Full-shielding anti-interference signal processing system and method for post insulator detection

Abstract

The invention discloses a full-shielding anti-interference signal processing system and method for post insulator detection, and relates to the technical field of post insulator detection. According to the full-shielding anti-interference signal processing system and method for post insulator detection, multiple frequencies are generated by arranging the signal excitation module, the signal acquisition module, the signal processing module, the defect identification module and the database management module. The multi-modal vibration response of the post insulator is stimulated through the multi-modal vibration sensor and excitation signals with different intensities, the defect detection sensitivity is improved, capturing and preprocessing of vibration response signals are achieved, and high-quality vibration characteristic parameters are obtained. Frequency spectrum extraction and modal analysis are carried out through the digital signal processor, accurate modal characteristic parameters are generated and compared with a preset defect characteristic library, rapid defect recognition is achieved through a frequency spectrum matching algorithm, a detection report is generated, the detection sensitivity and the anti-interference capacity are effectively improved, and the detection accuracy is improved. And the detection stability and accuracy in a high-pressure environment are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of post insulator detection, and in particular to a fully shielded anti-interference signal processing system and method for post insulator detection. Background Art

[0002] In the operation and maintenance of power systems, the safety performance of pillar porcelain insulators directly affects the stability of the power grid and the reliability of equipment. During long-term use, high-voltage pillar porcelain insulators may develop internal defects or cracks due to material aging, environmental impact or mechanical stress. If these defects are not discovered in time, they may cause serious power accidents, leading to unplanned power outages, equipment damage and even major economic losses. Therefore, real-time detection technology for pillar porcelain insulators has become an important demand in power system maintenance. The vibroacoustic method has gradually attracted attention because it can detect the quality characteristics of insulators in a live state, greatly improving safety and efficiency while ensuring detection accuracy.

[0003] At present, the traditional technologies used for the detection of pillar porcelain insulators mainly include ultrasonic detection and infrared detection. However, these methods have certain limitations. Ultrasonic detection usually requires power outage operation, which cannot meet the needs of real-time online detection, and the detection process is time-consuming and labor-intensive; infrared detection is greatly affected by the external environment, especially when the weather conditions are poor or the background temperature is high, the detection accuracy is difficult to guarantee. In addition, the existing vibration and acoustic detection methods mostly rely on manual judgment of defects, which is not only inefficient but also inaccurate. The existing methods still lack a signal processing system with full shielding and anti-interference characteristics, which makes the signal quality and stability in high-voltage environments insufficient, and it is difficult to fully adapt to the application needs in complex on-site environments. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a fully shielded anti-interference signal processing system and method for post insulator detection, which solves the problems of the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a fully shielded anti-interference signal processing system for post insulator detection, comprising the following modules: a signal excitation module, a signal acquisition module, a signal processing module, a defect recognition module, and a database management module; the signal excitation module is used to generate excitation signals of multiple frequencies and different intensities to excite the vibration modes of the post porcelain insulators; the signal acquisition module is used to capture the vibration response signal generated by the post porcelain insulator after being excited by the signal excitation module, and perform preprocessing to obtain vibration characteristic parameters; the signal processing module is used to perform spectrum feature extraction and modal analysis on the vibration characteristic parameters through a digital signal processor to generate modal characteristic parameters; the defect recognition module is used to compare the modal characteristic parameters with a preset defect feature library, identify the defect characteristics of the post porcelain insulator, obtain the defect index through a spectrum feature matching algorithm, and generate a defect detection report; the database management module is used to store and manage the spectrum feature data of insulator defects.

[0006] Furthermore, the vibration response signal includes a frequency response signal, a vibration acceleration and displacement signal, a time domain signal, noise and an interference signal.

[0007] Furthermore, the specific process of preprocessing the vibration response signal and obtaining the vibration characteristic parameters is as follows: bandpass filtering the vibration response signal to filter out noise and irrelevant frequency band signals and highlight the vibration signal of the target frequency band; amplifying the filtered signal to enhance the effective amplitude of the signal; applying Fourier transform to perform spectrum analysis on the vibration signal to extract frequency response characteristics; obtaining vibration acceleration and displacement time domain characteristic parameters through time domain analysis method; combining the spectrum characteristics with the time domain characteristics to generate vibration characteristic parameters.

