Time-varying Power Research Articles

Stochastic analysis of the diffusion LMS algorithm for cyclostationary white Gaussian inputs

This paper studies the stochastic behavior of a specific version of the Diffusion Least-Mean Square (DLMS) algorithm in a system identification framework for a cyclostationary white Gaussian input. The considered DLMS version has a fusion center. The input cyclostationary signal is modeled by a white random process with periodically time-varying power. The system parameters vary according to a random walk model. The paper focusses on the behavior of the fusion center for DLMS for the special case when the nodes communicate only with the fusion center and vice versa. Mathematical models are derived for the mean and mean-square-deviation (MSD) behavior of the fusion center adaptive weights as a function of the input cyclostationarity. It is shown that the behavior of the fusion center is the same for both Combine-Then-Adapt (CTA) and Adapt-Then-Combine (ATC) diffusion strategies. Monte Carlo simulations are shown in excellent agreement with the theory. Finally the model is used to study the design of the DLMS algorithm for different nodal step-sizes, cyclostationarities, noise powers and weighting coefficients.

Correcting for Non-stationarity in BOLD-fMRI Connectivity Analyses.

In this work fMRI BOLD datasets are shown to contain slice-dependent non-stationarities. A model containing slice-dependent, non-stationary signal power is proposed to address time-varying signal power during BOLD data acquisition. The impact of non-stationary power on functional MRI connectivity is analytically derived, establishing that pairwise connectivity estimates are scaled by a function of the time-varying signal power, with magnitude upper bound by 1, and that the variance of sample correlation is increased, thereby inducing spurious connectivity. Consequently, we make the observation that time-varying power during acquisition of BOLD timeseries has the propensity to diminish connectivity estimates. To ameliorate the impact of non-stationary signal power, a simple correction for slice-dependent non-stationarity is proposed. Our correction is analytically shown to restore both signal stationarity and, subsequently, the integrity of connectivity estimates. Theoretical results are corroborated with empirical evidence demonstrating the utility of our correction. In addition, slice-dependent non-stationary variance is experimentally determined to be optimally characterized by an inverse Gamma distribution. The resulting distribution of a voxel's signal intensity is analytically derived to be a generalized Student's-t distribution, providing support for the Gaussianity assumption typically imposed by fMRI connectivity methods.

Networking of Smart Meters Based on Time-Varying Feature of Low-Voltage Power Line Channel in Microgrid

In order to manage the electricity consumption information of microgrid users, the reliability of electricity information collection is studied in this paper. The normal communication between the acquisition terminal and the smart meter is a key factor affecting the accurate collection of power information; it is the basis for ensuring the operation of the microgrid as well. In order to improve the reliability of the low power line communication between the acquisition terminal and smart meters, this article first uses the static networking method to layer the smart meters and select relays from them and then select the optimal communication path based on integrating communication quality and relay forwarding number dynamically, which could avoid the signal conflict problem caused by simultaneous communication. Finally, by analyzing the influence of the time-varying power line channel on the smart meter communication, a method based on the integrated communication quality and the relay number to consider the time variability of low power line communication is proposed. Choosing the optimal path of the smart meters when the communication path is abnormal can not only establish a new communication path for communication in time, but also avoid communication failures caused by the time-varying channel. Through MATLAB simulation, the time-varying dynamic network of the power line channel is introduced in this paper, which improves the reliability of the smart meter communication and has certain guiding significance for the actual smart meter network construction in microgrid.

Adaptive tracking control of stochastic nonlinear systems with unknown time-varying powers

This paper investigates the adaptive output tracking problem for a class of high-order stochastic nonlinear systems with unknown time-varying powers and nonlinear parameterized uncertainties. By using the parameter separation technique and adding a power integrator design method, an adaptive controller with upper and lower bounds of the unknown time-varying power is successfully designed to guarantee that all the states of the closed-loop system are bounded in probability and the output tracking error can be regulated into a small neighborhood of the origin in probability. Finally, a simulation example is provided to illustrate the effectiveness of the designed controllers.

Cointegration in high frequency data

In this paper, we consider a framework adapting the notion of cointegration when two asset prices are generated by a driftless Itô-semimartingale featuring jumps with infinite activity, observed regularly and synchronously at high frequency. We develop a regression based estimation of the cointegrated relations method and show the related consistency and central limit theory when there is cointegration within that framework. We also provide a Dickey-Fuller type residual based test for the null of no cointegration against the alternative of cointegration, along with its limit theory. Under no cointegration, the asymptotic limit is the same as that of the original Dickey-Fuller residual based test, so that critical values can be easily tabulated in the same way. Finite sample indicates adequate size and good power properties in a variety of realistic configurations, outperforming original Dickey-Fuller and Phillips-Perron type residual based tests, whose sizes are distorted by non ergodic time-varying variance and power is altered by price jumps. Two empirical examples consolidate the Monte-Carlo evidence that the adapted tests can be rejected while the original tests are not, and vice versa.

