CN112710930A - Online evaluation method for insulation state in capacitor voltage transformer - Google Patents
Online evaluation method for insulation state in capacitor voltage transformer Download PDFInfo
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
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Abstract
The invention provides an online evaluation method for the insulation state in a capacitor voltage transformer, which comprises the following steps: constructing a typical state sample set based on the correlation between the operation error state and the internal insulation state of the capacitor voltage transformer and fusing a physical model and a data model of the capacitor voltage transformer; establishing a characteristic parameter of an online operation error state of the capacitor voltage transformers by taking an electrical connection relationship among a plurality of capacitor voltage transformers in the same transformer substation as a constraint condition; and evaluating the internal insulation state of the capacitor voltage transformer on line according to the matching relation between the characteristic parameters and the typical state, and diagnosing the abnormal type and the abnormal degree of the abnormal state. According to the method for online evaluation of the insulation state in the capacitor voltage transformer, provided by the embodiment of the invention, the transformer does not need to be powered off and quit running, other calibration equipment is not needed, the method is easy to realize, and the evaluation result is accurate and visual.
Description
Technical Field
The invention relates to the technical field of state evaluation of power transmission and distribution equipment, in particular to an online evaluation method for the insulation state in a capacitor voltage transformer.
Background
A Capacitor Voltage Transformer (CVT) is the most widely used Voltage Transformer in the high-Voltage power system today, and is one of the most important measuring devices in a Transformer substation. Compared with the traditional electromagnetic voltage transformer, the CVT has the obvious advantages of high insulating property, low production cost and the like, but the inner structure of the CVT is more complex, in the long-term operation process, the inner insulating property of the CVT is degraded due to thermal aging and electrical aging, the overvoltage of the electric power system and the local discharge of the inner part of the CVT can cause irreversible damage to the inner insulating structure of the CVT due to factors such as overvoltage and partial discharge of the CVT, the breakdown of a voltage-dividing capacitor occurs, the dielectric loss exceeds the standard and other abnormal states, the metering accuracy of the CVT is directly influenced, and the CVT and even the safety and stability of the electric power.
At present, the most common evaluation method for the insulation state in the CVT is based on a direct access method and a self-excitation method of a digital bridge, and different methods are adopted to load test voltage under the condition that the CVT stops running due to power failure, and a voltage dividing capacitor of the CVT is compared with a standard capacitor in the digital bridge, so that the capacitance and the dielectric loss of a high-voltage capacitor and a medium-voltage capacitor of the CVT are respectively obtained. However, the CVT internal insulation evaluation method based on the digital bridge has the following problems:
(1) the detection period is long, and the power failure maintenance plan is difficult to arrange, so that a large number of CVTs with undetected overperiod and unknown internal insulation states exist in the power system;
(2) the test needs to disassemble and assemble the primary lead of the CVT, the field test workload is large, and the CVT primary lead which is disassembled and assembled for many times has certain potential safety hazard;
(3) if the CVT does not have an intermediate voltage test tap, a capacitive voltage divider and an electromagnetic unit of the CVT need to be detached in part of tests, and the sealing performance of a CVT shell is affected.
In order to solve the above problems, the conventional CVT internal insulation state evaluation method further includes a signal analysis method based on capacitive current of a capacitive voltage divider, where the capacitance of the voltage-dividing capacitor is obtained according to a relationship between primary voltage and capacitive current of the capacitive voltage divider, and the dielectric loss of the voltage-dividing capacitor is obtained according to a relationship between output voltage and capacitive current of the capacitive voltage divider. The method can realize real-time evaluation of the insulation state in the CVT, the CVT and a primary lead wire of the CVT do not need to be disassembled and assembled, however, the method needs to additionally install a current sensor, the sealing performance of the CVT can be influenced, and certain hidden danger is caused to the safe operation of the CVT.
Therefore, a new method for evaluating the insulation state in the CVT is needed to solve this problem.
Disclosure of Invention
The invention provides an online evaluation method for the insulation state in a capacitor voltage transformer, which is used for realizing the real-time evaluation of the insulation state in a CVT (constant voltage transformer) under the conditions of no power failure, no disassembly and assembly of the CVT body and a primary lead wire and no additional installation of a sensor.
