Fault traveling wave head calibration method, device and electronic equipment

Abstract

The present invention provides a method for calibrating the head of a fault traveling wave. The method comprises obtaining a measurement value sequence of a current traveling wave signal; performing mean filtering on the measurement value sequence according to a fixed-width sliding window to obtain a filtered data sequence; roughly determining the position of the traveling wave head based on the filtered data sequence, selecting noise samples, and obtaining a noise sample data set; calculating noise distribution parameters based on the noise sample data set; constructing a recursive filter based on the noise distribution parameters; recursively filtering the measurement value sequence based on the recursive filter to obtain a signal estimation value sequence; and performing head calibration based on the signal estimation value sequence. The present invention has the ability to adaptively identify noise characteristics, can effectively filter out noise in the fault traveling wave measurement signal, improve the signal-to-noise ratio of the traveling wave signal, and has high head calibration accuracy and reliability.

Description

Technical Field

[0001] The present invention relates to the field of power grid traveling wave fault location measurement, and in particular to a fault traveling wave head calibration method, device and electronic equipment. Background Art

[0002] High-voltage transmission lines are prone to short-circuit failures due to their wide geographical distribution, long line distances, and complex operating environments. Accurately determining the fault location when a short-circuit occurs can shorten repair time, reduce power outage losses, and improve the safe and stable operation of the power grid. Currently, fault location methods for transmission lines are primarily divided into impedance and traveling wave methods. The traveling wave method, compared to the impedance method, is unaffected by factors such as fault point resistance, line structure, and transformer conversion errors, and has therefore been widely researched and applied.

[0003] The accuracy of fault location technology based on the traveling wave method depends primarily on the accurate calibration of the arrival time of the fault traveling wave head at the measurement point. Current traveling wave ranging products often use time-frequency analysis methods, such as wavelet transform and Hilbert-Huang transform, to detect signal singularities to calibrate the arrival time of the traveling wave. However, time-frequency analysis methods are susceptible to noise interference. Especially for long-distance transmission lines, the traveling wave signal is slowed down by dispersion effects during transmission. The small signal output by the transformer must then be transmitted through a cable a certain distance before being connected to the traveling wave signal acquisition device. This further attenuates the traveling wave signal and introduces electrical noise. Therefore, performing time-frequency analysis on weak traveling wave signals containing noise interference is prone to miscalibration and omission of the traveling wave head, directly affecting the reliability and accuracy of traveling wave ranging.

[0004] Currently, the introduction of noise interference is unavoidable during the transmission and acquisition of traveling wave signals. Traditional filtering methods, such as mean filtering and frequency-domain filtering, while filtering out noise, also cause loss of high-frequency components in the traveling wave signal, reducing the signal's singularity and making it difficult to calibrate the traveling wave head. Therefore, it is of great significance to develop an algorithm that can achieve noise filtering and traveling wave head calibration while preserving the signal's singularity. Summary of the Invention

[0005] The purpose of this application is to propose a fault traveling wave head calibration method to solve the problem of inaccurate and unreliable fault traveling wave head calibration under noise interference.

[0006] In order to achieve the above objectives, the solution of this application is:

[0007] As a first aspect of the present application, a method for calibrating a fault traveling wave head is proposed, comprising the following steps:

[0008] Acquiring a sequence of measurement values ​​of a current traveling wave signal;

[0009] Performing mean filtering on the measurement value sequence according to a fixed-width sliding window to obtain a filtered data sequence;

[0010] A traveling wave head position is roughly determined based on the filtered data sequence, and a noise sample is selected to obtain a noise sample data set;

[0011] Calculating noise distribution parameters based on the noise sample data set;

[0012] constructing a recursive filter based on the noise distribution parameters;

[0013] Recursively filtering the measurement value sequence based on the recursive filter to obtain a signal estimation value sequence;

[0014] Wave head calibration is performed based on the signal estimation value sequence.

