A method and system for analyzing the nonlinear vibration behavior of GIS equipment

Abstract

The present application discloses a method and system for analyzing the nonlinear vibration behavior of GIS equipment, including: using the on-site background noise as the reference noise, processing the nonlinear vibration signal through a two-step filtering algorithm of low-pass filtering and Gaussian threshold filtering; combining the time domain, frequency domain, time-frequency domain, phase trajectory, modal vibration shape, and Poincare mapping section to perform multi-type spectrum characterization on the waveform of the nonlinear vibration signal; extracting the features of multi-dimensional parameters of the nonlinear vibration signal from the multi-type spectrum; and performing visual quantitative analysis of the nonlinear vibration signal based on the extracted features. This method can solve the problem of environmental interference, realize all-round spectrum characterization, intuitively show the regular characteristics of the nonlinear vibration spectrum of the equipment, and realize the visual quantitative characterization of the abnormal nonlinear vibration of the GIS equipment, which helps the operation and maintenance personnel to intuitively and accurately grasp the mechanical vibration state of the GIS equipment and effectively ensure the safe and reliable operation of the GIS equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of power grids, and in particular to a method and system for analyzing nonlinear vibration behavior of GIS equipment. Background Art

[0002] Gas insulated metal-enclosed switchgear (GIS) is a key equipment in the power grid. In recent years, GIS equipment has frequently experienced abnormal noise and vibration due to latent mechanical defects such as structural looseness, spring deterioration, and poor contact, which can cause local ablation, gas leakage, and insulation failure. Therefore, it is urgent to strengthen the detection or monitoring of the vibration status of GIS equipment to ensure the safe operation of GIS equipment.

[0003] GIS equipment has various forms of dynamic and static contact connection assembly structures, including low-potential components such as housings, support frames, bellows, bolts, and high-potential components such as guide rods, springs, contacts, and conductor seats. Constraints include bolt fixation, clamps, spring preloads, and other forms. Normally fixed and assembled equipment is an approximately linear stable system with a small vibration amplitude and a single spectrum. However, GIS equipment with structural looseness defects will experience mechanical instability, complex contact, cross-scale and time-varying friction under operating conditions, resulting in amplitude enhancement, multi-frequency vibration, bifurcation and other nonlinear phenomena, and even exciting violent chaotic responses. Therefore, real-time measurement and parameter analysis of vibration phenomena caused by mechanical defects in GIS equipment is an effective means to achieve effective early warning of mechanical defects in GIS equipment.

[0004] At present, the existing achievements are mainly focused on the mechanical defect simulation and vibration signal acquisition of GIS equipment. The time / frequency domain analysis method is mainly used to characterize the vibration spectrum, and the analysis method is relatively simple. However, the mechanical vibration defects of GIS equipment have nonlinear vibration phenomena such as multiple frequencies, bifurcation and chaos, which manifest as complex vibration laws such as time domain non-periodicity and frequency domain instability. Only the existing time / frequency domain analysis method cannot effectively characterize the nonlinear vibration phenomenon of GIS equipment. In addition, there is strong environmental noise interference when GIS equipment is running, which increases the difficulty of analyzing vibration characteristic parameters. At present, there are no reports on waveform analysis and quantitative feature analysis of nonlinear vibration behavior of GIS equipment. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a method and system for analyzing the nonlinear vibration behavior of GIS equipment, which can solve the problem of environmental interference, intuitively show the regular characteristics of the nonlinear vibration spectrum of the equipment, and realize the visualized quantitative characterization of the nonlinear vibration of the equipment. The specific scheme is as follows:

[0006] A method for analyzing nonlinear vibration behavior of GIS equipment, comprising:

[0007] The background noise on site is used as the reference noise, and the nonlinear vibration signal is processed by a two-step filtering algorithm of low-pass filtering and Gaussian threshold filtering.

[0008] The waveform of the nonlinear vibration signal is characterized by multiple types of graphs from six angles: time domain, frequency domain, time-frequency domain, phase trajectory, mode vibration shape, and Poincare mapping section;

[0009] Extracting features of multi-dimensional parameters of the nonlinear vibration signal from multiple types of spectra;

[0010] A visual quantitative analysis of the nonlinear vibration signal is performed based on the extracted features.

[0011] Preferably, in the above-mentioned GIS equipment nonlinear vibration behavior analysis method provided by the embodiment of the present invention, the two-step filtering algorithm of low-pass filtering and Gaussian threshold filtering is used to process the nonlinear vibration signal, including:

[0012] The nonlinear vibration signal is processed by low-pass filtering based on the upper detection limit of the frequency response range calibrated by the sensor;

[0013] The nonlinear vibration signal after low-pass filtering is subjected to Gaussian threshold filtering to filter out environmental random noise.

