Direct-current line fault section positioning method and system

Abstract

The invention discloses a DC line fault section positioning method and system, and the method comprises the steps: setting sampling points at the head end of a line at intervals of a line, and collecting the voltage data of each sampling point at the same time, and obtaining the voltage sampling data; if any sampling point meets the voltage variation criterion, fault section positioning is started; meanwhile, fault voltage data after the fault moment is collected, a frequency spectrum distribution curve of the fault voltage data is extracted, and the oscillation main frequency of the fault voltage data is determined according to the frequency spectrum distribution curve; calculating the instantaneous amplitude of the fault voltage data; calculating deviation data of each sampling point, and obtaining a maximum deviation sequence number in a period corresponding to the first oscillation main frequency of each sampling point; and judging whether the fault is in the interval or not according to whether the difference of the oscillation main frequencies and the difference of the maximum deviation serial numbers of the adjacent sampling points simultaneously meet a fault positioning criterion or not, and finally determining the position of the fault. According to the method, fault features can be accurately extracted, and faults can be accurately identified under the condition that current changes are not obvious.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network fault detection, and more specifically, relates to a method and system for locating a fault section of a DC line. Background Art

[0002] AC and DC distribution networks are becoming increasingly important components of modern power systems. DC lines are widely used for large-capacity, long-distance power distribution, playing a crucial role in inter-station power distribution. DC lines not only effectively alleviate pressure on AC grids but also improve the stability and economic efficiency of power transmission. As the scale of DC lines continues to expand, the ability to quickly and accurately locate fault sections on them has become a key factor impacting their reliability and safety.

[0003] However, the current and voltage waveforms of DC lines differ significantly from those of AC power grids. Fault signals are weak and less distinct. Traditional AC power grid fault detection methods make it difficult to effectively distinguish and locate faults, making fault location even more challenging. Therefore, accurately locating the faulty section has become a crucial technical tool for ensuring stable DC line operation.

[0004] According to existing research, the methods for locating fault sections of DC lines are generally divided into several categories, including traditional voltage and current signal positioning methods, reverse power measurement-based positioning methods, and time difference analysis-based positioning methods.

[0005] Among them, the traditional voltage and current signal positioning method determines whether a fault has occurred by monitoring the sudden change of current and voltage. The disadvantages of this method are: the voltage and current signal characteristics are not obvious and the positioning accuracy is low.

[0006] Methods based on reverse power measurement and time difference analysis locate faults by monitoring reverse power flow in the line and analyzing signal propagation time differences. However, these two methods require additional hardware and precise clock synchronization equipment, increasing system complexity and cost. Summary of the Invention

[0007] To address the deficiencies in the prior art, the present invention provides a method and system for locating the fault section of a DC line based on voltage frequency and amplitude detection. The method collects fault voltage data when a fault occurs, and accurately determines the fault section through main frequency identification and instantaneous amplitude analysis.

[0008] The present invention adopts the following technical solutions.

[0009] A first aspect of the present invention provides a method for locating a fault section of a DC line, comprising:

[0010] Collect voltage data at each sampling point to obtain voltage sampling data;

[0011] Determine whether the voltage sampling data meets the voltage change criterion. If any sampling point meets the voltage change criterion, it is determined that there is a fault in the line and the fault section location is started;

[0012] Collect the fault voltage data within a set time after the fault moment at each sampling point, extract the spectrum distribution curve of the fault voltage data, and determine the main oscillation frequency of the fault voltage data based on the spectrum distribution curve;

[0013] Calculate instantaneous amplitude data based on fault voltage data;

[0014] The instantaneous amplitude data and the fault voltage data are subtracted to obtain the deviation data of each sampling point, and the sampling number corresponding to the maximum value of the deviation data within the period corresponding to the first oscillation main frequency of each sampling point is taken as the maximum deviation sequence number;

[0015] The fault location is determined based on whether the difference between the main oscillation frequencies and the difference between the maximum deviation numbers of adjacent sampling points simultaneously meet the fault location criteria.

[0016] Preferably, the voltage variation criterion is expressed as follows:

[0017]

[0018] Where:

[0019] u k 、u k+1 、u k+3 They are the kth, k+1th, and k+3th voltage sampling data of a certain sampling point respectively;

[0020] U N is the rated value of the DC link voltage.

[0021] Preferably, when collecting voltage data at each sampling point and collecting fault voltage data at each sampling point within a set time after the moment of the fault, the sampling frequency is not less than 4000 Hz.

[0022] Preferably, the set time after the fault moment is 0.2 seconds.

