High Frequency Signal Research Articles

Low Speed Online Inertia Identification Considering Inner Disturbances for Dual-Stator Flux Modulated Machine Based on High-frequency Signal Injection

Low Speed Online Inertia Identification Considering Inner Disturbances for Dual-Stator Flux Modulated Machine Based on High-frequency Signal Injection

A Novel Vector Magnetic Circuit Based Position Observer for IPMSM Drives Using High-Frequency Signal Injection

A Novel Vector Magnetic Circuit Based Position Observer for IPMSM Drives Using High-Frequency Signal Injection

Diagnosis of Inter-Turn Short Circuit Fault in BLDCM Based on Coupled High-Frequency Signal Injection

Diagnosis of Inter-Turn Short Circuit Fault in BLDCM Based on Coupled High-Frequency Signal Injection

Research methods of geological structures over hydrocarbon accumulation

The article presents the results of research methods of geological structures over hydrocarbon accumulation. Measurements of the characteristics of these structures at the Geological hydrocarbon accumulation of the Republic of Belarus are made. Irradiation with a dual-frequency electromagnetic wave and reception of the reflected wave in modes with the low-frequency signal exceeding 10 times the highfrequency signal and the high-frequency signal exceeding 10 times the low-frequency signal were used. It has been established that measurement of reflection coefficient contrast between a geological structure and a homogeneous underlying profile increases the accuracy of hydrocarbon isolation. Methods of hydrocarbon deposit search based on the measurement of phase components of surface impedance in the mode of amplitude-frequency-modulated signals and determination of reflective properties of the medium over hydrocarbon accumulation in the mode of dual-frequency signals have been developed. The data of the obtained studies can be used to develop methods of hydrocarbon deposits detection.

An Amplitude-Adaption High-Frequency Signal Injection Method for SynRMs at Zero-Low Speed

An Amplitude-Adaption High-Frequency Signal Injection Method for SynRMs at Zero-Low Speed

(Invited, Digital Presentation) 2-D Van Der Waal Materials for Advanced Interconnect Applications

Advanced interconnects require new materials to minimize high frequency signal loss and manage high temperature excursions due Joule heating. In this talk, we will review our on-going material selection modeling, and the thermo-mechanical reliability testing of interconnect prototypes that incorporate 2-D van der Waal thin films. Specifically, we will discuss our use of broadband microwave spectroscopy, that enables the accurate in-operando analysis of the electrical and magnetic properties without compromising the kinetic conditions of the experiment, to investigate high speed signal loss in 3-D integrated test circuits. The BDS method is sensitive to the actual electronic structure of the constituent materials and interfacial processes in the test structures. Using BDS, we have studied and characterized the 2-D van Der Wall, and other chalcogenide thin films in purposely built prototypical 3-D integrated circuit (3D-IC) during cycled high-temperature storage. We show that the microwave signal loss in these devices is attributable to the energy dissipation through the signal’s interactions with the copper barrier / cladding materials.

Research on a Non-Intrusive Load Recognition Algorithm Based on High-Frequency Signal Decomposition with Improved VI Trajectory and Background Color Coding

Against the backdrop of the current Chinese national carbon peak and carbon neutrality policies, higher requirements have been put forward for the construction and upgrading of smart grids. Non-intrusive Load Monitoring (NILM) technology is a key technology for advanced measurement systems at the end of the power grid. This technology obtains detailed power information about the load without the need for traditional hardware deployment. The key step to solve this problem is load decomposition and identification. This study first utilized the Long Short-Term Memory Denoising Autoencoder (LSTM-DAE) to decompose the mixed current signal of a household busbar and obtain the current signals of the multiple independent loads that constituted the mixed current. Then, the obtained independent current signals were combined with the voltage signals to generate multicycle colored Voltage–Current (VI) trajectories, which were color-coded according to the background. These color-coded VI trajectories formed a feature library. When the Convolutional Neural Network (CNN) was used for load recognition, in light of the influence of the hyperparameters on the recognition results, the Bayesian Optimization Algorithm (BOA) was used for optimization, and the optimized CNN network was employed for VI trajectory recognition. Finally, the proposed method was validated using the PLAID dataset. The experimental results show that the proposed method exhibited better performance in load decomposition and identification than current methods.