The embodiment of the invention provides an online evaluation method for the insulation state in a capacitor voltage transformer, which comprises the following steps:
s1, constructing a typical state sample set based on the correlation between the operation error state and the internal insulation state of the capacitor voltage transformer and by fusing a physical model and a data model of the capacitor voltage transformer;
s2, constructing characteristic parameters of the online operation error state of the capacitor voltage transformers by taking the electrical connection relation among the plurality of capacitor voltage transformers in the same transformer substation as a constraint condition;
and S3, evaluating the internal insulation state of the capacitor voltage transformer on line according to the matching relation between the characteristic parameters and the typical state.
Further, in step S1, based on the correlation between the operation error state and the internal insulation state of the capacitor voltage transformer, and fusing the physical model and the data model of the capacitor voltage transformer, a typical state sample set is constructed, which includes:
s11, establishing an equivalent physical model of the capacitor voltage transformer based on the evaluated device parameters of the capacitor voltage transformer, and calculating additional errors corresponding to various typical abnormal internal insulation states;
s12, synchronously acquiring output voltage amplitude and phase data of all capacitor voltage transformers in the same voltage level in the transformer substation, and constructing an original sample set;
and S13, copying the original sample set for a plurality of times, and then respectively superposing additional errors corresponding to different abnormal internal insulation states to construct the typical state sample set.
Further, in step S2, constructing characteristic parameters of the online operation error state of the capacitor voltage transformers by using the electrical connection relationship among the plurality of capacitor voltage transformers in the same substation as a constraint condition, including:
s21, converting the typical state sample set into a typical state parameter set by taking the electrical connection relationship among a plurality of capacitance voltage transformers in the same transformer substation as a constraint condition;
s22, carrying out fuzzy clustering analysis on the typical state parameter group to obtain a typical state group;
and S23, synchronously acquiring output voltage data of all the capacitor voltage transformers during operation, constructing an evaluation sample set, and converting the evaluation sample set into an evaluation sample parameter group.
Further, in step S3, the online evaluation of the internal insulation state of the capacitor voltage transformer according to the matching relationship between the characteristic parameter and the typical state includes:
and S30, calculating the attribution degree of the evaluation sample parameter group relative to each typical state group by using a fuzzy clustering method, and evaluating the internal insulation state of the capacitor voltage transformer.
Further, in step S11, based on the evaluated device parameters of the capacitor voltage transformer, an equivalent physical model of the capacitor voltage transformer is established, and additional errors corresponding to various typical abnormal internal insulation states are calculated, including:
obtaining device parameters of the capacitor voltage transformer according to equipment information and related experiments, and establishing an equivalent physical model of the capacitor voltage transformer containing internal insulation parameters;
and calculating additional errors corresponding to the m abnormal internal insulation states by taking the output voltage data of the mutual inductor in the normal state as a reference.
Further, in step S12, output voltage amplitude and phase data of all the capacitive voltage transformers in the same voltage class in the substation are synchronously collected, and an original sample set is constructed, including:
constructing a magnitude data set Vr＝[Vi-1，Vi，Vi+1]And phase data set
Wherein, Vi、
Respectively corresponding to the amplitude, phase and V of the i-th group of three-phase transformersi＝[ViA，ViB，ViC]、
Further, in step S13, after copying the original sample set several times, respectively superimposing additional errors corresponding to different abnormal internal insulation states, and constructing the typical state sample set, including:
setting several times as m times, respectively superposing additional errors of different types to obtain [ V ]i1，Vi2，...，Vim]、
Constructing a typical state sample set:
further, in step S21, the typical state parameter set is:
wherein the amplitude ratio parameter is:
the set of phase difference parameters is:
further, in step S22, performing fuzzy clustering analysis on the typical state parameter set to obtain a typical state population, including:
based on a fuzzy clustering principle, carrying out iterative clustering analysis on the typical state parameter group to respectively obtain m +1 clustering centers and m +1 typical state groups, wherein each group corresponds to a typical internal insulation state;
in step S23, output voltage data of all the capacitive voltage transformers during operation are synchronously collected, an evaluation sample set is constructed, and the evaluation sample set is converted into an evaluation sample parameter set, which includes:
after a typical state group is obtained, output voltage amplitude and phase data of all the capacitor voltage transformers are synchronously acquired, and an amplitude data set V is respectively constructedmAnd phase data set
And calculating an evaluation sample parameter group containing the current internal insulation state information from the evaluation sample set
Further, in step S30, the method for evaluating the internal insulation state of the capacitor voltage transformer by using fuzzy clustering to find the degree of attribution of the evaluation sample parameter set to each typical state group includes:
calculating the membership and the typicality of the evaluation sample parameter set relative to each typical state population;
and evaluating the attribution degree of the target transformer relative to each typical state group according to the membership degree and the typicality so as to judge the internal insulation state of the capacitor voltage transformer.
