Traveling wave head detection method and related equipment

Abstract

The invention discloses a traveling wave head detection method and related equipment, relates to the technical field of power transmission line fault positioning, and solves the problem that the anti-interference performance of traveling wave head detection is relatively weak. The method comprises the following steps: acquiring a current traveling wave signal, a voltage traveling wave signal and a high-frequency electric field signal of a power transmission line; extracting time-frequency energy characteristics and instantaneous phase characteristics of the current traveling wave signal, the voltage traveling wave signal and the high-frequency electric field signal; through a D-S evidence theory, based on the time-frequency energy characteristic and the instantaneous phase characteristic, calculating a joint confidence coefficient of the existence of the traveling wave head; and outputting a timestamp of the traveling wave head based on the joint confidence and a preset threshold. Traveling wave detection is carried out by acquiring current, voltage and electric field three-mode data, so that cross validation can be carried out among different source data, and the failure risk of a single sensor is inhibited.

Description

Technical Field

[0001] The present invention relates to the technical field of transmission line fault location, and particularly to a traveling wave head detection method and related equipment. Background Art

[0002] Accurate fault location of transmission lines in a power system can quickly narrow down the fault location range, reduce the burden of line patrol, and shorten the fault elimination time, which is of great significance for improving the power supply reliability of the power system and reducing power outage losses.

[0003] Currently, the commonly used fault location methods include impedance method, traveling wave method, and fault analysis method. Among them, the traveling wave location method is widely used due to its advantages such as being unaffected by factors such as line type and grounding impedance and having high location accuracy. The key to traveling wave location is to find the arrival time of the traveling wave head. With the large-scale operation of traveling wave location devices such as fault recorders, the identification of the traveling wave head using a single current traveling wave signal is vulnerable to interference and has poor reliability, and it is difficult to extract the traveling wave head when locating transmission line faults.

[0004] In view of this, a traveling wave head detection method and related equipment are needed. Summary of the Invention

[0005] Aiming at the problem of weak anti-interference performance in the existing traveling wave head detection method, the present invention provides a traveling wave head detection method and related equipment, which can improve the robustness and reliability of wave head detection. The specific technical solutions are as follows:

[0006] In a first aspect, an embodiment of the present application provides a traveling wave head detection method, including:

[0007] Obtain the current traveling wave signal, voltage traveling wave signal, and high-frequency electric field signal of the transmission line; extract the time-frequency energy characteristics and instantaneous phase characteristics of the current traveling wave signal, voltage traveling wave signal, and high-frequency electric field signal; calculate the combined confidence of the existence of the traveling wave head based on the time-frequency energy characteristics and the instantaneous phase characteristics through the D-S evidence theory; and output the time stamp of the traveling wave head based on the combined confidence and a preset threshold.

[0008] Preferably, the time-frequency energy feature includes an energy peak, and the instantaneous phase feature includes an instantaneous phase consistency; extracting the time-frequency energy feature and the instantaneous phase feature of the current traveling wave signal, the voltage traveling wave signal, and the high-frequency electric field signal includes: performing an S transform on the current traveling wave signal, the voltage traveling wave signal, and the high-frequency electric field signal respectively to obtain a first time-frequency matrix, a second time-frequency matrix, and a third time-frequency matrix; extracting the energy peaks of the first time-frequency matrix, the second time-frequency matrix, and the third time-frequency matrix in the frequency band of 20-100 kHz to obtain a first energy peak, a second energy peak, and a third energy peak; calculating the instantaneous phase consistency of the current traveling wave signal, the voltage traveling wave signal, and the high-frequency electric field signal based on the first time-frequency matrix, the second time-frequency matrix, and the third time-frequency matrix.

