CN107894552A - A kind of fault traveling wave detection method - Google Patents
A kind of fault traveling wave detection method Download PDFInfo
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- CN107894552A CN107894552A CN201711044077.9A CN201711044077A CN107894552A CN 107894552 A CN107894552 A CN 107894552A CN 201711044077 A CN201711044077 A CN 201711044077A CN 107894552 A CN107894552 A CN 107894552A
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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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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/52—Outage or fault management, e.g. fault detection or location
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Abstract
A kind of fault traveling wave detection method, its step are：S1：Travelling wave signal detection means is installed in each transformer station；S2：During line failure, the signal of detection is transmitted back to system main website and carries out signal processing analysis；S3：Travelling wave signal monostable accidental resonance model is built firstAdjust parameter a, b and sampling step length h in travelling wave signal model so that travelling wave signal reaches the state that cooperates with noise signal three, and noise transmits energy maximization to travelling wave signal, is effectively separated useful signal in noise signal is submerged in；S4：Travelling wave signal is resolved into some intrinsic rotational components and trend component using intrinsic time Scale Decomposition, calculate the instantaneous frequency of each component, the mutation of instantaneous frequency is presented as the mutation of travelling wave signal, and the mutation moment of instantaneous frequency is exactly the due in of travelling wave signal, extracts travelling wave signal.The present invention has the advantages that principle is simple, accuracy of detection is high, easily realized.
Description
Technical Field
The invention mainly relates to the technical field of electric power detection, in particular to a fault traveling wave detection method which is suitable for detecting fault traveling wave signals of a power transmission line.
Background
With the continuous expansion of the scale of the power system, the improvement of the voltage grade and the higher and higher requirements of users on the safety of the power grid, the accurate fault location becomes an important guarantee for rapidly removing faults and improving the transient stability of the system. The accuracy of the detection of the traveling wave signal directly affects the accuracy of fault traveling wave positioning and the reliability of traveling wave protection, and the accurate detection technology of the traveling wave signal becomes the key of traveling wave positioning and protection technology development.
Experts at home and abroad deeply research the traveling wave detection technology and obtain a large amount of research results. The method for extracting the traveling wave signal by utilizing the wavelet transform can effectively extract the traveling wave signal in noise to a certain degree, but the wavelet transform needs to artificially select the type and the decomposition layer number of a wavelet basis and has no self-adaptability, and different results can be obtained by different wavelet bases and decomposition scales, so that the condition of inconsistent detection results is easy to occur when the traveling wave signal is synchronously detected at multiple points, and the fault positioning precision and the protection reliability are influenced; the fault traveling wave signal detection method based on Hilbert-Huang Transform (HHT) has self-adaptability, can be used for detecting the fault traveling wave signal, and has the defects of mode aliasing and end effect; in addition, the traveling wave signal detection method based on Intrinsic Time-Scale Decomposition (ITD) is a self-adaptive signal Decomposition method, can avoid the phenomena of over-enveloping and under-enveloping existing in the traveling wave head signal Decomposition, is less influenced by an endpoint effect, and has a better wave head detection effect.
At present, a plurality of methods for detecting traveling wave signals by using software have good effect under the condition of no noise or small noise interference; however, due to the complex electromagnetic interference in the transformer substation, the traveling wave signal is easily submerged by various noise signals, and the difficulty in accurately detecting the traveling wave signal is increased.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the technical problems in the prior art, the invention provides the fault traveling wave detection method which is simple in principle, high in detection precision and easy to realize.
In order to solve the technical problems, the invention adopts the following technical scheme:
a fault traveling wave detection method comprises the following steps:
s1: installing a traveling wave signal detection device in each transformer substation;
s2: when the line has a fault, transmitting the detected signal back to the system main station for signal processing and analysis;
s3: firstly, a traveling wave signal monostable stochastic resonance model is constructedParameters a and b and a sampling step length h in the traveling wave signal model are adjusted, so that the traveling wave signal and the noise signal are in a cooperative state, the energy transferred from the noise to the traveling wave signal is maximized, and useful signals submerged in the noise signal are effectively separated;
s4: the traveling wave signal is decomposed into a plurality of inherent rotation components and trend components by inherent time scale decomposition, the instantaneous frequency of each component is calculated, the sudden change of the instantaneous frequency is reflected as the sudden change of the traveling wave signal, the moment of the sudden change of the instantaneous frequency is the arrival moment of the traveling wave signal, and the traveling wave signal is extracted.
