11 results about "Modulus maxima" patented technology

The invention discloses a line fault detection method and device based on morphological wavelets, relates to the technical field of line detection, and solves the problem that in the prior art, when a single-phase grounding fault occurs in a small-current grounding system, the fault current is small, the characteristics are not obvious, and when line fault detection is carried out by using time domain, frequency domain and time frequency analysis methods, the fault current is not obvious. And the technical problems of low fault line selection accuracy and poor reliability exist. The method comprises the following steps: acquiring zero-sequence current signals in a period before and after a line fault occurs; pre-processing the zero sequence current signal; decomposing the preprocessed zero sequence current signal by using a morphological Haar wavelet; calculating modulus maxima of the plurality of lines according to the decomposition result; correcting the modulus maxima of the plurality of lines based on the line length; screening fault lines according to the corrected modulus maxima; the accuracy and reliability of single-phase earth fault line selection can be improved.

The invention belongs to the technical field of logging data processing and interpretation, and discloses a logging curve layered prediction method and system based on optimal window wavelet transform, and the method comprises the steps: carrying out the preprocessing of logging curves of a cored well and a non-cored well; carrying out artificial layering and sedimentary microfacies analysis on the natural gamma logging curve; respectively carrying out wavelet transformation on the natural gamma logging curve and the natural potential logging curve to obtain an optimal window size; layering the non-cored well according to the size of the optimal window; and merging the layering points to obtain an optimal automatic layering effect of the logging curve. According to the method, the optimal window size of the wavelet transform modulus maxima of the natural gamma GR curve and the natural potential SP curve is determined by using the loss function through the mutual comparison constraint of manual layering and automatic layering, layering is performed by using the GR curve and the SP curve, and finally, the layering post-processing is performed, so that the missing division and the wrong division are effectively reduced, and the optimal automatic layering effect is obtained.

The invention relates to the technical field of ground penetrating radar detection application, provides a tunnel ground penetrating radar signal denoising analysis method based on wavelet decomposition, and analyzes the local singularity of attenuation speed type measurement signals of local modulus maxima under different scales by using the discontinuity of tunnel ground penetrating radar signals or the discontinuity of derivatives of the tunnel ground penetrating radar signals. The method comprises the following steps: firstly, carrying out wavelet transformation and wavelet decomposition on an obtained signal, calculating a modulus maximum value of a detail coefficient of each wavelet scale, setting a threshold value on the maximum scale, comparing each extreme point with the threshold value to obtain a new modulus maximum value point on the maximum scale, carrying out the calculation on a lower scale, and repeating step by step to obtain a new modulus maximum value point on the maximum scale. And carrying out non-linear interpolation on the modulus maxima of the second scale until reaching the second scale, taking the modulus maxima of the second scale as the modulus maxima of the first scale, and finally reconstructing the signal by adopting the reserved wavelet transform modulus maxima, thereby achieving the purpose of removing the noise of the tunnel ground penetrating radar data signal.

The embodiment of the present invention discloses a method, device, and electronic device for obtaining step response parameters of a direct current transformer. The method includes: obtaining a transient step signal from the secondary side of a current transformer; inputting a step current signal from the primary side of the current transformer; processing the transient step signal using a wavelet modulus maximum algorithm to sequentially obtain multiple modulus maxima; dividing the multiple modulus maxima into a step low value interval and a step high value interval; obtaining a first time window of a step low value corresponding to the step low value interval; obtaining a second time window of a step high value corresponding to the step high value interval; filtering the transient step signal in each time window using a preset filtering method to obtain a transient step signal to be used in each time window; obtaining a step value based on the transient step signal to be used in each time window, the step value including a step low value and a step high value; and obtaining a step response parameter based on each step value.

The application discloses a frequency band range selection method for fast traveling wave protection and a related device, and the method comprises the following steps: collecting positive electrode voltage data and negative electrode voltage data in a preset data window, and determining a fault initial line mode voltage traveling wave based on the positive electrode voltage data and the negative electrode voltage data; analyzing the fault initial line mode voltage traveling wave, and extracting a plurality of wavelet transform modulus maxima of different scales; comparing the plurality of wavelet transform modulus maxima of different scales with preset conditions, and determining a frequency band range of fast traveling wave protection based on a comparison result. The method realizes accurate selection of the frequency band range of fast traveling wave protection, and provides strong support for guaranteeing the reliability of fast traveling wave protection of a flexible direct current power transmission system.

This invention belongs to the field of positioning technology, specifically relating to a transient traveling wave positioning method for broken strand faults in overhead insulated conductors. The method includes: acquiring a single-end transient current signal of the line, obtaining a reconstructed signal through bandpass filtering, and performing an initial time-frequency domain transformation to obtain an initial time-frequency distribution; calculating the power spectrum and constructing a parallel time-varying parameter oscillator array to obtain multiple narrowband enhanced signals; performing a second time-frequency domain transformation on the enhanced signals to construct a fused time-frequency energy matrix; extracting the modulus maxima propagation path from the matrix; determining the arrival times of the initial traveling wave and the reverse polarity fault reflection wave based on the moment when the singularity characteristic index first enters the abrupt change threshold interval; and calculating the fault distance based on the calibrated traveling wave velocity and the single-end traveling wave ranging principle. This method effectively suppresses noise and enhances weak features, significantly improving the accuracy of wavefront identification and the reliability of single-end positioning.

