Traveling wave detection method and system based on high-frequency current sensor

Abstract

The present invention discloses a traveling wave detection method and system based on a high-frequency current sensor. The high-frequency current sensor includes a main coil and an auxiliary coil; the traveling wave detection method includes the following steps: synchronously acquiring the power frequency current signal output by the main coil and the high-frequency traveling wave signal output by the auxiliary coil; fusing the power frequency current signal and the high-frequency traveling wave signal to obtain a fused signal; performing notch filtering on the fused signal, and making the notch center frequency dynamically track the background noise peak of the distribution network; performing Teager energy operator transformation on the filtered signal to capture the sudden change point of the fault traveling wave head and determine the arrival time of the traveling wave. The traveling wave detection method and system based on a high-frequency current sensor provided by the present invention can eliminate the problem of signal connection distortion in traditional solutions through a three-level processing architecture of fusion, filtering and TEO detection.

Description

Technical Field

[0001] The present invention relates to the technical field of traveling wave detection, and in particular to a traveling wave detection method and system based on a high-frequency current sensor. Background Art

[0002] In the process of intelligent distribution network upgrades, integrated primary and secondary pole-mounted circuit breakers, as core equipment, play a vital role in line fault isolation. However, their identification accuracy is relatively low when facing high-resistance ground faults. The root cause is that existing traveling wave detection technology is limited by the performance bottleneck of current sensors, which mainly manifests in the following defects:

[0003] (1) Traditional electromagnetic CT has insufficient bandwidth and severe attenuation of high-frequency signals.

[0004] Distribution network traveling wave signals contain abundant high-frequency components in the MHz range (e.g., the rising edge frequency of lightning impulse waves ranges from 1MHz to 10MHz, and high-frequency oscillations of fault arcs range from 2MHz to 20MHz). However, the operating frequency band of traditional electromagnetic CTs is typically limited to 50Hz to 10kHz, with signal attenuation exceeding -20dB above 100kHz. For example, the amplitude of a 1MHz traveling wave signal is attenuated to 10% of its theoretical value, and the 10MHz component is almost completely lost. This makes it difficult to effectively extract the traveling wave's head features, resulting in a high rate of missed detection of high-resistance faults.

[0005] (2) Intrinsic defects in the core material cause signal distortion.

[0006] Traditional CTs generally use silicon steel sheets or ordinary ferrite cores, which have two major flaws: when a short circuit occurs in the line, the core saturates and causes the output waveform to be clipped and distorted; the phase error is large in the high-frequency band, resulting in not only large detection deviations but also large positioning errors at the front moment of the traveling wave.

[0007] (3) Lack of anti-interference mechanism and low signal-to-noise ratio.

[0008] Distribution networks are plagued by numerous MHz-level electromagnetic interference sources, such as switching oscillations and arc discharge noise. Traditional CTs rely solely on single-layer metal enclosures for shielding, lacking optimization for high-frequency interference. This results in a low measured signal-to-noise ratio, which causes traveling wave signals to be overwhelmed by noise and increases the probability of false fault detection.

[0009] (4) The installation structure is rigid and has poor adaptability to different scenarios.

[0010] Most existing sensors are closed ring structures and cannot adapt to complex distribution network scenarios, making it impossible to deploy monitoring equipment at some key nodes.

[0011] In order to solve at least one of the above technical problems, the present invention provides a traveling wave detection method and system based on a high-frequency current sensor. Summary of the Invention

[0012] The purpose of the present invention is to provide a traveling wave detection method and system based on a high-frequency current sensor, which can eliminate the problem of signal connection distortion in traditional solutions through a three-level processing architecture of fusion, filtering and TEO detection.

[0013] The purpose of the present invention is achieved by the following technical solutions:

[0014] On the one hand, the present invention provides a traveling wave detection method based on a high-frequency current sensor, characterized in that the high-frequency current sensor includes a main coil and an auxiliary coil;

[0015] The traveling wave detection method comprises the following steps:

[0016] Synchronously obtain the power frequency current signal output by the main coil and the high frequency traveling wave signal output by the auxiliary coil;

[0017] Fusing the power frequency current signal and the high frequency traveling wave signal to obtain a fused signal;

[0018] Perform notch filtering on the fusion signal and make the notch center frequency dynamically track the peak value of the distribution network background noise;

[0019] Teager energy operator transformation is performed on the filtered signal to capture the sudden change point of the fault traveling wave head and determine the arrival time of the traveling wave.