[0008] Furthermore, the specific process of extracting spectral features and performing modal analysis on vibration characteristic parameters through a digital signal processor is as follows: performing secondary spectral analysis on the vibration characteristic parameters, and obtaining the cepstrum characteristics of the vibration response through inverse Fourier transform; performing multi-scale analysis on the spectral characteristics by adopting wavelet transform, and extracting local characteristic information of different frequency bands; extracting the natural frequency and damping ratio modal parameters through the modal parameter identification algorithm, and generating the modal spectrum of the post insulator.

[0009] Furthermore, the specific process of generating modal characteristic parameters is as follows: extract the natural frequency, damping ratio and modal vibration shape information of each mode according to spectrum analysis and cepstrum analysis, and perform normalization processing; eliminate noise interference modes through modal screening algorithm and retain key modal characteristics; fuse multi-modal characteristics to form composite modal characteristic parameters to enhance the description ability of insulator status, and store the generated modal characteristic parameters in the database management module.

[0010] Furthermore, the modal feature parameters are compared with a preset defect feature library. The specific process of identifying the defect features of the pillar porcelain insulator is as follows: calling the stored defect feature library from the database management module, including the standardized modal feature parameters of various defects; comparing the modal feature parameters extracted in real time with the parameter set in the defect feature library item by item; analyzing the matching degree based on the comparison result, judging whether there are defects that meet the features, and recording the matching details; when a match that meets the defect features is detected, generating a preliminary defect identification result.

[0011] Furthermore, the specific process of obtaining the defect index through the spectrum feature matching algorithm is as follows: the spectrum feature matching algorithm is used to quantify the similarity coefficient between the modal characteristics and the defect characteristics, and the defect similarity index is calculated; the similarity coefficients of each frequency band are integrated, and a weighted defect index is generated according to the preset weight coefficient; according to the defect index and the set threshold, the defect degree is evaluated and the results are stored in a graded manner.

[0012] Furthermore, the database management module is used to store and manage the insulator defect spectrum characteristic data, including the excitation source resonance frequency, sampling rate, excitation source maximum output power, and single insulator energization detection time.

[0013] The invention discloses a fully shielded anti-interference signal processing method for post insulator detection, comprising the following steps: S1. generating excitation signals of multiple frequencies and different intensities to excite the vibration modes of post porcelain insulators; S2. capturing the vibration response signal generated by the post porcelain insulator after being excited by the signal excitation module, and performing preprocessing to obtain vibration characteristic parameters; S3. performing spectrum feature extraction and modal analysis on the vibration characteristic parameters through a digital signal processor to generate modal characteristic parameters; S4. comparing the modal characteristic parameters with a preset defect feature library to identify the defect characteristics of the post porcelain insulators, obtaining the defect index through a spectrum feature matching algorithm, and generating a defect detection report; S5. storing and managing the spectrum characteristic data of insulator defects.

[0014] The present invention has the following beneficial effects:

[0015] (1) The fully shielded anti-interference signal processing system for pillar insulator detection has a signal excitation module that generates excitation signals of multiple frequencies and different intensities to excite the vibration modes of the pillar porcelain insulators. The signal acquisition module captures the vibration response signal and pre-processes it to obtain vibration characteristic parameters, thereby stimulating various modal vibration responses, improving the system's response sensitivity to various defects, and ensuring stable and accurate data acquisition, providing a guarantee for subsequent analysis.

[0016] (2) The fully shielded anti-interference signal processing method for post insulator detection uses a digital signal processor to extract spectrum features and perform modal analysis on vibration characteristic parameters to generate modal characteristic parameters; these parameters are compared with a preset defect feature library, and the defect index is calculated based on a spectrum feature matching algorithm to quickly identify and generate a test report, thereby achieving efficient and accurate defect identification; the insulator defect spectrum feature data is stored and managed to facilitate data support for subsequent fault analysis and maintenance decisions.

[0017] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention is a flow chart of a fully shielded anti-interference signal processing system for post insulator detection.

[0019] Figure 2 The present invention is a flow chart of a fully shielded anti-interference signal processing system for post insulator detection. DETAILED DESCRIPTION

[0020] The embodiments of the present application solve the problems in the prior art of signal susceptibility to interference, insufficient detection accuracy, and difficulty in achieving real-time online detection when detecting post insulator defects under high voltage environments through a fully shielded anti-interference signal processing system and method for post insulator detection.

[0021] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0022] Generate excitation signals of multiple frequencies and different intensities to stimulate the vibration modes of the post porcelain insulators.

[0023] The vibration response signal generated by the rear support porcelain insulator after being excited by the signal excitation module is captured and preprocessed to obtain the vibration characteristic parameters.

