CN116298666A - Power transmission line fault single-ended traveling wave ranging method based on waveform dominant characteristic simulation inference - Google Patents
Power transmission line fault single-ended traveling wave ranging method based on waveform dominant characteristic simulation inference Download PDFInfo
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Abstract
The invention relates to a single-ended traveling wave distance measurement method of a power transmission line fault based on waveform leading characteristic simulation inference, which comprises the steps of collecting a line single-ended current traveling wave signal by using a traveling wave device, establishing a same bus type simulation model to obtain an offline simulation sample library, and reading fault traveling wave data and corresponding line simulation samples; constructing an improved mode distance similarity measure, comparing and selecting fault recording data with simulation data in a simulation sample library, and obtaining a simulation sample nearest to the fault recording data; reading a determined wave head prompt interval and a corresponding ranging formula of the nearest simulation sample, pre-calibrating actual measurement data through the existing wave head calibration technology, and judging whether actual measurement wave head calibration is effective according to whether the pre-calibrated wave head is in the prompt interval and whether the polarity is consistent with that of the simulation wave head; and substituting the arrival time difference between the effective wave head and the head wave head into a ranging formula provided by the nearest simulation sample for calculation to obtain a ranging sequence, and judging the ranging according to the standard deviation of the fault distance in the sequence.
Description
Technical Field
The invention relates to a power transmission line fault single-ended traveling wave distance measurement method based on waveform leading characteristic simulation inference, and belongs to the field of relay protection of power systems.
Background
At present, a power system rapidly develops, high voltage, long distance, large capacity, alternating current and direct current hybrid connection and regional power grid combination become reality, and the single-ended current traveling wave recording device of the power transmission line is widely applied to power transmission lines with voltage levels of 110kV and above due to the fact that the single-ended current traveling wave recording device is economical, high-speed and free of influence of factors such as system oscillation. Therefore, the fault position is accurately and timely positioned after the fault occurs, and the fault removal has an important effect on improving the power supply reliability.
The key point of the traveling wave ranging is to accurately identify an initial wave head, fault point reflected waves or opposite side bus reflected waves. However, current single-ended traveling wave ranging is affected by factors such as transition resistance, channel attenuation, channel noise interference of a wave recording line, zero mode components and the like, so that the automatic calibration work difficulty of a traveling wave head is high in practical application, and the automatic judgment of whether a marking error exists or not is difficult to realize. The simulation sample has the characteristics of regularity, no noise interference, obvious traveling wave leading characteristic and known fault position, and can strengthen the traveling wave head leading characteristic and stabilize the noise interference. Therefore, the invention provides the wave head calibration method which is used for obtaining the most similar simulation sample of the measured data through similarity measurement comparison and selection and integrating the simulation sample after prompting, the method can increase the wave heads capable of measuring the distance, reduce the wave head calibration of the measured data errors and utilize a plurality of identified wave heads to test the correctness of the distance measurement result.
Disclosure of Invention
The invention aims to solve the technical problem of providing the transmission line fault single-ended traveling wave ranging method based on waveform dominant characteristic simulation inference, which can effectively eliminate the error calibration wave head in the current measured data for ranging and can detect and verify the correctness of the measured fault distance, thereby solving the technical problem that the automatic calibration is difficult for the wave head calibration in the past measured data.
The technical scheme of the invention is as follows: a transmission line fault single-ended traveling wave distance measurement method based on waveform leading characteristic simulation inference comprises the following specific steps:
step1: reading fault phase current traveling wave recording data and corresponding fault line offline simulation sample set A m . And establishing a simulation model which is consistent with the topology type and the line length of the local line and has high resistance and low resistance as transition resistance, traversing the whole length of the transmission line by faults at intervals of 2km, and obtaining an offline sample library of the line faults.
Step2: and constructing an improved mode distance similarity measurement algorithm, comparing the selected fault wave recording data with simulation data in a simulation sample library, and obtaining a simulation sample which is most similar to the actually measured fault data.
Step3: reading simulation sample prompting interval R i ＝[R is ，R ie ]And corresponding to the ranging formula, pre-calibrating the measured data respectively by the existing wave head calibration technology to obtain a measured wave head sequence t. Judging whether the data wave head to be measured is in the simulation prompt interval or not, and judging whether the pre-calibrated actual measurement wave head is calibrated effectively or not according to the fact that whether the polarities of the data wave head to be measured are consistent.
