Partial discharge positioning method and system for GIS equipment

Abstract

The invention relates to a partial discharge positioning method and system for GIS equipment, and the method comprises the steps: carrying out the collection of a synchronous signal through a plurality of ultrahigh frequency sensors, constructing a multi-channel signal matrix based on a discharge measurement signal, and obtaining a time-aligned multi-sensor signal; performing signal filtering processing on the time-aligned multi-sensor signals to obtain a target frequency band signal corresponding to each ultrahigh frequency sensor; performing Hilbert transformation on the multiple groups of target frequency band signals to determine the envelope of each group of target frequency band signals; performing threshold detection according to the envelopes of the multiple groups of target frequency band signals to obtain a signal first arrival time corresponding to each ultrahigh frequency sensor; and determining the signal time delay of each pair of ultrahigh frequency sensors based on a GCC-PHAT algorithm and the first arrival time of each signal, and carrying out joint solution based on the signal time delay of each pair of ultrahigh frequency sensors so as to determine the position information of the partial discharge source and the corresponding signal propagation speed.

Description

Technical Field

[0001] This invention relates to the field of partial discharge detection technology, and in particular to a method and system for locating partial discharge in gas-insulated switchgear (GIS) equipment. Background Technology

[0002] For current methods of partial discharge detection in GIS equipment using ultra-high frequency (UHF) electromagnetic waves, the advantages of UHF electromagnetic wave detection lie in its high sensitivity and strong anti-interference ability, enabling accurate capture of partial discharge signals under high-voltage environments. However, in practical applications, UHF electromagnetic wave detection also faces problems such as signal attenuation and noise interference, making the accurate location of partial discharge sources a technical challenge that requires attention during partial discharge detection.

[0003] Currently, to ensure accurate location of partial discharge sources during the detection process, there are three corresponding methods: amplitude method, time difference method, and equivalent distance method. Although these three methods can address the location problem of partial discharge sources in the overall detection process to a certain extent, they are all based on the premise that the signal propagation speed is known and constant. Therefore, when performing partial discharge detection on GIS equipment, factors such as the temperature change of the equipment itself and the characteristics of the medium will affect the signal propagation speed, thus leading to poor location accuracy of partial discharge sources.

[0004] Chinese patent application publication number CN116338397A discloses a method, device, electronic device, and storage medium for GIS partial discharge localization. It achieves automated localization of GIS partial discharge by calculating the arrival time and time difference of pulse signals in the GIS channel. However, it requires multiple localization attempts and uses a distribution map of the results and the number of attempts as the localization outcome, resulting in a long processing time and the need for a large amount of data.

[0005] In summary, there is currently a lack of a method and system for locating partial discharges in GIS equipment to solve or partially solve the aforementioned problems. Summary of the Invention

[0006] The purpose of this invention is to overcome the defects of the prior art by providing a method and system for locating partial discharge in GIS equipment, so as to solve or partially solve the problem.

[0007] The objective of this invention can be achieved through the following technical solutions: According to one aspect of the present invention, a method for locating partial discharge in GIS equipment is provided, comprising: By synchronously acquiring signals from multiple ultra-high frequency sensors, a multi-channel signal matrix based on discharge measurement signals is constructed to obtain time-aligned multi-sensor signals; the multi-channel signal matrix is ​​generated based on the discharge measurement signals acquired by the multiple ultra-high frequency sensors. The time-aligned multi-sensor signals are subjected to signal filtering to obtain the target frequency band signal corresponding to each of the ultra-high frequency sensors; Hilbert transform is performed on multiple sets of the target frequency band signals to determine the envelope of each set of the target frequency band signals; Threshold detection is performed based on the envelopes of multiple sets of target frequency band signals to obtain the first arrival time of the signal corresponding to each UHF sensor; the first arrival time of the signal is used to characterize the moment when the signal envelope of the target frequency band signal corresponding to each UHF sensor first exceeds the detection threshold; Based on the GCC-PHAT algorithm and the first arrival time of each signal, the signal time delay of each pair of UHF sensors is determined, and a joint solution is performed based on the signal time delay of each pair of UHF sensors to determine the location information of the partial discharge power source and the corresponding signal propagation speed.

[0008] As a preferred technical solution, the process of performing threshold detection based on the envelopes of multiple sets of target frequency band signals to obtain the first arrival time of the signal corresponding to each ultra-high frequency sensor includes: For each target frequency band signal, the baseline noise segment, median, and median absolute deviation of the baseline noise segment are extracted. A detection threshold is constructed based on the median and the absolute deviation of the median for each of the baseline noise segments; Each target frequency band signal is scanned point by point, and the sampling point of the signal that first exceeds the detection threshold is determined as the first arrival time of the signal.

[0009] As a preferred technical solution, the process of performing signal filtering on the time-aligned multi-sensor signals to obtain the target frequency band signal includes: Based on the first-order difference algorithm, the time-aligned multi-sensor signal is subjected to high-pass filtering to obtain the high-pass filtered multi-sensor signal. Based on the bandpass filter, the DC component and noise and interference within the preset frequency band range in the multi-sensor signal after high-pass filtering are removed to obtain the target frequency band signal.

[0010] As a preferred technical solution, the process of determining the signal time delay of each pair of ultra-high frequency sensors based on the GCC-PHAT algorithm and the first arrival time of each signal includes: Based on the preset window truncation interval and the first arrival time of each signal, a window truncation is performed to obtain the signal window corresponding to the first arrival time of each signal; Based on the GCC-PHAT algorithm and the signal window corresponding to the first arrival time of each signal, the signal time delay of each UHF sensor is calculated and determined.