[0015] Preferably, the sequence of obtaining measurement values ​​of the current traveling wave signal includes:

[0016] At the measurement point, the current traveling wave signals before and after the fault are collected through the current transformer and high-pass filter circuit to obtain the measurement value sequence of the current traveling wave signal:

[0017] I=[i1,i2,...,i N ]

[0018] Where I represents the measurement value sequence, N represents the total length of the signal measurement sequence, i1 represents the measurement value corresponding to the first sampling point, i2 represents the measurement value corresponding to the second sampling point, and i N Indicates the measurement value corresponding to the Nth sampling point.

[0019] Preferably, the calculation formula of the filtered data sequence is as follows:

[0020]

[0021] Where M is a positive integer, the fixed width of the sliding window is 2M+1; N represents the total length of the signal measurement sequence; P represents the filtered data sequence; p M+1 Indicates the filtered data value corresponding to the M+1th sampling point, p j Represents the filtered data value corresponding to the jth sampling point, M+1≤j≤NM,p N-M Represents the filtered data value corresponding to the NMth sampling point; i x Indicates the measurement value corresponding to the x-th sampling point.

[0022] Preferably, the roughly determining the traveling wave head position based on the filtered data sequence, selecting noise samples, and obtaining a noise sample data set includes:

[0023] Set a comparison threshold Pa, compare the filtered data sequence point by point starting from the first data point, and obtain the first data point that is greater than the comparison threshold. p The sampling moment is roughly determined as the position of the traveling wave head;

[0024] From the roughly determined traveling wave head position, remove b data forward to obtain the data interception position a=a p -b, take the measurement value sequence data at the data interception position and before as the noise sample, and obtain the noise sample data set W, namely:

[0025] W=[i1,i2,...,i a ];

[0026] Among them, a is the length of the noise sample data set, i1 represents the measurement value corresponding to the first sampling point, i2 is the measurement value corresponding to the second sampling point, and i a Indicates the measurement value corresponding to the a-th sampling point.

[0027] Preferably, the calculation of the noise distribution parameters based on the noise sample data set includes: obtaining the Gaussian distribution model N(μ,σ) of the noise based on the noise sample data set using the maximum likelihood estimation method. 2 ) parameters:

[0028]

[0029] Among them, a is the length of the noise sample data set, i k is the measured value at the kth sampling moment, 1≤k≤a, μ is the expectation of the noise distribution, σ 2 is the variance of the noise distribution.

[0030] Preferably, in constructing the recursive filter based on the noise distribution parameters, the recursive equation of the filter is:

[0031]

[0032] in, is the filter output value at the kth sampling moment, is the filter output value at the k-1th sampling moment, L k is the filter gain at the kth sampling moment;

[0033] where the filter gain is given by the following recursive equation:

[0034]

[0035] Among them, ε m,n is the measured value i at the mth sampling moment m and the filter output value at the nth sampling moment The absolute value of the difference between m,n ) is the discrete value ω in the noise N(μ,σ 2 ) distribution is located in [-ε m,n ,ε m,n ] interval, e represents a natural constant.

[0036] Preferably, recursively filtering the measured value sequence based on the recursive filter to obtain the signal estimation value sequence includes: presetting the initial value based on the recursive filter Equal to the expected value μ of the noise distribution, recursively filter the measurement value sequence I to obtain the filtered signal estimation value sequence:

[0037]

[0038] in, is the filter output value at the first sampling moment, is the filter output value at the second sampling moment, is the filter output value at the N-5th sampling moment, is the filter output value at the N-4th sampling moment.

[0039] Preferably, the performing wave head calibration based on the signal estimation value sequence includes:

[0040] Performing first-order difference calculation on the signal estimation value sequence to obtain a first-order difference sequence;

[0041] According to the length a of the noise sample data set, the maximum value of the first a data in the first-order difference sequence is multiplied by the margin coefficient to obtain the wave head calibration threshold;

[0042] The first-order difference sequence is compared with the wave head calibration threshold point by point, and the moment corresponding to the first data point greater than the wave head calibration threshold is the wave head arrival time.

