CN115730497A - Method, device and equipment for identifying degradation of porcelain insulator string - Google Patents
Method, device and equipment for identifying degradation of porcelain insulator string Download PDFInfo
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Abstract
The application relates to a method, a device and equipment for identifying degradation of a porcelain insulator string, which relate to the technical field of detection of insulators of power transmission lines and comprise the steps of creating a solid model of the porcelain insulator string of an alternating-current power transmission line, and carrying out electric field simulation analysis on the solid model with different positions and different piece number degradation to obtain electric field data reflecting the axial electric field distribution condition of insulators at each position in the insulator string; normalizing the electric field data to obtain normalized data; processing the normalized data based on a preset mean value-standard deviation algorithm, obtaining a confidence interval corresponding to each position insulator in the insulator string when the insulator is in a non-degradation state through the combined standard deviation obtained through processing and an effectiveness comparison result between the standard deviation mean values, taking the lower limit value of the confidence interval as a state partition line, and judging whether the insulator to be detected is degraded or not based on the state partition line. The method and the device realize numerical identification of the insulator degradation, and effectively improve identification accuracy of the insulator degradation.
Description
Technical Field
The application relates to the technical field of transmission line insulator detection, in particular to a method, a device and equipment for identifying degradation of a porcelain insulator string.
Background
In power systems, insulators play an important role in supporting wires and electrical insulation. However, the insulation performance of the insulator gradually decreases or even deteriorates due to the influence of weather, dirt, mechanical stress, strong electric field, and the like. The existence of the degraded insulator threatens the safe operation of the power system, so that the timely detection of the degraded insulator is very important.
In the prior art, a voltage distribution method is used as a commonly used detection method for the existing deteriorated insulator, and has the defects of poor safety, low detection efficiency and the like; the electric field rule is gradually applied to practice due to the advantages of simple operation, high safety performance, low cost and the like, but the identification process of the insulator string degradation is still limited to the distortion degree of an electric field curve, but whether the insulator is degraded or not is difficult to judge through the distortion of the electric field curve of the porcelain insulator string of the alternating-current transmission line, namely, the accurate identification of the insulator degradation cannot be realized through the distortion of the electric field curve of the insulator string.
Disclosure of Invention
The application provides a method, a device and equipment for identifying degradation of a porcelain insulator string, and aims to solve the problem that the degradation of an insulator cannot be accurately identified through distortion of an electric field curve of the insulator string in the related technology.
In a first aspect, a method for identifying degradation of a porcelain insulator string is provided, which comprises the following steps:
creating a solid model of the porcelain insulator string of the alternating-current transmission line, and performing electric field simulation analysis on the solid model at different positions and with different sheet number degradation to obtain electric field data reflecting the axial electric field distribution condition of the insulator at each position in the insulator string;
carrying out normalization processing on the electric field data to obtain normalized data;
processing the normalized data based on a preset mean-standard deviation algorithm, obtaining a confidence interval corresponding to each position in the insulator string in a non-degraded state through the combined standard deviation obtained by processing and an effectiveness comparison result between the standard deviation means, and taking a lower limit value of the confidence interval as a state partition line;
and judging whether the insulator to be tested is degraded or not according to the relative position between the normalization value corresponding to the actual electric field data of the insulator to be tested and the state dividing line.
In a second aspect, there is provided a porcelain insulator string degradation recognition apparatus comprising:
the simulation unit is used for creating a solid model of the porcelain insulator string of the alternating-current transmission line, and performing electric field simulation analysis on the solid model with different positions and different piece number degradation to obtain electric field data reflecting the axial electric field distribution condition of the insulator at each position in the insulator string;
the processing unit is used for carrying out normalization processing on the electric field data to obtain normalized data; processing the normalized data based on a preset mean-standard deviation algorithm, obtaining a corresponding confidence interval when each position in the insulator string is in a non-degraded state through the combined standard deviation obtained by processing and an effectiveness comparison result between the standard deviation means, and taking a lower limit value of the confidence interval as a state partition line;
and the identification unit is used for judging whether the insulator to be tested has degradation or not according to the relative position between the normalization value corresponding to the actual electric field data of the insulator to be tested and the state dividing line.
