46 results about "Modulus maxima" patented technology

The invention relates to a same-tower double-circuit DC transmission line double end range finding method having wave velocity correction and belongs to the technical field of electric power system protection. The method includes the following steps: firstly acquiring a line rectification side and inversion side voltage fault component, adopting a phase-model transformation matrix to decouple the voltage fault component and resolve an aerial mode fault component voltage traveling wave; calculating the wavelet transform modulus maxima of the aerial mode fault component voltage traveling wave, obtaining the time t"M, t"N corresponding to the first and the second modulus maxima points; earth faults are arranged every 10 km along the whole length of the same-tower double-circuit DC transmission line and resolving the wave velocities of the earth faults, fitting the wave velocities of the earth faults to obtain an aerial mode wave velocity curve, adopting the iterative method to resolve an aerial mode fault voltage wave velocity v; secondly substituting the aerial mode fault voltage wave velocity v and the time t"M and t"N corresponding to the wavelet transform modulus maxima to a fault range finding formula to resolve x; based on the polarity of the front two modulus maxima points of the aerial mode fault component voltage traveling wave at the rectification side M terminal after head wave alignment, it can be determined that the fault point occurs within a half line length from the rectification side M terminal or out of the half line length.

The invention relates to a method for ranging the single terminal fault of a single outlet power transmission line based on opposite terminal bus reflected wave identification, and belongs to the technical field of fault ranging of power systems. When a power transmission line has a fault, the method includes the following steps: acquiring three-phase currents of the line from a head terminal of the line and separately resolving a zero modulus component and an aerial modulus component of the three-phase currents; separately performing continuous wavelet transform on the zero modulus componentsand the aerial modulus components to resolve the modulus maxima thereof, and separately determining the time t<0>, t<1> when the zero modulus current travelling wave and the aerial modulus current travelling wave arrive at the measurement point at the head terminal; determining the wave head of the zero modulus current travelling wave of the opposite terminal bus reflected wave, determining the time t<2> when the zero modulus current reflected wave which is reflected by the opposite terminal bus arrives at the measurement point at the head terminal; based on the determined wave head of the zero modulus current travelling wave of the opposite terminal bus reflected wave, determining the wave head of the opposite terminal bus reflected wave that corresponds to the aerial modulus current travelling wave, and determining the time t3 when the measurement point arrives at the head terminal; and resolving the fault distance x by using the wave head of the aerial modulus current travelling wave and a novel fault ranging formula of the wave head of the opposite terminal bus reflected wave.

The invention relates to an ear image recognition method combining wavelet analysis and moment features, which is characterized in that the method comprises following steps: (1) preprocessing collected ear images: using wavelet transform modulus maxima to denoise for the collected ear images, and extracting ear image edge to gain wavelet decomposition modulus maxima edge images in various scales; (2) extracting characteristic value of the ear images: making use of an improved wavelet moment invariants arithmetic to calculate the wavelet moment invariants used for extracting the characteristic value of the ear images; (3) recognizing the ear: processing weighting and classifying the characteristic value of the ear images gained in the step (2), and recognizing the ear. The invention processes to extract the characteristic value of the ear via wavelet modulus maxima and the improved wavelet moment invariants arithmetic, and not only has a high calculation accuracy and a fast calculation speed, but also can improve discrimination of the collected ear images in the environment of poor illumination, illumination change and noise jamming.

The invention relates to a method for extracting lightning strike signals and transient harmonic signals in a power system, which overcomes the problem of inaccurate extraction of the method for extracting the lightning strike signals and the transient harmonic signals in the power system in the prior art. The method comprises the steps of firstly converting the collected current signals to digital signals, further carrying out fast Fourier transform on the digital signals, determining the sampling frequency and the decomposition scale of wavelet decomposition according to the transform result, then carrying out wavelet decomposition on the digital signals, sequentially carrying out modulus maxima extraction, singularity detection and generalized wavelet entropy calculation on wavelet coefficients or single-branch wavelet reconstruction signals after the decomposition, and finally extracting the lightning strike signals and the transient harmonic signals according to the calculation result. The method can overcome the deficiencies of the prior art and be applicable to representing information features of the lightning signals and the transient harmonic signals in the time-frequencydomain.

