CN115469260A - Hausdorff-based current transformer abnormity identification method and system - Google Patents
Hausdorff-based current transformer abnormity identification method and system Download PDFInfo
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Abstract
The invention relates to a current transformer abnormity identification method based on Hausdorff, which comprises the following steps: acquiring operating current data of the current transformer, carrying out multi-stage difference on the operating current data and screening out stable current data; based on the stationary current data, three-phase asymmetric current components of the lines are constructed, and evaluation statistics and variable quantity of each line are calculated; according to the Hausdorff distance of the unbalance degree of the three-phase asymmetric current component of the line, the variable quantity of the evaluation statistic and the kirchhoff current law, constructing a line abnormality recognition model and a phase sequence diagnosis model through different SVM algorithms; and carrying out abnormity identification on the current transformer by using a line abnormity identification model and a phase sequence diagnosis model. The invention combines Hausdorff distance and PCA to extract current characteristics, and identifies the abnormity of the current transformer through a plurality of SVM models, thereby realizing the online monitoring of the metering error state of the current transformer.
Description
Technical Field
The invention belongs to the technical field of power equipment measurement, and particularly relates to a method and a system for identifying abnormality of a current transformer based on Hausdorff.
Background
Current transformers (Current transformers) are important measurement devices in electrical power systems. The primary winding is connected in series in the main loop of the power transmission and transformation, and the secondary winding is respectively connected with equipment such as a measuring instrument, a relay protection or an automatic device and the like according to different requirements, and is used for changing the large current of the primary loop into the small current of the secondary side for the measurement and control protection metering equipment to safely collect. The method is accurate and reliable, and has great significance for safe operation, control protection, electric energy metering and trade settlement of the power system.
Being different from a voltage transformer, the current transformer is characterized in that: 1. the physical relationship in the current transformer group is relatively complex, the physical constraint condition is concealed, and the physical relationship in the voltage transformer group is highlighted. The measured values of voltage transformers at the same node in the transformer substation are consistent, the measured values can be compared with each other among groups for judgment, and the line currents are independent from each other and cannot be compared with each other for realization. 2. The voltage amplitude in the steady-state transformer substation is 110% -120% of rated voltage change, voltage fluctuation is small, overall data characteristics of voltages in different transformer substations are kept consistent, voltage information characteristics are highlighted and have universality, and information physical fusion based on voltage signals is easy to achieve. The line current changes in different transformer substations are independent from each other, the amplitude changes from 0-120% of rated current, the fluctuation is great, and the line current has hidden information characteristics, so that the online monitoring of the metering error state of the current transformer is difficult to realize.
Disclosure of Invention
In order to solve the problem that the metering error state of the current transformer is difficult to monitor on line, the invention provides a current transformer abnormity identification method based on Hausdorff in a first aspect, which comprises the following steps: acquiring operating current data of the current transformer, carrying out multi-stage difference on the operating current data and screening out stable current data; constructing three-phase asymmetric current components of one or more lines based on the stationary current data; calculating the Hausdorff distance of the unbalance degree of the three-phase asymmetric current components of each line and other lines under the same bus according to the three-phase asymmetric current components; constructing characteristic parameters of the line current based on the stationary current data, and constructing a calculation model by using a principal component analysis method; calculating the evaluation statistic and the variable quantity of each line according to the calculation model; according to the Hausdorff distance of the degree of unbalance of the three-phase asymmetric current components of each line and other lines under the same bus and the variable quantity of the evaluation statistic, constructing a line anomaly identification model by using a first SVM algorithm; constructing a phase sequence diagnosis model by utilizing a second SVM algorithm based on the kirchhoff current law and the Hausdorff distance of the abnormal line; and carrying out abnormity identification on one or more current transformers of the line to be evaluated by utilizing the line abnormity identification model and the phase sequence diagnosis model.