[0011] As a preferred technical solution, the process of jointly solving for the signal time delay of each pair of ultra-high frequency sensors to determine the location information of the partial discharge source and the corresponding signal propagation speed includes: Based on the preset error sum of squares objective function, the signal time delay of each pair of UHF sensors is jointly solved by the least squares method to determine the location information of the local discharge power source and the corresponding signal propagation speed.

[0012] As a preferred technical solution, after determining the signal time delay of each of the ultra-high frequency sensors, the method further includes: Oversampling techniques and parabolic refinement are employed to improve the detection accuracy of signal correlation for the time delay of the signal.

[0013] In another aspect, the present invention provides a partial discharge location system for GIS equipment, used to implement the aforementioned partial discharge location method for GIS equipment, the system comprising: The signal acquisition module is used to synchronously acquire signals through multiple ultra-high frequency sensors, construct a multi-channel signal matrix based on discharge measurement signals, and obtain time-aligned multi-sensor signals. The filtering module is used to perform signal filtering processing on the time-aligned multi-sensor signals to obtain the target frequency band signal corresponding to each of the ultra-high frequency sensors; The transformation processing module is used to perform Hilbert transform on multiple sets of target frequency band signals to determine the envelope of each set of target frequency band signals; The detection module is used to perform threshold detection based on the envelopes of multiple sets of target frequency band signals to obtain the first arrival time of the signal corresponding to each UHF sensor; the first arrival time of the signal is used to characterize the moment when the signal envelope of the target frequency band signal corresponding to each UHF sensor first exceeds the detection threshold; The joint solution module is used to determine the signal time delay of each pair of UHF sensors based on the GCC-PHAT algorithm and the first arrival time of each signal, and to perform a joint solution based on the signal time delay of each pair of UHF sensors to determine the location information of the partial discharge power source and the corresponding signal propagation speed.

[0014] As a preferred technical solution, the detection module is used for: For each target frequency band signal, the baseline noise segment, median, and median absolute deviation of the baseline noise segment are extracted. A detection threshold is constructed based on the median and the absolute deviation of the median for each of the baseline noise segments; Each target frequency band signal is scanned point by point, and the sampling point of the signal that first exceeds the detection threshold is determined as the first arrival time of the signal.

[0015] As a preferred technical solution, the filtering module is used for: Based on the first-order difference algorithm, the time-aligned multi-sensor signal is subjected to high-pass filtering to obtain the high-pass filtered multi-sensor signal. Based on the bandpass filter, the DC component and noise and interference within the preset frequency band range in the multi-sensor signal after high-pass filtering are removed to obtain the target frequency band signal.

[0016] As a preferred technical solution, the joint solution module includes a delay calculation unit, which is used for: Based on the preset window truncation interval and the first arrival time of each signal, a window truncation is performed to obtain the signal window corresponding to the first arrival time of each signal; Based on the GCC-PHAT algorithm and the signal window corresponding to the first arrival time of each signal, the signal time delay of each UHF sensor is calculated and determined.

[0017] Compared with the prior art, the present invention has at least one of the following beneficial effects: (1) Accurate arrival time calculation: This invention first performs signal filtering, then calculates the generalized cross-correlation function (GCC-PHAT) to determine the signal time delay of each pair of sensors. Combined with sophisticated signal processing techniques, it improves the correlation detection accuracy and ensures the accuracy of time delay measurement. Finally, by jointly solving the local discharge source location information and signal propagation speed, it breaks through the limitation of fixed propagation speed in traditional methods. It can dynamically adapt to the changes in propagation speed in the complex medium environment within GIS equipment, eliminate the positioning error caused by speed assumption deviation, and achieve high-precision estimation of the local discharge source location.

[0018] (2) Improved anti-interference capability and positioning accuracy: This invention uses multiple UHF sensors to synchronously acquire signals and perform time alignment, ensuring that the signals from multiple sensors are compared and processed under a unified time reference, avoiding errors caused by time asynchrony, and laying the foundation for subsequent accurate analysis. The signal filtering process removes DC components, low-frequency noise, and high-frequency interference in a targeted manner, retaining the target frequency band signal, effectively improving the signal-to-noise ratio and reducing noise interference to positioning. The Hilbert transform is applied to the target frequency band signal to obtain the envelope, which enhances the signal amplitude characteristics. Combined with threshold detection to determine the first arrival time of the signal, this method uses statistical means to reduce the impact of noise, accurately captures the signal starting point, and provides a reliable time reference for time difference measurement. Overall, through signal acquisition and processing, time reference determination, accurate measurement of time delay, and joint optimization of propagation speed, the anti-interference capability and positioning accuracy are effectively improved, providing a reliable solution for the accurate positioning of partial discharge in GIS equipment and ensuring the safe and stable operation of the power system. Attached Figure Description

[0019] Figure 1 This is a flowchart of the partial discharge location method for GIS equipment in the embodiment; Figure 2 This is a flowchart of the method for obtaining the first arrival time of the signal in the embodiment; Figure 3 This is a schematic diagram of the signal time delay between the first pair of ultra-high frequency sensors in the embodiment; Figure 4 This is a schematic diagram illustrating the signal time delay between the second pair of ultra-high frequency sensors in the embodiment; Figure 5 This is a schematic diagram illustrating the signal time delay between the third pair of ultra-high frequency sensors in the embodiment; Figure 6 This is a schematic diagram of the partial discharge location and selection system for GIS equipment in the embodiment. Detailed Implementation