Analysis of vibrational resonance in an oscillator with exponential mass variation

This study investigated vibrational resonance (VR) in a Duffing-type oscillator with position-dependent mass (PDM) distribution defined by spatially varying exponential function. The role of two PDM parameters, the fixed rest mass m0 and nonlinear strength k on observed resonances was investigated from the analytical and numerical computation of response amplitude Q, which is a measure of the amplification of a low-frequency (LF) signal through the introduction and modulation of a high-frequency (HF) signal in a weakly driven nonlinear system. The method of direct separation of motion was used to analytically compute the response amplitude, while the numerically computed response amplitude was obtained from the Fourier spectrum of the output signal. Single resonance peaks with good agreement between the numerically and the analytically computed responses were observed for the traditional HF-induced VR and the PDM-induced resonances. The results demonstrated that spatial mass perturbation can play the roles of HF signals typically used in traditional VR setups. The results of this investigation corroborate earlier reports that stated PDM parameters can complement the HF signal to control the observed resonance peaks. However, the exponentially varying PDM parameters did not initiate double or multiple resonances as reported for other mass distributions such as the regular mass function and the doubly-singular mass function. This study communicates that the nature of the PDM distribution actually determines the possibility of generating new peaks from observed resonances.

Auditory Spatial Discrimination and Sound Localization in Single-Sided Deaf Participants Provided with a Cochlear Implant

Introduction: Spatial hearing is most accurate using both ears, but accuracy decreases in persons with asymmetrical hearing between ears. In participants with deafness in one ear but normal hearing in the other ear (single-sided deafness [SSD]), this difference can be compensated by a unilateral cochlear implant (CI). It has been shown that a CI can restore sound localization performance, but it is still unclear to what extent auditory spatial discrimination can be improved. Methods: The present study investigated auditory spatial discrimination using minimum audible angles (MAAs) in 18 CI-SSD participants. Results were compared to 120 age-matched normal-hearing (NH) listeners. Low-frequency (LF) and high-frequency (HF) noise bursts were presented from 4°, 30°, and 60° azimuth on the CI side and on the NH side. MAA thresholds were tested for correlation with localization performance in the same participants. Results: There were eight good performers and ten poor performers. There were more poor performers for LF signals than for HF signals. Performance on the CI side was comparable to performance on the NH side. Most difficulties occurred at 4° and at 30°. Eight of the good performers in the localization task were also good performers in the MAA task. Only the localization ability at 4° on the CI side was positively correlated with the MAA at that location. Conclusion: Our data suggest that a CI can restore localization ability but not necessarily auditory spatial discrimination at the same time. The ability to discriminate between adjacent locations may be trainable during rehabilitation to enhance important auditory skills.

A High-Frequency Signal Injection-Based Sensorless Control Method Robust to Single and Double Open-Phase Faults for a Dual Three-Phase Motor

A High-Frequency Signal Injection-Based Sensorless Control Method Robust to Single and Double Open-Phase Faults for a Dual Three-Phase Motor

VeHIF current arcing, volatility and vegetation ignition development: An EMD based volatility analysis for earth-fault classification

Vegetation High Impedance Fault (VeHIF) events pose severe risks to public safety, wildlife, and forests. Overcurrent protection schemes generally cannot clear VeHIF events, which have previously resulted in bushfires. This research presents a VeHIF classification method based on the statistical variance analysis of volatility in the empirical mode decomposed feeder current. The author applied the proposed scheme to a dataset of 130 earth-faults with 100 % success rate and a mean classification time of 10 s (standard deviation of 14.32 s). While faster VeHIF fault classification should ideally be achieved, research findings show that faster classification speeds may not always be feasible or necessary. This finding relates to the fact that bushfire risk increases, when charring starts to spread over a branch with a breakout of flames. The present work further validates a positive pairwise correlation between a fault current's volatility and arcing, demonstrating the efficacy of High Frequency (HF) signals as reliable VeHIF volatility signatures.

Fast sparse Bayesian learning-based seismic resolution enhancement

Seismic high resolution processing plays a crucial role in seismic interpretation and reservoir characterization. However, the low and high frequency of seismic signal are lost owing to the effects of earth filtering and diffraction. The missing of low-frequency increases the energy of wavelet side lobe, which not only enhances the interference with adjacent events, but also lead to side lobe artifacts. High frequency attenuation is more serious, which significantly reduces the seismic resolution. In addition, the current resolution enhancement methods are difficult to achieve desirable result in noise contaminated environment. A novel resolution enhancement method that extends high and low frequencies simultaneously and improves the signal-to-noise ratio (SNR) under the framework of sparse decomposition theory is proposed. Firstly, an over-complete dictionary is constructed using non-zero phase Ricker wavelet and then the effective seismic signal is extracted by sparse decomposition using the fast sparse Bayesian learning (FSBL) algorithm. Secondly, the effective frequency range of the seismic signal is determined through a series of atoms obtained by sparse decomposition. Then, a desired broadband spectrum can be identified depending on this range. By approximating the broadband spectrum of seismic data, the weak high-frequency information can be extended. Furthermore, a correction term is introduced in frequency domain to suppress the side lobes of atoms. The side lobes are well suppressed without distorting the main lobe, meanwhile, both the low and high frequencies are extended. Finally, the validity of this method is verified by synthetic and field data. The results show that this method has better performance than the ordinary deconvolution and spectral whitening methods in processing high resolution data.

