CN112926188B - Impact capacitor parameter identification method and insulation diagnosis method based on extended debye model - Google Patents
Impact capacitor parameter identification method and insulation diagnosis method based on extended debye model Download PDFInfo
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Abstract
The invention discloses an impact capacitor parameter identification method based on an extended debye model, which comprises the steps of firstly constructing a Hankle matrix based on collected depolarization current signal data, carrying out SVD (singular value decomposition) on the Hankle matrix, then obtaining singular value entropy increment by utilizing singular values obtained by SVD decomposition, determining the number of model branches, constructing a matrix according to the number of model branches and the singular value matrix obtained by SVD decomposition, and further determining time constants and RC parameters of each branch of the model by the characteristic values of the constructed matrix; the identification method can extract all effective messages from the original signals to obtain more reasonable model branch parameters and impulse capacitor parameters. The invention further provides an impact capacitor insulation diagnosis method, RC parameters of branches corresponding to the maximum time constant of the extended Debye model of the impact capacitor, which are obtained by utilizing the identification method, can accurately reflect the aging degree of the impact capacitor, and a more reasonable evaluation index is provided for evaluating the insulation state of the impact capacitor.
Description
Technical Field
The invention belongs to the technical field of capacitor insulation, relates to a shock capacitor insulation state diagnosis technology, and particularly relates to a shock capacitor parameter identification method based on an extended Debye model and application of the shock capacitor parameter identification method in shock capacitor insulation diagnosis.
Background
The development of the modern society has an increasing demand for power supply, so that the scale and capacity of a power distribution network are also increasing, and in order to improve the power transmission efficiency, a great amount of manpower and material resources are invested in China to develop an extra-high voltage alternating current/direct current power transmission technology. In an extra-high voltage direct current transmission line, overvoltage generated by lightning stroke and manual operation is easily born on a neutral line of a bus, and an impact capacitor is generally used for absorbing the generated overvoltage in engineering, so that the method has important significance for effectively diagnosing the insulation state of the impact capacitor.
The traditional diagnosis method is to measure the capacitance value of the capacitor by using the bridge, but the capacitance value of the capacitor is not changed greatly in the middle and front stages of aging, once the capacitor is broken down, the capacitance value is rapidly reduced, and the capacitance value is measured at the moment without great significance, so that the failure of the capacitor is difficult to be prevented by using the method. The patent application document with the application number of CN201220281713.6 discloses an intelligent online monitoring system for a high-voltage parallel capacitor of a transformer substation, the capacity of the capacitor is obtained by analyzing data acquired in real time, and when the capacity of any capacitor in the high-voltage parallel capacitor is changed, a monitoring center sends an alarm signal; as noted above, when the capacitor capacity changes, the capacitor will quickly break down, and thus such monitoring systems have difficulty effectively preventing capacitor failure. The patent application document with the application number of CN201310227870.8 discloses a system and a method for monitoring and diagnosing the faults, wherein a fault cause matrix is constructed by local and internal fuse wire fusing, internal breakdown of a capacitor element, broken wire of a core and the like, a fault sign matrix is constructed by capacitance increase, large sub-force of an oil tank, high temperature, large dielectric loss, leakage oil and the like, and then a set of system capable of monitoring and diagnosing the faults is obtained by training a BP neural network; on one hand, the neural network learning needs to be constructed based on a large amount of data, and the accuracy of the neural network obtained by training a small amount of data is difficult to ensure; this is to monitor the capacitor based on capacitance and related factors, and still is related to capacitance changes, so that it is still difficult to effectively prevent the failure of the capacitor.
Disclosure of Invention
Aiming at the technical situation that effective monitoring of the insulation state of the capacitor is difficult to realize in the existing capacitor monitoring technology, the invention aims to provide an impact capacitance parameter identification method based on an extended Debye model, which can effectively acquire the branch number of the equivalent extended Debye model of the impact capacitance, thereby extracting key parameters which can accurately reflect the insulation aging degree of the impact capacitor and providing reasonable indexes for evaluating the insulation state of the capacitor.