Filters Optimized Tuning for Wind Farms Reactive Power Calculation

Fast reactive power variation in wind farms creates flicker. Static VAR compensators (SVCs) are the most commonly used devices for flicker mitigation. The reactive power calculation unit is the main component in the control system of SVCs. This article deals with two new approaches for reactive power calculation. In both methods, the main parameters of a filter-based technique are optimized using a novel optimization approach. The first method has a non monotonic step response which is converted to a desirable monotonic response by the second method. This results in a better response to a time-varying reactive power. The problem formulation and definition of a new objective function are presented. A data pool is gathered from Manjil wind farm, Iran, for different conditions. This data pool is used in the objective function to derive the optimal setting. The performance of the proposed methods is assessed using this dataset and is compared with other techniques. Also, real-time implementation of the algorithm is evaluated using TMDSCNCD28335 as the processor. To do so, the reactive power variation in a wind generation unit is emulated in a prototype test bench and the algorithm is run in real-time by the processor during the system operation.

Two-Stage Energy Management Strategy of EV and PV Integrated Smart Home to Minimize Electricity Cost and Flatten Power Load Profile

The efficient use of the incorporation of photovoltaic generation (PV) and an electric vehicle (EV) with the home energy management system (HEMS) can play a significant role in improving grid stability in the residential area and bringing economic benefit to the homeowner. Therefore, this paper presents an energy management strategy in a smart home that integrates an electric vehicle with/without PV generation. The proposed strategy seeks to reduce the household electricity costs and flatten the load curve based on time-of-use pricing, time-varying household power demand, PV generation profile, and EV parameters (arrival and departure times, minimum and maximum limit of the state-of-charge, and initial state-of-charge). The proposed control strategy is divided into two stages: Stage A, which operates in three operating modes according to the unavailability of PV power generation, and Stage B, which operates in five operating modes according to the availability of PV generation. In this study, the proposed strategy enables controlling the amount of energy absorbed by the EV from the grid and/or PV and the amount of energy injected from the EV to the load to ensure that the household electricity costs are minimized, and the household power load profile is flattened. The findings show that both household electricity costs reduction and flattening of the power load profile are achieved. Moreover, the corresponding simulation results exhibit that the proposed strategy for the smart home with EV and PV provides better results than the smart home with EV and without PV in terms of electricity costs reduction and power load profile flattening.

Experimental Investigations on Polarization Switching and Bistability in a 1550 nm VCSEL Subject to Orthogonal Optical Injection With Time-Varying Injection Power

Polarization switching (PS) and polarization bistability (PB) characteristics of a 1550 nm vertical-cavity surface-emitting laser (VCSEL) subject to orthogonal optical injection (OOI) with time-varying injection power are experimentally investigated for the first time. To implement the optical injection, a Mach-Zehnder modulator (MZM) modulated by a triangular radio wave is adopted, and the influences of some typical parameters on the performances of PS and PB are analyzed. Under suitable operation conditions, PB in the VCSEL subject to OOI with time-varying injection power can be observed only for the input power P0 of MZM locating within a determined range, and the width of hysteresis loop gradually decreases with the increase of P0 from the minimum P0, min to the maximum P0, max. Under different frequency detuning Δ ν between the injection light and the X polarization mode of the VCSEL, the determined ranges of P0 required for PB are different. With the variation of Δ ν from a negative value to a positive value, both P0, min and P0, max present a trend of decrease first and then increase. Additionally, with the increase of the modulation frequency, the hysteresis loop width firstly increases rapidly and then gradually tends saturation.

Assessment of a Scalogram-Based Pilot-Induced Oscillation Metric with Flight-Test and Simulation Data‡‡

This paper presents a wavelet scalogram-based metric, which identifies pilot-induced oscillations (PIOs) from flight and when they have occurred. The method considers the time-varying peak pilot input power as a function of the controlled-element phase at the frequency of the peak power, all of which are elements of the defined PIO signature. A flight-test database generated in the evaluation of fixed-wing PIO mitigation concepts is first used to assess the utility of the proposed approach. Further independent evaluation and validation using a previously unassessed aircraft configuration and evaluation maneuvers are made using data generated from pilot-in-the-loop simulations of the XV-15 at the University of Liverpool’s HELIFLIGHT simulation facility. The results of the assessments found that the new metric was successful at identifying correctly both PIO and non-PIO cases.

