Partial discharge positioning method, device and equipment based on TDR principle and medium
By employing a partial discharge localization method based on the TDR principle, utilizing high-frequency current transformers and wavelet transform filtering techniques, combined with threshold judgment and correlation analysis, the problem of insufficient signal anti-interference capability and localization accuracy in traditional methods is solved, achieving rapid and accurate partial discharge localization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-16
- Publication Date
- 2026-03-10
AI Technical Summary
Traditional partial discharge detection methods are insufficient in terms of signal anti-interference capability and positioning accuracy in complex electromagnetic environments. In particular, the signals detected by high-frequency current transformers contain a lot of noise, which affects the accurate identification and positioning of partial discharge signals.
A partial discharge localization method based on the TDR principle is adopted. The signal is acquired through a high-frequency current transformer. By combining wavelet transform filtering, threshold judgment method and correlation analysis method, the starting point of pulse and reflection signal is determined, the distance between the partial discharge source and the high-frequency current transformer is calculated, and the precise location is achieved by using the time-domain reflection method.
It enables rapid and accurate partial discharge location in power systems with strong electromagnetic interference, reduces the impact of noise interference, improves the signal-to-noise ratio, and ensures the reliability and accuracy of the location.
Smart Images
Figure CN121633733A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power technology, and in particular to a partial discharge location method, device, equipment, and medium based on the TDR principle. Background Technology
[0002] In the field of power systems, power supply reliability is highly dependent on the safe and stable operation of the system, and the detection and location of partial discharge phenomena are crucial to ensuring the overall stability of the power system.
[0003] Traditional partial discharge detection methods primarily rely on pulsed current methods. While this method can detect live signals, its anti-interference capability and positioning accuracy remain insufficient in complex electromagnetic environments. In recent years, high-frequency current transformer (HFCT) technology has been widely used in partial discharge detection due to its excellent high-frequency response characteristics. HFCT captures partial discharge signals by detecting high-frequency current pulses on the grounding wire, offering advantages such as non-invasiveness and no power outage required. However, the signals detected by HFCT often contain significant noise, which may originate from surrounding electromagnetic interference or other electrical equipment, thus affecting the accurate identification and positioning of partial discharge signals. Summary of the Invention
[0004] This invention provides a partial discharge localization method, device, equipment, and medium based on the TDR principle, which can obtain rapid and accurate partial discharge localization results.
[0005] In a first aspect, embodiments of the present invention provide a partial discharge localization method based on the TDR principle, comprising:
[0006] The original partial discharge signal collected by the high-frequency current transformer is acquired, and the original partial discharge signal is filtered by wavelet transform to obtain the final partial discharge signal.
[0007] Based on the final partial discharge signal, the starting point of the pulse signal and the starting point of the reflected signal are determined by combining the threshold judgment method and the correlation analysis method. Based on the starting point of the pulse signal and the starting point of the reflected signal, the distance between the partial discharge source and the high-frequency current transformer is calculated, so as to determine the location of the partial discharge source based on the distance.
[0008] This invention acquires partial discharge signals in a power system using a high-frequency current transformer, leveraging the high response of the transformer to fully capture the partial discharge signal. The original partial discharge signal is filtered using wavelet transform to obtain a final partial discharge signal, thus denoising and extracting features from the original signal, resulting in a clearer and more distinct final partial discharge signal, providing reliable data support for subsequent partial discharge location. The distance between the partial discharge source and the high-frequency current transformer is calculated using the Time Domain Reflectometry (TDR) method, allowing for partial discharge location based on this distance. Since high-frequency current transformers are typically installed on grounding wires, they have good coupling to high-frequency pulses and a high compatibility with TDR, enabling accurate partial discharge location results while simplifying the calculation process. Compared to existing technologies, this application achieves fast and accurate partial discharge location results.
[0009] Furthermore, the step of filtering the original partial discharge signal using wavelet transform to obtain the final partial discharge signal specifically involves:
[0010] The original partial discharge signal is decomposed into multiple layers to decompose it into approximation coefficients and detail coefficients of different frequencies. The original partial discharge signal is then reconstructed based on the approximation coefficients and detail coefficients to obtain the final partial discharge signal.
[0011] This invention utilizes wavelet transform to effectively eliminate noise interference and accurately extract partial discharge signals, ensuring high reliability of the partial discharge detection even in power system environments with strong electromagnetic interference. Furthermore, the filtered signal should have a higher signal-to-noise ratio, more clearly reflecting the characteristics of the partial discharge signal and providing a more accurate signal basis for subsequent partial discharge localization.
[0012] Furthermore, before reconstructing the original partial discharge signal based on the approximation coefficients and detail coefficients, the method further includes thresholding the detail coefficients, wherein the thresholding of the detail coefficients specifically involves:
[0013] The noise characteristics and signal strength of the original partial discharge signal are extracted, and the threshold of each detail coefficient is determined based on the noise characteristics and the signal strength.
[0014] The embodiments of the present invention dynamically determine the threshold of each detail coefficient, thereby better adapting to filtering requirements under different signal and noise conditions.
