GIS-based interference source positioning method based on VFTO analysis, electronic device and storage medium

Abstract

The application discloses a GIS interference source positioning method based on VFTO analysis, electronic equipment and a storage medium, and is used for a WAPI device of a GIS system. The method comprises the following steps: extracting VFTO waveform data in a wireless local area network security protocol AP of the GIS system; performing noise reduction processing on the VFTO waveform data, and decomposing the VFTO waveform data after noise reduction into a plurality of intrinsic mode functions of different frequency components; performing Hilbert transformation on each intrinsic mode function, and constructing a Hilbert spectrum; and positioning an interference source based on the propagation characteristics of the interference signal and the physical layout of the system through the Hilbert spectrum. The VFTO generated by the WAPI device is analyzed and processed, and the interference source is determined, so that the complex industrial environment of strong electromagnetic interference can be coped with, and the stability and reliability of the WAPI wireless environment are ensured.

Description

Technical Field

[0001] This invention belongs to the technical field of transient signal processing within GIS, specifically relating to a method for locating interference sources in GIS based on VFTO analysis, electronic equipment, and storage media. Background Technology

[0002] Geographic Information System (GIS) is a spatial information system. Very Fast Transient Overvoltage (VFTO) can occur due to the switching operations of devices such as disconnectors and circuit breakers within a GIS, or external lightning strikes. The VFTO generated during disconnector operation is the most severe. When the disconnector begins to open at the voltage peak, the voltage on the unloaded bus remains at Em, while the voltage on the power supply side continues to change sinusoidally. When the rate of increase of the transient overvoltage exceeds or equals the electron avalanche threshold of SF6, arcing breakdown occurs. At this point, current flows through the arc from the power supply side to the unloaded bus side, causing the unloaded side voltage to jump to synchronize with the power supply voltage, accompanied by high-frequency oscillations, generating VFTO. Since the disconnector lacks arc-extinguishing capability, the arc will continue until the high-frequency current decays, and the arc will extinguish when it can no longer sustain its energy. Since the voltage on the power supply side continues to change sinusoidally while the voltage on the no-load side remains the residual voltage at the moment the arc breaks, when the recovery voltage between the contacts exceeds the breakdown voltage again, the contacts will break down again. This cycle repeats until the recovery voltage no longer exceeds the breakdown voltage between the contacts, at which point the contacts are completely disconnected and the tripping ends.

[0003] Since the bandwidth equipment in the converter station uniformly uses WAPI wireless LAN for access, in order to cope with the complex industrial environment with strong electromagnetic interference and ensure the stability and reliability of the WAPI wireless environment, it is necessary to assess the impact and harm of VFTO on WAPI equipment, and thus it is necessary to analyze the sources of interference. Summary of the Invention

[0004] The purpose of this invention is to provide a VFTO-based interference source localization method for GIS systems that can identify interference sources, thereby avoiding strong electromagnetic interference and providing a stable and reliable operating environment for WAPI wireless environments.

[0005] To achieve the above objectives, this invention proposes a method for locating interference sources in a GIS system based on VFTO analysis, for use with WAPI devices in the GIS system. The method includes: extracting VFTO waveform data from the wireless LAN security protocol AP of the GIS system; performing noise reduction processing on the VFTO waveform data; decomposing the noise-reduced VFTO waveform data into eigenmode functions of several different frequency components; performing Hilbert transform on each eigenmode function to construct a Hilbert spectrum; and locating the interference source based on the propagation characteristics of the interference signal and the physical layout of the system using the Hilbert spectrum.

[0006] In one optional implementation, the interference source is located using the Hilbert spectrum based on the propagation characteristics of the interference signal and the physical layout of the system. Specifically, this includes: performing a Hilbert transform on each intrinsic mode function to extract time-frequency information, including instantaneous frequency and instantaneous amplitude; and accumulating the instantaneous frequencies and amplitudes of all components of the intrinsic mode functions to construct the Hilbert spectrum. In the formula, δ(f-ω) k (t) is a unit impulse function, |IMF k (t)| is the instantaneous amplitude.