The present invention discloses a novel wavelet transform modulus maximum calibration method based on distribution fault traveling wave location, including: performing wavelet transform decomposition and reconstruction on fault traveling wave data to obtain the waveform of the reconstructed first-layer high-frequency detail coefficients; dividing the time horizontal axis of the waveform of the reconstructed first-layer high-frequency detail coefficients into several equal-length time periods, each time period containing the same number of time points, and sequentially marking the maximum amplitude of the waveform of the reconstructed first-layer high-frequency detail coefficients in each time period. Starting from the amplitude at the 0 time point of the original reconstructed waveform, sequentially connecting the maximum amplitudes of each time period to obtain the upper envelope of the original reconstructed waveform. On this basis, extract all modulus maxima and their corresponding time points in the upper envelope. Finally, set a floating threshold value, compare all modulus maxima with the floating threshold value, and obtain the arrival time of each fault wave head according to the time point corresponding to the modulus maximum, thereby completing the calibration.

The invention belongs to the technical field of aircraft airborne testing, and particularly relates to a rapid fault diagnosis method for an airborne vibration sensor. Comprising the following steps that 1, flight test sensor record data are obtained, data of a vibration sensor are separated according to parameter names, and the data of the sensor comprise a fault sample data sequence and a normal sample data sequence; 2, respectively forming a training set, a verification set and a test set according to a random selection mode; 3, respectively constructing normal sampling training subsets for the training set, and constructing an unbalanced sampling training subset by diluting normal samples; 4, wavelet transformation is carried out on the normal sampling training subset data sequence and the unbalanced sampling training subset data sequence respectively, and a normal sampling training subset input matrix and an unbalanced sampling training subset input matrix based on wavelet transformation modulus maxima are constructed respectively; 5, inputting the normal sampling training subset input matrix and the unbalanced sampling training subset input matrix into the bilateral branch network model for training, and obtaining prediction output through different weight accumulation learning of two sample sets; step 6, using a cross entropy loss function to realize convergence of the network model, and completing network model training; and 7, performing sensor fault judgment on output of the sensor and prediction output of the network model by adopting a majority consistent voting principle. The problem of sensor abnormal phenomenon detection or abnormal missing in a flight test is solved.

This invention belongs to the field of positioning technology, specifically relating to a transient traveling wave positioning method for broken strand faults in overhead insulated conductors. The method includes: acquiring a single-end transient current signal of the line, obtaining a reconstructed signal through bandpass filtering, and performing an initial time-frequency domain transformation to obtain an initial time-frequency distribution; calculating the power spectrum and constructing a parallel time-varying parameter oscillator array to obtain multiple narrowband enhanced signals; performing a second time-frequency domain transformation on the enhanced signals to construct a fused time-frequency energy matrix; extracting the modulus maxima propagation path from the matrix; determining the arrival times of the initial traveling wave and the reverse polarity fault reflection wave based on the moment when the singularity characteristic index first enters the abrupt change threshold interval; and calculating the fault distance based on the calibrated traveling wave velocity and the single-end traveling wave ranging principle. This method effectively suppresses noise and enhances weak features, significantly improving the accuracy of wavefront identification and the reliability of single-end positioning.

This application provides a traveling wave ranging device, distribution terminal, fault monitoring system, and method for distribution networks, relating to the field of distribution network fault monitoring technology. The device includes: a current transformer for acquiring voltage / current traveling wave signals at corresponding detection points; a data processing unit, including: a phase-mode transformation module for performing phase-mode transformation on the voltage / current traveling wave signals to extract the line-mode components of each voltage / current traveling wave signal; a wavelet analysis module for decomposing the extracted line-mode components based on wavelet transform to obtain corresponding wavelet transform coefficients, performing modulus maxima search on all obtained wavelet transform coefficients, and generating a first detection signal representing the detection of the target modulus maxima if a target modulus maxima representing a line fault is determined; and a ranging calculation unit for determining the first fault location information based on the first detection signal. This application enables high-speed sampling and processing of traveling wave data, effectively improving the accuracy of distribution network fault location.

The invention provides an FPGA (Field Programmable Gate Array)-based improved continuous wavelet traveling wave fault wave head identification system and method. The system comprises a discrete wavelet module, a windowing module and a continuous wavelet module. In the discrete wavelet module, real-time fault pre-identification is carried out on the traveling wave signals based on a multilayer discrete wavelet algorithm; the windowing module performs windowing processing on the fault section signal; the continuous wavelet module obtains a wavelet coefficient through Fourier transform, frequency domain product and inverse Fourier transform, searches a modulus maximum line according to the multilayer wavelet coefficient, judges whether a local modulus maximum is caused by noise or a fault wave head, and achieves the precise positioning of the fault wave head. Compared with a traditional discrete wavelet traveling wave head identification method, the method is higher in frequency resolution. And more wavelet decomposition coefficients can be decomposed for the fault frequency band of the fault traveling wave, and the positioning precision is higher.