An on-line monitoring device and method for fault location of transmission lines in harsh environments

Abstract

This application relates to the technical field of transmission line fault location, and specifically relates to an on-line monitoring device and method for transmission line fault location in harsh environments. The method includes: using the data monitoring module of the device to obtain the wave velocities of steady-state traveling waves at different frequencies, and obtaining the fault signals received at both ends of the line segment with a fault and the arrival times of their wave fronts from the traveling wave signals emitted at the fault point in the line segment with a fault; analyzing the propagation characteristics of the fault signals to determine the wave velocity of the traveling wave signals emitted at the fault point, and combining the length of the line segment with a fault to locate the fault point in the line segment with a fault. This application solves the interference of environmental factors and noise on the evaluation of the traveling wave velocity during the traveling wave location process, and improves the location accuracy of the fault position on the transmission line.

Description

Technical Field

[0001] This application relates to the technical field of transmission line fault location, and particularly relates to an on-line monitoring device and method for transmission line fault location in harsh environments. Background Art

[0002] The operating state of a transmission line is related to the power supply quality of the power system. It is inevitable that faults occur in the transmission line during the operation of the power system. Therefore, after a fault occurs in the transmission line, accurately locating the fault location of the transmission line is of great significance for improving the reliability of line power supply. At present, the traveling wave ranging method has become an important method for transmission line fault location. Among them, the double-end traveling wave ranging method has more accurate fault ranging calculation results compared with the single-end traveling wave ranging method and is widely used in transmission line fault location. The traveling wave velocity is an essential parameter for both methods, and the accuracy of the traveling wave velocity affects the accuracy of transmission line fault location.

[0003] The existing traveling wave velocity is mainly obtained by using the formula calculation method. This method calculates the wave velocity using the line parameters of the transmission line. However, this method does not consider the correlation between the wave velocity and frequency of the traveling wave, and thus ignores the influence of the low-frequency components in the fault traveling wave on its wave velocity. At the same time, it ignores the influence of impulse corona in the transmission line on the circuit parameters. Especially in harsh weather environments, the transmission line is extremely vulnerable to the influence of impulse corona, which will cause the wave velocity result to deviate from the actual wave velocity due to the change of line parameters when using the traditional formula calculation method to obtain the wave velocity of the traveling wave, thereby reducing the positioning accuracy of the fault location on the transmission line. Summary of the Invention

[0004] To solve the above technical problems, the purpose of this application is to provide an on-line monitoring device and method for transmission line fault location in harsh environments. The specific technical solutions adopted are as follows:

[0005] In the first aspect, an embodiment of this application provides an on-line monitoring method for transmission line fault location in harsh environments. The method includes the following steps:

[0006] S1: Obtain the wave velocities of steady-state traveling waves at different frequencies, and obtain the fault signals received at both ends of the line segment where the fault occurs and the arrival times of their wave fronts from the traveling wave signals emitted at the fault point.

[0007] S2: Analyze the propagation characteristics of the fault signals to determine the wave velocity of the traveling wave signals emitted at the fault point, specifically:

[0008] S201: Based on the wave velocities of the steady-state traveling wave at different frequencies and combined with an optimization algorithm, construct a wave velocity-frequency fitting equation; decompose each fault signal into multiple modal components, construct a set of modal components based on the differences in the central frequencies of all modal components between the fault signals at both ends, and determine the eigenvalues of each set of modal components by analyzing the similarity between the spectrograms of all modal components in each set of modal components, so as to screen out the characteristic set from all sets of modal components;

[0009] S202: Synthesize the central frequencies of all modal components in each characteristic set to determine the characteristic frequencies of each characteristic set, substitute the characteristic frequencies into the wave velocity-frequency fitting equation to obtain the characteristic wave velocities of each characteristic set, determine the characteristic weights of the characteristic wave velocities based on the energies of all modal components in each characteristic set, and combine the characteristic wave velocities to determine the wave velocity of the traveling wave signal emitted at the fault point;

[0010] S3: Locate the fault point in the faulty line segment based on the wave velocity of the traveling wave signal at the fault point and the length of the faulty line segment.

[0011] Preferably, the construction method of the wave velocity-frequency fitting equation is as follows:

[0012] Fit the wave velocities of the traveling wave signal at different frequencies to obtain an initial wave velocity-frequency fitting equation. Randomly generate a set of data within the range of fluctuating the coefficients of each term in this equation by a preset ratio as the initial particle population of the optimization algorithm, and take the optimal value output as the final coefficients of each term. Traverse all term coefficients to obtain the wave velocity-frequency fitting equation.

