Wavelet Transform Modulus Maximum Research Articles

A waveform-similarity-based protection scheme for the VSC-HVDC transmission lines

The protection for the voltage source converter based high voltage direct current (VSC-HVDC) transmission lines must detect the faulty DC lines and faulty poles within a few milliseconds, because the fault current rises quickly. The traditional protections are easily influenced by fault resistance and signal noise. What is worse, the double-pole-to-ground (DPG) fault with its positive-pole fault resistance unequal to negative-pole fault resistance cannot be detected accurately. To solve the problems, a non-unit protection scheme based on the waveform similarity and wavelet transform modulus maximum (WTMM) theories for the VSC-HVDC lines is proposed in this paper. A four-terminal VSC-HVDC power network is modeled in PSCAD to verify the protection. Extensive simulations of various fault situations prove that the protection scheme can detect high-fault-resistance, DPG, and remote faults. Moreover, it is insensitive to the sampling frequency, signal noise, smoothing reactor, and start delay. Furthermore, it can meet the rapidity requirement. Therefore, the proposed protection can identify the DC faults accurately, which minimizes the disturbance to power grids and improves system security and stability.

Detection of Traffic Pattern Based on Fuzzy Clustering and Wavelet Analysis Model at Different Signaling Positioning Frequencies

Signaling positioning technology provides a new opportunity to understand an individual’s travel characteristics. In recent studies, the travel parameters obtained are mainly macroscopic travel information. However, extracting detailed trip chain information, such as the trip mode and mode-switching time point, remains a challenge. Furthermore, because of the iterative development of wireless networks, existing communication operators usually store different frequencies and accuracy (2G/3G and 4G) of signaling data simultaneously, making the refined identification of travel information more difficult. Therefore, this paper proposes a new method. First, we use the shortest distance algorithm to match the signaling data with the road network. Second, a wavelet transform modulus maximum (WTMM) algorithm is proposed to divide multimodal travel trajectories into single-mode trip segments; thus, spatiotemporal information related to mode transfer can be obtained. Finally, an unsupervised fuzzy kernel c-means clustering (FKCM) algorithm is proposed to distinguish travel modes. As comparison data, smartphone GPS and travel log data are also collected to analyze the detection result and improve the method. The identification errors of mode-switching time points at different frequencies are all less than 360 s. The average correct rate of traffic mode identification for 2G is 65.1%, and the average correct rate of traffic mode identification for 3G is 78.2%. 4G intensive cellular positioning data has a significantly better recognition effect than low-frequency data; the average trip mode detection accuracy reaches 89.6%, and the mode-switching time point detection errors are within 300 s.

Roundness error separation based on singularity detection and exact-stop of spindle in on-machine measurement of spindle rotation error

At present, non-contact sensors are being extensively used for the measurement of spindle rotation errors. However, as the measuring target of the sensors, the roundness error of the artifact inevitably mixes into the error motion of the spindle. Moreover, the challenging nature of manufacturing high-precision artifacts has resulted in the separation of the roundness error of artifacts from the spindle rotation error being considered an urgent research prospect. In this study, a roundness error separation method based on singularity detection and exact-stop of spindle was proposed to perform on-machine measurement of spindle rotation error, wherein compared to the existing methods, the proposed method implements the Donaldson reversal method. It is independent of the index signal of the spindle encoder, which is usually unavailable on actual machine tools. The singularity of measured motion was detected using the wavelet transform modulus maximum (WTMM) ridges and their Lipschitz exponents, to locate the exact-stop and rotating stages of spindle in the measured motion. Consequently, a WTMM ridge extraction method was established to extract the continuous optimal WTMM ridges of the measured motion. Further, the synchronous motion of the measured motion was used for roundness error separation to promote the separation accuracy. Finally, the effectiveness of the proposed method was verified using the measurement results of a coordinate measuring machine (CMM). Thus, the measurement error caused by the roundness error of the artifact can be reduced significantly by applying the proposed method to the on-machine measurement of the spindle error motion.