[0015] Furthermore, the step of determining the pulse signal start point based on the final partial discharge signal, combined with threshold judgment and correlation analysis, specifically involves:
[0016] Extract the maximum value from the final partial discharge signal, and determine the pulse signal threshold based on the maximum value;
[0017] Based on the pulse signal threshold, the final partial discharge signals are traversed, and the point corresponding to the first final partial discharge signal that exceeds the pulse signal threshold during the traversal is determined as the first pulse signal starting point.
[0018] Calculate the correlation coefficient between the final partial discharge signal and the preset pulse template signal, and determine the starting point of the second pulse signal based on the correlation coefficient;
[0019] Determine whether the starting point of the first pulse signal and the starting point of the second pulse signal are the same point. If they are the same point, select the starting point of the first pulse signal as the starting point of the pulse signal; otherwise, select the starting point of the second pulse signal as the starting point of the pulse signal.
[0020] This invention uses a combination of threshold judgment and correlation analysis to determine the pulse signal starting point based on the final partial discharge signal. By obtaining the pulse signal starting point through two methods and verifying the results of the two methods, a more reliable and accurate pulse signal starting point is obtained, providing reliable data preparation for subsequent partial discharge localization.
[0021] Furthermore, the step of determining the starting point of the reflected signal based on the final partial discharge signal, combined with the threshold judgment method and the correlation analysis method, specifically involves:
[0022] Extract the maximum value from the final partial discharge signal, and determine the reflected signal threshold based on the maximum value;
[0023] Based on the reflected signal threshold, the final partial discharge signals are traversed, and the point corresponding to the first final partial discharge signal that exceeds the reflected signal threshold during the traversal is determined as the first reflected signal starting point.
[0024] Calculate the correlation coefficient between the final partial discharge signal and the preset reflection template signal, and determine the starting point of the second reflection signal based on the correlation coefficient;
[0025] Determine whether the starting point of the first reflected signal and the starting point of the second reflected signal are the same point. If they are the same point, select the starting point of the first reflected signal as the starting point of the reflected signal; otherwise, select the starting point of the second reflected signal as the starting point of the reflected signal.
[0026] This invention uses a method combining threshold judgment and correlation analysis to determine the starting point of the reflected signal based on the final partial discharge signal. This allows for the acquisition of the starting point of the reflected signal through two methods, and the results of the two methods are cross-validated to obtain a more reliable and accurate starting point of the reflected signal, providing reliable data preparation for subsequent partial discharge localization.
[0027] Furthermore, before calculating the distance between the partial discharge source and the high-frequency current transformer, it is also necessary to calculate the time difference between the pulse signal and the reflected signal. Specifically, the calculation of the time difference between the pulse signal and the reflected signal involves:
[0028] Calculate the difference between the starting point of the pulse signal and the starting point of the reflected signal to obtain the starting point distance difference;
[0029] Obtain the preset high-frequency current transformer sampling frequency, and calculate the time difference between the pulse signal and the reflected signal based on the high-frequency current transformer sampling frequency and the distance difference from the starting point.
[0030] This invention provides data support for subsequent distance calculations by calculating the time difference between the pulse signal and the reflected signal.
[0031] Furthermore, the calculation of the distance between the partial discharge power source and the high-frequency current transformer specifically involves:
[0032] The signal propagation speed of the original partial discharge signal is obtained, and the distance between the partial discharge source and the high-frequency current transformer is calculated based on the time difference and the signal propagation speed.
[0033] This invention calculates the distance between the partial discharge source and the high-frequency current transformer based on the principle of time-domain reflectometry (TDR). It makes good use of the perfect coupling between TDR and the high-frequency current transformer, reducing the computational complexity and thus obtaining accurate and efficient partial discharge location results. Compared with complex location models based on deep learning algorithms, which are easily limited by the available resources of the equipment, this invention can adapt to equipment in various scenarios and has better applicability.
[0034] Secondly, embodiments of the present invention provide a partial discharge location device based on the TDR principle, comprising a partial discharge signal acquisition module and a partial discharge location module, wherein,
[0035] The partial discharge signal acquisition module is used to acquire the original partial discharge signal collected by the high-frequency current transformer, and to filter the original partial discharge signal through wavelet transform to obtain the final partial discharge signal.
[0036] The partial discharge location module is used to determine the pulse signal start point and the reflection signal start point based on the final partial discharge signal by combining threshold judgment and correlation analysis. Based on the pulse signal start point and the reflection signal start point, the module calculates the distance between the partial discharge source and the high-frequency current transformer to determine the location of the partial discharge source based on the distance.
[0037] This invention utilizes a local signal acquisition module with wavelet transform to effectively eliminate noise interference and accurately extract partial discharge signals, ensuring high reliability of partial discharge detection even in power system environments with strong electromagnetic interference. By combining the signal characteristics filtered by wavelet transform with the partial discharge location module and the principle of time-domain reflection, a partial discharge source location model can accurately calculate the location of the partial discharge source. Compared to traditional fault location methods, this technology significantly improves location accuracy, enabling rapid fault location, thereby effectively shortening power outage time and reducing maintenance costs. Furthermore, this invention does not require excessively large equipment resources and is applicable to various working environments, demonstrating good applicability.
