High-voltage discharge method detection method capable of realizing nondestructive monitoring

Abstract

The invention provides a high-voltage discharge method detection method capable of realizing nondestructive monitoring, and relates to the technical field of state monitoring of high-voltage electrical equipment. The high-voltage discharge method detection method capable of realizing nondestructive monitoring comprises the following steps: S1, before detection, acquiring an environment background noise signal of a detected area through a sensor array; s2, carrying out feature extraction on the noise signal by adopting an adaptive filtering algorithm, and producing a dynamic noise line concentration model; and S3, in the high-voltage discharge detection process, subtracting the noise line concentration from the original signal in real time, and dynamically adjusting the amplification gain according to the noise intensity to prevent signal saturation. According to the method, the environmental noise is collected in advance, the dynamic noise baseline is established, and the influence of external interference on the discharge signal is reduced by combining the adaptive filtering technology and the gain adjustment technology, so that the detection reliability in a complex scene is improved through dynamic noise modeling and gain adjustment; the problem that the false detection rate is increased due to the fact that a traditional method is easily interfered by background noise in a complex industrial environment is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of high-voltage electrical equipment status monitoring, in particular to a high-voltage discharge detection method capable of non-destructive monitoring. Background Art

[0002] As an important means of monitoring the condition of electrical equipment, high-voltage discharge method evaluates insulation material defects by detecting partial discharge (PD) signals and is widely used in fault diagnosis of key power facilities such as cables, transformers, and GIS equipment. Traditional methods mainly rely on pulse current method, ultrasonic method, or ultra-high frequency (UHF) method to determine the defect type by analyzing the amplitude, phase, or spectral characteristics of the discharge signal. However, existing technologies face two major bottlenecks in practical applications: first, electromagnetic interference and mechanical vibration noise in complex industrial environments will significantly reduce the detection signal-to-noise ratio; second, existing detection methods are mostly limited to single-modal signal analysis, which makes it difficult to achieve accurate three-dimensional spatial positioning of defects, resulting in insufficient recognition of hidden defects (such as internal tiny air gaps and deep cracks).

[0003] A search revealed that existing methods generally employ fixed-threshold filtering (e.g., bandpass filter banks) for ambient noise suppression. This static compensation mode cannot adapt to dynamically changing field noise (e.g., inverter harmonics and mechanical vibration noise), resulting in signal distortion when the effective signal and noise bands overlap. Experimental data shows that when ambient noise fluctuates by more than 15dB, the signal-to-noise ratio of traditional methods deteriorates to below -8dB, resulting in a missed detection rate of up to 32% for tiny discharge signals. Existing single-modal detection technologies also have inherent limitations: while electromagnetic signals can accurately time (±0.1μs), they cannot resolve spatial coordinates; while ultrasonic arrays can achieve planar positioning (XY axis error ≤5mm), depth (Z axis) measurement errors exceed 10mm due to the anisotropy of the material's sound velocity; and while optical detection can characterize surface discharges, it struggles to penetrate opaque media. Consequently, existing systems can only output two-dimensional projection images and are unable to construct true three-dimensional defect models. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In view of the shortcomings of the existing technology, the present invention provides a high-voltage discharge detection method capable of non-destructive monitoring, which solves the problem that effective signals are mistakenly filtered out or covered by background noise due to insufficient anti-interference ability.

[0006] (2) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] A high-voltage discharge detection method capable of non-destructive monitoring comprises the following steps:

[0009] S1. Before testing, the sensor array collects the ambient background noise signal of the tested area;

[0010] S2. Use adaptive filtering algorithm to extract features from noise signals and generate dynamic noise cluster model;

[0011] S3. During high-voltage discharge detection, the noise baseline is subtracted from the original signal in real time, and the amplification gain is dynamically adjusted based on the noise intensity to prevent signal saturation.

[0012] S4. Apply a controllable high-voltage pulse signal to the object under test to stimulate partial discharge;

[0013] S5. Synchronously collect electromagnetic signals, ultrasonic signals, and optical signals generated during discharge;

[0014] S6. Perform time-frequency analysis on the electromagnetic signal to determine the phase and time window of the discharge. Calculate the direction of arrival of the ultrasonic signal. Determine the two-dimensional coordinates of the defect (X and Y axes) based on the difference in reception delay between each sensor. Analyze the intensity attenuation characteristics of the optical signal and, combined with the material transmittance parameters, construct a defect depth model (Z axis).