[0020] The beneficial effects of this solution are as follows: The main coil (with thick wire) ensures stable power-frequency current measurement, while the auxiliary coil (with fine wire and dense winding) focuses on capturing high-frequency traveling waves. This overcomes the bandwidth limitations of traditional sensors, expanding the effective bandwidth from the traditional 5MHz to 20MHz and reducing the amplitude error of the traveling wave signal. Furthermore, the three-stage processing architecture of fusion, filtering, and TEO detection eliminates the signal connection distortion problem found in traditional solutions.

[0021] Furthermore, the fusing of the power frequency current signal and the high frequency traveling wave signal to obtain the fused signal includes:

[0022] Perform wavelet packet decomposition on the high-frequency traveling wave signal, extract the sub-signal energy of the first frequency band, and calculate the dynamic weight coefficient based on the power frequency current signal and the sub-signal energy;

[0023] Based on the dynamic weight coefficient, the fusion signal is determined using the power frequency current signal and the high frequency traveling wave signal;

[0024] The first frequency band is determined according to a fault characteristic frequency band.

[0025] The beneficial effects of the above solution are: the present invention accurately extracts the energy of the characteristic frequency band of the traveling wave in the first frequency band through wavelet packet decomposition, improving the signal-to-noise ratio under switching interference. In addition, dynamic weighting can avoid false operation caused by high-frequency noise under normal operating conditions.

[0026] Furthermore, the high-frequency current sensor is deployed at a preset node of the 35kV line;

[0027] The dynamic weight coefficients include:

[0028] When a short circuit fault occurs on the line, the energy of the sub-signal increases sharply, and the dynamic weight coefficient is 1;

[0029] When the line operates normally, the sub-signal energy approaches zero and the dynamic weight coefficient is 0.

[0030] The beneficial effects of the above scheme are: the present invention uses dynamic weights to enhance the ability to capture traveling wave characteristics in the event of a fault, ensure the transmission of traveling wave signals without attenuation, and improve the protection speed; under normal circumstances, it maintains the power frequency measurement accuracy, completely isolates high-frequency channel interference, and reduces power frequency measurement errors.

[0031] Furthermore, an auxiliary induction coil is provided inside the shielding layer of the high-frequency current sensor;

[0032] The step of performing notch filtering on the fusion signal and making the notch center frequency dynamically track the peak value of the background noise of the distribution network includes:

[0033] The bus voltage signal is collected through the voltage sensor, and the power frequency noise component is extracted;

[0034] Collect spatial electromagnetic noise signals through auxiliary induction coils;

[0035] Performing FFT calculation on the spatial electromagnetic noise within the first sliding time window to identify the main peak frequency of the background noise;

[0036] When the main peak frequency is the power frequency noise component or is greater than the first preset frequency band, and the same-frequency verification is established, the notch center frequency is updated to the main peak frequency;

[0037] The first preset frequency band is determined according to high-frequency interference and effective traveling wave signals.

[0038] The beneficial effect of the above solution is that it can simultaneously suppress power frequency harmonics and high-frequency interference. In addition, a dual verification mechanism (main peak detection and frequency verification) prevents mistracking of the traveling wave signal itself, reducing the mistracking rate in arc fault scenarios.

[0039] Furthermore, the same-frequency verification includes:

[0040] Detect whether the fused signal has a spectrum peak greater than a first preset peak at the main peak frequency of the background noise:

[0041] If it exists, the same-frequency verification is established;

[0042] The first preset peak value is determined according to the interference amplitude.

[0043] The beneficial effects of the above solution are: by setting the first preset peak value, the present invention ensures that only significant interference is suppressed, thereby avoiding excessive filtering and reducing the waveform distortion rate of the traveling wave head. In addition, the first preset peak value can be used to accommodate different voltage levels, reducing the workload of on-site calibration.

[0044] Furthermore, the Teager energy operator transformation is performed on the filtered signal to capture the fault traveling wave head mutation point and determine the traveling wave arrival time, including the following steps:

[0045] Discretize the filtered signal to calculate the instantaneous energy;

[0046] Calculating an energy mean and an energy standard within a second sliding time window based on the instantaneous energy, and determining a traveling wave trigger threshold based on the energy mean and the energy standard;

[0047] When the instantaneous energy is greater than a first preset condition, it is the traveling wave arrival moment, wherein the first preset condition includes a traveling wave trigger threshold.

[0048] The beneficial effect of the above scheme is that the present invention can adapt to the noisy environment through the real-time dynamic traveling wave trigger threshold, solve the problems of false operation in thunderstorms and missed detection of high-resistance faults caused by fixed thresholds, and improve the detection rate of high-resistance grounding.

[0049] Furthermore, the first preset condition includes:

[0050] During the first duration, the instantaneous energy is greater than the traveling wave trigger threshold, and the energy rise rate is greater than the rising threshold;

[0051] The first duration is determined according to the duration characteristics of the traveling wave head; and the rising threshold is determined according to the pulse interference and the oscillation attenuation wave.