[0009] Preferably, based on the time-frequency energy feature and the instantaneous phase feature, calculating the joint confidence of the existence of the traveling wave head through the D-S evidence theory includes: defining a basic probability assignment function, and the expression of the basic probability assignment function includes:

[0010] m a (A b );

[0011]

[0012] m a (A2)=β· noise energy a / total energy a ;

[0013] m a (Θ)=1 - m a (A1)-m a (A1);

[0014] Among them, m a represents the probability calculated based on the time-frequency matrix a, a ∈ (i, u, E), i represents the current traveling wave signal, u represents the voltage traveling wave signal, and E represents the high-frequency electric field signal; A b ∈ (A1, A2, Θ), A1 represents the existence of a wave head, A2 represents the non-existence of a wave head, and Θ represents uncertainty; represents the energy peak of the time-frequency matrix a at time τ, and the total energy a represents the full-band energy of the time-frequency matrix a, and the noise energy a represents the energy of the time-frequency matrix a in the non-wave head frequency band, and β is an empirical coefficient; Δφ(τ) represents the instantaneous phase consistency; based on the basic probability assignment function, calculating the joint confidence through the Dempster combination rule; the expression for calculating the joint confidence includes:

[0015]

[0016] Wherein, K is a conflict factor, and B, C, and D are respectively independent proposition sets corresponding to the current traveling wave signal, the voltage traveling wave signal, and the high-frequency electric field signal; the propositions in the independent proposition set include the existence of a wavefront, the non-existence of a wavefront, and uncertainty.

[0017] Preferably, based on the joint confidence level and a preset threshold, outputting the timestamp of the traveling wavefront includes: when the joint confidence level is greater than the preset threshold, marking the moment corresponding to the joint confidence level as the candidate wavefront moment; when three consecutive moments are all the candidate wavefront moments, outputting the first moment among the three consecutive moments as the timestamp of the traveling wavefront.

[0018] Preferably, the time-frequency energy feature includes the noise standard deviation; after the timestamp of the output traveling wavefront, the method further includes: adjusting the preset threshold based on the noise standard deviation; the expression for adjusting the preset threshold is:

[0019] θ(τ) = α·σ(τ) + θ0;

[0020] Wherein, θ(τ) is the adjusted preset threshold at the moment τ, α is an adjustment coefficient, σ(τ) is the noise standard deviation at the moment τ, and θ0 is the initial value of the preset threshold.

[0021] Preferably, before extracting the time-frequency energy feature and the instantaneous phase feature of the current traveling wave signal, the voltage traveling wave signal, and the high-frequency electric field signal, the method further includes: when one of the current traveling wave signal, the voltage traveling wave signal, and the high-frequency electric field signal is missing, reconstructing the missing waveform through the phase consistency of the other two of them.

[0022] In a second aspect, an embodiment of the present application provides a traveling wavefront detection system, which is applied to the method as described in the first aspect. The system includes:

[0023] An acquisition module, configured to acquire the current traveling wave signal, the voltage traveling wave signal, and the high-frequency electric field signal of a transmission line;

[0024] An extraction module, configured to extract the time-frequency energy feature and the instantaneous phase feature of the current traveling wave signal, the voltage traveling wave signal, and the high-frequency electric field signal;

[0025] A calculation module, configured to calculate the joint confidence level of the existence of the traveling wavefront based on the time-frequency energy feature and the instantaneous phase feature through the D-S evidence theory;

[0026] An output module, configured to output the timestamp of the traveling wavefront based on the joint confidence level and a preset threshold.

[0027] Preferably, the acquisition module acquires the current traveling wave signal through a Rogowski coil; acquires the voltage traveling wave signal through a capacitive voltage transformer; and acquires the high-frequency electric field signal through a broadband electric field sensor.

[0028] In a third aspect, an embodiment of the present application provides a computing device, including: a memory for storing a program; a processor for loading the program to execute the method as described in the first aspect.

