Multilayer composite medium power distribution network signal distinguishing method based on acoustic resonance spectrum

Abstract

The invention discloses a multi-layer composite medium power distribution network signal distinguishing method based on an acoustic resonance spectrum, and belongs to the technical field of electric power, and the method comprises the steps: obtaining the historical detection data of a multi-layer 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; key feature points and feature parameters are determined; fitting the characteristic parameters and the material characteristics based on the key characteristic points to obtain a relation model of the characteristic parameters and the material characteristics; obtaining the relationship between the signal complexity and the defect type; and obtaining a to-be-detected ultrasonic signal, and respectively obtaining a defect size prediction result and a defect type prediction result based on the relation model of the characteristic parameters and the material characteristics and the relation between the signal complexity and the defect type. According to the invention, quantitative characterization of defect types and sizes is realized.

Description

Technical Field

[0001] The invention belongs to the field of electric power technology, and in particular relates to a multi-layer composite medium distribution network signal resolution method based on acoustic resonance spectrum. Background Art

[0002] In the distribution network system, medium-voltage switchgear plays a core role in power transmission and distribution. Its stability and reliability are crucial to ensuring the safe operation of the power grid. However, these devices often contain multiple layers of composite media. During the long-term power transmission and distribution process, these media may gradually develop tiny 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 loads. 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 tiny defects. On the one hand, due to the structural complexity and material diversity of multi-layer composite media, the propagation characteristics of ultrasonic signals in them become difficult to predict and control, resulting in significant signal attenuation, scattering and reflection, which increases the difficulty of defect detection. On the other hand, the operating environment of the distribution network is often accompanied by complex operating noise, such as electromagnetic interference, mechanical vibration, etc. These noises will seriously interfere with the collection and analysis of ultrasonic signals, making it difficult for traditional methods to effectively distinguish defect signals from noise signals. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a multi-layer composite medium distribution network signal resolution method based on acoustic resonance spectrum, which realizes the quantitative characterization of defect types and sizes.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a multi-layer composite medium distribution network signal resolution method based on acoustic resonance spectrum, comprising:

[0006] Obtain historical detection data of multi-layer composite media;

[0007] Extracting initial acoustic resonance spectrum data from 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 feature extraction algorithm, feature points are extracted from preprocessed acoustic resonance spectrum data to determine key feature points and feature parameters;

[0009] Based on the key characteristic points, the characteristic parameters and material properties are fitted to obtain the relationship model between the characteristic parameters and material properties;

[0010] Based on the historical detection data of multi-layer composite media, the relationship between signal complexity and defect type is obtained;

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

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

[0013] Furthermore, the method extracts initial acoustic resonance spectrum data from the historical detection data of the multilayer composite medium, and preprocesses the initial acoustic resonance spectrum data to obtain preprocessed acoustic resonance spectrum data, specifically: according to the historical detection data of the multilayer composite medium, extracts the frequency, amplitude and modal information of the historical ultrasonic signal to obtain the initial acoustic resonance spectrum data; denoises and filters the initial acoustic resonance spectrum data, standardizes the amplitude of the initial acoustic resonance spectrum data, and removes discrete points based on the frequency to obtain the preprocessed acoustic resonance spectrum data.

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

[0015] Furthermore, the expression of the relationship model between the characteristic parameters and the material properties 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] Where E is Young's modulus; a1, a2 and a n are the fitting coefficients of characteristic parameters and Young's modulus; A(·) is the amplitude function with respect to frequency; f1 is the frequency of the first key characteristic point; f2 is the frequency of the second key characteristic point; M(·) is the modal function with respect to frequency; f n is the frequency of the nth key feature point; S is the layered area; b1, b2 and b n are the fitting coefficients of characteristic parameters and delamination area; C is the defect size; c1, c2 and c n are the fitting coefficients of characteristic parameters and defect size.

[0020] Furthermore, the relationship between signal complexity and defect characteristics is obtained based on historical detection data of multi-layer composite media, specifically: preprocessing the historical ultrasonic signal, and performing time-frequency analysis on the preprocessed historical ultrasonic signal to obtain the time-frequency analysis result of the ultrasonic signal; based on the time-frequency analysis result of the ultrasonic signal, calculating the signal complexity, fitting the signal complexity with the defect type, and obtaining the relationship between the signal complexity and the defect type.

[0021] The beneficial effects of the present invention are as follows: the present invention collects acoustic resonance spectrum data through a high-precision and high-sensitivity ultrasonic sensor, and combines data processing, feature recognition and extraction, model building and correlation law determination to achieve accurate detection of early-stage tiny defects in multilayer composite media. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0023] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0024] like Figure 1 As shown, in one embodiment of the present invention, a multi-layer composite dielectric distribution network signal resolution method based on acoustic resonance spectrum includes:

[0025] Obtain historical detection data of multi-layer composite media;

[0026] Extracting initial acoustic resonance spectrum data from historical detection data of the multi-layer composite medium, and preprocessing the initial acoustic resonance spectrum data to obtain preprocessed acoustic resonance spectrum data;

[0027] Using feature extraction algorithm, feature points are extracted from preprocessed acoustic resonance spectrum data to determine key feature points and feature parameters;

[0028] Based on the key characteristic points, the characteristic parameters and material properties are fitted to obtain the relationship model between the characteristic parameters and material properties;

[0029] Based on the historical detection data of multi-layer composite media, the relationship between signal complexity and defect type is obtained;

[0030] The ultrasonic signal to be tested is obtained, and based on the relationship model between characteristic parameters and material properties and the relationship between signal complexity and defect type, the defect size prediction results and defect type prediction results are obtained respectively.

