Phase Angle Data Research Articles

Study on the Application of Kramers-Kronig Relation for Polyurethane Mixture.

Polyurethane (PU) mixture, which is a new pavement mixture, exhibits different dynamic properties compared to a hot-mixed asphalt mixture (HMA). This paper analyzed whether the Kramers-Kronig (K-K) relation and thermorheologically simple properties applied to the PU mixture. Based on the results, the PU mixture exhibited thermorheologically simple properties within the test conditions. The time-temperature superposition principle (TTSP) was applicable for the PU mixture to construct a dynamic modulus master curve using the standard logistic sigmoidal (SLS) model, the generalized logistic sigmoidal (GLS) model, and the Havriliak-Negami (HN) model. The Hilbert integral transformed SLS and GLS models for the phase angle can accurately fit the measured phase angle data with newly fitted shift factors and predict the phase angle within the viscoelastic range. The core-core and black space diagrams both displayed single continuous smooth curves, which can be utilized to characterize the viscoelastic property of the PU mixture. The K-K relation is applicable for the PU mixture to obtain the phase angle master curve model, storage modulus, and loss modulus from the complex modulus test results with the test temperatures and loading frequencies. The phase angle of the PU mixture at extremely high or low test temperatures cannot be derived from the dynamic modulus data.

Disk-integrated and disk-resolved photometry of the Moon with GaoFen-4 space observations

The GaoFen-4 (GF-4) satellite obtained high-resolution images of the entire lunar disk in a single exposure without atmospheric disturbance. These data are favorable for the study of both disk-resolved and disk-integrated photometric properties of the Moon. Using the GF-4 observations, we modeled the lunar reflectance phase functions with the Hapke model and analyzed the lunar photometric properties. The results indicate that the lunar surface exhibits pronounced backscattering, and the maria appear to be smoother compared to the highlands. The disk-resolved and disk-integrated models produced different best-fit parameters for the whole Moon, with the disk-integrated model fitting the small phase angle data better, and the disk-resolved model showing a more favorable appearance at large phase angles. By calculating the disk-integrated phase function with a disk-resolved method, the study confirms the equivalence of the two models, albeit with a small discrepancy due to an imperfect approximation of the roughness function. This discrepancy is due to the different forms of the phase function used in the models. The geometric albedo of the Moon derived from the disk-resolved model is 0.137 and the spherical albedo is 0.063 for Band 3 of GF-4 at 560.6 nm. This study observed an arched phase curve of the ratio of reflectance in two wavelengths, indicating a phenomenon of phase reddening at small phase angles and bluing at large phase angles.

Synchrophasor network-based detection and classification of power system events: A singular value decomposition approach

Timely detection and classification of power system events are essential for situation awareness and reliable electricity grid operation. It is also a crucial step with regard to synchrophasor network data management and archiving. In this paper, an event detection and classification method based on the singular value decomposition (SVD) of synchrophasor data is proposed. The detection algorithm exploits the low-dimensionality characteristics of synchrophasor data and identifies the changes in the dimensionality of a sliding data matrix. The SVD-based method assigns several detection flags indicating events and outliers in voltage magnitude, phase angle and frequency data. The proposed classification algorithm comprises a decision tree employing detection flags and singular values to classify events into several categories, e.g., fault, voltage magnitude and phase angle events, and generation-load mismatch events. Moreover, the proposed algorithm identifies whether events are spatially correlated. Field synchrophasor data collected from a smart grid are used to evaluate the performance of the proposed method. The numerical results show that the proposed method can successfully detect and classify different types of events even in the presence of measurement uncertainty.