Improved Parameter Identification of SPMSM Using High-Frequency Signal Injection Methods at Standstill

Improved Parameter Identification of SPMSM Using High-Frequency Signal Injection Methods at Standstill

Detection and localization of LV winding radial deformation in transformers using electromagnetic waves - a feasibility study

Recently, online methods based on electromagnetic waves have been proposed to detect the mechanical defects of high voltage (HV) windings in power transformers. In this article, for the first time, the possibility of online detecting and locating the radial deformation (RD) of low voltage (LV) windings using electromagnetic waves is presented. In the proposed method, a high frequency and wideband signal is sent to a simplified model of transformer winding via small antennas. The antennas are connected to the inner side of the oil tank cover through Radio Frequency (RF) cables, so that online monitoring can be realized with minimal changes in the transformer structure. The data of the reflected signals in different states is recorded in a database and sorted as primary data bank. By comparing the results of the sound state with measured signals, the possible faults can be detected. In this paper, the detection and location of radial deformation is estimated by using regression tree based on three features. Several cases are simulated by Computer Simulation Technology (CST) software, and their verification are conducted by a setup in laboratory. Based on comparison results, it can be claimed that the proposed method has LV windings radial deformation online detecting and locating ability with an acceptable accuracy.

Driving Signal and Geometry Analysis of a Magnetoelastic Bending Mode Pressductor Type Sensor.

The paper deals with a brief overview of magnetoelastic sensors and magnetoelastic sensors used in general for sensing bending forces, either directly or sensing bent structures, and defines the current state of the art. Bulk magnetoelastic force sensors are usually manufactured from transformer sheets or amorphous alloys. In praxis, usually, a compressive force is sensed by bulk magnetoelastic sensors; however, in this paper, the sensor is used for the measurement of bending forces, one reason being that the effect of such forces is easily experimentally tested, whereas compressive forces acting on a single sheet make buckling prevention a challenge. The measurement of the material characteristics that served as inputs into a FEM simulation model of the sensor is presented and described. The used material was considered to be mechanically and magnetically isotropic and magnetically nonlinear, even though the real sheet showed anisotropic behavior to some degree. A sinusoidal magnetizing current waveform was used in the experimental part of this paper, which was created by a current source. The effects of various frequencies, amplitudes, and sensor geometries were tested. The experimental part of this paper studies the sensors' RMS voltage changes to different loadings that bend the sheet out of its plane. The output voltage was the induced voltage in the secondary coil and was further analyzed to compute the linearity and sensitivity of the sensor at the specific current characteristic. It was found that for the given material, the most favorable operating conditions are obtained with higher frequency signals and higher excitation current amplitudes. The linearity of the sensor can be improved by placing the holes of the windings at different angles than 90° and by placing them further apart along the sheet's length. The current source was created by a simple op-amp voltage-to-current source controlled by a signal generator, which created a stable waveform. It was found that transformer sheet bending sensors with the dimensions described in this paper are suitable for the measurement of small forces in the range of up to 2 N for the shorter sensors and approximately 0.2 N for the longer sensors.

High-frequency signal transmission in a coplanar waveguide structure with different surface finishes

Continuous pursuits for higher data rate, larger network capacity, low-latency communication, and better energy efficiency have motivated the development of the fifth generation (5 G) mobile communication technologies in recent years. Under a high-frequency transmission, the alternating electric current (AC) tends to distribute to the outer of a conductor due to the so-called “skin effect”; therefore, the surface coatings of Cu interconnect becomes an indispensable factor of the signal transmission characteristics. The focus of this study is to deeply probe into the effect of surface coatings on the high-frequency signal transmission (1–100 GHz) of Cu interconnects. We investigated six different surface finishes over a coplanar waveguide (CPW) transmission line structure, including immersion tin (ImSn), immersion silver (ImAg), immersion gold (IG), electroless palladium/immersion gold (EPIG), immersion gold/electroless palladium/immersion gold (IGEPIG), and ultrathin-Ni(P)-type ENEPIG. The high-frequency signal performance of the CPW transmission line was analyzed through the finite element analysis (FEA) method by using a 3D electromagnetic simulation software. Furthermore, experimental measurements were conducted by using a vector network analyzer (VNA) to characterize the signal loss arising from different surface finishes, so as to validate the numerical simulation results. The FEA and VNA results showed that a noticeable current redistribution might be induced by a high conductivity/permeability of surface coating(s), i.e., ImSn and ultrathin-Ni(P)-type ENEPIG, which significantly increases the signal insertion loss at high frequencies. Thus, an appropriate surface coating over Cu interconnects was necessary for enhancing the high-frequency signal transmission performance.