In some embodiments of the present invention, the constructing the line anomaly identification model by using the first SVM algorithm according to the Hausdorff distance of the imbalance degree of the three-phase asymmetric current components of each line and other lines under the same bus and the variation of the evaluation statistic includes: constructing a feature vector according to the Hausdorff distance of the unbalance degree of the three-phase asymmetric current components of each line and other lines under the same bus and the variable quantity of the evaluation statistic; determining a target function and a kernel function of a first SVM algorithm, and constructing a line anomaly identification model according to the target function and the kernel function; the objective function is expressed as:
wherein the content of the first and second substances,
in order to be the weight of the weight,y j e { +1, -1} represents a class label that line j is normal or abnormal,v j is the eigenvector of line j;
is a threshold value;Ca penalty factor is represented which is a function of,
represents a relaxation factor; the kernel function is represented as:
，
is a gaussian radial basis kernel function.
Further, optimizing the C, g parameter in the first SVM model by using a gold eagle optimization algorithm.
In some embodiments of the invention, the constructing the phase sequence diagnostic model using the second SVM algorithm based on kirchhoff's current law and the Hausdorff distance of the abnormal line comprises: calculating the Hausdorff distance between the three-phase current of the abnormal line and the three-phase currents of the other lines on the same node based on the kirchhoff current law; calculating the variation of the contribution rate of each phase of current in the abnormal line to the evaluation statistic; and constructing a phase sequence recognition model by adopting a second SVM based on the contribution rate variation and the Hausdorff distance.
Further, the contribution rate is calculated by:
wherein the content of the first and second substances,
is a contribution rate array at time tcont(t) the first to
An element which is also the second element
Counter current transformer pair statisticsQ(t) a contribution rate;
is denoted by tAt the first moment
Real-time data after mutual sensor standardization;
is composed of
Projection in the principal component space.
In the foregoing embodiment, the calculating, according to the three-phase asymmetric current components, a Hausdorff distance of an imbalance degree of the three-phase asymmetric current components of each line and other lines under the same bus includes: calculating zero sequence unbalance and negative sequence unbalance of each line based on three-phase asymmetric current components; and calculating Hausdorff distances between zero sequence unbalance characteristic parameters and negative sequence unbalance characteristic parameters of each line and other lines in the same period for the lines on the same bus.
In a second aspect of the present invention, a current transformer abnormality identification system based on Hausdorff is provided, including: the acquisition module is used for acquiring the operating current data of the current transformer, performing multi-stage difference on the operating current data and screening out stable current data; constructing three-phase asymmetric current components of one or more lines based on the stationary current data; calculating the Hausdorff distance of the unbalance degree of the three-phase asymmetric current components of each line and other lines under the same bus according to the three-phase asymmetric current components; the calculation module is used for constructing characteristic parameters of the line current based on the steady current data and constructing a calculation model by using a principal component analysis method; calculating the evaluation statistic and the variable quantity of each line according to the calculation model; the first construction module is used for constructing a line abnormity identification model by utilizing a first SVM algorithm according to the Hausdorff distance of the unbalance degree of the three-phase asymmetric current components of each line and other lines under the same bus and the variable quantity of the evaluation statistic; the second construction module is used for constructing a phase sequence diagnosis model by utilizing a second SVM algorithm based on the kirchhoff current law and the Hausdorff distance of the abnormal line; and the identification module is used for carrying out abnormity identification on one or more current transformers of the line to be evaluated by utilizing the line abnormity identification model and the phase sequence diagnosis model.
In a third aspect of the present invention, there is provided an electronic apparatus comprising: one or more processors; the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the Hausdorff-based current transformer abnormity identification method provided by the invention in the first aspect.
In a fourth aspect of the present invention, a computer readable medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the Hausdorff-based current transformer anomaly identification method provided in the first aspect of the present invention.