[0038] Thirdly, embodiments of the present invention provide a terminal device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;
[0039] The memory is used to store at least one executable instruction that causes the processor to perform the operation of the partial discharge localization method based on the TDR principle as described in any of the above.
[0040] Fourthly, embodiments of the present invention provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device / apparatus where the computer-readable storage medium is located to perform the partial discharge localization method based on the TDR principle as described in any one of the above.
[0041] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description
[0042] Figure 1 A schematic diagram of a partial discharge localization method based on the TDR principle provided in an embodiment of the present invention;
[0043] Figure 2 A flowchart illustrating a wavelet transform filtering process, as exemplified by an embodiment of the present invention;
[0044] Figure 3 A flowchart illustrating partial discharge source localization is provided as an example of an embodiment of the present invention.
[0045] Figure 4 A schematic flowchart illustrating a partial discharge localization method based on the TDR principle, as exemplified by an embodiment of the present invention;
[0046] Figure 5 This is a structural diagram of a partial discharge location device based on the TDR principle provided in an embodiment of the present invention. Detailed Implementation
[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0048] Example 1:
[0049] like Figure 1 As shown, an embodiment of the present invention provides a partial discharge localization method based on the TDR principle, comprising the following steps:
[0050] The original partial discharge signal collected by the high-frequency current transformer is acquired, and the original partial discharge signal is filtered by wavelet transform to obtain the final partial discharge signal.
[0051] Based on the final partial discharge signal, the starting point of the pulse signal and the starting point of the reflected signal are determined by combining the threshold judgment method and the correlation analysis method. Based on the starting point of the pulse signal and the starting point of the reflected signal, the distance between the partial discharge source and the high-frequency current transformer is calculated, so as to determine the location of the partial discharge source based on the distance.
[0052] It should be noted that high-frequency current transformers (HFCTs) are primarily designed for partial discharge detection in power systems. Their operating frequency range is typically 1MHz-30MHz, catering to the characteristics of high-frequency pulse signals generated by partial discharges and the frequencies of common interference signals in power systems. This provides an accurate signal foundation for subsequent signal processing and partial discharge localization.
[0053] To ensure accurate capture of the high-frequency pulse current signal generated by partial discharge, a high-frequency current transformer (HFCT) must be installed on the grounding wire. The frequency range of this transformer must match the frequency range of the partial discharge signal to guarantee accurate capture. The HFCT should be installed near areas prone to partial discharge for more sensitive signal detection. A high-precision data acquisition card should be connected to the HFCT, with an appropriate sampling rate (e.g., 50MHz or higher) to ensure accurate recording of the detailed characteristics of the partial discharge signal. The data acquisition card should have sufficient storage capacity and fast data transmission capabilities to acquire and store large amounts of signal data in a short time.
[0054] Furthermore, the HFCT should be installed using a dedicated fixing device to ensure tight contact and secure fixation with the grounding wire, preventing poor contact or loosening due to equipment vibration or other factors. The fixing device can be a clamp-type structure made of high-strength insulating material, with an arc-shaped groove on the inner side of the clamp that matches the outer diameter of the grounding wire to ensure a tight fit. During installation, a torque wrench must be used to tighten the bolts on the clamp to the specified torque value to ensure the stability and reliability of the HFCT installation.
[0055] Preferably, the initial processing of the acquired raw partial discharge (PD) signal, which is typically weak, requires amplification by a low-noise amplifier to increase the signal-to-noise ratio. The amplification factor should be adjusted according to the actual signal strength and detection requirements to ensure that the amplified signal, while effectively removing high-frequency noise and low-frequency interference causing saturation distortion, will not significantly impact subsequent processing, nor will it cause saturation distortion. The design of the analog filter should consider the frequency characteristics of the PD signal, and its passband should encompass the main frequency components of the PD signal while effectively suppressing other interference signals. For example, in an optional embodiment, a band filter with a passband range of 1MHz-30MHz is selected to meet the detection requirements of the PD signal.
[0056] In this embodiment, the step of filtering the original partial discharge signal using wavelet transform to obtain the final partial discharge signal specifically involves: performing multi-level decomposition on the original partial discharge signal to decompose it into approximation coefficients and detail coefficients of different frequencies, and reconstructing the original partial discharge signal based on the approximation coefficients and detail coefficients to obtain the final partial discharge signal.
[0057] In this embodiment, before reconstructing the original partial discharge signal based on the approximation coefficients and detail coefficients, a thresholding process is performed on the detail coefficients. Specifically, the thresholding process involves extracting the noise characteristics and signal intensity of the original partial discharge signal, and determining a threshold for each detail coefficient based on the noise characteristics and the signal intensity.