A Deep-Learning-Based Solution for Securing the Power Grid Against Load Altering Threats by IoT-Enabled Devices

The growing integration of high-wattage Internet-of-Things (IoT)-enabled electrical appliances at the consumer end has created a new attack surface that an adversary can exploit to disrupt power grid operations. Specifically, dynamic load-altering attacks (D-LAAs), accomplished by an abrupt or strategic manipulation of a large number of consumer appliances in a botnet-type attack, have been recognized as major threats that can potentially destabilize power grid control loops. This paper introduces a novel approach based a multi-output network (two-dimensional convolutional neural networks classifier and reconstruction decoder)-called “2DR-CNN”-to detect and localize D-LAAs with high resolution. To achieve this, we leverage the frequency and phase angle data of the generator buses monitored by phasor measurement units (PMUs) installed in the power grid. To verify the effectiveness of the proposed method, simulations are conducted on IEEE 14-and 39-bus systems. The performance of the 2DR-CNN method is compared against several benchmark machine learning-based approaches. The results confirm that the proposed method outperforms other techniques in detection and localizing D-LAAs with high resolution in a number of practical scenarios, including PMU measurement noises and missing measurements.

Development of a Generalized Photothermal Measurement Model for the Layer Thickness Determination of Multi-Layered Coating Systems

In this article, a general model for 1D thermal wave interference is derived for multi-layered coating systems (with n∈N coating layers) applied on a thermally thick substrate. Such a model means the first step to building a non-contact photothermal measurement device that is able to determine the coating thickness of each layer. Test objects are to be illuminated on the surface using planar, sinusoidal excitation waves with fixed frequencies leading to the generation of thermal waves inside the object. Due to the multi-layered structure, each of these thermal waves is reflected and transmitted at layer interfaces. This process leads to infinitely many wave trains that need to be tracked to formulate the final surface temperature as a superposition of all waves. A mathematical and physical formulation of thermal wave interference is needed to model this process and relate the dependencies of the layer thicknesses, the materials, and the frequencies to the phase angle data, which then can be measured using, e.g., an infrared camera. In practice, the thermal properties of the layers might be unknown, which makes the process even more difficult. This article presents a concept to determine the thermal properties in advance. Finally, numerical experiments are presented that demonstrate the feasibility of the introduced layer thickness determination process.

Molecular, Metabolic, and Nutritional Changes after Metabolic Surgery in Obese Diabetic Patients (MoMen): A Protocol for a Multicenter Prospective Cohort Study.

Metabolic surgery is an essential option in the treatment of obese patients with type 2 diabetes (T2D). Despite its known advantages, this surgery still needs to be introduced in Malaysia. In this prospective study, the pathophysiological mechanisms at the molecular level will be studied and the metabolomics pathways of diabetes remission will be explored. The present study aims to evaluate the changes in the anthropometric measurements, body composition, phase angle, diet intake, biochemistry parameters, adipokines, microRNA, and metabolomics, both pre- and post-surgery, among obese diabetic patients in Malaysia. This is a multicenter prospective cohort study that will involve obese patients (n = 102) with a body mass index (BMI) of ≥25 kg/m2 (Asian BMI categories: WHO/IASO/IOTF, 2000) who will undergo metabolic surgery. They will be categorized into three groups: non-diabetes, prediabetes, and diabetes. Their body composition will be measured using a bioimpedance analyzer (BIA). The phase angle (PhA) data will be analyzed. Venous blood will be collected from each patient for glycated hemoglobin (HbA1c), lipids, liver, renal profile, hormones, adipokines, and molecular and metabolomics analyses. The serum microRNA will be measured. A gene expression study of the adipose tissue of different groups will be conducted to compare the groups. The relationship between the 1HNMR-metabolic fingerprint and the patients' lifestyles and dietary practices will be determined. The factors responsible for the excellent remission of T2D will be explored in this study.

