CN110161376B - Traveling wave fault time extraction algorithm - Google Patents
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- CN110161376B CN110161376B CN201910548184.8A CN201910548184A CN110161376B CN 110161376 B CN110161376 B CN 110161376B CN 201910548184 A CN201910548184 A CN 201910548184A CN 110161376 B CN110161376 B CN 110161376B
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/085—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution lines, e.g. overhead
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
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Abstract
The invention discloses a traveling wave fault time extraction algorithm with higher traveling wave positioning accuracy. The algorithm collects two paths of traveling wave voltage signals and two paths of current signals at the same position of the power transmission line and at the same time, and obtains GPS time service signals of a period of time before and after the time of collecting the traveling wave signals; the invention adopts the comprehensive comparison algorithm of the voltage sampling signal and the current sampling signal, greatly improves the reliability of the sampling of the traveling wave fault signal, adopts the algorithm of cyclic trigger acquisition and useless signal rejection, can avoid missing the signal, saves the storage space and the communication bandwidth, carries out the classification time extraction method on the real traveling wave fault signal obtained by discrimination, and extracts the accurate time data step by step, so that the accuracy of the finally obtained traveling wave fault signal at the occurrence time is higher, the accuracy is 20ns magnitude and is far higher than the existing accuracy of 50nm, the positioning accuracy can be improved to 150-200 m, and the positioning requirement of a complex power distribution network can be met. Is suitable for popularization and application in the field of on-line monitoring.
Description
Technical Field
The invention relates to the field of online monitoring, in particular to a traveling wave fault moment extraction algorithm.
Background
The existing developed and mature fault location method for power supply lines mainly comprises an impedance method and a traveling wave method. Compared with an impedance method, the traveling wave method is applied to fault location of the power transmission line, and the location accuracy and stability are not affected by the characteristics of transition resistance, traction load and the like. When the traveling wave method is used for ranging, the time for the traveling wave signal to reach the traveling wave signal detection device and the traveling wave speed are generally determined at the same time, so that the fault point can be accurately positioned. The extraction of the fault time of the traveling wave is very important, the existing traveling wave fault time extraction algorithm is basically established on the basis of GPS time service, a 20MHz sampling clock is used for collecting time service synchronous signals, the fault occurrence time point is obtained through analysis and calculation of traveling wave signals, the time precision calculated under the sampling frequency is 50ns magnitude, the positioning precision is about 300 m-500 m, and the positioning requirement of a complex power distribution network cannot be met, so that a scheme with higher positioning precision is needed to realize fault positioning.
Disclosure of Invention
The invention aims to solve the technical problem of providing a traveling wave fault time extraction algorithm with higher traveling wave positioning accuracy.
The technical scheme adopted by the invention for solving the technical problems is as follows: the traveling wave fault moment extraction algorithm comprises the following steps:
s1, collecting two traveling wave voltage signals and two traveling wave current signals at the same position of the power transmission line and at the same time, and acquiring a GPS time service signal a period of time before the time of collecting the traveling wave signals;
s2, comparing and analyzing the two traveling wave voltage signals to determine whether the traveling wave signal is a fault signal, if the result of the comparison and analysis of the two traveling wave voltage signals is that the traveling wave signal is a fault signal, determining that the collected traveling wave signal is a real traveling wave fault signal, and going to step S3; if the judgment result of the comparison and analysis of the two traveling wave voltage signals is that the traveling wave signal is not a fault signal, the collected traveling wave signal is determined not to be a real traveling wave fault signal, the collected traveling wave signal is deleted, and the step S1 is repeated;
s3, comparing and analyzing the two traveling wave current signals to judge whether the traveling wave signals are fault signals, if the judgment result of the comparison and analysis of the two traveling wave current signals is that the traveling wave signals are fault signals, the collected traveling wave signals are determined to be real traveling wave fault signals, the step S4 is carried out, if the judgment result of the comparison and analysis of the two traveling wave current signals is that the traveling wave signals are not fault signals, the collected traveling wave signals are determined to be not real traveling wave fault signals, the collected traveling wave signals are deleted, and the step S1 is repeated;
s4, extracting the time of the real traveling wave fault signal obtained in step S3, the specific method is as follows:
1) analyzing the GPS time service signal acquired in the step S1 to extract UTC time information; the initial time point of the UTC time information is marked as T1;
2) extracting 1pps time information from the UTC time information in the step 1) by using clock sampling, dividing the 1pps time information into M ms-level time periods, and then matching which ms-level time period the fault signal occurs in, wherein the initial time of the ms-level time period of the fault signal is marked as T2;
3) extracting N ns-level time periods from the ms-level time period of the fault signal obtained in the step 2) by using a clock sampling counter, and then matching which ns-level time period the fault signal occurs in, wherein the initial time of the ns-level time period of the fault signal is marked as T3;
4) extracting the starting time point of the real fault signal occurring in the ns-level time period by using a wavelet algorithm and recording the starting time point as T4;
5) and the occurrence time T of the traveling wave fault signal is T1+ T2+ T3+ T4.