The invention has the beneficial effects that:
the invention provides a current transformer abnormity identification method based on Hausdorff, which comprises the steps of screening stable section data according to the current transformer range and current fluctuation, and respectively calculating the Hausdorff distance ratio and the Q statistic variable quantity of a distance line by respectively adopting a Hausdorff distance algorithm and a Q statistic quantity algorithm according to the preprocessed stable three-phase current dataΔQ(ii) a Then, constructing a line anomaly identification model by adopting an improved SVM algorithm; based on an abnormal line model, a Hausdorff distance algorithm and the variable quantity of the contribution rate index are adoptedΔcont i And (t) constructing an identification model of the abnormal phase sequence, thereby realizing the online identification of the abnormal mutual inductor from the line current data with large volatility and hidden characteristics.
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FIG. 1 is a schematic basic flow diagram of a Hausdorff-based current transformer anomaly identification method in some embodiments of the present invention;
fig. 2 is a schematic flow chart of a specific method for identifying an anomaly of a current transformer based on Hausdorff in some embodiments of the present invention;
FIG. 3 is a schematic diagram of a Hausdorff-based current transformer anomaly identification system in some embodiments of the present invention;
fig. 4 is a schematic structural diagram of an electronic device in some embodiments of the invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth to illustrate, but are not to be construed to limit the scope of the invention.
Referring to fig. 1, in a first aspect of the present invention, there is provided a current transformer abnormality identification method based on Hausdorff, including: s100, obtaining operating current data of the current transformer, carrying out multi-stage difference on the operating current data, and screening out stable current data; constructing three-phase asymmetric current components of one or more lines based on the stationary current data; calculating the Hausdorff distance of the unbalance degree of the three-phase asymmetric current components of each line and other lines under the same bus according to the three-phase asymmetric current components; s200, constructing characteristic parameters of the line current based on the steady current data, and constructing a calculation model by using a principal component analysis method; calculating the evaluation statistic and the variable quantity of each line according to the calculation model; s300, constructing a line abnormity identification model by using a first SVM algorithm according to the Hausdorff distance of the unbalance degree of the three-phase asymmetric current components of each line and other lines under the same bus and the variable quantity of the evaluation statistic; s400, constructing a phase sequence diagnosis model by utilizing a second SVM algorithm based on the kirchhoff current law and the Hausdorff distance of the abnormal line; s500, performing anomaly identification on one or more current transformers of the line to be evaluated by using the line anomaly identification model and the phase sequence diagnosis model.
In step S100 of some embodiments of the present invention, operation current data of the current transformer is obtained, and multi-step difference is performed on the operation current data, and stable current data is screened out; specifically, collecting current data of the current transformer, preprocessing the current data by adopting a first-order difference and a second-order difference, and screening stable current data;
for the current transformer, when the line current is lower than the rated current, the error of the current transformer is large, and the data quality is low, so that current data with the rated range of 80% -120% are screened. Meanwhile, the current fluctuation in the power grid is large, and more data breakpoints exist in current data, so that the collected current amplitude data is subjected to first-order and second-order differential processing according to the formula (1) and the formula (2), and the current data breakpoints are screened out.
First order difference:
； （1），
second order difference:
； （2），
wherein the content of the first and second substances,x(Ω) is current amplitude data, Δ 1 x(Ω)、Δ 2 xAnd (omega) the current amplitude data has first-order and second-order difference values, and omega is a data point. When in use
、
Then, the first and second order data are judged to be stable, wherein
、
Is a set threshold.
It can be understood that the stable current data is screened out based on the first-order and second-order difference results. Constructing three-phase asymmetric current components of one or more lines based on the stationary current data; calculating the Hausdorff distance of the unbalance degree of the three-phase asymmetric current components of each line and other lines under the same bus according to the three-phase asymmetric current components; specifically, based on the screened current data, a zero sequence current component and a negative sequence current component of the line are constructed, and the ratio of the lines is calculated by respectively adopting Hausdorff distance
、
(ii) a The current data in the power grid fluctuate greatly, the transient process is frequent, the amplitude fluctuation is large, no fixed rule exists, and the construction of an operation error monitoring model is difficult to realize according to the amplitude phase characteristics. However, the negative sequence unbalance and the zero sequence unbalance are relatively stable and have a certain rule, so that the model construction can be realized by taking the negative sequence unbalance and the zero sequence unbalance as characteristic quantities.