[0013] Preferably, the construction method of the set of modal components is as follows:

[0014] Combine all modal components of the fault signals at both ends of the faulty line to form a fault set, which are represented by letters P and Q respectively. For the i-th modal component in the fault set P, find the modal component with the smallest central frequency difference from the i-th modal component in the fault set Q, and form a set of modal components with the i-th modal component. Traverse all modal components in the fault set P to obtain all sets of modal components.

[0015] Preferably, the eigenvalue of each set of modal components is the similarity between the spectrograms of all modal components in each set of modal components.

[0016] Preferably, screening out the characteristic set from all sets of modal components includes:

[0017] Take the eigenvalues of all modal components in each set of modal components as the input of the threshold segmentation algorithm, output the segmentation threshold, and denote the set of modal components with eigenvalues greater than the segmentation threshold as the feature set.

[0018] Preferably, the characteristic frequency of each feature set is the mean of the central frequencies of all modal components in each feature set.

[0019] Preferably, the method for determining the characteristic weight of the characteristic wave velocity is as follows:

[0020] Take the normalized value of the energy mean of all modal components in each feature set as the characteristic weight of the characteristic wave velocity of each feature set.

[0021] Preferably, the expression for the wave velocity of the traveling wave signal emitted at the fault point is: ; where represents the wave velocity of the traveling wave signal emitted at the fault point; represents the characteristic weight of the characteristic wave velocity of the i-th feature set; represents the wave velocity of the i-th feature set; I represents the number of all feature sets.

[0022] Preferably, the positioning of the fault point in the line segment with a fault includes:

[0023] The position coordinates of the fault point are: (L1, L2), where , ; In the expressions of L1 and L2, L represents the length of the line segment with a fault; , respectively represent the arrival times of the wavefronts of the fault signals at both ends of the line segment with a fault; represents the wave velocity of the traveling wave signal emitted at the fault point.

[0024] In a second aspect, the embodiments of the present application further provide an on-line monitoring device for power transmission line fault location in a harsh environment, which implements the method for on-line monitoring of power transmission line fault location in a harsh environment described in any one of the above. The device includes a data monitoring module, a wave velocity calculation module, and a fault location module:

[0025] The data monitoring module is used to divide the power transmission line to be measured into multiple line segments, install traveling wave ranging terminals at both ends of each line segment, in the line segment with a fault, a traveling wave signal is emitted at the fault point, and the signals received by the two ranging terminals are denoted as fault signals, obtain the wave velocities of the steady-state traveling waves at different frequencies, and the arrival times of the wavefronts of the two fault signals at the corresponding traveling wave ranging terminals, and denote this time as the wavefront arrival time, and input the obtained data into the wave velocity calculation module;

[0026] The wave velocity calculation module is used to process the data output by the data monitoring module, analyze the propagation characteristics of the fault signal, and determine the wave velocity of the traveling wave signal emitted at the fault point. Specifically, based on the wave velocities of the steady-state traveling wave at different frequencies and in combination with an optimization algorithm, a wave velocity-frequency fitting equation is constructed. Each fault signal is decomposed into multiple modal components. Based on the differences in the central frequencies of all modal components between the fault signals at both ends, a modal component set is constructed. By analyzing the similarities between the spectrograms of all modal components in each modal component set, the eigenvalue of each modal component set is determined to screen out the feature set from all modal component sets. By synthesizing the central frequencies of all modal components in each feature set, the characteristic frequency of each feature set is determined. The characteristic frequency is substituted into the wave velocity-frequency fitting equation to obtain the characteristic wave velocity of each feature set. Based on the energy of all modal components in each feature set, the characteristic weight of the characteristic wave velocity is determined, and in combination with the characteristic wave velocity, the wave velocity of the traveling wave signal emitted at the fault point is determined. The processed data is input into the fault location module;

[0027] The fault location module is used to perform fault location by using the data output by the wave velocity calculation module. Based on the wave velocity of the traveling wave signal at the fault point and the length of the line segment with a fault, the fault point in the line segment with a fault is located.

