High-voltage GIS partial discharge ultrahigh-frequency signal double-frequency-band harmonic wavelet feature extraction method and system
By using the octet band harmonic wavelet feature extraction method, the problem of identifying partial discharge types in the field withstand voltage test of GIS equipment was solved, and the accurate identification of single discharge signals was achieved, thus improving the accuracy of discharge type identification.
Patent Information
- Application Number
- CN202511128010.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-13
- Publication Date
- 2025-11-18
AI Technical Summary
In existing technologies, during on-site withstand voltage tests of GIS equipment, the number of discharge pulses captured by the partial discharge acquisition system is relatively small, making it impossible to effectively accumulate and form a discharge signal spectrum, which leads to difficulties in accurately identifying the discharge type.
The octave band harmonic wavelet feature extraction method is adopted. By using the octave band harmonic wavelet mother function and fast calculation, the features of the ultra-high frequency signal of partial discharge are extracted, so as to realize the discharge type identification of a single discharge signal.
This method achieves effective feature extraction from single discharge signals, solves the identification difficulties caused by insufficient discharge counts in traditional methods, and improves the accuracy of discharge type identification.
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Figure CN120971906A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application relates to the technical field of signal processing, in particular to a high-voltage GIS partial discharge ultrahigh-frequency signal frequency band harmonic wave feature extraction method and system. BACKGROUND
[0002] After the ultra-high-voltage GIS device is assembled on site, a field withstand voltage test needs to be carried out to find defects introduced or omitted in the installation stage of the device, so as to ensure the safe and stable operation of the device after being put into operation. Foreign matters may be left in the device during the GIS assembly process, and the foreign matters and defects will cause the insulation breakdown of the GIS device during the field withstand voltage test. Before the breakdown of the device, occasional partial discharge ultrahigh-frequency signals usually occur.
[0003] However, due to the short duration of the withstand voltage test, the number of discharge pulses captured by the partial discharge acquisition system is small, and an effective partial discharge signal pattern cannot be accumulated, so it is difficult to realize accurate discharge type recognition through traditional pattern feature analysis. There is an urgent need for a GIS internal discharge type recognition method based on a small amount of discharge signals. SUMMARY
[0004] In view of the above existing problems, the application is proposed.
[0005] Therefore, the application provides a high-voltage GIS partial discharge ultrahigh-frequency signal frequency band harmonic wave feature extraction method, which can realize discharge type recognition only by a single discharge signal compared with the traditional pattern feature analysis method which needs multiple discharge signal accumulations.
[0006] To solve the above technical problems, the application provides the following technical scheme, a high-voltage GIS partial discharge ultrahigh-frequency signal frequency band harmonic wave feature extraction method, comprising: adopting a frequency band harmonic wave mother function as a wavelet decomposition base function; performing frequency band harmonic wavelet transform fast calculation on the partial discharge ultrahigh-frequency discrete signal; determining an effective frequency band range according to a signal sampling rate and length, and extracting wavelet coefficients of a specified frequency band layer as features.
[0007] As a preferred scheme of the high-voltage GIS partial discharge ultrahigh-frequency signal frequency band harmonic wave feature extraction method, the wavelet mother function is a frequency band harmonic wavelet mother function, and is specifically represented as:
[0008]
[0009] wherein w(t) is a frequency band wavelet mother function, i is an imaginary unit, and t is a time variable;
[0010] The wavelet decomposition adopts harmonic wavelet transform, and the harmonic wavelet function is:
[0011]
[0012] wherein w m,n (t) is a harmonic wavelet function, m is a lower band parameter, and n is an upper band parameter.
[0013] As a preferred scheme of the high-voltage GIS partial discharge ultrahigh frequency signal frequency band harmonic wavelet feature extraction method, the fast calculation includes setting the discrete signals corresponding to the equal interval sampling of the partial discharge ultrahigh frequency signal f(t) as f0, f1,..., fn-1. N-1 wherein N is the total number of sampling points, N = 2 n and is a positive integer set, and let the discrete harmonic wavelet transform coefficient be a s s = 0, 1,..., N-1, s is a coefficient index.