[0024] The frequency spectrum feature extraction and modal analysis of the vibration characteristic parameters are performed through a digital signal processor to generate modal characteristic parameters.

[0025] The modal characteristic parameters are compared with the preset defect feature library to identify the defect characteristics of the pillar porcelain insulator, and the defect index is obtained through the spectrum feature matching algorithm to generate a defect detection report.

[0026] Store and manage insulator defect spectrum characteristic data.

[0027] See also Figure 1The embodiment of the present invention provides a technical solution: a fully shielded anti-interference signal processing system for post insulator detection, comprising the following modules: a signal excitation module, a signal acquisition module, a signal processing module, a defect recognition module, and a database management module; the signal excitation module is used to generate excitation signals of multiple frequencies and different intensities to excite the vibration mode of the post porcelain insulator; the signal acquisition module is used to capture the vibration response signal generated by the post porcelain insulator after being excited by the signal excitation module, and perform preprocessing to obtain vibration characteristic parameters; the signal processing module is used to perform spectrum feature extraction and modal analysis on the vibration characteristic parameters through a digital signal processor to generate modal characteristic parameters; the defect recognition module is used to compare the modal characteristic parameters with a preset defect feature library, identify the defect characteristics of the post porcelain insulator, obtain the defect index through a spectrum feature matching algorithm, and generate a defect detection report; the database management module is used to store and manage the spectrum feature data of insulator defects.

[0028] In this embodiment, in the power system, the post insulator is a porcelain or composite component used to support and isolate high-voltage lines, ensuring that the current can be transmitted stably without contacting other conductive parts. The integrity of the post insulator directly affects the safety and reliability of the power system. Fully shielded anti-interference signal processing system: This means that the system has the function of shielding external electromagnetic interference and can ensure the stability of signal acquisition and processing in a strong electromagnetic environment. This system has enhanced anti-interference capabilities in design to ensure that the signal processing module is protected from external electromagnetic interference when working in a high-voltage environment, thereby improving the accuracy of detection. Signal excitation module: This module generates an excitation signal of a specific frequency and intensity to stimulate the vibration response of the post insulator. Multi-frequency and different intensity excitation signals are used to detect defects of different characteristics inside the insulator, and reflect the internal state of the insulator more comprehensively through different vibration modes. Vibration mode: In this system, the vibration mode refers to the vibration characteristics exhibited by the insulator after being subjected to an excitation signal of a specific frequency and intensity. Different defects will cause differences in vibration modes, so that defects inside the insulator can be identified through modal analysis. Signal acquisition module: This module is used to capture the vibration response signal generated by the insulator after being excited and pre-process the signal. Pre-processing refers to the noise reduction, filtering and other processing of the collected signal to ensure the accuracy and clarity of the data, laying the foundation for subsequent analysis. Digital signal processor (DSP): A digital signal processor is a special microprocessor designed for rapid processing and conversion of digital signals. In this system, the DSP is used to extract the spectral features and modal features in the vibration signal to help identify possible defects in the insulator. Spectral feature extraction: This refers to converting the collected vibration signal into spectral data to reveal the distribution of each frequency component in the vibration signal. The vibration response of different defects will produce different spectral features. By analyzing these features, the state of the insulator can be judged. Defect identification module: This module is used to compare and judge the results of the spectral analysis, and identify defects by comparing with the preset defect feature library. The defect feature library contains the modal feature parameters of various known defects. The defect identification module determines whether the insulator has defects by comparing these parameters. Spectrum feature matching algorithm: This algorithm is used to compare the spectrum features of the vibration signal with the data in the defect feature library, calculate the "defect index" by analyzing the matching degree, judge the possibility and severity of the defect, and generate the corresponding inspection report. Database management module: This module is used to store and manage the spectrum feature data generated during the insulator inspection process to form a defect feature library. The defect feature library is a data set formed based on historical inspection data and accumulated experience, which is helpful for comparing and identifying defects in subsequent inspections.

[0029] Specifically, the vibration response signal includes a frequency response signal, a vibration acceleration and displacement signal, a time domain signal, noise, and an interference signal.