[0020] 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, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0022] Example 1 To address the problems of the aforementioned existing technologies and ensure accurate localization of partial discharge sources during the detection process, this embodiment provides a partial discharge localization method for GIS equipment. First, multiple ultra-high frequency sensors synchronously acquire signals and perform time alignment to ensure that the signals from multiple sensors are compared and processed under a unified time reference, avoiding errors caused by time asynchrony and laying the foundation for subsequent accurate analysis. The signal filtering process specifically removes DC components, low-frequency noise, and high-frequency interference, retaining the target frequency band signal, effectively improving the signal-to-noise ratio and reducing noise interference in localization. Hilbert transform is applied to the target frequency band signal to obtain the envelope, enhancing the signal amplitude characteristics. Combined with threshold detection, the first arrival time of the signal is determined. This method uses statistical methods to reduce the impact of noise, accurately capturing the signal start point and providing a reliable time reference for time difference measurement. The signal time delay of each pair of sensors is determined based on the GCC-PHAT algorithm, and refined signal processing techniques are used to improve the correlation detection accuracy, ensuring the accuracy of time delay measurement. Finally, by jointly solving the location information of the local discharge source and the signal propagation speed, the limitation of fixed propagation speed in traditional methods is overcome. It can dynamically adapt to the changes in propagation speed in the complex medium environment within the GIS equipment, eliminate the positioning error caused by the deviation of speed assumption, and realize high-precision estimation of the location of the local discharge source.

[0023] Overall, this method effectively improves anti-interference capability and positioning accuracy through technological innovations in multiple aspects, including signal acquisition and processing, time reference determination, precise measurement of time delay, and joint optimization of propagation speed. It provides a reliable solution for the accurate positioning of partial discharge in GIS equipment and ensures the safe and stable operation of the power system.

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0025] See Figure 1 The method in this embodiment specifically includes the following steps: S101: Synchronous signal acquisition is performed through multiple UHF sensors to obtain time-aligned multi-sensor signals; the multi-channel signal matrix is ​​generated based on the discharge measurement signals acquired by multiple UHF sensors.

[0026] In this embodiment, multiple ultra-high frequency (UHF) sensors are installed at different locations on the GIS equipment according to a preset layout. They synchronously acquire UHF electromagnetic wave signals generated by partial discharge using the same sampling frequency and triggering mechanism, ensuring that each sensor captures signal waveforms under the same time reference. Since there may be slight deviations in the sampling clocks of each sensor or signal transmission delays during actual acquisition, time calibration techniques such as linear interpolation are used to uniformly calibrate the time axis of each channel signal. This ensures that the signal data from different sensors are strictly aligned on the time scale, forming a multi-sensor signal sequence that can be compared and analyzed at the same time point. Based on these synchronously acquired and time-aligned discharge measurement signals, a multi-channel signal matrix is ​​further generated. This matrix systematically integrates information such as the sampling time and voltage amplitude of each sensor, providing a data foundation with standardized structure and a unified time reference for subsequent signal processing.

[0027] In one possible implementation, three UHF sensors are set up in the GIS equipment. The three UHF sensors simultaneously input signals from an oscilloscope. The data obtained by each sensor consists of sampling time and voltage amplitude. Through linear interpolation, it is ensured that the signals of each sensor can be compared and processed at the same point in time.

[0028] This synchronous acquisition and time alignment technology effectively avoids signal feature misalignment caused by sensor time asynchrony, ensuring that subsequent processing steps such as filtering, envelope detection, and time delay calculation can be carried out based on precisely corresponding signal data, fundamentally reducing the impact of time reference errors on positioning results. The generation of a multi-channel signal matrix facilitates collaborative processing of multi-sensor data, enabling subsequent signal feature extraction and correlation analysis to be performed within a unified data framework. This ensures the comparability and correlation between signals from different sensors, laying a solid data foundation for accurately obtaining the time difference of signals arriving at each sensor and improving the accuracy of the positioning algorithm.

[0029] S102: Perform signal filtering on the time-aligned multi-sensor signals to obtain the target frequency band signal corresponding to each ultra-high frequency sensor.

[0030] Specifically, this step is implemented through the following two steps: Step 1: Based on the first-order difference algorithm, perform high-pass filtering on the time-aligned multi-sensor signals to obtain the high-pass filtered multi-sensor signals.

[0031] The process of filtering a multi-channel signal matrix to obtain the target frequency band signal first involves high-pass filtering using a first-order differential algorithm. For time-aligned multi-sensor signals, the first-order differential operation represents the calculation of the difference between adjacent sampling points in the time domain, and corresponds to the suppression of low-frequency components in the frequency domain. This processing method effectively removes low-frequency drift and static noise from the signal, while preserving and enhancing the transient change characteristics of the high-frequency band. Since the ultra-high-frequency signal generated by partial discharge is dominated by high-frequency components, after first-order differential high-pass filtering, the high-frequency components of the signal are strengthened, the influence of low-frequency noise is significantly weakened, and the signal's abrupt changes, such as rising edges, become more pronounced, providing a signal foundation with a higher signal-to-noise ratio for subsequent processing. This step does not require complex filter design, has low computational complexity, and does not introduce phase delay, avoiding the phase distortion problems that traditional filters may cause, ensuring that the signal's temporal characteristics are not destroyed, and laying the foundation for accurately capturing the signal's arrival time.