[0007] In one optional implementation, the interference source is located using the Hilbert spectrum based on the propagation characteristics of the interference signal and the physical layout of the system. Specifically, this includes: extracting the time-varying pattern of the instantaneous frequency from the intrinsic mode functions to obtain the main characteristic frequency components of the denoised VFTO waveform data; comparing the characteristic frequencies and amplitudes of each test point using the Hilbert spectrum based on the main characteristic frequency components to determine the region with the strongest spectral energy; based on the region with the strongest spectral energy and the time starting point difference of the instantaneous frequency in the Hilbert spectrum, inferring the propagation path of the VFTO waveform data at different test points to obtain the inferred distance, calculated using the following formula: d = v × Δt; where d is the distance between the test point and the interference source, defined as the inferred distance, v is the signal propagation speed, and Δt is the arrival time difference of the instantaneous frequency; and obtaining the location of the interference source based on the inferred distance.

[0008] In one optional implementation, the interference source is located using the Hilbert spectrum, based on the propagation characteristics of the interference signal and the physical layout of the system. Specifically, this includes: comparing the time-frequency energy distribution of different test points using the Hilbert spectrum to obtain the frequency band with the highest instantaneous energy; obtaining the test point based on the frequency band with the highest instantaneous energy; analyzing the attenuation law of the instantaneous energy of the characteristic frequency of the test point with distance, and establishing an energy attenuation model, wherein the formula for calculating the instantaneous energy is as follows: In the formula, E(d) is the instantaneous energy at the distance between the measured point and the interference source, E0 is the optimal initial energy, and α m d is the optimal attenuation coefficient, and d is the predicted distance.

[0009] In one optional implementation, the location of the interference source is obtained by fitting the energy distribution based on the energy attenuation model, specifically including: estimating the attenuation coefficient and initial energy; and solving for the fitted attenuation coefficient and initial energy according to the error function to obtain the optimal attenuation coefficient and optimal initial energy, wherein the error function is as follows:

[0010] Based on the optimal attenuation coefficient, optimal initial energy, and energy attenuation model, the distance from the interference source to the test point is obtained, calculated using the following formula: In the formula, E0 is the optimal initial energy, E1 is the energy at a certain point to be measured, and E i Let d be the instantaneous energy at the distance of the i-th test point from the interference source. i Let α be the distance between the i-th test point and the interference source. m d is the optimal attenuation coefficient, and d is the estimated distance; the location of the interference source is obtained based on the estimated distance.

[0011] In one optional implementation, the denoised VFTO waveform data is decomposed into several intrinsic mode functions (EMFs) of different frequency components. Specifically, this includes: decomposing the denoised VFTO waveform data into a time-series signal using the EMD algorithm to obtain multiple EMFs, calculated as follows: In the formula, ξ(t) is the original signal, and IMF i There are K intrinsic mode functions, r K It is the remainder term after subtracting the intrinsic mode function from the original signal.

[0012] In one optional implementation, a Hilbert transform is performed on each of the intrinsic mode functions to extract time-frequency information, which includes instantaneous frequency and instantaneous amplitude. Specifically, this includes: assuming a component of a certain intrinsic mode function is IMF... k (t), then the result of the Hilbert transform is: In the formula, H represents the Hilbert transform operation, IMF k (t) is the k-th eigenmode function of the signal; the instantaneous frequency and instantaneous amplitude are obtained through the Hilbert transform; wherein, the formula for calculating the instantaneous frequency is as follows: In the formula, arg() represents the phase calculation operation; the formula for calculating the instantaneous amplitude is as follows: In the formula, j is the imaginary unit, and t represents time.

[0013] In one alternative implementation, the Hilbert spectrum is used for time-spectrum visualization, amplitude analysis, oscillation mode identification, and frequency transition detection.

[0014] The present invention also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the interference source localization method based on VFTO analysis of any of the claims.