[0015] S7. Input the time-frequency characteristics of the electromagnetic signal, the plane coordinates of the ultrasonic wave, and the depth information of the optical signal into the data fusion module, and then generate a three-dimensional defect imaging image based on the fused data, and annotate the spatial location, size, and type of the defect;

[0016] S8. Display the three-dimensional image and detection results generated in step S7 on the display screen, and store the data in the database.

[0017] Furthermore, the environmental background noise signal collected in step S1 includes noise from electromagnetic interference and mechanical working vibration.

[0018] Furthermore, during the high voltage discharge in step S3, the gain decreases by 3dB for every 10dB increase in noise.

[0019] Furthermore, when the controllable high-voltage pulse signal is applied in step S4, the frequency is 0.1-100 kHz and the amplitude is 1-50 kV.

[0020] Furthermore, the electromagnetic signal in step S5 and step S6 refers to the time domain waveform of the discharge pulse obtained by the electromagnetic sensor, the ultrasonic signal is the sound wave generated by the discharge received by the piezoelectric sensor array, and the optical signal is the discharge light radiation captured by the ultraviolet and visible light sensors.

[0021] Furthermore, steps S1 to S3 include achieving environmental noise compensation through adaptive filtering and dynamic gain adjustment.

[0022] (3) Beneficial effects

[0023] The present invention provides a high-voltage discharge detection method capable of non-destructive monitoring. It has the following beneficial effects:

[0024] 1. The present invention provides a high-voltage discharge detection method that can be non-destructively monitored. By pre-collecting environmental noise and establishing a dynamic noise baseline, combined with adaptive filtering technology and gain adjustment technology, the impact of external interference on the discharge signal is reduced. Through dynamic noise modeling and gain adjustment, the detection reliability in complex scenarios is improved, avoiding the problem that traditional methods are easily affected by background noise in complex industrial environments, resulting in an increased false detection rate.

[0025] The present invention provides a high-voltage discharge detection method that can be used for non-destructive monitoring. It realizes three-dimensional spatial reconstruction of defects through electromagnetic signal triggering time synchronization, combined with acoustic positioning and optical intensity analysis, and realizes spatial positioning and visualization of defects by fusing electromagnetic, acoustic and optical signals, thus avoiding the problem in the existing technology that it is difficult to accurately locate the planar position and depth information of the defect at the same time. DETAILED DESCRIPTION

[0026] The following is a clear and complete description of the technical solution of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0027] Example 1:

[0028] A high-voltage discharge detection method capable of non-destructive monitoring comprises the following steps:

[0029] S1. Before testing, the sensor array collects the ambient background noise signal of the tested area;

[0030] S2. Use the adaptive filtering algorithm to extract features from the noise signal and generate a dynamic noise baseline model. The adaptive filtering algorithm formula is:

[0031] (1) Filter output: γ(n) = W T (n)X(n)

[0032] Where: W is the filter coefficient vector, X is the input signal vector;

[0033] (2) Error calculation: e(n) = d(n) - γ(n)

[0034] Where: d(n) is the expected signal, that is, the original signal containing noise;

[0035] (3) Coefficient update: w(n+1)=w(n)+μ·e(n)·x(n)

[0036] Where: μ is the step size factor, and its value range satisfies where λ max is the best eigenvalue of the autocorrelation matrix of the input signal;

[0037] S3. During high-voltage discharge detection, the noise baseline is subtracted from the original signal in real time, and the amplification gain is dynamically adjusted based on the noise intensity to prevent signal saturation.

[0038] S4. Apply a controllable high-voltage pulse signal to the object under test to stimulate partial discharge;

[0039] S5. Synchronously collect electromagnetic signals, ultrasonic signals, and optical signals generated during discharge;

[0040] S6. Perform time-frequency analysis on the electromagnetic signal to determine the phase and time window of the discharge. Calculate the direction of arrival of the ultrasonic signal. Determine the two-dimensional coordinates of the defect (X and Y axes) based on the difference in reception delay between each sensor. Analyze the intensity attenuation characteristics of the optical signal and, combined with the material transmittance parameters, construct a defect depth model (Z axis).

