High-voltage switch operation state monitoring method based on voiceprint detection information

Abstract

The invention relates to the technical field of high-voltage switch operation state monitoring methods, and discloses a high-voltage switch operation state monitoring method based on voiceprint detection information, and the detection method comprises the steps: collecting voiceprint signals generated during the operation of a high-voltage switch, and carrying out the preprocessing of the voiceprint signals, and processing the voiceprint signal by using a preset voiceprint analysis algorithm, and extracting characteristic parameters reflecting the running state of the high-voltage switch. According to the high-voltage switch operation state monitoring method based on the voiceprint detection information, high-quality collection and processing of voiceprint signals are ensured through a high-sensitivity microphone array and a preprocessing technology, the signal-to-noise ratio and reliability are improved, characteristic parameters reflecting the operation state of a high-voltage switch are extracted through an advanced voiceprint analysis algorithm, and the high-voltage switch operation state monitoring method based on the voiceprint detection information is obtained. The method improves the accuracy and reliability of feature extraction, and then achieves the precise judgment of the operation state of the high-voltage switch through the standardization processing and similarity calculation, and timely discovers the abnormal conditions.

Description

Technical Field

[0001] This invention relates to the technical field of high-voltage switch operation status monitoring methods, specifically a high-voltage switch operation status monitoring method based on acoustic fingerprint detection information. Background Technology

[0002] High-voltage switch operation status monitoring is a technical means for real-time monitoring of the operating status of high-voltage switchgear. It aims to collect and analyze various signals generated by the high-voltage switch during operation using various sensors and technologies to determine whether its operating status is normal and to issue timely alarms or maintenance suggestions. This method is of great significance for ensuring the safe and stable operation of the power system. However, in existing technologies, the sampling frequency of acoustic fingerprint signals is low, which easily leads to signal distortion and makes it impossible to accurately capture the subtle acoustic fingerprint information generated by the high-voltage switch during operation. Furthermore, the extraction of feature parameters is relatively simple, usually only extracting a few parameters such as the basic frequency and amplitude, which cannot comprehensively reflect the operating status of the high-voltage switch. At the same time, existing technologies suffer from high noise levels during acoustic fingerprint signal acquisition, which seriously affects signal quality and leads to inaccurate feature parameter extraction. Summary of the Invention

[0003] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for monitoring the operating status of high-voltage switches based on voiceprint detection information, the detection method comprising,

[0006] Collect the acoustic fingerprint signals generated during the operation of the high-voltage switch and preprocess the acoustic fingerprint signals;

[0007] The voiceprint signal is processed using a preset voiceprint analysis algorithm to extract characteristic parameters that reflect the operating status of the high-voltage switch.

[0008] The characteristic parameters are compared with preset standard values ​​or historical data to determine whether the high-voltage switch is operating normally.

[0009] Based on the judgment results, corresponding alarms or maintenance suggestions will be output.

[0010] As a further aspect of the present invention: the collection of acoustic signature signals generated during the operation of the high-voltage switch includes,

[0011] A high-sensitivity microphone array is employed, positioned at key locations around the high-voltage switch. The array consists of N microphones, evenly distributed around the switch to cover as large a spatial area as possible. The sampling frequency of each microphone is set to f0. s The expression is:

[0012] S i (t)=A·e -αt ·sin(2πf0t+R i )+N(t);

[0013] Among them, S i (t) represents the voiceprint signal collected by the i-th microphone at time t, where A is the amplitude of the voiceprint signal, α is the attenuation factor used to simulate the attenuation of sound waves in air, f0 is the fundamental frequency of the voiceprint signal, and R... i Let N(t) be the phase difference between the i-th microphone and the other microphones, and let N(t) be the ambient noise, assumed to be Gaussian white noise.

[0014] As a further aspect of the present invention: the preprocessing of the voiceprint signal includes,

[0015] The signals acquired by each microphone are filtered to remove high-frequency noise and low-frequency interference, and then normalized to ensure that all signals are in the same dimension. The expression is as follows:

[0016]

[0017] Among them, S′ i H(t) is the preprocessed voiceprint signal of the i-th microphone at time t, and H(f) is the frequency response function of the filter, which is assumed to be a bandpass filter that allows signals within a specific frequency range to pass through while suppressing signals of other frequencies.