[0015] The present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, implements any of the interference source localization methods for a GIS system based on VFTO analysis as described in the present invention.

[0016] The beneficial effects of this invention are: by analyzing and processing the VFTO generated by WAPI devices to identify the interference source, it can cope with complex industrial environments with strong electromagnetic interference and ensure the stability and reliability of the WAPI wireless environment. Attached Figure Description

[0017] Figure 1 A flowchart of an interference source localization method based on VFTO analysis in a GIS system provided in an embodiment of the present invention;

[0018] Figure 2 This is a block diagram of an electronic device provided in an embodiment of the present invention.

[0019] Explanation of reference numerals in the attached figures:

[0020] 110. Processor; 120. Memory. Detailed Implementation

[0021] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] As power transmission corridors expand their coverage, transmission lines inevitably traverse icy regions with complex terrain and cold climates. In recent years, with increasing global warming and frequent extreme disasters, transmission tower collapses in plateau regions have become increasingly common. The main reason is that existing transmission tower structures, designed according to current standards, are no longer adequate to meet the challenges of the new environment. There is an urgent need to develop a set of transmission tower standards that meet the strength requirements of this new environment, to verify the strength of existing transmission tower structures, and to provide guidance for subsequent upgrades and renovations.

[0023] like Figure 1As shown, according to an embodiment of the present invention, in one aspect, a method for locating interference sources based on VFTO analysis in a GIS system is provided for WAPI devices in a GIS system. The method for locating interference sources based on VFTO analysis in a GIS system includes the following steps:

[0024] Step S101: Extract the VFTO waveform data of the access point of the wireless LAN security protocol of the GIS system.

[0025] Step S102: Perform noise reduction processing on the VFTO waveform data;

[0026] Step S103: Decompose the denoised VFTO waveform data into eigenmode functions of several different frequency components.

[0027] Step S105: Perform a Hilbert transform on each eigenmode function to construct the Hilbert spectrum.

[0028] Step S107: Locate the interference source using the Hilbert spectrum, based on the propagation characteristics of the interference signal and the physical layout of the system.

[0029] The interference source localization method in this embodiment is particularly applicable to WLAN Authentication and Privacy Infrastructure (WAPI) devices in GIS systems, and can effectively identify and locate interference sources in GIS systems.

[0030] VFTO waveform data is extracted from the access point (WAPI-AP) of the wireless LAN security protocol in the GIS system. The VFTO waveform data contains the time-domain characteristics of the interference signal and is key information for analyzing the interference source.

[0031] Among these methods, the transient value of the WAPI-AP voltage to ground, i.e., the VFTO waveform, can be measured by using a voltage probe and an oscilloscope when the circuit breaker or disconnector inside the GIS is opened or closed. The transient current of the WAPI-AP's POE network cable can also be measured by using a current probe and an oscilloscope. The transient current is presented as a voltage on the oscilloscope.

[0032] The extracted VFTO waveform data is denoised to improve the accuracy of subsequent analysis. The denoised data is then decomposed into several intrinsic mode functions (IMFs) of different frequency components. This decomposition is based on the empirical mode decomposition (EMD) method, which can decompose complex signals into a series of simple basic modes.

[0033] Then, a Hilbert transform is performed on each eigenmode function to construct the Hilbert spectrum. The Hilbert transform is a mathematical tool used to analyze the instantaneous frequency characteristics of a signal, while the Hilbert spectrum provides a detailed view of how the signal frequency changes over time.

[0034] By utilizing the constructed Hilbert spectrum and based on the propagation characteristics of the interference signal and the physical layout information of the GIS system, the interference source can be accurately located. By analyzing the signal propagation path and attenuation characteristics, the possible location of the interference source can be inferred, thus providing an important reference for the maintenance and optimization of the GIS system.