[0058] Preferably, before performing wavelet transform, the system adaptively selects a suitable wavelet basis function. This selection process needs to comprehensively consider factors such as signal characteristics, the complexity of calculating the wavelet transform, and the filtering effect. In addition, a comprehensive evaluation should be conducted based on the specific frequency distribution characteristics and noise level of the partial discharge signal. In one optional embodiment, for high-frequency partial discharge signals, wavelet basis functions with better high-frequency resolution, such as the Daubechies wavelet (db4), are preferred; in another optional embodiment, for low-frequency partial discharge signals, wavelet basis functions with better low-frequency resolution, such as the Coiflet wavelet (coif3), are selected.
[0059] Furthermore, during wavelet decomposition, the system will rationally determine the number of decomposition levels based on the frequency distribution range and noise level of the signal. Generally, for partial discharge signals with a wide frequency range and high noise level, 4-5 levels of wavelet decomposition can be selected; for partial discharge signals with a narrow frequency range and low noise level, 3-4 levels of wavelet decomposition can be selected.
[0060] Furthermore, when thresholding the detail coefficients, the system employs an adaptive thresholding method, dynamically determining the threshold for each detail coefficient based on the noise characteristics and signal strength of the partial discharge signal. In the specific implementation, Donoho's general thresholding theorem is used, combined with local characteristics of the partial discharge signal, such as local energy and local extrema, to adaptively adjust the threshold. For signal regions with high local energy, the threshold can be appropriately increased to remove more noise interference; for signal regions with low local energy, the threshold can be appropriately decreased to retain more signal details.
[0061] Furthermore, after thresholding the detail coefficients, the wavelet number is reconstructed based on the processed detail coefficients and approximation coefficients. Wavelet reconstruction restores the complete signal shape while removing noise interference and preserving the effective characteristics of the partial discharge signal. The filtered signal should have a higher signal-to-noise ratio, more clearly reflecting the characteristics of the partial discharge signal and providing a more accurate signal basis for subsequent partial discharge localization.
[0062] To better illustrate the working principle and steps of filtering the original partial discharge signal using wavelet transform in this embodiment, please refer to... Figure 2This is one example, which is a flowchart illustrating a wavelet transform filtering process according to an embodiment of the present invention.
[0063] In this embodiment, determining the pulse signal start point based on the final partial discharge signal, combined with a threshold judgment method and a correlation analysis method, specifically involves: extracting the maximum value in the final partial discharge signal and determining a pulse signal threshold based on the maximum value; traversing the final partial discharge signal according to the pulse signal threshold, and determining the point corresponding to the first final partial discharge signal exceeding the pulse signal threshold during the traversal as the first pulse signal start point; calculating the correlation coefficient between the final partial discharge signal and a preset pulse template signal, and determining the second pulse signal start point based on the correlation coefficient; determining whether the first pulse signal start point and the second pulse signal start point are the same point; if they are the same point, selecting the first pulse signal start point as the pulse signal start point; otherwise, selecting the second pulse signal start point as the pulse signal start point.
[0064] In this embodiment, determining the starting point of the reflected signal based on the final partial discharge signal, combined with a threshold judgment method and a correlation analysis method, specifically involves: extracting the maximum value in the final partial discharge signal and determining a reflected signal threshold based on the maximum value; traversing the final partial discharge signal according to the reflected signal threshold, and determining the point corresponding to the first final partial discharge signal exceeding the reflected signal threshold during the traversal as the first reflected signal starting point; calculating the correlation coefficient between the final partial discharge signal and a preset reflected template signal, and determining the second reflected signal starting point based on the correlation coefficient; determining whether the first reflected signal starting point and the second reflected signal starting point are the same point, and if they are the same point, selecting the first reflected signal starting point as the reflected signal starting point, otherwise selecting the second reflected signal starting point as the reflected signal starting point.
[0065] It should be noted that TDR can be easily coupled with high-frequency current transformers (HFCTs), which are typically installed on the grounding wire and have good coupling effect on high-frequency PD pulses, making it one of the key coupling methods for achieving single-ended measurement. Based on the principle of time-domain reflectometry (TDR), the filtered partial discharge signal is analyzed to determine the source location of the partial discharge. The key mechanism of TDR technology is that once a partial discharge occurs internally, a high-frequency pulse signal is triggered. The propagating signal is reflected when it passes through discontinuities such as contacts or terminals. The time difference between the pulse signal and the reflected signal is accurately measured and determined, and the distance between the partial discharge location and the detection device is also accurately calculated, thereby achieving precise location of the partial discharge.
[0066] Preferably, before performing TDR positioning, the filtered partial discharge signal is preprocessed to ensure signal stability and consistency. The preprocessing steps include:
[0067] 1) Normalization: The signal amplitude is normalized to the [0,1] interval to eliminate differences between different signal amplitudes, facilitating subsequent processing. The normalization formula is:
[0068]
[0069] Among them, s norm For the normalized signal, s filt The filtered signal, s min s is the minimum value of the filtered signal. max This represents the maximum value of the filtered signal.
