A multi-layer composite medium power distribution network signal resolution method based on acoustic resonance spectrum

Abstract

The application discloses a kind of based on acoustic resonance spectrum multilayer composite medium power distribution network signal resolution method, belong to electric power technical field, this method includes obtaining multilayer composite medium historical detection data;From multilayer composite medium historical detection data, extract initial acoustic resonance spectrum data, and the initial acoustic resonance spectrum data is preprocessed, obtain the acoustic resonance spectrum data after preprocessing;Determine key feature point and characteristic parameter;Based on key feature point, the characteristic parameter is fitted with material characteristic, and the relationship model of characteristic parameter and material characteristic is obtained;Obtain the relationship between signal complexity and defect type;Obtain the ultrasonic signal to be measured, based on the relationship model of characteristic parameter and material characteristic and the relationship between signal complexity and defect type, respectively, obtain defect size prediction result and defect type prediction result.The application realizes the quantitative characterization of defect type and size.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of electric power, and particularly relates to a multi-layer composite medium power distribution network signal distinguishing method based on acoustic resonance spectrum. BACKGROUND

[0002] In the power distribution network system, the medium voltage switch complete equipment plays a core role in power transmission and distribution, and its stability and reliability are crucial for ensuring the safe operation of the power grid. However, these devices often contain multi-layer composite media, which may gradually develop small but potentially dangerous defects such as delamination, cracks, inclusions, and changes in material properties due to material aging, mechanical stress, environmental factors (such as temperature and humidity changes), and repeated effects of electrical load during long-term power transmission and distribution. In order to find these potential dangers, ultrasonic detection methods are generally used for detection.

[0003] Traditional ultrasonic detection methods face many challenges in detecting these early small defects. On the one hand, due to the complexity of the structure and the diversity of the materials of the multi-layer composite medium, the propagation characteristics of the ultrasonic signal in it become difficult to predict and control, resulting in significant signal attenuation, scattering and reflection phenomena, increasing the difficulty of defect detection. On the other hand, the operating environment of the power distribution network is often accompanied by complex working noise such as electromagnetic interference and mechanical vibration, which can seriously interfere with the collection and analysis of ultrasonic signals, making it difficult for traditional methods to effectively distinguish between defect signals and noise signals. SUMMARY

[0004] In view of the above-mentioned deficiencies in the prior art, the multi-layer composite medium power distribution network signal distinguishing method based on acoustic resonance spectrum provided by the application realizes quantitative characterization of defect type and size.

[0005] In order to achieve the above-mentioned application purposes, the technical scheme adopted by the application is as follows: a multi-layer composite medium power distribution network signal distinguishing method based on acoustic resonance spectrum, comprising:

[0006] Obtaining historical detection data of the multi-layer composite medium;

[0007] Extracting initial acoustic resonance spectrum data from the historical detection data of the multi-layer composite medium, and preprocessing the initial acoustic resonance spectrum data to obtain preprocessed acoustic resonance spectrum data;

[0008] Using a feature extraction algorithm to extract feature points from the preprocessed acoustic resonance spectrum data, and determining key feature points and feature parameters;

[0009] Based on the key feature points, fitting the feature parameters and material properties to obtain a relationship model of the feature parameters and material properties;

[0010] According to the multi-layer composite medium historical detection data, the relationship between the signal complexity and the defect type is obtained.

[0011] The to-be-tested ultrasonic signal is obtained, and based on the relationship model of the characteristic parameters and the material characteristics and the relationship between the signal complexity and the defect type, a defect size prediction result and a defect type prediction result are respectively obtained.

[0012] Further, the multi-layer composite medium historical detection data includes historical ultrasonic signals, defect types and defect sizes.

[0013] Further, the initial acoustic resonance spectrum data is extracted from the multi-layer composite medium historical detection data, and the initial acoustic resonance spectrum data is preprocessed to obtain preprocessed acoustic resonance spectrum data, specifically: according to the multi-layer composite medium historical detection data, the frequency, amplitude and modal information of the historical ultrasonic signal are extracted to obtain the initial acoustic resonance spectrum data; the initial acoustic resonance spectrum data is denoised and filtered, the amplitude of the initial acoustic resonance spectrum data is standardized, and based on the frequency, the discrete points are removed to obtain the preprocessed acoustic resonance spectrum data.

[0014] Further, the characteristic parameters include frequency, amplitude and modal information; and the material characteristics include Young's modulus, delamination area and defect size.

