CN113884839A - Multi-parameter insulation state evaluation method and system for capacitor voltage transformer - Google Patents
Multi-parameter insulation state evaluation method and system for capacitor voltage transformer Download PDFInfo
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Abstract
The invention discloses a multi-parameter insulation state evaluation method, system and equipment for a capacitor voltage transformer, and relates to the technical field of power transmission and transformation equipment. A multi-parameter insulation state evaluation method for a capacitor voltage transformer comprises the following steps: collecting sample data, and carrying out normalization processing on the sample data, wherein the sample data comprises CVT aging time, CVT insulation characteristic parameters and CVT residual breakdown field strength parameters; establishing an insulation characteristic parameter prediction model by adopting a linear kernel function to obtain a CVT insulation parameter prediction value; establishing a residual breakdown field strength prediction model by adopting a Gaussian radial basis kernel function to obtain a residual breakdown field strength prediction value; and evaluating the insulation state of the CVT according to the predicted value of the residual breakdown field strength. According to the method, the evaluation of the insulation state of the CVT is realized by establishing the mapping relation between the insulation characteristic parameters of the CVT and the residual breakdown field strength, and the safe operation of the CVT and the safe reliability of a power grid are ensured.
Description
Technical Field
The invention relates to the technical field of power transmission and transformation equipment, in particular to a method, a system and equipment for evaluating the multi-parameter insulation state of a capacitor voltage transformer.
Background
With the development of economy, the development of the power industry has increasingly highlighted proportion in national economic construction. In order to accurately measure electric energy, the investment of power transformers is becoming more and more extensive. The function of the power transformer is very important as one of key devices for data acquisition in a power grid. In recent years, the transmission capacity of a power system in China is continuously expanded, the long-distance transmission capacity is rapidly increased, and the voltage level of a power grid is gradually improved. Compared with the traditional electromagnetic voltage transformer, the Capacitor Voltage Transformer (CVT) has the advantages of small volume, no generation of ferromagnetic resonance and the like, gradually replaces the electromagnetic voltage transformer, and is widely applied to a power grid with the voltage level of 110kV or above. According to statistics, in a 110 kV-1000 kV voltage class power system, the utilization rate of a CVT in the power system reaches over 90%, and the CVT becomes a first-choice device for gateway metering and measurement and control signal sources, so that great economic benefits and social benefits are generated. Due to the influence of factors such as raw materials, manufacturing experience, system overvoltage and the like, fault defects such as voltage division ratio change, breakdown of an intermediate transformer, a compensation reactor and the like caused by insulation aging can occur in the operation process of the CVT, and the defects directly influence the safety operation and the fairness of metering of a power grid. Therefore, in order to ensure safe and stable operation of the power equipment, accurate evaluation and display of the insulation state of the CVT are necessary.
Disclosure of Invention
In order to overcome the above problems or partially solve the above problems, an object of the present invention is to provide a method, a system, and a device for evaluating a multi-parameter insulation state of a capacitor voltage transformer, which ensure that a power device can operate safely and stably.
The invention is realized by the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method for evaluating a multi-parameter insulation state of a capacitor voltage transformer, including the following steps: s101, collecting sample data, and performing normalization processing on the sample data, wherein the sample data comprises a sample set A: CVT aging time, sample set B: CVT insulation characteristic parameter, sample set C: a CVT residual breakdown field strength parameter; s102, establishing an insulation characteristic parameter prediction model by adopting a linear kernel function based on the sample set A and the sample set B to obtain a sample set D: predicting a CVT insulation parameter; s103, based on the sample set C and the sample set D, establishing a residual breakdown field strength prediction model by adopting a Gaussian radial basis function to obtain a sample set E: predicting the residual breakdown field intensity; and S104, evaluating the insulation state of the CVT according to the sample set E.
Based on the first aspect, in some embodiments of the present invention, the CVT insulation characteristic parameter at least includes one of a leakage current, a dielectric tangent, a capacitance change amount, and a partial discharge parameter of the CVT.
Based on the first aspect, in some embodiments of the invention, the formula is utilized:
normalizing the sample data, wherein x, y belongs to R, x is sampling value, x isminIs a minimum sample value, xmaxIs the maximum sample value.