[0023] Preferably, collecting fault voltage data within a set time after the fault moment of each sampling point, extracting a frequency spectrum distribution curve of the fault voltage data, and determining the oscillation main frequency of the fault voltage data according to the frequency spectrum distribution curve includes:

[0024] The linear predictive coding algorithm is used to reconstruct the fault voltage data, which is expressed as follows:

[0025]

[0026] Where:

[0027] U k is the k-th fault voltage data;

[0028] e k is the prediction error;

[0029] is the predicted value of the k-th fault voltage data;

[0030] p is the prediction order, which is selected according to the actual operation;

[0031] a c is the prediction coefficient, which is the quantity to be determined;

[0032] U k-c is the historical sample data of fault voltage;

[0033] The original fault voltage data is segmented, and each segment of the fault voltage data is Fourier transformed to obtain the logarithmic spectrum of each segment of the fault voltage data. The logarithmic spectrum of each segment of the fault voltage data is inversely Fourier transformed to obtain the cepstrum of each segment of the fault voltage data.

[0034] Extract the prediction coefficient a of each segment of fault voltage data from the cepstrum of each segment of fault voltage data c , calculate the predicted value of each section of fault voltage data according to the formula for reconstructing fault voltage data and merge;

[0035] According to the predicted value of the combined fault voltage data Obtain the spectrum distribution curve, find the point with the largest ordinate in the spectrum distribution curve, and the abscissa of the point is the main oscillation frequency of the fault voltage data.

[0036] Preferably, calculating instantaneous amplitude data according to fault voltage data includes:

[0037] Calculate the Hilbert transform of the fault voltage data to obtain a set of data with a phase difference of 90 degrees from the fault voltage data;

[0038] The instantaneous amplitude is calculated using the following formula:

[0039]

[0040] Where:

[0041] U k is the kth fault voltage data;

[0042] U k ′ is the kth data obtained by Hilbert transform;

[0043] Ak is the kth instantaneous amplitude data.

[0044] Preferably, the step of obtaining the maximum deviation sequence number further includes:

[0045] For each sampling point, the least square method is used to fit the deviation data, and the fitted deviation data is used to replace the original deviation data;

[0046] Eliminate the Nth q The data before the Nth data point and z The data after data points, where N q and N z Calculated based on the main oscillation frequency and sampling frequency;

[0047] Find the deviation data after fitting from the Nth q data points to the Nth z The maximum value of the data points is obtained, the sampling number of the data point corresponding to the maximum value is recorded, and the sampling number is defined as the maximum deviation sequence number of the deviation data of the sampling point.

[0048] Preferably, the variable N q and variable N z Calculated by the following formula:

[0049]

[0050] Where:

[0051] f c is the sampling frequency;

[0052] f is the main oscillation frequency.

[0053] Preferably, the fault location criterion is expressed as follows:

[0054]

[0055] Where:

[0056] j and j+1 are sampling point numbers, j=1, 2, ..., m-1;

[0057] i and i+1 are the sampling point numbers in the summation operation, i=1, 2, ..., m-1;

[0058] f j and f j+1 are the main oscillation frequencies of the jth sampling point and the j+1th sampling point respectively;

[0059] f i and f i+1are the main oscillation frequencies of the jth sampling point and the j+1th sampling point respectively;

[0060] X j and X j+1 are the maximum deviation numbers of the j-th sampling point and the j+1-th sampling point respectively;

[0061] X i and X i+1 are the maximum deviation numbers of the j-th sampling point and the j+1-th sampling point respectively.

[0062] A second aspect of the present invention provides a DC line fault section locating system, comprising: a plurality of IED devices and a fault section locating device;

[0063] The IED devices are configured at various sampling points on the line, including:

[0064] The voltage acquisition module is used to collect voltage data at each sampling point to obtain voltage sampling data, and to collect fault voltage data within a set time after the fault moment at each sampling point;

[0065] The data calculation module includes a logic operation unit, an oscillation main frequency calculation unit and a maximum deviation sequence number calculation unit, which is used to determine whether the voltage sampling data meets the voltage change criterion. If so, the oscillation main frequency and the maximum deviation sequence number are calculated based on the voltage sampling data;

[0066] A communication transmission module, used to communicate with the fault section locating device, including a wireless communication interface and a wireless transmission device;

[0067] The fault section locating device comprises:

[0068] A communication receiving module, used to communicate with the IED device on the line, including a wireless communication interface and a wireless receiving device;

[0069] The data storage module is used to store the oscillation main frequency data and maximum deviation sequence number data received by the communication, and provide them to the fault section location judgment module;

[0070] Fault section location judgment module, used to determine the fault location;

[0071] The result display module is used to display the fault location.