[0014] Preferably, in the above-mentioned GIS equipment nonlinear vibration behavior analysis method provided in an embodiment of the present invention, the waveform of the nonlinear vibration signal is characterized by multiple types of graphs from six angles: time domain, frequency domain, time-frequency domain, phase trajectory, modal vibration shape, and Poincare mapping section, including:

[0015] Adaptively adjusting the waveform amplitude of the nonlinear vibration signal, or performing local and overall scaling on the waveform to obtain a time domain spectrum;

[0016] Performing fast Fourier transform and filtering processing on the nonlinear vibration signal to obtain a frequency domain sequence and a frequency domain spectrum;

[0017] Performing time-frequency domain vibration analysis on the nonlinear vibration signal by short-time Fourier transform, wavelet analysis method or Wigner-Willi distribution method to obtain a time-frequency domain spectrum;

[0018] Performing phase trajectory analysis on the nonlinear vibration signal to obtain a phase trajectory spectrum;

[0019] Adopting a complementary set empirical mode decomposition algorithm to perform adaptive mode decomposition on the nonlinear vibration signal, obtaining an IMF signal, and obtaining a mode vibration shape spectrum;

[0020] The nonlinear vibration signal is characterized by using the Poincare mapping method to obtain a Poincare mapping spectrum.

[0021] Preferably, in the above-mentioned GIS equipment nonlinear vibration behavior analysis method provided by an embodiment of the present invention, the use of a complementary set empirical mode decomposition algorithm to adaptively decompose the nonlinear vibration signal to obtain an IMF signal includes:

[0022] Applying white noise to the nonlinear vibration signal multiple times in positive and negative complementary directions to obtain doped signals in positive and negative directions;

[0023] Perform empirical mode decomposition on the obtained doping signals in the positive and negative directions respectively to obtain an IMF sequence group in the complementary direction;

[0024] All IMF sequence groups to which white noise is added multiple times are averaged to obtain the final decomposed IMF signal.

[0025] Preferably, in the above-mentioned GIS equipment nonlinear vibration behavior analysis method provided in an embodiment of the present invention, the feature extraction of multi-dimensional parameters of the nonlinear vibration signal from the multi-type spectrum includes:

[0026] The features of the nonlinear vibration signal, including average amplitude, center of gravity frequency, energy value, kurtosis, harmonic distortion rate and chaos index, are extracted from the multi-type spectrum.

[0027] Preferably, in the above-mentioned GIS equipment nonlinear vibration behavior analysis method provided in an embodiment of the present invention, the visual quantitative analysis of the nonlinear vibration signal according to the extracted features includes:

[0028] According to the extracted features, the spectrum distribution, energy, attenuation characteristics, waveform quality and chaotic characteristics of the nonlinear vibration signal are visually and quantitatively analyzed.

[0029] Preferably, in the above-mentioned GIS equipment nonlinear vibration behavior analysis method provided in the embodiment of the present invention, before performing the visual quantitative analysis of the nonlinear vibration signal according to the extracted features, it also includes:

[0030] The extracted features are normalized using the inverse tangent function.

[0031] The embodiment of the present invention further provides a GIS equipment nonlinear vibration behavior analysis system, comprising:

[0032] The signal noise filtering module is used to process the nonlinear vibration signal by using the on-site background noise as the reference noise through a two-step filtering algorithm of low-pass filtering and Gaussian threshold filtering;

[0033] A multi-type graphical characterization and analysis module is used to perform multi-type graphical characterization on the waveform of the nonlinear vibration signal from six angles: time domain, frequency domain, time-frequency domain, phase trajectory, mode vibration shape, and Poincare mapping section;

[0034] The characteristic parameter numerical analysis module is used to extract the characteristics of multi-dimensional parameters of the nonlinear vibration signal from multiple types of graphs; and perform visual quantitative analysis of the nonlinear vibration signal based on the extracted characteristics.

[0035] Preferably, in the above-mentioned GIS equipment nonlinear vibration behavior analysis system provided in an embodiment of the present invention, the characteristic parameter numerical analysis module is specifically used to extract the features of the average amplitude, center of gravity frequency, energy value, kurtosis, harmonic distortion rate, and chaos index of the nonlinear vibration signal from multiple types of spectra.

[0036] Preferably, in the above-mentioned GIS equipment nonlinear vibration behavior analysis system provided by the embodiment of the present invention, the characteristic parameter numerical analysis module is specifically used to perform a visual quantitative analysis of the spectral distribution, energy, attenuation characteristics, waveform quality, and chaotic characteristics of the nonlinear vibration signal based on the extracted features.

[0037] It can be seen from the above technical scheme that the nonlinear vibration behavior analysis method of GIS equipment provided by the present invention includes: using the on-site background noise as the reference noise, and processing the nonlinear vibration signal through a two-step filtering algorithm of low-pass filtering and Gaussian threshold filtering; combining the time domain, frequency domain, time-frequency domain, phase trajectory, modal vibration shape, and Poincare mapping section to perform multi-type spectrum characterization on the waveform of the nonlinear vibration signal; extracting the features of multi-dimensional parameters of the nonlinear vibration signal from the multi-type spectrum; and performing visual quantitative analysis of the nonlinear vibration signal based on the extracted features.

