Transmission line sound wave positioning method based on wavelet analysis

Abstract

The invention relates to the technical field of high-voltage transmission line fault positioning, and discloses a transmission line sound wave positioning method based on wavelet analysis, comprising the following steps: S1, installing acoustic sensors at two ends of a transmission line; s2, decoupling processing is carried out on the collected current signals; s3, performing Mallat algorithm decomposition on the current line mode component to obtain a wavelet coefficient of an approximate part and a wavelet coefficient of a detail part; s4, further de-noising processing is carried out on the current line mode component # imgabs0 #; and S5, obtaining the time when the fault sound wave arrives at the measuring end. According to the method, denoising processing is carried out on the wavelet coefficient # imgabs 1 # of the detail part, misjudgment information introduced by environmental noise in the signal is reduced, the wavelet coefficient is mapped to a unified range [0, 1] through normalization processing, and a variable # imgabs 2 # is introduced to carry out dynamic filtering on the signal, so that residual misjudgment information can be effectively filtered out, and the accuracy of the signal processing is improved. And the beneficial effect of accurately positioning the fault is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of high-voltage transmission line fault location, and in particular to a transmission line acoustic wave location method based on wavelet analysis. Background Art

[0002] At present, the main methods for transmission line fault distance measurement include fault analysis method, traveling wave method, and acoustic wave positioning method. The fault analysis method is a method of calculating the fault distance by measuring the current and voltage after a line fault occurs, and establishing corresponding equations and analyzing them in combination with the operating parameters of the system. The traveling wave law detects the time it takes for the transient traveling wave signal (electromagnetic wave) generated after the fault to reach the measuring end, combined with the wave speed, to complete the fault distance measurement. Acoustic wave positioning is a method of obtaining sound signals through acoustic sensors, using the time it takes for the direct sound wave to reach the sensor, combined with the sound wave speed for distance measurement.

[0003] The fault analysis method is easily interfered by the load current, and there is also a certain delay problem in the transmission of data, which makes it impossible to achieve accurate positioning. The traveling wave method is simple and easy to use, and is widely used in line fault distance measurement. However, since the detected transient traveling wave signal propagates at the speed of light, the sensitivity of the detection equipment is required to be high, and the investment cost is high. Especially for the two-end traveling wave distance measurement method, it is necessary to ensure that the synchronization accuracy of both ends of the transmission line reaches the microsecond level, which brings great challenges to fault distance measurement. The distance measurement principle of the acoustic wave location method is similar to that of the traveling wave method. Both methods measure the arrival time of the traveling wave signal and complete the fault distance measurement by combining the wave speed. Since the propagation speed of the sound wave is much lower than that of the electromagnetic wave (the speed of light), the acoustic wave location method has lower sensitivity requirements for the acoustic sensor and lower investment costs, which makes the acoustic wave location method more and more widely used in transmission line fault distance measurement. However, the interference of environmental noise on the transmission of the acoustic wave signal is a major problem in line distance measurement, especially the "misjudgment information" caused by the noise will directly affect the determination of the arrival time of the sound wave, and ultimately affect the accuracy of the line fault distance measurement. Summary of the invention

[0004] Technical issues solved: In view of the shortcomings of the prior art, the present invention provides a transmission line acoustic wave positioning method based on wavelet analysis, which has the advantages of accurate fault positioning, fast ranging convergence speed and high positioning accuracy, and solves the problems of the above-mentioned technology.

[0005] Technical solution: To achieve the above object, the present invention provides the following technical solution: a method for acoustic wave positioning of a power transmission line based on wavelet analysis, comprising the following steps: S1. Install acoustic sensors at both ends of the transmission line to detect the sound wave signals generated after the line fault. The acoustic sensors convert the sound signals into electrical signals, and select the current signals for analysis; S2. In order to eliminate the imbalance caused by coupling between transmission lines, the collected current signal is decoupled, and the collected current signal is transformed into a symmetrical component to obtain a current line mode component and a current zero mode component, and one of the current line mode components is selected for analysis; S3, decomposing the current line mode component by Mallat algorithm to obtain the wavelet coefficients of the approximate part and the wavelet coefficients of the detail part; S4. Since the acoustic wave signals collected from the line often contain certain noise, and the noise signal will form certain misjudgment information and interfere with the identification of the wave head, in order to eliminate the influence of noise on the positioning result, the current line mode component is Perform further denoising processing; S5, the wavelet coefficients of the details in S4 After normalization, the corresponding signal is obtained ,Will With variables By multiplying and filtering out misjudgment information, the positioning curve can be obtained. , by the positioning curve Get the time it takes for the fault sound wave to reach the measuring end.