Characterization of the healing potential of asphalt binders with separation of thixotropy after cyclic loading

Considering the self-healing property of asphalt binders in material selection and pavement design can help improve pavement performance and reduce maintenance costs. The study of self-healing of asphalt materials gradually is becoming an important step in the design of durable pavements. Traditionally, the performance recovery of asphalt binders has been attributed only to microcrack healing, which may lead to an overestimation of the self-healing ability. This is because an increasing number of studies have shown that the driving mechanism of asphalt binder performance recovery includes not only self-healing but also thixotropy. In order to accurately quantify the self-healing ability of asphalt binders without the influence of thixotropy, a novel test procedure and a three-step analysis method are proposed. The novel test procedure includes the large amplitude time sweep test and the small amplitude time sweep test. The three-step analysis method consists of establishing a thixotropy relationship between the dynamic shear modulus and the phase angle, calculating the effective modulus from the phase angle data via the thixotropy relationship, and determing the healing degree of asphalt binders by separating the thixotropy. The results show that the recovery of the dynamic shear modulus of asphalt materials results from the combined action of thixotropy and self-healing. And thixotropy plays a prominent role in the dynamic shear modulus recovery at the initial resting period. The contribution of self-healing to dynamic shear modulus recovery gradually increases with increasing resting time. Without excluding the influence of thixotropy, the self-healing ability of asphalt binders is overestimated, especially the short-term self-healing ability.

Improved SVD-based data compression method for synchronous phasor measurement in distribution networks

The integration of phasor measurement units (PMUs) greatly improves the operation monitoring level of distribution networks. However, high sampling rates in PMUs generate huge volumes of measurement data, which creates heavy transmission and storage burdens in information and communication systems. In this paper, an improved singular value decomposition (SVD)-based data compression method for PMU measurements in distribution networks is proposed. First, a lossless phase angle conversion method is proposed, which converts the discontinuous phase angle data of PMU into continuous data sequence to enhance the compression performance. Then, a PMU data compression method is proposed based on SVD, and the compression capability is further enhanced by a lossless compression that utilizes the orthogonal property of the two sub-matrices generated by SVD. Moreover, an error control strategy is designed to dynamically optimizes the scale of transmitted data according to the accuracy requirement of different applications in distribution networks. Finally, case studies are performed using real PMU measurement data from a pilot project in China to validate the compression performance and advantages of the proposed method.

Real-Time Lossless Compression for Ultrahigh-Density Synchrophasor and Point-on-Wave Data

Modern advanced phasor measurement units are developed with ultrahigh reporting rates to meet the demand for monitoring the power systems dynamics in detail. Due to the large volume of data, the communication and storage systems are seriously challenged with the presence of ultrahigh-density (UHD) synchrophasor and point-on-wave (POW) data. Therefore, it is an urgent task to compress the UHD data for more efficient communication and data storage. This article proposes several methods to compress the synchrophasor and POW data in a lossless manner. First, an improved time-series special compression (ITSSC) method is proposed to compress the UHD frequency data. Second, a delta-difference Huffman method is combined with the time-series special compression algorithm to compress the UHD phase angle data. Finally, a cyclical high-order delta modulation method is proposed to compress the UHD POW data. The proposed models are extensively tested and compared with different existing lossless compression algorithms using the field-collected synchrophasor and POW data at different reporting rates. The results indicate that the proposed algorithms are efficient in performing lossless compression for the UHD synchrophasor and POW data in real time.

Online Static Voltage Stability Monitoring for Power Systems Using PMU Data

A new online static voltage stability monitoring method for power systems is proposed by using phasor measurement unit (PMU) data in this paper. This approach uses the real-time power, voltage, and phase angle data collected by the PMU to estimate the power flow Jacobian matrix of the system, and then the static voltage stability is monitored via the minimum singular values (MSVs) of the power flow Jacobian matrix. The novelty of the approach lies in the fact that it only utilizes PMU data for implementing online monitoring of the power system static voltage stability, independent of the physical model and its parameters. The application results on the IEEE 57-bus test system verify the effectiveness of the proposed approach.

Phase angle and vector analysis from multifrequency segmental bioelectrical impedance analysis: new reference data for older adults.