Asymptotic Tracking Control of State-Constrained Nonlinear Systems With Time-Varying Powers.

This article investigates the asymptotic tracking control problem for full-state-constrained nonlinear systems with unknown time-varying powers. By introducing a nonlinear state-dependent transformation, a continuous bounded scalar function, and lower and higher powers into adding a power integrator control design, full-state constraints are skillfully handled without imposing frequently used feasibility conditions in traditional barrier Lyapunov function-based methods, and an asymptotic tracking control design is provided. It is proved that all the closed-loop signals are bounded, full-state constraints are not transgressed, and the asymptotic tracking is achieved.

A Novel Frequency Regulation Control Method for Deloaded Wind Turbines

The recent proliferation of converter-based power generating systems used within renewable energy sources has increased the need to consider how renewable energy sources, including wind turbines, may participate in ancillary services to ensure the security of the power grid. In this paper, we discuss a modern control methodology known as exact output regulation (EOR) and investigate its application to the problem of wind turbine provision of grid frequency support services. The EOR method assumes that both wind preview information from LIDAR measurements and the power demand curve from the grid are available in the design of the turbine control law. Our simulation study will compare the performance of EOR with a baseline controller, for the tracking of a time-varying power demand curve. Our work focuses on region 3 wind signals, however it has the capability to extend to other wind speed region. The simulations will employ the FAST simulator for a 5MW wind turbine, with wind signals generated by TurbSim. The simulations show that the LIDAR-enhanced EOR method has the potential to substantially improve power demand tracking performance, relative to a baseline controller.

Mechanics of walking and running up and downhill: A joint-level perspective to guide design of lower-limb exoskeletons.

Lower-limb wearable robotic devices can improve clinical gait and reduce energetic demand in healthy populations. To help enable real-world use, we sought to examine how assistance should be applied in variable gait conditions and suggest an approach derived from knowledge of human locomotion mechanics to establish a ‘roadmap’ for wearable robot design. We characterized the changes in joint mechanics during walking and running across a range of incline/decline grades and then provide an analysis that informs the development of lower-limb exoskeletons capable of operating across a range of mechanical demands. We hypothesized that the distribution of limb-joint positive mechanical power would shift to the hip for incline walking and running and that the distribution of limb-joint negative mechanical power would shift to the knee for decline walking and running. Eight subjects (6M,2F) completed five walking (1.25 m s-1) trials at -8.53°, -5.71°, 0°, 5.71°, and 8.53° grade and five running (2.25 m s-1) trials at -5.71°, -2.86°, 0°, 2.86°, and 5.71° grade on a treadmill. We calculated time-varying joint moment and power output for the ankle, knee, and hip. For each gait, we examined how individual limb-joints contributed to total limb positive, negative and net power across grades. For both walking and running, changes in grade caused a redistribution of joint mechanical power generation and absorption. From level to incline walking, the ankle’s contribution to limb positive power decreased from 44% on the level to 28% at 8.53° uphill grade (p < 0.0001) while the hip’s contribution increased from 27% to 52% (p < 0.0001). In running, regardless of the surface gradient, the ankle was consistently the dominant source of lower-limb positive mechanical power (47–55%). In the context of our results, we outline three distinct use-modes that could be emphasized in future lower-limb exoskeleton designs 1) Energy injection: adding positive work into the gait cycle, 2) Energy extraction: removing negative work from the gait cycle, and 3) Energy transfer: extracting energy in one gait phase and then injecting it in another phase (i.e., regenerative braking).

Power Load Disaggregation of Households with Solar Panels Based on an Improved Long Short-term Memory Network

With the increasing application of small distributed renewable energy systems in household power supplies, when a large number of distributed renewable energy power generation systems are connected to the power grid, the time-varying output power of small solar energy, wind turbines, etc. Disaggregation and analysis of regional household electricity and renewable energy power supply systems connected to household electricity will help grid companies to conduct power dispatch management. This paper employed a two-way two-layer Long Short-term Memory deep learning network with improved input form to perform non-intrusive load disaggregation on household power with solar panels, which can monitor the load status of household electrical appliances and the output power of solar power generation system in real time. The power situation provides a decision basis for optimizing the response value of household energy demand and improving the demand of the power system from the response management level. The combined dataset from UK-DALE and kaggle’solar panel power generation data is adopted to train and test the proposed improved Long Short-term Memory network. The test results show that the proposed algorithm is applied to the household electric load disaggregation with solar panels, with high accuracy and reliability.