According to the online evaluation method for the internal insulation state of the capacitor voltage transformer, provided by the embodiment of the invention, the online evaluation of the internal insulation state of the capacitor voltage transformer is realized based on the correlation between the error state and the internal insulation state, the evaluation method does not need to stop running of the transformer when power is cut off, other check equipment is not needed, the evaluation method is easy to realize, and the evaluation result is accurate and visual.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of an online evaluation method for the insulation state in a capacitor voltage transformer according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of the overall implementation of the present invention;
FIG. 3 is a CVT equivalent physical model including internal insulation parameters provided by an embodiment of the present invention;
FIG. 4 is a diagram illustrating fuzzy clustering effects of typical state parameter groups according to an embodiment of the present invention;
fig. 5 is a schematic diagram illustrating determination of an insulation state in a CVT according to an embodiment of the present invention;
fig. 6 is a schematic diagram showing the results of sample CVT internal insulation state evaluation 1 provided by the embodiment of the present invention;
fig. 7 is a schematic result view of a CVT internal insulation state evaluation sample 2 according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Fig. 1 is a schematic flow chart of an online evaluation method for an insulation state in a capacitor voltage transformer according to an embodiment of the present invention, and as shown in fig. 1, the online evaluation method for an insulation state in a capacitor voltage transformer according to an embodiment of the present invention includes, but is not limited to, the following steps:
s1, constructing a typical state sample set based on the correlation between the operation error state and the internal insulation state of the capacitor voltage transformer and by fusing a physical model and a data model of the capacitor voltage transformer;
s2, constructing characteristic parameters of online operation error states of the capacitor voltage transformers by taking the electrical connection relations among the plurality of capacitor voltage transformers in the same transformer substation as constraint conditions;
and S3, evaluating the internal insulation state of the capacitor voltage transformer on line according to the matching relation between the characteristic parameters and the typical state.
The method for evaluating the internal insulation state according to the embodiment of the present invention is applied to a CVT in a substation of 110kV or higher voltage class, and the evaluation target in the embodiment is a 220kV CVT, and the method for evaluating the internal insulation state of a CVT of another voltage class not mentioned is similar to the above.
Fig. 2 is a schematic diagram of an overall implementation flow provided by an embodiment of the present invention, and as shown in fig. 2, the embodiment of the present invention provides a CVT internal insulation state evaluation method based on a correlation between an error state and an internal insulation state, the method takes the correlation between the CVT error state and the internal insulation state as a basis, fuses a physical model and a data model of the CVT, calculates additional errors corresponding to multiple abnormal internal insulation states, combines the additional errors with normal output voltage data to construct a typical state sample set containing multiple types of internal insulation state error information, then constructs a characteristic parameter of the CVT online operation error state with an electrical connection relationship between multiple CVTs as a constraint condition, implements online evaluation of the internal insulation state of the CVT according to a matching relationship between the error characteristic parameter and the typical state, and diagnoses an abnormal type and an abnormal degree of the abnormal state.
According to the online evaluation method for the internal insulation state of the capacitor voltage transformer, provided by the embodiment of the invention, the online evaluation of the internal insulation state of the capacitor voltage transformer is realized based on the correlation between the error state and the internal insulation state, the evaluation method does not need to stop running of the transformer when power is cut off, other check equipment is not needed, the evaluation method is easy to realize, and the evaluation result is accurate and visual.