[0028] This application has at least the following beneficial effects:

[0029] According to the wave velocities of the steady-state traveling wave at different frequencies and in combination with an optimization algorithm, this application constructs a wave velocity-frequency fitting equation, that is, by combining the characteristics of the fault signal, the fitting relationship between the wave velocity and frequency of the traveling wave signal in the normal operation state of the circuit is corrected to adapt to the fitting relationship between the wave velocity and frequency of the traveling wave signal at the fault point during a circuit fault. Thus, the impact corona generated in the transmission line segment where the fault signal is located in a harsh environment is reduced when evaluating the wave velocities of the traveling wave components in subsequent different frequency bands, which helps to improve the positioning accuracy of the traveling wave components. Further, by analyzing the similarities between the spectrograms of all modal components in each modal component set, this application screens out the feature set from all modal component sets, which can effectively reduce the interference of the noise components in the fault signal on the evaluation of the wave velocity of the traveling wave signal, thereby improving the positioning accuracy of the fault location on the transmission line. Further, by synthesizing the energy characteristics of the modal components and the propagation theory of the traveling wave signal, this application obtains the wave velocity of the traveling wave signal emitted at the fault, and thus locates the fault position according to this wave velocity, more accurately evaluates the wave velocity when the traveling wave propagates in the transmission line, and improves the positioning accuracy of the fault in the transmission line. Description of the Drawings

[0030] To more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0031] Figure 1 It is a flowchart of the steps of an on-line monitoring method for transmission line fault location in a harsh environment provided by an embodiment of the present application;

[0032] Figure 2 It is a schematic diagram of the wave velocity extraction process of the traveling wave signal emitted at the fault point provided by an embodiment of the present application. Detailed implementation manners

[0033] In order to further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following will, in combination with the drawings and preferred embodiments, detail the specific implementation manners, structures, features and effects of an on-line monitoring device and method for transmission line fault location in a harsh environment proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs.

[0035] The following will specifically describe the specific solutions of an on-line monitoring device and method for transmission line fault location in a harsh environment provided by the present application with reference to the drawings.

[0036] Please refer to Figure 1 , which shows a flowchart of the steps of an on-line monitoring method for transmission line fault location in a harsh environment provided by an embodiment of the present application. The method includes the following steps:

[0037] S1: Use the data monitoring module of an on-line monitoring device for transmission line fault location in a harsh environment to obtain the wave velocities of the traveling wave signals emitted at the fault point in a faulty line segment at different frequencies, as well as the fault signals at both ends of the line segment and the arrival times of their wave fronts.

[0038] An on-line monitoring device for transmission line fault location in a harsh environment in this embodiment includes a data monitoring module, a wave velocity calculation module, and a fault location module.

[0039] In the data monitoring module, in this embodiment, a distributed traveling wave ranging device is used to perform online fault detection on the power transmission line to be measured. Among them, the power transmission line to be measured is divided into multiple line segments, and traveling wave ranging terminals are installed at both ends of each line segment. When a fault occurs in a certain line segment of the power transmission line, a traveling wave signal is emitted at the fault point, and the fault recording unit in the traveling wave ranging terminal will record the received signal, which is denoted as the fault signal for subsequent analysis, and detect and record the time when the wavefront of the fault signal reaches the traveling wave ranging terminal, which is hereinafter referred to as the arrival time of the wavefront of the fault signal. At the same time, the length of the line segment where the fault exists is obtained for subsequent fault location calculation.

[0040] In addition, in this embodiment, an electromagnetic transient simulation software PSCAD / EMTDC (power systems computer aided design / electro magnetic transient in DC system) is used to establish a simulation model of the power transmission line. The busbars in the simulation model are equivalent to a concentrated distributed line model. The voltage level and line parameters in the simulation model are consistent with the voltage level and line parameters of the power transmission line to be measured during normal operation. The line parameters include the resistance, inductance, and capacitance of positive sequence, negative sequence, and zero sequence per unit length.

[0041] Among them, the process of constructing a simulation model using the electromagnetic transient simulation software PSCAD / EMTDC is a well-known technology, and its specific principle will not be elaborated here.

[0042] Furthermore, the steady-state traveling wave is used as the input of the simulation model, and the wave velocity of the traveling wave signal at different frequencies is output for subsequent determination of the wave velocity of the traveling wave signal in the power transmission line to be measured. Finally, all the data obtained above are input into the wave velocity calculation module for data processing.