[0029] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and when the program runs, it controls the device where the computer-readable storage medium is located to execute the method as described in the first aspect.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows: By acquiring current, voltage, and electric field three-modal data, performing time-frequency feature extraction and joint confidence calculation, cross-verification can be performed between different source data, suppressing the risk of single-sensor failure, and solving problems such as weak anti-interference ability and saturation failure in traditional traveling wave detection, providing a highly reliable solution for intelligent power grid fault diagnosis. Description of the Drawings

[0031] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts do not necessarily draw according to the actual scale.

[0032] Figure 1 It is a schematic flowchart of a traveling wave head detection method provided by an embodiment of the present application;

[0033] Figure 2 It is a schematic structural diagram of a traveling wave head detection system provided by an embodiment of the present application;

[0034] Figure 3 It is a schematic structural diagram of a computing device provided by an embodiment of the present application. Detailed Embodiments

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the protection scope of the present invention.

[0036] It should be understood that when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0037] It should also be understood that the terms used in the specification of the present invention are for the purpose of describing particular embodiments only and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0038] It should be further understood that the term "and / or" used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0039] To solve the problem of weak anti-interference performance in traditional traveling wave head detection methods, the present invention provides a traveling wave head detection method and related devices, which can improve the robustness and reliability of wave head detection.

[0040] Please refer to Figure 1 , Figure 1 which is a schematic flow chart of a traveling wave head detection method provided by an embodiment of the present application. This method is applied to a computing device; as Figure 1 shown, the method includes:

[0041] Step 101, the computing device acquires the current traveling wave signal, voltage traveling wave signal and high-frequency electric field signal of the transmission line.

[0042] Among them, the computing device can be a computing module, control module or monitoring and acquisition module arranged on the transmission line, which can directly acquire the current traveling wave signal, voltage traveling wave signal and high-frequency electric field signal on the transmission line; it can also be a server, or a personal computer or tablet and other intelligent terminals directly operated by power system management personnel or maintenance personnel, which are communicatively connected to the management device, monitoring device or sensor of the transmission line by wired or wireless means, and acquire the current traveling wave signal, voltage traveling wave signal and high-frequency electric field signal collected by these devices from them.

[0043] Specifically, the computing device can acquire the current traveling wave signal through a Rogowski coil; acquire the voltage traveling wave signal through a capacitive voltage transformer; acquire the high-frequency electric field signal through a broadband electric field sensor.

[0044] Specifically, the computing device can align the sampling time axes of the three signals through GPS second pulses and use the cross-correlation algorithm to compensate for the sensor hardware delay, so that the alignment accuracy is less than or equal to 0.1 microseconds. It can be understood that through the multi-source data obtained synchronously, the computing device can not only calculate more accurate waveform signal characteristics at the same moment based on the multi-source data at the same moment, but also, in the case of the absence of one of the signals, reconstruct the missing signal based on the other two signals, so that the subsequent calculation steps can have accurate data input, improving the anti-interference ability of the wavefront detection.

[0045] Preferably, when one of the current traveling wave signal, the voltage traveling wave signal, and the high-frequency electric field signal is missing, the computing device reconstructs the missing waveform through the phase consistency of the other two of them.

[0046] Among them, the situation of signal loss includes the case where when a short-circuit fault occurs in the current sensor, the short-circuit current is too large, causing the iron core of the current sensor to enter the magnetic saturation state, resulting in the sensor being unable to accurately measure and reflect the actual current; it also includes the case where the sensor element is damaged or polarized, resulting in a decrease in sensitivity, and the sensor may not be able to generate sufficient electrical signals to be detected by the acquisition system.

[0047] Among them, taking the absence of the current signal as an example, the computing device can reconstruct the current signal through the following formula:

[0048]

[0049] where i rec (t) represents the reconstructed current traveling wave signal that changes with time t, and u(t), E(t), and i(t) are the voltage traveling wave signal, the high-frequency electric field signal, and the current traveling wave signal collected by the computing device that change with time t respectively; ∫ T ()dt represents the time-related integral function.

[0050] Step 102: The computing device extracts the time-frequency energy characteristics and instantaneous phase characteristics of the current traveling wave signal, the voltage traveling wave signal, and the high-frequency electric field signal.