The invention provides a crosslinked polyethylene high-voltage cable partial discharge feature extraction method, and belongs to the technical field of high-voltage cables. The high-voltage cable partial discharge feature extraction method aims to solve the problem that partial discharge feature extraction is inaccurate in an existing high-voltage cable partial detection method. According to the method, firstly, metal sheath grounding current signals generated when crosslinked polyethylene high-voltage cable partial discharge happens is acquired through a high-frequency current mutual inductor; then, current signals are converted into digital signals; next, wavelet packet decomposition is conducted on the digital signals, modulus maxima extraction and singularity detection are sequentially conducted on decomposed wavelet packet node coefficients or reconstructed signals, and wavelet packet energy index entropy calculation is conducted; finally, a discharge feature curve is drawn according to the operation result, and crosslinked polyethylene high-voltage cable partial discharge feature extraction is achieved. By means of the crosslinked polyethylene high-voltage cable partial discharge feature extraction method, the cable partial discharge feature extraction accuracy can be improved, and technical support is provided for next high-voltage cable partial discharge.

The invention relates to a novel double end fault location method for double DC circuits on same tower and belongs to the technical field of power system relay protection. The method includes steps of obtaining voltage of a measuring end M and a measuring end N and solving a fault component and solving a line mode voltage component by adopting phase mode conversion when the double DC circuits on same tower meets a grounding fault; applying a Bergeron transfer equation and the measuring end line mode voltage component for calculating line-side voltage and current travelling wave variations; cubing a product of the line-side voltage traveling wave variation of the a measuring end M and the line-side current traveling wave variation of the measuring end N and constructing a fault location function through integrating in an observation time window; realizing fault location through combining line-side mutation rules of the fault location function and the polarity of the wavelet transform modulus maxima of fault voltage traveling waves. According to the invention, fault location is performed for the double DC circuits on same tower and the principle is simple. Wave heads of the fault traveling waves do not need to marked and influence caused by fault instantaneity, fault transition resistance variation and the like is eliminated. The fault location result is accurate and reliable.

The invention discloses a method for identifying oil spilling and suspected objects in an SAR (synthetic aperture radar) dark shadow image. The method comprises inputting an SAR image, establishing an oil-spilling image library and a suspected object image library; choosing M pictures at random to form a training sample image library, calculating a fractal characteristic vector of each image in the training sample image library by using a difference box counting method and a wavelet transformation modulus maxima method, forming a training sample set; using the training sample set and labels of the training samples to train an SVM grader; determining a to-be-identified dark shadow area in the SAR image by using a self-adaptive threshold value method, scanning spots pixel by pixel in the dark shadow area, calculating fractal characteristic vector thereof to form a test sample set; and using the trained SVM grader to classify test samples, and outputting a classification result. The method has relatively high operation efficiency, and can be used in identification and classification of oil spilling and suspected objects in the SAR dark shadow image.

The invention discloses a method for detecting a surface defect area of a bearing outer ring. The method comprises the steps: carrying out multi-scale decomposition on an original bearing outer ring image, solving modulus maxima of each scale, and realizing edge preselection of a defect area of the bearing outer ring; clustering the edge of the defect area on the surface of the bearing outer ring to obtain each cluster, and calculating the center of each cluster to determine a growth seed point of the defect area; taking the growth seed point as a base point, taking four neighborhood pixels of the base point as a to-be-selected growth point set, traversing each pixel in the to-be-selected growth point set, and judging whether a growth termination condition is met or not so as to realize the detection of the surface defect area of the bearing outer ring. According to the bearing outer ring surface defect area detection method, higher accuracy is shown when the problem of multi-surface multi-type defect detection of the bearing outer ring is solved, meanwhile, the robustness is higher, the method can adapt to surface defect area detection under different brightness and illumination conditions, and the detection effect better than that of other bearing surface defect detection algorithms is obtained.