[0043] It should be supplementary explained that the steady-state traveling wave is a traveling wave generated by the power source along the power transmission line during the normal operation of the power system.

[0044] Among them, the process of obtaining the wave velocity of the traveling wave at different frequencies using the simulation model is a well-known technology, and the specific process will not be elaborated here.

[0045] S2: Analyze the propagation characteristics of the fault signal to determine the wave velocity of the traveling wave signal emitted at the fault point.

[0046] S201: Based on the wave velocities of the steady-state traveling waves at different frequencies and in combination with an optimization algorithm, construct a wave velocity-frequency fitting equation; decompose each fault signal into multiple modal components, construct a set of modal components based on the differences in the central frequencies of all modal components between the fault signals at both ends, and determine the eigenvalues of each set of modal components by analyzing the similarities between the spectrograms of all modal components in each set of modal components, so as to screen out the characteristic set from all sets of modal components.

[0047] Generally, the wave velocity of the traveling wave in the transmission line is positively correlated with the frequency of the traveling wave. And within a short period of time, it is considered that the line parameters in the transmission line are unchanged. Therefore, in this embodiment, by simulating the transmission line, the fitting relationship between the wave velocity of the traveling wave in the transmission line and its frequency is obtained, and the wave velocity and frequency of the collected steady-state traveling wave signal are used to correct the parameters in the fitting relationship, so as to obtain the fitting relationship between the wave velocity of the traveling wave in this line segment and its frequency under the line parameters of the line segment where the fault signal is located, so as to reduce the influence of the change in the line parameters caused by the impact corona in the line segment on the evaluation of the wave velocity of the traveling wave signal, improve the accuracy of the evaluation of the wave velocity of the traveling wave signal, and further improve the positioning accuracy of the fault location in the transmission line to be measured.

[0048] In the wave velocity calculation module, based on the above analysis, in this embodiment, first, based on the wave velocities of the traveling wave signal at different frequencies and in combination with an optimization algorithm, construct a wave velocity-frequency fitting equation. The specific process is as follows:

[0049] Fit the wave velocities of the traveling wave at different frequencies. Use the frequency as the abscissa and the wave velocity as the ordinate, and adopt a fitting method to fit the wave velocities of the traveling wave at all frequencies to obtain an initial wave velocity-frequency fitting equation, which is used to characterize the fitting relationship between the wave velocity of the steady-state traveling wave in the transmission line to be measured and its frequency when the transmission line to be measured is operating normally.

[0050] It should be noted that there are many commonly used fitting methods. In this embodiment, the least squares fitting method is used to fit the wave velocity and frequency. In the actual application process, as other implementation manners, the implementer can also adopt other fitting methods such as polynomial function fitting. Regarding the selection of the fitting method, this embodiment does not make special restrictions.

[0051] Among them, the least squares fitting method is a well-known technology, and its specific principle will not be elaborated here.

[0052] Further, calculate the ratio of the length of the faulty line segment to the time interval between the arrival times of its corresponding wavefronts, denoted as the reference wave velocity. Randomly generate a set of data within the range where each coefficient in the initial wave velocity-frequency fitting equation fluctuates by a preset ratio based on this reference wave velocity, and use this as the initial particle swarm of the optimization algorithm. Take the deviation between the reference wave velocity and all the fitting values on the corresponding curve of the fitting equation as the objective function of the optimization algorithm. The finally output optimal value is used as the final coefficient for each term. Traverse all the term coefficients to obtain the wave velocity-frequency fitting equation, which is used to evaluate the fitting relationship between the wave velocity and frequency of the traveling wave signal in the faulty line segment.

[0053] Among them, in this embodiment, the number of the initial particle swarm is set to 100. In the actual application process, the implementer can also set it according to the specific situation by himself / herself, and this embodiment does not make special restrictions.

[0054] For the sake of easy understanding, the above content is further supplemented. Assume that the expression of the initial wave velocity-frequency fitting equation is: , taking the coefficient of the first-order term as an example, set the value range of the coefficient of the first-order term as: , , where represents the coefficient of the first-order term of the initial wave velocity-frequency fitting equation. The initial wave velocity-frequency fitting equation is obtained by fitting the above polynomial. Therefore, is a known and determined value here. Further, randomly generate a set of data within the value range of the coefficient of the first-order term as the initial particle swarm of the coefficient of the first-order term in the optimization algorithm.