[0032] Specifically, let the signal sequence be x'(n), and the first-order difference is defined as: ; The transfer function corresponding to this difference operation in the frequency domain is: ; The amplitude response is: ; After differential processing, the signal exhibits more pronounced abrupt changes in the time domain and concentrated energy after low-frequency suppression in the frequency domain, which helps enhance the instantaneous change characteristics of the signal. Compared with traditional high-order filters, the first-order differential method in this step does not require the design of complex filter structures, does not introduce phase delay, and has extremely low computational complexity. It is particularly suitable for subsequent TDOA processing modules with time as the core variable, avoiding the introduction of systematic errors due to phase nonlinearity.

[0033] Step 2: Based on the bandpass filter, remove the DC component and noise and interference in the multi-sensor signal after high-pass filtering to obtain the target frequency band signal.

[0034] After high-pass filtering, a band-pass filter is further used to process the signal to remove interference outside the specific frequency band. The operating frequency range of the band-pass filter is automatically adjusted according to the actual sampling rate. It can effectively filter out DC components and low-frequency noise, avoiding their impact on the signal baseline, and suppress unstructured interference in the high-frequency band, preventing it from contaminating the signal details. Through this dual filtering mechanism, only the target frequency band components that conform to the characteristics of partial discharge are retained in the signal. DC offset is eliminated, low-frequency noise and high-frequency interference are effectively suppressed, and the waveform profile of the signal is clearer, with more prominent pulse leading edges and peak points. This filtering process not only improves the purity of the signal but also ensures that the signal bandwidth matches the sampling rate, avoiding signal information loss due to insufficient bandwidth or the introduction of additional noise due to excessive bandwidth. This provides a high-quality signal input for subsequent extraction of the signal envelope through Hilbert transform and accurate detection of the first arrival time of the signal. It also creates favorable conditions for time delay calculation based on cross-correlation algorithms, further ensuring the accuracy and reliability of the positioning process from the perspective of frequency domain processing.

[0035] Specifically, let's set F s Given the actual sampling rate of the current signal, the cutoff frequency of the filter is defined as: ; ; in, F min This is the minimum bandwidth limit specified. F max =0.98 F s / 2 This ensures the filter doesn't approach the Nyquist frequency, avoiding edge leakage. α is the sampling rate percentage coefficient.

[0036] S103: Perform Hilbert transform on multiple sets of target frequency band signals to determine the envelope of each set of target frequency band signals.

[0037] The process of applying Hilbert transform to multiple target frequency band signals to determine their envelopes is a crucial step in accurately extracting the characteristics of partial discharge signals. As a nonlinear signal processing method, Hilbert transform converts real-valued target frequency band signals into analytic signals. These analytic signals are composed of the original signal and its orthogonal components obtained through the Hilbert transform. The envelope of the original signal can be obtained by taking the modulus of the analytic signal. This envelope signal can intuitively reflect the amplitude modulation characteristics of the original signal in the time domain, transforming the energy abrupt changes implicit in the partial discharge signal into significant amplitude variation characteristics. Even in high-noise environments, it can effectively highlight the pulse profile and rising edge of the signal, providing clear characteristic evidence for subsequent accurate detection of the signal's first arrival time.

[0038] For each target frequency band signal, the Hilbert transform constructs its orthogonal components through integration. The combination of these components to form an analytic signal, followed by envelope calculation, further enhances the instantaneous amplitude characteristics of the signal, highlighting key information such as the starting point and peak position of the weak discharge signal that was previously submerged in noise. Considering the potential impact of high-frequency disturbances on the envelope signal's characteristic judgment, a moving average filter is introduced in actual processing to smooth the envelope, preserving the overall signal trend while suppressing local high-frequency fluctuations, resulting in a more stable and representative envelope curve.

[0039] Specifically, let a signal be... x(t) Its Hilbert transform is denoted as Then the analytical form of the signal is: ; The envelope of a signal is defined as the magnitude of the analytic signal: ; Each signal's local energy abrupt change in the time domain will be reflected as a sudden rise in the envelope function, which can then be used for subsequent analysis through peak extraction and other methods. In one possible implementation, to avoid high-frequency envelope perturbations interfering with the judgment, a moving average filter is introduced to smooth the envelope signal, resulting in a more representative fluctuation curve.

[0040] S104: Perform threshold detection based on the envelopes of multiple target frequency band signals to obtain the first arrival time of the signal corresponding to each UHF sensor; the first arrival time of the signal is used to characterize the signal envelope of the target frequency band signal corresponding to each UHF sensor, and the time when it first exceeds the detection threshold.

[0041] This step requires a detailed explanation with reference to the accompanying drawings of a specific process implementation. See attached drawings. Figure 2 The figure is a flowchart illustrating the method for obtaining the first arrival time of a signal according to an embodiment of this application, specifically including the following steps: S1041: For each target frequency band signal, extract the baseline noise segment, the median of the baseline noise segment, and the median absolute deviation of the median.

[0042] To determine the first arrival time of signals by threshold detection of the envelopes of multiple target frequency band signals, baseline noise characteristic analysis must first be performed on each target frequency band signal. Specifically, for each signal, the initial stage before signal arrival is selected in its time series as the baseline noise segment. During this period, the sensor has not yet received the effective signal generated by partial discharge, and only environmental noise and inherent equipment noise are recorded. By calculating the median and absolute deviation of the median of this baseline noise segment, the distribution characteristics of the noise can be accurately characterized. The median, as a measure of the central tendency of the noise signal, is robust to outliers and avoids the problem of mean calculation being interfered with by extreme noise points. The absolute deviation of the median reflects the dispersion of the noise signal and is more suitable for non-Gaussian noise environments than the standard deviation, and can more reliably describe the fluctuation range of the noise.