As a further improvement of the invention: in the step S3, the feature information of the traveling wave signal is accurately detected by using an ITD method.
As a further aspect of the inventionThe improvement is that: in the step S3, the traveling wave signal is extracted by adopting a variable step length stochastic resonance method, and h is made by changing the sampling step length h of the traveling wave signal>1/f s Wherein f is s The empirical value range of h is 0.1-1 for signal sampling frequency, and the extraction of the traveling wave signal is enabled to reach the optimal state by changing the parameters a, b and h in the traveling wave signal monostable model.
As a further improvement of the invention: in the step S4, X is assumed t The method comprises the steps of defining L as a base line extraction operator as a traveling wave signal, defining L as a time, acting the traveling wave signal on the L, defining the rest signal as an inherent rotation component, and if an inherent rotation extraction factor is represented by H, H =1-L; thereby to X t Further decomposing into: x t ＝LX t +(1-L)X t ＝L t +H t Let { τ be k K =1,2, \8230; } is X t Defining a local extreme point of 0 =0; when X is present t At a constant value in a certain time interval, its extreme value tau k Selecting as the right end point of the time interval; to simplify notation, let X k And L k Respectively represent X (tau) k ) And L (τ) k )。
As a further improvement of the invention: suppose L t And H t Has a domain of [0, τ ] k ]，X k Has a domain of [0, τ ] k+2 ](ii) a At successive extreme points (τ) k ，τ k+1 ]Within the scope, a baseline extraction operator L is defined:
in the formula L k+1 The calculation process of (c) is as follows:
in the formula, alpha is more than 0 and less than 1, and generally the alpha is 0.5.
As a further improvement of the invention: after defining the baseline signalAnd obtaining an inherent rotation extraction operator H: HX t ＝(1-L)X t ＝H t ＝X t -L t And once decomposition, continuously decomposing the obtained baseline signal as an input signal again until a high-precision traveling wave signal is obtained.
Compared with the prior art, the invention has the advantages that:
the fault traveling wave detection method is simple in principle, high in detection precision and easy to realize, is a novel traveling wave signal detection method based on SR-ITD, comprehensively utilizes the capability of detecting the traveling wave signal under strong noise by a stochastic resonance method and extracts traveling wave mutation point characteristics with inherent time scale decomposition, effectively improves the signal-to-noise ratio of the traveling wave signal, can accurately detect the traveling wave signal, and solves the problem of low detection precision of the conventional traveling wave detection method under strong noise interference of a transformer substation. Through further verification, compared with the existing method, the method has obvious advantages, accurate fault positioning of the traveling wave signal is realized, the application of the traveling wave positioning technology is effectively promoted, and the method has important application value.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
Fig. 2 is a schematic diagram of the detection of a fault traveling wave signal in the present invention.
FIG. 3 is a waveform diagram of a traveling wave signal after being processed and transformed according to the present invention; the method comprises the following steps of (a) superposing a white noise on an A end to obtain a current traveling wave signal, (b) converting the A end into a traveling wave signal waveform diagram through an SR, and (c) converting the A end into an SR-ITD waveform diagram.
FIG. 4 is a comparison of other traveling wave detection methods of the present invention; wherein, (a) extracts the travelling wave form map for wavelet transform under strong noise, and (b) extracts the travelling wave form map for HHT transform under strong noise.
Detailed Description
The invention will be described in further detail below with reference to the drawings and specific examples.
According to the fault traveling wave detection method, when a power transmission line has a fault, firstly, a traveling wave signal with strong noise interference on site is processed by using a stochastic resonance method, and the purpose of improving the signal-to-noise ratio of the traveling wave signal is achieved by adjusting model parameters and step length; then decomposing the traveling wave signal into a plurality of inherent rotation components and trend components by utilizing the inherent time scale decomposition better traveling wave signal detection capability, and calculating the instantaneous frequency of each component; and finally, obtaining the arrival time of the traveling wave signal according to the mutation time of the instantaneous frequency, thereby realizing the accurate extraction of the traveling wave signal and applying the traveling wave signal to the positioning protection of the power transmission line. The invention comprehensively utilizes the stronger signal extraction effect of stochastic resonance and the better traveling wave detection capability of inherent time scale decomposition, and effectively combines the stronger signal extraction effect and the better traveling wave detection capability, thereby realizing the accurate and reliable detection of fault characteristic information contained in the traveling wave signal under the strong noise interference of the transformer substation, and better solving the problem of accurate extraction of the fault traveling wave signal of the transformer substation under the strong noise interference.
