A Study of Algorithm and Application in Transient Signals Wavelet Post-Analysis Methods

Time–frequency analysis (TFA) methods can characterize the instantaneous frequency (IF) features of nonstationary signals by constructing the time-domain signal model. However, the traditional TFA methods are ineffective for transient signals that are discontinuous in the time domain and characterized by sharp frequency variation. Therefore, this article proposes a new TFA method called generalized transient-squeezing transform (GTST) to process transient signals by constructing a signal model in the frequency domain. First, the generalized group delay (GD) estimation is calculated based on the frequency-domain signal model. Then, the generalized transient-squeezing operator is constructed by iterative operations. Finally, the time–frequency (TF) energy is rearranged according to the constructed operator. The GTST provides a more compact TF representation (TFR) of the transient signal while maintaining reversibility. Numerical validation shows that the proposed method has higher TF concentration and accuracy in analyzing transient signals compared with other advanced TFA methods. Experimental validation shows the effectiveness of the proposed method in practical transient signal analysis.

We are investigating countermeasure techniques against electric noise on telecommunication and related equipment. There are many types of electric noise, and we focus on the characterization of transient noise such as the switching noise of switching power supplies. We are developing a method of transient signal analysis based on linear predictive coding (LPC) for the purpose. Unfortunately, as standard LPC contains Yule-Walker (YW) approximation, which replaces a given time series with a periodic time series for simplification, it is not suitable for transient signal analysis. Therefore, our method of transient signal analysis does not contain YW approximation. Instead, we use the local linearization technique, which was developed in the field of quantum mechanics. Our method makes it possible to obtain instantaneous frequencies and instantaneous decay rates simultaneously with high precision from a small number of samples. We discuss our LPC method avoid using YW approximation and using the local linearization technique and its application to repetitive ringing signals, which mimics the switching noise of a switching power supply, to show the efficiency of our method.

Transient dynamical analysis of strain signals in sheet metal stamping processes

This paper presents a new method for transient signal analysis in the frequency domain based on the continuous wavelet transform (CWT). The study involved testing signals obtained from an electronic switch that considers opening and closing operations. The source is connected to inductive, resistive, and capacitive loads. The resonance behaviors were analyzed, obtaining the result that the signal processing performed by CWT is better for spectrum analysis than the Fourier Transform (FT). The results show that the CWT achieves greater reliability, repeatability, high noise attenuation, and softness of the analyzed spectrum. This study shows the potential of WT for analyzing transient signals, specifically in the connection and disconnection of loads.

Integrity monitoring and flaw diagnostics of flat beams and tubular structures was investigated in this research task using guided acoustic signals. A piezo-sensor suite was deployed to activate and collect Lamb wave signals that propagate along metallic specimens. The dispersion curves of Lamb waves along plate and tubular structures are generated through numerical analysis. Several advanced techniques were explored to extract representative features from acoustic time series. Among them, the Hilbert-Huang transform (HHT) is a recently developed technique for the analysis of non-linear and transient signals. A moving window method was introduced to generate the local peak characters from acoustic time series, and a zooming window technique was developed to localize the structural flaws. The time-frequency analysis and pattern recognition techniques were combined for classifying structural defects in brass tubes. Several types of flaws in brass tubes were tested, both in the air and in water. The techniques also proved to be effective under background/process noise. A detailed theoretical analysis of Lamb wave propagation was performed and simulations were carried out using the finite element software system ABAQUS. This analytical study confirmed the behavior of the acoustic signals acquired from the experimental studies. The report presents the background the analysis of acoustic signals acquired from piezo-electric transducers for structural defect monitoring. A comparison of the use of time-frequency techniques, including the Hilbert-Huang transform, is presented. The report presents the theoretical study of Lamb wave propagation in flat beams and tubular structures, and the need for mode separation in order to effectively perform defect diagnosis. The results of an extensive experimental study of detection, location, and isolation of structural defects in flat aluminum beams and brass tubes are presented. The results of this research show the feasibility of on-line monitoring of small structural flaws by the use of transient and nonlinear acoustic signal analysis, and its implementation by the proper design of a piezo-electric transducer suite.

Numerical relays have become essential tools for carrying out the protection and surveillance tasks of electrical power systems. These relays implement their functions from the phasors of the electrical network signals, estimated by digital processing using digital filters. Digital filters must meet certain requirements, such as providing a fast and effective response to increasingly complex transient signals made up of components that make the estimation process difficult. In addition to the decreasing exponential (decaying dc offset), harmonics, and signal noise, it must be added that the interharmonic components that in recent years have acquired great relevance, mainly because of the increase in non-linear loads and the extensive use of power electronic systems. The presence of these interharmonic components causes a poor response in most of the filters implemented today. This article presents the design of a new digital filter, C-CharmDF (Cleaned Characteristic Harmonic Digital Filter) for phasor estimation on noisy transient signals with decreasing exponential components, harmonics, and interharmonics. A detailed study was carried out for severe transient situations and stationary signals. It was found that the method can be suitable for relays that implement both fault location functions and system protection functions.

Linear and quadratic time‐frequency transforms are used to investigate and characterize transient signals from several wave propagation problems in structural acoustics. The transforms include: the short‐time spectrum, Wigner type representations, and wavelet methods. Advantages and disadvantages of the various representations within the present context are presented. Particular emphasis is placed on using the wavelet packet method of Coifman to analyze and characterize complex transient signals from various structural‐acoustic transmission paths with dispersive characteristics. Adaptive procedures are used to determine optimal time‐frequency localization by using the best basis functions for the class of dispersive signals of interest. Various features and parameter extraction methods based on the local maxima of the wavelet transform at each scale are presented for several complex transient dispersive signals. [Work supported by ONR.]