[0014] The discrete sequence f r r = 0, 1,..., N-1 is subjected to fast Fourier transform (FFT) to obtain:
[0015] [F v |v = 0, 1,..., N / 2-1} = FFT [{f r |r = 0, 1,..., N-1}]
[0016] The frequency domain sequence {F v |v = 0, 1,..., N / 2-1} is segmented and each segment is subjected to inverse fast Fourier transform (IFFT) to obtain:
[0017]
[0018] wherein j = -1, 0, 1,..., n-2 and represents the maximum integer not exceeding x.
[0019] As a preferred scheme of the high-voltage GIS partial discharge ultrahigh frequency signal frequency band harmonic wavelet feature extraction method, the fast calculation further includes using the frequency band harmonic wavelet transform coefficients of the latter half part after calculation,
[0020]
[0021] to make the frequency band harmonic wavelet transform meet the Parseval theorem, and taking a N / 2 = F N / 2 :
[0022]
[0023] wherein k is an integer translation parameter.
[0024] As a preferred scheme of the high-voltage GIS partial discharge ultrahigh frequency signal frequency band harmonic wavelet feature extraction method, wherein: the wavelet coefficient of the specified frequency band is extracted as a feature, including determining the effective frequency band range of the GIS partial discharge signal acquisition system.
[0025] According to the frequency band division rule of the frequency band harmonic wavelet transform, the wavelet coefficient level covering the effective frequency band is selected; all wavelet coefficients in the selected level are extracted.
[0026] As a preferred scheme of the high-voltage GIS partial discharge ultrahigh frequency signal frequency band harmonic wavelet feature extraction method, wherein: the frequency band harmonic wavelet transform, the lowest layer has only one coefficient a0, and the corresponding discrete frequency is infinitesimal, which is considered as 0.
[0027] The j=0 layer has only one coefficient a1, and the corresponding discrete frequency is F SR / 2 n .
[0028] The j=1 layer has two coefficients a2 and a3, and the corresponding discrete frequencies are (2 1 +0)*F SR / 2 n and (2 1 +1)*F SR / 2 n , respectively, * is a conjugate operation.
[0029] The j=b layer, 0≤b≤n-2, has 2 b coefficients {a s |s=2 b ,2 b +1,···,2 b+1 -1}, and the corresponding discrete frequencies are {s*F SR / 2 n |s=2 b ,2 b +1,···,2 b+1 -1}, respectively.
[0030] The j=n-1 layer, i.e. the Nyquist layer, has only one coefficient a N / 2 , and the corresponding discrete frequency is the Nyquist frequency, i.e. F SR / 2.
[0031] As a preferred scheme of the high-voltage GIS partial discharge ultra-high frequency signal frequency multiplication band harmonic wave feature extraction method, the wavelet coefficient group is arranged into a harmonic wave time-frequency diagram for intuitive display, the coefficients of different j are arranged along the frequency axis, the coefficients of the same j are arranged along the time axis, the wavelet coefficients are processed, and a wavelet time-frequency diagram is obtained,
[0032]
[0033] Wherein, h map is a wavelet time-frequency diagram.
[0034] Another object of the present application is to provide a high-voltage GIS partial discharge ultra-high frequency signal frequency multiplication band harmonic wave feature extraction system.
[0035] As a preferred scheme of the high-voltage GIS partial discharge ultra-high frequency signal frequency multiplication band harmonic wave feature extraction system, the system comprises a signal acquisition module, a feature calculation module and a feature extraction module.
[0036] The signal acquisition module is configured to acquire GIS partial discharge ultra-high frequency discrete signals at a preset sampling rate.
[0037] The feature calculation module is connected to the signal acquisition module, loads a frequency multiplication band harmonic wave mother function, and performs fast calculation of frequency multiplication band harmonic wave transformation.
[0038] The feature extraction module is connected to the feature calculation module, selects a frequency multiplication layer according to the effective frequency band range, extracts and outputs the wavelet coefficient feature vector of the selected layer.
[0039] The present application provides a computer device comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of the high-voltage GIS partial discharge ultra-high frequency signal frequency multiplication band harmonic wave feature extraction method when executing the computer program.
[0040] The present application provides a computer readable storage medium having a computer program stored thereon, the computer program is executed by a processor to implement the steps of the high-voltage GIS partial discharge ultra-high frequency signal frequency multiplication band harmonic wave feature extraction method.
