CN118091329B - Travelling wave trigger threshold dynamic adjustment method based on background interference level - Google Patents

Travelling wave trigger threshold dynamic adjustment method based on background interference level Download PDF

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CN118091329B
CN118091329B CN202410495618.3A CN202410495618A CN118091329B CN 118091329 B CN118091329 B CN 118091329B CN 202410495618 A CN202410495618 A CN 202410495618A CN 118091329 B CN118091329 B CN 118091329B
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钱之银
张锐
刘富利
周幸福
卓睿
徐华琛
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SHANGHAI HAINENG INFORMATION TECHNOLOGY CO LTD
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Abstract

The invention relates to the technical field of traveling wave monitoring, in particular to a traveling wave trigger threshold dynamic adjustment method based on background interference level. Step S1, collecting n high-frequency characteristic signals, and recording the maximum value of each high-frequency characteristic signal to form a background interference level data set; step S2, fitting the background interference level data set by adopting a probability distribution model to obtain an accumulated probability distribution function of the background interference level data set; step S3, calculating a background interference level value which appears under the condition of setting probability; and S4, calculating a trigger threshold of the traveling wave monitoring terminal. According to the invention, the triggering threshold value of the monitoring terminal is dynamically adjusted based on the statistical characteristics of the background interference level of the power transmission line, so that the problems of frequent false triggering caused by overlarge background interference and missing of fault traveling wave caused by overhigh triggering threshold value are solved, and the reliability of the traveling wave current monitoring terminal and the operation and maintenance level of the power transmission line are improved.

