Multi-type interference pulse suppression method based on signal waveform analysis

Abstract

The invention discloses an interference pulse suppression technology, and aims to provide a multi-type interference pulse suppression method based on signal waveform analysis, and the key point of the technical scheme is that the method comprises the steps: S1, building a partial discharge test system, and collecting and measuring partial discharge waveforms of different defect types; s2, collecting a partial discharge waveform of the to-be-detected cable; s3, preprocessing the collected partial discharge pulses of different defect types and the partial discharge waveform of the cable to be detected, analyzing the pulse waveforms corresponding to different defects and noise sources, and using an envelope line and a sine function to construct a mathematical model of partial discharge signals to extract pulse waveform characteristics; and S4, drawing a feature distribution two-dimensional diagram (beta-alpha and Ap-At) and a three-dimensional diagram (f-beta-alpha and f-Ap-At) through the extracted feature parameters, establishing an interference pulse and defect knowledge base, and realizing effective separation of partial discharge signals and interference pulses. The method is suitable for the technical field of interference pulse suppression.

Description

Technical Field

[0001] The present invention relates to an interference pulse suppression technology, and more specifically, to a multi-type interference pulse suppression method based on signal waveform analysis. Background Art

[0002] According to the analysis of the degradation patterns of different insulating materials in high-voltage electrical systems, partial discharge is extremely common in all insulating materials. Cables are widely used in various fields of the national economy due to their high reliability, small footprint, large capacity and underground burial characteristics, providing important support for various industries, national defense construction and major construction projects. Cable failures often cause losses in power transmission, which seriously threatens power supply security.

[0003] The interference types in the cable partial discharge measurement signal are complex and diverse with different characteristics, including periodic interference pulses and random interference pulses. Their time domain waveforms and spectra are highly similar to partial discharge pulse signals. The presence of interference pulses increases the difficulty of partial discharge pulse extraction and identification, affecting the accuracy of insulation fault judgment. When conducting partial discharge online testing, the PRPD mode cannot effectively suppress interference pulses in many cases, so it is necessary to apply effective technology to filter background noise and classify the pulses corresponding to the sources involved. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a multi-type interference pulse suppression method based on signal waveform analysis, which can suppress most interference pulses before multi-source local discharge separation, can effectively simplify the local discharge data set, and is conducive to improving the accuracy of interference pulse suppression and subsequent multi-source discharge separation.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for suppressing multi-type interference pulses based on signal waveform analysis, comprising the following steps:

[0006] S1. Establish a partial discharge test system to collect and measure partial discharge waveforms of different defect types;

[0007] S2, collecting partial discharge waveform of the cable to be tested;

[0008] S3, pre-processing the collected partial discharge pulses of different defect types and partial discharge waveforms of the cable to be tested respectively, analyzing the pulse waveforms corresponding to different defects and noise sources, and constructing a mathematical model of the partial discharge signal using envelope curves and sine functions to extract pulse waveform features;

[0009] S4. Draw the two-dimensional (β-α and Ap-At) and three-dimensional (f-β-α and f-Ap-At) characteristic distribution graphs through the extracted characteristic parameters, establish the interference pulse and defect knowledge base, and realize the effective separation of partial discharge signals and interference pulses.

[0010] The present invention is further configured as follows: in step S1:

[0011] The HFCT sensor is installed on the grounding wire of the cable end connected to the high voltage generator, and the partial discharge signal of the HFCT sensor is collected by the data acquisition equipment.

[0012] The present invention is further configured as follows: in step S2:

[0013] Design various types of defect models, replace the test product with the defect model, and obtain the local discharge signal of each defect according to the detection steps of claim 2.

[0014] The present invention is further configured as follows: in step S3:

[0015] Wavelet filters are used to pre-process a large number of collected pulses to reduce random noise signals in the detection signals.

[0016] The present invention is further configured as follows: in step S3:

[0017] For the pulse detected by the wavelet filter, the optimal parameters are calculated. The parameters I0, α, β and t0 are selected as characteristic parameters to characterize the waveform of the partial discharge signal, and the envelope function h3(t) is determined by applying the least squares method to achieve the best fit for the waveform, which is calculated by the following formula:

[0018]

[0019] (1).