[0043] S1042: Construct a detection threshold based on the median and median absolute deviation of each baseline noise segment.

[0044] After obtaining the statistical characteristics of the baseline noise, a detection threshold is constructed based on the median and the median absolute deviation. The detection threshold is set based on noise characteristics, typically using the median plus a certain multiple of the median absolute deviation as the threshold benchmark. The specific multiple can be adjusted according to the actual noise level and detection sensitivity requirements. This threshold construction method can dynamically adapt to the noise differences in the environments where different sensors are located, avoiding the problems of misjudgment or missed detection caused by fixed thresholds due to environmental changes, and maintaining the stability of the threshold when noise fluctuates. By comparing the signal envelope with this dynamic threshold, the amplitude changes of noise fluctuations and actual discharge signals can be effectively distinguished, ensuring that the threshold is neither too strict, thus missing effective signals, nor too lenient, thus introducing noise interference.

[0045] S1043: Scan each target frequency band signal point by point, and determine the first arrival time of the signal as the sampling point of the signal that first exceeds the monitoring threshold.

[0046] After threshold construction, each target frequency band signal is scanned and detected point-by-point. Starting from the signal's initial moment, the signal envelope value is compared with the detection threshold sequentially according to the sampling points. When the envelope value of a sampling point first exceeds the threshold, the time corresponding to that point is determined as the signal's first arrival time. This moment characterizes the critical time point when the envelope of the signal collected by the ultra-high frequency sensor first breaks through the noise level, and is the core basis for judging the time difference of the discharge signal arriving at different sensors. During the scanning process, the point-by-point detection principle is strictly followed to ensure that no possible signal starting point is missed. At the same time, the continuity characteristics of the signal envelope are combined to avoid misjudgment caused by accidental noise spikes. Through this dynamic threshold detection method based on baseline noise characteristics, the true arrival time of the signal can be accurately captured in complex electromagnetic environments, providing a high-precision time reference for subsequent multi-sensor signal time delay calculation and discharge localization, effectively improving the reliability and positioning accuracy of the partial discharge detection system.

[0047] The above is a flowchart describing the method for determining the first arrival time of the signal in step S104. The following will continue to combine... Figure 1 The last step S105 in the method of the embodiments of this application will be described in detail.

[0048] S105: Based on the GCC-PHAT algorithm and the first arrival time of each signal, determine the signal time delay of each pair of UHF sensors, and perform joint solution based on the signal time delay of each pair of UHF sensors to determine the location information of the partial discharge power source and the corresponding signal propagation speed.

[0049] Specifically, this step is divided into two actions. First, the signal time delay of each pair of UHF sensors is determined. Then, based on the signal time delay of each pair of UHF sensors, a joint solution is performed to determine the location information of the local power source and the corresponding signal propagation speed. The following sections will describe these two actions in turn.

[0050] First, regarding the determination of the signal time delay for each pair of UHF sensors, the signal time delay for each pair of UHF sensors is mainly achieved through the following two steps: Step 1: Based on the preset window truncation interval and the first arrival time of each signal, perform window truncation to obtain the signal window corresponding to the first arrival time of each signal.

[0051] Determining the signal time delay between sensors based on the GCC-PHAT algorithm and the signal's first arrival time requires first selectively truncating the signal within a pre-defined window. This pre-defined window typically extends forward and backward from the signal's first arrival time, forming a valid data segment containing complete signal characteristics. The core objective of this operation is to accurately extract key waveform components related to partial discharge events from long-term acquired signals, avoiding irrelevant noise and interference from subsequent signals, while reducing data processing volume to improve computational efficiency. Specifically, using the signal's first arrival time from each sensor as a reference point, a signal window is truncated according to a pre-set time length (e.g., a reasonable interval covering the signal's rising edge, peak value, and part of the attenuation process). This ensures that the window contains both the main energy distribution of the signal and excludes the influence of irrelevant signal components as much as possible. This precise truncation based on arrival time provides clean data focused on the valid signal segment for subsequent time delay calculations, avoiding interference from noise accumulation in long-term signals and laying a data foundation for high-precision time delay estimation.

[0052] Step 2: Based on the GCC-PHAT algorithm and the signal window corresponding to the first arrival time of each signal, calculate and determine the signal time delay of each UHF sensor.

[0053] After the signal window is truncated, the time delay of the signal window for each pair of sensors is calculated using the GCC-PHAT algorithm. As an improved method of generalized cross-correlation technique, the GCC-PHAT algorithm can effectively suppress the influence of signal energy differences and noise interference by performing phase transformation processing on the cross-power spectrum in the frequency domain, thereby enhancing the peak sharpness of the cross-correlation function and improving the accuracy of time delay estimation.

[0054] In practice, the signal windows corresponding to each pair of sensors are first converted to the frequency domain, and their cross-power spectral density is calculated. Then, a phase transformation is performed on the cross-power spectrum, typically using amplitude normalization. The processed frequency domain signal is then converted back to the time domain using an inverse Fourier transform to obtain the enhanced cross-correlation function. The peak position of the cross-correlation function corresponds to the time delay between the two sensor signals. To further improve the estimation accuracy, oversampling techniques are also incorporated in the actual processing, such as zero-filling to extend the frequency domain resolution and fitting discrete points near the cross-correlation peak using parabolic interpolation to achieve sub-sampling level time delay estimation. This processing method, combining signal window truncation and the GCC-PHAT algorithm, not only uses the arrival time to locate the effective range of the signal but also enhances the noise resistance and accuracy of the time delay estimation through algorithm optimization. This ensures that the signal time difference between sensor pairs can be accurately obtained in complex electromagnetic environments, providing a reliable time reference for subsequent time delay-based partial discharge location calculations.