[0052] The above solution has the following advantages: by verifying the energy mutation detection through the first duration, the present invention can not only filter out most pulse interference but also eliminate oscillation decay waves. In addition, by verifying the steepness of the traveling wave head, the true traveling wave head can be identified.

[0053] In another aspect, the present invention provides a traveling wave detection system based on a high-frequency current sensor, comprising:

[0054] An edge computing unit has a built-in FPGA chip, which is used to execute the traveling wave detection method and output the traveling wave arrival time and fault direction identification.

[0055] The beneficial effect of the above solution is that the present invention can identify the fault direction based on the traveling wave polarity, and solve the problem of fault line selection in multi-branch distribution networks.

[0056] Furthermore, the traveling wave detection system based on the high-frequency current sensor further includes:

[0057] The fault diagnosis cloud platform is used to receive multi-node traveling wave arrival time data and calculate the fault distance through the dual-end traveling wave positioning method.

[0058] The beneficial effects of the above solution are: the present invention can dynamically calibrate the traveling wave speed through dual-end traveling wave positioning, shortening the fault finding time. In addition, multi-node data fusion can identify complex faults and improve the accuracy of fault diagnosis.

[0059] Furthermore, the edge computing unit integrates a noise learning module, which is used to periodically collect background noise spectrum and update notch filter parameters.

[0060] The beneficial effects of the above scheme are: the present invention maintains a high noise suppression capability by optimizing the notch center frequency; and can improve seasonal adaptability by optimizing the spectrum feature library.

[0061] Compared with the prior art, the beneficial effects of the present invention include at least:

[0062] This invention uses a primary coil to ensure stable power-frequency current measurement, while an auxiliary coil focuses on capturing high-frequency traveling waves. This overcomes the bandwidth limitations of traditional sensors, expanding the effective bandwidth from the traditional 5MHz to 20MHz and reducing traveling-wave signal amplitude errors. Furthermore, a three-stage processing architecture of fusion, filtering, and TEO detection eliminates signal connection distortion issues found in traditional solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of a traveling wave detection method based on a high-frequency current sensor according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concepts of the example embodiments to those skilled in the art. Identical reference numerals in the drawings represent identical or similar structures, and thus repeated descriptions thereof will be omitted.

[0065] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, features defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0066] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0067] The high-frequency current sensor of the present invention can be used for traveling wave detection of a 35kV line. Specifically, the high-frequency current sensor is deployed at a preset node of the 35kV line.

[0068] In some embodiments, the high-frequency current sensor is deployed at the outgoing line terminal, branch point or cable joint of the switch station of the 35kV line.

[0069] The high-frequency current sensor of the present invention comprises: a composite magnetic core module, a distributed signal acquisition module, a double-layer electromagnetic shielding structure and a detachable installation structure.

[0070] The composite magnetic core module of the present invention is composed of an inner nanocrystalline alloy ring and an outer ferrite ring coaxially nested, and the effective bandwidth is extended from less than 5MHz of traditional sensors to 20MHz, covering the full frequency band requirements of traveling wave signals.

[0071] Specifically, the nanocrystalline alloy ring accounts for 70% to 80% of the total thickness of the magnetic core. In addition, the nanocrystalline alloy ring is a Fe-Si-B based nanocrystalline strip with a thickness of 0.02 mm to 0.03 mm and an initial magnetic permeability greater than or equal to 5×10 4 @1kHz, significantly improves the sensitivity of mid- and low-frequency signals from 0.1Hz to 10MHz.

[0072] The ferrite ring accounts for 20% to 30% of the total core thickness. Furthermore, the ferrite ring is nickel-zinc ferrite (Ni-Zn) with a magnetic permeability greater than or equal to 5000 @ 1MHz, compensating for the high-frequency attenuation of nanocrystals.

[0073] The distributed signal acquisition module of this invention comprises a main coil and an auxiliary coil coaxially wound on a composite magnetic core module. The main coil is wound with thick wire to reduce resistance and mitigate thermal noise caused by power-frequency current. The auxiliary coil is wound with fine wire, densely wound with a high number of turns, improving high-frequency signal coupling efficiency and enhancing high-frequency response. Furthermore, the main and auxiliary coils are coaxially wound and their outputs are combined through an impedance matching circuit.

[0074] Specifically, the axial spacing between the main coil and the auxiliary coil is 0.5mm to 1.0mm, and their output terminals are combined into a single signal output terminal through an impedance matching circuit. Furthermore, the main coil is wound with 8 to 12 turns of enameled copper wire with a wire diameter of 1.0mm to 1.5mm, while the auxiliary coil is wound with 90 to 110 turns of high-frequency enameled wire with a wire diameter of 0.1mm to 0.2mm.