[0038] The above method provided by the present invention can solve the problem of environmental interference, realize all-round spectrum representation, intuitively grasp the regular characteristics of the nonlinear vibration spectrum of GIS equipment, and realize the visualized quantitative representation of abnormal nonlinear vibration of GIS equipment, which helps operation and maintenance personnel to intuitively and accurately grasp the mechanical vibration state of GIS equipment, discover vibration defects in time, and more reasonably take effective operation and maintenance measures for defective equipment, thereby effectively ensuring the safe and reliable operation of GIS equipment.

[0039] In addition, the present invention also provides a corresponding system for the nonlinear vibration behavior analysis method of GIS equipment, which further makes the above method more practical, and the system has corresponding advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0041] Figure 1 A flow chart of a method for analyzing nonlinear vibration behavior of GIS equipment provided by an embodiment of the present invention;

[0042] Figure 2 A schematic diagram of the structure of a nonlinear vibration behavior analysis system for GIS equipment provided by an embodiment of the present invention;

[0043] Figure 3 A functional schematic diagram of each module in the nonlinear vibration behavior analysis system of GIS equipment provided by an embodiment of the present invention;

[0044] Figure 4a and Figure 4b They are respectively schematic diagrams of the time domain waveform and phase trajectory output results corresponding to the busbar air chamber normal state vibration signal before and after filtering provided by the embodiments of the present invention;

[0045] Figure 5a and Figure 5b They are schematic diagrams of the time domain waveform and phase trajectory output results corresponding to the busbar air chamber abnormal sound state vibration signal before and after filtering provided by the embodiments of the present invention;

[0046] Figures 6a to 6f They are respectively the multi-type spectrum characterization and analysis effects of the vibration signal of the busbar air chamber in the normal state provided by the embodiments of the present invention;

[0047] Figures 7a to 7f The multi-type spectrum characterization and analysis effects of the vibration signal of the abnormal sound state of the busbar air chamber provided by the embodiments of the present invention;

[0048] Figure 8 The numerical analysis result of the characteristic parameters of the vibration signal of the busbar air chamber in the normal state provided by the embodiment of the present invention;

[0049] Fig. 9 The numerical analysis results of characteristic parameters of the vibration signal of the busbar air chamber abnormal sound state provided by the embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] The present invention provides a method for analyzing the nonlinear vibration behavior of GIS equipment. Figure 1 As shown, the following steps are included:

[0052] S101, using the on-site background noise as the reference noise, and processing the nonlinear vibration signal through a two-step filtering algorithm of low-pass filtering and Gaussian threshold filtering;

[0053] S102, combine the time domain, frequency domain, time-frequency domain, phase trajectory, mode vibration shape, and Poincare mapping section to perform multi-type spectrum characterization on the waveform of nonlinear vibration signal;

[0054] Specifically, the multi-type spectra may include time domain spectra, frequency domain spectra, time-frequency domain spectra, phase trajectory spectra, modal vibration shape spectra, and Poincare mapping spectra.

[0055] S103, extracting features of multi-dimensional parameters of nonlinear vibration signals from multi-type spectra;

[0056] S104. Perform visual quantitative analysis of the nonlinear vibration signal based on the extracted features.

[0057] The above-mentioned GIS equipment nonlinear vibration behavior analysis method provided in the embodiment of the present invention has three advantages: first, the on-site background noise is used as the reference noise, and a two-step filtering algorithm is designed for signal processing to solve the problem of environmental interference; second, the vibration signal waveform is comprehensively represented by combining six angles of time domain, frequency domain, time-frequency domain, phase trajectory, modal vibration shape, and Poincare mapping section, which intuitively shows the nonlinear vibration characteristics of the GIS equipment; third, the multi-dimensional parameter design of the vibration signal is carried out to extract features and visualize quantitative analysis, and the visualized quantitative representation of the abnormal nonlinear vibration of the GIS equipment is realized, which helps the operation and maintenance personnel to intuitively and accurately grasp the mechanical vibration state of the GIS equipment, timely discover vibration defects, and more reasonably take effective operation and maintenance measures for defective equipment, effectively ensuring the safe and reliable operation of the GIS equipment.

[0058] In specific implementation, in the above-mentioned GIS equipment nonlinear vibration behavior analysis method provided in the embodiment of the present invention, step S101 processes the nonlinear vibration signal through a two-step filtering algorithm of low-pass filtering and Gaussian threshold filtering, which may include: first, low-pass filtering the nonlinear vibration signal with the upper limit of the frequency response interval detection of the sensor calibration as the boundary; then, Gaussian threshold filtering the nonlinear vibration signal after the low-pass filtering to filter out environmental random noise.

