CN106841930A - A kind of detection method of transmission line malfunction traveling wave - Google Patents
A kind of detection method of transmission line malfunction traveling wave Download PDFInfo
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
This application provides a kind of detection method of transmission line malfunction traveling wave, each traveling wave data is decomposed using empirical mode decomposition algorithm, obtain the characteristic quantity of each traveling wave data；Characteristic quantity to all traveling wave data carries out Gaussian Mixture cluster, obtain cluster result, classify with to the interference traveling wave and fault traveling wave in traveling wave data, the application can accurately identify the type of traveling wave data, it is accurate to interference traveling wave and fault traveling wave classification, the workload of staff's screening is reduced, operating efficiency is improved.
Description
Technical field
The application is related to power failure detection field, more particularly to a kind of detection method of transmission line malfunction traveling wave.
Background technology
At present, with the raising and the increase of transmission distance of transmission line of electricity voltage class, especially surpass, extra-high voltage transmission line
The fast development on road, new challenge is proposed to electric network protection technology and fault localization technology.Traveling wave data contain abundant letter
Breath, if the feature of traveling wave data can be excavated effectively, must lift the accuracy of travelling wave ranging, fault type and differentiate and nature of trouble
Identification capability, is that follow-up traveling wave data lay the foundation in the research work of the aspect such as fault localization and main website construction.
In order to detect the traveling wave of transmission line of electricity, transmission line of electricity installs substantial amounts of traveling wave measurement apparatus, for obtaining during failure
Quarter information.Existing traveling wave measurement apparatus use Sudden Changing Rate Starting mode, to ensure the monitoring to transmission line of electricity Weak fault, OK
Wave measuring apparatus are typically provided with relatively low traveling wave activation threshold value.But, traveling wave measurement apparatus record substantial amounts of interference traveling wave, cause
Interference traveling wave and fault traveling wave ratio serious unbalance, increase the workload of data identification and screening.
The content of the invention
It is a large amount of to solve traveling wave measurement apparatus record this application provides a kind of detection method of transmission line malfunction traveling wave
Interference traveling wave, cause interference traveling wave and fault traveling wave proportional imbalance, increase the problem of data identification and the workload of screening.
Include this application provides a kind of detection method of transmission line malfunction traveling wave：
Obtain multiple traveling wave data；
Each traveling wave data is decomposed using empirical mode decomposition algorithm, obtains the characteristic quantity of each traveling wave data；
Characteristic quantity to all traveling wave data carries out Gaussian Mixture cluster, obtains cluster result, with traveling wave data
Interference traveling wave and fault traveling wave are classified.
Further, it is described each traveling wave data is decomposed using empirical mode decomposition algorithm, obtain each traveling wave number
According to characteristic quantity, including：
Each traveling wave data is decomposed using empirical mode decomposition algorithm, obtains M layers of IMF component of each traveling wave data；
According to the M layers of IMF component, selection meets pre-conditioned n-layer IMF components；The n is the just whole of no more than M
Number；
According to the n-layer IMF components, the characteristic quantity of each traveling wave data is obtained.
Further, it is described each traveling wave data is decomposed using empirical mode decomposition algorithm, obtain each traveling wave number
According to M layers of IMF component, including：
The maximum point and minimum point of each traveling wave data are obtained, maximum point sequence and minimum point sequence is obtained；
Row interpolation is entered to maximum point sequence and minimum point sequence respectively using cubic spline function method, obtains upper and lower
Envelope；
According to upper and lower envelope, average envelope line m (t) is calculated；
Calculate according to the following formula, obtain the traveling wave data and the average difference including line,
H (t)=y (t)-m (t),
Wherein, y (t) represents traveling wave data, and m (t) represents average envelope line；
If difference h (t) is unsatisfactory for the condition of intrinsic mode function, using h (t) as new traveling wave data, weight
The step of obtaining the maximum point and minimum point of each traveling wave data again；
If difference h (t) meets the condition of intrinsic mode function, difference h (t) is defined as the traveling wave
The IMF components of data；
By the IMF components from corresponding traveling wave data separating, traveling wave data discrepance is obtained；
If the traveling wave data discrepance can continue to decompose, using the traveling wave data discrepance as new traveling wave number
According to repetition is the step of obtain the maximum point and minimum point of each traveling wave data；
If the traveling wave data discrepance can not continue to decompose, M layers of component of each traveling wave data is obtained.