[0018] As a further aspect of the present invention: the step of processing the voiceprint signal using a preset voiceprint analysis algorithm to extract characteristic parameters reflecting the operating status of the high-voltage switch includes,

[0019] The preprocessed voiceprint signal is converted to a frequency domain representation, as shown in the expression:

[0020]

[0021] Among them, X i (ω,n) represents the short-time Fourier transform of the i-th microphone at frequency ω and time frame n, S′ i w(m) is the preprocessed audioprint signal of the i-th microphone at time t, w(m) is the window function, j is the imaginary unit, ω is the angular frequency, and n is the time frame index.

[0022] The Mel frequency cepstral coefficients are extracted from the frequency domain representation, and the expression is as follows:

[0023]

[0024] Among them, MFCC i (k,n) represents the Mel frequency cepstral coefficients of the i-th microphone at the k-th Mel frequency band and time frame n, X i (ω m (n) represents the frequency ω of the i-th microphone. m The short-time Fourier transform result at time frame n, W k (ω m ) is the weighting function of the triangular filter in the k-th Mel frequency band, and M is the number of frequency segments;

[0025] The extracted feature parameters are combined into a feature vector, expressed as follows:

[0026] F i (n)=[MFCC i (1,n),MFCC i (2,n),…,MFCC i (K,n)] T ;

[0027] Among them, F i (n) is the feature vector of the i-th microphone at time frame n, and K is the number of Mel frequency bands.

[0028] As a further aspect of the present invention: comparing the characteristic parameters with preset standard values ​​or historical data to determine whether the high-voltage switch is operating normally includes,

[0029] The extracted feature parameters are standardized so that they can be compared with preset standard values ​​or historical data under the same dimension.

[0030] Calculate the similarity between the feature parameters and standard values ​​or historical data;

[0031] Based on the similarity results, determine whether the high-voltage switch is operating normally.

[0032] As a further aspect of the present invention: the extracted feature parameters are standardized so that they are compared with preset standard values ​​or historical data under the same dimension. The standardization expression is as follows:

[0033]

[0034] Among them, F′ i (n) is the normalized feature vector of the i-th microphone at time frame n, F i(n) is the feature vector of the i-th microphone at time frame n, μ is the mean of the feature vector, and σ is the standard deviation of the feature vector.

[0035] As a further aspect of the present invention: in calculating the similarity between the feature parameters and the standard values ​​or historical data, the similarity calculation expression is as follows:

[0036]

[0037] Among them, S i (n) represents the similarity of the i-th microphone at time frame n, F′ i (n) is the normalized feature vector of the i-th microphone at time frame n, F std Let ||.||2 be the predefined standard feature vector, and ||.||2 be the L2 norm, representing the Euclidean distance of the vector.

[0038] As a further aspect of the present invention: the threshold judgment expression is:

[0039]

[0040] Among them, Status i (n) represents the operating state of the i-th microphone at time frame n, S i (n) represents the similarity of the i-th microphone at time frame n, and θ is the preset similarity threshold.

[0041] As a further aspect of the present invention: the step of outputting corresponding alarms or maintenance suggestions based on the judgment result includes,

[0042] Summarize the operating status of all microphones to determine the overall operating status of the high-voltage switch;

[0043] Based on the overall operating status, generate corresponding alarm information or maintenance suggestions;

[0044] Alarm information and maintenance suggestions are displayed to users through a visual interface.

[0045] As a further aspect of the present invention: the step of outputting corresponding alarms or maintenance suggestions based on the judgment result also includes,

[0046] It supports remote access and control functions, allowing users to monitor the operating status of high-voltage switches in real time and receive alarm information and maintenance suggestions through mobile applications or other remote access methods;

[0047] Provides encrypted data transmission functionality to ensure the security and privacy of user data;

[0048] It supports the storage and analysis of historical data, enabling long-term trend analysis and fault prediction.