As shown in fig. 1, the method for detecting a traveling fault wave according to the present invention includes the following steps:
s1: installing corresponding traveling wave signal detection devices in each transformer substation;
s2: when the line has a fault, transmitting the detected signal back to the system master station for signal processing and analysis;
s3: firstly, a traveling wave signal monostable stochastic resonance model is constructedParameters a and b and a sampling step length h in the traveling wave signal model are adjusted, so that the traveling wave signal and the noise signal achieve a collaborative state, the energy transferred by the noise to the traveling wave signal is maximized, useful signals submerged in the noise signal are effectively separated, and the purpose of improving the signal-to-noise ratio of the traveling wave signal is achieved;
s4: the traveling wave signal is decomposed into a plurality of inherent rotation components and trend components by inherent time scale decomposition, the instantaneous frequency of each component is calculated, the sudden change of the instantaneous frequency is reflected as the sudden change of the traveling wave signal, and the moment of the sudden change of the instantaneous frequency is the arrival moment of the traveling wave signal, so that the traveling wave signal can be accurately extracted.
In the process, the invention constructs a traveling wave signal monostable stochastic resonance model and carries out variable step length stochastic resonance processing on the parameters a, b and h in the traveling wave signal. When the stochastic resonance setting parameters, the traveling wave signals and the noise signals reach a cooperative state, the transmission energy of the noise to the traveling wave signals is maximized, and useful signals submerged in the noise signals are effectively separated out, so that weak traveling wave signals in the noise can be effectively detected, and the signal-to-noise ratio of the traveling wave signals is effectively improved. However, the traveling wave information processed by the stochastic resonance method has limited identification precision, and the ITD method has better traveling wave signal detection effect, so the invention selects the ITD method to accurately detect the characteristic information of the traveling wave signal.
In the process, the invention adopts a step length-variable stochastic resonance method to extract the traveling wave signal in step S3, and the sampling step length h of the traveling wave signal is changed to enable h to be h>1/f s (f s Signal sampling frequency), the empirical value range of h is 0.1-1, and the traveling wave signal is extracted to reach the optimal state by changing the parameters a, b and h in the traveling wave signal monostable model.
In the above process, in step S4, X is assumed t (t is time) is a traveling wave signal, L is defined as a base line extraction operator, L is applied to the traveling wave signal, and the remaining signal is defined as an intrinsic rotation component, and if an intrinsic rotation extraction factor is represented by H, H =1-L. Thereby to X t Further decomposing into: x t ＝LX t +(1-L)X t ＝L t +H t Let { tau } be k K =1,2, \8230; } is X t Defining a local extreme point of (c), defining 0 =0. When X is t At a constant value in a certain time interval, its extreme value tau k Is selected as the right end of the time interval. To simplify notation, let X k And L k Respectively represent X (tau) k ) And L (τ) k )。
Suppose L t And H t Has a domain of [0, τ ] k ]，X k Has a domain of [0, τ ] k+2 ]. At successive extreme points of (τ k ，τ k+1 ]Within the scope, a baseline extraction operator L can be defined:
in the formula L k+1 The calculation process of (2) is as follows:
in the formula, alpha is more than 0 and less than 1, and generally the alpha is 0.5.
Having defined the baseline signal, we get the intrinsic rotation extraction operator H: HX t ＝(1-L)X t ＝H t ＝X t -L t And once decomposition, continuously decomposing the obtained baseline signal as an input signal again until a high-precision traveling wave signal is obtained.