Further, described according to the M layers of IMF component, selection meets pre-conditioned n-layer IMF components, including,
According to the M layers of IMF component, every layer of spectrum kurtosis of IMF components is obtained；
According to every layer of spectrum kurtosis of IMF, n-layer IMF component of the spectrum kurtosis more than preset value is chosen.
Further, it is described according to the n-layer IMF components, the characteristic quantity of each traveling wave data is obtained, including,
According to the n-layer IMF components, calculate according to the following formula, obtain the corresponding traveling wave data of the n-layer IMF components
Energy-Entropy.
The Energy-Entropy is defined as the characteristic quantity of correspondence traveling wave data.
From above technical scheme, this application provides a kind of detection method of transmission line malfunction traveling wave, using warp
Test mode decomposition algorithm to decompose each traveling wave data, obtain the characteristic quantity of each traveling wave data；To all traveling wave data
Characteristic quantity carry out Gaussian Mixture cluster, obtain cluster result, carried out with to the interference traveling wave and fault traveling wave in traveling wave data
Classification, the application can accurately identify the type of traveling wave data, accurate to interference traveling wave and fault traveling wave classification, reduce staff
The workload of screening, improves operating efficiency.
Brief description of the drawings
In order to illustrate more clearly of the technical scheme of the application, letter will be made to the accompanying drawing to be used needed for embodiment below
Singly introduce, it should be apparent that, for those of ordinary skills, without having to pay creative labor,
Other accompanying drawings can also be obtained according to these accompanying drawings.
A kind of flow chart of the detection method of transmission line malfunction traveling wave that Fig. 1 is provided for the application.
Specific embodiment
Referring to Fig. 1, include this application provides a kind of detection method of transmission line malfunction traveling wave：
Step 11：Obtain multiple traveling wave data.
Traveling wave data are the data of traveling wave measurement apparatus record, and traveling wave data include interference traveling wave and fault traveling wave.Interference
Traveling wave is the waveform of traveling wave measurement apparatus record after causing traveling wave measurement apparatus error starting.Fault traveling wave is due to transmission line of electricity
Failure and cause traveling wave measurement apparatus start after, traveling wave measurement apparatus record waveform.
Step 12：Each traveling wave data is decomposed using empirical mode decomposition algorithm, obtains each traveling wave data
Characteristic quantity.
Empirical mode decomposition algorithm (Empirical Mode Decomposition, abbreviation EMD), the algorithm is according to certainly
The time scale feature of body carries out signal decomposition, without presetting any basic function, sophisticated signal can be made to be decomposed into limited
Card modular function (Intrinsic Mode Function, abbreviation IMF), can decomposite the individual IMF components for coming and contain original signal
The Local Features signal of different time scales.Experience skyscraping decomposition algorithm can make the Non-stationary Data carry out steady words treatment, due to
Basic function is to be decomposed to obtain in itself by data, and it is the local feature based on signal sequence time scale to decompose, with Fu in short-term
In the method such as leaf transformation, wavelet decomposition compared to having more adaptivity.
Step 13：Characteristic quantity to all traveling wave data carries out Gaussian Mixture cluster, obtains cluster result, with to traveling wave number
Interference traveling wave and fault traveling wave in are classified.
From above technical scheme, this application provides a kind of detection method of transmission line malfunction traveling wave, using warp
Test mode decomposition algorithm to decompose each traveling wave data, obtain the characteristic quantity of each traveling wave data；To all traveling wave data
Characteristic quantity carry out Gaussian Mixture cluster, obtain cluster result, carried out with to the interference traveling wave and fault traveling wave in traveling wave data
Classification, the application can accurately identify the type of traveling wave data, accurate to interference traveling wave and fault traveling wave classification, reduce staff
The workload of screening, improves operating efficiency.