In a third aspect, there is provided a porcelain insulator string degradation recognition apparatus comprising: the device comprises a memory and a processor, wherein at least one instruction is stored in the memory, and is loaded and executed by the processor to realize the porcelain insulator string degradation identification method.
The application provides a method, a device and equipment for identifying degradation of a porcelain insulator string, which comprises the steps of establishing a solid model of the porcelain insulator string of an alternating-current transmission line, and carrying out electric field simulation analysis on the solid model with different positions and different sheet number degradation to obtain electric field data reflecting the axial electric field distribution condition of insulators at each position in the insulator string; normalizing the electric field data to obtain normalized data; processing the normalized data based on a preset mean-standard deviation algorithm, obtaining a corresponding confidence interval when each position in the insulator string is in a non-degraded state through the combined standard deviation obtained by processing and an effectiveness comparison result between the standard deviation means, and taking a lower limit value of the confidence interval as a state partition line; and judging whether the insulator to be tested is degraded or not according to the relative position between the normalization value corresponding to the actual electric field data of the insulator to be tested and the state dividing line. According to the method and the device, the distribution accuracy of the confidence interval corresponding to each position in the insulator string in the non-degradation state is guaranteed through the mean value-standard deviation algorithm and the obtained validity comparison result between the combined standard deviation and the standard deviation mean value, a good data base is provided for the follow-up accurate distinguishing of the degradation state or the good state of the porcelain insulator, the numerical identification of the insulator degradation is realized, and the identification accuracy of the insulator degradation is effectively improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for identifying degradation of a porcelain insulator string according to an embodiment of the present application;
FIG. 2 is a simulation solid model I of the porcelain insulator string provided by the embodiment of the application;
FIG. 3 is a second simulation solid model of the porcelain insulator string provided in the embodiment of the present application;
FIG. 4 is a state dividing line of a 110kV porcelain insulator provided in the embodiment of the present application;
fig. 5 is a 220kV porcelain insulator state dividing line provided in the embodiment of the present application;
FIG. 6 is a graph of a test verification result under a good 110kV condition, provided by an embodiment of the present application;
FIG. 7 is a graph of experimental verification results under 110kV deterioration conditions provided in the embodiments of the present application;
fig. 8 is a graph of a test verification result in a good state of 220kV provided in the embodiment of the present application;
FIG. 9 is a graph of the experimental verification results under 220kV deterioration state provided in the embodiments of the present application;
fig. 10 is a schematic structural diagram of a device for identifying degradation of a porcelain insulator string according to an embodiment of the application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. 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 application.
The embodiment of the application provides a method, a device and equipment for identifying degradation of a porcelain insulator string, and solves the problem that the degradation of an insulator cannot be accurately identified through distortion of an electric field curve of the insulator string in the related technology.
Fig. 1 is a method for identifying degradation of a porcelain insulator string provided by an embodiment of the application, and the method comprises the following steps:
step S10: and creating a solid model of the porcelain insulator string of the alternating-current transmission line, and performing electric field simulation analysis of different positions and different sheet number degradation on the solid model to obtain electric field data reflecting the axial electric field distribution condition of the insulator at each position in the insulator string.
Exemplarily, in the present embodiment, referring to fig. 2 and 3, a solid model of the porcelain insulator string of the alternating-current transmission line is built in the solid simulation platform, and the built solid model is input into finite element simulation software to perform electric field simulation under preset working conditions such as different positions and different piece number degradation, so as to obtain corresponding electric field data capable of reflecting the axial electric field distribution condition of the insulator at each position in the insulator string.
The following examples will illustrate and explain the principle of the method for identifying the degradation of the porcelain insulator string in the present embodiment by taking 110kV seven XP-70 porcelain insulators and 220kV fourteen XP-70 porcelain insulators as examples. It should be noted that the current method is also applicable to 500kV porcelain insulators, and modeling analysis can be performed according to the implementation method disclosed in the current embodiment.
Step S20: and carrying out normalization processing on the electric field data to obtain normalized data.
Exemplarily, in this embodiment, axial electric field data of the porcelain insulator string of the alternating-current transmission line, which is obtained by simulation and is in the voltage class of 110kV and 220kV, is normalized, and a simulation database of the porcelain insulator string of the alternating-current transmission line, which is deteriorated at different positions and different working conditions at 110kV and 220kV, is constructed based on the normalized data.