[0030] In this embodiment, frequency response signal: The frequency response signal refers to the reaction of the insulator to different frequency excitations after being excited. It reveals the resonant frequency, rigidity and damping characteristics of the insulator structure by analyzing the response intensity and phase difference of the insulator at each frequency. Different types of defects will change the frequency response characteristics, so the analysis of the frequency response signal helps to identify abnormalities inside the insulator. Vibration acceleration signal: refers to the acceleration value of the vibration, that is, the rate of change of the vibration velocity per unit time. The distribution of vibration intensity and energy can be analyzed through the acceleration signal. Displacement signal: It is the displacement change of the insulator during the vibration process, which is usually used to determine the amplitude of the vibration and the flexibility of the insulator. The acceleration and displacement signals provide vibration information at different levels respectively, and the combined analysis helps to fully reflect the vibration state and structural characteristics of the insulator. Time domain signal: The time domain signal refers to the performance of the vibration response on the time axis, including the change of the vibration amplitude over time. Through time domain analysis, the transient characteristics in the vibration of the insulator can be extracted, which is a key indicator for identifying defects, fatigue and damage of the insulator. Noise and interference signals: Noise and interference signals refer to stray signals generated by non-target signals such as electromagnetic interference, environmental vibration, and line noise during vibration detection. Since the post insulator is in a high-voltage environment, it may be interfered by power equipment and the external environment. Effectively identifying and filtering out noise and interference signals is crucial to obtaining accurate vibration response data. This is usually achieved through filtering and denoising algorithms to ensure the signal quality of the final analysis.

[0031] Specifically, the specific process of preprocessing the vibration response signal and obtaining the vibration characteristic parameters is as follows: bandpass filtering the vibration response signal to filter out noise and irrelevant frequency band signals and highlight the vibration signal of the target frequency band; amplifying the filtered signal to enhance the effective amplitude of the signal; applying Fourier transform to perform spectrum analysis on the vibration signal to extract frequency response characteristics; obtaining vibration acceleration and displacement time domain characteristic parameters through time domain analysis method; combining spectrum characteristics with time domain characteristics to generate vibration characteristic parameters.

[0032] In this implementation, bandpass filtering is used: the vibration response signal is usually interfered by noise, especially in a high-voltage environment, so it is necessary to use a bandpass filter to filter out noise signals in irrelevant frequency bands and retain valid signals in the target frequency band. Bandpass filtering formula: x filtered (t) = H bp (f low , f h )*x(t);x filtered (t): Bandpass filtered signal, H bp (f low , f h): The transfer function of the bandpass filter limits the signal from f_low (lower frequency) to f h (upper frequency). x(t): The original vibration signal, which is obtained by bandpass filtering to obtain a signal that only contains the target frequency band, which can effectively exclude environmental noise. Signal amplification: The filtered signal may be weak, and its effective amplitude is increased by signal amplification. This step helps to enhance the signal characteristics, making it easier for the system to detect small vibration changes, especially when the vibration amplitude is low. Spectral analysis: The amplified signal is subjected to spectral analysis, and the time domain signal is converted into a frequency domain signal through Fourier transform. In the frequency domain, the vibration characteristics of the insulator at different frequencies, such as the resonant frequency, can be identified. This feature is very important for identifying structural defects because specific defects will produce unique frequency responses. Time domain feature extraction: In addition to frequency domain features, the system also extracts instantaneous vibration features in the time domain, such as acceleration and displacement features. These time domain parameters can reflect the dynamic changes of the insulator during the vibration process, which helps to identify mutation signals caused by structural problems such as cracks. Feature fusion: The spectrum features are combined with the time domain features to generate the final vibration feature parameters. These parameters integrate the response characteristics of the insulator and provide complete information input for the defect identification module to improve the accuracy and reliability of detection.

[0033] Specifically, the specific process of spectral feature extraction and modal analysis of vibration characteristic parameters through a digital signal processor is as follows: a secondary spectral analysis is performed on the vibration characteristic parameters, and the cepstrum characteristics of the vibration response are obtained through inverse Fourier transform; multi-scale analysis of the spectral characteristics is performed by using wavelet transform to extract local characteristic information of different frequency bands; the natural frequency and damping ratio modal parameters are extracted through the modal parameter identification algorithm, and the modal spectrum of the post insulator is generated.