Further, in step S2, the method for comparing and analyzing the two traveling wave voltage signals to determine whether the traveling wave signal is a fault signal is as follows:
A. setting an acquisition amplitude threshold, an amplitude difference range, an amplitude similarity ratio, waveform trigger polarity and a frequency range of the traveling wave voltage signal;
B. respectively judging the acquisition amplitude threshold of the two acquired traveling wave voltage signals, eliminating the traveling wave voltage signals smaller than the acquisition amplitude threshold, and respectively converting the remaining traveling wave voltage signals into digital signals through an ADC (analog-to-digital converter) to obtain two paths of sampling data;
C. comparing the difference values of the amplitudes of the two paths of sampling data to obtain amplitude difference values, judging the obtained amplitude difference values and a set amplitude difference range, counting the amplitude number falling in the amplitude difference range, calculating the proportion value of the amplitude number falling in the amplitude difference range and the total number of the two paths of sampling, comparing the obtained proportion with the set amplitude similarity ratio, judging the waveforms of the two paths of sampling data to be valid traveling wave fault signals if the calculated proportion is larger than the set amplitude similarity ratio, judging the waveforms of the two paths of sampling data to be invalid traveling wave fault signals if the calculated proportion is smaller than or equal to the set amplitude similarity ratio, rejecting the corresponding two paths of invalid sampling data, and re-sampling;
D. c, judging the polarity of the two paths of sampling data obtained after the processing in the step C to obtain the waveform trigger polarities of the two paths of sampling data, comparing and judging each obtained waveform trigger polarity with a set waveform trigger polarity, if the waveform trigger polarities of the two paths of sampling data are the same, judging the waveforms of the two paths of sampling data to be effective traveling wave fault signals, if the waveform trigger polarities of the two paths of sampling data are different, judging the waveforms of the two paths of sampling data to be ineffective traveling wave fault signals, eliminating the corresponding two paths of ineffective sampling data, and sampling again;
E. and D, performing filtering processing and Fourier transform processing on the two paths of sampling data obtained after the processing in the step D to obtain the frequencies of the two paths of sampling data, comparing and judging the frequency of each path of obtained sampling data with a set frequency range, judging the waveform of the path of sampling data to be an effective traveling wave fault signal if the frequency of the path of sampling data falls within the set frequency range, judging the waveform of the path of sampling data to be an ineffective traveling wave fault signal if the frequency of the path of sampling data does not fall within the set frequency range, and removing the corresponding ineffective sampling data to obtain two paths of traveling wave voltage signals to perform comparison analysis and judgment on a result.
Further, in step S3, the method for comparing and analyzing the two traveling wave current signals to determine whether the traveling wave signal is a fault signal is as follows:
A. setting an acquisition amplitude threshold, an amplitude difference range, an amplitude similarity ratio, a waveform trigger polarity and a frequency range of the traveling wave current signal;
B. respectively judging the acquisition amplitude threshold of the two acquired traveling wave current signals, eliminating the traveling wave current signals smaller than the acquisition amplitude threshold, and respectively converting the remaining traveling wave current signals into digital signals through an ADC (analog-to-digital converter) to obtain two paths of sampling data;
C. comparing the difference values of the amplitudes of the two paths of sampling data to obtain amplitude difference values, judging the obtained amplitude difference values and a set amplitude difference range, counting the number of the amplitudes falling into the amplitude difference range, calculating the proportion value of the number of the amplitudes falling into the amplitude difference range and the total number of the paths of sampling, comparing the obtained proportion values with the set amplitude similarity ratio for judgment, judging the waveforms of the two paths of sampling data to be valid traveling wave fault signals if the calculated proportion is greater than the set amplitude similarity ratio, judging the waveforms of the two paths of sampling data to be invalid traveling wave fault signals if the calculated proportion is less than or equal to the set amplitude similarity ratio, rejecting the corresponding two paths of invalid sampling data, and re-sampling;
D. c, judging the polarity of the two paths of sampling data obtained after the processing in the step C to obtain the waveform trigger polarities of the two paths of sampling data, comparing and judging each obtained waveform trigger polarity with a set waveform trigger polarity, if the waveform trigger polarities of the two paths of sampling data are the same, judging the waveform of the sampling data to be an effective traveling wave fault signal, if the waveform trigger polarities of the two paths of sampling data are different, judging the waveform of the sampling data to be an ineffective traveling wave fault signal, eliminating the corresponding ineffective sampling data, and sampling again;
E. d, performing filtering processing and Fourier transform processing on the two paths of sampling data obtained after the processing in the step D to obtain the frequencies of the two paths of sampling data, comparing and judging the frequency of each path of obtained sampling data with a set frequency range, and judging the waveform of the path of sampling data to be an effective traveling wave fault signal if the frequency of the path of sampling data falls within the set frequency range; if the frequency of the two paths of sampling data does not fall within the set frequency range, judging that the waveforms of the two paths of sampling data are invalid traveling wave fault signals, and eliminating the corresponding two paths of invalid sampling data, thereby obtaining a judgment result of comparing and analyzing the two paths of traveling wave current signals.