It should be noted that the currently related working condition degradation conditions include conditions of single-piece degradation, two-piece continuous degradation, two-piece discontinuous degradation, three-piece continuous degradation, and the like of the porcelain insulator string at the voltage levels of 110kV and 220kV, and the positions of the degraded porcelain insulators are distributed at different positions from low voltage to high voltage, that is, the porcelain insulators corresponding to different voltage positions in the insulator string have degradation.
Step S30: and processing the normalized data based on a preset mean-standard deviation algorithm, obtaining a confidence interval corresponding to each position in the insulator string in a non-degraded state through the combined standard deviation obtained by processing and an effectiveness comparison result between the standard deviation means, and taking the lower limit value of the confidence interval as a state partition line.
Exemplarily, in the present embodiment, the merged standard deviation and the standard deviation mean are obtained by processing the normalized data through a mean-standard deviation algorithm, then, the confidence interval corresponding to each position in the insulator string in the non-degraded state is determined according to the validity comparison result between the merged standard deviation and the standard deviation mean obtained, and the curve formed by the lower limit values of all the confidence intervals is used as the state partition line, so as to ensure the distribution accuracy of the confidence interval corresponding to each position in the insulator string in the non-degraded state, and provide a good data basis for subsequently and accurately distinguishing whether the porcelain insulator is in the degraded state or in the good state.
Step S40: and judging whether the insulator to be tested is degraded or not according to the relative position between the normalization value corresponding to the actual electric field data of the insulator to be tested and the state dividing line.
Exemplarily, in this embodiment, when the electric field normalization value of the insulator to be tested is below the state dividing line, it indicates that the insulator has been degraded, and otherwise, it indicates that the insulator is in a good state. In conclusion, the embodiment realizes the numerical identification of the insulator degradation, and further effectively improves the identification accuracy of the insulator degradation.
In one embodiment, in step S10, creating a solid model of the porcelain insulator string of the ac transmission line, and performing electric field simulation analysis on the solid model with different positions and different sheet number degradations includes: establishing a solid model of the ceramic insulator string of the alternating-current transmission line based on a preset solid simulation platform; and calling finite element analysis software to perform electric field simulation analysis on the entity model under a preset working condition, wherein the preset working condition comprises at least one of single-piece degradation, two-piece continuous degradation, two-piece discontinuous degradation, three-piece continuous degradation and three-piece discontinuous degradation.
For example, it should be understood that the preset conditions include, but are not limited to, single-piece degradation, two-piece continuous degradation, two-piece discontinuous degradation, three-piece continuous degradation, and three-piece discontinuous degradation, and it should be noted that the number of preset conditions and the specific condition setting may be determined according to actual requirements, and are not limited herein.
In one embodiment, before the step of processing the normalized data based on the preset mean-standard deviation algorithm, the method further includes: creating a training set according to the normalized data, wherein the training set comprises m subgroups of samples, each subgroup of samples comprises n subgroups of samples, m represents the number of insulator pieces corresponding to the entity model, and n represents the number of working conditions corresponding to electric field simulation; and executing the step of processing the normalized data based on the preset mean-standard deviation algorithm based on the normalized data in the training set.
Exemplarily, it can be understood that, in the present embodiment, simulation data under various operating conditions recorded in the simulation database are classified according to insulator serial numbers, so as to facilitate subsequent data management and invocation. Thereafter, training set a will be further constructed based on the simulation data recorded in the simulation database. The training set a includes m sub-group samples, and each sub-group sample is composed of n sub-sample quantities.
For example, for simulation analysis of seven porcelain insulators at a voltage level of 110kv, if a corresponding insulator at each position represents one subsample, since the insulator string includes 7 insulators, the number m =7 of constructed subsamples; the design of the sub-sample amount may specifically be set, in combination with the condition degradation condition indicated in step S20, that each sub-sample includes n sub-sample amounts. Exemplary, n ∈ [15,20].
For another example, for simulation analysis of 14 porcelain insulators at a voltage level of 220kv, if the insulator string corresponding to each position represents one sub-sample, since the insulator string includes 14 insulators, the number m =14 of currently constructed sub-samples; for the design of the sub-sample amount in this case, the sub-sample amount n included in each sub-group sample may be specifically considered in combination with the condition degradation condition indicated in step S20, and is not further limited at present.