[0020] The present invention is further configured as follows: in step S3:

[0021] Select the parameters frequency f and phase As a characteristic parameter, the sine function g(t) is well fitted at the zero crossing point of the collected signal, which is calculated by the following formula:

[0022]

[0023] Among them, the frequency f is determined by analyzing the spectrum of the sampled signal and applying Fourier transform F(f), which is specifically calculated by the following formula:

[0024]

[0025] f corresponds to the arithmetic mean of the values ​​in the interval where the spectrum energy is more significant. There is a cutoff frequency at the high frequency end and the low frequency end. This interval determines the dominant frequency range with 70% of the maximum spectrum amplitude as the limit.

[0026] The present invention is further configured as follows: in step S3:

[0027] When implementing the processing, the ground current to be measured by the sensor can be composed of a mathematical model i consisting of an envelope function h3(t) modulated by a sine function g(t) e (t) to characterize:

[0028]

[0029] The present invention is further configured as follows: in step S3:

[0030] By analyzing the normalized envelope function h pu * (t) Obtain two new parameters related to the area of ​​the front and rear regions, improve the classification process, and obtain lower discreteness. The two new parameters are peak area A p And the tail area A t . Peak area A p Corresponding function h pu * (t) The area from its beginning to the maximum amplitude value, the tail area A t The response function h pu * (t) from its maximum amplitude to the end area,

[0031] Normalized h pu * (t) is calculated by the following formula:

[0032]

[0033] The peak value of the signal I p Calculated by the following formula:

[0034]

[0035] Reach peak I p Time t p Calculated by the following formula:

[0036]

[0037] The present invention is further configured as follows: in step S4:

[0038] According to the characteristic parameters f, α, β, A calculated during the pulse classification process p and A t, draw a two-dimensional graph of the characteristic distribution (β-α and A p -A t ) and three-dimensional graphs (f-β-α and fA p -A t ), the 3D plot can be manually rotated in the software application to visually discern the best separation between clusters, allowing for efficient classification of discharges and interferences.

[0039] The present invention is further configured as follows: the data detection device includes a voltage regulator, a protection resistor, a transformer, a capacitive voltage divider, an HFCT sensor and a digital oscilloscope. Within the frequency range (0,1-20MHz), the proposed interference pulse suppression method can successfully suppress most interference pulses.

[0040] The beneficial effects of the present invention are:

[0041] 1. The process of obtaining classification parameters is fully automatic, i.e. no prior settings in the measuring instrument are required;

[0042] 2. Suppressing most interference pulses before multi-source partial discharge separation can effectively simplify the partial discharge data set, which is beneficial to the accuracy of subsequent multi-source discharge separation and can improve the accuracy of insulation fault judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The figure is a principle block diagram of the method for suppressing multiple types of interference pulses based on signal waveform analysis of the present invention.

[0044] Figure 2 It is a schematic diagram of the detection of the multi-type interference pulse suppression method based on signal waveform analysis of the present invention. DETAILED DESCRIPTION

[0045] Reference Figure 1-2 The embodiment of the method for suppressing multiple types of interference pulses based on signal waveform analysis of the present invention is further described.

[0046] For ease of explanation, spatial relative terms such as "upper", "lower", "left", "right" and the like are used in the embodiments to illustrate the relationship of one element or feature shown in the figure relative to another element or feature. It should be understood that, in addition to the orientation shown in the figure, the spatial terms are intended to include different orientations of the device in use or operation. For example, if the device in the figure is inverted, the element described as being "under" other elements or features will be positioned "on" other elements or features. Therefore, the exemplary term "under" can include both upper and lower orientations. The device can be positioned in other ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used here can be interpreted accordingly.

[0047] Furthermore, relational terms such as “first” and “second” and the like are merely used to distinguish one component from another having the same name, but do not necessarily require or imply any such actual relationship or order between these components.