[0051] Among them, the traveling wave front has significant characteristics in the traveling wave signal. When the traveling wave front appears, the current, voltage, and electric field corresponding to the traveling wave signal will all change rapidly, and these changes form the characteristics of the wave front, enabling it to be distinguished from noise. Therefore, the computing device can extract the characteristics of the current traveling wave signal, the voltage traveling wave signal, and the high-frequency electric field signal to invert the arrival time of the wave front.

[0052] Preferably, the time-frequency energy feature includes an energy peak, and the instantaneous phase feature includes instantaneous phase consistency; the computing device can perform an S transform on the current traveling wave signal i(t), the voltage traveling wave signal u(t), and the high-frequency electric field signal E(t) respectively to obtain a first time-frequency matrix ST i (τ,f), a second time-frequency matrix ST u (τ,f), and a third time-frequency matrix ST E (τ,f); extract the energy peak P i (τ,f), the second time-frequency matrix ST u (τ,f), and the third time-frequency matrix ST E (τ,f) in the frequency band of 20 - 100 kHz to obtain a first energy peak max P P P Based on the first time-frequency matrix, the second time-frequency matrix, and the third time-frequency matrix, calculate the instantaneous phase consistency Δφ(τ) of the current traveling wave signal, the voltage traveling wave signal, and the high-frequency electric field signal.

[0053] Among them, the energy peak corresponds to the point or region where the signal has the maximum energy at a specific time and frequency. By finding the energy peak, the time-frequency position where the energy of the signal is concentrated can be quickly located, and these positions often correspond to the main components or key features of the signal; the instantaneous phase consistency can reflect the phase change of the signal at different time and frequency points. In the time-frequency matrix, it helps to reveal the local structural features of the signal. Based on this, the computing device can calculate these two features for subsequent wavefront detection.

[0054] Among them, when the computing device focuses on the frequency band of 20 - 100 kHz for feature extraction of the time-frequency matrix, it can make full use of the better frequency response and performance of the measurement device or sensor in the frequency band of 20 - 100 kHz, and at the same time can avoid interference and noise. Specifically, the energy peak is the maximum energy value of the time-frequency matrix at time τ in the frequency band of 20 - 100 kHz.

[0055] Among them, the calculation formula of the S transform includes:

[0056]

[0057] Among them, t is the time variable of the original signal s(t), representing the value of the signal on the entire time axis; τ is the time shift variable, indicating the specific time point of interest during analysis; f is the frequency variable, and j is the imaginary unit.

[0058] Among them, the formula for calculating the instantaneous phase consistency includes:

[0059]

[0060] Among them, arg() represents the argument of a complex number, and ST * () represents the complex conjugate of the time-frequency matrix.

[0061] Step 103: The computing device calculates the joint confidence of the existence of the traveling wave head based on the D-S evidence theory, the time-frequency energy feature, and the instantaneous phase feature.

[0062] Among them, after calculating the traveling wave signal features in three modes, the computing device can further calculate the possibility of the existence of the wave head based on this feature, and then this possibility marks the arrival time of the wave head.

[0063] Among them, the D-S (Dempster-Shafer) evidence theory is a mathematical method for dealing with uncertain information and is commonly used in multi-source information fusion. The computing device can calculate the joint confidence (also known as the fusion confidence) of the existence of the traveling wave head based on the D-S evidence theory, the above time-frequency energy feature, and the instantaneous phase feature.