Step4: for each presentation section, if t exists in t u Satisfy R is <t u <R ie Wherein Δt is i ＝t u -t 0 (t u ≠t 0 ) Substituting the fault distance x into a corresponding distance measurement formula to calculate to obtain a fault distance x k Ranging sequence consisting of x in sequence k The number and the sequence standard deviation determine whether the ranging result is valid.
The Step1 specifically comprises the following steps:
step1.1: and collecting data through a traveling wave recording device, and recording current traveling wave data of more than 2ms before and after the fault.
Step1.2: in PSCAD/EMTDC, constructing a simulation model by using topology which keeps the adjacent bus types of the two-end lines identical, has consistent fault line length and has high resistance and low resistance transition resistance. The fault angle, the number of outgoing lines of the bus line and the fault type can not change the polarity and the abrupt change moment of the traveling wave, and can be set to be a fixed value.
Step1.3: the simulation model traverses the full length of a fault line by faults every 2km, and performs batch simulation to obtain two groups of offline simulation sample libraries numbered according to the sequence of the distance between faults.
The Step2 specifically comprises the following steps:
step2.1: the improved mode distance is obtained by calculating the slopes of two time sequences, and segmenting the time sequences into m segments according to the positive and negative values and the amplitude values of the slopes. Each segment pattern set is { steeply rising, steeply falling }, its corresponding pattern is denoted c= {1, -1}. Time series after piecewise linearization:
S＝{(t 1 ,y 1s ,y 1l ),…(t i ,y is ,y ie ),…(t m ,y ms ,y me )}(1)
wherein y is is 、y ie Respectively representing the start value and the end value of the ith sequence, t i Is the initial time of the ith sequence.
Step2.2: starting point of first mutation moment (t) 1 ，c 1 ) The following mode combination is formed by each section of corresponding mode (sharply rising or sharply falling) and each section of corresponding time according to time sequence:
S＝{(t 1 ,c 1 ),(t 2 ,c 2 ),…(t i ,c i ),…(t m ,c m )}(2)
wherein, c i E c, representing the pattern corresponding to the nth segment. The corresponding mode and time of each sequence can be obtained, the ith segment (t i ，c i ) And (5) performing analysis and comparison.
Step2.3: based on the actual measurement data, only the fault appears obvious macroscopic morphological characteristics in a short time, and only the most obvious first 3 time sequences in the simulation and actual measurement data are analyzed. The first wave head arrives first, is strongest and obvious, and has no multi-path interference, so the first wave head serves as a reference. Then after the head of the first wave is aligned, comparing the polarity and time difference corresponding to the two sections of the sequences with the most obvious residual, and calculating the improved mode distance of the two time sequences by the formula (3), wherein the mathematical expression is as follows:
wherein S is s ,S a Time series of measured data and simulated data, f (c) i )，f(t i ) Respectively corresponding to the polarity distance and the time distance alpha of each section of mode sequence i ,β i Is the weight. Wherein f (c) i )＝|c ai -c bi And the polarity of the traveling wave is represented by the I, c= {1, -1}, and when the polarities of the two time sequences are consistent, the polarity distance is 0. In contrast, the polarity distance is 2. Setting the time distance and the polarity distance to be the same in magnitude, and the difference of time is also [0,2]To perform linearization reduction and set saturation cut-off, namely:
wherein Δt is the difference between the end moments of the segments corresponding to the two time sequences, and Δt is less than t l The time distance is 0. Greater than t h The time distance was 2. Between t l And t h The time distance is determined by a linear function generated by the time threshold and the distance threshold.
Step2.4: sorting the improved pattern distances, and comparing to obtain minimum D m ，D m Less than the mode distance threshold D th And determining that the mth sample is the nearest neighbor simulation sample. When D is m Greater than the modified mode distance threshold D th And if the sample library has no most similar sample, reporting the data as special data.
The Step3 specifically comprises the following steps:
step3.1: prompt interval R for reading nearest neighbor simulation sample i And a corresponding ranging equation.
Step3.2: pre-calibrating fault current t to be measured u And polarity p u Obtaining N traveling wave head sequences t= [ t ] 0 ,t 1 …t N ]，t u E t. Step3.3: judgment of t u Whether or not in the presentation section R i In, the discriminant is:
namely, the polarities of the measured data and the simulation sample are kept consistent, and the measured data is calibrated to form a wave head in the simulation sample prompting section R i ＝[t i -ε，t i +ε]And judging that the actual measurement calibration wave head is effective at the time.