[0055] It should be noted that the value of the preset ratio is set artificially. In this embodiment, the value of the preset ratio is 0.5. The embodiment can also be set according to the specific situation by itself, and this embodiment does not make special restrictions.

[0056] It is further explained that there are many commonly used optimization algorithms. In this embodiment, the particle swarm optimization algorithm is used to determine the coefficients of each term in the wave velocity-frequency fitting equation. In the actual application process, the implementer can also use other methods such as the simulated annealing optimization algorithm. Regarding the selection of the optimization algorithm, this embodiment does not make special restrictions.

[0057] Among them, the particle swarm optimization algorithm is a well-known technology, and its specific principle will not be elaborated here.

[0058] Further, since the collected fault signals will be interfered by noise during the signal acquisition and transmission process, in order to reduce the influence of the noise components in the fault signals on the evaluation of the wave velocity of the traveling wave components corresponding to each useful signal component at the corresponding frequency in the subsequent fault signals, the following processing is carried out:

[0059] In the faulty line segment, the fault signals received by the traveling wave ranging terminal originate from the traveling wave signals generated by the same fault location. Therefore, these two fault signals should have a similar frequency distribution when not affected by noise interference.

[0060] Therefore, in this embodiment, by decomposing each fault signal into multiple modal components, based on the differences in the central frequencies of all modal components between the fault signals at both ends, a set of modal components is constructed. By analyzing the similarity between the spectrograms of all modal components in each set of modal components, the eigenvalue of each set of modal components is determined to screen out the feature set from all sets of modal components, judge whether the fault signal is affected by noise interference, and eliminate the influence of noise interference on the accuracy of fault location. The specific process is as follows:

[0061] As an implementation method, in this embodiment, first, each fault signal is used as the input of the time-frequency conversion algorithm to output a frequency signal, and the frequency-domain signal is used as the input of the modal decomposition algorithm. The number of modal components is set to M, and M modal components are output. Among them, in this embodiment, the fast Fourier transform algorithm is used to convert the fault signal from the time domain signal to the frequency domain, and the variational mode decomposition (VMD) algorithm is used to perform modal decomposition on the fault signal. In the actual application process, as other implementation methods, the implementer can also use other methods such as wavelet transform to perform time-frequency conversion on the signal, and can also use other modal decomposition algorithms such as empirical mode decomposition (EMD) in combination with specific situations. This embodiment does not make special restrictions.

[0062] Among them, the fast Fourier transform algorithm and the empirical mode decomposition algorithm are both well-known technologies, and their specific principles will not be elaborated here.

[0063] It should be added that the setting of the number of modal components M is manually set. In this embodiment, the value of the number of modal components M is 8. The implementer can also set it by himself in combination with specific situations. This embodiment does not make special restrictions.

[0064] Furthermore, all the modal components of the fault signals at both ends of the faulty line are combined into a fault set, which are represented by the letters P and Q respectively. For the i-th modal component in the fault set P, find the modal component with the smallest central frequency difference from the i-th modal component in the fault set Q, and form a set of modal components with the i-th modal component. Traverse all the modal components in the fault set P to obtain all sets of modal components.

[0065] Furthermore, in this embodiment, the similarity between the spectrograms of all modal components in each set of modal components is used as the eigenvalue of each set of modal components to reflect the similarity between modal components. The greater the similarity, the greater the similarity between modal components, indicating that the possibility of the traveling wave signal being affected by noise interference during transmission is smaller.

[0066] It should be noted that there are many methods to measure the similarity between spectrograms of modal components. In this embodiment, the cosine similarity between the spectrograms of all modal components in each set of modal components is used as the similarity between the spectrograms of all modal components in each set of modal components. In the actual application process, the implementer can also select other methods according to the specific situation, and this embodiment does not make special restrictions.

[0067] Among them, the process of obtaining the spectrogram and the calculation method of the cosine similarity are both well-known technologies, and their specific principles will not be elaborated here.

[0068] Furthermore, the eigenvalues of all modal components in each set of modal components are used as the input of the threshold segmentation algorithm, and the segmentation threshold is output. The set of modal components with eigenvalues greater than the segmentation threshold is denoted as the feature set, which is used to represent the set composed of all modal components corresponding to the non-noise signal components in the fault signal.