[0015] Further, the expression of the relationship model of the characteristic parameters and the material characteristics is:

[0016] E=a1A(f1)+a2A(f2)+...+a n M(f n )

[0017] S=b1A(f1)+b2A(f2)+...+b n M(f n )

[0018] C=c1A(f1)+c2A(f2)+...+c n M(f n )

[0019] Wherein, E is Young's modulus; a1, a2 and a n are fitting coefficients of the characteristic parameters and Young's modulus; A(·) is an amplitude function with respect to frequency; f1 is the frequency of the first key feature point; f2 is the frequency of the second key feature point; M(·) is a modal function with respect to frequency; f n is the frequency of the n-th key feature point; S is the delamination area; b1, b2 and b n are fitting coefficients of the characteristic parameters and the delamination area; C is the defect size; c1, c2 and c n are fitting coefficients of the characteristic parameters and the defect size.

[0020] Further, the relationship between signal complexity and defect characteristics is obtained according to historical detection data of the multilayer composite medium, specifically: the historical ultrasonic signal is preprocessed, and time-frequency analysis is performed on the preprocessed historical ultrasonic signal to obtain a time-frequency analysis result of the ultrasonic signal; based on the time-frequency analysis result of the ultrasonic signal, the signal complexity is calculated, the signal complexity is fitted with the defect type, and the relationship between the signal complexity and the defect type is obtained.

[0021] The present application has the advantages that: the present application collects acoustic resonance spectrum data through high-precision and high-sensitivity ultrasonic sensors, and combines data processing, feature recognition and extraction, model establishment and correlation rule determination, etc., to realize accurate detection of early micro-defects in the multilayer composite medium. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 The present application has the advantages that: the present application collects acoustic resonance spectrum data through high-precision and high-sensitivity ultrasonic sensors, and combines data processing, feature recognition and extraction, model establishment and correlation rule determination, etc., to realize accurate detection of early micro-defects in the multilayer composite medium. DETAILED DESCRIPTION

[0023] The specific embodiments of the present application are described below to facilitate understanding of the present application by those skilled in the art, but it should be clear that the present application is not limited to the scope of the specific embodiments, and for those skilled in the art, it is obvious that various changes are within the spirit and scope of the present application defined and determined by the appended claims, and all applications utilizing the concept of the present application are within the scope of protection.

[0024] As shown in the figure, in one embodiment of the present application, a multilayer composite medium power distribution network signal resolution method based on acoustic resonance spectrum includes: Figure 1

[0025] Obtain historical detection data of the multilayer composite medium;

[0026] Extract initial acoustic resonance spectrum data from the historical detection data of the multilayer composite medium, and preprocess the initial acoustic resonance spectrum data to obtain preprocessed acoustic resonance spectrum data;

[0027] Use a feature extraction algorithm to extract feature points from the preprocessed acoustic resonance spectrum data, determine key feature points and feature parameters;

[0028] Based on the key feature points, fit the feature parameters with the material characteristics to obtain a relationship model of the feature parameters and the material characteristics;

[0029] According to the historical detection data of the multilayer composite medium, the relationship between signal complexity and defect type is obtained;

[0030] ​Obtaining an ultrasonic signal to be tested, and based on a relationship model of a characteristic parameter and a material characteristic and a relationship between signal complexity and defect type, obtaining a defect size prediction result and a defect type prediction result respectively.

[0031] The multi-layer composite medium historical detection data includes historical ultrasonic signals, defect types and defect sizes.

[0032] The initial acoustic resonance spectrum data is extracted from the multi-layer composite medium historical detection data, and the initial acoustic resonance spectrum data is preprocessed to obtain preprocessed acoustic resonance spectrum data, specifically: according to the multi-layer composite medium historical detection data, the frequency, amplitude and modal information of the historical ultrasonic signal are extracted to obtain the initial acoustic resonance spectrum data; the initial acoustic resonance spectrum data is denoised and filtered, the amplitude of the initial acoustic resonance spectrum data is standardized, and discrete points are removed based on the frequency to obtain the preprocessed acoustic resonance spectrum data.

[0033] The characteristic parameters include frequency, amplitude and modal information; and the material characteristics include Young's modulus, delamination area and defect size.