[0006] Preferably, the Mallat algorithm in S3 decomposes the current line mode component by a decomposition filter, wherein Corresponding to the wavelet function, it is a bandpass filter. Corresponding to the scale function, it is a low-pass filter. Let the current line mode component be , the decomposition process of Mallat algorithm is:

[0007]

[0008]

[0009] in: is a discrete time sequence number, , It is Layer: wavelet coefficients of the approximate part on the low-frequency part, For the Layer: wavelet coefficients of the detail part of the high-frequency part.

[0010] Preferably, the S5 pair After normalization, The data in is mapped to [0,1], specifically:

[0011] in: for The maximum value in the data, for The minimum value in the data, is the signal after normalization.

[0012] Preferably, in S5 After the data mapping is completed, considering the normalized signal If there is still misjudgment information, it is necessary to introduce variables right Filter out the misjudged information in:

[0013] The normalized signal With variables Multiply to get the positioning curve :

[0014] By positioning curve The high-order mutation points in the fault determine the time when the fault sound wave reaches the measurement end.

[0015] Preferably, the symmetric component in S2 is calculated as: Zero sequence component

[0016] Positive sequence component

[0017] Negative sequence component

[0018] in: They are three current signals respectively.

[0019] Preferably, in S4 Perform wavelet transform to obtain approximate coefficients and detail coefficients:

[0020]

[0021] in: is the original current line mode component signal, including noise; For the The approximate wavelet coefficients obtained in the first-order wavelet transform reflect the low-frequency part of the signal; For the The detail wavelet coefficients obtained from the first-order wavelet transform reflect the high-frequency part of the signal and contain noise.

[0022] Preferably, the S4 further includes setting a threshold , used for denoising.

[0023] Preferably, the S4 soft threshold processing is calculated as:

[0024] in: The threshold value is set to distinguish signal from noise, which is selected by empirical rules or cross-validation; is the detail wavelet coefficient after soft threshold processing, indicating the signal details after denoising.

[0025] Preferably, the denoising process in S4 further includes reconstructing a signal, and the reconstructed signal is calculated as:

[0026] in: is the denoised signal and the signal after inverse wavelet transform.

[0027] Preferably, the normalization process in S5 also includes using an adaptive normalization method to dynamically adjust the normalization range according to the statistical characteristics of the signal: mean and standard deviation, so as to improve the robustness of signal processing:

[0028] in: yes The mean of is the standard deviation.

[0029] Compared with the prior art, the present invention provides a method for acoustic wave positioning of power transmission lines based on wavelet analysis, which has the following beneficial effects: 1. The present invention discloses a transmission line acoustic wave positioning method based on wavelet analysis, which eliminates the imbalance phenomenon caused by coupling between lines by decoupling the collected current signal, ensures that the analyzed signal is more accurate, reduces the possibility of misjudgment, and decomposes the current line mode component by Mallat algorithm to obtain the wavelet coefficients of the approximate part and the detail part, so that the low-frequency and high-frequency components in the signal can be effectively separated, which is beneficial to the analysis of the fault signal. De-noising is performed to reduce the misjudgment information introduced by environmental noise in the signal, ensuring that the signal obtained in subsequent analysis is relatively clean, thereby improving the accuracy of fault location. The wavelet coefficients are mapped to a unified range of [0,1] through normalization processing, and the variable Dynamically filter the signal to effectively filter out residual misjudgment information, enhance the signal recognizability, and help accurately determine the fault moment. The positioning curve obtained by combining the processed signal with the filter , providing timing information for fault location. Through the analysis of high-order mutation points, the time when the fault sound wave reaches the measurement end can be accurately determined, thereby inferring the specific time when the fault occurs, achieving the beneficial effect of accurate fault location.

