CN111308281A - Partial discharge pulse extraction method - Google Patents
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- CN111308281A CN111308281A CN201911276242.2A CN201911276242A CN111308281A CN 111308281 A CN111308281 A CN 111308281A CN 201911276242 A CN201911276242 A CN 201911276242A CN 111308281 A CN111308281 A CN 111308281A
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- 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
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
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Abstract
The invention discloses a partial discharge pulse extraction method, and belongs to the field of power transformer insulation detection technology and application thereof. The method comprises the following steps: calculating an initial threshold value of the partial discharge pulse sampling sequence by adopting a maximum entropy threshold value calculation method, and preliminarily dividing the partial discharge signal into a noise signal and a discharge signal; then, calculating the mean value and the variance of the noise part and the mean value and the variance of the discharge signal part; forming a mixed Gaussian distribution map according to the calculated variance and mean; confirming an optimal threshold value according to the Gaussian mixture distribution map; the discharge pulse is confirmed and extracted. According to the method, the local discharge pulse sampling sequences are preliminarily distinguished by using a maximum entropy threshold method, and an optimal threshold is determined according to the Gaussian mixture distribution map, so that the limitation of an empirical threshold is avoided; the accuracy of searching the pulse edge is improved by filtering and eliminating the noise, so that the method is suitable for partial discharge online monitoring and data analysis under different environments.
Description
Technical Field
The invention relates to the technical field of partial discharge detection of power transformers, in particular to a partial discharge pulse extraction method.
Background
The power transformer is one of the most important devices in the power system, and the safe operation of the large-scale high-voltage transformer of the power plant and the transformer substation is the key for ensuring the normal power supply of a large area. The transformer insulation system is an important component of a power transformer, and the system determines the reliability and the economical efficiency of the operation of the transformer to a great extent. The operation life of the power transformer under the working voltage is closely related to the existence of partial discharge in the insulation, and the weaker the partial discharge is, the longer the normal operation life is. Partial discharge can not only cause turn insulation breakdown, but can even cause turn-to-turn and interlayer short circuits, which frequently occurs in domestic large transformers. The partial discharge detection is based on a phenomenon such as electricity or light generated when a partial discharge occurs, and the state of the partial discharge is represented by a physical quantity capable of expressing the phenomenon. However, due to the existence of background noise, signals acquired by the online detection of the partial discharge of the power transformer contain a large amount of noise, so that the edge of a single partial discharge pulse signal is not obvious; in addition, a large number of interference pulses exist in the partial discharge detection site, and certain difficulties are brought to the analysis and identification of the signal characteristics of the partial discharge pulse in the later period and the statistics of the discharge times. Therefore, determining the edge position of the partial discharge pulse signal, and accurately searching the partial discharge pulse waveform from the background noise and the interference pulse have become one of the key problems of the partial discharge detection technology of the power transformer. The method is characterized in that the edge of a partial discharge pulse signal is searched as a key step of a partial discharge measurement signal preprocessing stage, and is a basis for deep analysis of the partial discharge signal.
The existing method for extracting the partial discharge pulse sets a threshold value according to an empirical value, and extracts the starting position and the ending position of the pulse based on a sliding window by using the threshold value. The threshold value is set mainly by an empirical value, and the improper selection of the threshold value can cause wrong discharge pulse judgment, so that the method has large limitation. The other method is to perform FIR smooth filtering on a pulse sequence, then detect the pulse width and the turn-off time, set a down-sampling scale, perform smooth filtering again, further segment the pulse by detecting a transition point, and adjust the filtering scale by mistake according to the segmentation result, and segment again until the segmentation is correct, but the calculation process of the pulse width and the turn-off time adopts a density distribution statistical averaging method to perform histogram drawing on the pulse on a calibration coordinate paper, the process is too complex and cumbersome to calculate and is not suitable for on-line monitoring and analysis of partial discharge, chinese patent application publication No. CN103487788A discloses a "rapid automatic extraction method of a sequence pulse signal", that is, the method is adopted to realize the method. The Chinese patent application is named as a radar pulse extraction method based on adaptive threshold (publication number: CN 101762808A), and the method comprises the steps of extracting an envelope amplitude of a radar signal, performing smooth filtering on the radar envelope amplitude, performing a K-means clustering algorithm on the filtered envelope amplitude, and calculating a radar pulse extraction threshold. The algorithm has two limitations, first, the process of using the K-means clustering algorithm first determines an initial cluster center for each cluster. The performance of the clustering is related to the selection of the initial cluster center. The determination of the initial clustering center has great influence on clustering results during clustering convergence, and improper initial values can cause the results to converge to an undesirable minimum point and influence the convergence speed; secondly, in the calculation process of the mean value of the lower approximation area and the boundary area, the K-means algorithm only adds the objects and divides the added objects by the number of the objects in the corresponding area, i.e. the weight of each data object is considered to be the same. In the actual calculation process of the cluster mean value, the weight is adjusted according to the density of the area where each data point is located, and the obtained mean value point can better represent the cluster. The clustering is a dynamic process, along with the change of upper approximation and lower approximation from the early stage to the later stage of the clustering process, the fixed empirical weight cannot well adapt to the characteristics of the early stage and the later stage of the clustering, meanwhile, the algorithm is easily interfered by abnormal noise points, and a small amount of data can greatly influence the average value. The Chinese patent application name is a partial discharge single pulse extraction method based on a sliding window (publication number: CN 104635126), the mean value and the variance of background noise are obtained by counting the background signals of a pulse-free area of a sampling sequence, and the threshold value of discharge pulse extraction is set. In the background signal statistical process, a sufficiently long sampling sequence needs to be intercepted, and the longer the interception sequence is, the more accurate the mean value and variance of the background noise obtained through statistics are. The processing process needs to count a large number of partial discharge time sequences, so that the calculation amount is increased; secondly, the specific positions of the partial discharges of different defects are different, and the background signals need to be intercepted and counted again, so that the pulse extraction rate is reduced.