[0035] The propagation characteristics of the interference signal are as follows: When the isolating switch begins its opening operation at the voltage peak, the voltage on the unloaded bus remains at Em, while the voltage on the power supply side continues to change sinusoidally. When the rise rate of the transient overvoltage is greater than or equal to the electron avalanche critical value of SF6, arcing breakdown will occur. At this time, current will flow from the power supply side to the unloaded bus side through the arc, causing the unloaded side voltage to jump to be synchronized with the power supply side voltage, accompanied by high-frequency oscillation, generating VFTO. Since the isolating switch has no arc-extinguishing capability, the arc will continue until the high-frequency current decays and the arc can no longer sustain its energy to continue burning, at which point the arc will extinguish. Because the power supply side voltage continues to change sinusoidally while the unloaded side voltage maintains the residual voltage at the moment the arc is broken, when the recovery voltage between the contacts is greater than the breakdown voltage again, the contacts will break down again, repeating this cycle until the recovery voltage is no longer greater than the breakdown voltage between the contacts, at which point the contacts are completely disconnected, and the opening operation ends.

[0036] The physical layout of a GIS system refers to how the physical layout of the internal system directly affects the propagation path, peak value, and waveform characteristics of VFTO (Voltage-Free Transmission) by influencing factors such as the relative positions of electrical equipment, grounding methods, cable and conductor configurations, and switch operations. When VFTO propagates within a GIS system, it may be reflected when it encounters different electrical components, such as circuit breakers, busbars, and conductors. The interaction between the reflected wave and the original wave may lead to an amplification of the voltage waveform, resulting in a higher VFTO.

[0037] Furthermore, since the noise inside GIS is relatively strong, it will directly affect the key features of the signal. In order to avoid the interference of noise on the IMF component, it is necessary to use wavelet threshold denoising method to preprocess the signal before applying the EMD algorithm to decompose the signal, reduce noise interference, avoid the interference of noise on the IMF component, and ensure that the EMD decomposition results are more reliable.

[0038] The noise reduction process for VFTO waveform data is as follows:

[0039] Signal preprocessing: Check whether the signal contains obvious DC components or low-frequency interference. Low-frequency interference can be removed and high-frequency components can be retained by using a high-pass filter.

[0040] Wavelet decomposition: Select a suitable wavelet basis, such as Daubechies db4 or Symlet sym6, to represent the peak transient signal, and set the number of wavelet decomposition layers according to the signal length.

[0041] Noise thresholding: The threshold is determined using a threshold formula.

[0042]

[0043] Where λ is the threshold, σ is the noise standard deviation, the noise standard deviation can be estimated by high-frequency wavelet coefficients, and N is the signal length.

[0044] Since traditional threshold functions are divided into soft thresholding and hard thresholding, hard thresholding tends to cause discontinuities in the function, while soft thresholding tends to cause changes in the wavelet decomposition coefficient values. In order to eliminate the discontinuities of the threshold function while making the function quickly approach the hard threshold function, the following threshold function can be used:

[0045]

[0046] Signal reconstruction: The signal is reconstructed using the denoised wavelet coefficients and the inverse wavelet transform IDWT to ensure that the spike characteristics of VFTO are preserved, and the reconstruction is verified in the time and frequency domains.

[0047] The above processing steps completed the noise reduction of the VFTO waveform data.

[0048] Based on step S105, a Hilbert transform is performed on each eigenmode function to construct the Hilbert spectrum, which specifically includes the following steps:

[0049] Step S1051: Perform Hilbert transform on each intrinsic mode function to extract time-frequency information, which includes instantaneous frequency and instantaneous amplitude.

[0050] Step S1053: Accumulate the instantaneous frequencies and instantaneous amplitudes of all components of the intrinsic mode functions to construct the Hilbert spectrum:

[0051]

[0052] In the formula, δ(f-ω) k (t) is a unit impulse function, |IMF k (t)| is the instantaneous amplitude.

[0053] In this embodiment, a Hilbert transform is performed on each intrinsic mode function to extract its time-frequency information.

[0054] Time-frequency information refers to the frequency and amplitude at different points in time, specifically including instantaneous frequency and instantaneous amplitude.