[0059] Specifically, a two-step filtering algorithm is used to filter out the complex background noise in the detection signal. The first step is to detect the upper limit f of the frequency response range calibrated by the sensor for high-frequency noise. H The sampling signal of the fast Fourier transform is processed by low-pass filtering. Step 2: For the environmental high-frequency random noise mixed in the main characteristic signal y(n), the present invention adopts Gaussian threshold filtering. The vibration signal y under the action of 0A current is collected for each type of defect. e (n) as background noise, and e (n) After low-pass filtering 0~f H The frequency domain sequence y of the range e (k) Perform statistical analysis to solve for y e The mean value M of (k) e and standard deviation σ e , then y e (k) satisfies Gaussian distribution:

[0060]

[0061] According to the 3σ principle, the upper threshold T is solved H =M e +3σ e , and then use T H Perform Gaussian threshold filtering on the defect signal y(k):

[0062]

[0063] When the values ​​of all points in the frequency domain interval are less than or equal to the threshold T H When , the values ​​of all points in the interval are set to 0, thereby filtering out 99.74% of the random noise in the environment.

[0064] In the specific implementation, in the above-mentioned GIS equipment nonlinear vibration behavior analysis method provided in the embodiment of the present invention, step S102 combines the six angles of time domain, frequency domain, time-frequency domain, phase trajectory, mode vibration shape, and Poincare mapping section to perform multi-type spectrum characterization on the waveform of the nonlinear vibration signal, which may include the following steps:

[0065] Step 1: adaptively adjust the waveform amplitude of the nonlinear vibration signal, or perform local and overall scaling on the waveform to obtain a time domain spectrum;

[0066] Specifically, this part can display the original signal y(n) or the filtered vibration signal y1(n) in time domain. The time domain spectrum can also realize the adaptive adjustment of the software according to the waveform amplitude, and can also manually amplify the waveform locally and zoom in the whole.

[0067] Step 2: Perform fast Fourier transform and filtering on the nonlinear vibration signal to obtain a frequency domain sequence and a frequency domain spectrum;

[0068] Specifically, this part can display the original signal y(n) or the filtered vibration signal y1(n) in frequency spectrum. After Fast Fourier Transform (FFT) and filtering, y(n) can obtain the frequency domain sequence y(k) to realize the frequency domain vibration spectrum display. The default display range of the frequency domain vibration spectrum is 0-4000Hz, and the spectrum display range can be manually scaled or widened.

[0069] Step 3: Perform time-frequency domain vibration analysis on the nonlinear vibration signal by short-time Fourier transform, wavelet analysis method or Wigner-Willi distribution method to obtain a time-frequency domain spectrum;

[0070] Specifically, users can choose the time-frequency spectrum conversion method; among them,

[0071] The first is the short-time Fourier transform (STFT). STFT is based on the FFT transform. It adds an instantaneous window function in the time domain to it and continuously moves the center position of the window function to obtain the Fourier transform at different times. 2 (R)), its short-time Fourier transform STFT can be expressed as:

[0072]

[0073] Among them, L 2 (R) represents the square integrable function space, τ represents the time differential, Ω represents the frequency, STFT is a two-dimensional function equation about the time domain and frequency domain variables (t,Ω), g(τ) is a short time window function equation, and its analytical expression is as follows:

[0074] g t,Ω (τ) = g(t-τ)e jΩτ (4)

[0075] ||g(τ)||=||g t,Ω(τ)||=1 (5) Then the energy of the short-time Fourier transform signal spectrum E y The distribution is shown in formula (6):

[0076] E y =∫∫|S TFTy (y,Ω)| 2 dtdΩ (6)

[0077] The second is the wavelet transform method, which can better solve the contradiction between time and frequency resolution: the window of wavelet transform is an adjustable time-frequency window, using a short window at high frequency and a wide window at low frequency. 2 (R)), the wavelet transform of y(t) is defined as:

[0078]

[0079] Among them, a and b are scale factors, ψ a,b (t) is the two-dimensional basic wavelet function:

[0080] ω=ω * / a (8)

[0081] Where ω represents the signal frequency, ω * Represents the frequency after scaling by the scale factor.

[0082] The third is the Wigner-Ville distribution (WVD), which is defined as the Fourier transform of the signal center covariance function. It has many excellent properties, such as symmetry, time shift, combination, complex conjugate relationship, etc. It will not lose the amplitude and phase information of the signal, and has a clear concept of instantaneous frequency and group delay.

[0083] L 2 (R)), whose WVD is defined as:

[0084] W y (t,ω)=∫y(t+τ / 2)y * (t-τ / 2)e -j2πωτ dτ (9)

[0085] In addition, the pseudo-Wigner-Wiley distribution (PWVD) (Equation (10)) and smoothed pseudo-Wigner-Wiley distribution (SPWVD) (Equation (11)) are also provided to improve the cross-term interference of WVD:

[0086] PW y (t,ω)=∫h(τ)y(t+τ / 2)y* (t-τ / 2)e -j2πωτ dτ (10)

[0087] SPW y (t,ω)=∫h(τ)∫g(st)y(s+τ / 2)y * (t-τ / 2)dse -j2πωτ dτ (11)

[0088] Among them, PW y (t,ω) represents PWVD transformation, SPW y (t,ω) represents the SPWVD transformation.