Based on the first aspect, in some embodiments of the present invention, the above-mentioned sample set a is based on: CVT aging time and sample set B: the CVT insulation characteristic parameter adopts a support vector machine algorithm, and the construction of an insulation characteristic parameter prediction model comprises the following steps: s1021, establishing an insulation characteristic parameter prediction model by adopting a linear kernel function, and performing a linear kernel function on the sample set A: CVT aging time as input, sample set B: using the CVT insulation characteristic parameter as output, and using a support vector machine as an algorithm to obtain a punishment parameter C1 of the insulation characteristic parameter prediction model; and S1022, training the insulation characteristic parameter prediction model based on the penalty parameter C1 to obtain a trained insulation characteristic parameter prediction model.
Based on the first aspect, in some embodiments of the present invention, the expression of the linear kernel function is:
xjand xiRespectively representing the jth and ith eigenvalues in the sample acquisition.
Based on the first aspect, in some embodiments of the present invention, constructing a residual breakdown field strength prediction model based on the sample set D and the sample set C by using a support vector machine algorithm includes: s1031, establishing a residual breakdown field strength prediction model by adopting a Gaussian radial basis function, and establishing a residual breakdown field strength prediction model by adopting a support vector machine algorithm by taking the sample set C and the sample set D as input to obtain a sample set E; s1032, training the residual breakdown field strength prediction model based on the penalty parameter C2 and the kernel function parameter to obtain a trained residual breakdown field strength model.
Based on the first aspect, in some embodiments of the present invention, the gaussian radial basis function expression is: k (x)j,xi)＝exp(-g||xj-xi||2) G is a kernel function parameter, xjAnd xiRespectively representing the jth and ith eigenvalues in the sample acquisition.
Based on the first aspect, in some embodiments of the present invention, the evaluating the insulation state of the CVT based on the sample set E includes: s1041, analyzing and summarizing through a simulation experiment to obtain an insulation state score standard table, wherein different score ranges in the table correspond to different CVT state evaluation states; s1042, comparing the sample set E with the table 1 through normalization processing, and predicting the insulation state condition of the CVT; and S1043, determining a score range where the insulation state score is located, and giving a state evaluation conclusion corresponding to the score range.
In a second aspect, an embodiment of the present invention provides a system for evaluating a multi-parameter insulation state of a capacitor voltage transformer, including: a data acquisition module: the system comprises a sample data acquisition module, a sample data processing module and a data processing module, wherein the sample data acquisition module is used for acquiring sample data and carrying out normalization processing on the sample data, and the sample data comprises a sample set A, a sample set B and a sample set C; a first model building module: based on the sample set A and the sample set B, constructing an insulation characteristic parameter prediction model by adopting a support vector machine algorithm, and obtaining a sample set D by utilizing the insulation characteristic parameter prediction model; a second model building module: the method comprises the steps of constructing a residual breakdown field strength prediction model by adopting a support vector machine algorithm based on the sample set C and the sample set D, and obtaining a sample set E by utilizing the residual breakdown field strength prediction model; an evaluation module: for determining from the sample set E: and (5) estimating the insulation state of the CVT by using the predicted value of the residual breakdown field strength.
In a third aspect, an embodiment of the present invention provides an electronic device, including: at least one processor, at least one memory, and a data bus; wherein, the processor and the memory complete mutual communication through the data bus; the memory stores program instructions executable by the processor, which invokes the program instructions to perform the one or more programs or methods.
Compared with the prior art, the invention at least has the following advantages and beneficial effects:
according to the method, the mixed kernel function support vector machine algorithm is adopted to establish the mapping relation between the CVT insulation characteristic parameters and the residual breakdown field intensity, the residual breakdown field intensity parameters of the CVT in the sample time are accurately predicted, the insulation state of the CVT is evaluated, the intelligent operation and maintenance level of the CVT is improved, and the safe operation of the CVT and the safe reliability of a power grid are guaranteed.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and that for those skilled in the art, other related drawings can be obtained from these drawings without inventive effort. In the drawings:
FIG. 1 is a schematic flowchart of an embodiment of a multi-parameter insulation state evaluation method for a capacitive voltage transformer;
FIG. 2 is a flowchart illustrating detailed steps of an embodiment of a method for estimating a multi-parameter insulation state of a capacitive voltage transformer;
FIG. 3 is a block diagram of an embodiment of a multi-parameter insulation state assessment system for a capacitive voltage transformer;
fig. 4 is a block diagram of an electronic device.