[0070] 2) Remove DC bias: By subtracting the mean of the signal, the DC bias component in the signal is removed, making the signal centered at zero. The processing formula is:
[0071] s zm =s norm -μ norm
[0072] Among them, s zm To remove the signal after DC bias, μ norm The mean of the normalized signal.
[0073] Furthermore, based on the final partial discharge signal, the starting point of the pulse signal is determined by combining the threshold judgment method and the correlation analysis method.
[0074] In an optional embodiment, the pulse signal start point determination process is as follows:
[0075] 1) Set a threshold, typically a certain percentage (e.g., 30%) of the maximum signal value, to initially identify the starting point of the partial discharge pulse signal. The threshold formula is:
[0076] θ = 0.3 × s max_zm
[0077] Where θ is the threshold and smax_zm is the maximum value of the signal after removing the DC bias.
[0078] By traversing the signal, the first point that exceeds the threshold is found as the pulse start point.
[0079] 2) To improve the accuracy of pulse detection, correlation analysis is used to further verify the pulse start point. A template signal with characteristics similar to the partial discharge pulse signal is selected, and the correlation coefficient between the template signal and the input signal is calculated. The system can adjust the size of the correlation analysis window according to the frequency characteristics and noise level of the signal. The specific calculation formula for the correlation analysis is as follows:
[0080] r = corr(t,s) zm [i:i+w]
[0081] Where r is the correlation coefficient, t is the template signal, szm[i:i+w] is the signal segment of length w starting from the i-th sampling point, and w is the size of the correlation analysis window.
[0082] Near the pulse initiation point, the correlation coefficient will reach its maximum value, thus further confirming the location of the pulse initiation point.
[0083] Optionally, when inconsistencies arise between the threshold determination and the correlation analysis results, the system needs to adopt a comprehensive strategy to redetermine the pulse start point. Specifically, the threshold determination result may be affected by noise or signal amplitude variations, leading to misjudgment; while the correlation analysis result may be affected by insufficient matching between the template signal and the actual signal, or by changes in local signal characteristics resulting in inaccurate correlation coefficients.
[0084] In this scenario, the system prioritizes the results of correlation analysis, as it better captures local signal features. Simultaneously, the system adjusts the threshold appropriately, re-evaluates the threshold, and re-identifies the pulse initiation point. Furthermore, the system incorporates local features for further refinement. Near the threshold and correlation analysis results, the system analyzes the signal's local energy. If the local energy is high, it indicates a strong signal in that region, potentially representing the true pulse initiation point. The system then examines the signal's local extrema, and by combining the location and amplitude of these extrema, further confirms the pulse initiation point.
[0085] If the correlation analysis results are still inaccurate, the system will adjust the size of the correlation analysis window and recalculate the correlation coefficient. Multiple template signals are selected for correlation analysis, and the result with the highest correlation coefficient is used as the final judgment. Analysis is combined with time synchronization and signal propagation characteristics to ensure the accuracy of the time synchronization module and avoid misjudgments caused by time synchronization errors. The signal propagation path and reflection characteristics are analyzed by combining the signal propagation speed and the structural characteristics of the equipment to further verify the location of the pulse start point.
[0086] Through the comprehensive analysis described above, the system will ultimately determine the location of the pulse start point. If, after multiple adjustments and verifications, a consistent result cannot be determined, the signal will be marked as an "uncertain signal," and relevant data will be recorded for further analysis and algorithm optimization. Simultaneously, the system will continuously optimize the threshold judgment and correlation analysis algorithms based on data from actual applications, improving the system's accuracy. Through a feedback mechanism, the analysis results of uncertain signals are fed back into the system to adjust parameters such as thresholds, optimize template signals, and correlation analysis windows, gradually improving system performance.
[0087] Furthermore, based on the final partial discharge signal, the starting point of the reflected signal is determined by combining the threshold judgment method and the correlation analysis method.
[0088] In an alternative embodiment, the detection window for the reflected signal can be adjusted according to the signal propagation speed and the length of the device, for example, set to twice the signal propagation time.
[0089] After determining the pulse start point, the system enters the reflected signal detection phase. First, based on the device length L and the signal propagation speed v, the detection window T is set as follows:
[0090]
[0091] Within this window, the system uses a threshold to initially locate the starting point of the reflected signal. The threshold θ is set to a certain proportion p of the maximum signal value smax.
[0092] θ=p×s max
[0093] Subsequently, correlation analysis was performed using a template signal t that has similar characteristics to the reflected signal, and the starting point location was further confirmed by calculating the correlation coefficient r.
[0094]
[0095] Where s is the signal, , where are the means of the signal and the template signal, respectively, and w is the size of the correlation analysis window. The position corresponding to the maximum value of the correlation coefficient is the starting point of the reflected signal.
[0096] If the threshold judgment and the correlation analysis results are inconsistent, the system prioritizes the correlation analysis results and adjusts the threshold θ or the correlation analysis window w appropriately before re-detecting. Simultaneously, it incorporates local signal energy and extreme point characteristics for correction to ensure the accuracy of the reflected signal's starting point.