[0055] The explanation begins with formulas. In determining the signal time delay for each pair of UHF sensors, the first step is to analyze the sampling time sequence. t k Determine the index corresponding to the window boundary. k 1 , k 2 ,Right now t k1 , t k2 satisfy: , ,in t 0 This represents the time of the first arrival of the signal. Δt pre The length of time for backward tracing is used to preserve the background noise and system baseline before the start; Δt post The extended time length covers the tail of the complete signal and possible multipath or reflections. Further improvements are made to the stability of boundary transitions and to avoid spectral leakage introduced by truncation effects. After windowing, each signal is weighted using a window function. This invention uses a Tukey window, which effectively suppresses edge artifacts while ensuring complete preservation of passband information. The specific formulas are shown in the following two equations: ; ; Where α is the window function smoothing parameter, and in the exemplary application scenario of this embodiment, it is set to 0.25, which can achieve a balance between the rectangular window and the full cosine window.

[0056] To further understand the concept of signal time delay for each pair of ultra-high frequency sensors, this embodiment uses three ultra-high sensitivity sensors as a scenario example (there are three pairs of signal time delays), providing... Figures 3-5 As an example of the signal time delay between each pair of ultra-high frequency sensors, where, Figure 3 This is a schematic diagram illustrating the signal time delay between the first pair of ultra-high frequency sensors provided in an embodiment of this application. Figure 4 This is a schematic diagram illustrating the signal time delay between the second pair of ultra-high frequency sensors provided in an embodiment of this application. Figure 5 This is a schematic diagram of the signal time delay between the third pair of ultra-high frequency sensors provided in an embodiment of this application.

[0057] Next, we will introduce the joint solution based on the signal time delay of each pair of UHF sensors to determine the location information of the local discharge source and the corresponding signal propagation speed. This process is mainly achieved by pre-setting the objective function of the sum of squared errors and applying the least squares method. The specific steps are as follows: Step 1: Based on the preset error sum of squares objective function, the signal time delay of each pair of UHF sensors is jointly solved using the least squares method to determine the location information of the local discharge source and the corresponding signal propagation speed.

[0058] Specifically, unlike traditional methods, this invention does not treat propagation speed as a fixed, known constant, but rather as a variable to be optimized, integrating it into the entire positioning inversion model for joint estimation. This consideration stems from two important physical characteristics of the propagation process of partial discharge signals in actual GIS structures: First, due to the influence of conductor surfaces, multiple reflections, or cavity structures on the propagation path, the electromagnetic wave propagation path is non-linear, increasing the effective path length, but the path cannot be directly traced, thus equivalent to a slower propagation speed; Second, GIS structures contain various media (such as SF6 gas, basin insulators, metal walls, etc.), and these different media have fundamentally different propagation speeds for electromagnetic waves, and electric field lines may bend or reconstruct between different media, further affecting the equivalent propagation speed. Therefore, if the propagation speed is simply treated as a constant value, systematic errors will inevitably be introduced, leading to positioning deviations.

[0059] To account for both uncertainties mentioned above, this embodiment establishes a bivariate least squares optimization model, with the location x of the local discharge source and its propagation velocity c as unknowns, and the measured time delay of each pair of sensors as the variables. The objective fit is to minimize the sum of squares of the total deviations between the theoretical and measured time delays.

[0060] Using the application scenarios of the three ultra-high frequency sensors mentioned above as examples, assume that these three sensors are located at coordinates. x 1 , x 2 , x 3 The location of the power source is unknown. x The propagation speed is unknown. c The theoretical propagation time from the source to each sensor is: ; The theoretical time difference between any two channels is: ; The sum of squared errors between the theoretical time difference and the actual measured time difference: ; The advantage of this joint solution method lies in its full utilization of time delay data from multiple sensor pairs. It reduces the impact of single measurement errors through the fusion of redundant information, while also considering the potential uncertainties in signal propagation speed (such as differences in medium homogeneity), thus avoiding errors arising from the assumption of known propagation speed in traditional positioning methods. During iterative optimization, the algorithm automatically balances the measurement deviations of each sensor pair, ensuring that the solution satisfies both geometric positioning relationships and the physical laws of signal propagation. Ultimately, it obtains accurate coordinates of the partial discharge source location and corresponding propagation speed parameters, providing a reliable basis for the precise location and condition assessment of partial discharge faults in power equipment.

[0061] In particular, in one possible implementation, after determining the signal time delay of each UHF sensor, oversampling techniques and parabolic refinement can be used to improve the detection accuracy of signal correlation for signal time delay.