Icon: 1-a processor; 2-a memory; 3-a data bus; 100-a data acquisition module; 200-a first model building module; 300-a second model building module; 400-evaluation module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1
Referring to fig. 1 and fig. 2, an embodiment of the present invention provides a method for evaluating a multi-parameter insulation state of a capacitor voltage transformer, which includes the following steps:
s101, collecting sample data, and performing normalization processing on the sample data, wherein the sample data comprises a sample set A: CVT aging time, sample set B: CVT insulation characteristic parameter, sample set C: a CVT residual breakdown field strength parameter;
in order to reduce errors caused by large magnitude difference between different insulation characteristic parameters, in this embodiment, normalization processing is performed after sample data obtained from an experiment is sorted and calculated, and a sample normalization processing formula is as follows:
wherein x, y belongs to R, x is sampling value, xminIs a minimum sample value, xmaxIs the maximum sample value.
Wherein the sample data comprises a sample set A, a sample set B and a sample set C. Specifically, the sample of the insulation characteristic parameter of the CVT in the present embodiment includes one of a leakage current, a dielectric tangent value, a capacitance change amount, and a partial discharge parameter of the CVT.
Leakage current: capacitive devices are often considered to be purely capacitive media with good insulation, in which case if a voltage is applied across the device, no resistive current is generated, i.e. the current flowing through the device is purely capacitive. However, in actual operation, due to impurities, manufacturing processes, aging of the device after long-term operation, and the like, the device generates a lossy medium, that is, energy loss under the action of voltage, and the loss includes conduction loss and polarization loss, which is also called dielectric loss. As the insulation degradation of the device becomes more and more severe, the proportion of resistive current in the total current flowing through the device gradually increases and the dielectric loss of the device increases. Therefore, for the same capacitive device, the magnitude of the dielectric loss can reflect the quality of the insulation.
Dielectric loss tangent: the ratio of the active current to the reactive current in the dielectric medium under the action of the ac voltage is called the dielectric loss tangent. When the insulation is deteriorated, the dielectric conductance increases, and the active current component increases, so that the dielectric loss tangent increases. By measuring the dielectric loss tangent value, the insulation condition can be accurately known, and the distribution defects of the whole insulation, such as moisture, deterioration and the like, can be found.
Capacity variation amount: the capacitance test of the CVT is the most important means for ensuring the safe operation of the CVT so far, and is an important characteristic parameter capable of reflecting the overall moisture condition of the equipment. The CVT is composed of a capacitance voltage division unit and an electromagnetic unit, wherein the capacitance voltage division unit is formed by vertically overlapping a single-section or multi-section sleeve type coupling capacitor and a capacitance voltage divider. Each section of coupling capacitor or capacitive voltage divider unit is provided with a capacitive element consisting of dozens of film paper composite media which are connected in series, and is sealed by dodecyl benzene insulating oil. When the capacitor breaks down, the capacitance changes suddenly, and when the capacitor is affected by factors affecting the insulation performance such as temperature, humidity and aging, the capacitance changes accordingly. Therefore, detecting the change in the capacitance of the CVT is an effective way to check the insulation condition thereof.
Partial discharge parameters (including discharge amount, discharge phase, number of discharges): when a local defect occurs in the insulating medium, a local discharge occurs at the local defect, so that the insulation is gradually eroded and damaged. Partial discharge is a main cause and manifestation of insulation damage, and therefore, measurement of the intensity of the partial discharge can effectively detect intrinsic defects of internal insulation, and partial defects and development thereof due to long-term operation insulation aging.