Phase angle (PA) and bioelectrical impedance vector analysis (BIVA) have been recommended as useful prognostic markers in various clinical settings. However, reference data for older adults measured by the novel segmental multifrequency bioelectrical impedance analysis (SMF-BIA) technique are currently lacking. This study examined 567 (286 men, 281 women) healthy older adults (65 - 97 years) and new SMF-BIA-based PA and BIVA reference values were generated stratified according to gender and 3 age groups (65 - 75 years, 76 - 85 years, > 85 years). Mean PA-values (women: 4.30 ± 0.6°, men: 4.77 ± 0.7°) were significantly lower than those previously reported for a younger reference population. Age and gender were significant determinants of PA and BIVA. PA showed a significant decrease with increasing age in both genders. The greatest changes occurred in the age group > 85 years. Men had higher Pas compared to women (except for the oldest age group), but showed a substantially steeper decline in PA, possibly due to a more pronounced reduction of muscle mass. Compared to published reference data for younger adults, there was a clear downward migration of the BIVA vector points in older adults, indicating an age-related reduction of body cell mass. Accordingly, the equation for the BIVA chart generation was modified by adding the factor age. In conclusion, this is the first study to present SMF-BIA-determined PA and BIVA reference data for healthy subjects aged ≥ 65 years. These data can be used for clinical purposes to identify individuals at increased risk for adverse health events or to monitor treatment responses.

Robust Identification Method for Transmission Line Parameters That Considers PMU Phase Angle Error

Accurate transmission line parameters are the basis of power system calculations. The measured phasor measurement unit (PMU) phase angle data at both ends of a line may contain large errors caused by synchronization problems, which can seriously affect the accuracy of parameter identification. This paper proposes a robust PMU-based method for calculating transmission line parameters from PMU data at both ends of a line in such a way that a synchronization error between the ends does not degrade the results. Specifically, a $\pi $ -equivalent model for the transmission line is established, and a least square objective function with nonlinear equations for positive parameter identification is derived. Furthermore, to reduce the impact of noise and biased data, median estimation is used to obtain the final result. Finally, a simulation shows the effectiveness and robustness of the proposed method, and its practicality is demonstrated in a case study using measured PMU data.

Rheological behaviour of bitumen blending with self-healing microcapsule: Effects of physical and chemical interface structures

Rejuvenator-containing microcapsules for spontaneously healing microcracks in bitumen have been developed and are already commercially available. Such smart self-healing approaches are promising for use in developing long-life pavements. The aim of this work was to investigate the rheological behavior of bitumen blended with self-healing microcapsules effected by physical and chemical interface structures. Based on a point of colloids, three self-healing microcapsule samples were used, with average diameters of 10, 20, and 30 μm. Bitumen composites with various microcapsule contents (1%, 2%, 3%, 4%, and 5%) were produced. Morphological analysis showed that the microcapsules were spherical and did not agglomerate. Fluorescence microscopy and X-ray transmission computed tomography showed that the microcapsules were homogeneously dispersed in the bitumen and did not rupture or agglomerate. The smaller microcapsules had larger specific surface areas. The size and content of the microcapsules affected the physical parameters of the bitumen, e.g., penetration, softening point, and ductility. Fourier-transform infrared spectroscopy showed that the bitumen was chemically linked with the microcapsules via functional group reactions. A dynamic shear rheometer was used to investigate the rheological behaviors of the bitumen/microcapsule samples. The complex shear modulus and phase angle data showed that the microcapsule content and size greatly affected the rheological behavior of the bitumen. The bitumen viscoelasticity depended strongly on the contact area and chemical bonds. A mechanistic analysis was performed to clarify the rheological behavior changes in terms of physics and chemistry. This result of this preliminary analysis will be helpful in the screening and application of self-healing microcapsule products.