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

[0032] The method extracts initial acoustic resonance spectrum data from historical detection data of the multilayer composite medium, and preprocesses the initial acoustic resonance spectrum data to obtain preprocessed acoustic resonance spectrum data, specifically: extracts frequency, amplitude and modal information of historical ultrasonic signals according to historical detection data of the multilayer composite medium to obtain initial acoustic resonance spectrum data; denoises and filters the initial acoustic resonance spectrum data, standardizes the amplitude of the initial acoustic resonance spectrum data, and removes discrete points based on frequency to obtain preprocessed acoustic resonance spectrum data.

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

[0034] The expression of the relationship model between the characteristic parameters and material properties 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] Where E is Young's modulus; a1, a2 and a n are the fitting coefficients of characteristic parameters and Young's modulus; A(·) is the amplitude function with respect to frequency; f1 is the frequency of the first key characteristic point; f2 is the frequency of the second key characteristic point; M(·) is the modal function with respect to frequency; f n is the frequency of the nth key feature point; S is the layered area; b1, b2 and b n are the fitting coefficients of characteristic parameters and delamination area; C is the defect size; c1, c2 and c n are the fitting coefficients of characteristic parameters and defect size.

[0039] The method of obtaining the relationship between signal complexity and defect characteristics based on historical detection data of the multilayer composite medium is specifically as follows: preprocessing the historical ultrasonic signal, and performing time-frequency analysis on the preprocessed historical ultrasonic signal to obtain the time-frequency analysis result of the ultrasonic signal; based on the time-frequency analysis result of the ultrasonic signal, calculating the signal complexity, fitting the signal complexity with the defect type, and obtaining the relationship between the signal complexity and the defect type.

Claims

1. A signal resolution method for a multi-layer composite medium distribution network based on acoustic resonance spectrum, characterized in that: include: Obtain historical detection data of multi-layer composite media; Extracting initial acoustic resonance spectrum data from historical detection data of the multi-layer composite medium, and preprocessing the initial acoustic resonance spectrum data to obtain preprocessed acoustic resonance spectrum data; Using feature extraction algorithm, feature points are extracted from preprocessed acoustic resonance spectrum data to determine key feature points and feature parameters; Based on the key characteristic points, the characteristic parameters and material properties are fitted to obtain the relationship model between the characteristic parameters and material properties; Based on the historical detection data of multi-layer composite media, the relationship between signal complexity and defect type is obtained; The ultrasonic signal to be tested is obtained, and based on the relationship model between characteristic parameters and material properties and the relationship between signal complexity and defect type, the defect size prediction results and defect type prediction results are obtained respectively.

2. The method for distinguishing signals in a multi-layer composite medium distribution network based on acoustic resonance spectrum according to claim 1 is characterized in that: The multi-layer composite medium historical detection data includes historical ultrasonic signals, defect types and defect sizes.

3. The signal resolution method for a multi-layer composite medium distribution network based on acoustic resonance spectrum according to claim 1 is characterized in that: The method extracts initial acoustic resonance spectrum data from historical detection data of the multilayer composite medium, and preprocesses the initial acoustic resonance spectrum data to obtain preprocessed acoustic resonance spectrum data, specifically: extracts frequency, amplitude and modal information of historical ultrasonic signals according to historical detection data of the multilayer composite medium to obtain initial acoustic resonance spectrum data; denoises and filters the initial acoustic resonance spectrum data, standardizes the amplitude of the initial acoustic resonance spectrum data, and removes discrete points based on frequency to obtain preprocessed acoustic resonance spectrum data.

4. The signal resolution method for a multi-layer composite medium distribution network based on acoustic resonance spectrum according to claim 1 is characterized in that: The characteristic parameters include frequency, amplitude and modal information; the material properties include Young's modulus, delamination area and defect size.

5. The method for distinguishing signals in a multi-layer composite medium distribution network based on acoustic resonance spectrum according to claim 4 is characterized in that: The expression of the relationship model between the characteristic parameters and material properties is: <h2 style=";text-align:left;direction:ltr">E = a1A(f1)+a2A(f2)+...+a<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> M(f<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ) 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 are the fitting coefficients of characteristic parameters and Young's modulus; A(·) is the amplitude function with respect to frequency; f1 is the frequency of the first key characteristic point; f2 is the frequency of the second key characteristic point; M(·) is the modal function with respect to frequency; f n is the frequency of the nth key feature point; S is the layered area; b1, b2 and b n are the fitting coefficients of characteristic parameters and delamination area; C is the defect size; c1, c2 and c n are the fitting coefficients of characteristic parameters and defect size.

6. The method for distinguishing signals in a multi-layer composite medium distribution network based on acoustic resonance spectrum according to claim 1, characterized in that: The method of obtaining the relationship between signal complexity and defect characteristics based on historical detection data of the multilayer composite medium is specifically as follows: preprocessing the historical ultrasonic signal, and performing time-frequency analysis on the preprocessed historical ultrasonic signal to obtain the time-frequency analysis result of the ultrasonic signal; based on the time-frequency analysis result of the ultrasonic signal, calculating the signal complexity, fitting the signal complexity with the defect type, and obtaining the relationship between the signal complexity and the defect type.

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