[0041] The present application has the following advantages: by performing frequency multiplication band harmonic wave transformation and feature extraction on the ultra-high frequency original waveform signal of single discharge, the discharge type recognition under occasional discharge condition can be effectively realized, and the problem that the traditional graph feature recognition method is invalid due to the small number of signal times of occasional discharge is solved. BRIEF DESCRIPTION OF DRAWINGS
[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.
[0043] Figure 1 A flowchart of the high-voltage GIS partial discharge ultrahigh frequency signal frequency band harmonic wavelet feature extraction method provided by an embodiment of the present application is shown.
[0044] Figure 2 A fast algorithm diagram of the frequency band harmonic wavelet transform of the high-voltage GIS partial discharge ultrahigh frequency signal frequency band harmonic wavelet feature extraction method provided by an embodiment of the present application is shown.
[0045] Figure 3 A single sharp tip discharge excited ultrahigh frequency original signal diagram of the high-voltage GIS partial discharge ultrahigh frequency signal frequency band harmonic wavelet feature extraction method provided by an embodiment of the present application is shown.
[0046] Figure 4 A harmonic wavelet time-frequency diagram of the high-voltage GIS partial discharge ultrahigh frequency signal frequency band harmonic wavelet feature extraction method provided by an embodiment of the present application is shown. DETAILED DESCRIPTION
[0047] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should be within the scope of protection of the present application.
[0048] Embodiment 1, refer to Figures 1-4 As the first embodiment of the present application, the embodiment provides a high-voltage GIS partial discharge ultrahigh frequency signal frequency band harmonic wavelet feature extraction method, which comprises:
[0049] S1: adopt a frequency band harmonic wavelet mother function as a wavelet decomposition base function.
[0050] Furthermore, the current main orthogonal wavelets can be roughly divided into four categories, namely Daubechies wavelet, symmetrical wavelet, Coiflets wavelet and Meyer wavelet. These orthogonal wavelets generally have no explicit analytical expression, but are generated by the weighted combination of scaling functions. The harmonic wavelet transform proposed by Professor D. E. Newland is a linear transform with wavelet as the basis, which combines the advantages of short-time Fourier transform and continuous wavelet transform. The harmonic wavelet is a complex wavelet, which is compactly supported in the frequency domain and has an explicit function expression. Its stretching and translation constitute an orthonormal basis in L2 With the gradual increase of the number of coefficients in the two-scale difference equation, the frequency spectrum of the orthogonal wavelet gradually becomes compact and closer to the box type. The frequency spectrum Wm,n(ω) of the harmonic wavelet is completely box-shaped, that is, it has a constant amplitude in the frequency band [2πm, 2πn) and an amplitude of 0 outside the frequency band, and the specific expression is:
[0051]
[0052] The inverse Fourier transform of the above formula can obtain the harmonic wavelet function W m,n (t):
[0053]
[0054] The scaling function corresponding to the harmonic wavelet function is the case of m = 0. If m and n are taken from the sequence p, q, r, s, ··· (0 < p < q < r < s < ···) in turn, then the box-shaped spectrum of the harmonic wavelet function defined by the frequency spectrum has no overlapping coverage of the part of the non-negative frequency except [0, 2πp). Therefore, in the above two formulas, if m = 0 and n = p, then can be given by the following formula. That is, the harmonic wavelet function wm,n(t) can be regarded as a special case of
[0055]
[0056] Using the harmonic wavelet function W m,n (t), and m and n are taken from the sequence 0, p, q, r, s, ··· (0 < p < q < r < s < ···) in turn, then the partial discharge ultra-high frequency signal f(t) can be decomposed into mutually independent spaces without overlapping and without omission.
[0057] The decomposition result is:
[0058]
[0059] where * represents the conjugate operation, f(t) is the partial discharge ultra-high frequency signal, is the expansion coefficient of the scaling function at the translation k, w(t) is the mother function of wavelet, is the conjugate symmetric coefficient of the complex conjugate of the scaling function, k is an integer translation parameter, a m,n,k is the expansion coefficient of the wavelet function at the parameter (m, n, k), is the conjugate symmetric coefficient of the complex conjugate of the wavelet, is the complex conjugate of the wavelet function, is the complex conjugate of the scaling function. Using the harmonic wavelet, any weak energy detail signal can be separated from the strong signal noise background, which is beneficial to the extraction of the partial discharge ultrahigh frequency signal characteristics, and this advantage is not possessed by the dilation wavelet.