[0048] Figure 1 to Figure 2 A multi-type interference pulse suppression method based on signal waveform analysis is shown, and the steps include: establishing a partial discharge test system, collecting and measuring partial discharge waveforms of different defect types and cables to be tested; pre-processing the collected partial discharge waveforms respectively, using envelope curves and sine functions to construct a mathematical model of partial discharge signals to extract pulse waveform features; drawing a two-dimensional feature distribution graph (β-α and Ap-At) and a three-dimensional graph (f-β-α and f-Ap-At) through the extracted feature parameters, establishing an interference pulse and defect knowledge base, and realizing effective separation of partial discharge signals and interference pulses;

[0049] The specific steps are as follows:

[0050] S1. Establish a partial discharge test system to collect and measure partial discharge waveforms of different defect types;

[0051] S2, collecting partial discharge waveform of the cable to be tested;

[0052] S3, pre-processing the collected partial discharge pulses of different defect types and partial discharge waveforms of the cable to be tested respectively, analyzing the pulse waveforms corresponding to different defects and noise sources, and constructing a mathematical model of the partial discharge signal using envelope curves and sine functions to extract pulse waveform features;

[0053] S4. Draw the two-dimensional (β-α and Ap-At) and three-dimensional (f-β-α and f-Ap-At) characteristic distribution graphs through the extracted characteristic parameters, establish the interference pulse and defect knowledge base, and realize the effective separation of the partial discharge signal and the interference pulse;

[0054] In step S1:

[0055] The HFCT sensor is installed on the ground wire of the cable end connected to the high voltage generator, and the partial discharge signal of the HFCT sensor is collected by the data acquisition equipment;

[0056] In step S2:

[0057] Design various types of defect models, replace the test product with the defect model, and obtain the partial discharge signal of each defect according to the detection step of claim 2;

[0058] In step S3:

[0059] Use wavelet filter to pre-process a large number of collected pulses to reduce random noise signals in the detection signals;

[0060] In step S3:

[0061] For the pulse detected by the wavelet filter, the optimal parameters are calculated. The parameters I0, α, β and t0 are selected as characteristic parameters to characterize the waveform of the partial discharge signal, and the envelope function h3(t) is determined by applying the least squares method to achieve the best fit for the waveform, which is calculated by the following formula:

[0062] (1);

[0064] In step S3:

[0065] Select the parameters frequency f and phase As a characteristic parameter, the sine function g(t) is well fitted at the zero crossing point of the collected signal, which is calculated by the following formula:

[0066]

[0067] Among them, the frequency f is determined by analyzing the spectrum of the sampled signal and applying Fourier transform F(f), which is specifically calculated by the following formula:

[0068]

[0069] f corresponds to the arithmetic mean of the values ​​in the interval where the spectrum energy is more significant. There is a cutoff frequency at the high frequency end and the low frequency end. This interval is limited to 70% of the maximum spectrum amplitude to determine the dominant frequency range;

[0070] In step S3:

[0071] When implementing the processing, the ground current to be measured by the sensor can be composed of a mathematical model i consisting of an envelope function h3(t) modulated by a sine function g(t) e (t) to characterize:

[0072]

[0073] In step S3:

[0074] By analyzing the normalized envelope function h pu * (t) Obtain two new parameters related to the area of ​​the front and rear regions, improve the classification process, and obtain lower discreteness. The two new parameters are peak area A p And the tail area A t . Peak area A p Corresponding function h pu * (t) The area from its beginning to the maximum amplitude value, the tail area A tThe response function h pu * (t) from its maximum amplitude to the end area,

[0075] Normalized h pu * (t) is calculated by the following formula:

[0076]

[0077] The peak value of the signal I p Calculated by the following formula:

[0078]

[0079] Reach peak I p Time t p Calculated by the following formula:

[0080]

[0081] In step S4:

[0082] According to the characteristic parameters f, α, β, A calculated during the pulse classification process p and A t , draw a two-dimensional graph of the characteristic distribution (β-α and A p -A t ) and three-dimensional graphs (f-β-α and fA p -A t ), the 3D plot can be manually rotated in the software application to visually discern the best separation between clusters, thus achieving efficient classification of discharges and interferences;

[0083] The data detection equipment includes a voltage regulator, a protection resistor, a transformer, a capacitor voltage divider, a HFCT sensor and a digital oscilloscope. Within the frequency range (0,1-20MHz), the proposed interference pulse suppression method can successfully suppress most of the interference pulses.