Specifically, according to the pre-screened three-phase current modeling data, the asymmetric three-phase current phasor is decomposed into symmetric positive-sequence negative-sequence and zero-sequence current components according to a formula (3).
Wherein the content of the first and second substances,I a the phase a is selected as a reference phase,
namely the three-phase current is obtained,
corresponding a-phase positive sequence, negative sequence and zero sequence components. In the formula, operator
,
. And:
then:
。
then, extracting characteristic parameters: zero sequence imbalance and negative sequence imbalance;
zero-sequence unbalance:
negative sequence imbalance:
and obtaining the zero sequence unbalance and the negative sequence unbalance of the line based on the formula (4) and the formula (5).
2) Hausdorff distance algorithm principle
The Hausdorff distance is a metric describing the degree of similarity between the 2-point sets. Assume that there are 2 sets of points:
H(U,Z)=max(h(U,Z),h(Z,U)) （7），
wherein the content of the first and second substances,
。
3) And calculating Hausdorff distances among zero sequence unbalanced and negative sequence unbalanced characteristic parameters of each line in the same period for the lines on the same bus.
(1) Taking the zero sequence unbalance as an example, calculating the Hausdorff distance between the zero sequence unbalances of each line:
in the formula (8), n represents a line on the same busThe number of the paths is equal to that of the channels,H n1 the Hausdorff distance between the nth line and the 1 st line zero sequence unbalance degree.
Based on the matrix H, calculating the Hausdorff distance ratio of the j column: calculating the ratio:
（9），
in the formula (9), the reaction mixture is,r j is as followsjThe ratio of the columns is such that,H jmax =max{H j1 ,H j2 …H jn m =1,2, …, n; j =1,2, …, n, n is the number of lines;j≠n，r j is as followsjColumn ratio.
(2) Calculating the ratio of Hausdorff distance between zero-sequence unbalance degrees and negative-sequence unbalance degrees of each line of circuit based on the step (1)
、
。
In step S200 of some embodiments of the present invention, a characteristic parameter of the line current is constructed based on the stationary current data, and a calculation model is constructed using a principal component analysis method; calculating the evaluation statistic and the variable quantity of each line according to the calculation model; specifically, based on the screened stable current data, the line current is taken as a characteristic parameter, a PCA is adopted to construct a calculation model, and an evaluation standard quantity in a normal mode is calculated
And line real-time statisticsQ(t) calculating the variation Delta of the statistic of each lineQ(t); the more detailed procedure is as follows:
using current data under 80% -120% rated amplitude as characteristic parameters, adopting PCA to construct an evaluation calculation model under a normal mode, and calculating evaluation statistic
。
1) Constructing a sample set in a normal mode by using current data in the normal mode
Where N is the number of sample points. A. B, C represents three phases, x represents the sampled current data in the sample.
2) Normalization process
In order to avoid the influence caused by the difference of variable dimensions, firstly, the data samples are obtained
And (3) carrying out standardization treatment, wherein the standardized data matrix is as follows:
wherein N is the number of sampling points, and M is the number of mutual inductors.
， 
Wherein
Is a matrix Y 0 The mean of the vectors of the M-th column,
in which
Is a matrix Y 0 Variance of mth column vector.
