stochastic Analysis of the Recursive Least Squares Algorithm for Cyclostationary Colored Inputs

This paper studies the stochastic behavior of the recursive least squares (RLS) algorithm in a system identification framework for a cyclostationary colored input. The input cyclostationary signal is modeled by a colored random process with periodically time-varying power. The system parameters vary according to a random-walk. Mathematical models are derived for the mean and mean-square-deviation (MSD) behavior of the adaptive weights as a function of the input cyclostationarity. The MSD behaviors of the RLS and LMS algorithms are compared for cyclostationary colored input. Monte Carlo simulations provide strong support for the theory. A separate analysis for white Gaussian and non-Gaussian inputs is presented in support of the assumptions made for the mathematical model above.

Radio Resource Allocation Scheme for Reliable Demand Response Management Using D2D Communications in Smart Grid

Integration of renewable energy into power distribution network is a challenge because renewable output is intermittent. This problem can be tackled through demand response management which controls consumption demand by adjusting energy prices in real-time to match the available time-varying power supply. Such real-time pricing (RTP) requires an advanced communication network to collect data and to disseminate price information. We use cellular-assisted underlay device-to-device (D2D) communications to connect energy management units (EMUs) directly to the RTP control center. Such D2D transmissions can be interfered by concurrent cellular transmissions. This interference can affect reliability of the RTP operation. We quantify the reliability requirements given a tolerable financial loss and a supply-demand gap. The cost in satisfying the reliability requirement appears in the form of a lower sum-rate for all cellular nodes, because cellular rate is a revenue generator for communication service provider. We formulate an optimization problem to allocate radio resources in terms of communication channel and transmit power, to maximize the cellular sum-rate without compromising the RTP reliability requirements. Since the optimization problem is non-convex, we use a pair of iterative algorithms to assign communication channel using the Hungarian algorithm and to control transmit power using the difference of convex (DC) optimization. The effectiveness of the combination of the Hungarian algorithm and the DC optimization has been verified through extensive simulations, with the results compared against a benchmark scheme that randomly assigns communication channels.

Clock-Voltage Co-Regulator With Adaptive Power Budget Tracking for Robust Near-Threshold-Voltage Sequential Logic Circuits

This paper presents a post-regulation power management integrated circuit for energy harvesting applications. It incorporates a power budget tracking mechanism to adaptively control the power consumption of a near-threshold-voltage microcontroller based on the time-varying available input power. In addition, the proposed integrated circuit adapts the clock frequency of the microcontroller to the supply voltage under the time-varying input available power to prevent timing violations for the sequential logics of the microcontroller. A prototype shows the adaptive power budget tracking can correct timing violations with only 6.26uA quiescent current. It also properly operates sequential logic circuits at as low as 188mV supply voltage and 1.61MHz clock without interruption when powered by a solar energy harvester under the light illuminance range of 1000–6000lx. This adaptive power tracking structure enables the near-threshold-voltage microcontroller to maintain a continuous operation at optimized processing speed under the varying power condition without violating the timing requirements.

Simplified global fault accommodation control design of uncertain nonlinear systems with unknown time-varying powers

This paper addresses the problem of global robust fault accommodation tracking for a class of uncertain nonlinear systems with unknown powers and actuator faults. It is assumed that the powers of the concerned system are unknown time-varying functions, all system nonlinearities are unknown, and unknown actuator faults depend on the time-varying power of a control input. A fault accommodation state-feedback controller is explicitly constructed based on the nonlinear error transformation technique using time-varying performance functions. Global tracking with the preselected performance bounds is established in the presence of unknown time-varying powers and unexpected actuator faults. Different from the previous results dealing with the problem of unknown time-varying powers, the proposed tracking strategy does not require the knowledge of the bounds of the time-varying powers and the nonlinear bounding functions of system nonlinearities. An underactuated mechanical system is simulated to validate the effectiveness of the proposed theoretical approach.