[0043] As a second aspect of the present application, a fault traveling wave head calibration device is proposed, comprising:

[0044] An acquisition unit, configured to acquire a sequence of measurement values ​​of a current traveling wave signal;

[0045] a first filtering unit, configured to perform mean filtering on the measurement value sequence according to a fixed-width sliding window to obtain a filtered data sequence;

[0046] a noise sample acquisition unit, configured to roughly determine the position of the traveling wave head based on the filtered data sequence, select noise samples, and obtain a noise sample data set;

[0047] a noise distribution parameter determination unit, configured to calculate noise distribution parameters based on the noise sample data set;

[0048] A recursive filter construction unit, configured to construct a recursive filter based on the noise distribution parameters;

[0049] A second filtering unit, configured to recursively filter the measurement value sequence based on the recursive filter to obtain a signal estimation value sequence;

[0050] A wave head calibration unit is used to perform wave head calibration based on the signal estimation value sequence.

[0051] As a third aspect of the present application, an electronic device is proposed, including a processor and a memory, wherein the memory stores a program, and the program can be loaded by the processor to execute the above-mentioned fault traveling wave head calibration method.

[0052] The positive and beneficial technical effects of the present invention include:

[0053] The present invention statistically analyzes the noise parameters in the traveling wave acquisition signal and adaptively realizes the parameter identification of the noise Gaussian distribution model. On this basis, a recursive filtering algorithm based on the noise statistical parameters is constructed. This algorithm can effectively retain the singularity of the traveling wave signal while filtering out noise interference, greatly improving the signal-to-noise ratio of the traveling wave signal. It can accurately and reliably realize the calibration of the traveling wave head, thereby improving the reliability and accuracy of the traveling wave ranging system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A flow chart of a fault traveling wave head calibration method according to an exemplary embodiment of the present application is shown.

[0055] Figure 2 This is a structural diagram of a single-circuit simulation model for a dual-power system;

[0056] Figure 3 It is a curve diagram of the signal measurement value sequence and the data sequence after mean filtering;

[0057] Figure 4 1 shows the waveforms of traveling wave headers in a signal measurement value sequence, a signal estimation value sequence, a signal measurement value (solid line) and an estimation value (dashed line) sequence according to an embodiment of the present application;

[0058] Figure 5 The first-order difference sequence curve of the signal estimation value and the traveling wave head calibration result of the embodiment of the present application are shown.

[0059] Figure 6 A block diagram of a fault traveling wave head calibration device according to an exemplary embodiment of the present application is shown;

[0060] Figure 7 A block diagram of an electronic device according to an exemplary embodiment is shown. DETAILED DESCRIPTION

[0061] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied 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. Like reference numerals in the drawings represent like or similar parts, and thus repetitive description thereof will be omitted.

[0062] The described features, structures or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of these specific details, or other modes, components, materials, devices or operations may be employed. In these cases, well-known structures, methods, devices, implementations, materials or operations will not be shown or described in detail.

[0063] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0064] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0065] Research on traveling-wave fault location technology reveals that ranging accuracy depends crucially on the accurate calibration of the fault traveling-wave probe at the moment it reaches the measurement point. Current traveling-wave ranging products are susceptible to noise interference, but the introduction of noise interference during the transmission and acquisition of traveling-wave signals is unavoidable, making traveling-wave probe calibration difficult. Therefore, developing an algorithm that achieves noise filtering and traveling-wave probe calibration while preserving the singularity of traveling-wave signals is crucial.

[0066] According to an example embodiment of the present application, the position of the traveling wave head is roughly determined based on the acquired current traveling wave signal, a noise sample is selected based on the roughly determined position and the statistical characteristics of the noise are identified; then, a filtering algorithm based on the identification of noise statistical characteristics is used to filter the traveling wave signal before the head calibration is achieved.

[0067] The specific embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0068] Figure 1 A flow chart of a fault traveling wave head calibration method according to an exemplary embodiment of the present application is shown as follows: Figure 1 The fault traveling wave head calibration method shown includes the following steps:

[0069] S101: Acquire a measurement value sequence of a current traveling wave signal.