[0064] Preferably, the computing device can first define a basic probability assignment function, and the expression of this basic probability assignment function includes:

[0065] m a (A b );

[0066]

[0067] m a (A2) = β · noise energy a / total energy a ;

[0068] m a (Θ) = 1 - m a (A1) - m a (A1);

[0069] Among them, m a represents the probability calculated based on the time-frequency matrix a, a ∈ (i, u, E), i represents the current traveling wave signal, u represents the voltage traveling wave signal, and E represents the high-frequency electric field signal; A b ∈ (A1, A2, Θ), A1 represents the existence of a wave head, A2 represents the non-existence of a wave head, and Θ represents uncertainty; represents the energy peak of the time-frequency matrix a at time τ, and the total energy a represents the full-band energy of the time-frequency matrix a, and the noise energy a represents the energy of the time-frequency matrix a in the non-wave head frequency band; β is an empirical coefficient used to suppress the excessive influence of noise on false alarms. Preferably, the empirical coefficient β can be 0.2.

[0070] Among them, the total energy a The calculation formula may include:

[0071]

[0072] Among them, the noise energy a The calculation formula may include:

[0073] It can be understood that since the main energy of the wavefront is concentrated in the 20 - 100 kHz frequency band, the total energy of the high - frequency band (i.e., the non - wavefront frequency band) can be taken as the noise energy.

[0074] Then, the computing device can calculate the joint confidence based on this basic probability assignment function through the Dempster combination rule; the expression for calculating the joint confidence includes:

[0075]

[0076] Among them, K is the conflict factor, and B, C, and D are the sets of independent propositions corresponding to the current traveling - wave signal, voltage traveling - wave signal, and high - frequency electric - field signal respectively; the propositions of the set of independent propositions include the existence of a wavefront, the non - existence of a wavefront, and uncertainty.

[0077] Among them, the calculation formula for the conflict factor K is:

[0078]

[0079] The conflict factor K reflects the degree of contradiction between multi - source data, which is represented by the sum of the products of the confidences of all conflict combinations.

[0080] It can be understood that in the calculation of the joint confidence in this step, the proposition sets of B, C, and D can be regarded as only including the existence of a wavefront, the non - existence of a wavefront, and uncertainty. Therefore, the expression of the joint confidence can be simplified to:

[0081]

[0082] Preferably, the computing device can first normalize the calculation result of the basic probability assignment function and then calculate the joint confidence based on the normalized data.

[0083] Step 104, the computing device outputs the timestamp of the traveling - wave front based on the joint confidence and a preset threshold.

[0084] Among them, the computing device can determine the arrival time of the traveling wave head based on the magnitude relationship between the joint confidence and the preset threshold, and then output the time stamp of the traveling wave head based on the arrival time. It can be understood that the time stamp of the traveling wave head is used to indicate the time when the traveling wave head arrives at the traveling wave signal acquisition device.

[0085] Preferably, when the joint confidence is greater than the preset threshold, the computing device can mark the moment corresponding to the joint confidence as the candidate wave head moment; when three consecutive moments are all the candidate wave head moments, the computing device can output the first moment among the three consecutive moments as the time stamp of the traveling wave head.

[0086] Preferably, the time-frequency energy feature includes the noise standard deviation; after the time stamp of the output traveling wave head, the method further includes: adjusting the preset threshold based on the noise standard deviation; the expression for adjusting the preset threshold is:

[0087] θ(τ) = α·σ(τ) + θ0;

[0088] Among them, θ(τ) is the adjusted preset threshold at the τ moment, α is the adjustment coefficient, σ(τ) is the noise standard deviation at the τ moment, and θ0 is the initial value of the preset threshold. It can be understood that after the computing device calculates θ(τ), it can assign the value of θ(τ) to θ0 so that in the next round of calculation, the computing device can perform wave head detection based on the adjusted preset threshold, and then iterate the preset threshold again.

[0089] Among them, the computing device can calculate the noise standard deviation based on the noise energy of one or more time-frequency matrices calculated in step 103.

[0090] Optionally, when the computing device outputs the time stamp, it can also output the time stamp credibility rating obtained based on the joint confidence. Exemplarily, when the joint confidence is greater than 0.8, the computing device can output a high credibility rating and mark it as red; when the joint confidence is less than or equal to 0.8 and greater than 0.6, the computing device can output a medium credibility rating and mark it as yellow; when the joint confidence is less than or equal to 0.6, the computing device can output a low credibility rating and mark it as blue.