[0089] Step 4: Perform phase trajectory analysis on the nonlinear vibration signal to obtain a phase trajectory spectrum;

[0090] Specifically, this part uses the phase trajectory analysis method to effectively analyze the periodicity, bifurcation and chaotic characteristics of the vibration spectrum. The horizontal axis of the phase trajectory is the time domain vibration amplitude of the time series y(n), and the vertical axis is the derivative of the time domain response, that is, the velocity value dy / dt.

[0091] Step 5: Adopt the complementary set empirical mode decomposition algorithm to perform adaptive mode decomposition on the nonlinear vibration signal, obtain the IMF signal, and obtain the mode vibration shape spectrum;

[0092] In the specific implementation, in the execution step five, the complementary set empirical mode decomposition algorithm (complete ensemble empirical mode decomposition, CEEMD) is used to perform adaptive modal decomposition on the nonlinear vibration signal to obtain an independent modal function (IMF) signal, which can include: applying white noise to the nonlinear vibration signal multiple times in positive and negative complementary directions to obtain doped signals in the positive and negative directions; performing empirical mode decomposition on the obtained doped signals in the positive and negative directions respectively to obtain IMF sequence groups in complementary directions; and performing ensemble averaging on all IMF sequence groups to which white noise is added multiple times to obtain the final decomposed IMF signal.

[0093] Specifically, for a given input signal y(n), the i-th white noise ±W is applied in the positive and negative complementary directions. i (n), and obtain the doping signal y in both positive and negative directions i + (n) and y i - (n), where L is the total average number:

[0094]

[0095] For y i + (n) and y i - (n) are respectively subjected to empirical mode decomposition (EMD) to obtain a group of IMF sequences in complementary directions, which are respectively denoted as IMF i + and IMF i - , and generally each group has M = log2 N-1 IMF components, and N is the length of the discrete data. Taking the average of them can obtain the corresponding IMF i :

[0096]

[0097] Performing ensemble averaging on all groups of IMF sequences with added white noise L times, the finally decomposed IMF can be obtained:

[0098]

[0099] Step 6: Characterize the non-linear vibration signal by using the Poincaré mapping method to obtain the Poincaré mapping spectrum.

[0100] Specifically, the Poincaré mapping method is introduced in this part to characterize the non-linear characteristics of GIS equipment, where the excitation frequency Ω s is 100 Hz, and the fundamental frequency period T s = 1 / Ω s , and 100T s is taken as the observation time history of the signal chaos characteristics. Solving the first-order difference of the source signal y(n) Constructing the phase plane Setting the point (y(n) = 0, as the equilibrium position. At the same time, introducing time t to expand the system to three dimensions and constructing the Poincaré section:

[0101]

[0102] where t0() represents the relationship between the initial value point at t = 0 and the point reached after time mT s along the system phase trajectory path, and m represents the serial number of the observation period.

[0103] Establishing the relationship between the initial value point at t = 0 and the point reached after time mT s along the system phase trajectory path, where 1 < m ≤ 100. Further, a Poincaré discrete point mapping system can be established:

[0104] q i+1 = f(qi ) (16)

[0105] Where: For the system at t = mT s The state of the system is in discrete format after the mapping points move through m system cycles.

[0106] It should be noted that the present invention can realize the transformation and characterization analysis of the vibration signal of GIS equipment in the time / frequency domain, time-frequency domain, modal vibration shape, phase trajectory and Poincare mapping spectrum. Specifically, time-frequency analysis, complementary set empirical mode decomposition, Poincare mapping and phase trajectory analysis algorithm are introduced to convert the vibration signal into a spectrum, and the time domain spectrum and frequency domain spectrum are combined to realize the detailed waveform characterization of the nonlinear phenomenon of abnormal vibration of GIS equipment, ensuring that the regular characteristics of the nonlinear vibration spectrum of the equipment can be intuitively grasped.

[0107] In specific implementation, in the above-mentioned GIS equipment nonlinear vibration behavior analysis method provided in the embodiment of the present invention, step S103 performs feature extraction of multi-dimensional parameters of the nonlinear vibration signal from the multi-type spectrum, which may include: extracting features of average amplitude, center of gravity frequency, energy value, kurtosis, harmonic distortion rate, and chaos index of the nonlinear vibration signal from the multi-type spectrum.

[0108] The following is an explanation of the specific feature extraction calculation method:

[0109] The average amplitude is for the frequency domain signal y after low-pass filtering and Gaussian threshold filtering f (n), its average amplitude is A M Calculate as formula (17):

[0110]

[0111] Where M represents the number of observation periods.

[0112] The centroid frequency is the frequency domain signal y after low-pass filtering and Gaussian threshold filtering. f (k), its center of gravity frequency f z Calculate as formula (18):

[0113]

[0114] Among them, f(k) is the frequency value corresponding to the kth frequency domain signal point, and N is the frequency domain sequence y f (k) length.