In one embodiment, step S30 specifically includes:
step S301: processing the normalized data based on a preset mean-standard deviation algorithm to obtain standard deviations of insulators at different positions in a non-degradation state, and calculating to obtain a combined standard deviation and a standard deviation mean based on the standard deviations;
step S302: performing effectiveness comparative analysis by gamma recursion based on the combined standard deviation and the standard deviation mean value to obtain a target standard deviation;
step S303: and determining a confidence interval corresponding to each position insulator in the insulator string in a non-degraded state based on the target standard deviation, and taking the lower limit value of the confidence interval as a state partition line according to the change rule of sudden reduction of the electric field when the insulator is degraded.
Wherein, the step S301 specifically includes: calculating a corresponding normalized value mean value of each insulator in the solid model when the insulator is in a non-degradation state based on the normalized data; calculating the variance corresponding to each insulator according to the normalized value mean value, and calculating to obtain a standard deviation based on the variance; and according to the standard deviation obtained currently, calculating a combined standard deviation and a standard deviation mean value.
Based on the above embodiments, it should be noted that the mean value and the standard deviation of the insulators at different positions in good condition are calculated by the following formula:
wherein,
representing a subsample x ij Mean value of (1), x ij J is the value of the sample, s, representing the ith sub-sample 2 i Represents the variance, s, of the ith subgroup sample i The standard deviation of the ith subgroup sample is shown.
In addition, the combined standard deviation and the standard deviation mean are specifically calculated by the following formulas:
wherein s is p RepresentThe standard deviation is combined with the standard deviation,
mean standard deviation is indicated.
In one embodiment, the step S302 specifically includes:
step S3021: calculating a first unbiased estimator according to the combined standard deviation, and calculating a second unbiased estimator according to the standard deviation mean value;
step S3022: calculating to obtain a first variance according to the first unbiased estimator, and calculating to obtain a second variance according to the second unbiased estimator;
step S3023: when the ratio of the first variance to the second variance is determined to be larger than 1, judging that the merging standard deviation is valid, and taking the currently valid merging standard deviation as a target standard deviation;
step S3024: and when the ratio of the first variance to the second variance is smaller than 1, judging that the standard deviation mean is valid, and taking the currently valid standard deviation mean as a target standard deviation.
Specifically, the step S3021 specifically includes:
substituting the combined standard deviation into a first calculation formula to obtain a first unbiased estimator, wherein the first calculation formula is as follows:
substituting the standard deviation mean value into a second calculation formula to obtain a second unbiased estimate, wherein the second calculation formula is as follows:
in the formula,
representing a first unbiased estimate, s p It is shown that the standard deviations are combined,
representing a second unbiased estimate of the quantity,
mean standard deviation is indicated.
In one embodiment, the step S3022 specifically includes:
substituting the first unbiased estimator into a third calculation formula to obtain a first square error, wherein the third calculation formula is as follows:
substituting the second unbiased estimated quantity into a fourth calculation formula to obtain a second variance, wherein the fourth calculation formula is as follows:
in the formula,
which represents the first variance of the first signal,
representing the variance of the ith subgroup of samples,
represents a second variance; sigma represents a base [ E (.)] 2 The method of preliminary determinationDifference, wherein "+" indicates
Or
It should be noted that, in one aspect, consideration is given to
And
independently of each other in pairs
After simplification, we obtain:
on the other hand, consider s i And s j Independently of one another, give E(s) i s j )＝Es i Es j Therefore, the above formula
Can be simplified as follows:
in one embodiment, the confidence interval in step S303 includes an upper limit value and a lower limit value, and the calculation formula of the upper limit value UCL is:
the calculation formula of the lower limit LCL is as follows:
in the formula,
and (4) representing the mean value of the normalized values of the ith insulator, and theta represents a target standard deviation.
The following can be referred to for the overall implementation process of the above method:
(1) Based on the constructed training set A, the mean value of the normalized values of the electric field of the insulators at the different positions of 110kV and 220kV in the good state (namely in the non-degraded state) is respectively calculated by the following formulas (1) to (3)
And standard deviation s i ：
In the formula, x ij A jth sample value representing an ith sub-sample,
representing the variance of the ith subgroup sample.