[0072] Compared with the prior art, the beneficial effects of the present invention include at least:

[0073] (1) The present invention uses voltage signal processing and data-driven fault detection, frequency analysis, and instantaneous amplitude calculation to accurately extract fault characteristics, clarify relevant positioning indicators of the DC line fault section, and increase fault differentiation, especially when the current change is not obvious. It can also accurately identify faults.

[0074] (2) The present invention further enhances the accuracy of fault location by introducing dual criteria of oscillation main frequency and maximum deviation sequence number for analysis.

[0075] (3) The present invention can quickly identify the location of the fault, isolate the fault section in time, and ensure the stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 1 is a schematic diagram of functional modules of an IED device provided according to an embodiment of the present invention;

[0077] Figure 2 This is a schematic diagram of the functional modules of a fault section locating device provided in accordance with an embodiment of the present invention;

[0078] Figure 3 This is a flow chart of a method for locating a fault section of a DC line provided in accordance with an embodiment of the present invention;

[0079] Figures 4 to 8 This is a diagram of voltage sampling data of IED1 to IED5 provided according to an application example of the present invention;

[0080] Figures 9 to 13 It is a diagram of the instantaneous voltage amplitude of IED1 to IED5 provided according to an application example of the present invention;

[0081] Figures 14 to 18 This is a diagram of voltage deviation data of IED1 to IED5 provided according to an application example of the present invention. DETAILED DESCRIPTION

[0082] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without making creative efforts are all within the scope of protection of the present invention.

[0083] The present invention collects voltage sampling data from a DC line in real time and determines whether to initiate a fault section location program based on voltage variation criteria. After the fault section location program is initiated, fault voltage data is collected immediately after the fault occurs. A linear predictive coding algorithm, Hilbert transform, and instantaneous amplitude calculation method are employed to calculate the main oscillation frequency and maximum deviation sequence number of the fault voltage data. The main oscillation frequency and maximum deviation sequence number of the fault voltage data are used to locate the fault section of the DC line.

[0084] like Figure 3 As shown, embodiment 1 of the present invention provides a method for locating a fault section of a DC line, comprising the following steps:

[0085] Step 1: Set sampling points n1, n2, ..., n at the beginning of the line and every other distance of the line. m , and collect the voltage data of each sampling point at the same time. The voltage sampling data of each sampling point are numbered u1, u2, ..., u m Where m is the total number of sampling points on the line.

[0086] Preferably, the sampling frequency f c Not less than 4000Hz.

[0087] In step 2, based on the voltage sampling data from step 1, determine whether a DC line fault has occurred using a voltage change criterion. If the voltage sampling data at any sampling point meets the voltage change criterion, it indicates a significant and sustained drop in line voltage, indicating a line fault. Fault section location is initiated, and the process proceeds to step 3. Otherwise, the process returns to step 1.

[0088] Preferably, the voltage variation criterion is as shown in formula (1):

[0089]

[0090] Where:

[0091] k, k+1, and k+3 are sampling numbers;

[0092] u k 、u k+1 、u k+3 They are the kth, k+1th, and k+3th voltage sampling data of a certain sampling point respectively;

[0093] U N is the rated value of the DC link voltage.

[0094] In a DC line, if the sampling point n j and sampling point n j+1When a fault occurs in the line section between the two, the voltage signals upstream and downstream will show some obvious differences, including: differences in voltage signal characteristics. Both upstream and downstream of the fault point will show a significant oscillation and voltage value drop. Although it has not reached a complete power outage, the voltage level is significantly lower than that during normal operation due to the increase in impedance caused by the fault. DC lines usually do not show the power frequency changes in traditional AC lines, but due to the significant changes in the state before and after the fault, the capacity stored in the line-to-ground capacitance and circuit inductance will be released, resulting in oscillations; and, due to the oscillations, it will be charged and discharged repeatedly until it reaches a stable state. There is a long DC network upstream of the DC line, while the downstream line is shorter, which leads to obvious differences in characteristic quantities such as the main oscillation frequency and maximum deviation number upstream and downstream of the fault point.

[0095] Step 3: Simultaneously collect the fault voltage data of each sampling point within a set time T after the fault moment. The fault voltage data of each sampling point are numbered U1, U2, ..., U m Extract the spectrum distribution curve of the fault voltage data and find the main oscillation frequency of the fault voltage data according to the spectrum distribution curve. Among them, the spectrum distribution curve reflects the amplitude values corresponding to different frequency components in the signal. The horizontal axis corresponding to the spectrum distribution curve represents the frequency value, and the corresponding vertical axis represents the amplitude value; the sampling points n1, n2, ..., n m The corresponding main oscillation frequencies found are recorded as f1, f2, ..., f m .