Description

Travelling wave trigger threshold dynamic adjustment method based on background interference level
Technical Field
The invention relates to the technical field of traveling wave monitoring, in particular to a traveling wave trigger threshold dynamic adjustment method based on background interference level.
Background
The high-voltage transmission line has long transmission distance, is positioned in mountainous areas, hills, farmlands, gobi and other areas, has complex running environment and is extremely easy to fail, thereby causing line tripping and power failure. Under the traditional operation and maintenance mode, the fault point is generally searched by an electric power operation and maintenance personnel through line inspection, the inquiry time is long, and the positioning is difficult. If the faults can not be found and cleared in time, multiple faults are easy to cause, large-area power failure is caused, and the safe operation of the power grid is seriously endangered and the social stability is influenced. The rapid and accurate location of fault points has been a focus of attention in the power sector.
When the transmission line fails, the fault point generates a high-frequency traveling wave current signal, the high-frequency traveling wave current signal propagates to two ends of the line at a speed close to the speed of light, and the transmission line traveling wave current monitoring terminal is hung on the transmission line and can record the fault current traveling wave and locate the accurate position of the fault point through the time difference that the traveling wave reaches the monitoring terminals at two sides. The fault traveling wave positioning technology of the power transmission line is widely used in the field of power systems at home and abroad, plays a good positioning effect, greatly improves the operation reliability of the power transmission line, shortens the power failure recovery time and has wide application prospect.
The accurate positioning of the fault positioning of the transmission line depends on the reliability of the traveling wave current monitoring terminal, namely the traveling wave current monitoring terminal can reliably record fault current traveling waves, and the traveling wave current monitoring terminals on the market at present all adopt a threshold triggering mode, namely the traveling wave is recorded when a high-frequency current signal exceeding a preset threshold is detected, and is uploaded to a central master station in a wireless communication mode. However, because the ground environment of the power transmission line is complex and severe, the power transmission line is seriously affected by external electromagnetic interference, and the power transmission line also has corona interference, the background interference of the power transmission line is extremely complex, and the background interference levels also have great differences in different places and different times in the same place. The traveling wave trigger threshold of the monitoring terminal is usually a default fixed value set at the time of factory shipment. When the background interference level of the power transmission line is higher, if the triggering threshold is set to be too low, the monitoring terminal frequently triggers and transmits traveling wave data by mistake, the storage space of the terminal is consumed, the service life of a storage device is shortened, the communication flow is consumed, and the record of the real fault traveling wave is influenced; when the background interference level is low, if the amplitude of the real fault traveling wave current is small, such as in the case of a high-resistance ground fault, the trigger threshold value is set to be too high, so that the fault traveling wave current is missed, and the fault point cannot be located.
Disclosure of Invention
The invention aims to provide a traveling wave trigger threshold dynamic adjustment method based on background interference level, which solves the technical problems; the technical problems solved by the invention can be realized by adopting the following technical scheme: the method for dynamically adjusting the traveling wave trigger threshold based on the background interference level comprises the following steps that S1, a traveling wave monitoring terminal collects n high-frequency characteristic signals according to preset sampling duration delta T1 and sampling interval duration delta T2, the maximum E i of each high-frequency characteristic signal is recorded, a background interference level dataset S E is formed, n is a positive integer, and i is a positive integer from 1 to n; step S2, fitting the background interference level data set S E by adopting a set probability distribution model to obtain a cumulative probability distribution function f S (E) of the background interference level data set S E, and taking the cumulative probability distribution function f S (E) as a statistical probability model of the background interference level; step S3, calculating a background interference level value H (P=1-a%) which appears under the condition of setting the probability a% by adopting the statistical probability model of the background interference level, wherein the subscript P represents the probability; and S4, determining a trigger threshold tg of the traveling wave current monitoring terminal according to the background interference level value H (P=1-a%).
Preferably, in step S1, the sampling duration Δt1 is 0.1S to 1.0S, the sampling interval duration Δt2 is 1S to 60S, the number n of the high-frequency characteristic signals acquired is 1000 to 10000, and the background interference level data set S E={E1,E2 , E3 …En }.
Preferably, in step S2, the probability distribution model is one of a normal distribution, a poisson distribution, an exponential distribution, a gamma distribution, and a weibull distribution.
Preferably, in step S2, the cumulative probability distribution function f S (E) of the background interference level data set S E is expressed as,
Wherein,As a mean of the background interference level dataset S E,Is the standard deviation of the background interference level dataset S E.
Preferably, the mean value of the background interference level dataset S E in step S2The calculation formula of (a) is as follows,
Standard deviation of the background interference level data set SEThe calculation formula of (a) is as follows,
Preferably, the value range of a in the set probability a% is 0.01-1.00.
Preferably, in step S4, the calculation formula of the trigger threshold tg is that,
Wherein,Is a safety factor.