(2) On the basis, the combined standard deviation s of the insulators at each position is respectively obtained through the following formulas (4) to (5) p And mean of standard deviation
(3) And carrying out effectiveness comparison on the unbiased estimation quantity of the standard deviation mean and the unbiased estimation quantity of the combined standard deviation through gamma recursion, and taking the standard deviation with higher effectiveness as the unbiased estimation of the overall standard deviation. Since implementation details have been previously described, they are not described much at present.
(4) It should be understood that, in statistics,
and
both can be used as unbiased estimates of θ, where if:
then represents
Ratio of
The effectiveness is high. Thus, the two estimators are compared
And
for validity of (2), the variance of the two is calculated first. The calculation formulas for the two estimators described above, which have been described previously, are not described in greater detail at present.
(5) By the above two estimators
And
the ratio of the variances of (a) and (b) reflects the effectiveness of the unbiased estimation, and the ratio of the variances of (a) and (b) is expressed as follows:
it can be ascertained from equation (11) that if the ratio deff is greater than 1, this proves to be
Effective, i.e. more effective
The corresponding combined standard deviation is used as an unbiased estimate of the total standard deviation (i.e. participates in the calculation of the confidence interval as the target standard deviation); if the ratio deff is less than 1, this proves
Effective, i.e. more effective
The corresponding standard deviation mean value is used as an unbiased estimation of the total standard deviation; if the ratio deff is equal to 1, it proves
And
is equal, then one of the standard deviations and the mean of the standard deviations can be arbitrarily selected as an unbiased estimate of the total standard deviation.
(6) And according to theoretical analysis results, calculating a confidence interval when each position in the porcelain insulator string is in a good state according to each position and the standard deviation with higher effectiveness (the confidence interval represents that the axial electric field normalization value of the insulator falls within the interval when the insulator is in a good state under the corresponding voltage level). The calculation method of the confidence interval has been described previously, and is not described in detail at present.
It can be understood that when the insulator at a certain position in the insulator string is degraded, the axial electric field value at the position is suddenly reduced. Therefore, in order to simplify the calculation flow and improve the insulator degradation identification efficiency, in the present embodiment, the curve formed by the lower limit value of the confidence interval corresponding to each position is used as the state dividing line for dividing the good state and the degraded state of the porcelain insulator, and further, the state dividing lines of the porcelain insulator of 110kV and 220kV shown in fig. 4 and 5 are obtained.
(7) And inputting the actual electric field data measured in the actual working conditions of 110kV and 220kV into the identification method, and further judging whether the porcelain insulator is degraded or not according to the normalization value corresponding to the actual electric field data and the relative position of the state dividing line. Among them, the test results in the 110kV good state and the zero value degraded state are shown in fig. 6 and 7, and the test results in the 220kV good state and the zero value degraded state are shown in fig. 8 and 9. It should be noted that when the normalized value of the electric field of the insulator to be tested is below the state dividing line, it indicates that the insulator has been degraded, and conversely, it indicates that the insulator is in a good state.
Therefore, in the embodiment, a mode of combining theory and simulation analysis is adopted, the porcelain insulator string of the 110kV and 220kV transmission line is taken as an example, simulation analysis is performed on the deterioration conditions of different numbers of porcelain insulator strings and different positions, and a mean standard deviation deterioration identification algorithm is adopted to identify the deterioration of the porcelain insulator string based on simulation data, so that the following conclusion can be obtained:
1) Under two voltage levels, when three pieces of continuous degradation exist in the porcelain insulator string, the local electric field change rate amplitude is maximum; for the distribution positions of the deteriorated porcelain insulators, when the high-voltage end of the porcelain insulator is deteriorated, the amplitude of the change rate of the axial electric field of the porcelain insulator is the largest, and the low-voltage end is the next, and when the deteriorated insulator is positioned in the middle of the insulator string, the change rate of the axial electric field is the lowest.
2) Test data verifies that the mean-standard deviation recognition algorithm provided by the embodiment can accurately distinguish the deterioration state and the good state of the porcelain insulator.
The embodiment points out the situation that the existing electric field method depends on and is difficult to identify continuous degradation only by judging degradation through electric field curve distortion, and further provides a degradation identification method based on a mean-standard deviation algorithm, so that a theoretical basis is provided for realizing digitization by the electric field method.