[0069] It should be noted that there are many commonly used threshold segmentation algorithms. In this embodiment, the maximum inter-class variance algorithm is used to classify the set of modal components. In the actual application process, as other implementation methods, the implementer can also use other methods, and this embodiment does not make special restrictions.

[0070] Among them, the maximum inter-class variance algorithm is a well-known technology, and the specific principle of using it to classify data will not be elaborated here.

[0071] So far, through simulation and actual data correction, a wave velocity-frequency fitting equation has been constructed to describe the propagation characteristics of traveling waves in transmission lines, improving the accuracy of wave velocity prediction. Furthermore, the fault signal is decomposed into modes, and the similarity of the spectrograms of modal components is analyzed to screen out the feature set, eliminating the interference of noise on the evaluation of the propagation wave velocity of traveling wave signals, and thus improving the accuracy of fault location.

[0072] S202: Synthesize the central frequencies of all modal components in each feature set to determine the characteristic frequencies of each feature set, substitute the characteristic frequencies into the wave velocity-frequency fitting equation to obtain the characteristic wave velocities of each feature set, determine the characteristic weights of the characteristic wave velocities based on the energies of all modal components in each feature set, and combine the characteristic wave velocities to determine the wave velocity of the traveling wave signal emitted at the fault point.

[0073] In the power system, when a fault occurs in the current transmission line, a traveling wave signal will be generated. In order to accurately locate the fault location, it is necessary to first analyze the characteristics of the traveling wave signal. Therefore, in the wave velocity calculation module, by analyzing the characteristics of all modal components in the feature set, the wave velocity of the traveling wave signal emitted at the fault point is determined, so as to more accurately locate the fault location. Specifically:

[0074] In this embodiment, the mean value of the central frequencies of all modal components in each feature set is used as the characteristic frequency of each feature set, and the characteristic frequency is substituted into the wave velocity-frequency fitting equation to obtain the characteristic wave velocity of each feature set, which is used to characterize the velocity of the traveling wave component in the faulty line segment when propagating in the frequency band corresponding to the signal component of each modal component set.

[0075] Furthermore, the normalized value of the energy mean of all modal components in each feature set is used as the characteristic weight of the characteristic wave velocity of each feature set, which is used to represent the relative importance of signal components in different frequency bands when determining the final wave velocity. The larger the characteristic weight, the greater the importance of the characteristic wave velocity of the feature set in determining the final wave velocity. Therefore, a larger weight value is assigned to it.

[0076] Furthermore, this embodiment is based on the characteristic weight of the characteristic wave velocity and combines the characteristic wave velocity to determine the wave velocity of the traveling wave signal emitted at the fault point, specifically:

[0077] As an implementation manner, in this embodiment, the expression of the wave velocity V of the traveling wave signal emitted at the fault point is: ; where represents the characteristic weight of the characteristic wave velocity of the i-th feature set; represents the wave velocity of the i-th feature set; I represents the number of all feature sets.

[0078] Preferably, the schematic diagram of the wave velocity extraction process of the traveling wave signal emitted at the fault point provided in this embodiment is as Figure 2 shown.

[0079] So far, by comprehensively analyzing the characteristic frequencies and energies of each modal component, the wave velocity of the traveling wave at the fault point is calculated. This process not only considers the characteristics of the signal in the frequency domain but also combines with the traveling wave propagation theory, thereby realizing a more accurate judgment of the fault location and improving the accuracy and precision of fault location on the transmission line.

[0080] Step S3: Locate the fault point in the faulty line segment based on the wave velocity of the traveling wave signal at the fault point and the length of the faulty line segment.

[0081] In the power system, when a fault occurs in the transmission line, the fault point will generate a traveling wave signal and propagate to both ends of the refrigerator. By installing traveling wave ranging terminals at both ends of the transmission line, the fault signal and the arrival time of its wavefront can be collected. Combining the wave velocity of the traveling wave signal at the fault point obtained in steps S1 and S2, the fault point location is located in the fault location module, specifically:

[0082] In this embodiment, the position coordinates of the fault point are: (L1, L2), where , ; In the expressions of L1 and L2, L represents the length of the line segment with a fault. , respectively represent the arrival times of the wavefronts of the fault signals at both ends of the line segment with a fault. represents the wave velocity of the traveling wave signal emitted at the fault point.

[0083] So far, by using the arrival times of the wavefronts of the fault signals and the wave velocity of the traveling wave signal at the fault point to calculate the position of the fault point, the accuracy of fault location is effectively improved, which in turn helps the rapid repair of the power system.