In one embodiment, in step S1, constructing a typical state sample set based on the correlation between the operation error state and the internal insulation state of the capacitor voltage transformer and fusing the physical model and the data model of the capacitor voltage transformer, including:
s11, establishing an equivalent physical model of the capacitor voltage transformer based on the evaluated device parameters of the capacitor voltage transformer, and calculating additional errors corresponding to various typical abnormal internal insulation states;
s12, synchronously acquiring output voltage amplitude and phase data of all capacitor voltage transformers in the same voltage level in the transformer substation, and constructing an original sample set;
and S13, copying the original sample set for a plurality of times, and then respectively superposing additional errors corresponding to different abnormal internal insulation states to construct the typical state sample set.
In one embodiment, in step S11, based on the evaluated device parameters of the capacitor voltage transformer, an equivalent physical model of the capacitor voltage transformer is established, and additional errors corresponding to a plurality of typical abnormal internal insulation states are calculated, including:
obtaining device parameters of the capacitor voltage transformer according to equipment information and related experiments, and establishing an equivalent physical model of the capacitor voltage transformer containing internal insulation parameters;
and calculating additional errors corresponding to the m abnormal internal insulation states by taking the output voltage data of the mutual inductor in the normal state as a reference.
Specifically, a CVT equivalent physical model including internal insulation parameters such as equivalent high-voltage capacitance capacity, equivalent medium-voltage capacitance capacity, equivalent high-voltage capacitance dielectric loss resistance and equivalent medium-voltage capacitance dielectric loss resistance is established according to the device parameters of the CVT. The device parameters required by the model include: high voltage capacitor rated capacity, high voltage capacitor rated dielectric loss tangent, medium voltage capacitor rated capacity, medium voltage capacitor rated dielectric loss tangent, compensation reactor rated capacity, and intermediate transformer rated transformation ratio provided by equipment manufacturers; the short-circuit impedance and the excitation impedance of the intermediate transformer are obtained through a no-load test and a short-circuit test; the secondary load capacity of the CVT is measured on site. Adjusting internal insulation parameters in the model, respectively calculating secondary output voltages corresponding to different internal insulation states under the condition of rated primary voltage, and calculating additional specific difference and additional angle difference caused by other internal insulation states by taking the output voltage under the normal internal insulation condition as a reference.
FIG. 3 is an equivalent physical model of a CVT incorporating internal insulation parameters, as shown in FIG. 3, where C isHAnd CMEquivalent capacitors, R, of a high-voltage-dividing capacitor and a medium-voltage-dividing capacitor, respectivelyHAnd RMRespectively, dielectric loss equivalent resistance, LkFor compensating reactors, the intermediate transformer has been equivalent to a T-type equivalent circuit, XT1、XT2、R1、R2And ZmAre all equivalent parameters of the intermediate transformer, ZLIs the equivalent secondary load of the CVT. The model can be established according to device rated parameters provided by an equipment manufacturer, a no-load test and a short-circuit test result of the intermediate transformer and the field-measured CVT secondary load capacity, and parameters of the model in the embodiment are as follows:
then respectively adjusting the equivalent capacitance of the high-voltage capacitor and the medium-voltage capacitor in the model, and simulating the breakdown state of the high-voltage capacitor and the breakdown state of the medium-voltage capacitor of the CVT; and respectively adjusting the medium loss equivalent resistance of a high-voltage capacitor and a medium-voltage capacitor in the model, and simulating the overproof state of the medium loss of the CVT voltage-dividing capacitor. And on the basis of the model, the rated primary voltage is used as a simulation test voltage, secondary output voltages corresponding to normal internal insulation states and multiple abnormal insulation states are respectively calculated, and the output voltage when the internal insulation is normal is used as a reference, and additional specific differences and additional angular differences corresponding to other abnormal internal insulation states are calculated. In this example, five internal insulation states of normal internal insulation, five high-voltage capacitance breakdowns, ten high-voltage capacitance breakdowns, 0.2% high-voltage capacitance dielectric loss tangent, and 0.5% high-voltage capacitance dielectric loss tangent were collectively calculated.