[0070] In some embodiments, a current traveling wave signal before and after a fault is collected at a measurement point via a current transformer and a high-pass filter circuit to obtain a measurement value sequence of the current traveling wave signal:

[0071] I=[i1,i2,...,i N ]

[0072] Where I represents the measurement value sequence, N represents the total length of the signal measurement sequence, i1 represents the measurement value corresponding to the first sampling point, i2 represents the measurement value corresponding to the second sampling point, and i N Indicates the measurement value corresponding to the Nth sampling point.

[0073] S102: Perform mean filtering on the measurement value sequence according to a fixed-width sliding window to obtain a filtered data sequence. Mean filtering is also called linear filtering, and the main method used is the neighborhood averaging method.

[0074] In some embodiments, the calculation formula of the filtered data sequence is as follows:

[0075]

[0076] Where M is a positive integer, the fixed width of the sliding window is 2M+1; N represents the total length of the signal measurement sequence; P represents the filtered data sequence; p M+1 Indicates the filtered data value corresponding to the M+1th sampling point, p j Represents the filtered data value corresponding to the jth sampling point, M+1≤j≤NM,p N-M Represents the filtered data value corresponding to the NMth sampling point; i x Indicates the measurement value corresponding to the x-th sampling point.

[0077] S103: roughly determining the position of the traveling wave head based on the filtered data sequence, selecting noise samples, and obtaining a noise sample data set.

[0078] In some embodiments, the method specifically includes the following steps: setting a comparison threshold Pa, performing point-by-point comparison on the filtered data sequence starting from the first data point, and obtaining the ath data point corresponding to the first data point greater than the comparison threshold.p The sampling moment is roughly determined as the traveling wave head position. From the roughly determined traveling wave head position, remove b data forward to obtain the data interception position a=a p -b, take the measurement value sequence data at the data interception position and before as the noise sample, and obtain the noise sample data set W, namely:

[0079] W=[i1,i2,...,i a ];

[0080] Among them, a is the length of the noise sample data set, i1 represents the measurement value corresponding to the first sampling point, i2 is the measurement value corresponding to the second sampling point, and i a = represents the measurement value corresponding to the ath sampling point. Because the current position is the roughly determined traveling wave head, by removing b data points forward, it can be ensured that there is no traveling wave signal but only noise in the noise sample interval.

[0081] S104: Calculating noise distribution parameters based on the noise sample data set.

[0082] In some embodiments, based on the noise sample data set, the Gaussian distribution model N(μ,σ 2 ) parameters:

[0083]

[0084] Among them, a is the length of the noise sample data set, i k is the measured value at the kth sampling moment, 1≤k≤a, μ is the expectation of the noise distribution, σ 2 is the variance of the noise distribution.

[0085] S105: Constructing a recursive filter based on the noise distribution parameters.

[0086] In some embodiments, the recursive equation of the filter is:

[0087]

[0088] in, is the filter output value at the kth sampling moment, is the filter output value at the k-1th sampling moment, L k is the filter gain at the kth sampling moment;

[0089] where the filter gain is given by the following recursive equation:

[0090]

[0091] Among them, ε m,n is the measured value i at the mth sampling momentm and the filter output value at the nth sampling moment The absolute value of the difference between m,n ) is the discrete value ω in the noise N(μ,σ 2 ) distribution is located in [-ε m,n ,ε m,n ] interval, e represents a natural constant.

[0092] S106: Recursively filter the measurement value sequence based on the recursive filter to obtain a signal estimation value sequence.

[0093] In some embodiments, the method specifically includes: presetting an initial value based on the recursive filter Equal to the expected value μ of the noise distribution, recursively filter the measurement value sequence I to obtain the filtered signal estimation value sequence:

[0094]

[0095] in, is the filter output value at the first sampling moment, is the filter output value at the second sampling moment, is the filter output value at the N-5th sampling moment, is the filter output value at the N-4th sampling moment.

[0096] S107: Perform wave head calibration based on the signal estimation value sequence.

[0097] In some embodiments, performing wave head calibration based on the signal estimation value sequence specifically includes:

[0098] Performing first-order difference calculation on the signal estimation value sequence to obtain a first-order difference sequence;

[0099] According to the length a of the noise sample data set, the maximum value of the first a data in the first-order difference sequence is multiplied by the margin coefficient to obtain the wave head calibration threshold;

[0100] The first-order difference sequence is compared with the wave head calibration threshold point by point, and the moment corresponding to the first data point greater than the wave head calibration threshold is the wave head arrival time.