[0034] In this embodiment, secondary spectrum analysis and cepstrum feature acquisition: the frequency information of the vibration characteristic parameters is further refined through secondary spectrum analysis. Specifically, the cepstrum feature can be obtained from the secondary spectrum using inverse Fourier transform. The cepstrum feature is a spectrum of a spectrum that can reflect the structural changes of the post insulator, especially with high sensitivity for detecting periodic or repetitive defects. Wavelet transform and multi-scale spectrum analysis: multi-scale analysis of the spectrum features is performed using wavelet transform. Wavelet transform is a method that can provide high-resolution analysis in different frequency bands, so the spectrum features can be refined at multiple scales. In this way, local features in the vibration response of the post insulator, that is, small changes in different frequency bands, can be identified. This multi-scale analysis can more accurately capture the vibration characteristics associated with defects, thereby enhancing the detection accuracy. Modal parameter identification and modal spectrum generation: The modal parameter identification algorithm is used to extract key modal parameters, including natural frequency and damping ratio. The natural frequency represents the natural vibration frequency of the post insulator, while the damping ratio reflects its vibration attenuation characteristics. Through the extraction of these parameters, the modal spectrum of the insulator, that is, the vibration response mode at different frequencies, can be constructed. These modal parameters and spectra can be used to accurately locate and identify structural defects in insulators, such as cracks or material degradation.

[0035] Specifically, the specific process of generating modal characteristic parameters is as follows: the natural frequency, damping ratio and modal vibration shape information of each mode are extracted according to spectrum analysis and cepstrum analysis, and normalized; the noise interference mode is eliminated through the modal screening algorithm, and the key modal characteristics are retained; the multi-modal characteristics are fused to form composite modal characteristic parameters to enhance the description ability of the insulator state, and the generated modal characteristic parameters are stored in the database management module.

[0036] In this implementation scheme, the modal parameters are extracted and normalized: the key parameters of each mode, the natural frequency, the damping ratio and the modal vibration type information are extracted through spectrum analysis and cepstrum analysis. The natural frequency is the inherent vibration frequency of the material, the damping ratio indicates the rate of vibration attenuation, and the modal vibration type describes the relative motion characteristics of each point during vibration. To ensure the comparability of different modal parameters, the extracted parameters are normalized to unify the magnitude of the data. Modal screening to remove noise interference: To ensure the accuracy of the extracted modal characteristic parameters, the modal screening algorithm is used to eliminate the noise interference mode and retain the key modal features that are most indicative of the insulator state. This can reduce the interference of environmental noise and measurement errors on the detection results and enhance the representativeness of the modal parameters. Multimodal data fusion to form composite modal feature parameters: on the basis of retaining the key modal features, the modal features of each modal are further combined into a composite modal feature parameter through data fusion. The fusion of multimodal features provides a more comprehensive description of the insulator state and helps to more accurately identify the defects of the post insulator. Store modal characteristic parameters: Store the generated modal characteristic parameters in the database management module for subsequent analysis and management. This storage ensures the traceability of insulator status data and provides reliable data support for further feature comparison and status monitoring.

[0037] Specifically, the modal feature parameters are compared with the preset defect feature library, and the specific process of identifying the defect features of the pillar porcelain insulator is as follows: the stored defect feature library is called from the database management module, including the standardized modal feature parameters of various defects; the modal feature parameters extracted in real time are compared item by item with the parameter set in the defect feature library; the matching degree is analyzed based on the comparison result to determine whether there are defects that meet the characteristics, and the matching details are recorded; when a match that meets the defect characteristics is detected, a preliminary defect identification result is generated.

[0038] In this implementation scheme, the defect feature library is called: the system first extracts the defect feature library from the database management module, which contains standardized modal feature parameters of various defects, and the natural frequency, damping ratio, and vibration mode characteristics corresponding to the defect type. These standardized parameters provide a unified benchmark for comparison, ensuring the scientificity and consistency of the comparison process. Item-by-item parameter comparison: The modal feature parameters extracted in real time are compared item by item with the parameter set of each type of defect in the defect feature library. The comparison process includes the matching of the natural frequency, damping ratio, and vibration mode of the modal feature parameters to ensure that the comparison of each parameter meets the corresponding defect characteristics to improve the accuracy of defect identification. Matching degree analysis: By analyzing the matching degree of the modal feature parameters and the standard defect parameters, the degree of conformity between the two is judged. This step not only judges the numerical proximity, but also considers the overall matching of the parameters to improve the accuracy of defect determination. Record the matching details and generate preliminary identification results: Once a group of modal feature parameters is detected to be highly matched with a defect feature in the defect feature library, the system will record the details of the match, including the matching parameter items and matching degree information. At the same time, preliminary defect identification results are generated, laying the foundation for the generation of subsequent inspection reports.