Further, a Rogowski coil sensor is adopted to acquire a traveling wave current signal.
Further, a voltage sensor is adopted to acquire the traveling wave voltage signal.
Further, in step S1, the sampling frequency of the two traveling wave voltage signals and the two traveling wave current signals collected at the same position of the power transmission line and at the same time is 50 MHz.
Further, in step S1, a GPS time service module with a time service precision of 20ns is used to obtain a GPS time service signal a period of time before the time of collecting the traveling wave signal.
The invention has the beneficial effects that: the traveling wave fault moment extraction algorithm collects two traveling wave voltage signals and two traveling wave current signals at the same position of the power transmission line and at the same moment, and obtains a GPS time service signal a period of time before the moment of collecting the traveling wave signals; firstly, comparing and analyzing two traveling wave voltage signals to judge whether the traveling wave signals are fault signals, and then comparing and analyzing the two traveling wave current signals to judge whether the traveling wave signals are fault signals, the invention adopts a voltage sampling signal and current sampling signal comprehensive comparison algorithm, greatly improves the reliability of sampling the traveling wave fault signals, and adopts a cyclic trigger acquisition and useless signal elimination algorithm to only store possible traveling wave fault signals, thereby avoiding missing the signals, saving the storage space and the communication bandwidth, and carrying out a classification time extraction method on the real traveling wave fault signals obtained by discrimination, namely analyzing the GPS time service signals to extract UTC time information; the initial time point of the UTC time information is marked as T1; extracting 1pps time information from the UTC time information in the step 1) by using clock sampling, dividing the 1pps time information into M ms-level time periods, and then matching which ms-level time period the fault signal occurs in, wherein the initial time of the ms-level time period of the generated fault signal is marked as T2; then, extracting N ns-level time periods from the ms-level time period of the fault signal obtained in the step 2) by using a clock sampling counter, and then matching which ns-level time period the fault signal occurs in, wherein the initial time of the ns-level time period of the fault signal is marked as T3; finally, extracting the starting time point of the real fault signal occurring in the ns-level time period by using a wavelet algorithm and recording the starting time point as T4; the method can obtain the occurrence time T of the traveling wave fault signal which is T1+ T2+ T3+ T4, and extract accurate time data step by adopting a step time extraction method, so that the finally obtained accuracy of the occurrence time of the traveling wave fault signal is higher, the accuracy is in the order of 20ns and is far higher than the existing accuracy of 50nm, the positioning accuracy can be improved to 150-200 m, and the positioning requirement of a complex power distribution network can be met.
Detailed Description
The traveling wave fault moment extraction algorithm comprises the following steps:
s1, collecting two traveling wave voltage signals and two traveling wave current signals at the same position of the power transmission line and at the same time, and acquiring a GPS time service signal a period of time before the time of collecting the traveling wave signals;
s2, comparing and analyzing the two traveling wave voltage signals to determine whether the traveling wave signal is a fault signal, if the result of the comparison and analysis of the two traveling wave voltage signals is that the traveling wave signal is a fault signal, determining that the collected traveling wave signal is a real traveling wave fault signal, and going to step S3; if the judgment result of the comparison and analysis of the two traveling wave voltage signals is that the traveling wave signal is not a fault signal, the collected traveling wave signal is determined not to be a real traveling wave fault signal, the collected traveling wave signal is deleted, and the step S1 is repeated;
s3, comparing and analyzing the two traveling wave current signals to judge whether the traveling wave signals are fault signals, if the judgment result of the comparison and analysis of the two traveling wave current signals is that the traveling wave signals are fault signals, the collected traveling wave signals are determined to be real traveling wave fault signals, the step S4 is carried out, if the judgment result of the comparison and analysis of the two traveling wave current signals is that the traveling wave signals are not fault signals, the collected traveling wave signals are determined to be not real traveling wave fault signals, the collected traveling wave signals are deleted, and the step S1 is repeated;
s4, extracting the time of the real traveling wave fault signal obtained in step S3, the specific method is as follows:
1) analyzing the GPS time service signal acquired in the step S1 to extract UTC time information; the initial time point of the UTC time information is marked as T1;
2) extracting 1pps time information from the UTC time information in the step 1) by using clock sampling, dividing the 1pps time information into M ms-level time periods, and then matching which ms-level time period the fault signal occurs in, wherein the initial time of the ms-level time period of the fault signal is marked as T2;
3) extracting N ns-level time periods from the ms-level time period of the fault signal obtained in the step 2) by using a clock sampling counter, and then matching which ns-level time period the fault signal occurs in, wherein the initial time of the ns-level time period of the fault signal is marked as T3;
4) extracting the starting time point of the real fault signal occurring in the ns-level time period by using a wavelet algorithm and recording the starting time point as T4;
5) and the occurrence time T of the traveling wave fault signal is T1+ T2+ T3+ T4.