In summary, the existing partial discharge detection still has deficiencies, and is limited by discharge defects and detection methods to different degrees, and a method capable of satisfying the partial discharge online monitoring and data analysis under different environments is urgently needed.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a partial discharge pulse extraction method. The method selects the optimal threshold by utilizing a maximum entropy threshold method and Gaussian fitting distribution, can set the threshold according to different self-adaptations of sampling environment and background noise, and avoids the limitation of experience threshold; the online monitoring and data analysis of partial discharge under different environments are met.
In order to solve the technical problems, the invention is realized by the following technical scheme:
a partial discharge pulse extraction method, comprising the steps of:
step (1): performing a maximum entropy threshold calculation on the sequence of local discharge pulse samples x (i) to calculate an initial threshold th 0; dividing the partial discharge pulse sampling sequence X (i) into two types of C0 and C1 according to an initial threshold th 0;
step (2): extracting a signal sequence of C0 class;
and (3): extracting a signal sequence of C1 class;
and (4): calculating the variance delta of the C0 signal0And mean value mu0；
And (5): calculating the variance delta of the signal sequence of C1 class1And mean value mu1；
Step (6): according to the variance delta0Average value of μ0Variance δ1Average value of μ1Generating a Gaussian distribution curve;
and (7): finding out the optimal threshold value thr of the partial discharge signal according to the Gaussian distribution curve;
and (8): and extracting partial discharge signal pulses by adopting a sliding window method according to the optimal threshold value thr.
Specifically, the step (3) is specifically realized as follows: the partial discharge pulse sampling sequence x (i) is smaller than the threshold th0 and is denoted as C0.
Specifically, the step (4) is specifically realized as follows: the partial discharge pulse sampling sequence x (i) is greater than the threshold th0 and denoted as C1.
Specifically, the specific content of the step (7) is as follows:
based on a Gaussian partial curve, calculating the optimal threshold value thr in an iterative fitting mode; mu.sbeginMean, μ, of a gaussian distribution with small varianceendMean of Gaussian distribution with large variance, from μbeginPosition to muendThe direction is traversed to find the optimal threshold value thr of the partial discharge signal.
Specifically, the specific content of the step (8) is as follows:
step (81): adopting a threshold sliding window method, and according to the optimal threshold thr and the window width M;
step (82): moving the partial discharge signal removing sequence X (i) from the first point of the partial discharge signal removing sequence X (i) one by one, and when the absolute value of the sequence amplitude in the window reaching the point A is larger than a threshold value thr, taking the point A as the starting point of the pulse waveform and recording the point A as an index A;
step (83): continuing to move the partial discharge signal sequence X (i) until the absolute values of the sequence amplitudes in the window are all smaller than a threshold value thr when the point B is reached, namely taking the point B as the end point of the pulse waveform and recording the point B as an index B;
step (84): taking the signal sequence between the index A and the index B as the pulse waveform which is complete once and storing the pulse waveform;
step (85): repeating the steps (82) and (83) until the end point of the partial discharge signal sequence X (i) is reached, and stopping the movement.