Another object of the present invention is to provide a method for diagnosing insulation of a capacitor by using the impulse capacitor parameters extracted by the above identification method.
Since the insulation of the impulse capacitor adopts an oil film insulation structure, a polarization depolarization model of the impulse capacitor can be described by an extended debye model. The RC branch parameters of the extended Debye model are closely related to the aging degree of the study object. The values of the RC parameters of the different branches of the equivalent Debye model will change during the different aging phases of the subject.
Based on the above, the application firstly provides an impact capacitor parameter identification method based on an extended debye model, which comprises the following steps:
s1, determining a current model of the impulse capacitor in the depolarization process according to an equivalent extended debye model of the impulse capacitor:
wherein I is d (t) represents the equivalent current of the extended debye model during depolarization, A i For the current amplitude of the ith branch, i=1, 2, …, M is the number of equivalent extended debye model branches to be determined, t is the polarization time, τ i N (t) is the noise in the signal, which is the time constant of the i-th branch;
s2 is based on the acquired impulse capacitor depolarization current signal h k ，k＝1,2,…,N，N is the sampling point number, and a Hankle matrix H of (N-L) x (L+1) order is constructed (N-L)*(L+1) Abbreviated as H matrix:
wherein, the value of L is between N/5 and N/2; in the invention, L=N/4;
s3, SVD decomposition is carried out on the Hankle matrix, S is a left singular value matrix, V is a singular value matrix, elements on diagonal lines of the SVD are singular values of an H matrix, and D is a right singular value matrix;
H＝SVD T wherein S is a left singular value feature matrix of (N-L), D is a right singular value feature matrix of (L+1), V is a singular value matrix of (N-L), and the diagonal elements (i.e., singular values) thereof are denoted as beta ii (i∈[1,θ]θ∈min (N-L, l+1)), and the singular values are arranged from large to small on the main diagonal, the magnitude of the singular values does not decrease to zero due to noise contained in the signal;
s4, acquiring corresponding singular entropy increment according to each singular value of the H matrix, and taking the signal order before the singular entropy increment converges to a bounded value as an equivalent extended Debye model branch number M; representing the signal order by the number of singular values;
singular value beta ii Corresponding singular entropy increment delta E i The method comprises the following steps:
along with the increase of the signal order, the singular entropy increment is reduced and gradually converged to a very small bounded value, and the signal at the moment can be considered to contain all effective information of the original signal, so that the signal order i before converging to the bounded value can be used as an equivalent extended debye model branch number M, and the singular entropy increment after the bounded value can be considered to be caused by noise and can be disregarded;
s5, according to the determined Debye model branch number M, taking the first M columns of the V matrix to form V' (N-L)*M Taking the first M columns and the first L rows of the matrix D to form a matrix D' L*M Taking the front M columns and the back L rows of the matrix D to form a matrix D' L*M Combining the S matrix to respectively construct a matrix H 1 And H 2 ，H 1 ＝SV'D' T ，H 2 ＝SV'D” T The method comprises the steps of carrying out a first treatment on the surface of the Further from H 1 And H 2 Constructing a matrix g=h 1 + H 2 ，H 1 + For matrix H 1 Is a pseudo-inverse of (a); and the first M eigenvalues sigma of the matrix G are obtained by using the eigenvalue root method i (i＝1,2,…M)；
The process of obtaining the first M eigenvalues of the matrix G by using the eigenvalue method is specifically as follows:
solving the characteristic equation |sigma I-G|= 0,I to represent an identity matrix, obtaining L non-negative characteristic values of G, arranging the characteristic values from large to small, taking the first M characteristic values, and marking the characteristic values as sigma i (i=1, 2, … M); then σ= [ σ ] 1 ,σ 2 ,…,σ i ,…,σ M ]；
S6, calculating to obtain time constants and RC parameters of each branch of the equivalent extended Debye model according to the following formula:
C i ＝τ i /R i (6)；
in the formula, |r i I represents r i Re represents the real part, T s For sampling interval, U is polarization voltage, t dep For depolarization time r i Is the complex amplitude of the signal;
r i the value can be obtained by the least squares method according to the expression (7):
in the method, in the process of the invention,representing the characteristic value sigma i To the power of (k-1).