[0115] Energy is a measure of the response amplitude energy level of the vibration signal. f (k) Calculate the energy E as shown in formula (19):

[0116]

[0117] Kurtosis is a measure of the attenuation characteristics of the spectrum of the vibration signal. f (k) The kurtosis K is obtained as formula (20):

[0118]

[0119] Among them, E(y) and σ(y) represent the expectation and standard deviation of y respectively.

[0120] The harmonic distortion rate is the superharmonic response to abnormal mechanical vibration. As the nonlinearity increases, its high-frequency response becomes stronger and the waveform distortion becomes more serious. The vibration frequency response is divided into R frequency bands according to its spectrum. For the signal y in the rth frequency band, fr (k) Find its energy E fr (n), define the harmonic distortion rate H as formula (21):

[0121]

[0122]

[0123] The chaos index is the Poincare map discrete system obtained for the phase plane Σ0(y) Observation period N T is 100, and the chaos index D is defined as formula (23):

[0124]

[0125] Among them, a1 and a2 are the signal qi(y), Amplitude adjustment parameter during the observation period.

[0126] In specific implementation, in the above-mentioned GIS equipment nonlinear vibration behavior analysis method provided in the embodiment of the present invention, step S104 performs a visual quantitative analysis of the nonlinear vibration signal based on the extracted features, which may include: performing a visual quantitative analysis of the spectral distribution, energy, attenuation characteristics, waveform quality, and chaotic characteristics of the nonlinear vibration signal based on the extracted features.

[0127] It is understandable that nonlinear vibration behaviors include spectrum distribution, amplitude difference, chaotic characteristics, attenuation characteristics, waveform quality, etc. The present invention can perform visual quantitative analysis on these nonlinear vibration behaviors and realize quantitative characterization of abnormal vibration defects of GIS equipment.

[0128] In specific implementation, in the above-mentioned GIS equipment nonlinear vibration behavior analysis method provided in the embodiment of the present invention, in order to enhance the comparability of signal feature quantities, before executing step S104 to perform visual quantitative analysis of nonlinear vibration signals according to the extracted features, it can also include: normalizing the extracted features using an inverse tangent function.

[0129] Based on the same inventive concept, an embodiment of the present invention also provides a nonlinear vibration behavior analysis system for GIS equipment. Since the principle of solving the problem by this system is similar to that of the aforementioned nonlinear vibration behavior analysis method for GIS equipment, the implementation of this system can refer to the implementation of the nonlinear vibration behavior analysis method for GIS equipment, and the repeated parts will not be repeated.

[0130] In specific implementation, the GIS equipment nonlinear vibration behavior analysis system provided by the embodiment of the present invention is as follows: Figure 2 As shown, specifically including:

[0131] The signal noise filtering module 1 is used to process the nonlinear vibration signal by using the on-site background noise as the reference noise through a two-step filtering algorithm of low-pass filtering and Gaussian threshold filtering;

[0132] Multi-type graphical representation and analysis module 2 is used to perform multi-type graphical representation of the waveform of nonlinear vibration signals from six angles: time domain, frequency domain, time-frequency domain, phase trajectory, mode vibration shape, and Poincare mapping section;

[0133] The characteristic parameter numerical analysis module 3 is used to extract the characteristics of multi-dimensional parameters of nonlinear vibration signals from multi-type spectra; and to perform visual quantitative analysis of nonlinear vibration signals based on the extracted characteristics.

[0134] In the above-mentioned GIS equipment nonlinear vibration behavior analysis system provided in the embodiment of the present invention, the environmental interference problem can be solved through the interaction of the above-mentioned three modules, and a full range of spectrum representation can be achieved. The regular characteristics of the nonlinear vibration spectrum of the GIS equipment can be intuitively grasped, and the visualized quantitative representation of the abnormal nonlinear vibration of the GIS equipment can be achieved. This helps the operation and maintenance personnel to intuitively and accurately grasp the mechanical vibration state of the GIS equipment, discover vibration defects in time, and take effective operation and maintenance measures for the defective equipment more reasonably, thereby effectively ensuring the safe and reliable operation of the GIS equipment.

[0135] In a specific implementation, in the above-mentioned GIS equipment nonlinear vibration behavior analysis system provided by the embodiment of the present invention, as Figure 3 As shown, the characteristic parameter numerical analysis module 3 is specifically used to extract the features of average amplitude, center of gravity frequency, energy value, kurtosis, harmonic distortion rate, and chaos index of nonlinear vibration signals from multiple types of spectra, and is also specifically used to perform visual quantitative analysis on the spectrum distribution, energy, attenuation characteristics, waveform quality, and chaos characteristics of the nonlinear vibration signal based on the extracted features.

[0136] For more specific working processes of the above modules, please refer to the corresponding contents disclosed in the above embodiments, which will not be repeated here.