Compared with the prior art, the invention has the following advantages:
the method utilizes the maximum entropy method to carry out preliminary analysis on signals to obtain an initial threshold value, divides the partial discharge original sequence into two types according to the initial threshold value, forms a Gaussian mixture distribution map according to the two types of data, carries out iterative traversal to find out the optimal threshold value, thus avoiding the limitation of experience threshold value, generates a self-adaptive threshold value according to the actual environment and background noise, and meets the requirements of partial discharge online monitoring and data analysis under different environments.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flow chart of a partial discharge pulse extraction method of the present invention.
Fig. 2 is a time-domain waveform diagram of a partial discharge pulse of a certain defect type collected in the embodiment of the invention.
In fig. 2: the vertical lines represent the waveform raw data and the horizontal lines represent the found thresholds.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
A method of partial discharge pulse extraction comprising the steps of:
step (1): performing a maximum entropy threshold calculation on the sequence of local discharge pulse samples x (i) to calculate an initial threshold th 0; dividing the partial discharge pulse sampling sequence X (i) into two types of C0 and C1 according to an initial threshold th 0;
step (2): extracting a signal sequence of C0 class;
a partial discharge pulse sampling sequence x (i) smaller than a threshold th0 denoted as C0;
and (3): extracting a signal sequence of C1 class;
a partial discharge pulse sampling sequence x (i) greater than a threshold th0 denoted as C1;
and (4): calculating the variance delta of the C0 signal0And mean value mu0；
And (5): calculating the variance delta of the signal sequence of C1 class1And mean value mu1；
And (6): according to the variance delta0Average value of μ0Variance δ1Average value of μ1Generating a Gaussian distribution curve;
and (7): finding the optimal threshold thr of the partial discharge signal according to the gaussian distribution curve specifically as follows:
based on a Gaussian partial curve, calculating the optimal threshold value thr in an iterative fitting mode; mu.sbeginMean, μ, of a gaussian distribution with small varianceendMean of Gaussian distribution with large variance, from μbeginPosition to muendThe direction is traversed to find the optimal threshold value thr of the partial discharge signal.
And (8): extracting partial discharge signal pulses by adopting a sliding window method according to the optimal threshold value thr, which comprises the following steps:
step (81): adopting a threshold sliding window method, and according to the optimal threshold thr and the window width M;
step (82): moving the partial discharge signal removing sequence X (i) from the first point of the partial discharge signal removing sequence X (i) one by one, and when the absolute value of the sequence amplitude in the window reaching the point A is larger than a threshold value thr, taking the point A as the starting point of the pulse waveform and recording the point A as an index A;
step (83): continuing to move the partial discharge signal sequence X (i) until the absolute values of the sequence amplitudes in the window are all smaller than a threshold value thr when the point B is reached, namely taking the point B as the end point of the pulse waveform and recording the point B as an index B;
step (84): taking the signal sequence between the index A and the index B as the pulse waveform which is complete once and storing the pulse waveform;
step (85): repeating the steps (82) and (83) until the end point of the partial discharge signal sequence X (i) is reached, and stopping the movement.
The method utilizes the maximum entropy method to carry out preliminary analysis on signals to obtain an initial threshold value, divides the partial discharge original sequence into two types according to the initial threshold value, forms a Gaussian mixture distribution map according to the two types of data, carries out iterative traversal to find out the optimal threshold value, thus avoiding the limitation of experience threshold value, generates a self-adaptive threshold value according to the actual environment and background noise, and meets the requirements of partial discharge online monitoring and data analysis under different environments.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. It will be understood by those skilled in the art that any modification, equivalent replacement, or improvement made on the technical solutions or parts of the technical features described in the above embodiments can be included in the scope of protection of the present invention within the spirit and principle of the present invention.
Claims (5)
1. A partial discharge pulse extraction method is characterized by comprising the following steps:
step (1): performing a maximum entropy threshold calculation on the sequence of local discharge pulse samples x (i) to calculate an initial threshold th 0; dividing the partial discharge pulse sampling sequence X (i) into two types of C0 and C1 according to an initial threshold th 0;
step (2): extracting a signal sequence of C0 class;
and (3): extracting a signal sequence of C1 class;
and (4): calculating signals of class C0Variance δ of0And mean value mu0；
And (5): calculating the variance delta of the signal sequence of C1 class1And mean value mu1；
And (6): according to the variance delta0Average value of μ0Variance δ1Average value of μ1Generating a Gaussian distribution curve;
and (7): finding out the optimal threshold value thr of the partial discharge signal according to the Gaussian distribution curve;
and (8): and extracting partial discharge signal pulses by adopting a sliding window method according to the optimal threshold value thr.
2. The partial discharge pulse extraction method according to claim 1, characterized in that: the step (3) is specifically realized as follows: the partial discharge pulse sampling sequence x (i) is smaller than the threshold th0 and is denoted as C0.