[0060] Further, the frequency band harmonic wavelet is adopted. That is, m=2 j and n=2m (j=0, 1, 2, ···), and m=0 and n=1 correspond to the scaling function, that is, formula p=1. At this time, the decomposition result is converted into:
[0061]
[0062] Among them, the frequency band harmonic wavelet mother function w(t) is:
[0063]
[0064] Since the partial discharge ultrahigh frequency signal f(t) in the method is a real signal, therefore Therefore, only a j,k is used next.
[0065] S2: Perform frequency band harmonic wavelet transform fast calculation on the partial discharge ultrahigh frequency discrete signal.
[0066] Further, for the partial discharge ultrahigh frequency signal f(t), only a j,k can be calculated. The operation amount is too large by calculating the inner product to calculate the frequency band harmonic wavelet expansion coefficient, and the specific formula is:
[0067]
[0068] It should be noted that the method combines the box spectrum characteristics of the harmonic wavelet itself, and adopts the frequency band harmonic wavelet transform fast algorithm as shown in Figure 2 The specific implementation process is:
[0069] (1) Assuming that the partial discharge ultrahigh frequency signal f(t) corresponds to the discrete signal f0, f1, ···, f N-1 , after equal interval sampling, where N is the total number of sampling points, N=2 n and Let a s , s = 0, 1, ···, N-1, be the discrete harmonic wavelet transform coefficients for a set of positive integers
[0070] (2) First, perform a fast Fourier transform (FFT) on the discrete sequence f r , r = 0, 1, ···, N-1, to obtain: {F v |v = 0, 1, ···, N / 2-1} = FFT[{f r |r = 0, 1, ···, N-1}];
[0071] (3) Segment the frequency domain sequence {F v |v = 0, 1, ···, N / 2-1} and perform an inverse fast Fourier transform (IFFT) on each segment to obtain:
[0072]
[0073] where j = -1, 0, 1, ···, n-2 and represents the maximum integer not exceeding x.
[0074] (4) Finally, use the calculated band-pass harmonic wavelet transform coefficients for the second half:
[0075]
[0076] (5) To ensure that the band-pass harmonic wavelet transform satisfies the Parseval theorem, take a N / 2 = F N / 2 :
[0077]
[0078] In this embodiment, the frequency domain sequence is directly segmented and then inverse fast Fourier transformed;
[0079] In an alternative embodiment, the frequency domain sequence segmentation can also be achieved by an overlapping window segmentation method. Specifically, after obtaining the frequency domain sequence, a smoothing window function is applied to the entire frequency domain data to suppress the amplitude fluctuations at the band edges, adapting to the wideband noise characteristics of the partial discharge signal. The windowed frequency domain sequence is divided into multiple segments, each corresponding to a band-pass range, and a 50% overlap is set between segments to capture the continuous changes in the signal.
[0080] Each overlapping segment is independently inverse fast Fourier transformed to obtain a time domain coefficient block. When combining the coefficients of each segment, the coefficients in the overlapping region are weighted and averaged to eliminate the boundary discontinuity, ensuring smooth transition of the final wavelet coefficients and reducing noise-induced artifacts.
[0081] In another optional embodiment, the frequency domain sequence segmentation can also be achieved by an adaptive frequency band segmentation method, specifically, based on the signal sampling rate and length, the energy distribution of each octave band is calculated, the dominant frequency band is identified, and the frequency domain sequence is divided into non-uniform segments, the low frequency band uses larger segment length to capture more details; the high frequency band uses smaller segment length to quickly process noise;
[0082] The inverse fast Fourier transform is independently performed on each non-uniform segment, at the segment boundary, a short-time smoothing filter is applied to fuse the coefficients, compensate for the amplitude jump caused by the change in segment size, and ensure the consistency of the coefficients.
[0083] S3: Determine the effective frequency band range according to the signal sampling rate and length, and extract the wavelet coefficients of the specified octave layer as features.