3) Based on
The covariance R of the residual error is subjected to singular value decomposition to determine a load matrix p of a residual error subspace e . From a modeling dataset in a normal modality
And corresponding residual subspace loading matrix p e Calculating evaluation standard quantity under confidence by using method based on nuclear density estimation
(ii) a From real-time data sets
And corresponding residual subspace loading matrix p e Computing real-time statisticsQ(t)。
In the formula, R on the left side is a covariance matrix, R on the right side is singular value decomposition,
is the eigenvalue of covariance matrix, and the arrangement order is satisfied
，[p m p e ]Representing a feature vector matrix; the eigenvector matrix [ p ] obtained at this time m p e ]Is the load matrix P. Forming a load matrix p of principal components by accumulating variance contributions m The rest forming the residual load matrix
。
4）QThe statistics are embodied as follows:
5) Evaluation-based statistics
And real-time statisticsQ(t) calculating a statistical change amount Δ of each lineQ(t)。
In step S300 of some embodiments of the present invention, the constructing a line anomaly identification model using a first SVM algorithm, including: s301, evaluating the variation delta of statistics according to the Hausdorff distance of the unbalance degree of the three-phase asymmetric current components of each line and other lines under the same busQ(t) constructing a feature vector;
based on
、
、ΔQAnd (t) constructing a feature vector, and constructing a recognition model by adopting an SVM algorithm. To be provided with
、
、ΔQ(t) constructing a feature vector at time tv：
In the formula (14), the compound represented by the formula (I),
representing the zero sequence unbalance Hausdorff distance ratio of the jth line and other lines,
representing the ratio of the negative sequence imbalance Hausdorff distance, delta, of the j-th line to the other linesQ(t) is the amount of change in the statistics of the jth line; j =1,2, …, n, n is the number of lines.
Based on the characteristic parameters of the samples, an SVM model is adopted to find an optimal hyperplane which can completely separate the samples of different classes. Considering that the classification performance of the SVM can be seriously influenced by outliers in the data, in order to make the model more stable, soft intervals and penalty terms are introduced, and the improved SVM objective function is as follows:
wherein, the first and the second end of the pipe are connected with each other,
in order to be the weight, the weight is,y j e { +1, -1} represents a class label that line j is normal or abnormal,v j is the eigenvector of line j;
is a threshold value;Ca penalty factor is represented which is a function of,
represents a relaxation factor; another important part of SVM is the kernel function and its kernel function parameters, which can help SVM deal with the non-linearity problem it cannot solve. The classification function of the SVM in the case of kernel mapping is:
wherein, the first and the second end of the pipe are connected with each other,
is a Lagrange multiplier and is a Lagrange multiplier,
is a function of the gaussian radial basis kernel,
. The key of SVM model construction is to solve the optimal value problem of kernel parameter g and punishment coefficient C. Therefore, the method introduces a gold eagle optimization algorithm to optimize the SVM model parameters, and improves the model performance. The method comprises the following specific steps:
1) Aggressive behavior
The attack vector of the gold eagle is:
in the formula (I), the compound is shown in the specification,
is a first
The attack vector of only the golden eagle,
the best hunting place (prey) to which the current falcon arrives,
is as follows
Only the current position of the falcon.
2) Cruising behaviour
The scalar form of the hyperplane in three-dimensional space is:
in the formula (I), the compound is shown in the specification,
the vector is a normal vector, and the vector is a vector,
is a variable vector. Here, the
The position of the golden eagle is shown and defineds 1 For the penalty factor C in the SVM model,s 2 is the kernel parameter g in the SVM model. Find the value of the fixed variable:
in the formula (I), the compound is shown in the specification,c k is the target point ofkThe number of the elements is one,
is the first of the attack vector
The number of the elements is one,a k is the first of the attack vectorkA vector. So that the random target point on the flying hyperplane can be found. The general representation of the target points is:
in the formula, random is in the shape of [0,1], and random number updating enables the falcon to be explored to a random target point.
3) Move to a new position
The displacement of the gold eagle is composed of an attack vector and a target position, and the iteration step vector is as follows:
in the formula (I), the compound is shown in the specification,p a in order to be a coefficient of attack,p c in order to be the cruise factor,
、
is [0,1]Random vector of (2).
Based on this, the next position of the eagle can be found:
where T represents the current iteration number and T represents the maximum iteration number.
、
Are respectively asp a The initial and final values of (a) are,
、
are respectively asp c Initial and final values of. In order to prevent the optimization algorithm from falling into local optimization, the golden eagle position updating algorithm is optimized:
In step S400 of some embodiments of the present invention, the constructing a phase sequence diagnostic model using the second SVM algorithm based on the kirchhoff' S law of current and the Hausdorff distance of the abnormal line includes: calculating the Hausdorff distance between the three-phase current of the abnormal line and the three-phase currents of the other lines on the same node based on the kirchhoff current law; calculating the variation of the contribution rate of each phase of current in the abnormal line to the evaluation statistic; and constructing a phase sequence recognition model by adopting a second SVM based on the contribution rate variation and the Hausdorff distance.