Empirical Frequency Band Analysis of Nonstationary Time Series

The time-varying power spectrum of a time series process is a bivariate function that quantifies the magnitude of oscillations at different frequencies and times. To obtain low-dimensional, parsimonious measures from this functional parameter, applied researchers consider collapsed measures of power within local bands that partition the frequency space. Frequency bands commonly used in the scientific literature were historically derived, but they are not guaranteed to be optimal or justified for adequately summarizing information from a given time series process under current study. There is a dearth of methods for empirically constructing statistically optimal bands for a given signal. The goal of this article is to provide a standardized, unifying approach for deriving and analyzing customized frequency bands. A consistent, frequency-domain, iterative cumulative sum based scanning procedure is formulated to identify frequency bands that best preserve nonstationary information. A formal hypothesis testing procedure is also developed to test which, if any, frequency bands remain stationary. The proposed method is used to analyze heart rate variability of a patient during sleep and uncovers a refined partition of frequency bands that best summarize the time-varying power spectrum. Supplementary materials for this article are available online.

Time-varying international market power for the Chinese iron ore markets

This study analyses the dynamics of iron ore's international market power on exports to China by Australia, Brazil, India and South Africa. It uses the residual demand elasticity (RDE) model and rolling window regression model and applies the monthly data for the international iron ore market over the period from January 2006 to December 2016. The results indicate that (1) Australia and Brazil exercise market power in the Chinese iron ore market, but India and South Africa have no market power in the iron ore market; (2) market power has a time-varying nature, particularly in Australia and Brazil; and (3) changes in the pricing regime in the international iron ore market have a profound impact on market power. Furthermore, to maintain the stability of the Chinese iron ore market, exporters are suggested to enhance their competitiveness and gain greater market power on the basis of maintaining market share, and China should optimize its trade structure.

Smoothing Tie-Line Power Fluctuations for Industrial Microgrids by Demand Side Control: An Output Regulation Approach

This paper investigates the problem of how to effectively smooth the short-term tie-line power fluctuations for grid-connected industrial microgrids (IMGs) from demand side. A wide-area closed-loop control architecture is proposed by directly and continuously modulating load power consumption, e.g., the aluminum electrolysis load. To optimally design the load smoothing controllers, an output regulation approach combined with LQ optimal control is developed. In particular, dominant frequencies of wind power fluctuations are analyzed and incorporated into the exogenous system that models time-varying disturbances. Simulations are conducted for a real industrial microgrid in the Inner Mongolia, China, to validate the practical feasibility and effectiveness of the proposed smoothing control strategy. It is demonstrated that the demand side control based on output regulation is able to achieve not only zero steady error in rejecting the time-varying wind power disturbance but also guarantee the satisfactory dynamic performance of modulated loads for the grid-connected IMG.

Distributed load-side control: Coping with variation of renewable generations

This paper addresses the distributed frequency control problem in a multi-area power system taking into account of unknown time-varying power imbalance. Particularly, fast controllable loads are utilized to restore system frequency under changing power imbalance in an optimal manner. The imbalanced power causing frequency deviation is decomposed into three parts: a known constant part, an unknown low-frequency variation and a high-frequency residual. The known steady part is usually the prediction of power imbalance. The variation may result from the fluctuation of renewable resources, electric vehicle charging, etc., which is usually unknown to operators. The high-frequency residual is also unknown and treated as an external disturbance. Correspondingly, in this paper, we resolve the following three problems in different timescales: (1) allocate the steady part of power imbalance economically; (2) mitigate the effect of unknown low-frequency power variation locally; (3) attenuate unknown high-frequency disturbances. To this end, a distributed controller combining consensus method with adaptive internal model control is proposed. We first prove that the closed-loop system is asymptotically stable and converges to the optimal solution of an optimization problem if the external disturbance is not included. We then prove that the power variation can be mitigated accurately. Furthermore, we show that the closed-loop system is robust against both parameter uncertainty and external disturbances. The New England system is used to verify the efficacy of our design.

Differentiation of skin incision and laparoscopic trocar insertion via quantifying transient bradycardia measured by electrocardiogram.