[0039] Specifically, the specific process of obtaining the defect index through the spectrum feature matching algorithm is as follows: the spectrum feature matching algorithm is used to quantify the similarity coefficient between the modal characteristics and the defect characteristics, and the defect similarity index is calculated; the similarity coefficients of each frequency band are integrated, and a weighted defect index is generated according to the preset weight coefficient; according to the defect index and the set threshold, the defect degree is evaluated and the results are stored in a graded manner.

[0040] In this implementation scheme, the similarity coefficient between the modal feature and the defect feature is quantified: At this stage, the modal feature extracted by spectrum analysis is compared with the preset defect feature. Specifically, the system first extracts the natural frequency and damping ratio characteristic parameters of each mode from the vibration signal. These parameters are compared with the defect features (including different types of defect features) recorded in the database, and the similarity coefficient is calculated by the feature matching algorithm to quantify the similarity between the modal feature and the defect feature. The higher the value of the similarity coefficient, the higher the matching degree between the current modal feature and the defect feature. The similarity coefficient of each frequency band is integrated: Since the vibration signal usually involves the characteristics of multiple frequency bands, the system will calculate the similarity coefficient for different frequency bands and combine them to form a comprehensive index. In this process, the system will adjust the contribution of the similarity coefficient of different frequency bands to the final result according to the importance or weight of each frequency band. Generate a weighted defect index based on the preset weight coefficient: In the process of spectrum feature matching, the system will weight the similarity coefficient of each frequency band according to the importance of each frequency band by applying the preset weight coefficient. The setting of the weight coefficient can be determined based on experience, actual application requirements or experimental data. The weighted similarity coefficients will be synthesized into a comprehensive defect index (DI), which can more accurately reflect the current status and defect type of the post insulator. Evaluate the degree of defects and store the results in grades: Evaluate the defect index by setting a threshold. If the defect index exceeds the set threshold, the system considers that there is an obvious defect and further grades the defect according to the size of the defect index. For example, a higher defect index may indicate severe damage, while a lower defect index indicates lighter damage. The detection results will be stored in grades and can be used for subsequent fault prediction, maintenance decisions and condition monitoring. The defect index formula is as follows: Formula parameter explanation: f r f : The actual resonant frequency of the excitation source. ref : The reference value of the resonant frequency, which is 50-55Hz. P: The actual output power of the excitation source. max : Maximum output power reference value (>5W). s : Sampling rate. min : Minimum sampling rate reference value. T max : The maximum time for live detection of a single insulator should be 30 seconds. d : Actual time of single insulator live detection. i S : Weight coefficient of the i-th frequency band. i : Similarity coefficient of the ith frequency band, similarity calculated based on spectral feature matching. n: Number of frequency bands.

[0041] Specifically, the database management module is used to store and manage the insulator defect spectrum characteristic data, including the excitation source resonance frequency, sampling rate, excitation source maximum output power, and single insulator energization detection time.

[0042] In this embodiment, the excitation source resonant frequency is stored: the excitation source resonant frequency is the frequency range of the excitation signal emitted by the signal excitation module. Since the resonant frequency of the excitation source is closely related to the vibration mode of the post insulator, it is an important parameter for detecting insulator defects. The database stores the excitation source resonant frequency used in each detection process for subsequent analysis and comparison.

[0043] Store sampling rate: Sampling rate refers to the sampling frequency during data collection, that is, the number of data points collected per second. A high sampling rate can ensure that more details in the signal are captured, improving the accuracy of the data and the precision of the analysis. The database stores the sampling rate used for each test to ensure that the appropriate sampling rate is used for data processing in subsequent analysis. Store the maximum output power of the excitation source: The maximum output power of the excitation source refers to the maximum energy output that the excitation module can provide, which is used to drive the post insulator to produce a vibration response. Different output powers will affect the amplitude and frequency characteristics of the vibration, so recording this parameter can help analyze and adjust the test conditions to ensure the accuracy of the results. Store the live detection time of a single insulator: The live detection time refers to the time limit for each live detection of a single post insulator. Since excessive detection time may cause errors or damage to the equipment and the test process, recording and managing the detection time can help ensure the accuracy of each detection and the safety of the equipment.

[0044] See also Figure 2 , a fully shielded anti-interference signal processing method for post insulator detection includes the following steps: S1. Generate multi-frequency and different strength excitation signals to excite the vibration mode of the post porcelain insulator; S2. Capture the vibration response signal generated by the post porcelain insulator after being excited by the signal excitation module, and pre-process it to obtain vibration characteristic parameters; S3. Perform spectrum feature extraction and modal analysis on the vibration characteristic parameters through a digital signal processor to generate modal characteristic parameters; S4. Compare the modal characteristic parameters with a preset defect feature library to identify the defect characteristics of the post porcelain insulator, obtain the defect index through a spectrum feature matching algorithm, and generate a defect detection report; S5. Store and manage the spectrum feature data of insulator defects.