As shown in fig. 2, a schematic diagram of detecting a traveling wave signal when the power transmission line is simulated to have a fault in a specific application example of the present invention is shown, and the analysis is performed by taking a case that a fault initial phase angle is small when a single-phase ground short circuit occurs in the power transmission line as an example, the total length of the power transmission line is set to 100km, a single-phase ground fault is set to occur at a position 45km away from an a terminal of a transformer substation, the fault initial phase angle is 10, signal acquisition devices are located at two terminals of the transformer substations, and the transformer substation traveling wave acquisition devices respectively acquire current traveling wave signals from fault points. The sampling frequency of the traveling wave device is set to be 2MHz, the sampling step length is 5 multiplied by 10 < -7 > s, and the total simulation time is 1ms. White noise signals are superposed in the simulation model to enable the traveling wave signals to be completely submerged, and the wave speed can be calculated to be 2.96 multiplied by 108m/s by using the given parameters of the line; and then, by utilizing a traveling wave double-end measurement principle, SR-ITD analysis is carried out on traveling wave signals collected at the two ends A and B, the arrival time of the traveling wave signals is accurately detected, and accurate fault positioning is realized.
As shown in fig. 3, (a) is a current traveling wave signal obtained by superimposing a white noise signal on the a terminal. As can be seen from the figure, the traveling wave signal is submerged in the white noise signal, and the traveling wave signal cannot be effectively identified. Wherein (b) is the superposition of white noise on the A endAnd (4) processing the acoustic traveling wave signal by an SR method to obtain a waveform diagram. According to the graph, the traveling wave signal and the white noise signal are subjected to stochastic resonance processing, and the signal-to-noise ratio of the traveling wave signal is improved by adjusting the model parameters a and b and the step length h, so that the traveling wave signal can be detected more accurately. However, a small amount of noise interference still exists near the traveling wave signal, and the traveling wave signal needs to be denoised. Wherein, the step (c) is a oscillogram obtained by SR-ITD conversion of the traveling wave signal at the A end, the traveling wave signal with white noise superimposed on the A end is processed by an SR method, and then the signal is further denoised by ITD conversion, so that the sudden change of the traveling wave signal is obvious, and the instantaneous frequency sudden change of the traveling wave signal is accurately extracted, namely the sudden change moment of the traveling wave signal. In the same way, the traveling wave acquisition device can extract the mutation moment of the current traveling wave signal after SR-ITD conversion at the B end, the number of the recorded points of the traveling wave signal reaching the A end traveling wave acquisition device is 304, the number of the recorded points of the traveling wave signal reaching the B end traveling wave acquisition device is 372, and the time interval of each sampling point is 5 multiplied by 10 -7 And s, according to a double-end traveling wave positioning formula, the distance between the fault and the end A is 44.968km, and the positioning error is easy to obtain and is 32m. Where l is the length of the distribution line, x is the position of the fault point distance A, and t A Time of arrival of travelling wave at line A end, t B V is the traveling wave propagation velocity.
The distance from the fault to the end A can be solved by a double-end positioning method as follows:
the distance A between the original fault points is 45km, and the measurement error is 32m.
According to simulation analysis and calculation, the SR-ITD method provided by the invention can effectively detect the fault traveling wave signal under strong noise interference, accurately extract the sudden change moment of the traveling wave signal and meet the precision requirement of power system fault positioning.
As can be seen from fig. 4 (a), when white noise is superimposed on the traveling wave signal, the noise greatly interferes with the wavelet transform, the wavelet denoising effect is poor, the wavelet transform cannot accurately extract the modulo maximum of the traveling wave signal, the traveling wave signal will be confused with the interference noise, and the traveling wave signal extraction effect is poor, so that the fault location error is increased, and thus the location failure may be caused. As can be seen from fig. 4 (b), the HHT conversion detection traveling wave signal is greatly affected by the noise interference under strong noise, and no obvious mutation point can be observed, so that modal aliasing occurs, and the time-frequency diagram waveform after conversion is randomly distributed, so that the instantaneous frequency mutation value of the traveling wave head cannot be accurately measured, and the traveling wave signal extraction effect is not good.
According to the analysis, when the traveling wave signal is detected under the strong noise interference, the fault characteristic information of the traveling wave signal can be accurately extracted, the traveling wave signal submerged under the strong noise interference can be effectively detected, and the traveling wave positioning protection precision is improved.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (6)
1. A fault traveling wave detection method is characterized by comprising the following steps:
s1: installing a traveling wave signal detection device in each transformer substation;
s2: when the line has a fault, transmitting the detected signal back to the system master station for signal processing and analysis;
s3: firstly, a traveling wave signal monostable stochastic resonance model is constructedAdjusting parameters a and b and a sampling step length h in the traveling wave signal model to enable the traveling wave signal and the noise signal to reach a cooperative state, maximizing the energy transferred from the noise to the traveling wave signal, and effectively separating useful signals submerged in the noise signal;
s4: the traveling wave signal is decomposed into a plurality of inherent rotation components and trend components by inherent time scale decomposition, the instantaneous frequency of each component is calculated, the sudden change of the instantaneous frequency is reflected as the sudden change of the traveling wave signal, the moment of the sudden change of the instantaneous frequency is the arrival moment of the traveling wave signal, and the traveling wave signal is extracted.