Specifically, based on kirchhoff's current law, the Hausdorff distances of the three phases A, B, C of the abnormal line and the three phases A, B, C of the rest lines on the same node are calculated respectivelyH ψ (ii) a Definition circuitL 1 、L 2 …L l The transmission lines are respectively connected with the branches connected with the bus, and the A, B, C three phases of the transmission lines meet kirchhoff's theorem:
wherein
Indicating lineL 1 The a-phase primary current sample value sequence of (1). Based on the line positioning result, selecting abnormal lineL y Memory for recordingL y Line A phase primary current vector is
. The sum of the phasors of the primary currents of the remaining lines is recorded
Then, then
Namely, it is
And with
Equal in amplitude and phase difference of
Then, based on the Hausdorff distance algorithm:
wherein the content of the first and second substances,
、
respectively represent pair
And with
And the current sampling sequence carries out per unit current data.
In the actual operation process, the influence of the operation error of the transformer is considered, and the calculation is carried out through the secondary side data of the current transformer:
wherein the content of the first and second substances,
for selected abnormal lineL y The A phase of (1) acquires per unit current data of the secondary current data,
is a lineL y Rated transformation ratio of (1);
and calculating current phasor and per unit current data for other lines of the same bus through secondary side current and rated transformation ratio. And calculating the distance between the abnormal line A, B, C three-phase (one phase or multiple phases) and the current data of the same phase of the other lines in one running time.
Further, the contribution rate is calculated by:
wherein, the first and the second end of the pipe are connected with each other,
contribution rate array for time tcont(t) the first to
An element which is also the second element
Counter current transformer pair statisticsQ(t) a contribution rate;
denoted as time t
Real-time data after mutual sensor standardization;
is composed of
Projection in the principal component space.
In the foregoing embodiment, the calculating, according to the three-phase asymmetric current components, a Hausdorff distance of an imbalance degree of the three-phase asymmetric current components of each line and other lines under the same bus includes: calculating zero sequence unbalance and negative sequence unbalance of each line based on three-phase asymmetric current components; and calculating Hausdorff distance between zero sequence unbalanced and negative sequence unbalanced characteristic parameters of each line and other lines in the same period for the lines on the same bus.
Referring to fig. 2, in an embodiment of the present invention, the method for identifying an abnormality of a current transformer based on Hausdorff includes: step A: collecting current data of the current transformer, preprocessing the current data by adopting a first-order difference and a second-order difference, and screening stable current data;
step A: constructing zero sequence current component and negative sequence current component of the line based on the screened current data, and calculating the ratio of the lines by respectively adopting Hausdorff distance
、
；
And B: based on screening stable current data, taking line current as characteristic parameter, adopting PCA to construct calculation model, and calculating evaluation standard quantity under normal modeQ α And line real-time statisticsQ(t) calculating the amount of change Δ of each line statisticQ(t)；
And C: calculating each line based on the step B, C
、
、ΔQAnd (t) constructing a line anomaly identification model by adopting an improved GEO-SVM learning algorithm.
Step D: based on kirchhoff's current law, hausdorff distances of three phases A, B, C of the abnormal line and three phases A, B, C of the rest lines on the same node are calculated respectively
；
Step E: calculating A, B, C three-phase pair statistic in abnormal line
Amount of change in the contribution rate of
；
Step F: based on the amount of change in the contribution rate
And Hausdorff distanceH i Adopting an SVM to construct a phase sequence recognition model, and positioning a phase sequence number of an abnormal transformer in an abnormal line;
step G: and inputting the line to be evaluated into the model to realize the abnormal recognition of the mutual inductor.