The application provides another embodiment, and a kind of detection method of transmission line malfunction traveling wave includes：
Step 21：Obtain multiple traveling wave data.
Step 22：The maximum point and minimum point of each traveling wave data are obtained, maximum point sequence and minimum is obtained
Point sequence.
Step 23：Row interpolation is entered to maximum point sequence and minimum point sequence respectively using cubic spline function method, is obtained
To upper and lower envelope.
Step 24：According to upper and lower envelope, average envelope line m (t) is calculated；
Step 25：Calculate according to the following formula, obtain the traveling wave data and the average difference including line,
H (t)=y (t)-m (t),
Wherein, y (t) represents traveling wave data, and m (t) represents average envelope line.
Step 26：If difference h (t) is unsatisfactory for the condition of intrinsic mode function, using h (t) as new traveling wave
Data, perform step 21；If difference h (t) meets the condition of intrinsic mode function, step 27 is performed.
Step 27：Difference h (t) is defined as the IMF components of the traveling wave data；By the IMF components from correspondence
Traveling wave data separating, obtain traveling wave data discrepance.
Step 28：If the traveling wave data discrepance can continue to decompose, using the traveling wave data discrepance as new
Traveling wave data, perform step 21；If the traveling wave data discrepance can not continue to decompose, each traveling wave data is obtained
M layers of IMF component.
Step 29：According to every layer of spectrum kurtosis of IMF, n-layer IMF component of the spectrum kurtosis more than preset value is chosen.
Alternatively, according to below equation, the spectrum kurtosis of every layer of IMF of each traveling wave data is calculated,
Wherein, D represents every layer of spectrum kurtosis of IMF, and ξ () represents and expect that μ and σ represents each layer IMF of former travelling wave signal x
The average and standard variance of component.
Optionally, the application is taken after spectrum kurtosis sorts from big to small, and the larger sequence of spectrum kurtosis is located at preceding four IMF point
Amount.Play a part of dimensionality reduction by composing kurtosis selection IMF components simultaneously.
Step 30：According to the n-layer IMF components, calculate according to the following formula, obtain the corresponding row of the n-layer IMF components
The Energy-Entropy of wave number evidence.
Wherein, WEEjRepresent j-th Energy-Entropy of traveling wave data, EjkRepresent j-th kth layer IMF component of traveling wave data
Energy value, k is the no more than positive integer of n；EjRepresent j-th gross energy of traveling wave data.
Step 31：The Energy-Entropy is defined as the characteristic quantity of correspondence traveling wave data.
Step 32：It is 2 to set Gaussian Mixture cluster classification, 2 traveling wave data of arbitrary extracting in multiple traveling wave data, meter
Calculate initial parameter value and start iteration；
Step 33：According to the following formula, according to "current" model parameter, the IMF components of traveling wave data are calculated to the traveling wave data
Responsiveness,
Wherein, p () represents probability-distribution function, xjRepresent j-th Energy-Entropy of traveling wave data, θkRepresent j-th traveling wave
The covariance matrix of the kth layer IMF components of data, αkRepresent j-th weight of the kth layer IMF components of traveling wave data.
Step 34：According to following formula, the model parameter of a new wheel iteration is calculated,
Wherein, μkRepresent the j-th average of the kth layer IMF components of traveling wave data, ∑ k2Represent the of j traveling wave data
The k layers of variance of IMF components, αkJ-th weight of the kth layer IMF components of traveling wave data is represented, N is the sum of traveling wave data.
Step 35：If clustering cluster center is less than or equal to preset value, cluster result is obtained, with to interference traveling wave or event
Barrier traveling wave classification, if clustering cluster center is more than preset value, the average that will be obtained, weight, variance iteration to step 33.