[0096] Preferably, the set time after the fault moment of each sampling point is 0.2 seconds.

[0097] In a preferred but non-limiting embodiment of the present invention, a linear predictive coding algorithm is used to process the fault voltage data, and step 3 specifically includes:

[0098] Step 3.1: Reconstruct the fault voltage data using the linear prediction coding algorithm, which includes: multiplying the historical sample data by the prediction coefficient and then accumulating it, and then adding the prediction error; the number of accumulations, i.e., the prediction order, is selected based on actual operation; the prediction coefficient is an unknown quantity and needs to be calculated according to subsequent steps; the prediction error is automatically determined after determining other parameters.

[0099] Further preferably, the prediction expression for reconstructing the fault voltage data is shown in formula (2):

[0100]

[0101] Where:

[0102] U k is the kth fault voltage data;

[0103] e kis the prediction error;

[0104] is the predicted value of the k-th fault voltage data;

[0105] p is the prediction order;

[0106] a c is the prediction coefficient;

[0107] U k-c is the historical sample data of fault voltage.

[0108] In step 3.2, the original fault voltage data is segmented into overlapping segments for local analysis. Each segment of the fault voltage data is Fourier transformed to obtain the logarithmic spectrum of each segment. The logarithmic spectrum of each segment of the fault voltage data is then inversely Fourier transformed to obtain the cepstrum of each segment of the fault voltage data.

[0109] Step 3.3: Extract the prediction coefficient a of each segment of fault voltage data from the cepstrum of each segment of fault voltage data. c , calculate the predicted value of each section of fault voltage data according to formula (2) and merge.

[0110] Step 3.4: Based on the predicted value of the combined fault voltage data Obtain the spectrum distribution curve, find the point with the largest ordinate in the spectrum distribution curve, and the abscissa of the point is the main oscillation frequency of the fault voltage data.

[0111] Step 4: Perform Hilbert transform on the fault voltage data obtained in step 3 and calculate the instantaneous amplitude to obtain instantaneous amplitude data of the fault voltage data.

[0112] In a preferred but non-limiting embodiment of the present invention, step 4 specifically comprises:

[0113] Step 4.1, calculate the Hilbert transform of the fault voltage data to obtain a set of data with a phase difference of 90 degrees with the fault voltage data.

[0114] Step 4.2, calculate the instantaneous amplitude data using formula (3):

[0115]

[0116] Where:

[0117] k is the sampling number of the fault voltage data, k∈[0,f c T];

[0118] U k is the kth fault voltage data;

[0119] U k ′ is the kth data obtained by Hilbert transform;

[0120] A k To obtain the kth instantaneous amplitude data.

[0121] It can be understood that by using the linear predictive coding algorithm to analyze the spectrum distribution of voltage sampling data and combining it with the Hilbert transform to calculate the instantaneous amplitude, the fault characteristics can be accurately extracted, the fault location can be quickly identified, and the fault section can be isolated in time.

[0122] Step 5: Calculate the deviation data of each sampling point based on the fault voltage data of step 2 and the instantaneous amplitude data of step 4, eliminate noise interference, occasional large deviation data interference and fault instantaneous interference, take the deviation data within the period corresponding to the first oscillation main frequency, and use the sampling number corresponding to the maximum value as the maximum deviation sequence number of the deviation data. Sampling points n1, n2, ..., n m The maximum deviation numbers obtained are recorded as X1, X2, ..., X m .

[0123] In a preferred but non-limiting embodiment of the present invention, step 5 specifically comprises:

[0124] Step 5.1: Calculate the deviation data using formula (4):

[0125] P k =A k -u k (4)

[0126] Where:

[0127] P k is the kth deviation data obtained.

[0128] Step 5.2: For each sampling point, find the Nth q Deviation data to Nth z Deviation data, N q and N z The variables are related to the sampling point position. The least squares fitting method is used to fit these data to eliminate noise interference and occasional large deviation data interference. The fitted data is used to replace the original deviation data in the deviation data. Find the Nth q data points to the Nth z The maximum value of the data points is obtained, the sampling number of the data point corresponding to the maximum value is recorded, and the sampling number is defined as the maximum deviation sequence number of the deviation data of the sampling point.

[0129] Step 5.3, in order to eliminate the instantaneous interference, the Nth q The data before the Nth deviation data are taken into consideration; in order to analyze the data within the first oscillation main frequency cycle, the Nth z The data after the deviation data are taken into consideration.