[0084] Based on the same inventive concept as the above method, the embodiment of the present application also provides an on-line monitoring device for fault location of a transmission line in a harsh environment, which implements any one of the above methods for on-line monitoring of fault location of a transmission line in a harsh environment. The device includes a data monitoring module, a wave velocity calculation module, and a fault location module:

[0085] The data monitoring module is used to divide the transmission line to be measured into multiple line segments, install traveling wave ranging terminals at both ends of each line segment, in the line segment with a fault, a traveling wave signal is emitted at the fault point, and the signals received by the two ranging terminals are recorded as fault signals, obtain the wave velocities of the steady-state traveling waves at different frequencies, and the arrival times of the wavefronts of the two fault signals at the corresponding traveling wave ranging terminals, and record this time as the wavefront arrival time, and input the obtained data into the wave velocity calculation module;

[0086] The wave velocity calculation module is used to process the data output by the data monitoring module, analyze the propagation characteristics of the fault signals, and determine the wave velocity of the traveling wave signal emitted at the fault point. Specifically: based on the wave velocities of the steady-state traveling waves at different frequencies, and combined with an optimization algorithm, construct a wave velocity-frequency fitting equation; decompose each fault signal into multiple modal components, based on the differences in the center frequencies of all modal components between the fault signals at both ends, construct a modal component set, and determine the eigenvalue of each modal component set by analyzing the similarity between the spectrograms of all modal components in each modal component set, so as to screen out the feature set from all modal component sets; synthesize the center frequencies of all modal components in each feature set to determine the characteristic frequency of each feature set, substitute the characteristic frequency into the wave velocity-frequency fitting equation to obtain the characteristic wave velocity of each feature set, determine the characteristic weight of the characteristic wave velocity based on the energy of all modal components in each feature set, and combine the characteristic wave velocity to determine the wave velocity of the traveling wave signal emitted at the fault point; input the processed data into the fault location module;

[0087] A fault location module is configured to perform fault location by using the data output by the wave velocity calculation module. Based on the wave velocity of the traveling wave signal at the fault point and the length of the line segment where the fault exists, the fault point in the line segment where the fault exists is located.

[0088] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0089] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the differences between each embodiment and other embodiments are emphasized.

[0090] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for online monitoring of transmission line fault location in a harsh environment, characterized in that: The method comprises the following steps: S1: Obtain the wave velocity of the steady-state traveling wave at different frequencies, and obtain the fault signal and its wave head arrival time received at both ends of the line section by the traveling wave signal transmitted at the fault point in the line section with the fault; S2: Analyze the propagation characteristics of the fault signal and determine the wave velocity of the traveling wave signal emitted at the fault point, specifically: S201: Based on the wave speed of the steady-state traveling wave at different frequencies and in combination with an optimization algorithm, a wave speed-frequency fitting equation is constructed; each fault signal is decomposed into multiple modal components, and a modal component set is constructed based on the difference in the central frequency of all modal components between the fault signals at the two ends, and the characteristic value of each modal component set is determined by analyzing the similarity between the frequency spectra of all modal components in each modal component set, so as to filter out a characteristic set from all modal component sets; S202: The central frequencies of all modal components in each feature set are integrated to determine the characteristic frequencies of each feature set, the characteristic frequencies are substituted into the wave velocity-frequency fitting equation to obtain the characteristic wave velocity of each feature set, the characteristic weights of the characteristic wave velocity are determined based on the energy of all modal components in each feature set, and the wave velocity of the traveling wave signal emitted at the fault point is determined in combination with the characteristic wave velocity; S3: Based on the wave velocity of the traveling wave signal at the fault point and the length of the line section where the fault occurs, the fault point in the line section where the fault occurs is located.

2. The method for online monitoring of transmission line fault location in a harsh environment as claimed in claim 1, characterized in that: The wave velocity-frequency fitting equation is constructed as follows: The wave velocity of the traveling wave signal at different frequencies is fitted to obtain an initial wave velocity-frequency fitting equation. The coefficients of each item in the equation fluctuate within a preset ratio on the basis of the equation, and a group of data is randomly generated as the initial particle population of the optimization algorithm. The optimal value of the output is used as the final coefficient of each item. All the coefficients of the items are traversed to obtain the wave velocity-frequency fitting equation.