[0091] In the embodiments of the present application, by synchronously acquiring current, voltage, and electric field three-modal data, performing time-frequency feature extraction and joint confidence calculation, cross-verification can be performed between different source data, the risk of single sensor failure can be suppressed, and problems such as weak anti-interference ability and saturation failure in traditional traveling wave detection are solved, providing a highly reliable solution for intelligent power grid fault diagnosis.

[0092] The method part provided by the embodiments of the present application has been described above, and the system part provided by the embodiments of the present application will be described below.

[0093] Please refer to Figure 2 , Figure 2 which is a schematic structural diagram of a traveling wave head detection system provided by an embodiment of the present application. As Figure 2 shown, the system 20 includes:

[0094] An acquisition module 201, configured to acquire current traveling wave signals, voltage traveling wave signals, and high-frequency electric field signals of a transmission line;

[0095] An extraction module 202, configured to extract time-frequency energy features and instantaneous phase features of the current traveling wave signal, the voltage traveling wave signal, and the high-frequency electric field signal;

[0096] A calculation module 203, configured to calculate a joint confidence degree of the existence of a traveling wave head based on the time-frequency energy feature and the instantaneous phase feature through D-S evidence theory;

[0097] An output module 204, configured to output a time stamp of the traveling wave head based on the joint confidence degree and a preset threshold.

[0098] Preferably, the time-frequency energy feature includes an energy peak, and the instantaneous phase feature includes an instantaneous phase consistency; the extraction module 202 is specifically configured to perform an S transform on the current traveling wave signal, the voltage traveling wave signal, and the high-frequency electric field signal respectively to obtain a first time-frequency matrix, a second time-frequency matrix, and a third time-frequency matrix; extract energy peaks of the first time-frequency matrix, the second time-frequency matrix, and the third time-frequency matrix in the 20-100 kHz frequency band to obtain a first energy peak, a second energy peak, and a third energy peak; calculate the instantaneous phase consistency of the current traveling wave signal, the voltage traveling wave signal, and the high-frequency electric field signal based on the first time-frequency matrix, the second time-frequency matrix, and the third time-frequency matrix.

[0099] Preferably, calculating the joint confidence degree of the existence of a traveling wave head based on the time-frequency energy feature and the instantaneous phase feature through D-S evidence theory includes: defining a basic probability assignment function, and the expression of the basic probability assignment function includes:

[0100] m a (A b )

[0101]

[0102] m a (A2) = β · noise energy a / total energy a ;

[0103] m a (Θ) = 1 - m a (A1) - m a(A1);

[0104] where m a represents the probability calculated based on the time-frequency matrix a, a ∈ (i, u, E), where i represents the current traveling wave signal, u represents the voltage traveling wave signal, and E represents the high-frequency electric field signal; A b ∈ (A1, A2, Θ), where A1 represents the existence of a wavefront, A2 represents the non-existence of a wavefront, and Θ represents uncertainty; represents the energy peak of the time-frequency matrix a at time τ, and the total energy a represents the full-band energy of the time-frequency matrix a, and the noise energy a represents the energy of the time-frequency matrix a in the non-wavefront frequency band, and β is an empirical coefficient; Δφ(τ) represents the instantaneous phase consistency; based on this basic probability assignment function, the joint confidence is calculated through the Dempster combination rule; the expression for calculating the joint confidence includes:

[0105]

[0106] where K is the conflict factor, and B, C, and D are the sets of independent propositions corresponding to the current traveling wave signal, voltage traveling wave signal, and high-frequency electric field signal, respectively; the propositions in the set of independent propositions include the existence of a wavefront, the non-existence of a wavefront, and uncertainty.

[0107] Preferably, the output module 204 is specifically configured to mark the time corresponding to the joint confidence as the candidate wavefront time when the joint confidence is greater than the preset threshold; when three consecutive times are all the candidate wavefront times, the first time among the three consecutive times is output as the timestamp of the traveling wave front.