S102, establishing an insulation characteristic parameter prediction model by adopting a linear kernel function based on the sample set A and the sample set B to obtain a sample set D: predicting a CVT insulation parameter;
in an exemplary embodiment, the specific steps of constructing the insulation characteristic parameter prediction model by using a support vector machine algorithm based on the sample set a and the sample set B include:
s1021, establishing an insulation characteristic parameter prediction model by adopting a linear kernel function, and obtaining a sample C by taking the sample set A and the sample set B as input and a support vector machine as an algorithm;
wherein, the expression of the linear kernel function is:
(xj,xi) To train the samples, xjAnd xiRespectively representing the jth and ith eigenvalues in the sample acquisition.
And S1022, training the insulation characteristic parameter prediction model based on the penalty parameter C1 to obtain a trained insulation characteristic parameter prediction model.
S103, based on the sample set C and the sample set D, establishing a residual breakdown field strength prediction model by adopting a Gaussian radial basis function to obtain a sample set E: predicting the residual breakdown field intensity;
in an exemplary embodiment, the specific steps of constructing the residual breakdown field strength prediction model by using a support vector machine algorithm based on the sample set C and the sample set D include:
s1031, establishing a residual breakdown field strength prediction model by adopting a Gaussian radial basis kernel function, taking the sample set C and the sample set D as input, and obtaining a penalty parameter C2 and a kernel function parameter of the residual breakdown field strength prediction model through a support vector machine algorithm;
wherein, the expression of the Gaussian radial basis kernel function is as follows: k (x)j,xi)＝exp(-g||xj-xi||2) G is a kernel function parameter, xjAnd xiAre respectively provided withRepresenting the jth and ith eigenvalues in the sample acquisition. In particular, a second penalty parameter (c)2) The calculation process comprises the following steps: 1) is selected c2And a group of initial ranges corresponding to the kernel function parameters g are roughly searched, an SVR parameter optimization contour map and a 3D view are drawn, and c is selected2The initial variation range of g is 2-8 to 28, the step size is 0.5; 2) obtaining the area with the highest cross validation accuracy rate in the contour map and the 3D view by checking the contour map and the 3D view, and taking the area as the parameter range of the next grid search; 3) adjusting the step size, repeating the grid search to find the optimal parameter value c with the highest accuracy2And g.
S1032, training the residual breakdown field strength prediction model based on the penalty parameter C2 and the kernel function parameter to obtain a trained residual breakdown field strength model.
Based on the obtained penalty parameter C2 and the kernel function parameter, a complete residual breakdown field strength model is obtained through training. In this embodiment, the CVT insulation characteristic parameters include leakage current, dielectric tangent, capacitance variation, and partial discharge parameter of the CVT, which are nonlinear with the residual breakdown field strength. For a nonlinear SVM, a method for solving the problem of non-linear inseparability is to map a sample set of a low-dimensional space to a high-dimensional space through a kernel function so that the sample set can be linearly separable in the high-dimensional space. CVT insulation characteristic parameter xiThe form after being mapped to the high-dimensional feature space through the kernel function is phi (x)i) For a linearly separable sample phi (x)i) The SVM optimization problem is converted into a mathematical form:
taking the sample set D obtained by calculation in the step S102 as an input of the trained residual breakdown field intensity model, and obtaining a sample set E by using a prediction model: the residual breakdown field strength prediction value (here "second" to distinguish from the residual breakdown field strength parameter in the sample).
And S104, evaluating the insulation state of the CVT according to the sample set E.
Illustratively, in this embodiment, the implementation for this step includes the following sub-steps:
s1041, analyzing and summarizing through a simulation experiment to obtain an insulation state score standard table, wherein different score ranges in the table correspond to different CVT state evaluation conclusions;
illustratively, the CVT state characteristic value output value is defined between [0,1], and 3 grades are divided on the basis, and the result is shown in table 1:
TABLE 1
| Grade | Score value | CVT state assessment |
| A | [1，0.8) | Normal insulating state |
| B | [0.8，0.6) | Insulation state warning |
| C | [0.6，0] | Has insulation problem |
S1042, comparing the sample set E with the table 1 through normalization processing, and predicting the insulation state condition of the CVT;
the physical quantity selected in the present embodiment to reflect the insulation state of the CVT may include: and obtaining the residual breakdown field intensity value of the CVT through an SVM regression prediction model according to the leakage current, the dielectric loss tangent value, the capacitance variation, the discharge capacity, the discharge phase and the discharge frequency of the CVT. Since the SVM regression prediction model does not consider the influence of the winding temperature and the critical life on the insulation, weight values will be given to the winding temperature and the critical life of the CVT. In the normalized column average method, the average value of each row of the standard pairwise comparison matrix is the relative weight of the parameter, and in this embodiment, the relative weight of the winding temperature is calculated to be 0.2, and the relative weight of the critical life is calculated to be 0.8.