[0075] In some embodiments, an auxiliary induction coil is installed inside the shielding layer of the high-frequency current sensor. The double-layer electromagnetic shielding structure of the present invention comprises an inner permalloy magnetic shielding layer, tightly attached to the sensor housing, and an outer copper electrical shielding layer. The permalloy magnetic shielding layer suppresses low-frequency magnetic field interference, while the copper electrical shielding layer reflects high-frequency electromagnetic waves. This double-layer shielding improves noise suppression by 15dB to 20dB in the 1MHz to 20MHz frequency band.

[0076] Specifically, the permalloy magnetic shielding layer has a thickness of 0.1mm to 0.3mm, and the copper electrical shielding layer has a thickness of 0.5mm to 1.0mm. An insulating buffer layer with a thickness of 0.2mm to 0.5mm is placed between the permalloy magnetic shielding layer and the copper electrical shielding layer. The insulating buffer layer is made of silicone rubber or epoxy resin. The detachable mounting structure of the present invention includes a split open magnetic core and a matching spring clip. A self-locking mechanism is provided at the core opening.

[0077] The spring buckle has a built-in pressure sensor to detect the closing status of the magnetic core in real time and provide feedback on the installation sealing through an LED indicator.

[0078] In some embodiments, the detachable mounting structure further includes a standardized mounting interface at the bottom of the housing, the mounting interface including a guide rail groove and a bolt hole, and is compatible with guide rail installation inside the DTU cabinet and outdoor pole tower fixing.

[0079] The traveling wave detection method of the present invention can be implemented using high-frequency current sensors. Specifically, through a three-stage processing architecture consisting of fusion, filtering, and TEO detection, it eliminates the signal connection distortion problem found in traditional solutions. In actual measurements of a 35kV cable fault, the wave front detection delay was reduced from 5μs to 0.8μs.

[0080] refer to Figure 1 The traveling wave detection method of the present invention includes: steps SS1 to SS4.

[0081] Step SS1: synchronously obtain the power frequency current signal Imain(t) output by the main coil and the high frequency traveling wave signal Iaux(t) output by the auxiliary coil.

[0082] When applied, the distributed signal acquisition module synchronously acquires the power frequency current signal Imain(t) output by the main coil and the high frequency traveling wave signal Iaux(t) output by the auxiliary coil.

[0083] Step SS2: Fusing the power frequency current signal Imain(t) and the high frequency traveling wave signal Iaux(t) to obtain a fused signal Ifuse(t).

[0084] In order to improve the signal-to-noise ratio and avoid malfunction caused by high-frequency noise under normal working conditions, step SS2 of the present invention includes: step SS21 to step SS22.

[0085] Step SS21: performing wavelet packet decomposition on the high-frequency traveling wave signal Iaux(t), extracting the sub-signal energy Ehigh of the first frequency band, and calculating the dynamic weight coefficient α according to the power frequency current signal and the sub-signal energy.

[0086] During application, the first frequency band is determined based on the fault characteristic frequency band, ensuring that wavelet packet decomposition accurately extracts the fault characteristic frequency band and improves the signal-to-noise ratio. The high-frequency components of the fault traveling wave are concentrated in the 1MHz to 20MHz range, and the effective bandwidth of the high-frequency current sensor extends to 20MHz, so the first frequency band can be 1MHz to 20MHz.

[0087] In practical applications, the dynamic weight coefficient α includes the following formula:

[0088]

[0089] In the formula, α is the dynamic weight coefficient, Ehigh is the sub-signal energy, Imain(t) is the power frequency current signal, K1 is the dynamic adjustment coefficient, and K1 of the present invention is 0.1-0.5.

[0090] In some embodiments, to enhance the ability to capture traveling wave characteristics during faults, ensure unattenuated transmission of traveling wave signals, and improve protection responsiveness: when a short circuit occurs on the line, the sub-signal energy Ehigh surges, and the dynamic weight coefficient α is 1. In some embodiments, to maintain power frequency measurement accuracy during normal operation, completely isolate high-frequency channel interference, and reduce power frequency measurement errors: when the line operates normally, the sub-signal energy Ehigh approaches zero, and the dynamic weight coefficient α is 0.

[0091] Step SS22: Based on the dynamic weight coefficient, the fusion signal Ifuse(t) is determined using the power frequency current signal Imain(t) and the high frequency traveling wave signal Iaux(t).

[0092] When applied, the fused signal Ifuse(t) includes the following formula:

[0093] Ifuse(t)=α·Iaux(t)+(1-α)·Imain(t)

[0094] Where Ifuse(t) is the fusion signal, Iaux(t) is the high-frequency traveling wave signal, Imain(t) is the power frequency current signal, and α is the dynamic weight coefficient.