By the impact capacitor parameter identification method based on the extended debye model, the time constant and RC parameters of each branch of the extended debye model can be obtained. The applicant finds that the RC parameter of the branch corresponding to the maximum time constant changes with the increase of the insulation aging time of the impact capacitor, so as to reflect the insulation aging degree of the impact capacitor.
Based on this, the present invention further provides a surge capacitor insulation diagnosis method comprising the steps of:
step one, obtaining time constants and RC parameters of each branch of an extended debye model of the impact capacitor according to the method;
and secondly, evaluating the insulation aging degree of the impact capacitor according to the RC parameters of the corresponding branch circuit of the maximum time constant.
According to the research, as the aging time of the impact capacitor increases, the resistance value of the branch corresponding to the maximum time constant of the extended debye model of the impact capacitor is obviously reduced, and the capacitance value is obviously increased; therefore, the resistance value and the capacitance value of the branch corresponding to the maximum time constant of the extended debye model of the impact capacitor can be compared with the resistance value and the capacitance value of the branch corresponding to the maximum time constant of the new impact capacitor debye model which is just put into use (namely, the base ratio), and the insulation aging degree of the impact capacitor is judged according to the reduction degree of the resistance value and the increase degree of the capacitance value; when the insulation state of the impact capacitor reaches the set fault level, the impact capacitor is repaired in time or replaced by a new impact capacitor.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the impact capacitor parameter identification method based on the extended Debye model, firstly, a Hankle matrix is constructed based on collected depolarization current signal data, SVD decomposition is carried out on the Hankle matrix, singular value entropy increment is obtained by using singular values obtained by SVD decomposition, the number of model branches is determined, a matrix is constructed according to the number of model branches and the singular value matrix obtained by SVD decomposition, and time constants and RC parameters of each branch of the model are further determined according to characteristic values of the constructed matrix; by means of the improved matrix beam algorithm, noise influence can be removed, all effective messages are extracted from the original signals, and more reasonable model branch parameters and impact capacitor parameters are obtained.
2. According to the impact capacitor parameter identification method based on the extended debye model, provided by the invention, the influence of subjective factors in the process of determining the number of branches can be effectively avoided through an improved matrix beam algorithm, so that the identification result is more accurate.
3. According to the impact capacitor insulation diagnosis method based on the extended Debye model, the RC parameters of the branches corresponding to the maximum time constant of the extended Debye model of the impact capacitor, which are obtained by the identification method, can accurately reflect the aging degree of the impact capacitor, and can evaluate the deterioration speed of the insulation state of the capacitor more timely, so that a more reasonable evaluation index is provided for evaluating the insulation state of the impact capacitor, and accidents caused by the failure of the capacitor are prevented.
Drawings
Fig. 1 is a schematic diagram of a shock capacitor performing a polarization-depolarization test.
Fig. 2 is a schematic diagram of an extended debye model of a surge capacitor.
Fig. 3 is a flow chart of the impact capacitor parameter identification method based on the extended debye model of the present invention.
FIG. 4 is a graph showing the singular value increment corresponding to different impulse capacitor samples as a function of the order; (a) Corresponding to an aged 0h sample (i.e., a standard sample that is not aged), (b) corresponding to an aged 180h sample.
Fig. 5 is a graph showing the RC parameter variation of the corresponding branch circuit of the maximum time constant of the impulse capacitor according to the embodiment.
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 one of ordinary skill in the art without undue burden on the person of ordinary skill in the art based on the embodiments of the present invention, are within the scope of the present invention.
Examples
In the embodiment, based on an extended debye model, impact capacitors with different aging degrees are identified by using a matrix beam algorithm, so that the corresponding equivalent extended debye model branch number, the time constant of each branch and RC parameters are obtained, the change rule of the RC parameters of the branch corresponding to the maximum time constant of the equivalent debye model in the aging process of the capacitor is obtained, and the working state of the capacitor is evaluated.