Enhancing gesture decoding performance using signals from posterior parietal cortex: a stereo-electroencephalograhy (SEEG) study

Objective. Hand movement is a crucial function for humans’ daily life. Developing brain-machine interface (BMI) to control a robotic hand by brain signals would help the severely paralyzed people partially regain the functional independence. Previous intracranial electroencephalography (iEEG)-based BMIs towards gesture decoding mostly used neural signals from the primary sensorimotor cortex while ignoring the hand movement related signals from posterior parietal cortex (PPC). Here, we propose combining iEEG recordings from PPC with that from primary sensorimotor cortex to enhance the gesture decoding performance of iEEG-based BMI. Approach. Stereoelectroencephalography (SEEG) signals from 25 epilepsy subjects were recorded when they performed a three-class hand gesture task. Across all 25 subjects, we identified 524, 114 and 221 electrodes from three regions of interest (ROIs), including PPC, postcentral cortex (POC) and precentral cortex (PRC), respectively. Based on the time-varying high gamma power (55-150 Hz) of SEEG signal, both the general activation in the task and the fine selectivity to gestures of each electrode in these ROIs along time was evaluated by the coefficient of determination r 2. According to the activation along time, we further assessed the first activation time of each ROI. Finally, the decoding accuracy for gestures was obtained by linear support vector machine classifier to comparatively explore if the PPC will assist PRC and POC for gesture decoding. Main results. We find that a majority(L: 60%, R: 40%) of electrodes in all the three ROIs present significant activation during the task. A large scale temporal activation sequence exists among the ROIs, where PPC activates first, PRC second and POC last. Among the activated electrodes, 15% (PRC), 26% (POC) and 4% (left PPC) of electrodes are significantly selective to gestures. Moreover, decoding accuracy obtained by combining the selective electrodes from three ROIs together is 5%, 3.6%, and 8% higher than that from only PRC and POC when decoding features across, before, and after the movement onset, were used. Significance. This is the first human iEEG study demonstrating that PPC contains neural information about fine hand movement, supporting the role of PPC in hand shape encoding. Combining PPC with primary sensorimotor cortex can provide more information to improve the gesture decoding performance. Our results suggest that PPC could be a rich neural source for iEEG-based BMI. Our findings also demonstrate the early involvement of human PPC in visuomotor task and thus may provide additional implications for further scientific research and BMI applications.

Factors and Considerations for Modeling Loss of a GaN-based Inverter

The article investigates the impacts of four often-neglected factors on the loss model of a GaN-based full-bridge inverter: parasitic capacitance of the devices, dynamics of junction temperature ( Tj ) under time-varying power dissipation ( P loss), case temperature estimation, and detailed considerations of the passive components. Procedures to calculate the converter loss considering the above factors are proposed and implemented. A 4.5-kW hard-switching inverter prototype using gallium nitrite (GaN) high-electron-mobility transistors is used to experimentally demonstrate the impact of each factor on the converter loss model. It is found that the accuracy of a converter loss model is mainly affected by the passive components at the light load condition, whereas the thermal and loss models of the active components become the major factors as the output power increases. The results show that after considering the above factors, the converter loss discrepancy between calculation and measurement can be reduced from 30.6 W (28%) to 2.5 W (less than 3%) at heavy load ( Po = 4.5 kW), while at the light load condition ( Po = 500 W), it is reduced from 3.9 W (28%) to 2.6 W (16%). Furthermore, the difference between simulated and measured case temperature of the GaN devices is within 6 °C.

Removing Feasibility Conditions on Adaptive Neural Tracking Control of Nonlinear Time-Delay Systems With Time-Varying Powers, Input, and Full-State Constraints.

This article investigates the tracking control for input and full-state-constrained nonlinear time-delay systems with unknown time-varying powers, whose nonlinearities do not impose any growth assumption. By utilizing the auxiliary control signal and nonlinear state-dependent transformation (NSDT) to counteract the effect of input saturation and cope with full-state constraints, respectively, and then introducing lower and higher powers and Lyapunov-Krasovskii (L-K) functionals in control design together with the adaptive neural-networks (NNs) method, an adaptive neural tracking control design is provided without feasibility conditions. It is proved that NNs approximation is valid, all the closed-loop signals are semiglobally bounded, and input and full-state constraints are not violated.

Pain Detection using a Smartphone in Real Time.