[0095] Step SS3: Perform notch filtering on the fused signal Ifuse(t), and make the notch center frequency dynamically track the noise peak of the distribution network background.

[0096] When applicable, the noise peak can be 50Hz, 150Hz or 250Hz.

[0097] In order to suppress both power frequency harmonics and high frequency interference, and to prevent mistracking of the traveling wave signal itself, that is, to reduce the mistracking rate in an arc fault scenario, step SS3 of the present invention includes: steps SS31 to SS34.

[0098] Step SS31: Collect the bus voltage signal through the voltage sensor and extract the power frequency noise component.

[0099] In application, the power frequency noise components include: the power frequency noise fundamental wave and its main harmonic components. Among them, the power frequency noise fundamental wave is 50Hz, and the main harmonic components of the power frequency noise fundamental wave are 150Hz or 250Hz.

[0100] Step SS32: Collect the spatial electromagnetic noise signal Nref(t) through the auxiliary induction coil.

[0101] When applied, the spatial electromagnetic noise signal Nref(t) covers the frequency band from 100kHz to 20MHz.

[0102] Step SS33: Perform FFT calculation on the spatial electromagnetic noise Nref(t) within the first sliding time window to identify the main peak frequency fnoise of the background noise.

[0103] When applied, the window length of the first sliding time window may be 10 ms.

[0104] Step SS34: When the main peak frequency fnoise is the power frequency noise component or is greater than the first preset frequency band, and the same-frequency verification is established, the notch center frequency is updated to the main peak frequency fnoise.

[0105] During application, the first preset frequency band is determined based on the high-frequency interference and the effective traveling wave signal to ensure that the notch filter only tracks significant noise above this threshold to avoid filtering out the effective components of the traveling wave. The effective bandwidth of the high-frequency current sensor is extended to 20MHz, and the effective traveling wave signal covers 1MHz to 20MHz. High-frequency interference in the background noise of the distribution network, such as switching operation oscillation and arc noise, is mainly concentrated in the frequency band greater than 1MHz. Therefore, the first preset frequency band can be 1MHz. The power frequency noise component includes: the power frequency noise fundamental wave and its main harmonic components. Among them, the power frequency noise fundamental wave is 50Hz, and the main harmonic components of the power frequency noise fundamental wave are 150Hz or 250Hz.

[0106] The same-frequency verification of the present invention includes: detecting whether the fusion signal Ifuse(t) has a spectrum peak greater than a first preset peak at the main peak frequency fnoise of the background noise: if so, the same-frequency verification is established; if not, the same-frequency verification is not established.

[0107] When applied, the traditional solution has a low signal-to-noise ratio. In order to suppress significant noise such as power frequency harmonics and high-frequency interference while avoiding distortion of the traveling wave head caused by excessive filtering, the first preset peak value is determined according to the interference amplitude. Based on the noise attenuation defect of the traditional solution at signals greater than 100kHz and the actual measurement comparison of the improved noise suppression capability of the high-frequency current sensor of the present application in the frequency band of 1MHz to 20MHz, the interference amplitude is greater than or equal to 10dB. Therefore, the first preset peak value can be 10dB to ensure that only significant interference is suppressed to avoid excessive filtering and reduce the waveform distortion rate of the traveling wave head. In some embodiments, it is possible to reduce the workload of on-site calibration by adjusting the value below the first preset peak value to be compatible with different voltage levels.

[0108] Step SS4: Perform Teager energy operator transformation on the filtered signal x(t) to capture the sudden change point of the fault traveling wave head and determine the arrival time t0 of the traveling wave.

[0109] In some embodiments, the continuous fault traveling wave head mutation point comprises the following formula:

[0110]

[0111] Where Ψ[x(t)] is the sudden change point of the continuous fault traveling wave head, and x(t) is the filtered signal.

[0112] In some embodiments, the discrete form of the fault traveling wave head mutation point comprises the following formula:

[0113] Ψ[n]=x²[n]-x[n-1]·x[n+1]

[0114] Where Ψ[n] is the discrete fault traveling wave head mutation point, and n is the discrete time series.

[0115] In some embodiments, in order to adapt to noisy environments, solve problems such as false operation in thunderstorms and missed detection of high-resistance faults caused by fixed thresholds, and improve the detection rate of high-resistance grounding, step SS4 of the present invention includes: steps SS41 to SS43.

[0116] Step SS41: Discretize the filtered signal x(t) to calculate the instantaneous energy Ψ(t).

[0117] When applied, the sampling rate fs can be greater than or equal to 40MHz.