Example 2
Referring to fig. 3, in a second aspect of the present invention, there is provided a Hausdorff-based current transformer abnormality recognition system 1, including: the acquisition module 11 is used for acquiring the operating current data of the current transformer, performing multi-stage difference on the operating current data and screening out stable current data; constructing three-phase asymmetric current components of one or more lines based on the stationary current data; calculating the Hausdorff distance of the unbalance degree of the three-phase asymmetric current components of each line and other lines under the same bus according to the three-phase asymmetric current components; the calculation module 12 is used for constructing characteristic parameters of the line current based on the stationary current data and constructing a calculation model by using a principal component analysis method; calculating the evaluation statistic and the variable quantity of each line according to the calculation model; the first construction module 13 is configured to construct a line anomaly identification model by using a first SVM algorithm according to a Hausdorff distance of the degree of unbalance between three-phase asymmetric current components of each line and other lines under the same bus and a variation of the evaluation statistic; the second construction module 14 is configured to construct a phase sequence diagnostic model by using a second SVM algorithm based on kirchhoff's current law and the Hausdorff distance of the abnormal line; and the identification module 15 is configured to perform anomaly identification on one or more current transformers of the line to be evaluated by using the line anomaly identification model and the phase sequence diagnosis model.
Further, the second building module 14 includes: the first calculation unit is used for calculating Hausdorff distances between three-phase currents of the abnormal line and three-phase currents of other lines on the same node based on the kirchhoff current law; a second calculation unit for calculating a contribution rate variation amount of each phase current in the abnormal line to the evaluation statistic; and the construction unit is used for constructing a phase sequence recognition model by adopting a second SVM based on the contribution rate variation and the Hausdorff distance.
Example 3
Referring to fig. 4, in a third aspect of the present invention, there is provided an electronic apparatus comprising: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method of the invention in the first aspect.
The electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following devices may be connected to the I/O interface 505 in general: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; a storage device 508 including, for example, a hard disk; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 4 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 4 may represent one device or may represent multiple devices as desired.
In particular, the processes described above with reference to the flow diagrams may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of embodiments of the present disclosure. It should be noted that the computer readable medium described in the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may be separate and not incorporated into the electronic device. The computer readable medium carries one or more computer programs which, when executed by the electronic device, cause the electronic device to:
computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, python, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.
Claims (10)
1. A current transformer abnormity identification method based on Hausdorff is characterized by comprising the following steps:
acquiring operating current data of the current transformer, carrying out multi-stage difference on the operating current data and screening out stable current data; constructing three-phase asymmetric current components of one or more lines based on the stationary current data; calculating the Hausdorff distance of the unbalance degree of the three-phase asymmetric current components of each line and other lines under the same bus according to the three-phase asymmetric current components;
constructing characteristic parameters of the line current based on the steady current data, and constructing a calculation model by utilizing a principal component analysis method; calculating the evaluation statistic and the variable quantity of each line according to the calculation model;
according to the Hausdorff distance of the unbalance degree of the three-phase asymmetric current components of each line and other lines under the same bus and the variable quantity of the evaluation statistic, constructing a line anomaly recognition model by utilizing a first SVM algorithm;
constructing a phase sequence diagnosis model by utilizing a second SVM algorithm based on the kirchhoff current law and the Hausdorff distance of the abnormal line;
and carrying out abnormity identification on one or more current transformers of the line to be evaluated by utilizing the line abnormity identification model and the phase sequence diagnosis model.
2. The method for identifying the abnormality of the Hausdorff-based current transformer according to claim 1, wherein the step of constructing the line abnormality identification model by using the first SVM algorithm according to the Hausdorff distance of the unbalance degree of the three-phase asymmetric current components of each line and other lines under the same bus and the variation of the evaluation statistic comprises the steps of:
constructing a feature vector according to the Hausdorff distance of the unbalance degree of the three-phase asymmetric current components of each line and other lines under the same bus and the variable quantity of the evaluation statistic;
determining a target function and a kernel function of a first SVM algorithm, and constructing a line anomaly identification model according to the target function and the kernel function; the objective function is expressed as:
wherein the content of the first and second substances,
in order to be the weight, the weight is,
a category label indicating that line j is normal or abnormal,v j is the eigenvector of line j;
is a threshold value;Ca penalty factor is represented which is a function of,
represents a relaxation factor; the kernel function is represented as:
，
is a gaussian radial basis kernel function.