Most surgical procedures involve structures deeper than the skin. However, the difference in surgical noxious stimulation between skin incision and laparoscopic trocar insertion is unknown. By analyzing instantaneous heart rate (IHR) calculated from the electrocardiogram, in particular the transient bradycardia in response to surgical stimuli, this study investigates surgical noxious stimuli arising from skin incision and laparoscopic trocar insertion, and their difference. Thirty-five patients undergoing laparoscopic cholecystectomy were enrolled in this prospective observational study. Sequential surgical steps including umbilical skin incision (11mm), umbilical trocar insertion (11mm), xiphoid skin incision (5mm), xiphoid trocar insertion (5mm), subcostal skin incision (3mm), and subcostal trocar insertion (3mm) were investigated. IHR was derived from electrocardiography and calculated by the modern time-varying power spectrum. Similar to the classical heart rate variability analysis, the time-varying low frequency power (tvLF), time-varying high frequency power (tvHF), and tvLF-to-tvHF ratio (tvLHR) were calculated. Prediction probability (PK) analysis and global pointwise F-test were used to compare the statistical performance between indices and the heart rate readings from the patient monitor. Analysis of IHR showed that surgical stimulus elicits a transient bradycardia, followed by the increase of heart rate. Transient bradycardia is more significant in trocar insertion than skin incision (p < 0.001 for tvHF). The IHR change quantifies differential responses to different surgical intensity. Serial PK analysis demonstrates de-sensitization in skin incision, but not in laparoscopic trocar insertion. Quantitative indices present the transient bradycardia introduced by noxious stimulation. The results indicate different effects between skin incision and trocar insertion.

Semi-automated EEG Enhancement Improves Localization of Ictal Onset Zone With EEG-Correlated fMRI.

Objective: To improve the accuracy of detecting the ictal onset zone, we propose to enhance the epilepsy-related activity present in the EEG signals, before mapping their BOLD correlates through EEG-correlated fMRI analysis.Methods: Based solely on a segmentation of interictal epileptic discharges (IEDs) on the EEG, we train multi-channel Wiener filters (MWF) which enhance IED-like waveforms, and suppress background activity and noisy influences. Subsequently, we use EEG-correlated fMRI to find the brain regions in which the BOLD signal fluctuation corresponds to the filtered signals' time-varying power (after convolving with the hemodynamic response function), and validate the identified regions by quantitatively comparing them to ground-truth maps of the (resected or hypothesized) ictal onset zone. We validate the performance of this novel predictor vs. that of commonly used unitary or power-weighted predictors and a recently introduced connectivity-based metric, on a cohort of 12 patients with refractory epilepsy.Results: The novel predictor, derived from the filtered EEG signals, allowed the detection of the ictal onset zone in a larger percentage of epileptic patients (92% vs. at most 83% for the other predictors), and with higher statistical significance, compared to existing predictors. At the same time, the new method maintains maximal specificity by not producing false positive activations in healthy controls.Significance: The findings of this study advocate for the use of the MWF to maximize the signal-to-noise ratio of IED-like events in the interictal EEG, and subsequently use time-varying power as a sensitive predictor of the BOLD signal, to localize the ictal onset zone.

Dynamic evolution of the soil pore size distribution and its connection to soil management and biogeochemical processes

Soil properties are determined by a complex arrangement of pores, particles, and aggregates, which may change in time as a result of both ecohydrological dynamics and land management processes. The soil pore size distribution (PSD), which typically is treated as a static component, is a key determinant of soil properties, and its accurate representation has the potential to improve hydrological and crop models. Following previous work by Or et al. (2000), a modeling framework is proposed for the time evolution of the PSD which takes into account processes such as tillage, consolidation, and changes in organic matter. A time-varying power law PSD is obtained as the solution of a special form of transport equation for the PSD, parameterized using data from the literature to capture, in a parsimonious and efficient manner, the changes in the PSD as a result of the soil processes considered. Alterations in soil properties brought about by tillage, consolidation, and organic matter are then discussed. The potential benefit of this method for determining soil properties over the more widely used pedotransfer functions (PTF) is that it allows for the history of the soil, rather than only its present state, to be taken into account when estimating soil properties, and it does so in a physically consistent manner, leading to the widely used power law expression for soil properties with few parameters.