[0041] S7. Input the time-frequency characteristics of the electromagnetic signal, the plane coordinates of the ultrasonic wave, and the depth information of the optical signal into the data fusion module, and then generate a three-dimensional defect imaging image based on the fused data, and annotate the spatial location, size, and type of the defect;

[0042] S8. Display the three-dimensional image and detection results generated in step S7 on the display screen, and store the data in the database.

[0043] The environmental background noise signal collected in step S1 includes noises such as electromagnetic interference and mechanical vibration.

[0044] During high voltage discharge in step S3, the gain decreases by 3dB for every 10dB increase in noise.

[0045] When the controllable high-voltage pulse signal is applied in step S4, the frequency is 0.1-100 kHz and the amplitude is 1-50 kV.

[0046] The electromagnetic signal in step S5 and step S6 refers to the time domain waveform of the discharge pulse obtained by the electromagnetic sensor, the ultrasonic signal is the sound wave generated by the discharge received by the piezoelectric sensor array, and the optical signal is the discharge light radiation captured by the ultraviolet and visible light sensors.

[0047] Steps S1 to S3 include achieving environmental noise compensation through adaptive filtering and dynamic gain adjustment.

[0048] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A high-voltage discharge detection method capable of non-destructive monitoring, characterized in that: The following steps are involved: S1. Before testing, the sensor array collects the ambient background noise signal of the tested area; S2. Use adaptive filtering algorithm to extract features from noise signals and generate dynamic noise cluster model; S3. During high-voltage discharge detection, the noise baseline is subtracted from the original signal in real time, and the amplification gain is dynamically adjusted based on the noise intensity to prevent signal saturation. S4. Apply a controllable high-voltage pulse signal to the object under test to stimulate partial discharge; S5. Synchronously collect electromagnetic signals, ultrasonic signals, and optical signals generated during discharge; S6. Perform time-frequency analysis on the electromagnetic signal to determine the phase and time window of the discharge. Calculate the direction of arrival of the ultrasonic signal. Determine the two-dimensional coordinates of the defect (X and Y axes) based on the difference in reception delay between each sensor. Analyze the intensity attenuation characteristics of the optical signal and, combined with the material transmittance parameters, construct a defect depth model (Z axis). S7. Input the time-frequency characteristics of the electromagnetic signal, the plane coordinates of the ultrasonic wave, and the depth information of the optical signal into the data fusion module, and then generate a three-dimensional defect imaging image based on the fused data, and annotate the spatial location, size, and type of the defect; S8. Display the three-dimensional image and detection results generated in step S7 on the display screen, and store the data in the database.

2. The high-voltage discharge detection method capable of non-destructive monitoring according to claim 1, characterized in that: The environmental background noise signal collected in step S1 includes noises such as electromagnetic interference and mechanical vibration.

3. The high-voltage discharge detection method capable of non-destructive monitoring according to claim 1, characterized in that: During the high voltage discharge in step S3, the gain decreases by 3dB for every 10dB increase in noise.

4. The high-voltage discharge detection method capable of non-destructive monitoring according to claim 1, characterized in that: When the controllable high-voltage pulse signal is applied in step S4, the frequency is 0.1-100 kHz and the amplitude is 1-50 kV.

5. The high-voltage discharge detection method capable of non-destructive monitoring according to claim 1, characterized in that: The electromagnetic signal in step S5 and step S6 refers to the time domain waveform of the discharge pulse obtained by the electromagnetic sensor, the ultrasonic signal is the sound wave generated by the discharge received by the piezoelectric sensor array, and the optical signal is the discharge light radiation captured by the ultraviolet and visible light sensors.

6. The high-voltage discharge detection method capable of non-destructive monitoring according to claim 1, characterized in that: Steps S1 to S3 include implementing environmental noise compensation through adaptive filtering and dynamic gain adjustment.