Sample preparation
The impact capacitor samples of different heat aging degrees used in this example were prepared as follows: the impulse capacitor is placed in the environment of 90 ℃ and the ageing time is respectively 0h, 48h, 132h, 156h, 180h and 204h.
(two) polarization-depolarization test
Carrying out polarization-depolarization current method test on impact capacitors with different aging degrees according to the test principle provided by FIG. 1, wherein when a switch is connected with S1, the current in a test loop is polarization current, after the polarization time is over, the switch is automatically switched to S2, and the current in the loop is the depolarization current; in this embodiment, the polarization voltage u=1000v, and the polarization time and the depolarization time are 90s. The depolarization current signal is denoted here as h k K=1, 2, …, N, n=1200 is the number of sampling points, and the sampling interval is T s ＝0.075s。
(III) impact capacitor parameter identification
As shown in fig. 2, the equivalent extended debye model based on the embodiment consists of a plurality of parallel branches, and the insulation resistance R of the impulse capacitor g And geometric capacitance C g Parallel to both ends of the branch.
As shown in fig. 3, based on the extended debye model, impact capacitors with different aging degrees were identified and analyzed by using a matrix beam algorithm according to the following steps:
s1, determining a current model of the impulse capacitor in the depolarization process according to an equivalent extended debye model of the impulse capacitor:
wherein I is d (t) represents the equivalent current of the extended debye model during depolarization, A i For the current amplitude of the ith branch, i=1, 2, …, M is the number of equivalent extended debye model branches to be determined, t is time, τ i N (t) is the noise in the signal, which is the time constant of the i-th branch;
s2 is based on the acquired impulse capacitor depolarization current signal h k Constructing a Hankle matrix H of (N-L) ×L+1 order (N-L)*(L+1) Abbreviated as H matrix:
wherein l=n/4=300;
s3, SVD decomposition is carried out on the Hankle matrix, S is a left singular value matrix, V is a singular value matrix, elements on diagonal lines of the SVD are singular values of an H matrix, and D is a right singular value matrix;
i.e. h=svd T Wherein S is a left singular value feature matrix of (N-L), D is a right singular value feature matrix of (L+1), V is a singular value matrix of (N-L), and the diagonal elements (i.e., singular values) thereof are denoted as beta ii (i∈[1,θ]θ∈min (N-L, l+1)), and the singular values are arranged from large to small on the main diagonal, the magnitude of the singular values does not decrease to zero due to noise contained in the signal;
s4, acquiring corresponding singular entropy increment according to each singular value of the H matrix, and taking the signal order before the singular entropy increment converges to the bounded value as an equivalent extended Debye model branch number M.
In this embodiment, each singular value β ii Corresponding singular entropy increment delta E i The method is calculated according to the following formula:
in this embodiment, as the signal order increases, the singular entropy increment decreases and gradually converges to the bounded value, and it can be considered that the signal at this time already contains all the effective information of the original signal, so the signal order i before converging to the bounded value can be regarded as the equivalent extended debye model branch number M, and the singular entropy increment after the bounded value can be considered as noise, and can be disregarded.
The capacitor equivalent model scaling for 0h and 180h aging is described below as an example.
As can be seen from fig. 4 a, the singular entropy increment value of the aged 0h capacitor is considered to have included all the information in the original current signal when the order is 3 (m=3), and the singular entropy increment of the 4 th and subsequent orders converges to a very small non-zero bounded value, which is considered to be caused by noise in the signal, and is not considered. Similarly, as can be seen from fig. 4 b, the singular entropy increment value of the aged 180h capacitor is considered to have already included all the information in the original current signal when the order is 2 (m=2), and the singular entropy increment of 3 rd order and later converges to a very small non-zero bounded value, which is considered to be caused by noise in the signal, and is not considered.
The number of branches M of the debye model determined for impact capacitors of different aging levels is shown in table 1.