We developed an objective real-time pain detection method using a smartphone and a wrist-worn wearable device to collect electrodermal activity (EDA) signals. Recently, various researchers have developed pain management applications. However, they rely on subjective self-reported pain scores or the video camera of a smartphone to detect pain, but the latter method's accuracy needs further improvement. In our work, we use a wrist-worn EDA device which transmits data via Bluetooth to a smartphone. A smartphone application was developed to analyze the EDA data so that near real-time processed pain detection information can be displayed. The analysis of EDA is based on estimating time-varying spectral power in the frequency range (0.08-0.24 Hz) associated with the sympathetic nervous system. This time-varying characterization of EDA is termed TVSymp. In this work, we also examined whether removing baseline EDA fluctuations from TVSymp would provide more accurate results. This was carried out by taking the moving average of the EDA response prior to stimulus and subtracting that value from the EDA response post stimulus. This approach is termed modified TVSymp (MTVSymp). Pain stimuli were induced in ten subjects using a thermal grill, which gives intense pain perception without damaging skin tissues. We compared both TVSymp and MTVSymp in detecting pain induced by the thermal grill using machine learning approaches. We found the accuracy of pain detection of TVSymp and MTVSymp to be 80% and 90%, respectively.

Robust H∞-Fuzzy Logic Control for Enhanced Tracking Performance of a Wheeled Mobile Robot in the Presence of Uncertain Nonlinear Perturbations.

Motion control involving DC motors requires a closed-loop system with a suitable compensator if tracking performance with high precision is desired. In the case where structural model errors of the motors are more dominating than the effects from noise disturbances, accurate system modelling will be a considerable aid in synthesizing the compensator. The focus of this paper is on enhancing the tracking performance of a wheeled mobile robot (WMR), which is driven by two DC motors that are subject to model parametric uncertainties and uncertain deadzones. For the system at hand, the uncertain nonlinear perturbations are greatly induced by the time-varying power supply, followed by behaviour of motion and speed. In this work, the system is firstly modelled, where correlations between the model parameters and different input datasets as well as voltage supply are obtained via polynomial regressions. A robust -fuzzy logic approach is then proposed to treat the issues due to the aforementioned perturbations. Via the proposed strategy, the controller and the fuzzy logic (FL) compensator work in tandem to ensure the control law is robust against the model uncertainties. The proposed technique was validated via several real-time experiments, which showed that the speed and path tracking performance can be considerably enhanced when compared with the results via the controller alone, and the with the FL compensator, but without the presence of the robust control law.

Rare disaster and renewable energy in the USA: new insights from wavelet coherence and rolling-window analysis

The increasing trend of economic and political crises in different parts of the world has made global economies highly vulnerable because of having globally as well as regionally integrated economic systems. In such an environment, switching to alternative energy products, such as renewable energy production, may be devastating. Therefore, the aim of this paper is to provide novel insights for the relationship between rare disaster risks and renewable energy production (REN) of the USA by utilizing the time series monthly data from 1973 to 2016. Using time-varying continuous wavelet power spectrum, the wavelet coherence, and the modified bootstrap rolling-window analysis, the results reveal significant linkages between all the categories of rare disaster risks and renewable energy production. Rare disaster risks and REN are linked with each other, and both the variables have time-varying cyclic and anti-cyclic effects on each other with robust and significant predictability from rare disasters to REN. These findings have novel implications for many stakeholders. For instance, producers of energy may safely switch to renewable energy production since disasters are found to have potential to leave cyclic effect on renewable energy at most.

Numerical Analysis of Eddy Current Induced by z-Gradient Coil in a Superconducting MRI Magnet

In magnetic resonance imaging (MRI), pulsed magnetic gradient fields induce eddy currents in surrounding conducting components of the MRI scanner. These eddy currents distort MRI image and introduce thermal load in cryostat. Ohmic heating of superconducting magnet due to eddy currents changes the operating temperature of magnet, which can lead to system quench. Numerical simulation of these eddy current is important to compensate/control their adverse effects. In this paper, a coupled circuit network method is outlined to model eddy currents in a superconducting magnet that induced by z-gradient coil. According to the axial symmetry, the cylindrical cryostat vessels are divided into several concentric slices with a finite thickness. Each slice is modeled as a series circuit composed of a resistor and an inductor, and inductively coupled with other slices and gradient coil. To improve the calculation efficiency, the Eigen matrix technique is used to simplify the coupled differential matrix equation. The undesired eddy current effects including time-varying secondary magnetic field and power losses are analyzed. In addition, the eddy current behavior due to mechanical errors of gradient coil is discussed. Compared with the simulation results of ideal gradient coil design, the misalignment between primary and secondary layer of z-gradient coil can cause asymmetric secondary magnetic field along z-axis and further introduce $B_{0}$ -shift in imaging area.