[0118] In practical applications, the instantaneous energy Ψ(t) includes the following formula:

[0119] Ψ(t)=x(t)²-x(t-1)·x(t+1)

[0120] Where Ψ(t) is the instantaneous energy, x(t) is the filtered signal, and t is the time.

[0121] Step SS42: Calculate the energy mean μ and the energy standard σ in the second sliding time window according to the instantaneous energy Ψ(t), and determine the traveling wave trigger threshold Vth according to the energy mean μ and the energy standard σ.

[0122] When applied, the window length of the second sliding time window is 10ms.

[0123] In practical applications, the traveling wave trigger threshold Vth includes the following formula:

[0124] Vth=μ+K2·σ

[0125] μ=mean(Ψ[x(t)])

[0126] σ=std(Ψ[x(t)])

[0127] Where Vth is the traveling wave trigger threshold, μ is the energy mean, σ is the energy standard, and K2 is the dynamic coefficient, which can be 3.0 to 4.0.

[0128] Step SS43: When the instantaneous energy Ψ(t) is greater than a first preset condition, it is the traveling wave arrival moment, wherein the first preset condition includes a traveling wave triggering threshold Vth.

[0129] In some embodiments, the first preset condition of the present invention includes: within the first duration Δt, the instantaneous energy Ψ(t) is greater than the traveling wave trigger threshold Vth, and the energy rise rate Greater than the rising threshold. The present invention verifies energy mutation detection through the first duration, not only filtering out most pulse interference but also eliminating oscillatory decay waves. Furthermore, the steepness of the traveling wave head can be verified to identify the true traveling wave head.

[0130] In application, the first duration is determined based on the duration characteristics of the traveling wave head to ensure that the instantaneous energy mutation lasts long enough to be distinguished from transient interference. The mutation duration of the fault traveling wave is typically in the range of hundreds of nanoseconds to microseconds. Therefore, the first duration Δt can be greater than or equal to 200ns.

[0131] In practical applications, the rising threshold can be determined based on the voltage change and time change of the traveling wave head in the time domain; the rising threshold can also be determined based on the pulse interference and oscillation attenuation wave; the rising threshold can also be determined based on the voltage change and time change of the traveling wave head in the time domain, as well as the pulse interference and oscillation attenuation wave. In some embodiments, the rising threshold can be 0.5×10 9 (V 2 / s).

[0132] The traveling wave detection system of the present invention is based on the application of high-frequency current sensors.

[0133] The traveling wave detection system of the present invention includes an edge computing unit and further includes a fault diagnosis cloud platform.

[0134] In order to identify the fault direction based on the traveling wave polarity and solve the problem of fault line selection in multi-branch distribution networks, the edge computing unit of the present invention has a built-in FPGA chip. The FPGA chip is used to execute the above-mentioned traveling wave detection method and output the traveling wave arrival time t0 and the fault direction identifier.

[0135] When applied, in order to maintain a high noise suppression capability and improve seasonal adaptability, the edge computing unit of the present invention integrates a noise learning module. The noise learning module is used to regularly collect the background noise spectrum and update the notch filter parameters to suppress interference at specific frequencies, such as 5MHz switching noise.

[0136] In actual application, the noise learning module collects the background noise spectrum once every 24 hours.

[0137] In order to shorten the fault finding time and improve the fault diagnosis accuracy, the fault diagnosis cloud platform of the present invention is used to receive multi-node traveling wave arrival time t0 data and calculate the fault distance Lfault through a double-end traveling wave positioning method.

[0138] When applicable, the fault distance Lfault includes the following formula:

[0139]

[0140] Where Lfault is the fault distance, v is the traveling wave propagation velocity, L is the total line length, and t01 and t02 are the detection times of the sensors at both ends.

[0141] In some embodiments, the fault diagnosis cloud platform includes a built-in high-resistance fault identification model. The model inputs the traveling wave front steepness S and the first duration Δt. When the traveling wave front steepness S is less than a preset steepness Sset and the first duration Δt is greater than the preset duration, a high-resistance ground fault is determined.

[0142] When applied, the traveling wave head of a high-resistance grounding fault has a low steepness and a long duration. The duration of an arc fault can reach more than tens of microseconds. According to the predicted data, the preset duration can be greater than 50μs.

[0143] In practical applications, the traveling wave head steepness S includes the following formula:

[0144]

[0145] Where S is the steepness of the traveling wave head, Ψ[x(t)] is the sudden change point of the fault traveling wave head, and x(t) is the filtered signal.

[0146] In some embodiments, the present invention can also provide another traveling wave detection system based on a high-frequency current sensor.

[0147] The traveling wave detection system of the present invention comprises: an acquisition module, a fusion module, a filtering module and a determination module.