[0045] In this embodiment, S1. Multi-frequency and different intensity signals are generated by the signal excitation module. These signals are designed to cover the vibration frequency range of the pillar porcelain insulator and can excite its different vibration modes. By generating excitation signals of different frequencies and intensities, multiple vibration modes of the porcelain insulator can be fully excited, thereby improving the sensitivity and accuracy of defect detection. S2. Capture the vibration signal of the pillar insulator after responding to the excitation signal. These response signals are preprocessed, including filtering and denoising, to obtain effective vibration characteristic parameters. Through preprocessing, noise is removed and the signal of the target frequency band is highlighted to ensure the accuracy of the vibration characteristics and avoid external interference from affecting the detection results. S3. The preprocessed vibration characteristic parameters are subjected to spectrum analysis and modal analysis to extract the natural frequency and damping ratio modal characteristic parameters of the pillar insulator. These parameters represent the vibration characteristics of the insulator and can reflect its health status. Through spectrum feature extraction and modal analysis, representative modal characteristic parameters can be extracted from complex vibration signals to provide accurate basis for subsequent defect identification. S4. The extracted modal characteristic parameters are compared with the defect feature library stored in advance to identify whether there are signal features that match the defect features in the library. The defect index is calculated through the spectrum feature matching algorithm, and a test report is generated to indicate the type and severity of the defect. By comparing with the defect feature library and calculating the defect index, it is possible to effectively identify whether the insulator has defects and quantify its severity, thereby providing a basis for subsequent maintenance and replacement decisions. S5. The defect spectrum feature data (including excitation source frequency, vibration response characteristics, and modal characteristics) obtained from each inspection is stored in the database management module for subsequent analysis, query, and comparison. Through efficient storage and management, the integrity and traceability of all inspection data are ensured, and trend changes can be discovered in subsequent comparative analysis, system health can be evaluated, and historical data support can be provided for decision-making.

[0046] In summary, this application has at least the following effects:

[0047] The fully shielded anti-interference signal processing system and method for pillar insulator detection can fully stimulate the vibration characteristics of the insulator by generating excitation signals of multiple frequencies and different intensities and stimulating the vibration modes of the pillar porcelain insulator, ensuring that the vibration signals of potential defects can be accurately captured under various working conditions. The preprocessing step further filters out noise and interference, enhances the signal-to-noise ratio of the detection, and improves the detection accuracy. The fully shielded anti-interference signal processing method can effectively suppress the interference factors of the external environment and ensure the purity and stability of the vibration signal. In this way, the system can still maintain a high detection reliability in a complex working environment. Spectral feature extraction and modal analysis are performed by a digital signal processor, and matching is performed in combination with a preset defect feature library to achieve automatic defect identification. The system calculates the defect index based on the modal feature parameters and evaluates the severity of the defect, realizing fully automated defect detection and evaluation, reducing manual intervention, and improving efficiency and accuracy. The response time of the entire detection process is short, the live detection time of a single insulator can be controlled within 30 seconds, and the output time of the defect detection result is ≤10 seconds, which meets the needs of real-time monitoring and rapid response. The database management module is used to store and manage the insulator defect spectrum feature data to ensure the integrity and traceability of the detection data. This provides a solid data support foundation for subsequent trend analysis, comparative analysis and decision-making. By generating detailed defect detection reports, it helps operation and maintenance personnel quickly identify problems and formulate corresponding maintenance plans, avoiding equipment failures caused by failure to discover defects in a timely manner and ensuring the stable operation of the power system.

[0048] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0049] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0050] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0051] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0052] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0053] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A fully shielded anti-interference signal processing system for post insulator detection, characterized in that: It includes the following modules: signal excitation module, signal acquisition module, signal processing module, defect identification module, and database management module; The signal excitation module is used to generate excitation signals of multiple frequencies and different intensities to excite the vibration mode of the pillar porcelain insulator; The signal acquisition module is used to capture the vibration response signal generated by the support porcelain insulator after being excited by the signal excitation module, and perform preprocessing to obtain vibration characteristic parameters; The signal processing module is used to perform spectrum feature extraction and modal analysis on the vibration characteristic parameters through a digital signal processor to generate modal characteristic parameters; The defect recognition module is used to compare the modal characteristic parameters with a preset defect characteristic library, identify the defect characteristics of the pillar porcelain insulator, obtain the defect index through the spectrum characteristic matching algorithm, and generate a defect detection report; The database management module is used to store and manage insulator defect spectrum feature data.