3. The partial discharge pulse extraction method according to claim 1, characterized in that: the step (4) is specifically realized as follows: the partial discharge pulse sampling sequence x (i) is greater than the threshold th0 and denoted as C1.
4. The partial discharge pulse extraction method according to claim 1, characterized in that: the specific content of the step (7) is as follows:
based on a Gaussian partial curve, calculating the optimal threshold value thr in an iterative fitting mode; mu.sbeginMean, μ, of a gaussian distribution with small varianceendMean of Gaussian distribution with large variance, from μbeginPosition to muendThe direction is traversed to find the optimal threshold value thr of the partial discharge signal.
5. The partial discharge pulse extraction method according to claim 1, characterized in that: the specific content of the step (8) is as follows:
step (81): adopting a threshold sliding window method, and according to the optimal threshold thr and the window width M;
step (82): moving the partial discharge signal removing sequence X (i) from the first point of the partial discharge signal removing sequence X (i) one by one, and when the absolute value of the sequence amplitude in the window reaching the point A is larger than a threshold value thr, taking the point A as the starting point of the pulse waveform and recording the point A as an index A;
step (83): continuing to move the partial discharge signal sequence X (i) until the absolute values of the sequence amplitudes in the window are all smaller than a threshold value thr when the point B is reached, namely taking the point B as the end point of the pulse waveform and recording the point B as an index B;
step (84): taking the signal sequence between the index A and the index B as the pulse waveform which is complete once and storing the pulse waveform;
step (85): repeating the steps (82) and (83) until the end point of the partial discharge signal sequence X (i) is reached, and stopping the movement.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111999620A (en) * | 2020-09-22 | 2020-11-27 | 珠海华网科技有限责任公司 | Multi-channel joint positioning method for partial discharge of power equipment |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102854445A (en) * | 2012-10-18 | 2013-01-02 | 上海市电力公司 | Method for extracting waveform feature of local discharge pulse current |
| CN103675617A (en) * | 2013-11-20 | 2014-03-26 | 西安交通大学 | Anti-interference method for high-frequency partial discharge signal detection |
| CN104200440A (en) * | 2014-09-16 | 2014-12-10 | 哈尔滨恒誉名翔科技有限公司 | Spot image processing algorithm based on multi-scale wavelet transformation |
| CN104502812A (en) * | 2014-11-26 | 2015-04-08 | 国家电网公司 | Partial discharge acquisition method and apparatus |
| CN104635126A (en) * | 2015-01-27 | 2015-05-20 | 国家电网公司 | Local discharge single-pulse extraction method based on sliding window |
| CN107505552A (en) * | 2017-10-16 | 2017-12-22 | 云南电网有限责任公司电力科学研究院 | The lower shelf depreciation high-frequency signal extraction element of steep-front impact and measuring system |
| CN108446632A (en) * | 2018-03-20 | 2018-08-24 | 珠海华网科技有限责任公司 | It a kind of partial discharge pulse edge finds and shelf depreciation confirmation method |
-
2019
- 2019-12-12 CN CN201911276242.2A patent/CN111308281A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102854445A (en) * | 2012-10-18 | 2013-01-02 | 上海市电力公司 | Method for extracting waveform feature of local discharge pulse current |
| CN103675617A (en) * | 2013-11-20 | 2014-03-26 | 西安交通大学 | Anti-interference method for high-frequency partial discharge signal detection |
| CN104200440A (en) * | 2014-09-16 | 2014-12-10 | 哈尔滨恒誉名翔科技有限公司 | Spot image processing algorithm based on multi-scale wavelet transformation |
| CN104502812A (en) * | 2014-11-26 | 2015-04-08 | 国家电网公司 | Partial discharge acquisition method and apparatus |
| CN104635126A (en) * | 2015-01-27 | 2015-05-20 | 国家电网公司 | Local discharge single-pulse extraction method based on sliding window |
| CN107505552A (en) * | 2017-10-16 | 2017-12-22 | 云南电网有限责任公司电力科学研究院 | The lower shelf depreciation high-frequency signal extraction element of steep-front impact and measuring system |
| CN108446632A (en) * | 2018-03-20 | 2018-08-24 | 珠海华网科技有限责任公司 | It a kind of partial discharge pulse edge finds and shelf depreciation confirmation method |
Non-Patent Citations (2)
| Title |
|---|
| 何东健: "《数字图像处理》", 28 February 2015 * |
| 陈海鹏等: "采用高斯拟合的全局阈值算法阈值优化框架", 《计算机研究与发展》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111999620A (en) * | 2020-09-22 | 2020-11-27 | 珠海华网科技有限责任公司 | Multi-channel joint positioning method for partial discharge of power equipment |
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