And S1043, determining a score range where the insulation state score is located, and giving a state evaluation conclusion corresponding to the score range.
According to the step S1042, the value of the CVT running state can be calculated, then the value is compared and analyzed with the value in the table 1, the CVT insulation state can be obtained, and scientific and reasonable overhaul or replacement suggestions are given.
Example 2
Referring to fig. 3, in some embodiments of the present invention, a system for evaluating a multi-parameter insulation state of a capacitor voltage transformer is provided, including: the data acquisition module 100: the system comprises a sample data acquisition module, a sample data processing module and a data processing module, wherein the sample data acquisition module is used for acquiring sample data and carrying out normalization processing on the sample data, and the sample data comprises a sample set A, a sample set B and a sample set C; the first model building module 200: the method comprises the steps that an insulation characteristic parameter prediction model is constructed on the basis of the sample set A and the sample set B by adopting a support vector machine algorithm, and a sample set D is obtained by utilizing the insulation characteristic parameter prediction model; the second model building module 300: and the method is used for constructing a residual breakdown field strength prediction model by adopting a support vector machine algorithm based on the sample set C and the sample set D to obtain a sample set E, and evaluating the insulation state of the CVT according to the value of the sample set E.
The system provided by the embodiment of the invention can be used for executing the method described in the above embodiment, and the specific method steps are shown in embodiment 1. And will not be described in detail herein.
Example 3
An embodiment of the present invention provides an electronic device, including: at least one processor 1, at least one memory 2 and a data bus 3;
wherein, the processor 1 and the memory 2 complete the communication with each other through the data bus 3; the memory 2 stores program instructions executable by the processor 1, and the processor 1 calls the program instructions to execute the method in the embodiment, for example, to execute: s101, collecting sample data, and performing normalization processing on the sample data, wherein the sample data comprises a sample set A, a sample set B and a sample set C; s102, constructing an insulation characteristic parameter prediction model based on the sample set A and the sample set B by adopting a support vector machine algorithm, and predicting by using the insulation characteristic parameter prediction model to obtain a sample set D; s103, constructing a residual breakdown field strength prediction model by adopting a support vector machine algorithm based on the sample set C and the sample set D, and obtaining a sample set E by using the residual breakdown field strength prediction model; and S104, evaluating the insulation state of the CVT according to the sample set E.
Fig. is a schematic structural block diagram of an electronic device according to an embodiment of the present application. The electronic device comprises a memory 2, a processor 1 and a data bus 3, the memory 2, the processor 1 and the data bus 3 being electrically connected to each other, directly or indirectly, to enable transmission or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 2 can be used for storing software programs and modules, such as program instructions/modules corresponding to the electronic device provided in the embodiments of the present application, and the processor 1 executes the software programs and modules stored in the memory 2, thereby executing various functional applications and data processing. The data bus 3 can be used for signaling or data communication with other node devices.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A multi-parameter insulation state evaluation method for a capacitor voltage transformer is characterized by comprising the following steps:
collecting sample data, and carrying out normalization processing on the sample data, wherein the sample data comprises a sample set A: CVT aging time, sample set B: CVT insulation characteristic parameter, sample set C: a CVT residual breakdown field strength parameter;
based on the sample set A and the sample set B, establishing an insulation characteristic parameter prediction model by adopting a linear kernel function to obtain a sample set D: predicting a CVT insulation parameter;
based on the sample set C and the sample set D, establishing a residual breakdown field strength prediction model by adopting a Gaussian radial basis function to obtain a sample set E: predicting the residual breakdown field intensity;
and evaluating the insulation state of the CVT according to the sample set E.