2. The fully shielded anti-interference signal processing system for post insulator detection according to claim 1 is characterized in that: The vibration response signal includes a frequency response signal, a vibration acceleration and displacement signal, a time domain signal, noise and an interference signal.

3. The fully shielded anti-interference signal processing system for post insulator detection according to claim 2 is characterized in that: The specific process of preprocessing the vibration response signal and obtaining the vibration characteristic parameters is as follows: Perform bandpass filtering on the vibration response signal to filter out noise and irrelevant frequency band signals and highlight the vibration signal in the target frequency band; Amplify the filtered signal to enhance the effective amplitude of the signal; Apply Fourier transform to perform spectrum analysis on vibration signals and extract frequency response characteristics; Obtain the time domain characteristic parameters of vibration acceleration and displacement through time domain analysis method; The spectrum features are combined with the time domain features to generate vibration characteristic parameters.

4. The fully shielded anti-interference signal processing system for post insulator detection according to claim 3 is characterized in that: The specific process of spectral feature extraction and modal analysis of vibration characteristic parameters through digital signal processor is as follows: Perform secondary spectrum analysis on the vibration characteristic parameters and obtain the cepstrum characteristics of the vibration response through inverse Fourier transform; By using wavelet transform to perform multi-scale analysis on spectrum features, local feature information of different frequency bands can be extracted; The natural frequency and damping ratio modal parameters are extracted through the modal parameter identification algorithm, and the modal spectrum of the post insulator is generated.

5. The fully shielded anti-interference signal processing system for post insulator detection according to claim 4 is characterized in that: The specific process of generating modal characteristic parameters is as follows: The natural frequency, damping ratio and modal vibration shape information of each mode are extracted based on spectrum analysis and cepstrum analysis, and normalized; The noise interference mode is eliminated through the mode screening algorithm to retain the key mode characteristics; The multi-modal features are fused to form composite modal feature parameters to enhance the description capability of the insulator state, and the generated modal feature parameters are stored in the database management module.

6. The fully shielded anti-interference signal processing system for post insulator detection according to claim 5 is characterized in that: The specific process of comparing the modal characteristic parameters with the preset defect characteristic library and identifying the defect characteristics of the pillar porcelain insulator is as follows: Call the stored defect feature library from the database management module, including standardized modal feature parameters of various defects; Compare the modal feature parameters extracted in real time with the parameter set in the defect feature library item by item; Analyze the matching degree based on the comparison results, determine whether there are defects that meet the characteristics, and record the matching details; When a match that meets the defect characteristics is detected, a preliminary defect identification result is generated.

7. The fully shielded anti-interference signal processing system for post insulator detection according to claim 6 is characterized in that: The specific process of obtaining the defect index through the spectrum feature matching algorithm is as follows: The spectrum feature matching algorithm is used to quantify the similarity coefficient between the modal feature and the defect feature, and the defect similarity index is calculated; The similarity coefficients of each frequency band are integrated to generate a weighted defect index according to the preset weight coefficient; Based on the defect index and set threshold, the defect degree is evaluated and the results are stored in a graded manner.

8. The fully shielded anti-interference signal processing system for post insulator detection according to claim 7, characterized in that: The database management module is used to store and manage the insulator defect spectrum characteristic data, including the excitation source resonance frequency, sampling rate, excitation source maximum output power, and single insulator live detection time.

9. A fully shielded anti-interference signal processing method for post insulator detection, using the fully shielded anti-interference signal processing system for post insulator detection according to any one of claims 1 to 8, characterized in that: The following steps are involved: S1. Generate excitation signals of multiple frequencies and different intensities to stimulate the vibration modes of the pillar porcelain insulators; S2. Capture the vibration response signal generated by the post porcelain insulator after being excited by the signal excitation module, and pre-process it to obtain vibration characteristic parameters; S3. Performing spectrum feature extraction and modal analysis on the vibration characteristic parameters through a digital signal processor to generate modal characteristic parameters; S4. Compare the modal characteristic parameters with the preset defect characteristic library, identify the defect characteristics of the pillar porcelain insulator, obtain the defect index through the spectrum characteristic matching algorithm, and generate a defect detection report; S5. Store and manage insulator defect spectrum feature data.

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