[0137] The following is a detailed description of the effects of the above-mentioned GIS equipment nonlinear vibration behavior analysis method and system provided by the embodiment of the present invention using a specific example:

[0138] The present invention reads the abnormal sound and vibration signal obtained after data collection of a 110kV real GIS device under 1800A load conditions, and first inputs it into the signal noise filtering module of the system for denoising. Figure 4a and Figure 4b The time domain waveform and phase trajectory output results of the normal state vibration signal after filtering are shown respectively. Figure 5a and Figure 5b The time domain waveform and phase trajectory output results of the busbar air chamber abnormal sound state vibration signal after filtering are shown respectively. It can be seen that the signal has strong noise before filtering and the curve trajectory is chaotic. After filtering, the signal noise is small, the normal signal phase trajectory is a weakly bifurcated circular curve, and the busbar air chamber abnormal sound signal is a vibration waveform that fluctuates around multiple fixed points.

[0139] The spectrum characterization analysis module of the present invention is used for Figure 4a , Figure 4b , Figure 5a and Figure 5b The vibration signal waveform in the image is converted and displayed. Figures 6a to 6f They are the output results of normal vibration signal in time domain, frequency domain, phase trajectory, time-frequency domain, modal vibration shape, and Poincare mapping spectrum. Figures 7a to 7f They are the output results of the busbar air chamber abnormal sound state vibration signal in the time domain, frequency domain, phase trajectory, time-frequency domain, modal vibration shape, and Poincare mapping spectrum. It can be found that through the transformation of the original vibration signal by the spectrum analysis module of this system, the vibration condition of the equipment can be intuitively understood from a more comprehensive and detailed spectrum perspective, thereby grasping the abnormal sound vibration law of the equipment and the mechanical health status.

[0140] The multidimensional feature extraction module of the present invention is used to extract multidimensional features of the vibration signals of the normal state and the abnormal sound state of the busbar air chamber to obtain the vibration amplitude, energy value, kurtosis, harmonic distortion rate, center of gravity frequency, chaos index, etc. Figure 8 and Fig. 9 The results show that multidimensional feature extraction and analysis can achieve quantitative parameter calculation and characterization of the vibration law characteristics of GIS equipment, which helps operation and maintenance personnel to take more effective operation and maintenance measures for defective equipment.

[0141] It should be pointed out that the nonlinear vibration behavior analysis method and system of GIS equipment of the present invention realizes multi-dimensional visualization analysis of nonlinear response characteristics of GIS equipment based on multiple types of spectrum characterization algorithms such as time-frequency domain, modal vibration shape, phase trajectory, Poincare mapping, etc., and can numerically characterize vibration behaviors such as equipment waveform distortion characteristics, amplitude, and attenuation rate. Personnel engaged in power equipment operation and maintenance can use this patented achievement to conduct real-time status monitoring of the mechanical vibration characteristics of GIS / GIL equipment. Relevant personnel from GIS / GIL equipment manufacturers, colleges and universities, and research institutes can also use this patented method to test and analyze the mechanical vibration characteristics of GIS equipment, thereby providing effective technical means for optimizing the structural design of GIS equipment or developing new vibration detection technologies.

[0142] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0143] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0144] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0145] In summary, the embodiment of the present invention provides a method for analyzing the nonlinear vibration behavior of GIS equipment, including: using the on-site background noise as the reference noise, processing the nonlinear vibration signal through a two-step filtering algorithm of low-pass filtering and Gaussian threshold filtering; combining the time domain, frequency domain, time-frequency domain, phase trajectory, modal vibration shape, and Poincare mapping section to perform multi-type spectrum characterization on the waveform of the nonlinear vibration signal; extracting the characteristics of multi-dimensional parameters of the nonlinear vibration signal from the multi-type spectrum; and performing visual quantitative analysis of the nonlinear vibration signal based on the extracted characteristics. The above method can solve the problem of environmental interference, realize all-round spectrum characterization, intuitively grasp the regular characteristics of the nonlinear vibration spectrum of GIS equipment, and realize the visual quantitative characterization of abnormal nonlinear vibration of GIS equipment, which helps the operation and maintenance personnel to intuitively and accurately grasp the mechanical vibration state of GIS equipment, timely discover vibration defects, and more reasonably take effective operation and maintenance measures for defective equipment, so as to effectively ensure the safe and reliable operation of GIS equipment. In addition, the present invention also provides a corresponding system for the nonlinear vibration behavior analysis method of GIS equipment, which further makes the above method more practical, and the system has corresponding advantages.

[0146] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0147] The above is a detailed introduction to the nonlinear vibration behavior analysis method and system for GIS equipment provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for analyzing the nonlinear vibration behavior of GIS equipment. It is characterized in that include: The background noise on site is used as the reference noise, and the nonlinear vibration signal is processed by a two-step filtering algorithm of low-pass filtering and Gaussian threshold filtering. The waveform of the nonlinear vibration signal is characterized by multiple types of graphs from six angles: time domain, frequency domain, time-frequency domain, phase trajectory, mode vibration shape, and Poincare mapping section; Extracting features of multi-dimensional parameters of the nonlinear vibration signal from multiple types of spectra; A visual quantitative analysis of the nonlinear vibration signal is performed based on the extracted features.