[0084] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Common changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for suppressing multi-type interference pulses based on signal waveform analysis, characterized in that: The following steps are involved: S1. Establish a partial discharge test system to collect and measure partial discharge waveforms of different defect types; S2, collecting partial discharge waveform of the cable to be tested; S3, pre-processing the collected partial discharge pulses of different defect types and partial discharge waveforms of the cable to be tested respectively, analyzing the pulse waveforms corresponding to different defects and noise sources, and constructing a mathematical model of the partial discharge signal using envelope curves and sine functions to extract pulse waveform features; S4. Draw the two-dimensional (β-α and Ap-At) and three-dimensional (f-β-α and f-Ap-At) characteristic distribution graphs through the extracted characteristic parameters, establish the interference pulse and defect knowledge base, and realize the effective separation of partial discharge signals and interference pulses.

2. The method for suppressing multi-type interference pulses based on signal waveform analysis according to claim 1, characterized in that: In step S1: The HFCT sensor is installed on the grounding wire of the cable end connected to the high voltage generator, and the partial discharge signal of the HFCT sensor is collected by the data acquisition equipment.

3. The method for suppressing multi-type interference pulses based on signal waveform analysis according to claim 2 is characterized in that: In step S2: Design various types of defect models, replace the test product with the defect model, and obtain the local discharge signal of each defect according to the detection steps of claim 2.

4. The method for suppressing multi-type interference pulses based on signal waveform analysis according to claim 3 is characterized in that: In step S3: Wavelet filters are used to pre-process a large number of collected pulses to reduce random noise signals in the detection signals.

5. The method for suppressing multi-type interference pulses based on signal waveform analysis according to claim 4 is characterized in that: In step S3: For the pulse detected by the wavelet filter, the optimal parameters are calculated. The parameters I0, α, β and t0 are selected as characteristic parameters to characterize the waveform of the partial discharge signal, and the envelope function h3(t) is determined by applying the least squares method to achieve the best fit for the waveform, which is calculated by the following formula:

6. The method for suppressing multiple types of interference pulses based on signal waveform analysis according to claim 5, characterized in that: In step S3: Select the parameters frequency f and phase As a characteristic parameter, the sine function g(t) is well fitted at the zero crossing point of the collected signal, which is calculated by the following formula: Among them, the frequency f is determined by analyzing the spectrum of the sampled signal and applying Fourier transform F(f), which is specifically calculated by the following formula: f corresponds to the arithmetic mean of the values ​​in the interval where the spectrum energy is more significant. There is a cutoff frequency at the high frequency end and the low frequency end. This interval determines the dominant frequency range with 70% of the maximum spectrum amplitude as the limit.

7. The method for suppressing multiple types of interference pulses based on signal waveform analysis according to claim 6, characterized in that: In step S3: When implementing the processing, the ground current to be measured by the sensor can be composed of a mathematical model i consisting of an envelope function h3(t) modulated by a sine function g(t) e (t) to characterize: i e (t)=g(t)·h3(t) (4)。 8. The method for suppressing multi-type interference pulses based on signal waveform analysis according to claim 7, characterized in that: In step S3: By analyzing the normalized envelope function h pu * (t) Obtain two new parameters related to the area of ​​the front and rear regions, improve the classification process, and obtain lower discreteness. The two new parameters are peak area A p And the tail area A t . Peak area A p Corresponding function h pu * (t) The area from its beginning to the maximum amplitude value, the tail area A t The response function h pu * (t) from its maximum amplitude to the end area, Normalized h pu * (t) is calculated by the following formula: The peak value of the signal I p Calculated by the following formula: Reach peak I p Time t p Calculated by the following formula:

9. The method for suppressing multiple types of interference pulses based on signal waveform analysis according to claim 8, characterized in that: In step S4: According to the characteristic parameters f, α, β, A calculated during the pulse classification process p and A t , draw a two-dimensional graph of the characteristic distribution (β-α and A p -A t ) and three-dimensional graphs (f-β-α and fA p -A t ), the 3D plot can be manually rotated in the software application to visually discern the best separation between clusters, allowing for efficient classification of discharges and interferences.

10. The method for suppressing multiple types of interference pulses based on signal waveform analysis according to claim 9, characterized in that: The data detection device comprises a voltage regulator, a protection resistor, a transformer, a capacitive voltage divider, an HFCT sensor and a digital oscilloscope. Within the frequency range (0,1-20MHz), the proposed interference pulse suppression method can successfully suppress most interference pulses.

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