[0055] Several important parameters. Based on the extracted time-frequency information, the Hilbert spectrum is further constructed. The Hilbert spectrum is a tool that can describe in detail the distribution of a signal in the time-frequency plane. It combines the instantaneous frequency and instantaneous amplitude of the signal to form a three-dimensional representation, thus providing a powerful means to analyze the local characteristics of the signal.

[0056] Step S107, using the Hilbert spectrum, locates the interference source based on the propagation characteristics of the interference signal and the physical layout of the system, specifically including the following steps:

[0057] Step S1071: Extract the instantaneous frequency variation law from the intrinsic mode function to obtain the main characteristic frequency components of the denoised VFTO waveform data;

[0058] Step S1073: Based on the main characteristic frequency components, compare the characteristic frequencies and amplitudes of each test point using the Hilbert spectrum to determine the region with the strongest spectral energy;

[0059] Step S1075: Based on the region with the strongest spectral energy, and according to the time starting point difference of the instantaneous frequency in the Hilbert spectrum, infer the propagation path of the VFTO waveform data at different test points, and obtain the inferred distance. The calculation formula is as follows:

[0060] d = v × Δt.

[0061] In the formula, d is the distance between the point to be measured and the interference source, which is the estimated distance; v is the signal propagation speed; and Δt is the arrival time difference of the instantaneous frequency.

[0062] Step S1077: Obtain the location of the interference source based on the estimated distance.

[0063] In this embodiment, the instantaneous frequency variation over time is extracted from the intrinsic mode functions. This step obtains the main characteristic frequency components in the denoised VFTO waveform data. Based on these main characteristic frequency components, the characteristic frequencies and amplitudes at different test points are compared using Hilbert transform technology to determine the region with the most concentrated spectral energy. Based on the region with the strongest spectral energy, the propagation path of the VFTO waveform data at each test point is inferred by further utilizing the time starting point difference of the instantaneous frequency in the Hilbert transform spectrum, and finally, the specific distance between the interference source and the test point is calculated.

[0064] Based on the provided time-frequency information and Hilbert spectrum, a thorough time-frequency characteristic analysis is performed. This analysis allows for the calculation of the precise distance between the interference source and the test point. Using the distance calculated in the previous step, a comparative analysis of time-frequency energy is conducted. This comparison further determines the specific location of the interference source.

[0065] Step S107, using the Hilbert spectrum, based on the propagation characteristics of the interference signal and the physical layout of the system, locates the interference source, specifically including the following steps:

[0066] Step S1072: Based on the Hilbert spectrum, compare the time-frequency energy distribution of different test points to obtain the frequency band with the largest instantaneous energy.

[0067] Step S1074: Obtain the test point based on the frequency band with the highest instantaneous energy.

[0068] Step S1076: Analyze the attenuation law of the instantaneous energy of the characteristic frequency of the test point with the predicted distance, and establish an energy attenuation model. The formula for calculating the instantaneous energy is as follows:

[0069]

[0070] In the formula, E(d) is the instantaneous energy at the distance between the measured point and the interference source, E0 is the optimal initial energy, and α m The optimal attenuation coefficient is given by d, where d is the estimated distance.

[0071] Step S1078: Based on the energy decay model, obtain the location of the interference source by fitting the energy distribution.

[0072] By comparing the time-frequency energy distribution at different test points, the frequency band with the maximum instantaneous energy and the corresponding test point can be obtained, thus locating the point of maximum energy. The location of the point with the maximum instantaneous energy is usually close to the interference source. Once the frequency band with the maximum instantaneous energy is determined, the specific test point can be located based on this frequency band. Analyzing the attenuation law of the instantaneous energy of the characteristic frequency of the test point with distance allows for the establishment of an energy attenuation model. Based on the established energy attenuation model, the specific location of the interference source can be deduced by fitting the energy distribution.