[0130] More preferably, the variable N q and variable N z By formula (5), we can get:

[0131]

[0132] Where:

[0133] f c is the sampling frequency, f is the main oscillation frequency obtained in step 3, for sampling points n1, n2, ..., n m , the values of f are f1, f2, ..., f m .

[0134] Step 6: Determine whether the fault is within this interval based on whether the difference between the main oscillation frequency and the maximum deviation number between adjacent sampling points simultaneously meets the fault location criteria. The fault location criteria is composed of the calculation of the main oscillation frequency and the maximum deviation number, reflecting whether the difference between the main oscillation frequency and the maximum deviation number between two adjacent sampling points is significant.

[0135] Preferably, the fault location criterion is expressed as follows:

[0136]

[0137] Where:

[0138] j and j+1 are sampling point numbers, j=1, 2, ..., m-1;

[0139] i and i+1 are the sampling point numbers in the summation operation, i=1, 2, ..., m-1;

[0140] f j and f j+1 are sampling points n j and sampling point n j+1 The main frequency of oscillation;

[0141] f i and f i+1 are sampling points n i and sampling point n i+1 The main frequency of oscillation;

[0142] X jand X j+1 are sampling points n j and sampling point n j+1 The maximum deviation number;

[0143] X i and X i+1 are sampling points n i and sampling point n i+1 The maximum deviation number.

[0144] If formula (6) is satisfied, then the sampling point n j and sampling point n j+1 The difference between the main oscillation frequencies of the sampling points is greater than 2 times the average of the main oscillation frequency differences of all two adjacent sampling points on the line, and the sampling point n j and sampling point n j+1 The maximum deviation number difference of is greater than 2 times the average value of the maximum deviation number difference of all two adjacent sampling points on the line, which means that the sampling point n j and sampling point n j+1 The difference in the main oscillation frequency and the difference in the maximum deviation number are both significant.

[0145] If equation (6) is satisfied, the fault is determined to be located at sampling point n j and sampling point n j+1 If formula (6) is not satisfied, it is determined that the fault is not at sampling point n. j and sampling point n j+1 between.

[0146] It can be understood that the use of dual criteria of oscillation main frequency and maximum deviation number can increase the accuracy of locating the fault section of the DC line and reduce the risk of misjudgment compared with the traditional single criterion.

[0147] like Figure 2 As shown, embodiment 2 of the present invention provides a DC line fault section locating system, which runs the DC line fault section locating method described in embodiment 1, and includes: multiple IED devices and a fault section locating device.

[0148] The full name of IED is Intelligent Electronic Device. IED devices are pre-configured on the DC line in the following way: one IED device is configured at the line head end and every other line distance (i.e. at each sampling point), and they are numbered IED1, IED2, ..., IED m , where m is the total number of IEDs configured for the line. The IED devices specifically include:

[0149] The voltage acquisition module is used to sample the line voltage signal at the location of the IED device in real time to obtain voltage sampling data; and to collect fault voltage data within a set time after the fault occurs at the location of the IED device.

[0150] Preferably, when the voltage acquisition module acquires voltage sampling data and fault voltage data, the sampling frequency is not less than 4000 Hz, and the set time after the fault moment is 0.2 seconds.

[0151] The data calculation module includes a logic operation unit, an oscillation main frequency calculation unit and a maximum deviation sequence number calculation unit, which is used to determine whether the voltage sampling data meets the voltage change criterion. If so, the oscillation main frequency and the maximum deviation sequence number are calculated based on the fault voltage data.

[0152] Preferably, the voltage variation criterion is expressed as follows:

[0153]

[0154] Where:

[0155] u k 、u k+1 、u k+3 They are the kth, k+1th, and k+3th voltage sampling data of a certain sampling point respectively;

[0156] U N is the rated value of the DC link voltage.

[0157] Preferably, the data calculation module uses a linear predictive coding algorithm to reconstruct the fault voltage data, which can be expressed as follows:

[0158]

[0159] Where:

[0160] U k is the kth fault voltage data;

[0161] e k is the prediction error;

[0162] is the predicted value of the k-th fault voltage data;

[0163] p is the prediction order, which is selected according to the actual operation;

[0164] a c is the prediction coefficient, which is the quantity to be determined;

[0165] U k-c is the historical sample data of fault voltage;

[0166] The original fault voltage data is segmented, and each segment of the fault voltage data is Fourier transformed to obtain the logarithmic spectrum of each segment of the fault voltage data. The logarithmic spectrum of each segment of the fault voltage data is inversely Fourier transformed to obtain the cepstrum of each segment of the fault voltage data.