S5, taking the first M columns of the V matrix to form V 'according to the determined Debye model branch number M' (N-L)*M Taking the first M columns and the first L rows of the matrix D to form a matrix D' L*M Taking the front M columns and the back L rows of the matrix D to form a matrix D' L*M Combining the S matrix to respectively construct a matrix H 1 And H 2 ，H 1 ＝SV'D' T ，H 2 ＝SV'D” T The method comprises the steps of carrying out a first treatment on the surface of the Further from H 1 And H 2 Constructing a matrix For matrix H 1 Is a pseudo-inverse of the matrix of (a). Then using a characteristic root method to solve a characteristic equation |sigma I-G|=0 (I represents an identity matrix) to obtain L non-negative characteristic values of G, and arranging the characteristic values from large to small, namely sigma 1 ,σ 2 ,…,σ L The method comprises the steps of carrying out a first treatment on the surface of the Taking the first M characteristic values, and recording as sigma i (i=1, 2, … M); then σ= [ σ ] 1 ,σ 2 ,…,σ i ,…,σ M ]。
The following matrix is further constructed according to the depolarization current signal and the eigenvalue:
further, the signal complex amplitude r is obtained by the least square method according to (7) i 。
S6, calculating to obtain time constants and RC parameters of each branch of the equivalent extended Debye model according to the following formula:
C i ＝τ i /R i (6)；
in the formula, |r i I represents r i Re represents the real part, T s For sampling interval, U is polarization voltage, t dep Is the depolarization time.
For impulse capacitors of different aging levels, the time constants and RC parameters (including resistance and capacitance) of each branch of the Debye model determined according to steps S5-S6 are shown in Table 1.
TABLE 1 identification of equivalent extended Debye model parameters for impact capacitors at different aging levels
(IV) impact capacitor insulation diagnostics
As can be seen from table 1, as the aging time of the capacitor increases, the resistance value of the branch corresponding to the maximum time constant is significantly reduced, the capacitance value of the branch is significantly increased, and the RC parameter variation of the rest of the branches is not significantly changed. Fig. 5 shows the relationship between the resistance value and the capacitance value corresponding to the branch corresponding to the maximum time constant over the aging time, and it can be seen from the graph that, as the thermal aging degree of the impact capacitor increases, the resistance value of the branch corresponding to the maximum time constant of the equivalent extended debye model is obviously reduced, the capacitance value is obviously increased, and the increase amplitude of the capacitance value is larger than that of the resistance value.
Based on the above analysis, the present embodiment further provides a surge capacitor insulation diagnosis method, comprising the steps of:
step one, obtaining time constants and RC parameters of each branch of an extended debye model of the impact capacitor according to the method;
secondly, evaluating insulation aging degree of the impact capacitor according to RC parameters of the corresponding branch circuit of the maximum time constant;
for example, for an impact capacitor with an aging time of 156h, the equivalent extended debye model maximum time constant corresponding leg resistance and capacitance values are 6.77×10, respectively 7 Omega and 2.73 x 10 -7 F, compared with the impact capacitor without aging (the resistance and capacitance of the equivalent extended Debye model maximum time constant corresponding branch are 2.39×10 respectively 8 Omega and 2.58 x 10 -8 F) The resistance value is obviously reduced, and the capacitance value is obviously increased, so that the impact capacitor is seriously aged in insulation and needs to be subjected to insulation repair or replaced by a new impact capacitor.