From above technical scheme, this application provides a kind of detection method of transmission line malfunction traveling wave, using warp
Test mode decomposition algorithm to decompose each traveling wave data, obtain the characteristic quantity of each traveling wave data；To all traveling wave data
Characteristic quantity carry out Gaussian Mixture cluster, obtain cluster result, carried out with to the interference traveling wave and fault traveling wave in traveling wave data
Classification, the application can accurately identify the type of traveling wave data, accurate to interference traveling wave and fault traveling wave classification, reduce staff
The workload of screening, improves operating efficiency.
Claims (5)
1. a kind of detection method of transmission line malfunction traveling wave, it is characterised in that methods described includes：
Obtain multiple traveling wave data；
Each traveling wave data is decomposed using empirical mode decomposition algorithm, obtains the characteristic quantity of each traveling wave data；
Characteristic quantity to all traveling wave data carries out Gaussian Mixture cluster, obtains cluster result, with to the interference in traveling wave data
Traveling wave and fault traveling wave are classified.
2. the method for claim 1, it is characterised in that the utilization empirical mode decomposition algorithm is to each traveling wave data
Decomposed, obtained the characteristic quantity of each traveling wave data, including：
Each traveling wave data is decomposed using empirical mode decomposition algorithm, obtains M layers of IMF component of each traveling wave data；
According to the M layers of IMF component, selection meets pre-conditioned n-layer IMF components；The n is the no more than positive integer of M；
According to the n-layer IMF components, the characteristic quantity of each traveling wave data is obtained.
3. method as claimed in claim 2, it is characterised in that the utilization empirical mode decomposition algorithm is to each traveling wave data
Decomposed, obtained M layers of IMF component of each traveling wave data, including：
The maximum point and minimum point of each traveling wave data are obtained, maximum point sequence and minimum point sequence is obtained；
Row interpolation is entered to maximum point sequence and minimum point sequence respectively using cubic spline function method, upper and lower envelope is obtained
Line；
According to upper and lower envelope, average envelope line m (t) is calculated；
Calculate according to the following formula, obtain the traveling wave data and the average difference including line,
H (t)=y (t)-m (t),
Wherein, y (t) represents traveling wave data, and m (t) represents average envelope line；
If difference h (t) is unsatisfactory for the condition of intrinsic mode function, using h (t) as new traveling wave data, repetition is obtained
The step of taking the maximum point and minimum point of each traveling wave data；
If difference h (t) meets the condition of intrinsic mode function, difference h (t) is defined as the traveling wave data
IMF components；
By the IMF components from corresponding traveling wave data separating, traveling wave data discrepance is obtained；
If the traveling wave data discrepance can continue to decompose, using the traveling wave data discrepance as new traveling wave data,
The step of repetition obtains the maximum point and minimum point of each traveling wave data；
If the traveling wave data discrepance can not continue to decompose, M layers of component of each traveling wave data is obtained.
4. method as claimed in claim 2, it is characterised in that described according to the M layers of IMF component, chooses and meets default bar
The n-layer IMF components of part, including,
According to the M layers of IMF component, every layer of spectrum kurtosis of IMF components is obtained；
According to every layer of spectrum kurtosis of IMF, n-layer IMF component of the spectrum kurtosis more than preset value is chosen.