A Control Chart Approach to Power System Line Outage Detection Under Transient Dynamics

Online transmission line outage detection over the entire network enables timely corrective action to be taken, which prevents a local event from cascading into a large scale blackout. Line outage detection aims to detect an outage as soon as possible after it happened. Traditional methods either do not consider the transient dynamics following an outage or require a full Phasor Measurement Unit (PMU) deployment. Using voltage phase angle data collected from a limited number of PMUs, we propose a real-time dynamic outage detection scheme based on alternating current (AC) power flow model and statistical change detection theory. The proposed method can capture system dynamics since it retains the time-variant and nonlinear nature of the power system. The method is computationally efficient and scales to large and realistic networks. Extensive simulation studies on IEEE 39-bus and 2383-bus systems demonstrated the effectiveness of the proposed method.

Rheological characterization of BCC and FCC structures in aqueous diblock copolymer liquid crystals

Low molecular weight, amphiphilic diblock copolymers in selective solvent exhibit complex phase behavior and macroscopic properties that affect the processing and application of these materials. The mechanical properties of the crystalline phases seen in concentrated solutions are dependent on nanoscale structure and sample history. The goal of this study is to characterize macroscopic properties and thermal history effects of lyotropic liquid crystals in aqueous diblock copolymer solutions. Rheological temperature ramps are used to characterize three aqueous concentrations of diblock copolymer [Brij-58®, C16H33(CH2CH2O)20OH]. Between these three samples the order-disorder transitions (ODTs) for BCC and FCC are accessible in addition to the order-order transition (OOT) between BCC and FCC. These transitions are distinguished using rheology. Frequency sweeps are performed across a range of temperatures and parameterized with a loglinear fit to the phase angle data to extract the crossover frequency. We find that a single frequency sweep does not distinguish BCC and FCC structures. By normalizing the temperature with respect to the ODT, we are able to use a series of frequency sweeps to distinguish characteristic trends in the response of BCC and FCC structures to thermal history.

A Response-Based Approach for Online Prediction of Generating Unit Angular Stability

In this paper, first, a rotor angle trajectory model based on polynomial functions is proposed. Afterwards, a response-based approach for online prediction of power system angular instability is presented. The proposed method utilizes bus phase angle data measured by phasor measurement unit at the point of common coupling of power plant transformer to the bulk power grid. In the prediction process, by computing the second order derivative of post-fault data, the starting point of the calculation data window is determined. Next, a fifth-degree polynomial curve is fitted on the designated data window to predict the angular curve of generating unit. Based on the sign of the first order derivative of predicted curve, the angular stability of generating unit is judged. This approach is testified on the western system coordinating council standard test bed under different operation and fault type scenarios. Taking into account various fault conditions and their associated occurrence probability, a probabilistic index is also defined to sum up the overall performance of the new method. Simulation results confirm that the proposed method outperforms the existing ones in terms of both accuracy and speed. Prediction results could be used in generator rejection schemes to prevent severe power plant outages.

Kinetics of radical dimerization. Simple evaluation of rate constant from convolution voltammetry and faradaic phase angle data

Search for possible ways of the binary information storage evoked an interest in the mechanism of the electrochemical monomer/dimer switching. Thus, determination of the rate of radical-to-dimer conversion became an issue. Mathematical formulation describing the electrochemical generation of a radical followed by a bimolecular chemical reaction contains a quadratic term, in which case the inverse Laplace transformation does not exist. Until now, kinetic data were obtained by using finite difference simulations. We report an alternative and simpler evaluation method, which uses the frequency dependence of the faradaic impedance. Determination of the required surface concentration uses convoluted voltammogram recorded at the same bulk concentration as the impedance data. Functional dependence of the faradaic phase angle on frequency (cot ϕ vs. ω1/2) shows a maximum at lower frequencies containing information on the dimerization kinetics and a linear asymptote at the highest frequency range yields the electron transfer rate. This new procedure was applied to four different known redox systems, all involving radical dimerization process.