The traveling wave fault moment extraction algorithm collects two traveling wave voltage signals and two traveling wave current signals at the same position of the power transmission line and at the same moment, and obtains a GPS time service signal a period of time before the moment of collecting the traveling wave signals; firstly, comparing and analyzing two traveling wave voltage signals to judge whether the traveling wave signals are fault signals, and then comparing and analyzing the two traveling wave current signals to judge whether the traveling wave signals are fault signals, the invention adopts a voltage sampling signal and current sampling signal comprehensive comparison algorithm, greatly improves the reliability of sampling the traveling wave fault signals, and adopts a cyclic trigger acquisition and useless signal elimination algorithm to only store possible traveling wave fault signals, thereby avoiding missing the signals, saving the storage space and the communication bandwidth, and carrying out a classification time extraction method on the real traveling wave fault signals obtained by discrimination, namely analyzing the GPS time service signals to extract UTC time information; the initial time point of the UTC time information is marked as T1; extracting 1pps time information from the UTC time information in the step 1) by using clock sampling, dividing the 1pps time information into M ms-level time periods, and then matching which ms-level time period the fault signal occurs in, wherein the initial time of the ms-level time period of the generated fault signal is marked as T2; then, extracting N ns-level time periods from the ms-level time period of the fault signal obtained in the step 2) by using a clock sampling counter, and then matching which ns-level time period the fault signal occurs in, wherein the initial time of the ns-level time period of the fault signal is marked as T3; finally, extracting the starting time point of the real fault signal occurring in the ns-level time period by using a wavelet algorithm and recording the starting time point as T4; the method can obtain the occurrence time T of the traveling wave fault signal which is T1+ T2+ T3+ T4, and extract accurate time data step by adopting a step time extraction method, so that the finally obtained accuracy of the occurrence time of the traveling wave fault signal is higher, the accuracy is in the order of 20ns and is far higher than the existing accuracy of 50nm, the positioning accuracy can be improved to 150-200 m, and the positioning requirement of a complex power distribution network can be met.
In the above embodiment, the method for comparing and analyzing the two traveling wave voltage signals to determine whether the traveling wave signal is a fault signal in step S2 is as follows:
A. setting an acquisition amplitude threshold, an amplitude difference range, an amplitude similarity ratio, waveform trigger polarity and a frequency range of the traveling wave voltage signal; the specific parameter value can be set according to the actual situation;
B. respectively judging the acquisition amplitude threshold of the two acquired traveling wave voltage signals, eliminating the traveling wave voltage signals smaller than the acquisition amplitude threshold, and respectively converting the remaining traveling wave voltage signals into digital signals through an ADC (analog-to-digital converter) to obtain two paths of sampling data;
C. comparing the difference values of the amplitudes of the two paths of sampling data to obtain amplitude difference values, judging the obtained amplitude difference values and a set amplitude difference range, counting the amplitude number falling in the amplitude difference range, calculating the proportion value of the amplitude number falling in the amplitude difference range and the total number of the two paths of sampling, comparing the obtained proportion with the set amplitude similarity ratio, judging the waveforms of the two paths of sampling data to be valid traveling wave fault signals if the calculated proportion is larger than the set amplitude similarity ratio, judging the waveforms of the two paths of sampling data to be invalid traveling wave fault signals if the calculated proportion is smaller than or equal to the set amplitude similarity ratio, rejecting the corresponding two paths of invalid sampling data, and re-sampling;
D. c, judging the polarity of the two paths of sampling data obtained after the processing in the step C to obtain the waveform trigger polarities of the two paths of sampling data, comparing and judging each obtained waveform trigger polarity with a set waveform trigger polarity, if the waveform trigger polarities of the two paths of sampling data are the same, judging the waveform of the sampling data to be an effective traveling wave fault signal, if the waveform trigger polarities of the two paths of sampling data are different, judging the waveform of the sampling data to be an ineffective traveling wave fault signal, eliminating the corresponding ineffective sampling data, and sampling again;
E. and D, performing filtering processing and Fourier transform processing on the two paths of sampling data obtained after the processing in the step D to obtain the frequencies of the two paths of sampling data, comparing and judging the frequency of each path of obtained sampling data with a set frequency range, judging the waveform of the path of sampling data to be an effective traveling wave fault signal if the frequency of the path of sampling data falls within the set frequency range, judging the waveform of the path of sampling data to be an ineffective traveling wave fault signal if the frequency of the path of sampling data does not fall within the set frequency range, and removing the corresponding two paths of ineffective sampling data, thereby obtaining a judgment result of comparing and analyzing the two paths of traveling wave current signals.