[0049] Compared with the prior art, the beneficial effects of the present invention are:

[0050] By employing a high-sensitivity microphone array and preprocessing technology, high-quality acquisition and processing of voiceprint signals are ensured, improving the signal-to-noise ratio and reliability. Secondly, advanced voiceprint analysis algorithms are used to extract characteristic parameters reflecting the operating status of high-voltage switches, improving the accuracy and reliability of feature extraction. Next, through standardization processing and similarity calculation, accurate judgment of the operating status of high-voltage switches is achieved, enabling timely detection of abnormalities. Finally, through a visual interface and remote access function, real-time alarm information and maintenance suggestions are provided, enhancing the system's practicality and user experience, and ensuring the safe and stable operation of the power system. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating the steps of a high-voltage switch operation status monitoring method based on voiceprint detection information. Detailed Implementation

[0052] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0053] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0055] Example

[0056] Please see Figure 1 This embodiment of the invention provides a method for monitoring the operating status of high-voltage switches based on voiceprint detection information, including:

[0057] Acquisition and preprocessing of voiceprint signals:

[0058] An array of 10 high-sensitivity microphones was evenly distributed around the high-voltage switch, with each microphone's sampling frequency set to 44.1 kHz. The specific steps included:

[0059] Install the microphone array at predetermined locations around the high-voltage switch, ensuring that each microphone is evenly distributed and covers the largest possible area.

[0060] Turn on the high-voltage switch and begin collecting voiceprint signals;

[0061] The signals acquired by each microphone are bandpass filtered to remove high-frequency noise (>20kHz) and low-frequency interference (<20Hz);

[0062] The filtered signal is normalized to ensure that all signals are in the same dimension for subsequent processing.

[0063] Frequency domain transformation and feature parameter extraction of voiceprint signals:

[0064] The preprocessed voiceprint signal is converted into a frequency domain representation using short-time Fourier transform (STFT) technology with a window size of 1024 points and an overlap rate of 50%.

[0065] Mel frequency cepstral coefficients (MFCCs) are extracted from the frequency domain representation, and the first 13 coefficients are selected as characteristic parameters.

[0066] The extracted feature parameters are combined into a feature vector, and each feature vector contains 13 Mel frequency cepstral coefficients.

[0067] Feature parameter standardization and similarity calculation:

[0068] The extracted feature parameters are standardized, and the mean and standard deviation of each feature vector are calculated so that all feature vectors can be compared under the same scale.

[0069] Calculate the similarity between feature parameters and preset standard values ​​or historical data, and use the L2 norm to represent the Euclidean distance of the vectors;

[0070] The similarity threshold is set to 0.85. When the similarity is greater than or equal to 0.85, the operating status of the high-voltage switch is determined to be normal.

[0071] When the similarity is less than 0.85, the operating status of the high-voltage switch is determined to be abnormal.

[0072] Operational status assessment and output of alarms or maintenance suggestions:

[0073] Summarize the operating status of all microphones to determine the overall operating status of the high-voltage switch;

[0074] Based on the overall operating status, generate corresponding alarm information or maintenance suggestions;

[0075] At each time frame n, summarize the operating status of all 10 microphones;

[0076] If more than 80% of the microphones are judged to be normal, then the overall high-voltage switch is operating normally; otherwise, the overall high-voltage switch is operating abnormally.

[0077] Based on the overall operating status, generate corresponding alarm information or maintenance suggestions:

[0078] Normal state: Generates the message "High-voltage switch is operating normally";

[0079] Abnormal state:

[0080] Minor anomaly (similarity between 0.70 and 0.85): Generates the message "The high-voltage switch is in a slightly abnormal operating state; inspection and maintenance are recommended."

[0081] Severe anomaly (similarity less than 0.70): Generates the message "High-voltage switch operating status is severely abnormal, immediately stop and inspect";

[0082] Alarm information and maintenance suggestions are displayed to users through a visual interface:

[0083] The real-time operating status of the high-voltage switch is displayed using a visual interface, including the similarity value of each microphone and the overall operating status.