In one embodiment, in step S12, output voltage amplitude and phase data of all capacitor voltage transformers in the same voltage class in the substation are synchronously acquired, and an original sample set is constructed, which specifically includes:
on the premise that the internal insulation of all CVTs in a transformer substation is in a normal state, a CVT is used as an evaluation object, other two phases in the same group and other two groups of three-phase CVTs in the same voltage level are used as auxiliary transformers, the output voltage amplitude and phase data of all the CVTs are synchronously acquired, and an original sample set is constructed and comprises an amplitude data set Vr＝[Vi-1，Vi，Vi+1]And phase data set
Wherein, ViAnd
respectively corresponding to the amplitude and the phase of the ith group of three-phase mutual inductors: vi＝[ViA，ViB，ViC]、
Synchronously acquiring output voltage amplitude and phase of all CVTs under the same voltage level in the transformer substationData, the internal insulation state of all CVTs was good at this time. In the embodiment, the 220kV boosting transformer substation adopts a three-half connection mode, six groups of three-phase 220kV CVTs are provided in total, an acquisition system acquires output voltage data of the CVT once every 15 minutes, and continuous 1000 sampling points are selected as original sample data. The phase A of the 4 th group of three-phase CVT is used as an evaluation object, the 4 th group of B-phase CVT and C-phase CVT and the 3 rd group and 5 th group of three-phase CVT are used as auxiliary CVT, and the original amplitude data set is Vr＝[V3，V4，V5]The original phase data set is
Both are 1000 a in length.
In one embodiment, in step S13, after copying the original sample set several times, respectively superimposing additional errors corresponding to different abnormal internal insulation states, and constructing the typical state sample set includes:
setting several times as m times, respectively superposing additional errors of different types to obtain [ V ]i1，Vi2，...，Vim]、
Constructing a typical state sample set:
in particular, a representative state sample set is constructed based on the original sample set and additional errors caused by a plurality of representative internal insulation states. Taking evaluation of the phase A in the ith group of three-phase CVT as an example, additional errors caused by m typical abnormal internal insulation states are obtained through equivalent model simulation, original samples are concentrated into output voltage data of the CVT and are copied m times, different types of additional errors are respectively superposed, and [ V ] is obtainediA1，ViA2，...，ViAm]T、
Wherein, the superposition method of the additional ratio difference is：Vim＝(1+εm)Vi0The method for adding the angular difference comprises the following steps:
(Vi0、
is the raw output data, V, of the CVT within the raw sample setim、
Amplitude, phase, epsilon of CVT output voltage corresponding to m-th internal insulation statem、ρmAn additional ratio difference and an additional angle difference corresponding to the m-th internal insulation state). For the auxiliary CVT, output voltage data of the CVT in a normal internal insulation state are directly copied m times and combined with an evaluated CVT output data set containing typical additional errors to obtain a typical state sample set of internal insulation state evaluation:
and constructing a typical state sample set based on the original sample set and additional errors corresponding to various typical internal insulation states. In this embodiment, the equivalent physical model obtains additional errors caused by four abnormal insulation states, the phase a in the 4 th group of three-phase CVT is used as an evaluation object, and the additional ratio difference are respectively added to the V phase after the original sample data set is copied for 4 times4A、
The data for the other auxiliary CVTs are copied 4 times directly, resulting in a typical state sample set:
wherein, VijAnd
respectively represents the output voltage amplitude and the phase of the ith group of three-phase CVT in the jth internal insulation state.
In one embodiment, in step S2, the constructing the characteristic parameters of the online operation error state of the capacitor voltage transformers by using the electrical connection relationship among the plurality of capacitor voltage transformers in the same substation as the constraint condition specifically includes:
s21: and converting the typical state sample set into a typical state parameter set by taking the electrical connection relationship among a plurality of capacitance voltage transformers in the same transformer substation as a constraint condition.
S22, carrying out fuzzy clustering analysis on the typical state parameter group to obtain a typical state group;
and S23, synchronously acquiring output voltage data of all the capacitor voltage transformers during operation, constructing an evaluation sample set, and converting the evaluation sample set into an evaluation sample parameter group.