In summary, in this embodiment, according to different voltage levels and different numbers of insulators, a mean value and a standard deviation of the insulator at each position in the insulator string in the good state are calculated, a confidence interval of an electric field normalization value of the insulator at each position in the good state is calculated according to a mean value-standard deviation algorithm, and a lower limit value is used as a state dividing line for dividing the good state and the degraded state of the insulator, when the electric field normalization value of the insulator to be tested is below the state dividing line, it indicates that the insulator is degraded, and conversely, it indicates that the insulator is in the good state, that is, the insulator that is degraded can be accurately identified through the mean value-standard deviation algorithm, so that a certain theoretical basis is provided for realizing digitization on the stone-wall porcelain insulator by an electric field method.
The embodiment of the present application further provides a porcelain insulator string degradation recognition device, including:
the simulation unit is used for creating a solid model of the porcelain insulator string of the alternating-current transmission line, and performing electric field simulation analysis on the solid model with different positions and different piece number degradation to obtain electric field data reflecting the axial electric field distribution condition of the insulator at each position in the insulator string;
the processing unit is used for carrying out normalization processing on the electric field data to obtain normalized data; processing the normalized data based on a preset mean-standard deviation algorithm, obtaining a corresponding confidence interval when each position in the insulator string is in a non-degraded state through the combined standard deviation obtained by processing and an effectiveness comparison result between the standard deviation means, and taking a lower limit value of the confidence interval as a state partition line;
and the identification unit is used for judging whether the insulator to be tested has degradation or not according to the relative position between the normalization value corresponding to the actual electric field data of the insulator to be tested and the state dividing line.
Further, the simulation unit is specifically configured to:
establishing a solid model of the porcelain insulator string of the alternating-current transmission line based on a preset solid simulation platform;
and calling finite element analysis software to perform electric field simulation analysis on the entity model under a preset working condition, wherein the preset working condition comprises at least one of single-piece degradation, two-piece continuous degradation, two-piece discontinuous degradation, three-piece continuous degradation and three-piece discontinuous degradation.
Further, the processing unit is further configured to:
creating a training set according to the normalized data, wherein the training set comprises m subgroups of samples, each subgroup of samples comprises n subgroups of samples, m represents the number of insulator pieces corresponding to the entity model, and n represents the number of working conditions corresponding to electric field simulation;
and executing the step of processing the normalized data based on the preset mean-standard deviation algorithm based on the normalized data in the training set.
Further, the processing unit is specifically configured to:
processing the normalized data based on a preset mean-standard deviation algorithm to obtain standard deviations of insulators at different positions in a non-degradation state, and calculating to obtain a combined standard deviation and a standard deviation mean based on the standard deviations;
performing effectiveness comparative analysis through gamma recursion based on the combined standard deviation and the standard deviation mean value to obtain a target standard deviation;
and determining a confidence interval corresponding to each position insulator in the insulator string in a non-degraded state based on the target standard deviation, and taking the lower limit value of the confidence interval as a state partition line according to the change rule of sudden reduction of the electric field when the insulator is degraded.
Further, the processing unit is specifically further configured to:
calculating a corresponding normalized value mean value of each insulator in the solid model when the insulator is in a non-degradation state based on the normalized data;
calculating the variance corresponding to each insulator according to the normalized value mean value, and calculating to obtain a standard deviation based on the variance;
and calculating according to the standard deviation to obtain a combined standard deviation and a standard deviation mean value.
Further, the confidence interval includes an upper limit and a lower limit, and the calculation formula of the upper limit UCL is:
the calculation formula of the lower limit value LCL is as follows:
in the formula,
and (4) representing the average value of the normalized values of the ith insulator, and theta represents a target standard deviation.
Further, the processing unit is specifically further configured to:
calculating a first unbiased estimator according to the combined standard deviation, and calculating a second unbiased estimator according to the standard deviation mean value;
calculating to obtain a first variance according to the first unbiased estimator, and calculating to obtain a second variance according to the second unbiased estimator;
when the ratio of the first variance to the second variance is determined to be larger than 1, judging that the merging standard deviation is valid, and taking the currently valid merging standard deviation as a target standard deviation;
and when the ratio of the first variance to the second variance is smaller than 1, judging that the standard deviation mean is effective, and taking the currently effective standard deviation mean as a target standard deviation.