The Step4 specifically comprises the following steps:
step4.1: fault calibration wave head t for merging simulation sample prompt u Ranging according to the corresponding single-end ranging source provided by the simulation sample to obtain x i 。
Step4.2: the measured fault distance sequence x can be verified by calculating standard deviations of a plurality of fault distances i Accuracy of (3). When i is greater than 2, calculating a standard deviation sigma according to formula (6), setting a sigma threshold value as eta, when sigma<And when eta, the distance measurement sequence element has smaller phase difference, the distance measurement is correct, and the eta experience value can be 3. Standard deviation of wave head ranging:
wherein x is i I fault distances, u being x, are determined for the identified i+1 wave heads i Average value of N is x i Number of the pieces.
Step4.3: and automatically checking the fault data according to the wave head number and the standard deviation sigma. When i is more than or equal to 2 and the standard deviation sigma<When epsilon, the fault data passes detection verification, the ranging is correct, and a ranging result x is output 1 . When standard deviation sigma<Epsilon or i<2, if the fault data identifies that the number of wave heads is small or the difference between fault sequence elements is large, the distance measurement does not pass the detection verification, and a distance measurement result x is output 1 And the accuracy of the ranging result is further checked manually.
The beneficial effects of the invention are as follows: the invention is simple and efficient, and can be continuously applied before the fault line is not expanded and rebuilt after the off-line simulation sample library is established; the method can detect and verify the correctness of the detected faults, the number of wave heads available for ranging can be increased after the simulation sample prompt is integrated, a plurality of wave heads obtained through prompt can obtain a plurality of ranging distances, thereby verifying the correctness of ranging, and meanwhile, the nearest simulation sample can be used as an auxiliary prompt for manual checking. The method has important significance for solving the problems that the current single-ended traveling wave ranging is difficult to determine whether the calibration wave head is correct or not and the reliability of the calibration wave head is not high.
Drawings
FIG. 1 is a schematic diagram of the 220kV simulation model construction of the present invention;
FIG. 2 shows steps involved in the implementation of step1 of the present invention;
FIG. 3 shows steps involved in the implementation of step2 of the present invention;
FIG. 4 is a graph of off-line simulation sample library improvement pattern distance from step2 in example 2 of the present invention;
FIG. 5 is a simulation sample of the data to be tested and the nearest neighbor in step2 according to embodiment 2 of the present invention;
FIG. 6 shows steps involved in the implementation of step3 of the present invention;
FIG. 7 is a presentation interval of simulation data in step3 in embodiment 1 of the present invention;
FIG. 8 is a pre-calibration of the data to be measured in step3 of example 1 of the present invention;
FIG. 9 is a graph showing the correct calibration results after simulation prompts are incorporated into step3 of example 1 of the present invention;
FIG. 10 shows the steps involved in the implementation of step4 of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and detailed description.
Example 1: in PSCAD/EMTDC, constructing a simulation model with a topology keeping the adjacent bus types of the two-end lines identical and the lengths of the fault lines consistent, wherein the simulation model is shown in figure 1. The traveling wave recording device synchronously collects data by adopting a 1MHz sampling rate, and records traveling wave data of more than 2ms before and after a fault. And acquiring fault recording data of the 220kV transmission line in a certain region from the wave recording device as actual measurement data. The specific implementation steps are as follows:
step1: and (3) establishing a simulation model according to the topology of the 220kV transmission line at a certain place, and carrying out batch simulation on the changed parameters to obtain an offline simulation sample library, wherein the specific implementation steps are shown in fig. 2.
Step1.1: and collecting data through a traveling wave recording device, and recording current traveling wave data of more than 2ms before and after the fault.
Step1.2: the simulation model which is the same as the bus type of the power transmission line, has the same line length and has the transition resistance divided into high resistance and low resistance is established, and the fault angle, the number of outgoing lines of the bus line and the fault type in the model can take typical values at will because the polarity and the abrupt change moment of the traveling wave are not changed.
Step1.3: the line is established with off-line simulation samples, the full length of the line is 93km, 2 groups of simulation samples are generated by traversing the step length of the transition resistance with the high resistance of 200 omega and the low resistance of 50 omega and the fault position for 2km in batches, and 46 simulation samples are generated in each group.
Step2: and calculating the mode distance between each sample and the fault measured data in the off-line simulation sample library of the same fault line, and comparing and selecting to obtain the nearest neighbor simulation sample, wherein the specific implementation steps are shown in fig. 3.
Step2.1: and calculating the slope of the measured data and the simulation data respectively, and segmenting the time sequence into m segments according to the positive and negative values and the amplitude values of the slope. Obtaining a piecewise linearized time series S s ，S a 。
Step2.2: starting point of first mutation moment (t) 1 ，c 1 ) The multi-section mode combination is formed by each section of corresponding mode (rapid rising or rapid falling) and each section of corresponding time according to time sequence.