A traveling-wave-based protection scheme for the bipolar voltage source converter based high voltage direct current (VSC-HVDC) transmission lines in renewable energy integration

The voltage source converter based high voltage direct current (VSC-HVDC) technology is a feasible solution to realize wide-area complementarity and flexible consumption of the renewable clean energy resources but DC line faults are crucial problems. The traditional traveling wave protection for HVDC transmission lines are easily affected by the fault resistance and signal noise. Besides, it cannot detect the double-pole-to-ground fault. To overcome these problems, a single-ended protection scheme based on the phase-mode transformation and wavelet transform modulus maximum (WTMM) theories for DC lines in the VSC-HVDC transmission system is proposed in this paper. The protection scheme includes the protection startup unit, the internal fault identification unit, and the faulty pole detection unit. Extensive simulation tests carried out in PSCAD/EMTDC verify that the protection scheme has high selectivity in the high fault resistance faults, remote faults, and close-by faults with different fault types. And it does not rely too much on data synchronization. Furthermore, sensitivity analysis and comparisons with other methods indicate that the protection is insensitive to signal noise and suitable for different operational modes. And the effectiveness of the protection is independent of the high sampling frequency. Moreover, it can identify the double-pole-to-ground fault correctly.

Defect feature extraction of marine protective coatings by terahertz pulsed imaging

Feature extraction of marine protective coatings using Hilbert transform (HT) and wavelet transform modulus maximum (WTMM) on terahertz pulsed imaging (TPI) of the coatings was proposed. For TPI-based marine protective coating detection, it is difficult to locate exactly the reflected echoes form internal structure of coating system by the faint feature in the time domain due to the interference of background noise. However, those faint features were usually caused by the interface between two different medium layers whose refractive indices are very similar or caused by micro defects beneath the coating. The proposed algorithm was validated by simulated and experimental TPI waveform obtained from marine paint samples with or without defects. To extract the structure feature more clearly and intuitively, Hilbert transform procedure was carried out on detected terahertz signal to get Hilbert envelope for further processing. Subsequently, the modulus maximum of the stationary wavelet transform approximation coefficients were employed as the criteria for feature extraction of internal interfaces and defect features, according to the relationship between WTMM and signal singularity. The results demonstrated that the combination of HT and WTMM algorithms could be used to exactly extract the structure feature and to evaluate the position of defects beneath coatings.

Performance Improvement of Raman Distributed Temperature System by Using Noise Suppression

In Raman distributed temperature system, the key factor for performance improvement is noise suppression, which seriously affects the sensing distance and temperature accuracy. Therefore, we propose and experimentally demonstrate dynamic noise difference algorithm and wavelet transform modulus maximum (WTMM) to de-noising Raman anti-Stokes signal. Experimental results show that the sensing distance can increase from 3 km to 11.5 km and the temperature accuracy increases to 1.58 °C at the sensing distance of 10.4 km.

Frozen-in Fractals All Around: Inferring the Large-Scale Effects of Small-Scale Magnetic Structure

The large-scale structure of the magnetic field in the solar corona provides the energy to power large-scale solar eruptive events. Our physical understanding of this structure, and hence our ability to predict these events, is limited by the type of data currently available. It is shown that the multifractal spectrum is a powerful tool to study this structure, by providing a physical connection between the details of photospheric magnetic gradients and current density at all size scales. This uses concepts associated with geometric measure theory and the theory of weakly differentiable functions to compare Ampère’s law to the wavelet-transform modulus maximum method. The Hölder exponent provides a direct measure of the rate of change of current density across spatial size scales. As this measure is independent of many features of the data (pixel resolution, data size, data type, presence of quiet-Sun data), it provides a unique approach to studying magnetic-field complexity and hence a potentially powerful tool for a statistical prediction of solar-flare activity. Three specific predictions are provided to test this theory: the multifractal spectra will not be dependent on the data type or quality; quiet-Sun gradients will not persist with time; structures with high current densities at large size scales will be the source of energy storage for solar eruptive events.