In step S21, the method includes converting the typical state sample set into a typical state parameter set with the electrical connection relationship among multiple capacitive voltage transformers in the same substation as a constraint condition, and specifically includes:
and converting the typical state sample set into a typical state parameter set comprising an amplitude ratio parameter set and a phase difference parameter set by taking the electrical connection relation between the mutual inductors in the transformer substation as a constraint condition:
the j-th row phasor is calculated from the j-th row phasor in the typical state sample set S, all parameters do not contain a primary voltage component with random fluctuation, and the numerical value of the primary voltage component can represent the error state change of the related CVT. More specifically, the set of amplitude ratio parameters is:
the set of phase difference parameters is:
specifically, the typical state sample set is converted into a typical state parameter set by taking the electrical connection relationship between the transformers in the substation as a constraint condition. In this embodiment, there are six sets of three-phase CVTs for 220kV, three sets of CVTs are used for evaluation, and a typical state parameter set including 5 typical internal insulation states is:
wherein the typical state parameter set corresponding to the j-th typical internal insulation state comprises:
amplitude ratio parameter set:
set of phase difference parameters:
in one embodiment, in step S22, performing fuzzy clustering analysis on the typical state parameter set to obtain a typical state population, including:
and performing iterative clustering analysis on the typical state parameter group based on a fuzzy clustering principle to respectively obtain m +1 clustering centers and m +1 typical state groups, wherein each group corresponds to a typical internal insulation state.
Specifically, a probabilistic fuzzy C-means clustering algorithm (PFCM) is adopted to perform clustering analysis on the typical state parameter group C to obtain (m +1) clustering centers and (m +1) data clusters, and each clustering center and each data cluster respectively correspond to a typical internal insulation state. In addition, the fuzzy clustering result also comprises m +1 group membership degree and typical numerical value, and the clustering effect of the typical state parameter group can be evaluated by combining the membership degree and the typical numerical value.
Fig. 4 is a schematic diagram of a fuzzy clustering effect of a typical state parameter group provided in an embodiment of the present invention, and as shown in fig. 4, a fuzzy clustering analysis objective function is constructed for a typical state sample set C and a PFCM is used for iterative optimization until an algorithm stop condition is satisfied. Fuzzy clustering obtains 5 clustering centers and divides the typical state parameter groups into 5 typical state groups, wherein each clustering center and each state group correspond to a typical insulation state. In addition, 5 groups of membership degrees and typicality numerical values can be obtained by fuzzy clustering, and the membership degree and typicality of the data segment corresponding to each internal insulation state reach optimal values, which shows that the fuzzy clustering effect is good.
Further, after obtaining the typical state population, in step S23, the output voltage amplitude and phase data of all the aforementioned CVTs are synchronously collected to respectively construct an amplitude data set VmAnd phase data set
And calculating an evaluation sample parameter group containing the current internal insulation state information from the evaluation sample set
And after a typical state group is obtained, synchronously acquiring the amplitude and phase data of the output voltage of the CVT, and constructing an evaluation sample set. In the monitoring process, 5 high-voltage capacitor breakdown and 0.2% dielectric loss tangent abnormal internal insulation states of the high-voltage capacitor are set in the CVT to be evaluated respectively. The acquisition system acquires the output voltage of the CVT once every 15 minutes, and respectively and continuously acquires 500 sampling points as an evaluation sample set, wherein the corresponding evaluation time is 125 hours, and abnormal states all occur at the 200 th sampling point. Respectively calculating monitoring parameter groups C 'corresponding to the two abnormal states from the evaluation sample set'1、C′2Namely, evaluation of sample parameter set C'1、C′2。
In one embodiment, in step S3, the online evaluation of the internal insulation state of the capacitor voltage transformer according to the matching relationship between the characteristic parameter and the typical state includes:
and S30, calculating the attribution degree of the evaluation sample parameter group relative to each typical state group by using a fuzzy clustering method, and evaluating the internal insulation state of the capacitor voltage transformer.
In one embodiment, in step S30, the evaluating the internal insulation state of the capacitor voltage transformer by using a fuzzy clustering method to find the degree of attribution of the evaluation sample parameter set with respect to each typical state group includes:
calculating the membership and the typicality of the evaluation sample parameter set relative to each typical state population;
and evaluating the attribution degree of the target transformer relative to each typical state group according to the membership degree and the typicality so as to judge the internal insulation state of the capacitor voltage transformer.