Further, the processing unit is specifically further configured to:
substituting the combined standard deviation into a first calculation formula to obtain a first unbiased estimator, wherein the first calculation formula is as follows:
substituting the standard deviation mean value into a second calculation formula to obtain a second unbiased estimation quantity, wherein the second calculation formula is as follows:
in the formula,
representing a first unbiased estimate, s p It is shown that the standard deviations are combined,
representing a second unbiased estimated quantity of light,
mean standard deviation is indicated.
It should be noted that, as will be clear to those skilled in the art, for convenience and brevity of description, the specific working processes of the above-described apparatus and units may refer to the corresponding processes in the foregoing embodiments of the method for identifying degradation of a porcelain insulator string, and are not described herein again.
The apparatus provided by the above embodiment may be implemented in the form of a computer program which can be run on a porcelain insulator string degradation identification device as shown in fig. 10.
The embodiment of the application also provides a porcelain insulator chain degradation identification device, which comprises: the device comprises a memory, a processor and a network interface which are connected through a system bus, wherein at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor so as to realize all or part of the steps of the porcelain insulator string degradation identification method.
The network interface is used for performing network communication, such as sending distributed tasks. Those skilled in the art will appreciate that the architecture shown in fig. 10 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The Processor may be a CPU, other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being the control center of the computer device and the various interfaces and lines connecting the various parts of the overall computer device.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the computer device by executing or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a video playing function, an image playing function, etc.), and the like; the storage data area may store data (such as video data, image data, etc.) created according to the use of the cellular phone, etc. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, all or part of the steps of the method for identifying the degradation of the porcelain insulator string are realized.
The embodiments of the present application may implement all or part of the foregoing processes, and may also be implemented by a computer program instructing related hardware, where the computer program may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the foregoing methods. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer memory, read-Only memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. It should be noted that the computer-readable medium may contain suitable additions or subtractions depending on the requirements of legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer-readable media may not include electrical carrier signals or telecommunication signals in accordance with legislation and patent practice.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, server, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or system that comprises the element.
The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A porcelain insulator string degradation identification method is characterized by comprising the following steps:
establishing a solid model of the porcelain insulator string of the alternating-current transmission line, and performing electric field simulation analysis of different positions and different sheet number degradation on the solid model to obtain electric field data reflecting the axial electric field distribution condition of the insulator at each position in the insulator string;
carrying out normalization processing on the electric field data to obtain normalized data;
processing the normalized data based on a preset mean-standard deviation algorithm, obtaining a confidence interval corresponding to each position in the insulator string in a non-degraded state through the combined standard deviation obtained by processing and an effectiveness comparison result between the standard deviation means, and taking a lower limit value of the confidence interval as a state partition line;
and judging whether the insulator to be tested is degraded or not according to the relative position between the normalization value corresponding to the actual electric field data of the insulator to be tested and the state dividing line.
2. The method of claim 1, wherein the creating of the solid model of the porcelain insulator string of the alternating-current transmission line and the performing of electric field simulation analysis of different position and different sheet number degradation on the solid model comprise:
establishing a solid model of the porcelain insulator string of the alternating-current transmission line based on a preset solid simulation platform;
and calling finite element analysis software to perform electric field simulation analysis on the entity model under a preset working condition, wherein the preset working condition comprises at least one of single-piece degradation, two-piece continuous degradation, two-piece discontinuous degradation, three-piece continuous degradation and three-piece discontinuous degradation.
3. The porcelain insulator string degradation identification method of claim 2, wherein prior to the step of processing the normalized data based on a preset mean-standard deviation algorithm, further comprising:
creating a training set according to the normalized data, wherein the training set comprises m subgroups of samples, each subgroup of samples comprises n subgroups of samples, m represents the number of insulator pieces corresponding to the entity model, and n represents the number of working conditions corresponding to electric field simulation;
and executing the step of processing the normalized data based on the preset mean-standard deviation algorithm based on the normalized data in the training set.