[0062] Oversampling and parabolic refinement techniques improve the accuracy of signal time delay detection. Essentially, this involves dual precision optimization through enhanced frequency domain resolution and optimized peak location. The core of oversampling lies in overcoming the limitations of the original sampling rate. By zero-padding or interpolating the signal, the number of discrete frequency points in the frequency domain is increased, resulting in a higher-resolution time-domain cross-correlation sequence after inverse transformation. Specifically, when performing cross-correlation operations on a signal processed by the GCC-PHAT algorithm, the cross-correlation function at the original sampling rate may suffer from insufficient frequency resolution, causing the peak position to be limited by discrete sampling points, resulting in a "pick-up fence effect." This means the true peak may lie between two sampling points and cannot be accurately captured. Oversampling (e.g., extending the signal length to several times the original length) reduces the frequency interval in the frequency domain, resulting in a denser sampling point in the corresponding time-domain cross-correlation sequence. This allows for a more refined characterization of the waveform details of the cross-correlation function, improving the peak location accuracy from the original sampling interval to a smaller interval after oversampling, effectively reducing estimation errors caused by insufficient sampling rate. This technique is particularly suitable for scenarios with complex signal components and strong noise backgrounds. By increasing the density of data points, it provides richer local feature information for subsequent peak refinement.

[0063] Let any pair of synchronously acquired signals be x(t) and y(t) Frequency domain representation is X(f) and Y(f) The phase cross spectrum is: ; in, express The conjugate complex number of the cross spectrum. Normalizing the spectral amplitude effectively suppresses the differences in signal energy, ensuring that the cross-correlation operation only reflects the phase difference characteristics between signals. Subsequently, an inverse Fourier transform is performed on the cross spectrum to obtain the time-domain cross-correlation function: ; The peak position of this function represents the estimated time delay between the two signals. Due to the limited original sampling frequency, this invention employs oversampling to further improve the resolution of the time delay estimation. This involves zero-padding the signal in the frequency domain to expand the number of frequency points, which is equivalent to performing a subtle reconstruction of the cross-correlation function in the time domain. By expanding the number of sampling points to 32 times the original, the positioning accuracy of the cross-correlation peak can be improved from the original sampling period to a smaller time scale, thus supporting sub-nanosecond-level time delay estimation.

[0064] Furthermore, to avoid interpolation errors caused by oversampling, this invention introduces a parabolic thinning mechanism near the peak of the cross-correlation function. Let the cross-correlation function at a certain point... t 0 The maximum value is obtained by taking the three nearest neighboring sampling points as ( t 0 -Δ , r 1 ), ( t 0 , r 0 ), ( t 0 +Δ , r 2 If we construct a parabola to fit these three points, we can use the following analytical formula to obtain the subsampling level precise location of the peak: ; In this way, high-resolution localization of the main cross-correlation peak can be achieved without introducing additional filtering or complex models, effectively suppressing the limitation of the sampling rate on the estimated value.

[0065] Example 2 Based on Embodiment 1, this embodiment provides a partial discharge location method and system for GIS equipment to implement the method of Embodiment 1. The partial discharge location system for GIS equipment described below and the partial discharge location method for GIS equipment described above can be referred to in correspondence.

[0066] See Figure 6 A schematic diagram of a partial discharge location system for GIS equipment provided in this application embodiment specifically includes the following modules: The signal acquisition module 100 is used to synchronously acquire signals through multiple ultra-high frequency sensors to obtain time-aligned multi-sensor signals; the multi-channel signal matrix is ​​generated based on the discharge measurement signals acquired by multiple ultra-high frequency sensors. The filtering module 200 is used to perform signal filtering on time-aligned multi-sensor signals to obtain the target frequency band signal corresponding to each ultra-high frequency sensor. The transformation processing module 300 is used to perform Hilbert transform on multiple sets of target frequency band signals to determine the envelope of each set of target frequency band signals; The detection module 400 is used to perform threshold detection based on the envelopes of multiple sets of target frequency band signals to obtain the first arrival time of the signal corresponding to each UHF sensor; the first arrival time of the signal is used to characterize the signal envelope of the target frequency band signal corresponding to each UHF sensor, and the time when it first exceeds the detection threshold. The joint solution module 500 is used to determine the signal time delay of each pair of UHF sensors based on the GCC-PHAT algorithm and the first arrival time of each signal, and to perform joint solution based on the signal time delay of each pair of UHF sensors to determine the location information of the partial discharge source and the corresponding signal propagation speed.

[0067] In one possible implementation, the detection module 400 is specifically used for: For each target frequency band signal, extract the baseline noise segment, the median of the baseline noise segment, and the median absolute deviation of the median noise segment; A detection threshold is constructed based on the median and median absolute deviation of each baseline noise segment; For each target frequency band signal, a point-by-point scan is performed, and the sampling point of the signal that first exceeds the monitoring threshold is determined as the first arrival time of the signal.

[0068] In one possible implementation, the filtering module 200 is specifically used for: Based on the first-order difference algorithm, high-pass filtering is performed on time-aligned multi-sensor signals to obtain high-pass filtered multi-sensor signals. Based on the bandpass filter, the DC component and noise and interference in the preset frequency band range of the multi-sensor signal after high-pass filtering are removed to obtain the target frequency band signal.

[0069] In one possible implementation, the joint solver module 500 includes: a delayed computation unit, specifically used for: Based on the preset window truncation interval and the first arrival time of each signal, the window is truncated to obtain the signal window corresponding to the first arrival time of each signal; Based on the GCC-PHAT algorithm and the signal window corresponding to the first arrival time of each signal, the signal time delay of each UHF sensor is calculated and determined.