[0148] When applied, the acquisition module of the present invention is used to synchronously acquire the industrial frequency current signal output by the main coil and the high-frequency traveling wave signal output by the auxiliary coil; the fusion module is used to fuse the industrial frequency current signal and the high-frequency traveling wave signal to obtain a fused signal; the filtering module is used to perform notch filtering on the fused signal and make the notch center frequency dynamically track the background noise peak of the distribution network; the determination module is used to perform Teager energy operator transformation on the filtered signal to capture the sudden change point of the fault traveling wave head and determine the arrival time of the traveling wave.

[0149] In some embodiments, the fusion module can also be configured to perform wavelet packet decomposition on the high-frequency traveling wave signal, extract the sub-signal energy of the first frequency band, and calculate the dynamic weight coefficient based on the industrial frequency current signal and the sub-signal energy; based on the dynamic weight coefficient, the industrial frequency current signal and the high-frequency traveling wave signal are used to determine the fusion signal.

[0150] In some embodiments, the filtering module can also be configured to collect bus voltage signals through a voltage sensor and extract power frequency noise components; collect spatial electromagnetic noise signals through an auxiliary induction coil; perform FFT calculation on the spatial electromagnetic noise within a first sliding time window to identify the main peak frequency of the background noise; when the main peak frequency is the power frequency noise component or is greater than the first preset frequency band, and the same frequency verification is established, update the notch center frequency to the main peak frequency.

[0151] In some embodiments, the determination module can also be configured to discretize the filtered signal to calculate the instantaneous energy; based on the instantaneous energy, calculate the energy mean and the energy standard within the second sliding time window, and determine the traveling wave trigger threshold based on the energy mean and the energy standard; when the instantaneous energy is greater than the first preset condition, it is the arrival moment of the traveling wave, where the first preset condition includes the traveling wave trigger threshold.

[0152] As can be seen from the above embodiments, the present invention uses a main coil to ensure stable power frequency current measurement, while the auxiliary coil focuses on capturing high-frequency traveling waves. This addresses the bandwidth limitations of traditional sensors, expanding the effective bandwidth from the traditional 5 MHz to 20 MHz and reducing traveling wave signal amplitude errors. Furthermore, the three-stage processing architecture of fusion, filtering, and TEO detection eliminates the signal connection distortion issues inherent in traditional solutions. Furthermore, the present invention uses wavelet packet decomposition to accurately extract the characteristic frequency energy of the traveling wave in the first frequency band, improving the signal-to-noise ratio despite switching interference. Furthermore, dynamic weighting can prevent false tripping caused by high-frequency noise under normal operating conditions. Furthermore, the present invention uses dynamic weighting to enhance the ability to capture traveling wave characteristics during faults, ensuring unattenuated transmission of traveling wave signals and improving protection responsiveness. Under normal operating conditions, the present invention maintains power frequency measurement accuracy, completely isolates high-frequency channel interference, and reduces power frequency measurement errors. Furthermore, the present invention can simultaneously suppress power frequency harmonics and high-frequency interference. Furthermore, a dual verification mechanism (main peak detection and co-frequency verification) prevents mistracking of the traveling wave signal itself, reducing the mistracking rate in arc fault scenarios. Furthermore, the present invention ensures that only significant interference is suppressed by setting a first preset peak value, so as to avoid over-filtering and reduce the waveform distortion rate of the traveling wave head. In addition, the first preset peak value can be used to be compatible with different voltage levels, reducing the workload of on-site calibration. Furthermore, the present invention can adapt to the noise environment through a real-time dynamic traveling wave trigger threshold, solve the problems of false operation in thunderstorms and missed detection of high-resistance faults caused by fixed thresholds, and improve the detection rate of high-resistance grounding. Furthermore, the present invention verifies the energy mutation detection through the first duration, which can not only filter out most of the pulse interference, but also exclude the oscillation attenuation wave. In addition, the true traveling wave head can be identified by verifying the steepness of the traveling wave head.

[0153] Furthermore, this invention can identify fault directions based on traveling wave polarity, resolving the challenge of fault line selection in multi-branch distribution networks. Dual-end traveling wave positioning allows for dynamic calibration of traveling wave velocity, shortening fault-finding time. Furthermore, multi-node data fusion identifies complex faults, improving fault diagnosis accuracy. Optimizing the notch center frequency maintains high noise suppression capabilities, while optimizing the spectral signature library enhances seasonal adaptability.