[0073] Step S1078, based on the energy attenuation model, obtains the location of the interference source by fitting the energy distribution, specifically including the following steps:

[0074] Step S10781: Estimate the attenuation coefficient and initial energy.

[0075] Step S10783: Based on the error function, solve for the fitted attenuation coefficient and initial energy to obtain the optimal attenuation coefficient and optimal initial energy. The error function is as follows:

[0076]

[0077] Step S10785: Based on the optimal attenuation coefficient and initial energy, the predicted distance is obtained, calculated using the following formula:

[0078]

[0079] In the formula, E0 is the optimal initial energy, E1 is the energy at a certain point to be measured, and E i Let d be the instantaneous energy at the distance of the i-th test point from the interference source. i Let α be the distance between the i-th test point and the interference source. m d is the optimal attenuation coefficient, and d is the predicted distance.

[0080] Step S10787: Obtain the location of the interference source based on the estimated distance.

[0081] In the process of applying the energy decay model to locate interference sources, the fitting process can be achieved using the least squares method.

[0082] Choose an initial guess: estimate the attenuation coefficient and initial energy from some known propagation characteristics or measurements.

[0083] Minimum error: Solve for the fitting parameters α using the least squares method. m And E0, to minimize the error between the predicted energy value and the actual measured energy value:

[0084]

[0085] The optimal α is obtained by minimizing this error function. m And E0.

[0086] Among them, the least squares method is used for fitting, and α is determined. m And E0.

[0087] Inferring the location of the interference source: The location of the interference source is inferred from the known energy data of the measurement points and the fitted energy attenuation model.

[0088]

[0089] In this embodiment, the calculated estimated distance can be compared with the estimated distance calculated in the preceding steps. For ease of explanation, the estimated distance calculated above is named the first estimated distance, and the estimated distance calculated in subsequent steps is named the second estimated distance. The first estimated distance and the second estimated distance are compared. Only when the first estimated distance and the second estimated distance are relatively close can the interference source be identified, and the localization will be more accurate. If the difference between the first estimated distance and the second estimated distance is large, the second estimated distance needs to be calculated again until the difference between the second estimated distance and the first estimated distance is within a preset range.

[0090] Based on step S103, the denoised VFTO waveform data is decomposed into eigenmode functions of several different frequency components, specifically including the following steps:

[0091] Step S1031: Decompose the VFTO waveform data into a time-series signal using the EMD algorithm to obtain a series of intrinsic mode functions. The calculation formula is as follows:

[0092]

[0093] In the formula, ξ(t) is the original signal, and IMF i There are K intrinsic mode functions, r K It is the remainder term after subtracting the intrinsic mode function from the original signal.

[0094] In this embodiment, the waveform signal is decomposed into IMFs using the EMD algorithm.

[0095] The EMD algorithm is applied to the preprocessed VFTO waveform to obtain a series of intrinsic mode functions (IMFs). The EMD algorithm program terminates under two conditions: Condition 1: The average value of the mean line approaches 0, generally with a difference of less than 0.1. Condition 2: The difference between the number of extreme points of the original signal and the number of intersections of the original signal with y=0 cannot exceed 1. The program terminates when an IMF is found to be a monotonic function or lacks maximum / minimum points.

[0096] Generally, the Hilbert transform H[x(t)] of the original signal x(t) is usually written as:

[0097] z(t) = x(t) + jH[x(t)].

[0098] Here, x(t) is called the real part of the complex signal z(t), H[x(t) is called the imaginary part of the complex signal z(t), and z(t) is called the analytic signal of x(t).

[0099] Through the above steps, the original signal x(t) is decomposed into several intrinsic mode functions (IMFs). Each IMF can be regarded as a frequency component of the signal, and these frequency components are locally varying.

[0100] Based on step S1051, a Hilbert transform is performed on each intrinsic mode function to extract time-frequency information, which includes instantaneous frequency and instantaneous amplitude. Specifically, the steps are as follows:

[0101] Step S10511: Let the component of a certain intrinsic mode function be IMF. k (t), then the result of the Hilbert transform is:

[0102]

[0103] In the formula, H represents the Hilbert transform operation, IMF k (t) is the kth eigenmode function of the signal.