[0034] The expression of the relationship model of the characteristic parameter and the material characteristic is:

[0035] E=a1A(f1)+a2A(f2)+...+a n M(f n )

[0036] S=b1A(f1)+b2A(f2)+...+b n M(f n )

[0037] C=c1A(f1)+c2A(f2)+...+c n M(f n )

[0038] Wherein, E is Young's modulus; a1, a2 and a n are fitting coefficients of the characteristic parameters and Young's modulus; A(·) is an amplitude function with respect to frequency; f1 is the frequency of the first key feature point; f2 is the frequency of the second key feature point; M(·) is a modal function with respect to frequency; f n is the frequency of the nth key feature point; S is the delamination area; b1, b2 and b n are fitting coefficients of the characteristic parameters and the delamination area; C is the defect size; c1, c2 and c n are fitting coefficients of the characteristic parameters and the defect size.

[0039] The relationship between the signal complexity and the defect feature is obtained according to historical detection data of the multilayer composite medium, specifically: historical ultrasonic signals are preprocessed, and time-frequency analysis is performed on the preprocessed historical ultrasonic signals to obtain a time-frequency analysis result of the ultrasonic signals; based on the time-frequency analysis result of the ultrasonic signals, the signal complexity is calculated, the signal complexity is fitted with the defect type, and the relationship between the signal complexity and the defect type is obtained.

Claims

1. A method for signal resolution of power distribution networks based on acoustic resonant spectroscopy of multi-layered composite media, characterized by, The method comprises the following steps: acquiring historical detection data of a multilayer composite medium; extracting initial acoustic resonance spectrum data from the historical detection data of the multilayer composite medium, and preprocessing the initial acoustic resonance spectrum data to obtain preprocessed acoustic resonance spectrum data; extracting feature points from the preprocessed acoustic resonance spectrum data by using a feature extraction algorithm, and determining key feature points and feature parameters; fitting the feature parameters and material characteristics based on the key feature points to obtain a relationship model of the feature parameters and the material characteristics; obtaining a relationship between signal complexity and defect type according to the historical detection data of the multilayer composite medium; obtaining a to-be-detected ultrasonic signal, and obtaining a defect size prediction result and a defect type prediction result based on the relationship model of the feature parameters and the material characteristics and the relationship between the signal complexity and the defect type.

2. The method of claim 1, wherein the method is based on the acoustic resonant spectrum of a multi-layer composite dielectric power distribution network signal resolution. The historical detection data of the multilayer composite medium comprises historical ultrasonic signals, defect types and defect sizes.

3. The method of claim 1, wherein the method further comprises: The initial acoustic resonance spectrum data is extracted from the historical detection data of the multilayer composite medium, and the initial acoustic resonance spectrum data is preprocessed to obtain preprocessed acoustic resonance spectrum data, specifically: frequency, amplitude and modal information of the historical ultrasonic signals are extracted from the historical detection data of the multilayer composite medium to obtain initial acoustic resonance spectrum data; the initial acoustic resonance spectrum data is denoised and filtered, the amplitude of the initial acoustic resonance spectrum data is standardized, and discrete points are removed based on the frequency to obtain the preprocessed acoustic resonance spectrum data.

4. The method of claim 1, wherein the method further comprises: The feature parameters comprise frequency, amplitude and modal information; and the material characteristics comprise Young's modulus, delamination area and defect size.

5. The method of claim 4, wherein the method further comprises: The relationship model of the feature parameters and the material characteristics has the following expression: E = a1A(f1) + a2A(f2) +... + a n M(f n ) S = b1A(f1) + b2A(f2) +... + b n M(f n ) C = c1A(f1) + c2A(f2) +... + c n M(f n ) Where E is Young's modulus; a1, a2 and a n All are fitting coefficients for characteristic parameters and Young's modulus; A(·) is the amplitude function with respect to frequency; f1 is the frequency of the first key feature point; f2 is the frequency of the second key feature point; M(·) is the modal function with respect to frequency; f n Let S be the frequency of the nth key feature point; S be the layer area; b1, b2, and b n All are fitting coefficients for characteristic parameters and layered areas; C is the defect size; c1, c2, and c... n All are fitting coefficients for characteristic parameters and defect size.

6. The method of claim 1, wherein the method further comprises: The relationship between the signal complexity and the defect characteristics is obtained according to the historical detection data of the multilayer composite medium, specifically: the historical ultrasonic signals are preprocessed, and time-frequency analysis is performed on the preprocessed historical ultrasonic signals to obtain time-frequency analysis results of the ultrasonic signals; the signal complexity is calculated based on the time-frequency analysis results of the ultrasonic signals, the signal complexity is fitted with the defect type, and the relationship between the signal complexity and the defect type is obtained.

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