Specifically, the degree of membership u of the evaluation sample parameter set C' relative to each typical state population is calculatedikAnd "typicality" tik：
Wherein d isikTau, eta and b are fuzzy clustering parameters for monitoring Euclidean distance from the kth row phasor to the ith clustering center in the parameter group. The degree of attribution of the monitoring parameter set to each typical state population is determined by 'membership degree' uikAnd "typicality" tikAnd (4) jointly determining. More specifically, if uikAnd tikMeanwhile, when the internal insulation state of the CVT is close to the optimal value, the evaluated internal insulation state of the CVT is considered as the ith typical state; if it is
And is
There are two possibilities at this time: 1) the inner insulation state of the evaluated CVT is abnormal and the abnormal state does not belong to a typical state sample; 2) an abnormality occurs in the state of internal insulation of the auxiliary CVT. Because most CVTs with normal internal insulation states in the CVT group of the transformer substation are available, and only a few CVTs with abnormal internal insulation states are available, transformers can be randomly selected from the rest CVTs with the same voltage level to serve as auxiliary CVTs, a typical state sample set and an evaluation sample parameter set are reconstructed, and the CVTs with suspected abnormal internal insulation states are evaluated. If the evaluation result is normal, the auxiliary CVT is considered to have an abnormal mutual inductor during initial evaluation; if the internal insulation state of the evaluated CVT still does not belong to the typical state sample set, the internal insulation state of the evaluated CVT is considered to be abnormal and the typical state sample set does not contain the abnormality. Respectively calculating and evaluating sample parameter set C'1And C'2All data in (a) relative to the "degree of membership" u of each said representative state populationikAnd "typicality" tikDegree of attribution of each representative state population is defined by "degree of membership" uikAnd "typicality" tikFig. 5 is a schematic diagram illustrating determination of an insulation state in a CVT according to an embodiment of the present invention, and the determination method is shown in fig. 5.
Fig. 6 is a schematic diagram showing results of a sample evaluation example 1 of the insulation state in the CVT according to the embodiment of the present invention, fig. 7 is a schematic diagram showing results of a sample evaluation example 2 of the insulation state in the CVT according to the embodiment of the present invention, and fig. 6 and 7 are schematic diagrams showing results of the evaluation according to the present invention when two abnormal insulation states, i.e., a state in which 5 high-voltage capacitors are broken down and a dielectric loss tangent of 0.2%, occur, respectively. It can be seen that when the internal insulation state is abnormal, the membership degree and the typicality corresponding to the normal internal insulation state are both close to zero, the membership degree and the typicality corresponding to the abnormal state are both close to optimal values, and the two groups of results show that the abnormal type and the abnormal degree of the abnormal internal insulation state of the CVT are accurately evaluated by the method.
The embodiments of the present invention can be arbitrarily combined to achieve different technical effects.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An online evaluation method for the insulation state in a capacitor voltage transformer is characterized by comprising the following steps:
s1, constructing a typical state sample set based on the correlation between the operation error state and the internal insulation state of the capacitor voltage transformer and by fusing a physical model and a data model of the capacitor voltage transformer;
s2, constructing characteristic parameters of the online operation error state of the capacitor voltage transformers by taking the electrical connection relation among the plurality of capacitor voltage transformers in the same transformer substation as a constraint condition;
and S3, evaluating the internal insulation state of the capacitor voltage transformer on line according to the matching relation between the characteristic parameters and the typical state.
2. The online evaluation method for the internal insulation state of the capacitor voltage transformer according to claim 1, wherein in step S1, based on the correlation between the operation error state and the internal insulation state of the capacitor voltage transformer and by fusing the physical model and the data model of the capacitor voltage transformer, a typical state sample set is constructed, which comprises:
s11, establishing an equivalent physical model of the capacitor voltage transformer based on the evaluated device parameters of the capacitor voltage transformer, and calculating additional errors corresponding to various typical abnormal internal insulation states;
s12, synchronously acquiring output voltage amplitude and phase data of all capacitor voltage transformers in the same voltage level in the transformer substation, and constructing an original sample set;
and S13, copying the original sample set for a plurality of times, and then respectively superposing additional errors corresponding to different abnormal internal insulation states to construct the typical state sample set.