The invention relates to a through type cophase traction power supply system traction network fault interval traveling wave positioning method, and belongs to the technical field of electrified railway traction network fault interval positioning research. The method comprises the steps that A, after a fault occurs on a through type cophase traction power supply system traction network, the data acquisition devices of all traction substations acquire current within 10ms time window after occurrence of the fault; B, the fault current signal wavelet transform modulus maxima of the outlet of each traction substation is obtained; and C, the polarities of the current traveling wave signal modulus maximas of all the outputs are compared. If the polarities of the fault current traveling wave modulus maximas of the two adjacent traction substations are opposite, the fault occurs between the two traction substations; or the fault position is not between the two traction substations. According to the method, the through type cophase traction power supply system fault interval traveling wave positioning method is indicated to be accurate and reliable through the simulation result.

The invention discloses an equal-even length anti-symmetrical biorthogonal wavelet filter group based edge detection method. The equal-even length anti-symmetrical biorthogonal wavelet filter group based edge detection method comprises the following steps of performing three-layer wavelet decomposition on an image through an equal-even length anti-symmetrical biorthogonal wavelet by a Mallat algorithm; calculating a gradient vector modulus value graph and a phase angle graph according to every scale of wavelet decomposition data of the image; detecting a modulus maxima point of every scale according to the direction determined by the phase angle graph; removing pseudo boundary points by a threshold value method; integrating obtained scales of boundary point sets so as to obtain the accurate single pixel width of boundary. According to the equal-even length anti-symmetrical biorthogonal wavelet filter group based edge detection method, the wavelet decomposition is performed through an equal-even length anti-symmetrical biorthogonal wavelet filter group with the differential operator function, the boundary information of every scale is comprehensively utilized, and accordingly the integrity and the accuracy of edge detection of the image are improved and certain anti-noise performance is achieved.

The invention discloses a small current line selection method using wavelet analysis. Wavelet transform and modulus maximum representation thereof can accurately score fault characteristics of transient traveling waves; the method has a step of extracting grounded traveling wave information to form effective grounding line selection criterion; initial transient traveling waves of a fault line anda sound line have obvious characteristic difference. Therefore, the modulus maximum value of wavelet transform is used for representing the amplitude and polarity characteristics of the initial traveling wave on each line; according to the method, the fault line selection criterion can be formed according to the amplitude and polarity difference of the modulus maxima of the initial traveling wavesof all the lines, and line selection is accurate and high in speed according to the amplitude and polarity difference of the modulus maxima of the initial traveling waves of all the lines by means ofthe wavelet analysis small current line selection method.

The invention belongs to the technical field of wireless positioning, and relates to a wireless positioning method based on a Wavelet Transform (WT) modulus maximum value. The method mainly comprises the following steps of: performing multi-resolution analysis (MRA) on a signal at a signal preprocessing end; solving modulus maxima of each scale according to the wavelet transform coefficient on each scale; selecting a proper threshold value to process the modulus maximum value, and reserving extreme points greater than or equal to the threshold value; from the highest scale J to the scale 1, searching a propagation point of a modulus maximum value on an adjacent scale, and according to a propagation rule that the modulus extreme values of the signal and the noise are different, retaining the modulus extreme points of the signal and removing the modulus extreme points of the noise; and the processed modulus maximum point is utilized to reconstruct a signal for subsequent conventional positioning operation. According to the method, the signal noise reduction effect can be greatly improved, the signal-to-noise ratio (SNR) is improved, the positioning precision is improved, and convenience is provided for positioning.

The invention discloses a power distribution network fault section positioning method based on a BP neural network. The method comprises steps of firstly obtaining transient zero-sequence current sampling data in a 1/2 cycle after a single-phase grounding fault moment according to zero-sequence current collection equipment installed in a power distribution network; wavelet analysis being carried out on the measured transient zero-sequence current along the line under different fault sections to obtain a corresponding modulus maximum value; taking a certain number of transient zero-sequence current modulus maxima obtained under different fault sections and inputting the modulus maxima into the BP network for training to obtain a relationship between the modulus maxima and the fault sections; wavelet transformation being carried out on a group of transient zero-sequence current data of an actual fault to obtain a modulus maximum value, trained BP parameters being read in for testing, and a fault section positioning result being obtained. The method can avoid the influence caused by the branch line, and is not affected by the cable overhead hybrid line.