[0097] In this embodiment, the calculation of the time difference between the pulse signal and the reflected signal specifically involves: calculating the difference between the starting point of the pulse signal and the starting point of the reflected signal to obtain the starting point distance difference; obtaining a preset high-frequency current transformer sampling frequency; and calculating the time difference between the pulse signal and the reflected signal based on the high-frequency current transformer sampling frequency and the starting point distance difference.
[0098] In an optional embodiment, the time difference is calculated using the following formula:
[0099]
[0100] Where Δt is the time difference, f s r is the sampling frequency. p p represents the starting point position of the reflected signal. s This indicates the starting point position of the pulse signal.
[0101] In this embodiment, calculating the distance between the partial discharge source and the high-frequency current transformer specifically involves: obtaining the signal propagation speed of the original partial discharge signal, and calculating the distance between the partial discharge source and the high-frequency current transformer based on the time difference and the signal propagation speed.
[0102] In an optional embodiment, the distance calculation formula is:
[0103]
[0104] Where v is the signal propagation speed, and Δt is the time difference between the pulse signal and the reflected signal. Since the signal propagates back and forth between the partial discharge source and the detection point, the calculation result needs to be divided by 2.
[0105] To better illustrate the working principle and procedure of partial discharge localization in this embodiment, please refer to... Figure 3 One example is a partial discharge localization flowchart exemplified by an embodiment of the present invention.
[0106] Preferably, during partial discharge localization, the propagation time of the pulse signal in the filtered partial discharge signal is first measured through precise time synchronization and a pulse detection algorithm. Time synchronization ensures synchronized signal acquisition, avoiding time deviations caused by positioning errors. The pulse detection algorithm accurately identifies the start and reflection points of the partial discharge pulse signal, improving the accuracy of time measurement. When measuring the propagation time of the pulse signal in the filtered partial discharge signal, the time synchronization technology employs a high-precision clock synchronization module with nanosecond-level time synchronization accuracy, ensuring that the synchronization error of signal acquisition is within the microsecond level. The pulse detection algorithm uses a combination of threshold judgment and correlation analysis. By setting an appropriate threshold, the start and reflection points of the partial discharge pulse signal are initially identified. Correlation analysis is then used to further verify the accuracy of the identification results, thereby improving the accuracy of time measurement. When determining the signal propagation speed, in addition to experimental or theoretical calculations, a comprehensive analysis can be conducted considering the actual operating status of the power equipment and environmental factors. In experimental measurements, multiple measurements can be taken under different environmental conditions such as temperature and humidity to obtain more accurate signal propagation speed data. In theoretical calculations, the signal propagation speed can be corrected and adjusted based on the actual operating parameters of the power equipment, such as the aging degree of the equipment's insulation materials and the distribution of the electromagnetic field inside the equipment.
[0107] Preferably, determining the signal propagation speed is one of the key parameters for TDR positioning. The structure and materials of electrical equipment affect the signal propagation speed, which needs to be determined experimentally or theoretically based on the actual model and parameters. The signal propagation speed can be calculated by transmitting a pulse signal between two points of known distance and measuring its propagation time, or by theoretical calculation based on the structural and material characteristics of the electrical equipment and the formulas for the propagation speed of electromagnetic waves in different media.
[0108] Preferably, a partial discharge (PD) source localization model based on the TDR principle is established. This model combines the characteristics of the filtered PD signal with the structural parameters of the power equipment. Through mathematical modeling and algorithm optimization, the accuracy and reliability of PD source localization are improved. The localization model needs to establish accurate signal propagation and reflection models. Data fitting and machine learning methods are used to optimize the model and improve its accuracy. By analyzing and fitting a large amount of actual measurement data, the parameters in the model are adjusted to better match the actual PD signal transmission patterns. Machine learning algorithms are used to learn and classify the characteristics of PD signals, improving the model's ability to identify different PD types and locations, thus making the local PD source localization more accurate.
[0109] To better illustrate the complete working principle and process of this embodiment, please refer to... Figure 4 One example is a partial discharge localization flowchart based on the TDR principle, which is an embodiment of the present invention.
[0110] This invention acquires partial discharge signals in a power system using a high-frequency current transformer, leveraging the high response of the transformer to fully capture the partial discharge signal. The original partial discharge signal is filtered using wavelet transform to obtain a final partial discharge signal, thus denoising and extracting features from the original signal, resulting in a clearer and more distinct final partial discharge signal, providing reliable data support for subsequent partial discharge location. The distance between the partial discharge source and the high-frequency current transformer is calculated using the Time Domain Reflectometry (TDR) method, allowing for partial discharge location based on this distance. Since high-frequency current transformers are typically installed on grounding wires, they have good coupling to high-frequency pulses and a high compatibility with TDR, enabling accurate partial discharge location results while simplifying the calculation process. Compared to existing technologies, this application achieves fast and accurate partial discharge location results.