3. The method for online evaluation of the insulation state in the capacitor voltage transformer according to claim 2, wherein in step S2, the method for constructing the characteristic parameters of the online operation error state of the capacitor voltage transformer with the electrical connection relationship among the plurality of capacitor voltage transformers in the same substation as the constraint condition comprises:
s21, converting the typical state sample set into a typical state parameter set by taking the electrical connection relationship among a plurality of capacitance voltage transformers in the same transformer substation as a constraint condition;
s22, carrying out fuzzy clustering analysis on the typical state parameter group to obtain a typical state group;
and S23, synchronously acquiring output voltage data of all the capacitor voltage transformers during operation, constructing an evaluation sample set, and converting the evaluation sample set into an evaluation sample parameter group.
4. The online evaluation method for the internal insulation state of the capacitor voltage transformer according to claim 3, wherein in step S3, the online evaluation for the internal insulation state of the capacitor voltage transformer according to the matching relationship between the characteristic parameter and the typical state comprises:
and S30, calculating the attribution degree of the evaluation sample parameter group relative to each typical state group by using a fuzzy clustering method, and evaluating the internal insulation state of the capacitor voltage transformer.
5. The method for online evaluation of internal insulation state of capacitor voltage transformer according to claim 4, wherein in step S11, based on the device parameters of the evaluated capacitor voltage transformer, an equivalent physical model of the capacitor voltage transformer is established, and additional errors corresponding to various typical abnormal internal insulation states are calculated, including:
obtaining device parameters of the capacitor voltage transformer according to equipment information and related experiments, and establishing an equivalent physical model of the capacitor voltage transformer containing internal insulation parameters;
and calculating additional errors corresponding to the m abnormal internal insulation states by taking the output voltage data of the mutual inductor in the normal state as a reference.
6. The online evaluation method for the insulation state in the capacitor voltage transformer according to claim 5, wherein in step S12, the method comprises the steps of synchronously acquiring the output voltage amplitude and phase data of all capacitor voltage transformers in the same voltage class in the substation, and constructing an original sample set, and comprises the following steps:
constructing a magnitude data set Vr＝[Vi-1，Vi，Vi+1]And phase data set
Wherein, Vi、
Corresponding to the i-th group of three-phase mutual inductance respectivelyAmplitude, phase, V, of the devicei＝[ViA，ViB，ViC]、
7. The online evaluation method for the internal insulation state of the capacitor voltage transformer according to claim 6, wherein in step S13, the step of copying the original sample set several times and then respectively superimposing additional errors corresponding to different abnormal internal insulation states to construct the typical state sample set comprises:
setting several times as m times, respectively superposing additional errors of different types to obtain [ V ]i1，Vi2，...，Vim]、
Constructing a typical state sample set:
8. the method for online evaluating the insulation state in a capacitor voltage transformer according to claim 7, wherein in step S21, the typical state parameters are:
wherein the amplitude ratio parameter is:
the set of phase difference parameters is:
9. the online evaluation method for the insulation state in the capacitor voltage transformer according to claim 8, wherein in step S22, the fuzzy clustering analysis is performed on the typical state parameter set to obtain a typical state population, which comprises:
based on a fuzzy clustering principle, carrying out iterative clustering analysis on the typical state parameter group to respectively obtain m +1 clustering centers and m +1 typical state groups, wherein each group corresponds to a typical internal insulation state;
in step S23, output voltage data of all the capacitive voltage transformers during operation are synchronously collected, an evaluation sample set is constructed, and the evaluation sample set is converted into an evaluation sample parameter set, which includes:
after a typical state group is obtained, output voltage amplitude and phase data of all the capacitor voltage transformers are synchronously acquired, and an amplitude data set V is respectively constructedmAnd phase data set
And calculating an evaluation sample parameter group containing the current internal insulation state information from the evaluation sample set
10. The online evaluation method for the internal insulation state of the capacitor voltage transformer according to claim 9, wherein in step S30, the method for evaluating the internal insulation state of the capacitor voltage transformer by using a fuzzy clustering method to obtain the degree of attribution of the evaluation sample parameter set with respect to each typical state group comprises:
calculating the membership and the typicality of the evaluation sample parameter set relative to each typical state population;
and evaluating the attribution degree of the target transformer relative to each typical state group according to the membership degree and the typicality so as to judge the internal insulation state of the capacitor voltage transformer.
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