Preferably, in step S4, a trigger threshold upper boundary tg H and a trigger threshold lower boundary tg L are further provided, a maximum value of the trigger threshold tg is the trigger threshold upper boundary tg H, and a minimum value of the trigger threshold tg is the trigger threshold lower boundary tg L.
Preferably, in step S1, the duration of the statistical period of the traveling wave monitoring terminal is n×Δt2, and the traveling wave trigger threshold of the traveling wave monitoring terminal is dynamically adjusted in step S4 according to the background interference level data set S E of the previous statistical period at intervals of n×Δt2.
Preferably, the traveling wave monitoring terminal comprises a traveling wave current monitoring terminal and a traveling wave voltage monitoring terminal, and the high-frequency characteristic signal is a high-frequency current signal or a high-frequency voltage signal obtained by sampling the traveling wave monitoring terminal.
The invention has the beneficial effects that: by adopting the technical scheme, the invention changes the technical thought that the triggering threshold value of the traveling wave current monitoring terminal is set to be a fixed value, extracts the statistical characteristics of the background interference level based on real-time monitoring analysis of the background interference level of the power transmission line, and dynamically adjusts the triggering threshold value of the monitoring terminal according to the statistical characteristics, so as to solve the problems of frequent false triggering caused by overlarge background interference and missed acquisition of fault traveling waves caused by overhigh triggering threshold value, and improve the reliability of the traveling wave current monitoring terminal and the operation and maintenance level of the power transmission line.
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Fig. 1 is a schematic diagram of steps of a method for dynamically adjusting a traveling wave trigger threshold based on a background interference level in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
The invention is further described below with reference to the drawings and specific examples, which are not intended to be limiting.
The method for dynamically adjusting the traveling wave trigger threshold based on the background interference level is shown in fig. 1, and comprises the following steps that step S1, a traveling wave monitoring terminal collects n high-frequency characteristic signals according to preset sampling duration delta T1 and sampling interval duration delta T2, and records the maximum value E i of each high-frequency characteristic signal to form a background interference level dataset S E, wherein n is a positive integer, and i is a positive integer from 1 to n; step S2, fitting the background interference level data set S E by adopting a set probability distribution model to obtain a cumulative probability distribution function f S (E) of the background interference level data set S E, and taking the cumulative probability distribution function f S (E) as a statistical probability model of the background interference level; step S3, calculating a background interference level value H (P=1-a%) which appears under the condition of setting a% of probability by adopting a statistical probability model of the background interference level, wherein a subscript P represents the probability; and S4, determining a trigger threshold tg of the traveling wave current monitoring terminal according to the background interference level value H (P=1-a%).
Specifically, the invention changes the technical thought that the triggering threshold value of the traveling wave current monitoring terminal is set to be a fixed value, extracts the statistical characteristics of the background interference level based on real-time monitoring analysis of the background interference level of the power transmission line, and dynamically adjusts the triggering threshold value of the monitoring terminal according to the statistical characteristics, so as to solve the problems of frequent false triggering caused by overlarge background interference and missed acquisition of fault traveling waves caused by overhigh triggering threshold value, and improve the reliability of the traveling wave current monitoring terminal and the operation and maintenance level of the power transmission line.
The invention establishes a probability statistical model of background interference by monitoring the background interference level of the power transmission line in real time, and dynamically adjusts the triggering threshold value of the traveling wave current monitoring terminal based on the probability statistical model to achieve the aim of the invention.
The traveling wave monitoring terminal takes a traveling wave current monitoring terminal as an example, and the method comprises the following steps: the method comprises the steps that a high-frequency current signal on a high-speed sampling line is sampled in real time by a traveling wave current monitoring terminal of the power transmission line, the sampling duration of each section is delta T1, and the maximum value E of the high-frequency current signal in the time range is calculated; the sampling interval duration between each signal is delta T2; when n high-frequency current signals are collected, the maximum value of each high-frequency current signal is marked as E i, and a data set consisting of E 1、E2、E3、…、En is marked as S E,SE, namely a background interference level data set.
Further, the cumulative probability distribution function f S (E) of S E, that is, the probability that the background interference level is smaller than E is f S (E), that is, the statistical probability model of the background interference level, is obtained by fitting S E with a given probability distribution model. Using this model, the background interference level value that may occur given a very low probability of a% is further calculated as H (P=1-a%), where the subscript P represents the probability.
Then, a proper trigger threshold tg is determined according to H (P=1-a%), and the specific method is as follows:
where k is the safety factor.
In order to prevent the trigger threshold from being too large or too small, the invention provides that the trigger threshold be dynamically adjusted to an upper boundary tg H and a lower boundary tg L, i.e., the trigger threshold tg is preferably set to be maximum tg H and minimum tg L.
In a preferred embodiment, in step S1, the sampling duration Δt1 is 0.1S to 1.0S, the sampling interval duration Δt2 is 1S to 60S, the number n of the high-frequency characteristic signals is 1000 to 10000, and the background interference level data set S E={E1 ,E2 , E3 …En }.