[0167] Extract the prediction coefficient a of each segment of fault voltage data from the cepstrum of each segment of fault voltage data c , calculate the predicted value of each section of fault voltage data according to the formula for reconstructing fault voltage data and merge;

[0168] According to the predicted value of the combined fault voltage data Obtain the spectrum distribution curve, find the point with the largest ordinate in the spectrum distribution curve, and the abscissa of the point is the main oscillation frequency of the fault voltage data.

[0169] Preferably, the data calculation module calculates the Hilbert transform of the fault voltage data to obtain a set of data with a phase difference of 90 degrees from the fault voltage data;

[0170] The instantaneous amplitude is calculated using the following formula:

[0171]

[0172] Where:

[0173] U k is the kth fault voltage data;

[0174] U k ′ is the kth data obtained by Hilbert transform;

[0175] A k is the kth instantaneous amplitude data.

[0176] The instantaneous amplitude data and the fault voltage data are subtracted to obtain the deviation data of each sampling point. For each sampling point, the deviation data is fitted using the least squares method, and the fitted deviation data is used to replace the original deviation data;

[0177] Eliminate the Nth q The data before the Nth data point and z The data after data points, where N q and N z Calculated based on the main oscillation frequency and sampling frequency;

[0178] Find the deviation data after fitting from the Nth q data points to the Nth zThe maximum value of the data points is obtained, the sampling number of the data point corresponding to the maximum value is recorded, and the sampling number is defined as the maximum deviation sequence number of the deviation data of the sampling point.

[0179] More preferably, the variable N q and variable N z Calculated by the following formula:

[0180]

[0181] Where:

[0182] f c is the sampling frequency;

[0183] f is the main oscillation frequency.

[0184] The communication transmission module is used to communicate with the fault section locating device, and includes a wireless communication interface and a wireless transmission device.

[0185] The fault section locating device specifically includes:

[0186] Communication receiving module, used to communicate with IED1 on the line m The device communicates, including a wireless communication interface and a wireless receiving device.

[0187] The data storage module is used to store the oscillation main frequency data and maximum deviation sequence number data received by the communication receiving module, and provide them to the fault section positioning criterion module.

[0188] The fault section location judgment module is used to judge the fault section of the line and determine the fault location.

[0189] Preferably, the fault location criterion set in the fault section location criterion module is expressed by the following formula:

[0190]

[0191] Where:

[0192] j and j+1 are sampling point numbers, j=1, 2, ..., m-1;

[0193] i and i+1 are the sampling point numbers in the summation operation, i=1, 2, ..., m-1;

[0194] f j and f j+1 are the main oscillation frequencies of the jth sampling point and the j+1th sampling point respectively;

[0195] f i and f i+1are the main oscillation frequencies of the jth sampling point and the j+1th sampling point respectively;

[0196] X j and X j+1 are the maximum deviation numbers of the j-th sampling point and the j+1-th sampling point respectively;

[0197] X i and X i+1 are the maximum deviation numbers of the j-th sampling point and the j+1-th sampling point respectively.

[0198] The result display module is used to display the fault location.

[0199] In order to verify the effectiveness of the present invention, an application example is introduced below.

[0200] A DC line fault simulation model was built on MATLAB / Simulink software. The line rated voltage was set to 750V, and the signal sampling frequency was set to 10kHz. The line was divided into 4 sections, the resistance of each section was set to 0.055Ω, and the line inductance was set to 0.00129mH. An IED device was configured at the head end of the line and every other section of the line, and these IED devices were numbered IED1, IED2, IED3, IED4, and IED5 in sequence. A short circuit fault was artificially set on the second section of the line (i.e., between IED2 and IED3) in advance to verify the effectiveness of the present invention. After a short circuit fault occurs, the converters on both sides of the DC line are automatically locked, and the DC capacitors are used to provide the fault voltage and fault current.

[0201] The fault section location method described in Example 1 is applied to perform fault detection and fault location. The specific steps are as follows:

[0202] Step A.1: Install an IED at the line head end and every other distance of the line. These IEDs are numbered as IED1, IED2, IED3, IED4, IED5, IED6, IED7, IED8, IED9, IED10, IED111, IED212, IED32, IED43, IED544, IED112 m Where m is the total number of IEDs configured for the line. In this application example, the total number of IEDs is 5.

[0203] Step A.2: Each IED device samples the line voltage signal at its location in real time to obtain voltage sampling data.

[0204] Step A.3: Based on the voltage sampling data, determine whether a DC line fault has occurred using the voltage change criterion. Since the sampling data meets the voltage change criterion, indicating a significant and sustained drop in line voltage, a fault is determined and the fault section location procedure is initiated.