[0101] This method has the ability to adaptively identify noise characteristics, can effectively filter out noise in fault traveling wave measurement signals, improve the signal-to-noise ratio of traveling wave signals, and has high wave head calibration accuracy and reliability.

[0102] The following uses the simulated transmission line traveling wave waveform superimposed with strong random noise as an example to further introduce the method of the present application. Figure 2The figure shows a single-circuit simulation model of a dual-power system. The power supply voltage level is 500 kV, and the system frequency is 50 Hz. The total length of the transmission line is 200 km. G and H represent the measurement points at both ends of the line, respectively. FLT is the line fault point, which is 150 km away from the H measurement point.

[0103] The model was modeled and simulated in the simulation software, with the simulation step size set to 1us and the simulation time set to 0.2s. The transmission line adopted the frequency-variable parameter model of the overhead bare conductor, and the theoretical value of the traveling wave velocity was 298.95m / us. The simulated fault point FLT had a permanent single-phase grounding fault at time 0.161670s, with an initial fault phase angle of 30° and a transition resistance of 20Ω. In order to verify the effectiveness of the method of the present invention, strong random noise was added to the measurement point, and the noise accounted for 45% of the peak value of the traveling wave signal. The fault traveling wave head calibration method of the present application was used to calibrate the H-end fault traveling wave head, specifically including:

[0104] S201: At the H terminal, a current transformer is used to synchronously collect the current traveling wave signal through a high-pass filter circuit. The sampling rate is 1 MHz, and the cutoff frequency of the high-pass filter circuit is 1 kHz. The measurement start time of the recorded data is 0.161 s and the measurement end time is 0.163 s. The measurement value sequence containing the fault traveling wave signal is obtained:

[0105] I=[i1,i2,...,i N ]

[0106] Where N is equal to 2000, such as Figure 3 The upper curve in the figure shows the waveform of the measurement value sequence.

[0107] S202: Perform sliding window mean filtering with a window length of 2M+1 on the measurement sequence I to obtain a filtered data sequence:

[0108]

[0109] Among them, the half window length M of the mean filter is set to 30, such as Figure 3 The lower curve in the figure shows the waveform of the filtered data sequence P.

[0110] S203: Based on the filtered data sequence, the traveling wave head position is roughly determined, and noise samples are selected to obtain a noise sample data set. The comparison threshold Pa is set to 2.0, and the filtered data sequence P is calculated from the first data point p M+1 Start point-by-point comparison and get the sampling time of the first data point that is greater than the comparison threshold as a p , roughly determined as the traveling wave head position. Then remove b data from the roughly determined traveling wave head position forward to obtain the data interception position a=a p-b, take the measurement value sequence data at the data interception position and before as noise samples, and obtain the noise sample data set W:

[0111] W=[i1,i2,...,i a ]

[0112] Among them, the calculated a p is equal to 1156, b is set to 100, and a is equal to 1056, such as Figure 3 As shown in the lower curve.

[0113] S204: Based on the noise sample data set obtained in step S203, the Gaussian distribution model N(μ,σ 2 ) parameters:

[0114]

[0115] Among them, the calculated noise distribution expectation μ is equal to 0.10195, and the noise distribution variance σ 2 Equal to 1.78254.

[0116] S205: Construct a recursive filter based on the noise distribution parameters. The recursive equation of the filter is:

[0117]

[0118] in, is the filter output value at the kth sampling moment, is the filter output value at the k-1th sampling moment, L k is the filter gain at the kth sampling moment;

[0119] where the filter gain is given by the following recursive equation:

[0120]

[0121] Among them, ε m,n is the measured value i at the mth sampling moment m and the filter output value at the nth sampling moment The absolute value of the difference between m,n ) is the discrete value ω in the noise N(μ,σ 2 ) distribution is located in [-ε m,n ,ε m,n ] interval, e represents a natural constant.