[0084] Furthermore, for a partial discharge ultra-high frequency signal with a length of 2 n and a sampling rate of FSR, the octave band harmonic wavelet transform of different wavelet layers has the following properties:
[0085] The j=0 layer has only one coefficient a1, and the corresponding discrete frequency is F SR / 2 n ;
[0086] The j=1 layer has two coefficients a2 and a3, and the corresponding discrete frequencies are (2 1 +0)*F SR / 2 n and (2 1 +1)*F SR / 2 n , respectively, * is the conjugate operation;
[0087] The j=b layer, 0≤b≤n-2, has 2 b coefficients {a s |s=2 b ,2 b +1,···,2 b+1 -1}, and the corresponding discrete frequencies are {s*F SR / 2 n |s=2 b ,2 b +1,···,2 b+1 -1}, respectively;
[0088] The j=n-1 layer, i.e., the Nyquist layer, has only one coefficient a N / 2 , and the corresponding discrete frequency is the Nyquist frequency, i.e., F SR / 2.
[0089] The harmonic wavelet decomposition result can be directly displayed in the form of harmonic wavelet time-frequency diagram composed of the above coefficients, that is, the coefficients belonging to different j are arranged along the frequency axis, and the coefficients of the same j are arranged along the time axis.
[0090] The wavelet coefficients in the method are processed by the following formula to obtain the wavelet time-frequency diagram. The original signal of the ultra-high frequency excited by a single tip discharge is shown in FIG. 3, and the harmonic wavelet time-frequency diagram thereof is shown in FIG. 4. Figure 3
[0091]
[0092] wherein h map is the wavelet time-frequency diagram.
[0093] Furthermore, in the method, the sampling rate of the partial discharge ultra-high frequency signal is F SR = 2 x 109Sa / s, and the length N = 512 is taken, and at this time, n = log2N = 9. Then j = 8 corresponds to 1 GHz; j = 7 corresponds to 0.5 GHz-1 GHz; j = 6 corresponds to 0.25 GHz-0.5 GHz; j = 5 corresponds to 125 M-250 MHz; j = 4 corresponds to 62.5 M-125 MHz. The bandwidth of the partial discharge signal acquisition system is 0-1000 MHz, and therefore the harmonic wavelet coefficients of j = 4-6 and the first 6 harmonic wavelet coefficients of j = 7 are selected as the features of the extraction of a single ultra-high frequency original signal, and there are 118 harmonic wavelet transform coefficients.
[0094] Embodiment 2 is a second embodiment of the application, which is different from the previous embodiment in that:
[0095] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the application or the parts of the prior art that essentially contribute or the parts of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various storage medium that can store program codes.
[0096] The logic and / or steps represented in flow diagrams or otherwise described herein, for example, can be considered as a sequence of executable instructions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or a combination of the above. For the purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
[0097] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and stored in a computer memory.
[0098] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, the various steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following technologies can be used: discrete logic circuitry having logic gates for implementing logic functions upon data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and so on, as is known in the art.
[0099] Embodiment 3, which is an embodiment of the present application, provides a high-voltage GIS partial discharge ultrahigh frequency signal frequency band harmonic wavelet feature extraction system, comprising a signal acquisition module, a feature calculation module, and a feature extraction module;
[0100] The signal acquisition module is configured to acquire GIS partial discharge ultrahigh frequency discrete signals at a preset sampling rate.
[0101] The feature calculation module is connected to the signal acquisition module, loads a frequency band harmonic wavelet mother function, and performs fast calculation of frequency band harmonic wavelet transformation.
[0102] The feature extraction module is connected with the feature calculation module, selects a frequency multiplication layer according to the effective frequency band range, extracts and outputs a wavelet coefficient feature vector of the selected layer.
[0103] It should be noted that the above examples are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.
Claims
1. A method for extracting octave band harmonic wavelet features of ultra-high frequency partial discharge signals from high-voltage GIS, characterized in that: include, The octave band harmonic wavelet mother function is used as the wavelet decomposition basis function; Fast calculation of octave band harmonic wavelet transform for ultra-high frequency discrete partial discharge signals; The effective frequency band range is determined based on the signal sampling rate and length, and wavelet coefficients of the specified octave layer are extracted as features.
2. The method for extracting octave band harmonic wavelet features of high-voltage GIS partial discharge UHF signals as described in claim 1, characterized in that: The wavelet mother function is an octet band harmonic wavelet mother function, specifically expressed as follows: Where w(t) is the octave band wavelet mother function, i is the imaginary unit, and t is the time variable; Wavelet decomposition employs harmonic wavelet transform, where the harmonic wavelet function is: Among them, w m,n (t) Harmonic wavelet function, where m is the lower limit parameter of the frequency band and n is the upper limit parameter of the frequency band.