[0084] The generated alarm information and maintenance suggestions are displayed in pop-up windows and recorded in the log file;

[0085] Supports remote access and control functions:

[0086] Users can monitor the operating status of high-voltage switches in real time through mobile applications or web interfaces;

[0087] Users can remotely view alarm information and maintenance suggestions, and perform remote control operations, such as starting or stopping high-voltage switches;

[0088] Provides encrypted data transmission functionality to ensure the security and privacy of user data:

[0089] All data transmissions are encrypted using the SSL / TLS protocol to ensure data security and privacy during transmission;

[0090] Supports historical data storage and analysis, enabling long-term trend analysis and fault prediction:

[0091] The data from each monitoring session is stored in a database, including the similarity value of each microphone and the overall operating status;

[0092] It provides data statistics and analysis functions, allowing users to view trend charts of historical data and conduct long-term trend analysis;

[0093] Machine learning algorithms are used to analyze historical data, predict potential future failures, and generate preventative maintenance recommendations.

[0094] Comparison of high-voltage switch operating status monitoring data:

[0095]

[0096]

[0097] Data Analysis:

[0098] The sampling frequency used in this invention is 44.1kHz, which is much higher than the 22.05kHz of the prior art. The higher sampling frequency can capture the details of the acoustic signal more accurately, reduce signal distortion, and improve the accuracy of monitoring. Through bandpass filtering, the noise level is significantly reduced from 60dB to 30dB. The lower noise level means higher signal quality, which helps to extract feature parameters more accurately. Moreover, the number of feature parameters extracted by this invention is 13, which is much more than the 5 of the prior art. More feature parameters can more comprehensively reflect the operating status of the high-voltage switch and improve the accuracy of monitoring. At the same time, the similarity calculation error of this invention is only 5%, which is much lower than the 15% of the prior art. The lower error means that the status judgment is more accurate and reduces the possibility of misjudgment.

[0099] In summary, this invention has significant advantages in monitoring the operating status of high-voltage switches. It not only improves the accuracy and timeliness of monitoring, but also enhances the reliability and safety of the system. The improvement provides comprehensive support for the operation and maintenance of high-voltage switches, ensuring the safe and stable operation of the power system.

[0100] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for monitoring the operating status of a high-voltage switch based on voiceprint detection information, characterized in that: The detection method comprises: Collect the voiceprint signal generated when the high-voltage switch is running, and pre-process the voiceprint signal; The voiceprint signal is processed using a preset voiceprint analysis algorithm to extract characteristic parameters reflecting the operating state of the high-voltage switch; Comparing the characteristic parameter with a preset standard value or historical data to determine whether the operating state of the high-voltage switch is normal; Output corresponding alarms or maintenance suggestions based on the judgment results.

2. A method for monitoring the operating status of a high-voltage switch based on voiceprint detection information according to claim 1, characterized in that: The collecting of the voiceprint signal generated when the high-voltage switch is running includes: A high-sensitivity microphone array is used and arranged at key positions around the high-voltage switch. The microphone array consists of N microphones, and the microphones are evenly distributed around the high-voltage switch to cover the largest possible spatial range. The sampling frequency of each microphone is set to f s , the expression is: S i (t)=A·e -αt ·sin(2πf0t+R i )+N(t); Among them, S i (t) is the corresponding character, which is the voiceprint signal collected by the i-th microphone at time t. A is the amplitude of the voiceprint signal. α is the attenuation factor, which is used to simulate the attenuation of sound waves in the air. f0 is the basic frequency of the voiceprint signal. R i is the phase difference of the ith microphone relative to other microphones, and N(t) is the ambient noise, which is assumed to be Gaussian white noise.

3. A method for monitoring the operating status of a high-voltage switch based on voiceprint detection information according to claim 1, characterized in that: The preprocessing of the voiceprint signal includes: The signals collected by each microphone are filtered to remove high-frequency noise and low-frequency interference, and the signals are normalized to ensure that all signals are in the same dimension. The expression is: Among them, S′ i (t) is the voiceprint signal of the i-th microphone after preprocessing at time t, and H(f) is the frequency response function of the filter, which is assumed to be a bandpass filter that allows signals within a specific frequency range to pass through and suppresses signals of other frequencies.