[0205] Step A.4: Each IED device collects fault voltage data 0.2 seconds after the fault moment. At this time, the fault voltage data obtained by each IED device is as follows: Figures 4 to 8 shown.

[0206] The fault voltage data is processed using a linear predictive coding algorithm to obtain the spectrum distribution curve of the voltage signal. The spectrum distribution curve reflects the amplitude values corresponding to different frequency components in the signal. The horizontal axis of the spectrum distribution curve represents the frequency value, and the vertical axis represents the amplitude value. The dominant frequency of the voltage signal is found based on the spectrum distribution curve. Based on the voltage sampling data collected by IED1, IED2, IED3, IED4, and IED5, the dominant frequencies found are recorded as f1, f2, f3, f4, and f5 respectively. Their values are:

[0207] f1=29.99Hz f2=29.98Hz f3=24.98Hz f4=24.96Hz f5=24.99Hz

[0208] Step A.5: For the continuous voltage sampling data collected by each IED device 0.2 seconds after the fault moment, use Hilbert transform and instantaneous amplitude calculation method to obtain the instantaneous amplitude data of the voltage signal.

[0209] The instantaneous amplitude data changes with time. The instantaneous amplitude data of each IED device is shown in the figure as follows: Figures 9-13 shown.

[0210] Step A.6: Each IED device calculates the deviation data, such as Figures 14-18 As shown, excluding noise interference, occasional large deviation data interference, and instantaneous fault interference, take the data within the first main frequency cycle, calculate the maximum deviation sequence number of the deviation data, find the data sequence number corresponding to the maximum value in the deviation data, and define this data sequence number as the maximum deviation sequence number.

[0211] The maximum deviation numbers obtained by IED1, IED2, IED3, IED4, and IED5 are recorded as X1, X2, X3, X4, and X5 respectively. Their values are:

[0212] X1=140X2=142X3=178X4=176X5=174

[0213] Step A.7: Each IED sends the oscillation main frequency data and maximum deviation sequence number data to the fault section location system through the communication transmission module. The fault section location system determines the fault section location based on whether the oscillation main frequency and maximum deviation sequence number meet the fault location criteria.

[0214] Since the difference in the main frequencies and the difference in the maximum deviation numbers between IED2 and IED3 both satisfy equation (6), it can be determined that the fault is located between IED2 and IED3. Since the difference in the main frequencies and the difference in the maximum deviation numbers of the other IED devices do not satisfy equation (6), it is determined that the fault is not between other adjacent IED devices.

[0215] Compared with the prior art, the beneficial effects of the present invention include at least:

[0216] (1) The present invention uses voltage signal processing and data-driven fault detection, frequency analysis, and instantaneous amplitude calculation to accurately extract fault characteristics, clarify relevant positioning indicators of the DC line fault section, and increase fault differentiation, especially when the current change is not obvious. It can also accurately identify faults.

[0217] (2) The present invention further enhances the accuracy of fault location by introducing dual criteria of main frequency and maximum deviation number for analysis.

[0218] (3) The present invention can quickly identify the location of the fault, isolate the fault section in time, and ensure the stable operation of the power grid.

[0219] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0220] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for locating a fault section of a DC line, characterized in that: include: Collect voltage data at each sampling point to obtain voltage sampling data; Determine whether the voltage sampling data meets the voltage change criterion. If any sampling point meets the voltage change criterion, determine that a fault exists in the line. Collect fault voltage data within a set time after the fault moment at each sampling point, extract a frequency spectrum distribution curve of the fault voltage data, and determine the main oscillation frequency of the fault voltage data based on the frequency spectrum distribution curve. Calculate instantaneous amplitude data based on fault voltage data; The instantaneous amplitude data and the fault voltage data are subtracted to obtain the deviation data of each sampling point, and the sampling number corresponding to the maximum value of the deviation data within the period corresponding to the first oscillation main frequency of each sampling point is taken as the maximum deviation sequence number; The fault location is determined based on whether the difference between the main oscillation frequencies and the difference between the maximum deviation numbers of adjacent sampling points simultaneously meet the fault location criteria.

2. The method for locating a fault section of a DC line according to claim 1, wherein: The voltage change criterion is expressed as follows: Where: u k 、u k+1 、u k+3 They are the kth, k+1th, and k+3th voltage sampling data of a certain sampling point respectively; U N is the rated value of the DC link voltage.