[0030] 2. The present invention uses the soft threshold method to denoise the wavelet coefficients, which greatly reduces the false signal interference caused by environmental and equipment noise, improves the reliability and accuracy of the final positioning, and normalizes the wavelet coefficients to improve the comparability of the signal. The dynamic filtering variable is introduced Further removing potential misjudgment information increases the authenticity of the positioning data and ensures accurate identification of the fault location Fault analysis can be performed based on the high-order mutation points in the measurement, which can quickly identify the time when the sound wave arrives at the measuring end and distinguish the propagation characteristics of the sound wave, making the fault location faster and the positioning result more accurate. By analyzing the properties of the mutation points, various fault modes can be quickly classified, thereby improving the timeliness and accuracy of fault handling, helping to quickly locate and eliminate faults, and achieving the beneficial effects of fast ranging convergence speed and high positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Schematic diagram of traveling waves of a power transmission line fault in an embodiment of the present invention. Figure 2 Schematic diagram of traveling waves of a power transmission line fault in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] See also Figure 1 and Figure 2 , a transmission line acoustic wave positioning method based on wavelet analysis, comprising the following steps: S1. Install acoustic sensors at both ends of the transmission line to detect the sound wave signals generated after the line fault. The acoustic sensors convert the sound signals into electrical signals, and select the current signals for analysis; S2. In order to eliminate the imbalance caused by coupling between transmission lines, the collected current signal is decoupled, and the collected current signal is transformed into a symmetrical component to obtain a current line mode component and a current zero mode component, and one of the current line mode components is selected for analysis; S3, decomposing the current line mode component by Mallat algorithm to obtain the wavelet coefficients of the approximate part and the wavelet coefficients of the detail part; S4. Since the acoustic wave signals collected from the line often contain certain noise, and the noise signal will form certain misjudgment information and interfere with the identification of the wave head, in order to eliminate the influence of noise on the positioning result, the current line mode component is Perform further denoising processing; S5, the wavelet coefficients of the details in S4 After normalization, the corresponding signal is obtained ,Will With variables By multiplying and filtering out misjudgment information, the positioning curve can be obtained. , by the positioning curve Get the time it takes for the fault sound wave to reach the measuring end.

[0034] Real-time monitoring: Acoustic sensors are installed at both ends of the transmission line and can capture the acoustic wave signals generated after a fault in real time, which makes monitoring almost instantaneous and enables rapid response to faults.

[0035] Efficient signal processing: Through decoupling processing and wavelet transform methods, signal features can be quickly analyzed and extracted, which reduces the time of signal analysis and improves the convergence speed of ranging.

[0036] Noise Elimination: Through denoising processing, the impact of environmental noise and other interference on the signal is significantly reduced, thereby improving the reliability and accuracy of positioning results.

[0037] Standardized signal: The signal is normalized to enhance its consistency and comparability, making subsequent analysis more accurate and helping to locate the fault more precisely.

[0038] Fast fault identification: By analyzing the high-order mutation points in the positioning curve, the time when the sound wave reaches the measurement end can be quickly identified. This method effectively improves the analysis speed of the fault waveform, thereby improving the accurate judgment of the fault occurrence time.

[0039] Fault mode identification: By analyzing the mutation points, fault modes can be quickly identified and classified, thereby improving the efficiency of fault response and helping to quickly locate and troubleshoot faults.

[0040] Automated processing: The entire process is automated, reducing manual intervention in every link from signal acquisition to fault location, improving overall efficiency and shortening response time.

[0041] Strong processing power: The system can efficiently process large amounts of data and quickly produce accurate results even under high load conditions, ensuring timely and reliable signal processing.