2. The multi-parameter insulation state assessment method of a capacitor voltage transformer according to claim 1, wherein said CVT insulation characteristic parameters comprise at least one of leakage current, dielectric tangent, capacitance variation and partial discharge parameters of the CVT.
3. The multi-parameter insulation state assessment method for a capacitor voltage transformer according to claim 1, characterized by using the formula: f:
normalizing the sample data, wherein x, y belongs to R, x is a sampling value, and x isminIs a minimum sample value, xmaxIs the maximum sample value.
4. The multi-parameter insulation state evaluation method of the capacitor voltage transformer according to claim 1, wherein the establishing of the insulation characteristic parameter prediction model by using the linear kernel function based on the sample set a and the sample set B comprises:
establishing an insulation characteristic parameter prediction model by adopting a linear kernel function, taking the sample set A and the sample set B as input, and taking a support vector machine as an algorithm to obtain a punishment parameter C1 and a sample set D of the insulation characteristic parameter prediction model;
and training the insulation characteristic parameter prediction model based on the punishment parameter C1 to obtain a trained insulation characteristic parameter prediction model.
5. The multi-parameter insulation state assessment method for a capacitor voltage transformer according to claim 4, wherein the expression of the linear kernel function is:
xjand xiRespectively representing the jth and ith eigenvalues in the sample acquisition.
6. The method for estimating the multi-parameter insulation state of the capacitor voltage transformer as recited in claim 1, wherein the step of constructing a residual breakdown field strength prediction model based on the sample set C and the sample set D by using a support vector machine algorithm comprises the steps of:
establishing a residual breakdown field strength prediction model by adopting a Gaussian radial basis kernel function, taking the sample set C and the sample set D as input, and obtaining a penalty parameter C2 and a sample set E of the residual breakdown field strength prediction model through a support vector machine algorithm;
and training the residual breakdown field strength prediction model based on the penalty parameter C2 and the sample set E to obtain a trained residual breakdown field strength model.
7. The multi-parameter insulation state evaluation method of the capacitor voltage transformer according to claim 6, wherein the gaussian radial basis function expression is: k (x)j,xi)＝exp(-g||xj-xi||2) G is a kernel function parameter, xjAnd xiRespectively representing the jth and ith eigenvalues in the sample acquisition.
8. The multi-parameter insulation state evaluation method for the capacitor voltage transformer according to claim 1, wherein the evaluation of the insulation state of the CVT based on the sample set E comprises:
analyzing and summarizing through a simulation experiment to obtain an insulation state score standard table, wherein different score ranges in the table correspond to different CVT state evaluation conclusions;
comparing the sample set E after normalization processing with the insulation state score standard table, and predicting the insulation state condition of the CVT;
and determining the value range of the insulation state score to obtain the evaluation conclusion of the insulation state of the CVT.
9. A multi-parameter insulation state evaluation system of a capacitor voltage transformer is characterized by comprising:
a data acquisition module: collecting sample data, and carrying out normalization processing on the sample data, wherein the sample data comprises a sample set A, a sample set B and a sample set C;
a first model building module: based on the sample set A and the sample set B, establishing an insulation characteristic parameter prediction model by adopting a linear kernel function to obtain a sample set D: predicting a CVT insulation parameter;
a second model building module: based on the sample set C and the sample set D, establishing a residual breakdown field strength prediction model by adopting a Gaussian radial basis function to obtain a sample set E: predicting the residual breakdown field intensity;
an evaluation module: and evaluating the insulation state of the CVT according to the sample set E.
10. An electronic device, comprising: at least one processor, at least one memory, and a data bus;
the processor and the memory complete mutual communication through the data bus; the memory stores program instructions executable by the processor, the processor calling the program instructions to perform the method of any of claims 1 to 8.
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Cited By (3)
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| CN115932702A (en) * | 2023-03-14 | 2023-04-07 | 武汉格蓝若智能技术股份有限公司 | Voltage transformer online operation calibration method and device based on virtual standard device |
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