[0154] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0155] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0156] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0157] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0158] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A traveling wave detection method based on a high-frequency current sensor, characterized in that: The high-frequency current sensor includes a main coil and an auxiliary coil; The traveling wave detection method comprises the following steps: Synchronously obtain the power frequency current signal output by the main coil and the high frequency traveling wave signal output by the auxiliary coil; Fusing the power frequency current signal and the high frequency traveling wave signal to obtain a fused signal; Perform notch filtering on the fusion signal and make the notch center frequency dynamically track the peak value of the distribution network background noise; Perform Teager energy operator transformation on the filtered signal to capture the sudden change point of the fault traveling wave head and determine the arrival time of the traveling wave; The fusing of the power frequency current signal and the high frequency traveling wave signal to obtain a fused signal includes: Perform wavelet packet decomposition on the high-frequency traveling wave signal, extract the sub-signal energy of the first frequency band, and calculate the dynamic weight coefficient based on the power frequency current signal and the sub-signal energy; The dynamic weight coefficient α includes the following formula: ； Where α is the dynamic weight coefficient, Ehigh is the sub-signal energy, Imain(t) is the power frequency current signal, K1 is the dynamic adjustment coefficient, and K1 is 0.1-0.5; Based on the dynamic weight coefficient, the fusion signal is determined using the power frequency current signal and the high frequency traveling wave signal; The fused signal Ifuse(t) includes the following formula: Ifuse(t)=α·Iaux(t)+(1-α)·Imain(t); Where, Ifuse(t) is the fusion signal, Iaux(t) is the high-frequency traveling wave signal, and Imain(t) is the power frequency current signal; The first frequency band is determined according to a fault characteristic frequency band.

2. The traveling wave detection method based on a high-frequency current sensor according to claim 1, characterized in that: The high-frequency current sensor is deployed at a preset node of the 35kV line; The dynamic weight coefficients include: When a short circuit fault occurs on the line, the energy of the sub-signal increases sharply, and the dynamic weight coefficient is 1; When the line operates normally, the sub-signal energy approaches zero and the dynamic weight coefficient is 0.

3. The traveling wave detection method based on a high-frequency current sensor according to claim 1, characterized in that: An auxiliary induction coil is provided inside the shielding layer of the high-frequency current sensor; The step of performing notch filtering on the fusion signal and making the notch center frequency dynamically track the peak value of the background noise of the distribution network includes: The bus voltage signal is collected through the voltage sensor, and the power frequency noise component is extracted; Collect spatial electromagnetic noise signals through auxiliary induction coils; Performing FFT calculation on the spatial electromagnetic noise within the first sliding time window to identify the main peak frequency of the background noise; When the main peak frequency is the power frequency noise component or is greater than the first preset frequency band, and the same-frequency verification is established, the notch center frequency is updated to the main peak frequency; The first preset frequency band is determined according to high-frequency interference and effective traveling wave signals.

4. The traveling wave detection method based on a high-frequency current sensor according to claim 3, characterized in that: The same-frequency verification includes: Detect whether the fused signal has a spectrum peak greater than a first preset peak at the main peak frequency of the background noise: If it exists, the same-frequency verification is established; The first preset peak value is determined according to the interference amplitude.

5. The traveling wave detection method based on a high-frequency current sensor according to claim 1, characterized in that: The Teager energy operator transformation is performed on the filtered signal to capture the fault traveling wave head mutation point and determine the arrival time of the traveling wave, including the following steps: Discretize the filtered signal to calculate the instantaneous energy; Calculating an energy mean and an energy standard within a second sliding time window based on the instantaneous energy, and determining a traveling wave trigger threshold based on the energy mean and the energy standard; When the instantaneous energy is greater than a first preset condition, it is the traveling wave arrival moment, wherein the first preset condition includes a traveling wave trigger threshold.

6. The traveling wave detection method based on a high-frequency current sensor according to claim 5, characterized in that: The first preset condition includes: During the first duration, the instantaneous energy is greater than the traveling wave trigger threshold, and the energy rise rate is greater than the rising threshold; The first duration is determined according to the duration characteristics of the traveling wave head; and the rising threshold is determined according to the pulse interference and the oscillation attenuation wave.

7. A traveling wave detection system based on a high-frequency current sensor, characterized in that: include: An edge computing unit having a built-in FPGA chip, the FPGA chip being used to execute the traveling wave detection method according to any one of claims 1 to 6, and outputting the traveling wave arrival time and the fault direction identifier.

8. The traveling wave detection system based on a high-frequency current sensor according to claim 7, characterized in that: Also includes: The fault diagnosis cloud platform is used to receive multi-node traveling wave arrival time data and calculate the fault distance through the dual-end traveling wave positioning method.

9. The traveling wave detection system based on a high-frequency current sensor according to claim 7, characterized in that: The edge computing unit is integrated with a noise learning module, which is used to periodically collect background noise spectra and update notch filter parameters.

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