2. The traveling wave fault detection method according to claim 1, wherein in step S3, the characteristic information of the traveling wave signal is accurately detected by using an ITD method.
3. The traveling wave fault detection method according to claim 1, wherein in step S3, a step length-variable stochastic resonance method is adopted to extract the traveling wave signal, and the step length h of the traveling wave signal sampling is changed to enable h to be changed>1/f s Wherein f is s The empirical value range of h is 0.1-1 for signal sampling frequency, and the extraction of the traveling wave signal is enabled to reach the optimal state by changing the parameters a, b and h in the traveling wave signal monostable model.
4. A faulty traveling wave detection method according to claim 1,2 or 3, characterized in that in step S4, X is assumed to be t The method comprises the steps of defining L as a base line extraction operator as a traveling wave signal, defining L as a time, acting the traveling wave signal on the L, defining the rest signal as an inherent rotation component, and if an inherent rotation extraction factor is represented by H, H =1-L; thereby to X t Further decomposing into: x t ＝LX t +(1-L)X t ＝L t +H t Let { tau } be k K =1,2, \8230; } is X t Defining a local extreme point of 0 =0; when X is t At a constant value in a certain time interval, its extreme value tau k Selecting as the right end point of the time interval; to simplify notation, let X k And L k Respectively represent X (tau) k ) And L (τ) k )。
5. The traveling fault wave detection method of claim 4, wherein L is assumed t And H t Has a domain of [0, τ ] k ]，X k Has a domain of [0, τ ] k+2 ](ii) a At a continuous extreme valueDot (tau) k ，τ k+1 ]Within the scope, a baseline extraction operator L is defined:
in the formula L k+1 The calculation process of (2) is as follows:
in the formula, alpha is more than 0 and less than 1, and generally the alpha is 0.5.
6. The traveling fault wave detection method according to claim 5, wherein after defining the baseline signal, an intrinsic rotation extraction operator H is obtained: HX t ＝(1-L)X t ＝H t ＝X t -L t And once decomposition, continuously decomposing the obtained baseline signal as an input signal again until a high-precision traveling wave signal is obtained.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112418076A (en) * | 2020-11-20 | 2021-02-26 | 国网湖南省电力有限公司 | Accurate extraction method for photoelectric volume pulse wave signal characteristics |
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| CN114802343A (en) * | 2022-03-21 | 2022-07-29 | 北京全路通信信号研究设计院集团有限公司 | Steel rail damage monitoring method and system |
| CN116520096A (en) * | 2023-07-04 | 2023-08-01 | 常州长创力智能科技有限公司 | Traveling wave fault positioning method and device based on LMD decomposition |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112418076A (en) * | 2020-11-20 | 2021-02-26 | 国网湖南省电力有限公司 | Accurate extraction method for photoelectric volume pulse wave signal characteristics |
| CN113625101A (en) * | 2021-06-24 | 2021-11-09 | 国网青海省电力公司果洛供电公司 | Traveling wave signal processing method based on fruit fly algorithm and stochastic resonance |
| CN113625101B (en) * | 2021-06-24 | 2023-12-26 | 国网青海省电力公司果洛供电公司 | Travelling wave signal processing method based on Drosophila algorithm and stochastic resonance |
| CN114802343A (en) * | 2022-03-21 | 2022-07-29 | 北京全路通信信号研究设计院集团有限公司 | Steel rail damage monitoring method and system |
| CN114802343B (en) * | 2022-03-21 | 2024-01-19 | 北京全路通信信号研究设计院集团有限公司 | Rail damage monitoring method and system |
| CN116520096A (en) * | 2023-07-04 | 2023-08-01 | 常州长创力智能科技有限公司 | Traveling wave fault positioning method and device based on LMD decomposition |
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Application publication date: 20180410 |