Ultrasonic hearing abilities of the domestic cat assessed with auditory brainstem responses

Domestic cats (Felis catus) have sharp sensory abilities which they use in various circumstances. For example, they have acute hearing for detecting and localizing relevant auditory information, such as the presence of potential threats or prey items. However, there are notable discrepancies in the literature regarding the full extent of the cat’s hearing abilities. Here, we hypothesize that domestic cats can hear ultrasonic frequencies above 60kHz, since they might utilize their hearing abilities to detect the ultrasonic vocalizations emitted by rodent prey. We used auditory brainstem responses (ABRs), a more time efficient method compared to behavioral psychoacoustic techniques, to evaluate the sensitivity of the cat’s auditory system to ultrasonic frequencies. We presented artificial and behaviorally relevant stimuli containing ultrasonic frequencies to each cat (n = 6). We then recorded the resulting ABRs, measured the wave amplitudes and latencies, and determined the ABR thresholds to these stimuli. The ABR data presented here will be useful in conjunction with psychoacoustic experiments to provide insight into the neural mechanisms that might be involved when cats perceive high frequency signals. This work will ultimately contribute to a better understanding of the cat’s hearing abilities. [Work supported by the Natural Sciences and Engineering Research Council of Canada.]

Position Sensorless Control of Dual Three-Phase IPMSM Drives With High-Frequency Square-Wave Voltage Injection

Dual three-phase interior permanent-magnet synchronous motor (IPMSM) drives have received more attention by the merits of better fault-tolerant capability and lower torque ripples. To improve the reliability of the system against failures in position sensor, it is necessary to investigate the position estimation method for dual three-phase IPMSM drives. The high-frequency (HF) signals injection is utilized for position estimation in three-phase IPMSM drives during low-speed operation region. However, the torque ripples are inevitable owing to the mixing of fundamental and HF components in the same control space for three-phase IPMSM drives. In this paper, the position sensorless control method has been proposed by injecting HF square-wave voltage in the harmonic subspace for the dual three-phase IPMSM drives. Since the harmonic subspace is decoupled from the torque subspace, the torque disturbance can be mitigated well with the proposed HF signal injection based position sensorless method. Moreover, the digital filters in signal extraction and current feedback can be avoided and the signal separation process becomes easy with the proposed method. The experiments have been conducted to validate the performance of the proposed method.

Numerical analysis of neutral delay differential equations with high-frequency inputs

PurposeThe paper proposes an efficient and insightful approach for solving neutral delay differential equations (NDDE) with high-frequency inputs. This paper aims to overcome the need to use a very small time step when high frequencies are present. High-frequency signals abound in communication circuits when modulated signals are involved.Design/methodology/approachThe method involves an asymptotic expansion of the solution and each term in the expansion can be determined either from NDDE without oscillatory inputs or recursive equations. Such an approach leads to an efficient algorithm with a performance that improves as the input frequency increases.FindingsAn example shall indicate the salient features of the method. Its improved performance shall be shown when the input frequency increases. The example is chosen as it is similar to that in literature concerned with partial element equivalent circuit (PEEC) circuits (Bellen et al., 1999). Its structure shall also be shown to enable insights into the behaviour of the system governed by the differential equation.Originality/valueThe method is novel in its application to NDDE as arises in engineering applications such as those involving PEEC circuits. In addition, the focus of the method is on a technique suitable for high-frequency signals.

Computation of VHF Signal Strength for Point to Area Network using Machine Learning Modeling Techniques

In this paper, computation of very high frequency (VHF) signal strength for point to area network was carried out using machine learning modeling techniques. Seven different machine learning models were adopted: Decision Tree, Random Forest, AdaBoost, k-Nearest Neighbor, Support Vector Machine, Artificial Neural Network and Linear Regression. A total of 120 data points was used in computing the signal strength. 72 data points (60%) was used to train the model, while the remaining 48 data points (40%) were used as test data to determine the accuracy of the computation for all the models. From the results, it was observed that the accuracy of the computations was greatly influenced by the amount of training data that was used. Also, from the results, in highest order of accuracy, AdaBoost was adjudged the best model. This was followed by the Artificial Neural Network model. Generally, the error margin of computation obtained for these two models were low, hence indicating that the models can be effectively relied on for computation of signal strength in the study area.