4. The method according to claim 3, wherein the processing the normalized data based on a preset mean-standard deviation algorithm, obtaining a confidence interval corresponding to each position in the insulator string in a non-degraded state by processing the combined standard deviation and the validity comparison result between the standard deviation means, and using a lower limit value of the confidence interval as a state partition line comprises:
processing the normalized data based on a preset mean-standard deviation algorithm to obtain standard deviations of the insulators at different positions in a non-degraded state, and calculating to obtain a combined standard deviation and a standard deviation mean value based on the standard deviations;
performing effectiveness comparative analysis by gamma recursion based on the combined standard deviation and the standard deviation mean value to obtain a target standard deviation;
and determining a corresponding confidence interval when each position insulator in the insulator string is in a non-degraded state based on the target standard deviation, and taking the lower limit value of the confidence interval as a state parting line according to the change rule of sudden reduction of the electric field when the insulator is degraded.
5. The method for identifying the degradation of the porcelain insulator string according to claim 4, wherein the processing of the normalized data based on a preset mean-standard deviation algorithm to obtain standard deviations of the insulators in non-degraded states at different positions and the calculation of a combined standard deviation and a mean value of the standard deviations based on the standard deviations comprise:
calculating a corresponding normalized value mean value of each insulator in the solid model when the insulator is in a non-degradation state based on the normalized data;
calculating the variance corresponding to each insulator according to the normalized value mean value, and calculating to obtain a standard deviation based on the variance;
and calculating according to the standard deviation to obtain a combined standard deviation and a standard deviation mean value.
6. The porcelain insulator string degradation identification method according to claim 5, wherein the confidence interval comprises an upper limit value and a lower limit value, and the calculation formula of the upper limit value UCL is as follows:
the calculation formula of the lower limit value LCL is as follows:
in the formula,
and (4) representing the average value of the normalized values of the ith insulator, and theta represents a target standard deviation.
7. The method of claim 4, wherein performing a validity comparison analysis based on the combined standard deviation and the standard deviation mean and by gamma recursion to obtain a target standard deviation comprises:
calculating a first unbiased estimator according to the combined standard deviation, and calculating a second unbiased estimator according to the standard deviation mean value;
calculating to obtain a first variance according to the first unbiased estimator, and calculating to obtain a second variance according to the second unbiased estimator;
when the ratio of the first variance to the second variance is determined to be larger than 1, judging that the merging standard deviation is valid, and taking the currently valid merging standard deviation as a target standard deviation;
and when the ratio between the first variance and the second variance is determined to be less than 1, judging that the standard deviation mean is valid, and taking the currently valid standard deviation mean as a target standard deviation.
8. The porcelain insulator string degradation identification method of claim 7, wherein the calculating a first unbiased estimator from the combined standard deviation and a second unbiased estimator from the standard deviation mean comprises:
substituting the combined standard deviation into a first calculation formula to obtain a first unbiased estimator, wherein the first calculation formula is as follows:
substituting the standard deviation mean value into a second calculation formula to obtain a second unbiased estimate, wherein the second calculation formula is as follows:
in the formula,
representing a first unbiased estimate, s p It is shown that the standard deviations are combined,
representing a second unbiased estimate of the quantity,
the mean of the standard deviations is expressed as,
and s i Respectively, the variance and standard deviation of the ith subgroup sample.
9. A porcelain insulator string degradation recognition device, comprising:
the simulation unit is used for creating a solid model of the porcelain insulator string of the alternating-current transmission line, and performing electric field simulation analysis on the solid model with different positions and different piece number degradation to obtain electric field data reflecting the axial electric field distribution condition of the insulator at each position in the insulator string;
the processing unit is used for carrying out normalization processing on the electric field data to obtain normalized data; processing the normalized data based on a preset mean-standard deviation algorithm, obtaining a corresponding confidence interval when each position in the insulator string is in a non-degraded state through the combined standard deviation obtained through processing and an effectiveness comparison result between standard deviation means, and taking a lower limit value of the confidence interval as a state partition line;
and the identification unit is used for judging whether the insulator to be tested is degraded or not according to the relative position between the normalization value corresponding to the actual electric field data of the insulator to be tested and the state dividing line.
10. A porcelain insulator string degradation recognition apparatus, comprising: a memory and a processor, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to realize the porcelain insulator string degradation identification method according to any one of claims 1 to 8.
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