[0108] Preferably, the time-frequency energy feature includes the noise standard deviation; the system 20 further includes an adjustment module 205 for adjusting the preset threshold based on the noise standard deviation; the expression for adjusting the preset threshold is:

[0109] θ(τ) = α·σ(τ) + θ0;

[0110] where θ(τ) is the adjusted preset threshold at time τ, α is the adjustment coefficient, σ(τ) is the noise standard deviation at time τ, and θ0 is the initial value of the preset threshold.

[0111] Preferably, the system 20 further includes a reconstruction module 206 for reconstructing the missing waveform through the phase consistency of the other two of the current traveling wave signal, voltage traveling wave signal, and high-frequency electric field signal when one of them is missing.

[0112] Preferably, the acquisition module 201 acquires the current traveling wave signal through a Rogowski coil; acquires the voltage traveling wave signal through a capacitive voltage transformer; and acquires the high-frequency electric field signal through a broadband electric field sensor.

[0113] The traveling wave front detection system provided by the embodiments of the present application can be understood by referring to the corresponding content in the foregoing method embodiment section, and will not be repeated here.

[0114] As Figure 3 shown, Figure 3 FIG. is a possible schematic logical structure diagram of a computing device provided by an embodiment of the present application. The computing device 300 includes: a processor 301, a communication interface 302, a memory 303, and a bus 304. The processor 301, the communication interface 302, and the memory 303 are interconnected through the bus 304. In the embodiments of the present application, the processor 301 is used to control and manage the operations of the computing device 300. For example, the processor 301 is used to execute Figure 1 the steps in the embodiments and / or other processes for the technologies described herein. The communication interface 302 is used to support the computing device 300 to communicate. The memory 303 is used to store the program codes and data of the computing device 300.

[0115] Among them, the processor 301 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in combination with the disclosure of the present application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, and so on. The bus 304 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 3 only a thick line is shown in FIG., but it does not mean that there is only one bus or one type of bus.

[0116] In another embodiment of the present application, a computer-readable storage medium is further provided. The computer-readable storage medium includes instructions. When the instructions run on a computer, the computer is caused to execute the above Figure 1 embodiments.

[0117] Those of ordinary skill in the art can realize that the units of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0118] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0119] In several embodiments provided by the embodiments of the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0120] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0121] In addition, the functional units in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0122] If the above-mentioned functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present invention, and they should all be covered by the scope of the claims and the description of the present invention.

Claims

1. A traveling wave front detection method, characterized in that, Including: Obtaining current traveling wave signals, voltage traveling wave signals, and high-frequency electric field signals of a transmission line; Extracting time-frequency energy features and instantaneous phase features of the current traveling wave signals, the voltage traveling wave signals, and the high-frequency electric field signals; Calculating a combined confidence of the existence of a traveling wave head based on the time-frequency energy features and the instantaneous phase features through the D-S evidence theory; Outputting a time stamp of the traveling wave head based on the combined confidence and a preset threshold.

2. The method according to claim 1, characterized in that, The time-frequency energy features include energy peaks, and the instantaneous phase features include instantaneous phase consistency; the extracting the time-frequency energy features and the instantaneous phase features of the current traveling wave signals, the voltage traveling wave signals, and the high-frequency electric field signals includes: Performing S-transforms on the current traveling wave signals, the voltage traveling wave signals, and the high-frequency electric field signals respectively to obtain a first time-frequency matrix, a second time-frequency matrix, and a third time-frequency matrix; Extracting energy peaks of the first time-frequency matrix, the second time-frequency matrix, and the third time-frequency matrix in a frequency band of 20 - 100 kHz to obtain a first energy peak, a second energy peak, and a third energy peak; Calculating the instantaneous phase consistency of the current traveling wave signals, the voltage traveling wave signals, and the high-frequency electric field signals based on the first time-frequency matrix, the second time-frequency matrix, and the third time-frequency matrix.