[0111] Example 2:
[0112] like Figure 5 As shown, this embodiment provides a partial discharge location device based on the TDR principle, including a partial discharge signal acquisition module 001 and a partial discharge location module 002, wherein,
[0113] The partial discharge signal acquisition module 001 is used to acquire the original partial discharge signal collected by the high-frequency current transformer, and to filter the original partial discharge signal through wavelet transform to obtain the final partial discharge signal.
[0114] The partial discharge location module 002 is used to determine the starting point of the pulse signal and the starting point of the reflected signal based on the final partial discharge signal by combining threshold judgment and correlation analysis, and to calculate the distance between the partial discharge source and the high-frequency current transformer based on the starting point of the pulse signal and the starting point of the reflected signal, so as to determine the location of the partial discharge source based on the distance.
[0115] In this embodiment, the partial discharge signal acquisition module 001 performs wavelet transform to filter the original partial discharge signal to obtain the final partial discharge signal. Specifically, the partial discharge signal acquisition module 001 performs multi-level decomposition on the original partial discharge signal to decompose the original partial discharge signal into approximation coefficients and detail coefficients of different frequencies, and reconstructs the original partial discharge signal according to the approximation coefficients and detail coefficients to obtain the final partial discharge signal.
[0116] In this embodiment, the partial discharge location module 002 determines the pulse signal starting point based on the final partial discharge signal, combined with a threshold judgment method and a correlation analysis method. Specifically, the partial discharge location module 002 extracts the maximum value in the final partial discharge signal and determines a pulse signal threshold based on the maximum value; based on the pulse signal threshold, it traverses the final partial discharge signal and determines the point corresponding to the first final partial discharge signal exceeding the pulse signal threshold during the traversal as the first pulse signal starting point; it calculates the correlation coefficient between the final partial discharge signal and a preset pulse template signal and determines a second pulse signal starting point based on the correlation coefficient; it determines whether the first pulse signal starting point and the second pulse signal starting point are the same point. If they are the same point, the first pulse signal starting point is selected as the pulse signal starting point; otherwise, the second pulse signal starting point is selected as the pulse signal starting point.
[0117] Furthermore, the partial discharge location module 002 determines the starting point of the reflected signal based on the final partial discharge signal, using a threshold judgment method and a correlation analysis method. Specifically, the partial discharge location module 002 extracts the maximum value in the final partial discharge signal and determines a reflected signal threshold based on the maximum value; based on the reflected signal threshold, it traverses the final partial discharge signal and determines the point corresponding to the first final partial discharge signal exceeding the reflected signal threshold during the traversal as the first reflected signal starting point; it calculates the correlation coefficient between the final partial discharge signal and a preset reflected template signal and determines the second reflected signal starting point based on the correlation coefficient; it determines whether the first reflected signal starting point and the second reflected signal starting point are the same point. If they are the same point, the first reflected signal starting point is selected as the reflected signal starting point; otherwise, the second reflected signal starting point is selected as the reflected signal starting point.
[0118] Furthermore, the partial discharge location module 002 calculates the time difference between the pulse signal and the reflected signal. Specifically, the partial discharge location module 002 calculates the difference between the starting point of the pulse signal and the starting point of the reflected signal to obtain the starting point distance difference; obtains a preset high-frequency current transformer sampling frequency, and calculates the time difference between the pulse signal and the reflected signal based on the high-frequency current transformer sampling frequency and the starting point distance difference.
[0119] Furthermore, the partial discharge location module 002 calculates the distance between the partial discharge power source and the high-frequency current transformer. Specifically, the partial discharge location module 002 acquires the signal propagation speed of the original partial discharge signal and calculates the distance between the partial discharge power source and the high-frequency current transformer based on the time difference and the signal propagation speed.
[0120] This invention utilizes wavelet transform in the local signal acquisition module 001 to effectively eliminate noise interference and accurately extract partial discharge signals, ensuring high reliability of partial discharge detection even in power system environments with strong electromagnetic interference. The partial discharge location module 002, combining the signal characteristics filtered by wavelet transform with the time-domain reflection method, establishes a partial discharge source location model that can accurately calculate the location of the partial discharge source. Compared to traditional fault location methods, this technology significantly improves location accuracy, enabling rapid fault location, thereby effectively shortening power outage time and reducing maintenance costs. Furthermore, this invention does not require excessively large equipment resources and is applicable to various working environments, demonstrating good applicability.
[0121] Example 3:
[0122] This embodiment provides a terminal device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;
[0123] The memory is used to store at least one executable instruction that causes the processor to perform the operation of the partial discharge localization method based on the TDR principle as described in any of the above.
[0124] Example 4:
[0125] This invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device / apparatus containing the computer-readable storage medium to perform the partial discharge localization method based on the TDR principle as described above.
[0126] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.
[0127] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.
Claims
1. A partial discharge locating method based on the TDR principle, characterized in that, The method comprises the steps of: obtaining an original partial discharge signal collected by a high-frequency current transformer, and performing filtering processing on the original partial discharge signal through wavelet transform to obtain a final partial discharge signal; determining a pulse signal starting point and a reflected signal starting point according to the final partial discharge signal, in combination with threshold judgment and correlation analysis, and calculating a distance between a partial discharge source and the high-frequency current transformer according to the pulse signal starting point and the reflected signal starting point, so as to determine the position of the partial discharge source according to the distance.