Specifically, the sampling duration Δt1 may be 0.1S, the sampling interval duration Δt2 may be 1S, and the number n of the high-frequency current acquisitions may be 3600, at which time the background interference level data set S E={E1,E2,E3,…,E3600 }.
In a preferred embodiment, in step S2, the probability distribution model is one of a normal distribution, a poisson distribution, an exponential distribution, a gamma distribution, and a weibull distribution.
Specifically, the "set probability distribution model" of the present invention is not limited to a specific probability distribution, and common ones such as normal distribution, poisson distribution, exponential distribution, gamma distribution, weibull distribution, etc. may be selected.
In a preferred embodiment, in step S2, the cumulative probability distribution function f S (E) of the background interference level dataset S E is expressed as,
Wherein,Is the mean of the background interference level dataset S E,Is the standard deviation of the background interference level dataset S E.
In a preferred embodiment, the mean value of the background interference level dataset S E in step S2The calculation formula of (a) is as follows,
Standard deviation of background interference level dataset SEThe calculation formula of (a) is as follows,
In a preferred embodiment, the value range of a in the set probability a% is 0.01-1.00.
Specifically, the probability a% is set as a given extremely low probability, the value range of a is 0.01-1.00, more specifically, when normal distribution is taken, according to the probability and statistical basic theory, based on the fitting rule of normal distribution, the expression of the cumulative probability distribution function f S (E) of the background interference level dataset S E is,
Wherein,Is the mean of the background interference level dataset S E,Is the standard deviation of the background interference level dataset S E.
Mean of background interference level dataset S E The calculation formula of (a) is as follows,
Standard deviation of background interference level dataset SEThe calculation formula of (a) is as follows,
When x is a specific value, the probability fs (x) when the background interference level is smaller than x can be calculated by the above formula. Specifically, given that the probability a is 0.1, the probability that the background interference level value is H (P=1-0.1%), i.e., H (P=99.9%), i.e., the background interference signal value on the power line is less than H (P=99.9%) is 99.9%.
In a preferred embodiment, in step S4, the trigger threshold tg is calculated as,
Wherein,Is a safety factor.
In particular, the safety factorThe value range of (1) is 1.0-3.0, and the typical preferable safety factor k=1.3, i.e. the trigger threshold is dynamically adjusted to tg=1.3×h (P=99.9%).
In a preferred embodiment, in step S4, an upper trigger threshold boundary tg H and a lower trigger threshold boundary tg L are further provided, the maximum value of the trigger threshold tg is the upper trigger threshold boundary tg H, and the minimum value of the trigger threshold tg is the lower trigger threshold boundary tg L.
Specifically, taking a high-frequency characteristic signal as an example of a high-frequency current signal, the trigger threshold upper boundary tg H is 30A-100A, and the trigger threshold lower boundary tg L is 1A-10A; typically, the trigger threshold upper boundary tg H may be taken as 100A and the trigger threshold lower boundary tg L may be taken as 10A.
In a preferred embodiment, in step S1, the duration of the statistical period of the traveling wave monitoring terminal is n×Δt2, and the traveling wave trigger threshold of the traveling wave monitoring terminal is dynamically adjusted in step S4 according to the background interference level data set S E of the previous statistical period at intervals of n×Δt2.
Specifically, according to the above embodiment, the sampling interval duration Δt2 may be 1s, the number n of the high-frequency characteristic signals may be 3600s, where n is Δt2=3600 s, that is, the trigger threshold tg of the traveling wave monitoring terminal will be dynamically adjusted once according to the statistical feature of the background interference level of the previous hour, so as to dynamically adjust the trigger threshold of the traveling wave monitoring terminal according to the background interference.
In a preferred embodiment, the traveling wave monitoring terminal comprises a traveling wave current monitoring terminal and a traveling wave voltage monitoring terminal, and the high-frequency characteristic signal is a high-frequency current signal or a high-frequency voltage signal obtained by sampling the traveling wave monitoring terminal.
Specifically, the invention can execute the traveling wave trigger threshold dynamic adjustment method based on the background interference level aiming at the traveling wave current monitoring terminal and the traveling wave voltage monitoring terminal, and the working principle and the trigger mode are completely consistent, so the invention is applicable to the trigger threshold dynamic adjustment of the traveling wave current monitoring terminal and the trigger threshold dynamic adjustment of the traveling wave voltage monitoring terminal.
In summary, the method for dynamically adjusting the traveling wave trigger threshold based on the background interference level changes the traditional mode of setting the trigger threshold of the traveling wave current monitoring terminal to be a fixed value, and dynamically adjusts the trigger threshold of the traveling wave monitoring terminal according to the field background interference level of the power transmission line and the statistical characteristics thereof. The invention can effectively avoid frequent false triggering caused by overlarge line background interference and the problem of missing traveling wave acquisition caused by overhigh setting of the triggering threshold of the traveling wave monitoring terminal, greatly improves the operation reliability of the traveling wave current monitoring terminal, ensures the correct triggering and acquisition of the fault traveling wave of the power transmission line, and improves the operation and maintenance level and the power supply reliability of the power transmission line.
The foregoing description is only illustrative of the preferred embodiments of the present invention and is not to be construed as limiting the scope of the invention, and it will be appreciated by those skilled in the art that equivalent substitutions and obvious variations may be made using the description and illustrations of the present invention, and are intended to be included within the scope of the present invention.