3. The method for online monitoring of transmission line fault location in a harsh environment as claimed in claim 1, characterized in that: The method for constructing the modal component set is: All modal components of the fault signals at both ends of the faulty line are combined into a fault set, which are represented by letters P and Q respectively. For the i-th modal component in the fault set P, the modal component with the smallest center frequency difference with the i-th modal component is found in the fault set Q, and combined with the i-th modal component, a modal component set is formed. All modal components in the fault set P are traversed to obtain a set of all modal components.

4. The method for online monitoring of transmission line fault location in a harsh environment as claimed in claim 1, characterized in that: The characteristic value of each modal component set is the similarity between the frequency spectrograms of all modal components in each modal component set.

5. The method for online monitoring of transmission line fault location in a harsh environment as claimed in claim 1, characterized in that: The feature set is screened out from all modal component sets, including: The eigenvalues ​​of all modal components in each modal component set are used as the input of the threshold segmentation algorithm, and the segmentation threshold is output. The modal component set with eigenvalues ​​greater than the segmentation threshold is recorded as the feature set.

6. The method for online monitoring of transmission line fault location in a harsh environment as claimed in claim 1, characterized in that: The characteristic frequency of each feature set is the mean of the center frequencies of all modal components in each feature set.

7. The method for online monitoring of transmission line fault location in a harsh environment as claimed in claim 1, characterized in that: The method for determining the characteristic weight of the characteristic wave velocity is: The normalized value of the energy mean of all modal components in each feature set is used as the feature weight of the characteristic wave velocity of each feature set.

8. The method for online monitoring of transmission line fault location in a harsh environment as claimed in claim 1, characterized in that: The wave velocity of the traveling wave signal emitted at the fault point is expressed as: ; In the formula, Indicates the wave velocity of the traveling wave signal emitted at the fault point; The feature weight representing the feature wave velocity of the i-th feature set; represents the wave velocity of the i-th feature set; I represents the number of all feature sets.

9. The method for online monitoring of transmission line fault location in a harsh environment as claimed in claim 1, characterized in that: The locating of the fault point in the line section where the fault exists includes: The location coordinates of the fault point are: (L1, L2), where: , ; In the expressions of L1 and L2, L represents the length of the line section where the fault occurs; , They respectively represent the arrival time of the wave head of the fault signal at both ends of the line section where the fault exists; Indicates the wave velocity of the traveling wave signal emitted at the fault point.

10. An online monitoring device for locating faults in power transmission lines under harsh environments, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, implementing the method as claimed in claim 1, characterized in that: The device includes a data monitoring module, a wave speed calculation module, and a fault location module: The data monitoring module is used to divide the transmission line to be tested into multiple line sections, install traveling wave ranging terminals at both ends of each line section, transmit traveling wave signals at the fault point in the line section where a fault exists, record the signals received by the two ranging terminals as fault signals, obtain the wave speed of the steady-state traveling wave at different frequencies, and the time when the wave heads of the two fault signals arrive at the corresponding traveling wave ranging terminals, record the time as the wave head arrival time, and input the obtained data into the wave speed calculation module; The wave velocity calculation module is used to process the data output by the data monitoring module, analyze the propagation characteristics of the fault signal, and determine the wave velocity of the traveling wave signal emitted at the fault point, specifically: based on the wave velocity of the steady-state traveling wave at different frequencies, and in combination with the optimization algorithm, a wave velocity-frequency fitting equation is constructed; each fault signal is decomposed into multiple modal components, and a modal component set is constructed based on the difference in the center frequency of all modal components between the fault signals at the two ends, and the characteristic value of each modal component set is determined by analyzing the similarity between the frequency spectra of all modal components in each modal component set, so as to filter out a feature set from all modal component sets; the characteristic frequency of each feature set is determined by combining the center frequency of all modal components in each feature set, and the characteristic frequency is substituted into the wave velocity-frequency fitting equation to obtain the characteristic wave velocity of each feature set, and the characteristic weight of the characteristic wave velocity is determined based on the energy of all modal components in each feature set, and the wave velocity of the traveling wave signal emitted at the fault point is determined in combination with the characteristic wave velocity; Input the processed data into the fault location module; The fault location module is used to locate the fault using the data output by the wave speed calculation module, and locate the fault point in the line section with the fault based on the wave speed of the traveling wave signal at the fault point and the length of the line section with the fault.

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