2. A partial discharge locating method based on TDR principle as claimed in claim 1, wherein, The filtering processing on the original partial discharge signal through wavelet transform to obtain a final partial discharge signal is specifically: performing multi-layer decomposition on the original partial discharge signal to decompose the original partial discharge signal into approximate coefficients and detail coefficients of different frequencies, and reconstructing the original partial discharge signal according to the approximate coefficients and the detail coefficients to obtain a final partial discharge signal.
3. A partial discharge locating method based on TDR principle as claimed in claim 2, wherein, Before the reconstruction of the original partial discharge signal according to the approximate coefficients and the detail coefficients, the method further comprises threshold processing on the detail coefficients, wherein the threshold processing on the detail coefficients is specifically: extracting noise characteristics and signal intensity of the original partial discharge signal, and determining a threshold value of each detail coefficient according to the noise characteristics and the signal intensity.
4. A partial discharge locating method based on TDR principle as claimed in claim 1, wherein, The determination of the pulse signal starting point according to the final partial discharge signal in combination with threshold judgment and correlation analysis is specifically: extracting a maximum value in the final partial discharge signal, and determining a pulse signal threshold value according to the maximum value; traversing the final partial discharge signal according to the pulse signal threshold value, and determining a first pulse signal starting point corresponding to a point of the final partial discharge signal that first exceeds the pulse signal threshold value in the traversing process; calculating a correlation coefficient of the final partial discharge signal and a preset pulse template signal, and determining a second pulse signal starting point according to the correlation coefficient; determining whether the first pulse signal starting point and the second pulse signal starting point are the same point, and if so, selecting the first pulse signal starting point as the pulse signal starting point, otherwise, selecting the second pulse signal starting point as the pulse signal starting point.
5. A partial discharge locating method based on TDR principle as claimed in claim 1, wherein, The determination of the reflected signal starting point according to the final partial discharge signal in combination with threshold judgment and correlation analysis is specifically: extracting a maximum value in the final partial discharge signal, and determining a reflected signal threshold value according to the maximum value; traversing the final partial discharge signal according to the reflected signal threshold value, and determining a first reflected signal starting point corresponding to a point of the final partial discharge signal that first exceeds the reflected signal threshold value in the traversing process; calculating a correlation coefficient of the final partial discharge signal and a preset reflected template signal, and determining a second reflected signal starting point according to the correlation coefficient; determining whether the first reflected signal starting point and the second reflected signal starting point are the same point, and if so, selecting the first reflected signal starting point as the reflected signal starting point, otherwise, selecting the second reflected signal starting point as the reflected signal starting point.
6. A partial discharge locating method based on TDR principle as claimed in claim 1 wherein, Before the distance between the partial discharge source and the high-frequency current transformer is calculated, the time difference between the pulse signal and the reflected signal is also calculated, wherein the time difference between the pulse signal and the reflected signal is specifically: The difference between the pulse signal starting point and the reflected signal starting point is calculated to obtain a starting point distance difference; A preset high-frequency current transformer sampling frequency is obtained, and the time difference between the pulse signal and the reflected signal is calculated according to the high-frequency current transformer sampling frequency and the starting point distance difference.
7. A partial discharge locating method based on TDR principle as claimed in claim 6, wherein, The distance between the partial discharge source and the high-frequency current transformer is calculated, and the distance between the partial discharge source and the high-frequency current transformer is specifically: The signal propagation speed of the original partial discharge signal is obtained, and the distance between the partial discharge source and the high-frequency current transformer is calculated according to the time difference and the signal propagation speed.
8. A partial discharge locating device based on the TDR principle, characterized in that The method comprises a partial discharge signal acquisition module and a partial discharge positioning module, wherein The partial discharge signal acquisition module is configured to acquire an original partial discharge signal collected by a high-frequency current transformer, and perform filtering processing on the original partial discharge signal through wavelet transform to obtain a final partial discharge signal; The partial discharge positioning module is configured to determine a pulse signal starting point and a reflected signal starting point according to the final partial discharge signal through a method combining threshold judgment and correlation analysis, and calculate the distance between the partial discharge source and the high-frequency current transformer according to the pulse signal starting point and the reflected signal starting point, so as to determine the position of the partial discharge source according to the distance.
9. A terminal device, comprising: The method comprises: a processor, a memory, a communication interface and a communication bus, the processor, the memory and the communication interface complete mutual communication through the communication bus; The memory is configured to store at least one executable instruction, and the executable instruction causes the processor to execute the operation of the partial discharge positioning method based on the TDR principle in any one of claims 1 to 7.
10. A computer readable storage medium characterized by, The computer readable storage medium comprises a stored computer program, wherein when the computer program runs, the device or apparatus where the computer readable storage medium is located is controlled to execute the partial discharge positioning method based on the TDR principle in any one of claims 1 to 7.