Claims (10)

1. The method for dynamically adjusting the traveling wave trigger threshold based on the background interference level is characterized by comprising the following steps of,
Step S1, a traveling wave monitoring terminal collects n high-frequency characteristic signals according to a preset sampling duration delta T1 and a sampling interval duration delta T2, records the maximum value E i of each high-frequency characteristic signal, and forms a background interference level data set S E, wherein n is a positive integer, and i is a positive integer from 1 to n;
Step S2, fitting the background interference level data set S E by adopting a set probability distribution model to obtain a cumulative probability distribution function f S (E) of the background interference level data set S E, and taking the cumulative probability distribution function f S (E) as a statistical probability model of the background interference level;
step S3, calculating a background interference level value H (P=1-a%) which appears under the condition of setting the probability a% by adopting the statistical probability model of the background interference level, wherein the subscript P represents the probability;
step S4, determining a trigger threshold tg of the traveling wave monitoring terminal according to the background interference level value H (P=1-a%);
the traveling wave monitoring terminal comprises a traveling wave current monitoring terminal, and the high-frequency characteristic signal is a high-frequency current signal.
2. The method for dynamically adjusting the traveling wave trigger threshold based on the background interference level according to claim 1, wherein in the step S1, the sampling duration Δt1 is 0.1S-1.0S, the sampling interval duration Δt2 is 1S-60S, the number n of the high-frequency characteristic signals is 1000-10000, and the background interference level data set S E={E1 ,E2 , E3 …En }.
3. The method for dynamically adjusting a traveling wave trigger threshold based on a background interference level according to claim 1, wherein in step S2, the probability distribution model is one of a normal distribution, a poisson distribution, an exponential distribution, a gamma distribution, and a weibull distribution.
4. The method of claim 1, wherein in step S2, the cumulative probability distribution function f S (E) of the background interference level data set S E is expressed as,
Wherein,As a mean of the background interference level dataset S E,Is the standard deviation of the background interference level dataset S E.
5. The method for dynamic adjustment of traveling wave trigger threshold based on background interference level as claimed in claim 4, wherein the average value of the background interference level data set S E in step S2The calculation formula of (a) is as follows,
Standard deviation of the background interference level data set SEThe calculation formula of (a) is as follows,
6. The method for dynamically adjusting the traveling wave trigger threshold based on the background interference level according to claim 4, wherein the value range of a in the set probability a% is 0.01-1.00.
7. The method for dynamically adjusting traveling wave trigger threshold based on background interference level according to claim 1, wherein in step S4, the calculation formula of the trigger threshold tg is,
Wherein,Is a safety factor.
8. The method according to claim 1, wherein in step S4, there are further provided an upper trigger threshold tg H and a lower trigger threshold tg L, the maximum value of the trigger threshold tg is the upper trigger threshold tg H, and the minimum value of the trigger threshold tg is the lower trigger threshold tg L.
9. The method for dynamically adjusting a traveling wave trigger threshold based on a background interference level according to claim 1, wherein in step S1, the duration of the statistical period of the traveling wave monitoring terminal is n×Δt2, and the traveling wave trigger threshold of the traveling wave monitoring terminal is dynamically adjusted in step S4 according to the background interference level data set S E of the previous statistical period at intervals of n×Δt2.
10. The method for dynamically adjusting a traveling wave trigger threshold based on a background interference level according to claim 1, wherein the traveling wave monitoring terminal further comprises a traveling wave voltage monitoring terminal, and the high-frequency characteristic signal may be a high-frequency voltage signal.
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116243108A (en) * 2023-03-16 2023-06-09 国网江苏省电力有限公司南通市通州区供电分公司 Distribution network fault point positioning method, system, equipment and medium
CN117273552A (en) * 2023-11-22 2023-12-22 山东顺国电子科技有限公司 Big data intelligent treatment decision-making method and system based on machine learning

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN204631189U (en) * 2015-04-18 2015-09-09 山东安亚电子信息有限公司 Realize the device of distribution line failure travelling wave ranging high reliability
US11016133B2 (en) * 2018-12-12 2021-05-25 Hamilton Sunstrand Corporation Arc fault detection with sense wire monitoring
CN212540592U (en) * 2020-06-28 2021-02-12 国网河南省电力公司平顶山供电公司 Remote control device for fault recording equipment
CN113030636B (en) * 2021-02-26 2022-04-08 国网河南省电力公司电力科学研究院 Active intervention type arc suppression device test system

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116243108A (en) * 2023-03-16 2023-06-09 国网江苏省电力有限公司南通市通州区供电分公司 Distribution network fault point positioning method, system, equipment and medium
CN117273552A (en) * 2023-11-22 2023-12-22 山东顺国电子科技有限公司 Big data intelligent treatment decision-making method and system based on machine learning

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