Improving the spatial resolution in CZT detectors using charge sharing effect and transient signal analysis: Simulation study

Vector space representation of signals for transient signal analysis in transformers and shunt reactor

In response to the problem of difficulty in accurately extracting spectral trajectories of multi‐band non‐repetitive transient weak signals in industrial control networks under strong noise background, this paper proposes a method that introduces a fusion filtering algorithm to extract spectral trajectories of multi‐band non‐repetitive transient weak signals in industrial control networks. Build an XGBoost classifier for multi‐band signal states in industrial control networks, clarify the current non‐repetitive transient weak signal states in multi‐band industrial control networks, and achieve accurate identification of weak signal states. Based on the classifier results, combined with the Bloom filter, the classified signal sequence is deduplicated to eliminate redundancy caused by repeated transient signals. Further adopt a fusion filtering algorithm to filter the spectral trajectory of weak signals in multiple frequency bands of the input industrial control network, in order to restore the original signal; extract the single‐frequency spectral trajectory of multi‐band non‐repetitive transient weak signals through average sampling processing, and fuse the trajectory to obtain the complete weak signal spectral trajectory. The experimental data comparison results show that the proposed method can better extract weak signal spectral trajectory information under transient conditions, and the extraction effect is more stable and reliable, with high accuracy and anti‐interference ability.

Intensity analysis methods for transient signals

Transmission line protection is an important issue in power system engineering because 85-87% of power system faults are occurring in transmission lines. This paper presents a technique to detect and classify the different shunt faults on a transmission lines for quick and reliable operation of protection schemes. Discrimination among different types of faults on the transmission lines is achieved by application of evolutionary programming tools. PSCAD/EMTDC software is used to simulate different operating and fault conditions on high voltage transmission line, namely single phase to ground fault, line to line fault, double line to ground and three phase short circuit. The discrete wavelet transform (DWT) is applied for decomposition of fault transients, because of its ability to extract information from the transient signal, simultaneously both in time and frequency domain. The data sets which are obtained from the DWT are used for training and testing the SVM architecture. After extracting useful features from the measured signals, a decision of fault or no fault on any phase or multiple phases of a transmission line is carried out using three SVM classifiers. The ground detection task is carried out by a proposed ground index. Gaussian radial basis kernel function (RBF) has been used, and performances of classifiers have been evaluated based on fault classification accuracy. In order to determine the optimal parametric settings of an SVM classifier (such as the type of kernel function, its associated parameter, and the regularization parameter c), fivefold cross-validation has been applied to the training set. It is observed that an SVM with an RBF kernel provides better fault classification accuracy than that of an SVM with polynomial kernel. It has been found that the proposed scheme is very fast and accurate and it proved to be a robust classifier for digital distance protection.

This paper presents an approach to classifying power system faults using rough set methods. A knowledge-based fault detection and identification (FDI) system for power system faults has been introduced. The FDI system has the ability to detect and classify power system faults by combining conventional signal analysis methods (e.g., FFT, IFFT and wavelets) with granular computing and rough set methods. In granular computing, experimental data is partitioned into collections of data (called information granules) that are in some way similar. Rough set methods are based on set approximation, partition of each finite universe using an indiscernibility relation, attribute reduction, decision-rule derivation, and many useful measures such as approximation accuracy and rough inclusion. Traditional fuzzy set theory is also as part of fault signal feature extraction. The FDI system derives an indication of the type of faults that have occurred and also generates classification rules for the fault classification. This system has resulted from a study of fault files recorded by the Transcan Recording System (TRS) at the Manitoba Hydro Dorsey Station over several years. The contribution of this paper is the introduction of an approach to classifying power system faults using a combination of traditional signal analysis methods and a number of computational intelligence methods (granular computing, and rough set theory).

Faults in transmission lines cause instability of power system and result in degrading end users sophisticated equipment. Therefore, in case of fault and for the quick restoration of problematic phases, reliable and accurate fault detection and classification techniques are required to categorize the faults in a minimum time. In this work, 500 kV transmission line (Jamshoro‐New Karachi), Sindh, Pakistan has been modeled in MATLAB. The discrete wavelet transform (DWT) has been used to extract features from the transient current signal for different faults in 500 kV transmission line under various parameters such as fault location, fault inception angle, ground resistance and fault resistance and time series data has been obtained for fault classification. Moreover, the temporal convolutional neural network (TCN) is used for fault classification in 500 kV transmission network due to its robust framework. From simulation results, it is found that faults in 500 kV transmission line are classified with 99.9% accuracy. Furthermore, the simulation results of the TCN model compared to bidirectional long short‐term memory (BiLSTM) and Gated Recurrent Unit (GRU) and it has been found that TCN model is capable of classifying faults in 500 kV transmission line with high accuracy due to its ability to handle long receptive field size, less memory requirement and parallel processing due to dilated causal convolutions. Through this work, the meantime to repair of 500 kV transmission line can be reduced.

Smoothing reactors, together with the DC filter banks installed at both ends of the HVDC transmission line constitute the `natural boundary', which leads to significant difference in attenuation characteristic of different frequency components of fault transient signals detected by protection. By means of Hilbert-Huang Transform, which is a kind of non-stationary transient signal processing method, the energy of high frequency components(2000Hz~10kHz) is extracted based on the attenuation characteristic of the line boundary, then a novel algorithm for the non-unit transient-based protection is acquired. Moreover, a protection threshold that is adapt to various lengths of transmission line is obtained by extensive simulation work. Experimental results indicate that the new criteria can identify any internal fault on the whole line and clear it rapidly and credibly, what's more important, the accuracy and reliability of the criteria will not be influenced by the length of the transmission line.