2. The nonlinear vibration behavior analysis method of GIS equipment according to claim 1, It is characterized in that The two-step filtering algorithm of low-pass filtering and Gaussian threshold filtering is used to process the nonlinear vibration signal, including: The nonlinear vibration signal is processed by low-pass filtering based on the upper detection limit of the frequency response range calibrated by the sensor; The nonlinear vibration signal after low-pass filtering is subjected to Gaussian threshold filtering to filter out environmental random noise.

3. The nonlinear vibration behavior analysis method of GIS equipment according to claim 2, It is characterized in that The waveform of the nonlinear vibration signal is characterized by multiple types of graphs from six angles: joint time domain, frequency domain, time-frequency domain, phase trajectory, modal vibration shape, and Poincare mapping section, including: Adaptively adjusting the waveform amplitude of the nonlinear vibration signal, or performing local and overall scaling on the waveform to obtain a time domain spectrum; Performing fast Fourier transform and filtering processing on the nonlinear vibration signal to obtain a frequency domain sequence and a frequency domain spectrum; Performing time-frequency domain vibration analysis on the nonlinear vibration signal by short-time Fourier transform, wavelet analysis method or Wigner-Willi distribution method to obtain a time-frequency domain spectrum; Performing phase trajectory analysis on the nonlinear vibration signal to obtain a phase trajectory spectrum; Adopting a complementary set empirical mode decomposition algorithm to perform adaptive mode decomposition on the nonlinear vibration signal, obtaining an IMF signal, and obtaining a mode vibration shape spectrum; The nonlinear vibration signal is characterized by using the Poincare mapping method to obtain a Poincare mapping spectrum.

4. The nonlinear vibration behavior analysis method of GIS equipment according to claim 3, It is characterized in that The method of using a complementary set empirical mode decomposition algorithm to adaptively decompose the nonlinear vibration signal to obtain an IMF signal includes: Applying white noise to the nonlinear vibration signal multiple times in positive and negative complementary directions to obtain doped signals in positive and negative directions; Perform empirical mode decomposition on the obtained doping signals in the positive and negative directions respectively to obtain an IMF sequence group in the complementary direction; All IMF sequence groups to which white noise is added multiple times are averaged to obtain the final decomposed IMF signal.

5. The nonlinear vibration behavior analysis method of GIS equipment according to claim 4, It is characterized in that The step of extracting multi-dimensional parameters of the nonlinear vibration signal from the multi-type spectra includes: The features of the nonlinear vibration signal, including average amplitude, center of gravity frequency, energy value, kurtosis, harmonic distortion rate and chaos index, are extracted from the multi-type spectrum.

6. The method for analyzing nonlinear vibration behavior of GIS equipment according to claim 5, It is characterized in that The visual quantitative analysis of the nonlinear vibration signal according to the extracted features includes: According to the extracted features, the spectrum distribution, energy, attenuation characteristics, waveform quality and chaotic characteristics of the nonlinear vibration signal are visually and quantitatively analyzed.

7. The method for analyzing nonlinear vibration behavior of GIS equipment according to claim 6, It is characterized in that Before performing the visual quantitative analysis of the nonlinear vibration signal according to the extracted features, the method further includes: The extracted features are normalized using the inverse tangent function.

8. A nonlinear vibration behavior analysis system for GIS equipment, It is characterized in that include: The signal noise filtering module is used to process the nonlinear vibration signal by using the on-site background noise as the reference noise through a two-step filtering algorithm of low-pass filtering and Gaussian threshold filtering; A multi-type graphical characterization and analysis module is used to perform multi-type graphical characterization on the waveform of the nonlinear vibration signal from six angles: time domain, frequency domain, time-frequency domain, phase trajectory, mode vibration shape, and Poincare mapping section; The characteristic parameter numerical analysis module is used to extract the characteristics of multi-dimensional parameters of the nonlinear vibration signal from multiple types of graphs; and perform visual quantitative analysis of the nonlinear vibration signal based on the extracted characteristics.

9. The GIS equipment nonlinear vibration behavior analysis system according to claim 8, It is characterized in that The characteristic parameter numerical analysis module is specifically used to extract the characteristics of the average amplitude, center of gravity frequency, energy value, kurtosis, harmonic distortion rate and chaos index of the nonlinear vibration signal from multiple types of spectra.

10. The GIS equipment nonlinear vibration behavior analysis system according to claim 9, It is characterized in that The characteristic parameter numerical analysis module is specifically used to perform visual quantitative analysis on the spectrum distribution, energy, attenuation characteristics, waveform quality, and chaotic characteristics of the nonlinear vibration signal based on the extracted characteristics.

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