[0122] S206: Preset the initial value based on the recursive filter obtained in step S205 Implement recursive filtering of the measurement value sequence I to obtain the filtered signal estimation value sequence:

[0123]

[0124] Figure 4 The uppermost curve is the waveform of the signal measurement value sequence I. The middle curve is the signal estimation value sequence after filtering. Waveforms. The two bottom curves are partial enlargements of the wavefront portion of the signal measurement value sequence and the signal estimation value sequence placed together. The solid line represents the traveling wave front waveform in the signal measurement value sequence, and the dashed line represents the traveling wave front waveform in the signal estimation value sequence. It can be seen that the filtered signal estimation value sequence retains the singularity of the traveling wave signal while filtering out noise interference.

[0125] S207: Perform wave head calibration on the signal estimation value sequence obtained in step S206. Step S207-1: Perform first-order difference calculation on the signal estimation value sequence to obtain a first-order difference sequence:

[0126]

[0127] like Figure 5 The left curve in the middle is the first-order difference sequence curve of the signal estimation value.

[0128] Step S207-2: According to the length a of the noise sample data set, the first a data of the first-order difference sequence are intercepted, that is, Calculate the maximum value Multiply by the margin factor K cmp As the wave head calibration threshold i thrd :

[0129]

[0130] Among them, the margin coefficient K cmp When the value is greater than 1, the larger the margin coefficient, the lower the probability of misjudging the noise as a traveling wave head, and the more reliable the result, but it may not be able to detect weak traveling wave signals. Conversely, the smaller the margin coefficient, the higher the detection sensitivity for weak traveling wave signals, but the probability of misjudging the noise as a traveling wave head is also higher. In this embodiment, the value is 2.0, and the calculated i thrd is equal to 0.1032, Figure 5 The dotted line in the figure is the wave head calibration threshold.

[0131] Step S207-3: First-order difference sequence and wave head calibration threshold i thrd Compare point by point and get the first one that is greater than the wave head calibration threshold i thrdThe time corresponding to the data point is the time corresponding to sequence number 1172. Combined with the sampling rate of 1MHz and the measurement start time of 0.161s, the calibrated traveling wave head arrival time is:

[0132]

[0133] like Figure 5 The curve on the right is a partial enlargement of the left wave head position.

[0134] According to the simulation parameters, the distance from the fault point FLT to the H measurement point is 150 km, the traveling wave propagation speed is 298.95 m / us, and the fault occurs at 0.161670 s. Therefore, theoretically, the time when the fault traveling wave head reaches the H measurement point is:

[0135]

[0136] The measurement start time is 0.161s, so the corresponding theoretical wave head time scale is 1171.75us. Since the simulation step size is set to 1us and the sampling rate is 1MHz, the wave head time scale of 1172us calibrated by the method of the present invention is accurate. This shows that the method of the present invention has high wave head calibration accuracy and reliability for traveling wave head calibration under noisy conditions.

[0137] Figure 6 The device shown can execute the aforementioned fault traveling wave head calibration method according to the embodiment of the present application.

[0138] like Figure 6 As shown, the fault traveling wave head calibration device 300 may include: an acquisition unit 301, a first filtering unit 302, a noise sample acquisition unit 303, a noise distribution parameter determination unit 304, a recursive filter construction unit 305, a second filtering unit 306 and a wave head calibration unit 307.

[0139] The acquisition unit 301 is used to obtain a measurement value sequence of the current traveling wave signal.

[0140] The first filtering unit 302 is configured to perform mean filtering on the measurement value sequence according to a fixed-width sliding window to obtain a filtered data sequence.

[0141] The noise sample acquisition unit 303 is configured to roughly determine the position of the traveling wave head based on the filtered data sequence, select noise samples, and obtain a noise sample data set.

[0142] The noise distribution parameter determination unit 304 is configured to calculate noise distribution parameters based on the noise sample data set.

[0143] The recursive filter construction unit 305 is configured to construct a recursive filter based on the noise distribution parameters.

[0144] The second filtering unit 306 is configured to recursively filter the measurement value sequence based on the recursive filter to obtain a signal estimation value sequence.