[0070] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system and method embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments. The system and method embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components indicated as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the solution in this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0071] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for locating partial discharge in GIS equipment, characterized in that, include: By synchronously acquiring signals from multiple ultra-high frequency sensors, a multi-channel signal matrix based on discharge measurement signals is constructed to obtain time-aligned multi-sensor signals. The time-aligned multi-sensor signals are subjected to signal filtering processing to obtain the target frequency band signal corresponding to each of the ultra-high frequency sensors; The envelope of each set of target frequency band signals is determined by performing Hilbert transform on multiple sets of target frequency band signals; Threshold detection is performed based on the envelopes of multiple sets of target frequency band signals to obtain the first arrival time of the signal corresponding to each UHF sensor; the first arrival time of the signal is used to characterize the moment when the signal envelope of the target frequency band signal corresponding to each UHF sensor first exceeds the detection threshold; Based on the GCC-PHAT algorithm and the first arrival time of each signal, the signal time delay of each pair of UHF sensors is determined, and a joint solution is performed based on the signal time delay of each pair of UHF sensors to determine the location information of the partial discharge power source and the corresponding signal propagation speed.

2. The partial discharge location method for GIS equipment according to claim 1, characterized in that, The process of obtaining the first arrival time of the signal corresponding to each UHF sensor by performing threshold detection based on the envelopes of multiple sets of target frequency band signals includes: For each target frequency band signal, the baseline noise segment, median, and median absolute deviation of the baseline noise segment are extracted. A detection threshold is constructed based on the median and the absolute deviation of the median for each of the baseline noise segments; Each target frequency band signal is scanned point by point, and the sampling point of the signal that first exceeds the detection threshold is determined as the first arrival time of the signal.

3. The partial discharge location method for GIS equipment according to claim 1, characterized in that, The process of performing signal filtering on the time-aligned multi-sensor signals to obtain the target frequency band signal includes: Based on the first-order difference algorithm, the time-aligned multi-sensor signal is subjected to high-pass filtering to obtain the high-pass filtered multi-sensor signal. Based on the bandpass filter, the DC component and noise and interference in the multi-sensor signal after high-pass filtering are removed to obtain the target frequency band signal.

4. The partial discharge location method for GIS equipment according to claim 1, characterized in that, The process of determining the signal time delay of each pair of UHF sensors based on the GCC-PHAT algorithm and the first arrival time of each signal includes: Based on the preset window truncation interval and the first arrival time of each signal, a window truncation is performed to obtain the signal window corresponding to the first arrival time of each signal; Based on the GCC-PHAT algorithm and the signal window corresponding to the first arrival time of each signal, the signal time delay of each UHF sensor is calculated and determined.

5. The partial discharge location method for GIS equipment according to claim 1, characterized in that, The process of jointly solving for the signal time delay of each pair of UHF sensors to determine the location information of the partial discharge source and the corresponding signal propagation speed includes: Based on a preset error sum of squares objective function, the signal time delay of each pair of UHF sensors is jointly solved using the least squares method to determine the location information of the local discharge source and the corresponding signal propagation speed.

6. The partial discharge location method for GIS equipment according to claim 1, characterized in that, After determining the signal time delay of each of the ultra-high frequency sensors, the method further includes: Oversampling techniques and parabolic refinement are employed to improve the detection accuracy of signal correlation for the time delay of the signal.

7. A partial discharge location system for GIS equipment, characterized in that, For implementing the partial discharge location method for GIS equipment as described in any one of claims 1-6, the system comprises: The signal acquisition module is used to synchronously acquire signals through multiple ultra-high frequency sensors, construct a multi-channel signal matrix based on discharge measurement signals, and obtain time-aligned multi-sensor signals. The filtering module is used to perform signal filtering processing on the time-aligned multi-sensor signals to obtain the target frequency band signal corresponding to each of the ultra-high frequency sensors; The transformation processing module is used to perform Hilbert transform on multiple sets of target frequency band signals to determine the envelope of each set of target frequency band signals; The detection module is used to perform threshold detection based on the envelopes of multiple sets of target frequency band signals to obtain the first arrival time of the signal corresponding to each UHF sensor; the first arrival time of the signal is used to characterize the moment when the signal envelope of the target frequency band signal corresponding to each UHF sensor first exceeds the detection threshold; The joint solution module is used to determine the signal time delay of each pair of UHF sensors based on the GCC-PHAT algorithm and the first arrival time of each signal, and to perform a joint solution based on the signal time delay of each pair of UHF sensors to determine the location information of the partial discharge power source and the corresponding signal propagation speed.

8. The partial discharge positioning system for GIS equipment according to claim 7, characterized in that, The detection module is used for: For each target frequency band signal, the baseline noise segment, median, and median absolute deviation of the baseline noise segment are extracted. A detection threshold is constructed based on the median and the absolute deviation of the median for each of the baseline noise segments; Each target frequency band signal is scanned point by point, and the sampling point of the signal that first exceeds the detection threshold is determined as the first arrival time of the signal.

9. The partial discharge positioning system for GIS equipment according to claim 7, characterized in that, The filtering module is used for: Based on the first-order difference algorithm, the time-aligned multi-sensor signal is subjected to high-pass filtering to obtain the high-pass filtered multi-sensor signal. Based on the bandpass filter, the DC component and noise and interference in the multi-sensor signal after high-pass filtering are removed to obtain the target frequency band signal.

10. The partial discharge positioning system for GIS equipment according to claim 7, characterized in that, The joint solver module includes a delay calculation unit, which is used for: Based on the preset window truncation interval and the first arrival time of each signal, a window truncation is performed to obtain the signal window corresponding to the first arrival time of each signal; Based on the GCC-PHAT algorithm and the signal window corresponding to the first arrival time of each signal, the signal time delay of each UHF sensor is calculated and determined.

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