3. The method for locating a fault section of a DC line according to claim 1, wherein: When collecting voltage data at each sampling point and collecting fault voltage data within a set time after the fault moment at each sampling point, the sampling frequency shall not be less than 4000 Hz.

4. The method for locating a fault section of a DC line according to claim 3, wherein: The set time after the fault moment is 0.2 seconds.

5. The method for locating a fault section of a DC line according to claim 1, wherein: Collect the fault voltage data within a set time after the fault moment at each sampling point, extract the spectrum distribution curve of the fault voltage data, and determine the main oscillation frequency of the fault voltage data based on the spectrum distribution curve. The linear predictive coding algorithm is used to reconstruct the fault voltage data, which is expressed as follows: Where: U k is the k-th fault voltage data; e k is the prediction error; is the predicted value of the k-th fault voltage data; p is the prediction order, which is selected according to the actual operation; a c is the prediction coefficient, which is the quantity to be determined; U k-c is the historical sample data of fault voltage; The original fault voltage data is segmented, and each segment of the fault voltage data is Fourier transformed to obtain the logarithmic spectrum of each segment of the fault voltage data. The logarithmic spectrum of each segment of the fault voltage data is inversely Fourier transformed to obtain the cepstrum of each segment of the fault voltage data. Extract the prediction coefficient a of each segment of fault voltage data from the cepstrum of each segment of fault voltage data c , calculate the predicted value of each section of fault voltage data according to the formula for reconstructing fault voltage data and merge; According to the predicted value of the combined fault voltage data Obtain the spectrum distribution curve, find the point with the largest ordinate in the spectrum distribution curve, and the abscissa of the point is the main oscillation frequency of the fault voltage data.

6. The method for locating a fault section of a DC line according to claim 1, wherein: Calculating instantaneous amplitude data based on fault voltage data includes: Calculate the Hilbert transform of the fault voltage data to obtain a set of data with a phase difference of 90 degrees from the fault voltage data; The instantaneous amplitude is calculated using the following formula: Where: U k is the k-th fault voltage data; U k ′ is the kth data obtained by Hilbert transform; A k is the kth instantaneous amplitude data.

7. The method for locating a fault section of a DC line according to claim 1, wherein: The steps of obtaining the maximum deviation sequence number also include: For each sampling point, the least square method is used to fit the deviation data, and the fitted deviation data is used to replace the original deviation data; Eliminate the Nth q The data before the Nth data point and z The data after data points, where N q and N z Calculated based on the main oscillation frequency and sampling frequency; Find the deviation data after fitting from the Nth q data points to the Nth z The maximum value of the data points is obtained, the sampling number of the data point corresponding to the maximum value is recorded, and the sampling number is defined as the maximum deviation sequence number of the deviation data of the sampling point.

8. The method for locating a fault section of a DC line according to claim 7, wherein: variable N q and variable N z Calculated by the following formula: Where: f c is the sampling frequency; f is the main oscillation frequency.

9. The method for locating a fault section of a DC line according to claim 1, wherein: The fault location criterion is expressed as follows: Where: j and j+1 are sampling point numbers, j=1, 2, ..., m-1; i and i+1 are the sampling point numbers in the summation operation, i=1, 2, ..., m-1; f j and f j+1 are the main oscillation frequencies of the jth sampling point and the j+1th sampling point respectively; f i and f i+1 are the main oscillation frequencies of the jth sampling point and the j+1th sampling point respectively; X j and X j+1 are the maximum deviation numbers of the j-th sampling point and the j+1-th sampling point respectively; X i and X i+1 are the maximum deviation numbers of the j-th sampling point and the j+1-th sampling point respectively.

10. A DC line fault section location system, characterized in that: include: Multiple IED devices and fault section locating devices; The IED devices are configured at various sampling points on the line, including: The voltage acquisition module is used to collect voltage data at each sampling point to obtain voltage sampling data, and to collect fault voltage data within a set time after the fault moment at each sampling point; The data calculation module includes a logic operation unit, an oscillation main frequency calculation unit and a maximum deviation sequence number calculation unit, which is used to determine whether the voltage sampling data meets the voltage change criterion. If so, the oscillation main frequency and the maximum deviation sequence number are calculated based on the voltage sampling data; A communication transmission module, used to communicate with the fault section locating device, including a wireless communication interface and a wireless transmission device; The fault section locating device comprises: A communication receiving module, used to communicate with the IED device on the line, including a wireless communication interface and a wireless receiving device; The data storage module is used to store the oscillation main frequency data and maximum deviation sequence number data received by the communication, and provide them to the fault section location judgment module; Fault section location judgment module, used to determine the fault location; The result display module is used to display the fault location.

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