[0104] Step S10513: Obtain the instantaneous frequency and instantaneous amplitude through Hilbert transform.

[0105] The formula for calculating instantaneous frequency is as follows:

[0106]

[0107] In the formula, arg() represents the phase calculation operation.

[0108] The formula for calculating the instantaneous amplitude is as follows:

[0109]

[0110] In the formula, j is the imaginary unit, and t represents time.

[0111] In this embodiment, a Hilbert transform is performed on each IMF to extract the instantaneous frequency distribution in the transient overvoltage waveform. The Hilbert transform yields the instantaneous frequency and amplitude of each IMF, and a Hilbert spectrum is constructed. The Hilbert transform provides a complex analytic representation of the signal, thereby revealing the instantaneous characteristics of the signal, such as its instantaneous frequency and amplitude.

[0112] For each IMF component, IMF k (t) First, it is necessary to calculate the complex analytic signal of the signal, which can be represented by the combination of the original signal and the Hilbert transform:

[0113]

[0114] Where j is the imaginary unit, IMF k (t) is the k-th intrinsic mode function (IMF) of the signal. It is the IMF k Hilbert transform of (t).

[0115] In the process of calculating instantaneous amplitude, the amplitude of the complex analytic signal is the instantaneous amplitude, which represents the instantaneous intensity and amplitude of the signal at each time t. It is the modulus of the complex signal, which is the absolute value of the complex number. The calculation formula is as follows:

[0116]

[0117] Furthermore, time-frequency spectrum visualization, amplitude analysis, oscillation mode identification, and frequency transition detection are performed using Hilbert spectra.

[0118] Hilbert's spectrum is essentially a time-frequency plot. From this constructed time-frequency plot, the following can be performed:

[0119] (1) Time-frequency spectrum visualization: The Hilbert spectrum of a signal is visualized as a two-dimensional image by plotting a time-frequency graph, where the X-axis represents time, the Y-axis represents frequency, and color or brightness represents the amplitude or energy intensity. Therefore, the changes in frequency components of the signal over different time periods can be observed intuitively, such as frequency transitions or frequency change trends, which helps to locate the moment when an abnormal event occurs.

[0120] (2) Amplitude analysis: In the time spectrum, the amplitude is usually represented by color or brightness. Therefore, the transient amplitude change corresponding to each IMF can be viewed directly in the time spectrum. A sudden increase in amplitude can be used to judge electrical disturbances or fault events in a short period of time; while a rapid decay of amplitude can be used for subsequent analysis of VFTO.

[0121] (3) Oscillation pattern identification: When frequency components appear repeatedly in a time-frequency graph within certain time periods, it may indicate periodic oscillation; and when the oscillation frequency changes in different time periods, the intensity of the oscillation can be observed.

[0122] (4) Frequency transition detection: Observing frequency transition points on the time spectrum can be used to judge drastic changes in the system state; while the frequency rise or fall and the amplitude of the frequency transition can provide clues for fault diagnosis.

[0123] On another aspect, such as Figure 2 As shown, the present invention also provides an electronic device, comprising: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the interference source localization methods based on VFTO analysis in a GIS system.

[0124] On the other hand, the present invention also proposes a computer storage medium storing a computer program, which, when executed by a processor, implements a method for locating interference sources in a GIS system based on VFTO analysis.

[0125] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Dual Data SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus Direct RAM (RDRAM), Direct Memory Bus Dynamic RAM (DRDRAM), and Memory Bus Dynamic RAM (RDRAM). The various embodiments described in this specification are presented in a progressive manner, and similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, for embodiments of apparatus, devices, and non-volatile computer storage media, since they are substantially similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments.