The method for judging whether the traveling wave signals are fault signals by comparing and analyzing the two traveling wave voltage signals comprises the steps of judging acquisition amplitude thresholds of the two acquired traveling wave voltage signals respectively, removing the traveling wave voltage signals smaller than the acquisition amplitude thresholds, and converting the rest traveling wave voltage signals into digital signals through an ADC (analog to digital converter) respectively to obtain two paths of sampling data; and then comparing the amplitude, the waveform trigger polarity and the frequency of the two paths of sampling data to obtain similarity information of the two paths of sampling data, rapidly judging whether the traveling wave signal is a real traveling wave fault signal according to the similarity information, effectively removing clutter interference, and rapidly extracting the traveling wave fault signal.
Similarly, in step S3, the method for comparing and analyzing the two traveling wave current signals to determine whether the traveling wave signal is a fault signal is as follows:
A. setting an acquisition amplitude threshold, an amplitude difference range, an amplitude similarity ratio, a waveform trigger polarity and a frequency range of the traveling wave current signal; the specific parameter value can be set according to the actual situation;
B. respectively judging the acquisition amplitude threshold of the two acquired traveling wave current signals, eliminating the traveling wave current signals smaller than the acquisition amplitude threshold, and respectively converting the remaining traveling wave current signals into digital signals through an ADC (analog-to-digital converter) to obtain two paths of sampling data;
C. comparing the difference values of the amplitudes of the two paths of sampling data to obtain amplitude difference values, judging the obtained amplitude difference values and a set amplitude difference range, counting the amplitude number falling in the amplitude difference range, calculating the proportion value of the amplitude number falling in the amplitude difference range and the total number of the two paths of sampling, comparing the obtained proportion with the set amplitude similarity ratio, judging the waveforms of the two paths of sampling data to be valid traveling wave fault signals if the calculated proportion is larger than the set amplitude similarity ratio, judging the waveforms of the two paths of sampling data to be invalid traveling wave fault signals if the calculated proportion is smaller than or equal to the set amplitude similarity ratio, rejecting the corresponding two paths of invalid sampling data, and re-sampling;
D. c, judging the polarity of the two paths of sampling data obtained after the processing in the step C to obtain the waveform trigger polarities of the two paths of sampling data, comparing and judging each obtained waveform trigger polarity with a set waveform trigger polarity, if the waveform trigger polarities of the two paths of sampling data are the same, judging the waveform of the sampling data to be an effective traveling wave fault signal, if the waveform trigger polarities of the two paths of sampling data are different, judging the waveform of the sampling data to be an ineffective traveling wave fault signal, eliminating the corresponding ineffective sampling data, and sampling again;
E. and D, performing filtering processing and Fourier transform processing on the two paths of sampling data obtained after the processing in the step D to obtain the frequencies of the two paths of sampling data, comparing and judging the frequency of each path of obtained sampling data with a set frequency range, judging the waveform of the path of sampling data to be an effective traveling wave fault signal if the frequency of the path of sampling data falls within the set frequency range, judging the waveform of the path of sampling data to be an ineffective traveling wave fault signal if the frequency of the path of sampling data does not fall within the set frequency range, and rejecting the corresponding two paths of ineffective sampling data, thereby obtaining a judgment result of comparing and analyzing the two paths of traveling wave current signals.