Step2.3: after aligning the simulation waveform and the actual measurement waveform according to the head of the first wave, comparing the polarities and time differences corresponding to the two sections of the remaining most obvious sequences, and sequentially calculating the improved mode distances of the two time sequences of 46 simulation samples and the actual measurement data according to the formula (3), wherein D tl Is 1, the weight alpha in the formula (4) 2 ＝0.25，α 3 ＝0.25，β 2 ＝0.27，β 3 =0.23. The off-line simulation sample library pattern distances are shown in fig. 4.
Step2.4: the obtained 2 groups of improved pattern distances are ordered, from which it can be seen that sample No. 24 when the minimum improved pattern distance of the sample library is 50 omega of transition resistance, the polarity distance in the improved pattern distance is 0, and the time distance is 3.86 x 10 -5 The data to be tested and the nearest neighbor simulation sample are shown in fig. 5.
Step3: reading nearest neighbor sample wave head prompt interval R i And a corresponding ranging equation. Precalibrated fault current N traveling wave heads to be detected t= [ t ] 0 ,t 1 …t n ]，t u E t, t is determined by equation (5) u Whether or not to be in the sample presentation section R i In (c), and t u And R is R i Whether the polarities are consistent or not is determined, so that whether the calibration of the pre-calibrated actual measurement wave head is effective or not is determined, and the specific implementation steps are shown in fig. 6.
Step3.1: reading nearest neighbor sample wave head prompt interval R 3 As shown in table 3, and corresponding ranging equation (7). The nearest simulation sample interval boundary takes traveling wave transmission time corresponding to 10% of the total line length, namely 9.3km, as tolerance, namely 31 mu s. Nearest neighbor simulation sample hint interval calibration is shown in fig. 7.
TABLE 3 simulation prompt interval
Wherein v is the traveling wave velocity of fault current, the value is the light velocity, and Δt can be obtained from the prompting section of the simulation sample zi ＝t zi -t z0 ，Δt fi ＝t fi -t z0 ，t zi 、t fi The average value of the i-th interval with positive and negative polarities is shown.
Step3.2: the actual measurement data are calibrated through the existing wave head calibration method, 5 actual measurement calibration wave heads are obtained, the actual measurement calibration wave heads are shown in table 4, and the pre-calibration of the data to be measured is shown in fig. 8.
TABLE 4 polarity and time obtained by precalibration of the wave head of the data to be measured
Step3.3: the data to be tested are judged one by the formula (5). The polarities of the measured data and the simulation sample are kept consistent, and meanwhile, the measured data calibration wave head is effective in the simulation sample prompt interval. Obtaining a second measured wave head t f1 The wave head calibration of the measured data which is integrated into the simulation sample is shown in fig. 9, and the wave head calibration is not in the simulation prompt interval, and belongs to the error calibration of the measured data by the current calibration method.
Step4: and (3) ranging the effectively calibrated wave head by a ranging formula corresponding to the simulation sample prompting interval, and verifying the ranging effectiveness through a ranging sequence, wherein the specific implementation steps are shown in fig. 10.
Step4.1: for the actually measured wave head sequence t= [ t ] z0 ，t f2 ，t z3 ，t z4 ]There is t u ∈t(t u ≠t z0 ) T is calculated from formula (7) u And t z0 Can obtain the distance measurement sequence x i ＝[47.16km，48.13km，47.11km]。
Step4.2: the measured fault distance sequence x can be verified by calculating standard deviations of a plurality of fault distances i The standard deviation threshold η is empirically valued at 3. Ranging sequence x by (6) i Standard deviation was calculated to obtain σ=0.469.
Step4.3: and automatically checking the fault according to the wave head number and the standard deviation sigma. The number of the wave heads is 4, the sigma=0.469 < eta obtained by detection and verification is detected, the distance measurement is correct through automatic verification, and the distance measurement result is 47.16km.