GPS and Acceleration Data in Multimode Trip Data Recognition Based on Wavelet Transform Modulus Maximum Algorithm

The GPS-based travel survey is an emerging data collection method in transportation planning. The survey's application in trip mode detection has been explored in many studies. Most research on trip mode detection methods based on GPS data has been developed and tested with data collected from European and American countries. The methods cannot be easily adapted to Asian countries such as China, India, and Japan, which have much higher population densities, more complex road networks, and highly mixed travel modes during daily commuting. Furthermore, for trip segment division in multimode travel, existing algorithms use travel time and distance thresholds that are highly dependent on local travel behavior and lack universality across traffic environments. This paper proposes an innovative framework for detecting trip modes in complex urban environments. First, a smartphone application, GPSurvey, was developed to collect passive GPS trace data. Then a wavelet transform modulus maximum algorithm was developed for trip segment division. The algorithm has outstanding capabilities for identifying singularity features of a signal; this factor suits the task of detecting mode changes in a complex traffic environment. A neural network module was developed for mode detection on the basis of cell phone GPS location and acceleration data. The results indicate that the proposed method has promising performance. The average absolute detection error of mode transfer time was within 1 min, and the accuracy for detecting all modes was greater than 85%.

Digital discrimination of neutron and γ ray using an organic scintillation detector based on wavelet transform modulus maximum

A novel algorithm for the discrimination of neutron and γ-ray events with wavelet transform modulus maximum (WTMM) in an organic scintillation has been investigated. Voltage pulses arising from a BC501A organic liquid scintillation detector in a mixed radiation field have been recorded with a fast digital sampling oscilloscope. The WTMM method using frequency-domain features exhibits a strong insensitivity to noise and can be used to discriminate neutron and γ-ray events based on their different asymptotic decay trend between the positive modulus maximum curve and the negative modulus maximum curve in the scale-space plane. This technique has been verified by the corresponding mixed-field data assessed by the time-of-flight (TOF) method and the charge comparison (CC) method. It is shown that the characterization of neutron and γ ray achieved by the discrimination method based on WTMM is consistent with that afforded by the TOF method and better than the CC method. Moreover, the WTMM method itself has presented its ability to eliminate the noise without any pretreatment to the pulses.

The Application of Dyadic Wavelets Transform Modulus Maximum in the Seismic Signal De-Noising

This article first briefly introduces the characteristic of the wavelets transform ,explains the concrete operations process that the conjugate gradient method restructuring signal, then narrates in detail the modulus maximum value de-noising's algorithm, finally using the dyadic wavelet transform modulus maximum value de-noising method respectively carries the processing on the synthesis and noisy signal and the actual seismic signal, the experimental result indicates that this algorithm makes the good progress, the seismic data quality is enhanced greatly.

A New Wavelet Transform Modulus Maximum Denoising Algorithm for UWB System

The wavelet transform of pulse signal and noise have different properties, thus an improved wavelet transform modulus maximum algorithm was introduced to remove the noise in UWB system. Improvements include: a) a new adaptive threshold algorithm is used to process the wavelet transform modulus maxima on the largest scale level; b) getting modulus maxima on the first level through nonlinear least-square method. At last, Mallat alterative projection algorithm was adopted to reconstruct the signal. Simulation results show that the denoising effect of the novel method is far better than the traditional one, and it can locate the position of the narrowband pulse more accurately.

APPLICATION OF WAVELET-BASED SINGULARITY DETECTION TECHNIQUE IN AUTOMATIC INSPECTION SYSTEM

This paper describes an application of the wavelet transform in in-line solder paste inspection. In the development of a three-dimensional (3D) automatic inspection device of solder paste, it is necessary to detect the characteristic positions in images. In this paper, on the basis of the property of local wavelet transform modulus maximum (WTMM) and the deference filter, a practical algorithm is proposed for the detection of the characteristic positions. The proposed algorithm is applied to more than 20 actual images obtained from the 3D automatic inspection device. The detected errors are smaller than the maximum permissible error of 5 pixels. Also, the proposed algorithm is verified in-line. The experimental results show that the quality of solder paste can be successfully judged in-line. The proposed algorithm is superior to other possible techniques reported so far because of its implementation in assembly lines.

2D-discontinuity detection from scattered data

We describe a numerical approach for the detection of discontinuities of a two dimensional function distorted by noise. This problem arises in many applications as computer vision, geology, signal processing. The method we propose is based on the two-dimensional continuous wavelet transform and follows partially the ideas developed in [2], [6] and [8]. It is well-known that the wavelet transform modulus maxima locate the discontinuity points and the sharp variation points as well. Here we propose a statistical test which, for a suitable scale value, allows us to decide if a wavelet transform modulus maximum corresponds to a function value discontinuity. Then we provide an algorithm to detect the discontinuity curves fromscattered and noisy data.