[0145] The wave head calibration unit 307 is configured to perform wave head calibration based on the signal estimation value sequence.

[0146] The device performs functions similar to the method provided above. For other functions, please refer to the previous description and will not be repeated here.

[0147] Figure 7 A block diagram of an electronic device according to an exemplary embodiment is shown.

[0148] Refer to the following Figure 7 4 to describe an electronic device 400 according to this embodiment of the present application. Figure 7 The electronic device 400 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0149] like Figure 7 As shown, electronic device 400 is implemented as a general-purpose computing device. Components of electronic device 400 may include, but are not limited to, at least one processing unit 410, at least one storage unit 420, a bus 430 connecting various system components (including storage unit 420 and processing unit 410), a display unit 440, and the like.

[0150] The storage unit stores program codes, which can be executed by the processing unit 410 , so that the processing unit 410 executes the methods described in this specification according to various exemplary embodiments of the present application.

[0151] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 4201 and / or a cache memory unit 4202 , and may further include a read-only memory unit (ROM) 4203 .

[0152] The storage unit 420 may also include a program / utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0153] Bus 430 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0154] The electronic device 400 may also communicate with one or more external devices 400′ (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 400, and / or any device that enables the electronic device 400 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication may occur via an input / output (I / O) interface 450. Furthermore, the electronic device 400 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 460. The network adapter 460 may communicate with other modules of the electronic device 400 via the bus 430. It should be understood that, although not shown in the figures, other hardware and / or software modules may be used in conjunction with the electronic device 400, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0155] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software or by combining software with necessary hardware. The technical solution according to the embodiment of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiment of the present application.

[0156] The software product can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0157] Computer-readable storage media may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0158] The program code for performing the operations of the present application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0159] Those skilled in the art will appreciate that the above modules can be distributed in the device according to the description of the embodiment, or can be modified accordingly to be used in one or more devices that are different from the embodiment. The units of the above embodiments can be combined into one unit or further divided into multiple sub-units.

[0160] The present invention has been described herein with reference to specific exemplary embodiments only. It will be apparent to those skilled in the art that appropriate substitutions and modifications may be made without departing from the scope of the present invention. The exemplary embodiments are intended to be illustrative only and are not intended to limit the scope of the present invention, which is defined by the appended claims.

Claims

1. A fault traveling wave head calibration method, characterized in that: The following steps are involved: Acquiring a sequence of measurement values ​​of a current traveling wave signal; Performing mean filtering on the measurement value sequence according to a fixed-width sliding window to obtain a filtered data sequence; A traveling wave head position is roughly determined based on the filtered data sequence, and a noise sample is selected to obtain a noise sample data set; Calculating noise distribution parameters based on the noise sample data set; constructing a recursive filter based on the noise distribution parameters; Recursively filtering the measurement value sequence based on the recursive filter to obtain a signal estimation value sequence; Performing wave head calibration based on the signal estimation value sequence; The recursive equation of the filter is: in, is the filter output value at the kth sampling moment, is the filter output value at the k-1th sampling moment, L k is the filter gain at the kth sampling moment, i k is the measurement value at the kth sampling moment; where the filter gain is given by the following recursive equation: Among them, ε m,n is the measured value i at the mth sampling moment m and the filter output value at the nth sampling moment The absolute value of the difference between m,n ) is the discrete value ω in the noise N(μ,σ 2 ) distribution is located in [-ε m,n ,ε m,n ] interval, e represents a natural constant.

2. The method according to claim 1, wherein The sequence of obtaining the measured values ​​of the current traveling wave signal includes: At the measurement point, the current traveling wave signals before and after the fault are collected through the current transformer and high-pass filter circuit to obtain the measurement value sequence of the current traveling wave signal: I=[i1,i2,...,i N ] Where I represents the measurement value sequence, N represents the total length of the signal measurement sequence, i1 represents the measurement value corresponding to the first sampling point, i2 represents the measurement value corresponding to the second sampling point, and i N Indicates the measurement value corresponding to the Nth sampling point.