[0126] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for locating interference sources in a GIS system based on VFTO analysis, used in a WAPI device of a GIS system, characterized in that, The method includes: Extract VFTO waveform data from the wireless LAN security protocol AP in the GIS system; The VFTO waveform data is subjected to noise reduction processing; The denoised VFTO waveform data is decomposed into eigenmode functions of several different frequency components; Perform a Hilbert transform on each of the intrinsic mode functions to construct the Hilbert spectrum, specifically including: A Hilbert transform is performed on each of the intrinsic mode functions to extract time-frequency information, which includes instantaneous frequency and instantaneous amplitude. The instantaneous frequencies and instantaneous amplitudes of all components of the intrinsic mode functions are summed to construct the Hilbert spectrum: In the formula, δ(f-ω) k (t) is a unit impulse function, |IMF k (t)| is the instantaneous amplitude; Using the Hilbert spectrum, based on the propagation characteristics of the interference signal and the physical layout of the system, the interference source is located, specifically including: Based on the Hilbert spectrum, the time-frequency energy distribution at different test points is compared to obtain the frequency band with the maximum instantaneous energy. The formula for calculating the instantaneous energy is as follows: In the formula, E(d) is the instantaneous energy at the distance between the measured point and the interference source, E0 is the optimal initial energy, and α m The optimal attenuation coefficient is given by d, where d is the estimated distance. The test point is obtained based on the frequency band with the highest instantaneous energy. Analyze the attenuation law of the instantaneous energy of the characteristic frequency of the point under test with distance, and establish an energy attenuation model; based on the energy attenuation model, obtain the location of the interference source by fitting the energy distribution, specifically including: Estimate the attenuation coefficient and initial energy; Based on the error function, the fitted attenuation coefficient and initial energy are calculated to obtain the optimal attenuation coefficient and optimal initial energy. The error function is as follows: Based on the optimal attenuation coefficient and optimal initial energy, the predicted distance is obtained, calculated using the following formula: In the formula, E0 is the optimal initial energy, E1 is the energy at a certain point to be measured, and E i Let d be the instantaneous energy at the distance of the i-th test point from the interference source. i α is the distance between the i-th test point and the interference source; m The optimal attenuation coefficient is given by d, where d is the estimated distance. The location of the interference source is obtained based on the estimated distance.

2. The interference source localization method based on VFTO analysis in a GIS system according to claim 1, characterized in that, Perform a Hilbert transform on each of the intrinsic mode functions to extract time-frequency information, which includes instantaneous frequency and instantaneous amplitude, specifically including: Let the components of a certain intrinsic mode function be IMF. k (t), then the result of the Hilbert transform is: In the formula, H represents the Hilbert transform operation, IMF k (t) is the k-th eigenmode function of the signal; The instantaneous frequency and instantaneous amplitude are obtained through the Hilbert transform. The formula for calculating the instantaneous frequency is as follows: In the formula, arg() represents the phase calculation operation; The formula for calculating the instantaneous amplitude is as follows: In the formula, j is the imaginary unit, and t represents time.

3. The interference source localization method for a GIS system based on VFTO analysis according to claim 1 or 2, characterized in that, The denoised VFTO waveform data is decomposed into eigenmode functions of several different frequency components, specifically including: The VFTO waveform data after noise reduction is decomposed into a time-series signal using the EMD algorithm to obtain multiple intrinsic mode functions, calculated as follows: In the formula, ξ(t) is the original signal, and IMF i There are K intrinsic mode functions, r K It is the remainder term after subtracting the intrinsic mode function from the original signal.

4. The interference source localization method for a GIS system based on VFTO analysis according to claim 1 or 2, characterized in that, The Hilbert spectrum is used for time-frequency visualization, amplitude analysis, oscillation mode identification, and frequency transition detection.

5. An electronic device, characterized in that, include: At least one processor; A memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the interference source localization method based on VFTO analysis in a GIS system as described in any one of claims 1 to 4.

6. A computer storage medium, characterized in that, The system contains a computer program that, when executed by a processor, implements a method for locating interference sources in a GIS system based on VFTO analysis as described in any one of claims 1 to 4.

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