CN111141996A - Porcelain insulator infrared detection threshold optimization method and system based on generalized extreme value theory and storage medium - Google Patents
Porcelain insulator infrared detection threshold optimization method and system based on generalized extreme value theory and storage medium Download PDFInfo
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- 238000006731 degradation reaction Methods 0.000 description 3
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- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- 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
- G01R31/1218—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 using optical methods; using charged particle, e.g. electron, beams or X-rays
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- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
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- G01R27/02—Measuring real or complex resistance, reactance, impedance, or other two-pole characteristics derived therefrom, e.g. time constant
- G01R27/025—Measuring very high resistances, e.g. isolation resistances, i.e. megohm-meters
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- 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
- G01R31/1245—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 of line insulators or spacers, e.g. ceramic overhead line cap insulators; of insulators in HV bushings
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Abstract
The invention discloses a porcelain insulator infrared detection threshold value optimization method, a system and a storage medium based on a generalized extreme value theory, which comprises the following steps: testing the resistance value of the deteriorated insulator detected by infrared by using an insulation resistance meter, and selecting the insulation resistance as a sample set; solving temperature difference data of the degraded insulator; fitting the sample by using generalized extreme value distribution, carrying out maximum likelihood estimation on three parameters of the function according to a function expression of the generalized extreme value distribution to obtain distributed parameter values, and solving a mathematical model of the temperature difference of the degraded insulator; checking whether the sample conforms to the distribution by adopting K-S (K-S) checking; and acquiring a temperature difference threshold value of the infrared detection porcelain insulator. The method utilizes the extreme value theory to carry out fitting analysis on the sample to obtain the temperature difference threshold values of different degrees of the infrared detection porcelain insulator, and improves the judgment criterion, thereby improving the detection accuracy of the infrared detection porcelain insulator and greatly reducing the hidden danger of a power grid system.
Description
Technical Field
The invention relates to the technical field of operation, maintenance and overhaul of high-voltage power transmission and transformation equipment, in particular to a porcelain insulator infrared detection threshold value optimization method and system based on a generalized extreme value theory and a storage medium.
Background
At present, a large number of suspension porcelain insulators are used in overhead transmission lines and substations. If a degraded insulator is present in a string of insulators, it indicates that part of the insulation is shorted, and the probability of flashover increases accordingly. If a zero-value insulator string is subjected to power frequency flashover or lightning stroke, a large current flows through the interior of the zero-value insulator, and the heat effect generated by the strong current often causes the iron cap of the suspension insulator to be cracked or separated, so that serious accidents such as insulator string falling, wire falling and the like occur. Generally, for a porcelain insulator string, the string drop and wire grounding accidents only occur when degraded insulators exist in the string.
The State examination and repair test Specification (DL/T393) of the power transmission and transformation equipment stipulates that degradation detection should be carried out regularly on disc-shaped suspension porcelain insulators in lines and transformer substations. At present, a voltage distribution method (spark gap) is generally adopted for insulator degradation detection, and the method has the defects of high workload, poor safety, low efficiency, susceptibility to electromagnetic interference and high detection error (leakage) probability. According to statistics, various faults caused by the insulators are positioned at the first of all faults of the high-voltage transmission line. A disc-shaped suspension type porcelain insulator (insulator for short) detection technology based on the infrared thermography principle is provided in DL/T664-2016 electrified equipment infrared diagnosis application Specification, and insulation resistance less than or equal to 300M omega is specified as a degraded insulator, so that the efficiency and accuracy of zero-value and low-value insulator detection are improved, and the occurrence of accidents of power transmission and transformation equipment caused by insulator degradation is reduced. In the specification, the average temperature difference between a degraded insulator and two adjacent normal insulators is 1 ℃ as a judgment basis, but the fact that the temperature difference is large in actual infrared detection projects is found, so that the detection omission of most of the degraded insulators is easily caused, and great hidden danger is brought to the safety of a power grid. Because the heating power of the deteriorated insulator cap changes nonlinearly with the change of the insulation resistance value, a detection blind area exists when the deteriorated insulator is detected by infrared. When the upper and lower limits of the temperature difference range are respectively 0.2, 0.4 and 0.6 ℃, the percentage range of the insulator dead zone range with the distribution voltage of more than 15kV accounting for the zero value insulator is as follows: 3% -8.9%, 5% -15% and 15% -24%. The detection blind area is generally between 1 and 13M omega. Although the detection blind area range is small, the probability that the deteriorated insulator falls into the detection blind area is very small, but the deteriorated insulators in the detection blind area are zero-value insulators which have the greatest harm to a power grid. The problem of high zero value missed detection rate exists in the practical application process of the 1K temperature difference discrimination threshold value specified in DL/T664-2016 electrified equipment infrared diagnosis application Specification.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the porcelain insulator infrared detection threshold value optimization method based on the generalized extreme value theory is provided, and the accuracy of infrared detection of the deteriorated insulator can be effectively improved.