3. The Hausdorff-based current transformer abnormality identification method according to claim 2, further comprising: and optimizing the C, g parameter in the first SVM model by adopting a gold eagle optimization algorithm.
4. The method for identifying the abnormality of the Hausdorff-based current transformer according to claim 1, wherein the constructing of the phase sequence diagnosis model by using the second SVM algorithm based on the kirchhoff current law and the Hausdorff distance of the abnormal line comprises:
calculating the Hausdorff distance between the three-phase current of the abnormal line and the three-phase currents of the other lines on the same node based on the kirchhoff current law;
calculating the variation of the contribution rate of each phase of current in the abnormal line to the evaluation statistic;
and constructing a phase sequence recognition model by adopting a second SVM based on the contribution rate variation and the Hausdorff distance.
5. The Hausdorff-based current transformer abnormality recognition method according to claim 4,
the contribution rate is calculated as follows:
wherein the content of the first and second substances,
is a contribution rate array at time tcont(t) the first to
An element, which is also the first
Counter current transformer pair statisticsMeasurement ofQ(t) a contribution rate;
denoted as time t
Real-time data after mutual sensor standardization;
is composed of
Projection in the principal component space.
6. The Hausdorff-based current transformer abnormality identification method according to any one of claims 1 to 5, wherein the calculating of the Hausdorff distance of the degree of unbalance of the three-phase asymmetric current components of each line and other lines under the same bus according to the three-phase asymmetric current components comprises:
calculating zero sequence unbalance and negative sequence unbalance of each line based on three-phase asymmetric current components;
and calculating Hausdorff distances between zero sequence unbalance characteristic parameters and negative sequence unbalance characteristic parameters of each line and other lines in the same period for the lines on the same bus.
7. The utility model provides a current transformer abnormal recognition system based on Hausdorff which characterized in that includes:
the acquisition module is used for acquiring the operating current data of the current transformer, performing multi-stage difference on the operating current data and screening out stable current data; constructing three-phase asymmetric current components of one or more lines based on the stationary current data; calculating the Hausdorff distance of the unbalance degree of the three-phase asymmetric current components of each line and other lines under the same bus according to the three-phase asymmetric current components;
the calculation module is used for constructing characteristic parameters of the line current based on the steady current data and constructing a calculation model by using a principal component analysis method; calculating the evaluation statistic and the variable quantity of each line according to the calculation model;
the first construction module is used for constructing a line abnormity identification model by utilizing a first SVM algorithm according to the Hausdorff distance of the unbalance degree of the three-phase asymmetric current components of each line and other lines under the same bus and the variable quantity of the evaluation statistic;
the second construction module is used for constructing a phase sequence diagnosis model by utilizing a second SVM algorithm based on the kirchhoff current law and the Hausdorff distance of the abnormal line;
and the identification module is used for carrying out abnormity identification on one or more current transformers of the line to be evaluated by utilizing the line abnormity identification model and the phase sequence diagnosis model.
8. The Hausdorff based current transformer anomaly identification system according to claim 7, wherein the second building block comprises:
the first calculation unit is used for calculating Hausdorff distances between three-phase currents of the abnormal line and three-phase currents of other lines on the same node based on the kirchhoff current law;
a second calculation unit for calculating a contribution rate variation amount of each phase current in the abnormal line to the evaluation statistic;
and the construction unit is used for constructing a phase sequence recognition model by adopting a second SVM based on the contribution rate variation and the Hausdorff distance.
9. An electronic device, comprising: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the Hausdorff-based current transformer anomaly identification method as claimed in any one of claims 1 to 6.
10. A computer-readable medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the Hausdorff-based current transformer anomaly identification method according to any one of claims 1 to 6.
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