4. A method for monitoring the operating status of a high-voltage switch based on voiceprint detection information according to claim 1, characterized in that: The method of processing the voiceprint signal using a preset voiceprint analysis algorithm to extract characteristic parameters reflecting the operating state of the high-voltage switch includes: The preprocessed voiceprint signal is converted into frequency domain representation, and the expression is: Among them, X i (ω,n) is the short-time Fourier transform result of the i-th microphone at frequency ω and time frame n, S′ i (t) is the preprocessed voiceprint signal of the i-th microphone at time t, w(m) is the window function, j is the imaginary unit, ω is the angular frequency, and n is the time frame index; Extract the Mel frequency cepstrum coefficients from the frequency domain representation, the expression is: Among them, MFCC i (k,n) is the Mel-frequency cepstrum coefficient of the i-th microphone at the k-th Mel-frequency band and time frame n, X i (ω m ,n) is the frequency of the ith microphone at ω m and the short-time Fourier transform result at time frame n, W k (ω m ) is the triangular filter weight function of the kth Mel frequency band, M is the number of frequency segments; The extracted feature parameters are combined into a feature vector, expressed as: F i (n)=[MFCC i (1,n),MFCC i (2,n),…,MFCC i (K,n)] T ; Among them, F i (n) is the feature vector of the ith microphone at time frame n, and K is the number of Mel frequency bands.

5. The method for monitoring the operating status of a high-voltage switch based on voiceprint detection information according to claim 1 is characterized in that: The characteristic parameter is compared with a preset standard value or historical data to determine whether the operating state of the high-voltage switch is normal, including: The extracted characteristic parameters are standardized so that they can be compared with the preset standard values ​​or historical data in the same dimension; Calculate the similarity between feature parameters and standard values ​​or historical data; Based on the similarity results, determine whether the operating status of the high-voltage switch is normal.

6. A method for monitoring the operating status of a high-voltage switch based on voiceprint detection information according to claim 5, characterized in that: The extracted characteristic parameters are standardized so that they can be compared with the preset standard values ​​or historical data in the same dimension. The standardized processing expression is: Among them, F′ i (n) is the normalized feature vector of the ith microphone at time frame n, F i (n) is the eigenvector of the ith microphone at time frame n, μ is the mean of the eigenvector, and σ is the standard deviation of the eigenvector.

7. A method for monitoring the operating status of a high-voltage switch based on voiceprint detection information according to claim 5, characterized in that: In calculating the similarity between the characteristic parameter and the standard value or historical data, the similarity calculation expression is: Among them, S i (n) is the similarity of the ith microphone at time frame n, F′ i (n) is the normalized feature vector of the ith microphone at time frame n, F std is the preset standard feature vector, ||.||2 is the L2 norm, which represents the Euclidean distance of the vector.

8. A method for monitoring the operating status of a high-voltage switch based on voiceprint detection information according to claim 5, characterized in that: The threshold judgment expression is: Among them, Status i (n) is the operating state of the i-th microphone at time frame n, S i (n) is the similarity of the ith microphone at time frame n, and θ is the preset similarity threshold.

9. A method for monitoring the operating status of a high-voltage switch based on voiceprint detection information according to claim 1, characterized in that: The outputting of corresponding alarms or maintenance suggestions according to the judgment results includes: Summarize the operating status of all microphones to determine the operating status of the overall high-voltage switch; Generate corresponding alarm information or maintenance suggestions based on the overall operating status; The alarm information and maintenance suggestions are displayed to the user through a visual interface.

10. The method for monitoring the operating status of a high-voltage switch based on voiceprint detection information according to claim 1, characterized in that: The outputting of corresponding alarms or maintenance suggestions according to the judgment results also includes: Support remote access and control functions, allowing users to monitor the operating status of high-voltage switches in real time and receive alarm information and maintenance suggestions through mobile applications or other remote access methods; Provide data encryption transmission function to ensure the security and privacy of user data; Supports historical data storage and analysis, and performs long-term trend analysis and fault prediction.

Images