The technical scheme is as follows: in order to achieve the purpose, the invention provides a porcelain insulator infrared detection threshold value optimization method based on a generalized extreme value theory, which comprises the following steps:
s1: testing the insulation resistance value of the deteriorated insulator detected by infrared, setting a sample range of the insulation resistance value, and taking the deteriorated insulator positioned in the sample range as a sample set;
s2: solving temperature difference data of the deteriorated insulator in the sample set;
s3: fitting the deteriorated insulators in the sample set by using generalized extreme value distribution, carrying out maximum likelihood estimation according to a function expression of the generalized extreme value distribution to obtain distributed parameter values, and solving a deteriorated insulator temperature difference mathematical model according to the temperature difference data obtained in the step S2;
s4: obtaining the distribution of a fitting probability density function according to a mathematical model of the temperature difference of the deteriorated insulator, obtaining the probability value that the average temperature difference of the distribution is greater than a set temperature value by calculating the area of a shadow part according to the definition of the probability density function and the area of a fitting curve and an abscissa of the probability density function is the occurrence probability of corresponding data, and obtaining the temperature difference threshold value of the deteriorated insulator through the analysis of an infrared detection blind area according to the minimum temperature difference value in the deteriorated insulator in a sample set.
Further, the sample range of the insulation resistance value in the step S1 is equal to or less than 300M Ω.
Further, the step S3 is specifically:
the probability distribution function of the generalized extremum distribution is:
the probability density function of the generalized extremum distribution is:
where k is a shape parameter, μ is a position parameter, and σ is a scale parameter, and the obtained sample X is (X)1，x2，…，xn) And carrying out maximum likelihood estimation on parameters of the generalized extreme value distribution, wherein the log likelihood function is as follows:
order to
And (5) solving a mathematical model of the temperature difference of the degraded insulator.
And after the step S3 is executed, performing goodness-of-fit inspection on the samples in the sample set by adopting K-S inspection to inspect whether the samples accord with distribution. The goodness-of-fit test specifically comprises the following steps:
constructing a statistic:
KS＝max(|Fn(x)-G(x)|)
wherein Fn(x) And G (x) is an empirical distribution function obtained by a sample, a significant level value is set for the specified distribution function, and after K-S test, if a K-S test return value h is calculated to be 0, the sample data is proved to be in accordance with the generalized extreme value distribution by accepting the original hypothesis.
Further, the formula is used in the step S2
Finding out temperature difference data of deteriorated insulator, wherein TnRepresenting the temperature of the insulator cap of the n-th piece of the degraded insulator string.
The invention also discloses a porcelain insulator infrared detection threshold value optimization system based on the generalized extreme value theory, which comprises a network interface, a memory and a processor; wherein the content of the first and second substances,
the network interface is used for receiving and sending signals in the process of receiving and sending information with other external network elements;
the memory to store computer program instructions operable on the processor;
and the processor is used for executing the steps of the porcelain insulator infrared detection threshold value optimization method based on the generalized extreme value theory when the computer program instruction is run.
The invention also discloses a computer storage medium, wherein the computer storage medium stores a program of the porcelain insulator infrared detection threshold optimization method based on the generalized extreme value theory, and the program of the porcelain insulator infrared detection threshold optimization method based on the generalized extreme value theory realizes the steps of the porcelain insulator infrared detection threshold optimization method based on the generalized extreme value theory when being executed by at least one processor.