[0042] Specifically, the Mallat algorithm in S3 decomposes the current line mode components by using a decomposition filter, where Corresponding to the wavelet function, it is a bandpass filter. Corresponding to the scale function, it is a low-pass filter. Let the current line mode component be , the decomposition process of Mallat algorithm is:

[0043]

[0044]

[0045] in: is a discrete time sequence number, , It is Layer: wavelet coefficients of the approximate part on the low-frequency part, For the Layer: wavelet coefficients of the detail part of the high-frequency part.

[0046] Specifically, S5 After normalization, The data in is mapped to [0,1], specifically:

[0047] in: for The maximum value in the data, for The minimum value in the data, is the signal after normalization.

[0048] Specifically, in S5 After data mapping is completed, considering the normalized signal If there is still misjudgment information, it is necessary to introduce variables right Filter out the misjudged information in:

[0049] The normalized signal With variables Multiply to get the positioning curve :

[0050] By positioning curve The high-order mutation points in the fault determine the time when the fault sound wave reaches the measurement end.

[0051] Specifically, the symmetric component in S2 is calculated as: Zero sequence component

[0052] Positive sequence component

[0053] Negative sequence component

[0054] in: They are three current signals respectively.

[0055] Specifically, in S4 Perform wavelet transform to obtain approximate coefficients and detail coefficients:

[0056]

[0057] in: is the original current line mode component signal, including noise; For the The approximate wavelet coefficients obtained in the first-order wavelet transform reflect the low-frequency part of the signal; For the The detail wavelet coefficients obtained from the first-order wavelet transform reflect the high-frequency part of the signal and contain noise.

[0058] Specifically, S4 also includes setting a threshold , used for denoising.

[0059] Specifically, the S4 soft threshold processing is calculated as:

[0060] in: The threshold value is set to distinguish signal from noise, which is selected by empirical rules or cross-validation; is the detail wavelet coefficient after soft threshold processing, indicating the signal details after denoising.

[0061] Specifically, the denoising process in S4 also includes reconstructing the signal, and the reconstructed signal is calculated as:

[0062] in: is the denoised signal and the signal after inverse wavelet transform.

[0063] Specifically, the normalization process in S5 also includes using an adaptive normalization method to dynamically adjust the normalization range according to the statistical characteristics of the signal: mean and standard deviation, so as to improve the robustness of signal processing:

[0064] in: yes The mean of is the standard deviation.

[0065] Acoustic sensors are installed at both ends of the transmission line and can instantly detect the acoustic wave signals generated by the fault. This real-time performance ensures the agility of the response to the fault, thereby shortening the time interval between the occurrence of the fault and the detection, greatly improving the overall monitoring efficiency.

[0066] Secondly, the accuracy of signal processing is enhanced through decoupling and wavelet analysis. When the current signal is decoupled, the interference between different lines is minimized, ensuring the purity of the signal. On this basis, the use of wavelet transform can effectively separate the low-frequency and high-frequency components in the signal. This processing step not only improves the signal feature extraction capability, but also makes the analysis of fault signals more sensitive, thereby improving the accuracy of fault location.

[0067] In terms of noise processing, the enhanced denoising capability also significantly improves the reliability of fault location. By denoising the signal, the noise interference from the environment and the equipment itself is reduced, ensuring the extraction of effective signals. This is crucial for subsequent analysis, because clear data can significantly reduce the risk of misjudgment, which directly affects the accuracy of positioning.

[0068] Normalization also helps improve the accuracy of positioning by strengthening the consistency of data. By standardizing the signal, the error introduced by inconsistent data range can be reduced during the analysis process. In this way, subsequent analysis is more accurate, so that problem positioning can be focused on the most relevant data.

[0069] In addition, high-order mutation point analysis further enhances the accuracy of fault location. In the positioning curve, the timely identification of mutation points can quickly determine the moment when the fault sound wave reaches the measurement point. This real-time analysis capability ensures that the fault is quickly located and provides an effective time basis for maintenance and repair.

[0070] The automation and rapid processing capabilities of the entire method improve the efficiency of fault response. By simplifying each link from signal acquisition to fault diagnosis, the system can quickly produce accurate fault location results under high-load operation conditions, improving the timeliness and reliability of response.