3. The method for extracting octave band harmonic wavelet features of high-voltage GIS partial discharge UHF signals as described in claim 2, characterized in that: The rapid calculation includes setting the discrete signals corresponding to the ultra-high frequency partial discharge signal f(t) after equal-interval sampling as f0, f1, ..., f N-1 Where N is the total number of sampling points, N = 2 n and Let a be the set of positive integers, and let the discrete harmonic wavelet transform coefficients be a. s s = 0, 1, ..., N-1, where s is the coefficient index; For discrete sequence f r For r = 0, 1, ..., N-1, performing a Fast Fourier Transform (FFT) yields: {F v |v=0,1,···,N-1}=FFT[{f r |r=0,1,···,N-1}] For frequency domain sequence {F v Divide the string |v=0,1,···,N / 2-1} into segments and perform an Inverse Fast Fourier Transform (IFFT) on each segment to obtain: Where j = -1, 0, 1, ..., n-2 and ... Represents the largest integer not exceeding x.
4. The method for extracting octave band harmonic wavelet features of high-voltage GIS partial discharge UHF signals as described in claim 3, characterized in that: The rapid calculation also includes calculating the octave band harmonic wavelet transform coefficients in the latter half of the calculation. To make the octave band harmonic wavelet transform conform to Parseval's theorem, let a... N / 2 =F N / 2 : Where k is an integer translation parameter.
5. The method for extracting octave band harmonic wavelet features of high-voltage GIS partial discharge UHF signals as described in claim 4, characterized in that: The step of extracting wavelet coefficients of a specified frequency doubling layer as features includes determining the effective frequency band range of the GIS partial discharge signal acquisition system. Based on the frequency band division rules of octave band harmonic wavelet transform, select the wavelet coefficient level covering the effective frequency band; extract all wavelet coefficients within the selected level.
6. The method for extracting octave band harmonic wavelet features of high-voltage GIS partial discharge UHF signals as described in claim 5, characterized in that: The lowest level of the octave band harmonic wavelet transform has only one coefficient a0, and the corresponding discrete frequency is infinitesimal, which is considered to be 0. Layer j=0 has only one coefficient a1, corresponding to a discrete frequency F. SR / 2 n ; Layer j=1 has two coefficients a2 and a3, corresponding to discrete frequencies (2...). 1 +0)*F SR / 2 n and (2) 1 +1)*F SR / 2 n , * represents the conjugate operation; j = b layers, 0 ≤ b ≤ n-2, having 2 b Coefficients {a s |s=2 b ,2 b +1,···,2 b+1 -1}, the corresponding discrete frequencies are {s*F} in sequence. SR / 2 n |s=2 b ,2 b +1,···,2 b+1 -1}; The j=n-1 layer, i.e., the Nyquist layer, has only one coefficient a. N / 2 The corresponding discrete frequency is the Nyquist frequency, i.e., F. SR / 2.
7. The method for extracting octave band harmonic wavelet features of high-voltage GIS partial discharge UHF signals as described in claim 6, characterized in that: The wavelet coefficients are used to construct a harmonic wavelet time-frequency diagram for intuitive display. Coefficients belonging to different j are arranged along the frequency axis, while coefficients belonging to the same j are arranged along the time axis. Wavelet coefficient processing is then performed to obtain the wavelet time-frequency diagram. Among them, h map This is a wavelet time-frequency plot.
8. A system for extracting octave band harmonic wavelet features of high-voltage GIS partial discharge UHF signals, comprising the method for extracting octave band harmonic wavelet features of high-voltage GIS partial discharge UHF signals as described in any one of claims 1 to 7, characterized in that, include: Signal acquisition module, feature calculation module, feature extraction module; The signal acquisition module is configured to acquire UHF discrete signals of GIS partial discharge at a preset sampling rate. The feature calculation module is connected to the signal acquisition module, loads the octave band harmonic wavelet mother function, and performs fast calculation of octave band harmonic wavelet transform. The feature extraction module is connected to the feature calculation module. It selects a frequency doubling layer based on the effective frequency band range, extracts and outputs the wavelet coefficient feature vector of the selected layer.
9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the high-voltage GIS partial discharge ultra-high frequency signal octave band harmonic wavelet feature extraction method as described in any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the high-voltage GIS partial discharge ultra-high frequency signal octave band harmonic wavelet feature extraction method as described in any one of claims 1 to 7.