3. The method according to claim 1, wherein The calculation formula of the filtered data sequence is as follows: Where M is a positive integer, the fixed width of the sliding window is 2M+1; N represents the total length of the signal measurement sequence; P represents the filtered data sequence; p M+1 Indicates the filtered data value corresponding to the M+1th sampling point, p j Represents the filtered data value corresponding to the jth sampling point, M+1≤j≤NM,p N-M Represents the filtered data value corresponding to the NMth sampling point; i x Indicates the measurement value corresponding to the x-th sampling point.

4. The method according to claim 1, wherein The method of roughly determining the traveling wave head position based on the filtered data sequence and selecting noise samples to obtain a noise sample data set includes: Set a comparison threshold Pa, compare the filtered data sequence point by point starting from the first data point, and obtain the first data point that is greater than the comparison threshold. p The sampling moment is roughly determined as the position of the traveling wave head; From the roughly determined traveling wave head position, remove b data forward to obtain the data interception position a=a p -b, take the measurement value sequence data at the data interception position and before as the noise sample, and obtain the noise sample data set W, namely: W=[i1,i2,...,i a ]; Among them, a is the length of the noise sample data set, i1 represents the measurement value corresponding to the first sampling point, i2 is the measurement value corresponding to the second sampling point, and i a Indicates the measurement value corresponding to the a-th sampling point.

5. The method according to claim 1, wherein The calculation of the noise distribution parameters based on the noise sample data set includes: obtaining the Gaussian distribution model N(μ,σ 2 ) parameters: Among them, a is the length of the noise sample data set, i k is the measured value at the kth sampling moment, 1≤k≤a, μ is the expectation of the noise distribution, σ 2 is the variance of the noise distribution.

6. The method according to claim 1, wherein The method of recursively filtering the measured value sequence based on the recursive filter to obtain the signal estimation value sequence includes: presetting the initial value based on the recursive filter. Equal to the expected value μ of the noise distribution, recursively filter the measurement value sequence I to obtain the filtered signal estimation value sequence: in, is the filter output value at the first sampling moment, is the filter output value at the second sampling moment, is the filter output value at the N-5th sampling moment, is the filter output value at the N-4th sampling moment.

7. The method according to claim 1, wherein The performing wave head calibration based on the signal estimation value sequence includes: Performing first-order difference calculation on the signal estimation value sequence to obtain a first-order difference sequence; According to the length a of the noise sample data set, the maximum value of the first a data in the first-order difference sequence is multiplied by the margin coefficient to obtain the wave head calibration threshold; The first-order difference sequence is compared with the wave head calibration threshold point by point, and the moment corresponding to the first data point greater than the wave head calibration threshold is the wave head arrival time.

8. A fault traveling wave head calibration device, characterized in that: The device comprises: An acquisition unit, configured to acquire a sequence of measurement values ​​of a current traveling wave signal; a first filtering unit, configured to perform mean filtering on the measurement value sequence according to a fixed-width sliding window to obtain a filtered data sequence; a noise sample acquisition unit, configured to roughly determine the position of the traveling wave head based on the filtered data sequence, select noise samples, and obtain a noise sample data set; a noise distribution parameter determination unit, configured to calculate noise distribution parameters based on the noise sample data set; A recursive filter construction unit, configured to construct a recursive filter based on the noise distribution parameters; A second filtering unit, configured to recursively filter the measurement value sequence based on the recursive filter to obtain a signal estimation value sequence; A wave head calibration unit, configured to perform wave head calibration based on the signal estimation value sequence; The recursive equation of the filter is: in, is the filter output value at the kth sampling moment, is the filter output value at the k-1th sampling moment, L k is the filter gain at the kth sampling moment, i k is the measurement value at the kth sampling moment; where the filter gain is given by the following recursive equation: Among them, ε m,n is the measured value i at the mth sampling moment m and the filter output value at the nth sampling moment The absolute value of the difference between m,n ) is the discrete value ω in the noise N(μ,σ 2 ) distribution is located in [-ε m,n ,ε m,n ] interval, e represents a natural constant.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a program, and the program can be loaded by the processor to execute the method according to any one of claims 1 to 7.

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