The method for judging whether the traveling wave signals are fault signals by comparing and analyzing the two traveling wave current signals respectively judges the acquisition amplitude threshold of the two acquired traveling wave current signals, eliminates the traveling wave current signals smaller than the acquisition amplitude threshold, and respectively converts the rest traveling wave current signals into digital signals through an ADC (analog to digital converter) to obtain two paths of sampling data; and then comparing the amplitude, the waveform trigger polarity and the frequency of the two paths of sampling data to obtain similarity information of the two paths of sampling data, rapidly judging whether the traveling wave signal is a real traveling wave fault signal according to the similarity information, effectively removing clutter interference, and rapidly extracting the traveling wave fault signal.
In order to further ensure the positioning accuracy of the traveling wave fault signal, a Rogowski coil sensor is adopted to obtain a traveling wave current signal. And acquiring a traveling wave voltage signal by using a voltage sensor. In step S1, the sampling frequency of the two traveling wave voltage signals and the two traveling wave current signals collected at the same position of the power transmission line and at the same time is 50 MHz. In step S1, a GPS time service module with a time service precision of 20ns is used to obtain a GPS time service signal a period of time before the time of collecting the traveling wave signal.
Claims (7)
1. A traveling wave fault moment extraction algorithm is characterized by comprising the following steps:
s1, collecting two traveling wave voltage signals and two traveling wave current signals at the same position of the power transmission line and at the same time, and acquiring a GPS time service signal a period of time before the time of collecting the traveling wave signals;
s2, comparing and analyzing the two traveling wave voltage signals to determine whether the traveling wave signal is a fault signal, if the result of the comparison and analysis of the two traveling wave voltage signals is that the traveling wave signal is a fault signal, determining that the collected traveling wave signal is a real traveling wave fault signal, and going to step S3; if the judgment result of the comparison and analysis of the two traveling wave voltage signals is that the traveling wave signal is not a fault signal, the collected traveling wave signal is determined not to be a real traveling wave fault signal, the collected traveling wave signal is deleted, and the step S1 is repeated;
s3, comparing and analyzing the two traveling wave current signals to judge whether the traveling wave signals are fault signals, if the judgment result of the comparison and analysis of the two traveling wave current signals is that the traveling wave signals are fault signals, the collected traveling wave signals are determined to be real traveling wave fault signals, the step S4 is carried out, if the judgment result of the comparison and analysis of the two traveling wave current signals is that the traveling wave signals are not fault signals, the collected traveling wave signals are determined to be not real traveling wave fault signals, the collected traveling wave signals are deleted, and the step S1 is repeated;
s4, extracting the time of the real traveling wave fault signal obtained in step S3, the specific method is as follows:
1) analyzing the GPS time service signal acquired in the step S1 to extract UTC time information; the initial time point of the UTC time information is marked as T1;
2) extracting 1pps time information from the UTC time information in the step 1) by using clock sampling, dividing the 1pps time information into M ms-level time periods, and then matching which ms-level time period the fault signal occurs in, wherein the initial time of the ms-level time period of the fault signal is marked as T2;
3) extracting N ns-level time periods from the ms-level time period of the fault signal obtained in the step 2) by using a clock sampling counter, and then matching which ns-level time period the fault signal occurs in, wherein the initial time of the ns-level time period of the fault signal is marked as T3;
4) extracting the starting time point of the real fault signal occurring in the ns-level time period by using a wavelet algorithm and recording the starting time point as T4;
5) and the occurrence time T of the traveling wave fault signal is T1+ T2+ T3+ T4.
2. The traveling wave fault time extraction algorithm of claim 1, characterized in that: in step S2, the method for comparing and analyzing the two traveling wave voltage signals to determine whether the traveling wave signal is a fault signal is as follows:
A. setting an acquisition amplitude threshold, an amplitude difference range, an amplitude similarity ratio, waveform trigger polarity and a frequency range of the traveling wave voltage signal;
B. respectively judging the acquisition amplitude threshold of the two acquired traveling wave voltage signals, eliminating the traveling wave voltage signals smaller than the acquisition amplitude threshold, and respectively converting the remaining traveling wave voltage signals into digital signals through an ADC (analog-to-digital converter) to obtain two paths of sampling data;
C. comparing the difference values of the amplitudes of the two paths of sampling data to obtain amplitude difference values, judging the obtained amplitude difference values and a set amplitude difference range, counting the amplitude number falling in the amplitude difference range, calculating the proportion value of the amplitude number falling in the amplitude difference range and the total number of the two paths of sampling, comparing the obtained proportion with the set amplitude similarity ratio, judging the waveforms of the two paths of sampling data to be valid traveling wave fault signals if the calculated proportion is larger than the set amplitude similarity ratio, judging the waveforms of the two paths of sampling data to be invalid traveling wave fault signals if the calculated proportion is smaller than or equal to the set amplitude similarity ratio, rejecting the corresponding two paths of invalid sampling data, and re-sampling;
D. c, judging the polarity of the two paths of sampling data obtained after the processing in the step C to obtain the waveform trigger polarities of the two paths of sampling data, comparing and judging each obtained waveform trigger polarity with a set waveform trigger polarity, if the waveform trigger polarities of the two paths of sampling data are the same, judging the waveforms of the two paths of sampling data to be effective traveling wave fault signals, if the waveform trigger polarities of the two paths of sampling data are different, judging the waveforms of the two paths of sampling data to be ineffective traveling wave fault signals, eliminating the corresponding two paths of ineffective sampling data, and sampling again;
E. and D, performing filtering processing and Fourier transform processing on the two paths of sampling data obtained after the processing in the step D to obtain the frequencies of the two paths of sampling data, comparing and judging the frequency of each path of obtained sampling data with a set frequency range, judging the waveform of the path of sampling data to be an effective traveling wave fault signal if the frequency of the path of sampling data falls within the set frequency range, judging the waveform of the path of sampling data to be an ineffective traveling wave fault signal if the frequency of the path of sampling data does not fall within the set frequency range, and removing the corresponding two paths of ineffective sampling data, thereby obtaining a judgment result of comparing and analyzing the two paths of traveling wave current signals.