In summary, the invention can more accurately identify the equivalent extended debye model parameters of the impulse capacitor through the improved matrix beam algorithm, and provides a new basis for judging the insulation aging degree of the capacitor by taking the RC parameter change of the branch with the maximum time constant of the model as the characterization mode of the aging degree of the impulse capacitor.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (3)
1. An impact capacitor insulation diagnosis method is characterized by comprising the following steps:
the method comprises the steps that firstly, according to an impulse capacitor parameter identification method based on an extended debye model, time constants and RC parameters of all branches of the extended debye model of the impulse capacitor are obtained; comprises the following sub-steps:
s1, determining a current model of the impulse capacitor in the depolarization process according to an equivalent extended debye model of the impulse capacitor:
wherein I is d (t) represents the equivalent current of the extended debye model during depolarization, A i For the current amplitude of the ith branch, i=1, 2, …, M is the number of equivalent extended debye model branches to be determined, t is the polarization time, τ i N (t) is the noise in the signal, which is the time constant of the i-th branch;
s2 is based on the acquired impulse capacitor depolarization current signal h k K=1, 2, …, N is the number of sampling points, and a (N-L) ×l+1 rank Hankle matrix H is constructed (N-L)*(L+1) Abbreviated as H matrix:
wherein, the value of L is between N/5 and N/2;
s3, SVD decomposition is carried out on the Hankle matrix, S is a left singular value matrix, V is a singular value matrix, elements on diagonal lines of the SVD are singular values of an H matrix, and D is a right singular value matrix; h=svd T Wherein S is a left singular value feature matrix of (N-L), D is a right singular value feature matrix of (L+1), V is a singular value matrix of (N-L), and the diagonal element is singular value and is recorded as beta ii (i∈[1,θ]θ εmin (N-L, L+1)), and the singular values are arranged from large to small on the main diagonal;
s4, acquiring corresponding singular entropy increment according to each singular value of the H matrix, and taking the signal order before the singular entropy increment converges to a bounded value as an equivalent extended Debye model branch number M; representing the signal order by the number of singular values;
s5, according to the determined Debye model branch number M, taking the first M columns of the V matrix to form V' (N-L)*M Taking the first M columns and the first L rows of the matrix D to form a matrix D' L*M Taking the front M columns and the back L rows of the D matrix to form a matrix D' L*M Combining the S matrix to respectively construct a matrix H 1 And H 2 ，H 1 ＝SV'D' T ，H 2 ＝SV'D” T The method comprises the steps of carrying out a first treatment on the surface of the Further from H 1 And H 2 Constructing a matrix For matrix H 1 Is a pseudo-inverse of (a); and the first M eigenvalues sigma of the matrix G are obtained by using the eigenvalue root method i ，i＝1,2,…M；
S6, calculating to obtain time constants and RC parameters of each branch of the equivalent extended Debye model according to the following formula:
C i ＝τ i /R i (6)
in the formula, |r i I represents r i Re represents the real part, T s For sampling interval, U is polarization voltage, t dep For depolarization time r i Is the complex amplitude of the signal;
r i the value is obtained by the least square method according to equation (7):
in the method, in the process of the invention,representing the characteristic value sigma i The power of (k-1);
and secondly, evaluating the insulation aging degree of the impact capacitor according to the RC parameters of the corresponding branch circuit of the maximum time constant.
2. The impact capacitor insulation diagnosis method according to claim 1, characterized in that the singular value β ii Corresponding singular entropy increment ΔE i The method comprises the following steps:
3. the impact capacitor insulation diagnosis method according to claim 1, wherein in step S5, the process of obtaining the first M eigenvalues of the matrix G using the eigenvalue root method is specifically as follows:
solving the characteristic equation |sigma I-G|= 0,I to represent the identity matrix to obtain L GNon-negative characteristic values, arranging the characteristic values from large to small, taking the first M characteristic values, and marking as sigma i (i=1, 2, … M); then σ= [ σ ] 1 ,σ 2 ,…,σ i ,…,σ M ]。
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107621595A (en) * | 2017-03-28 | 2018-01-23 | 国网四川省电力公司电力科学研究院 | A kind of Condition assessment of insulation device and method of power cable |
| CN111913046A (en) * | 2020-05-27 | 2020-11-10 | 哈尔滨理工大学 | Principle for measuring steady-state relaxation polarizability of insulating dielectric medium based on polarization current time domain spectrum |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107621595A (en) * | 2017-03-28 | 2018-01-23 | 国网四川省电力公司电力科学研究院 | A kind of Condition assessment of insulation device and method of power cable |
| CN111913046A (en) * | 2020-05-27 | 2020-11-10 | 哈尔滨理工大学 | Principle for measuring steady-state relaxation polarizability of insulating dielectric medium based on polarization current time domain spectrum |
Non-Patent Citations (1)
| Title |
|---|
| 基于矩阵束算法的XLPE电力电缆绝缘介质响应参数辨识;张晨萌 等;《高电压技术》;20190528;第45卷(第5期);第1647-1653页 * |
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