5. method as claimed in claim 2, it is characterised in that described according to the n-layer IMF components, obtains each traveling wave number
According to characteristic quantity, including,
According to the n-layer IMF components, the Energy-Entropy of the corresponding traveling wave data of the n-layer IMF components is obtained；
The Energy-Entropy is defined as the characteristic quantity of correspondence traveling wave data.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109917223A (en) * | 2019-03-08 | 2019-06-21 | 广西电网有限责任公司电力科学研究院 | A kind of transmission line malfunction current traveling wave feature extracting method |
| CN110161320A (en) * | 2019-05-31 | 2019-08-23 | 北京无线电计量测试研究所 | A kind of waveform widths uncertainty determines method and system |
| CN112505481A (en) * | 2020-11-20 | 2021-03-16 | 云南电网有限责任公司普洱供电局 | 35kV power line fault traveling wave extraction method based on neighbor propagation clustering |
| CN115754599A (en) * | 2022-11-10 | 2023-03-07 | 海南电网有限责任公司乐东供电局 | Cable fault positioning method and device based on transfer learning |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101976826A (en) * | 2010-10-18 | 2011-02-16 | 昆明理工大学 | EMD (Empirical Mode Decomposition) based boundary element method for ultra high voltage DC transmission lines |
| CN102788926A (en) * | 2012-07-04 | 2012-11-21 | 河南理工大学 | Single-phase ground fault section positioning method of small-current ground system |
| CN103454562A (en) * | 2013-09-22 | 2013-12-18 | 福州大学 | One-phase grounding clustering line selection method of resonant grounding system |
| CN104597376A (en) * | 2015-01-07 | 2015-05-06 | 西安理工大学 | Method for measuring fault location of HVDC (High Voltage Direct Current) transmission line under consideration of measured wave velocity |
| CN106019046A (en) * | 2016-05-18 | 2016-10-12 | 成都理工大学 | Novel small-current grounding system transient line selection method |
-
2017
- 2017-04-13 CN CN201710239938.2A patent/CN106841930A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101976826A (en) * | 2010-10-18 | 2011-02-16 | 昆明理工大学 | EMD (Empirical Mode Decomposition) based boundary element method for ultra high voltage DC transmission lines |
| CN102788926A (en) * | 2012-07-04 | 2012-11-21 | 河南理工大学 | Single-phase ground fault section positioning method of small-current ground system |
| CN103454562A (en) * | 2013-09-22 | 2013-12-18 | 福州大学 | One-phase grounding clustering line selection method of resonant grounding system |
| CN104597376A (en) * | 2015-01-07 | 2015-05-06 | 西安理工大学 | Method for measuring fault location of HVDC (High Voltage Direct Current) transmission line under consideration of measured wave velocity |
| CN106019046A (en) * | 2016-05-18 | 2016-10-12 | 成都理工大学 | Novel small-current grounding system transient line selection method |
Non-Patent Citations (3)
| Title |
|---|
| 徐丽兰: "谐振接地系统单相接地暂态特征量聚类选线", 《中国优秀硕士学位论文全文数据库 工程科技II辑》 * |
| 李天友 等: "采用EMD谱带通滤波和近似熵识别的配电网单相接地故障区段定位新方法", 《供用电》 * |
| 黄忠棋: "采用行波固有频率的混合线路故障测距新方法", 《电力系统及其自动化学报》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109917223A (en) * | 2019-03-08 | 2019-06-21 | 广西电网有限责任公司电力科学研究院 | A kind of transmission line malfunction current traveling wave feature extracting method |
| CN110161320A (en) * | 2019-05-31 | 2019-08-23 | 北京无线电计量测试研究所 | A kind of waveform widths uncertainty determines method and system |
| CN110161320B (en) * | 2019-05-31 | 2021-08-06 | 北京无线电计量测试研究所 | Method and system for determining waveform width uncertainty |
| CN112505481A (en) * | 2020-11-20 | 2021-03-16 | 云南电网有限责任公司普洱供电局 | 35kV power line fault traveling wave extraction method based on neighbor propagation clustering |
| CN115754599A (en) * | 2022-11-10 | 2023-03-07 | 海南电网有限责任公司乐东供电局 | Cable fault positioning method and device based on transfer learning |
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Inventor after: Huang Ran Inventor after: Mu Runzhi Inventor after: Ma Yi Inventor after: Li Hao Inventor after: Zhang Guangbin Inventor after: Dong Jun Inventor after: Cao Pulin Inventor after: Wang Huichun Inventor before: Huang Ran Inventor before: Shen Yuan Inventor before: Ma Yi Inventor before: Mu Runzhi Inventor before: Zhang Guangbin Inventor before: Han Yiming Inventor before: Zhou Fangrong Inventor before: Dong Jun Inventor before: Qian Guochao Inventor before: Ma Yutang |
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Application publication date: 20170613 |