3. The traveling wave fault time extraction algorithm of claim 2, characterized in that: in step S3, the method for comparing and analyzing the two traveling wave current signals to determine whether the traveling wave signal is a fault signal is as follows:
A. setting an acquisition amplitude threshold, an amplitude difference range, an amplitude similarity ratio, a waveform trigger polarity and a frequency range of the traveling wave current signal;
B. respectively judging the acquisition amplitude threshold of the two acquired traveling wave current signals, eliminating the traveling wave current signals smaller than the acquisition amplitude threshold, and respectively converting the remaining traveling wave current signals into digital signals through an ADC (analog-to-digital converter) to obtain two paths of sampling data;
C. comparing the difference values of the amplitudes of the two paths of sampling data to obtain amplitude difference values, judging the obtained amplitude difference values and a set amplitude difference range, counting the amplitude number falling in the amplitude difference range, calculating the proportion value of the amplitude number falling in the amplitude difference range and the total number of the two paths of sampling, comparing the obtained proportion values with the set amplitude similarity ratio, judging the waveforms of the two paths of sampling data to be valid traveling wave fault signals if the calculated proportion is larger than the set amplitude similarity ratio, judging the waveforms of the two paths of sampling data to be invalid traveling wave fault signals if the calculated proportion is smaller than or equal to the set amplitude similarity ratio, rejecting the corresponding two paths of invalid sampling data, and re-sampling;
D. c, judging the polarity of the two paths of sampling data obtained after the processing in the step C to obtain the waveform trigger polarities of the two paths of sampling data, comparing and judging each obtained waveform trigger polarity with a set waveform trigger polarity, if the waveform trigger polarities of the two paths of sampling data are the same, judging the waveform of the sampling data to be an effective traveling wave fault signal, if the waveform trigger polarities of the two paths of sampling data are different, judging the waveform of the sampling data to be an ineffective traveling wave fault signal, eliminating the corresponding ineffective sampling data, and sampling again;
E. d, performing filtering processing and Fourier transform processing on the two paths of sampling data obtained after the processing in the step D to obtain the frequencies of the two paths of sampling data, comparing and judging the frequency of each path of obtained sampling data with a set frequency range, and judging the waveform of the path of sampling data to be an effective traveling wave fault signal if the frequency of the path of sampling data falls within the set frequency range; if the frequency of the two paths of sampling data does not fall within the set frequency range, judging that the waveforms of the two paths of sampling data are invalid traveling wave fault signals, and eliminating the corresponding two paths of invalid sampling data, thereby obtaining a judgment result of comparing and analyzing the two paths of traveling wave current signals.
4. The traveling wave fault time extraction algorithm of claim 1, characterized in that: and acquiring a traveling wave current signal by adopting a Rogowski coil sensor.
5. The traveling wave fault time extraction algorithm of claim 1, characterized in that: and acquiring a traveling wave voltage signal by using a voltage sensor.
6. The traveling wave fault time extraction algorithm of claim 1, characterized in that: in step S1, the sampling frequency of the two traveling wave voltage signals and the two traveling wave current signals collected at the same position of the power transmission line and at the same time is 50 MHz.