[0071] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for acoustic wave positioning of power transmission lines based on wavelet analysis, characterized in that: The following steps are involved: S1. Install acoustic sensors at both ends of the transmission line to detect the sound wave signals generated after the line fault. The acoustic sensors convert the sound signals into electrical signals, and select the current signals for analysis; S2. In order to eliminate the imbalance caused by coupling between transmission lines, the collected current signal is decoupled, and the collected current signal is transformed into a symmetrical component to obtain a current line mode component and a current zero mode component, and one of the current line mode components is selected for analysis; S3, decomposing the current line mode component by Mallat algorithm to obtain the wavelet coefficients of the approximate part and the wavelet coefficients of the detail part; S4. Since the acoustic wave signals collected from the line often contain certain noise, and the noise signal will form certain misjudgment information and interfere with the identification of the wave head, in order to eliminate the influence of noise on the positioning result, the current line mode component is Perform further denoising processing; S5, wavelet coefficients of the details after S4 denoising After normalization, the corresponding signal is obtained , the signal With variables By multiplying and filtering out misjudgment information, the positioning curve can be obtained. , by the positioning curve Get the time it takes for the fault sound wave to reach the measurement end.

2. The method for acoustic wave positioning of a power transmission line based on wavelet analysis according to claim 1, characterized in that: The Mallat algorithm in S3 decomposes the current line mode component by a decomposition filter, where Corresponding to the wavelet function, it is a bandpass filter. Corresponding to the scale function, it is a low-pass filter. Let the current line mode component be , the decomposition process of Mallat algorithm is: in: is a discrete time sequence number, , It is Layer: wavelet coefficients of the approximate part on the low-frequency part, For the Layer: wavelet coefficients of the detail part of the high-frequency part.

3. The method for acoustic wave positioning of a power transmission line based on wavelet analysis according to claim 2, characterized in that: The S5 After normalization, The data in is mapped to [0,1], specifically: in: for The maximum value in the data, for The minimum value in the data, is the signal after normalization.

4. The method for acoustic wave positioning of a power transmission line based on wavelet analysis according to claim 3 is characterized in that: The S5 After data mapping is completed, considering the normalized signal If there is still misjudgment information, it is necessary to introduce variables right Filter out the misjudged information in: The normalized signal With variables Multiply to get the positioning curve : By positioning curve The high-order mutation points in the fault determine the time when the fault sound wave reaches the measurement end.

5. The method for acoustic wave positioning of a power transmission line based on wavelet analysis according to claim 1, characterized in that: The symmetric component of S2 is calculated as: Zero sequence component Positive sequence component Negative sequence component in: They are three current signals respectively.

6. The method for acoustic wave positioning of a power transmission line based on wavelet analysis according to claim 5, characterized in that: The S4 Perform wavelet transform to obtain approximate coefficients and detail coefficients: in: is the original current line mode component signal, including noise; For the The approximate wavelet coefficients obtained in the first-order wavelet transform reflect the low-frequency part of the signal; For the The detail wavelet coefficients obtained from the first-order wavelet transform reflect the high-frequency part of the signal and contain noise.

7. The method for acoustic wave positioning of a power transmission line based on wavelet analysis according to claim 6, characterized in that: S4 also includes setting a threshold , used for denoising.

8. The method for acoustic wave positioning of a power transmission line based on wavelet analysis according to claim 7, characterized in that: The S4 soft thresholding is calculated as: in: The threshold value is set to distinguish signal from noise, which is selected by empirical rules or cross-validation; is the detail wavelet coefficient after soft threshold processing, indicating the signal details after denoising.

9. The method for acoustic wave positioning of a power transmission line based on wavelet analysis according to claim 8, characterized in that: The denoising process in S4 also includes reconstructing the signal, and the reconstructing signal is calculated as: in: is the denoised signal and the signal after inverse wavelet transform.

10. The method for acoustic wave positioning of a power transmission line based on wavelet analysis according to claim 4, characterized in that: The normalization process in S5 also includes using an adaptive normalization method to dynamically adjust the normalization range according to the statistical characteristics of the signal: mean and standard deviation, so as to improve the robustness of signal processing: in: yes The mean of is the standard deviation.

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