A Novel Method for Screening the PMU Phase Angle Difference Data Based on Hyperplane Clustering

Compared with the traditional supervisory control and data acquisition (SCADA) data, phasor measurement unit (PMU) data is characterized by phase angle measurement and high reporting speed (perhaps 100 Hz). The high reporting speed provides dynamic characteristics of the power system frequency, voltage, and current measurement. PMUs have become one of the important data sources for smart grid monitoring. PMU/WAMS (wide area measurement system) based advanced applications have been widely used in the dispatch centers. Some of the applications, such as line parameter identification and state estimation, depend not only on phase angle data but also on phase angle difference between different locations. Field data can suffer from errors, such as time synchronization error, transducer error, PMU algorithm error, hardware error or malicious attacks, etc. A time synchronization error can directly cause an error in the phase angle difference calculated between the two ends of a transmission line that could degrade a PMU based application. In this paper, a novel method to cluster the phase angle difference data, assess the data quality and screen out the bad PMU phase angle difference data is proposed. First, we develop the hyperplane cluster method to cluster the phase angle difference data. Second, in order to screen out the right data type, this paper compares the virtual reactance parameters of each data type obtained by voltage mean to the line reactance parameter given by the system model. Finally, the performance of the proposed methods has been verified by a simulation. The efficiency of the proposed method has been analyzed. The application of the proposed method using field measured PMU data shows the engineering practicability of the proposed method. In addition, the comparison of the proposed method with other clustering methods is discussed.

A wideband acoustic method for direct assessment of bubble-mediated methane flux

The bubble-mediated transport and eventual fate of methane escaping from the seafloor is of great interest to researchers in many fields. Acoustic systems are frequently used to study gas seep sites, as they provide broad synoptic observations of processes in the water column. However, the visualization and characterization of individual gas bubbles needed for quantitative studies has routinely required the use of optical sensors which offer a limited field of view and require extended amounts of time for deployment and data collection. In this paper, we present an innovative method for studying individual bubbles and estimating gas flux using a calibrated wideband from the Bolin Centre for Climate Research database: http://bolin.su.se/data/.and split-beam echosounder. The extended bandwidth (16 – 26 kHz) affords vertical range resolution of approximately 7.5 cm, allowing for the differentiation of individual bubbles in acoustic data. Split-aperture processing provides phase-angle data used to compensate for transducer beam-pattern effects and to precisely locate bubbles in the transducer field of view. The target strength of individual bubbles is measured and compared to an analytical scattering model to estimate bubble radius, and bubbles are tracked through the water column to estimate rise velocity. The resulting range of bubble radii (0.68–8.40 mm in radius) agrees with those found in other investigations with optical measurements, and the rise velocities trends are consistent with published models. Together, the observations of bubble radius and rise velocity offer a measure of gas flux, requiring nothing more than vessel transit over a seep site, bypassing the need to deploy time-consuming and expensive optical systems.

Wavelet transform applied to lock-in thermographic data for detection of inclusions in composite structures: Simulation and experimental studies

In this work, we focused on lock-in infrared thermography as a non-destructive testing method for detection of inclusions in glass fiber reinforced plastic composite structures. The sample consisted of artificial inserts made of copper sheets to simulate inclusions of different shapes and sizes at varying depth levels. In an experimental study, the sample was excited at several modulation frequencies by a sinusoidal heat flux, and a thermal infrared camera was utilised for monitoring of surface temperature of a thermal wave that propagated into the sample. In simulation, ThermoCalc™-3D software based on finite difference method was used for the replication of the physical phenomena that were present in the experiment. The effect of the applied heat flux and time for different sizes and depths of inclusions was analysed to investigate the thermal response. Wavelet transform was applied to compute phase angle data from the temperature-time history of each pixel for the assessment of the inclusions. The investigation into the effects of wavelet parameters; scale and shift, modulation frequencies, inclusion sizes, and depths on the phase contrast was conducted and discussed. The simulation results were found to have a good correlation with the experimental results, thus demonstrating potential in detecting inclusions.