3. The method according to claim 2, wherein The calculating the combined confidence of the existence of a traveling wave head through the D-S evidence theory based on the time-frequency energy features and the instantaneous phase features includes: Defining a basic probability assignment function, and an expression of the basic probability assignment function includes: m a (A b ); m a (A2) = β · noise energy a / total energy a ; m a (Θ) = 1 - m a (A1) - m a (A1); where m a represents the probability calculated based on the time-frequency matrix a, where a ∈ (i, u, E), i represents the current traveling wave signal, u represents the voltage traveling wave signal, and E represents the high-frequency electric field signal; A b ∈ (A1, A2, Θ), A1 represents the presence of a wavefront, A2 represents the absence of a wavefront, and Θ represents uncertainty; represents the energy peak of the time-frequency matrix a at time τ, and the total energy a represents the full-band energy of the time-frequency matrix a, and the noise energy a represents the energy of the time-frequency matrix a in the non-wavefront frequency band, and β is an empirical coefficient; Δφ(τ) represents the instantaneous phase consistency; Calculating the combined confidence through the Dempster combination rule based on the basic probability assignment function; an expression for calculating the combined confidence includes: where m(A1) represents the combined confidence of the existence of a wave head, K is a conflict factor, and B, C, and D are independent proposition sets corresponding to the current traveling wave signal, the voltage traveling wave signal, and the high-frequency electric field signal respectively; propositions in the independent proposition sets include the existence of a wave head, the non-existence of a wave head, and uncertainty.

4. The method according to any one of claims 1-3, characterized in that The outputting a time stamp of the traveling wave head based on the combined confidence and a preset threshold includes: When the combined confidence is greater than the preset threshold, marking the moment corresponding to the combined confidence as a candidate wave head moment; When three consecutive moments are all the candidate wave head moments, outputting the first moment among the three consecutive moments as the time stamp of the traveling wave head.

5. The method according to any one of claims 1 to 3, characterized in that, The time-frequency energy features include noise standard deviation; After the time stamp of the output traveling wave head, the method further includes: Adjusting the preset threshold based on the noise standard deviation; an expression for adjusting the preset threshold is: θ(τ) = α·σ(τ) + θ0; where θ(τ) is the adjusted preset threshold, α is an adjustment coefficient, σ(τ) is the noise standard deviation at moment τ, and θ0 is the initial value of the preset threshold.

6. The method according to any one of claims 1 to 3, characterized in that Before the extracting the time-frequency energy features and the instantaneous phase features of the current traveling wave signals, the voltage traveling wave signals, and the high-frequency electric field signals, the method further includes: In the case where one of the current traveling wave signal, the voltage traveling wave signal, and the high-frequency electric field signal is missing, the missing waveform is reconstructed by the phase consistency of the other two of the three signals.

7. A traveling wave front detection system, characterized in that Applied to the method according to any one of claims 1-6, the system includes: An acquisition module, configured to acquire a current traveling wave signal, a voltage traveling wave signal, and a high-frequency electric field signal of a transmission line; An extraction module, configured to extract time-frequency energy features and instantaneous phase features of the current traveling wave signal, the voltage traveling wave signal, and the high-frequency electric field signal; A calculation module, configured to calculate a combined confidence of the existence of a traveling wave head based on the time-frequency energy features and the instantaneous phase features through the D-S evidence theory; An output module, configured to output a timestamp of the traveling wave head based on the combined confidence and a preset threshold.

8. The system according to claim 7, wherein The acquisition module acquires the current traveling wave signal through a Rogowski coil; acquires the voltage traveling wave signal through a capacitive voltage transformer; and acquires the high-frequency electric field signal through a broadband electric field sensor.

9. A computing device, characterized in that, Comprising: A memory, configured to store a program; A processor, configured to load the program to execute the method according to any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein when the program runs, it controls the device where the computer-readable storage medium is located to execute the method according to any one of claims 1-6.

Images