7. The traveling wave fault time extraction algorithm of claim 1, characterized in that: in step S1, a GPS time service module with a time service precision of 20ns is used to obtain a GPS time service signal a period of time before the time of collecting the traveling wave signal.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201269915Y (en) * | 2008-07-23 | 2009-07-08 | 长沙理工大学 | Travelling wave positioning apparatus for electricity grid failure |
| CN102064827A (en) * | 2010-11-11 | 2011-05-18 | 国网电力科学研究院 | Rubidium oscillator-based standard frequency and time adjusting method |
| CN103076538A (en) * | 2012-12-26 | 2013-05-01 | 武汉大学 | Power transmission line traveling wave fault location method using atomic decomposition |
| CN103176107A (en) * | 2013-03-08 | 2013-06-26 | 山东大学 | High-voltage direct-current power transmission line hybrid fault ranging method |
| CN103235238A (en) * | 2013-04-23 | 2013-08-07 | 贵州电网公司输电运行检修分公司 | Transmission line travelling wave recording method |
| CN203643551U (en) * | 2013-06-06 | 2014-06-11 | 山东电力集团公司济宁供电公司 | Traveling wave positioner |
| CN104459470A (en) * | 2014-12-11 | 2015-03-25 | 东北大学 | Traveling wave fault positioning method suitable for multi-type bus structure |
| CN204359894U (en) * | 2015-01-15 | 2015-05-27 | 国家电网公司 | A kind of Distributed power circuit travelling wave ranging system |
| CN105137293A (en) * | 2015-09-24 | 2015-12-09 | 国网技术学院 | Positioning method of fault points in power distribution network mixed circuits |
| CN105353268A (en) * | 2015-10-10 | 2016-02-24 | 电子科技大学 | Method for judging and positioning distributed traveling wave fault of power transmission line |
| CN106600452A (en) * | 2016-11-11 | 2017-04-26 | 国网新疆电力公司巴州供电公司 | Time analysis matrix and clustering analysis-based power distribution network traveling wave fault location method |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104316833A (en) * | 2014-10-14 | 2015-01-28 | 广东电网有限责任公司电力科学研究院 | Line selection method for medium-voltage power distribution network fault traveling wave |
| CN105353275B (en) * | 2015-12-02 | 2018-08-24 | 杭州务实科技有限公司 | A kind of economical 66kV bands multipoint line Fault Locating Method and system |
| CN106093699B (en) * | 2016-05-30 | 2018-08-14 | 上海交通大学 | The replay method of transmission line malfunction current traveling wave waveform |
| CN109342884A (en) * | 2018-11-09 | 2019-02-15 | 南京大贺电力科技有限公司 | Travelling wave ranging and fault recording integrated device and implementation method |
-
2019
- 2019-06-24 CN CN201910548184.8A patent/CN110161376B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201269915Y (en) * | 2008-07-23 | 2009-07-08 | 长沙理工大学 | Travelling wave positioning apparatus for electricity grid failure |
| CN102064827A (en) * | 2010-11-11 | 2011-05-18 | 国网电力科学研究院 | Rubidium oscillator-based standard frequency and time adjusting method |
| CN103076538A (en) * | 2012-12-26 | 2013-05-01 | 武汉大学 | Power transmission line traveling wave fault location method using atomic decomposition |
| CN103176107A (en) * | 2013-03-08 | 2013-06-26 | 山东大学 | High-voltage direct-current power transmission line hybrid fault ranging method |
| CN103235238A (en) * | 2013-04-23 | 2013-08-07 | 贵州电网公司输电运行检修分公司 | Transmission line travelling wave recording method |
| CN203643551U (en) * | 2013-06-06 | 2014-06-11 | 山东电力集团公司济宁供电公司 | Traveling wave positioner |
| CN104459470A (en) * | 2014-12-11 | 2015-03-25 | 东北大学 | Traveling wave fault positioning method suitable for multi-type bus structure |
| CN204359894U (en) * | 2015-01-15 | 2015-05-27 | 国家电网公司 | A kind of Distributed power circuit travelling wave ranging system |
| CN105137293A (en) * | 2015-09-24 | 2015-12-09 | 国网技术学院 | Positioning method of fault points in power distribution network mixed circuits |
| CN105353268A (en) * | 2015-10-10 | 2016-02-24 | 电子科技大学 | Method for judging and positioning distributed traveling wave fault of power transmission line |
| CN106600452A (en) * | 2016-11-11 | 2017-04-26 | 国网新疆电力公司巴州供电公司 | Time analysis matrix and clustering analysis-based power distribution network traveling wave fault location method |
Non-Patent Citations (2)
| Title |
|---|
| 基于行波传感器的输电线路故障定位方法研究;曾祥君;《中国电机工程学报》;20020630;全文 * |
| 输电线路单端行波故障测距新算法;高洪雨;《电力***自动化》;20170531;全文 * |
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