While the present invention has been described in detail with reference to the drawings, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Claims (6)
1. A power transmission line fault single-ended traveling wave distance measurement method based on waveform dominant characteristic simulation inference is characterized in that:
step1: reading fault phase current traveling wave recording data and corresponding fault line offline simulation sample set A m Establishing a simulation model which is consistent with the topology type and the line length of a local line and has high resistance and low resistance as transition resistance, traversing the whole length of a transmission line by faults at intervals of 2km, and obtaining an offline sample library of the line faults;
step2: constructing an improved mode distance similarity measurement algorithm, comparing fault record data with simulation data in a simulation sample library, and obtaining a simulation sample most similar to actual measurement fault data;
step3: reading simulation sample prompting interval R i The method comprises the steps of obtaining a simulation prompt interval, and obtaining a corresponding ranging calculation formula, namely pre-calibrating actual measurement data through an existing wave head calibration technology to obtain an actual measurement wave head sequence t, judging whether the wave head of the data to be tested is in the simulation prompt interval or not, and judging whether the pre-calibrated actual measurement wave head is calibrated effectively or not according to the fact that the polarities of the wave head and the actual measurement wave head are consistent;
step4: and substituting the wave head pre-calibrated in the simulation prompt interval into a corresponding ranging formula for calculation to obtain a ranging sequence consisting of fault distances, and judging whether the ranging result is effective or not according to the number of the fault distances in the sequence and the sequence standard deviation.
2. The transmission line fault single-ended traveling wave ranging method based on waveform dominant characteristic simulation inference as claimed in claim 1, wherein Step1 specifically comprises:
step1.1: collecting data by a traveling wave recording device, and recording current traveling wave data of more than 2ms before and after a fault;
step1.2: setting up a simulation model in PSCAD/EMTDC by using topology which keeps the adjacent bus types of the two-end lines to be the same, the lengths of fault lines to be consistent and the transition resistance to be high resistance and low resistance, wherein the fault angle, the number of outgoing lines of the bus lines and the fault type can not change the polarity and the abrupt change moment of travelling waves, and can be set to be a fixed value;
step1.3: the simulation model traverses the full length of a fault line by faults every 2km, and performs batch simulation to obtain two groups of off-line simulation sample libraries with different transition resistances of high resistance and low resistance, which are numbered according to the sequence of the distance between faults.
3. The power transmission line fault single-ended traveling wave analysis and ranging method based on waveform dominant characteristic simulation inference as claimed in claim 1, wherein Step2 specifically comprises:
step2.1: respectively calculating the measured data and the simulation data, calculating the slope of the measured data and the simulation data, and segmenting the time sequence into m segments according to the positive and negative values and the amplitude values of the slope of the measured data and the simulation data to obtain a segmented linearized time sequence S s ，S a ；
Step2.2: aligning the first mutation time and the polarity of the two groups of traveling wave data as starting points t1 and c1, and sequentially forming a multi-section mode combination according to the corresponding mode of each section and the corresponding time of each section;
step2.3: after the first wave head is aligned at the moment, comparing the polarities and time differences corresponding to the two sections of the residual most obvious sequences, and calculating the improved mode distance between the simulation sample and the two time sequences of the measured data;
step2.4: sorting the improved pattern distances, comparing and selecting the minimum pattern distance, searching the line simulation sample library by utilizing the improved pattern distances to obtain nearest neighbor simulation samples, and calculating the improved pattern distance D between each simulation sample and fault wave recording data m Comparing and selecting to obtain the minimum D m ，D m Less than the mode distance minimum threshold D tl And determining that the mth sample is the nearest neighbor simulation sample.
4. The power transmission line fault single-ended traveling wave analysis and ranging method based on waveform dominant characteristic simulation inference as claimed in claim 2, wherein the step2.2 specifically comprises, in a mode corresponding to each segment: a sharp rise or a sharp fall.
5. The power transmission line fault single-ended traveling wave analysis and ranging method based on waveform dominant characteristic simulation inference as claimed in claim 1, wherein Step3 specifically comprises:
step3.1: prompt interval R for reading nearest continuous simulation sample i And a corresponding ranging equation;
step3.2: pre-calibrating fault current t to be tested by current existing wave head calibration algorithm u And polarity p u Obtaining N traveling wave head sequences t= [ t ] 0 ,t 1 …t N ]，t u ∈t；
Step3.3: judgment of t u Whether or not in the hint region R provided by the nearest neighbor simulation sample i And whether the polarities of the two are consistent, so as to judge whether the pre-calibrated actual measurement wave head is calibrated effectively.
6. The power transmission line fault single-ended traveling wave analysis and ranging method based on waveform dominant characteristic simulation inference as claimed in claim 1, wherein Step4 specifically comprises:
step4.1: fault calibration wave head t for merging simulation sample prompt u Substituting the single-end ranging formula to perform ranging to obtain x i ；
Step4.2: calculating standard deviations of a plurality of fault distances;
step4.3: and automatically checking the fault data according to the number of wave heads and the standard deviation sigma, and outputting a ranging result obtained by calculating the arrival time difference of the first two wave heads.
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