When the insulator is degraded, factors such as resistance value, reactance value, distributed voltage, a circulation path of leakage current, heating power and the like of the insulator are changed to different degrees, so that the temperature difference between a normal insulator and the degraded insulator is large. According to the infrared detection principle, the temperature of a normal insulator string is the same as the voltage distribution, namely, the asymmetric saddle-shaped distribution is presented, and the temperature distribution of a degraded insulator string can have an obvious peak-valley phenomenon due to the existence of degraded insulators. In DL/T664-2016 (Standard for Infrared diagnostic application of charged device) the temperature difference of the infrared detection porcelain insulator is 1K, and the numerical value has larger error in practical application, so that more insulators are missed to be judged, and the detection accuracy is influenced. The invention provides a porcelain insulator infrared detection threshold value optimization method based on a generalized extreme value theory, which is characterized in that the existing deteriorated insulator detected by infrared is tested by an insulation resistance method, and the temperature difference and the insulation resistance value are recorded; and (4) performing mathematical modeling fitting on the temperature difference sample, counting and concluding the corresponding insulation resistance value, and finally obtaining the infrared inspection porcelain insulator temperature difference threshold value based on the generalized extreme value theory.
Has the advantages that: compared with the prior art, the method utilizes the extreme value theory to perform fitting analysis on the sample to obtain the temperature difference threshold values of different degrees of the infrared detection porcelain insulator, and improves the judgment criterion, so that the detection accuracy of the infrared detection porcelain insulator is improved, the problem of high zero value missing rate is solved, and the hidden danger of a power grid system is greatly reduced.
Drawings
FIG. 1 is a fitted probability density function distribution plot;
FIG. 2 is a schematic diagram of an A-phase infrared image of a small-size side of a No. 3 framework of a bus interval at a section I;
FIG. 3 is a schematic view of a temperature difference sample scatter plot;
FIG. 4 is a schematic view of a zero insulation resistance scatter plot;
FIG. 5 is a schematic diagram of low value insulation resistance scattering.
Detailed Description
The invention is further elucidated with reference to the drawings and the embodiments.
The invention provides a porcelain insulator infrared detection threshold value optimization method based on a generalized extreme value theory, which comprises the following steps:
s1: collecting samples:
and testing the resistance value of the degraded insulator detected by infrared by using an insulation resistance meter, and selecting the insulation resistor with the resistance value less than or equal to 300M omega as a sample set.
S2: sample treatment:
using formulas
Finding out temperature difference data of deteriorated insulator, wherein TnRepresenting the temperature of the insulator cap of the n-th piece of the degraded insulator string.
S3: fitting the samples using a generalized extremum distribution:
the probability distribution function of the generalized extremum distribution is:
the probability density function of the generalized extremum distribution is:
where k is a shape parameter, μ is a position parameter, and σ is a scale parameter, and the obtained sample X is (X)1，x2，…，xn) And carrying out maximum likelihood estimation on parameters of the generalized extreme value distribution, wherein the log likelihood function is as follows:
order to
And solving a mathematical model of the temperature difference of the degraded insulator according to the temperature difference data.
S4: and (3) testing the goodness of fit of the sample:
and (3) adopting K-S test to test whether the sample accords with the distribution, and constructing statistic:
KS＝max(|Fn(x)-G(x)|)
wherein Fn(x) And G (x) is an empirical distribution function obtained by the sample, the significance level is set to be 0.05, and after K-S test, if the return value h of the K-S test is calculated to be 0, the sample data is proved to be in accordance with the generalized extreme value distribution by receiving the original hypothesis.
S5: acquisition of the temperature difference threshold value of the infrared detection porcelain insulator:
according to the fitting process, a fitting probability density function distribution graph can be obtained, specifically, as shown in fig. 1, a longitudinal line in fig. 1 represents a data sample, a transverse curve represents generalized extreme value distribution, according to the definition of the probability density function, the area of the fitting curve and an abscissa of the fitting curve is the probability of corresponding data occurrence, by calculating the area of a shadow part, a probability value that the average temperature difference of the distribution is greater than a set temperature value can be obtained, and as the minimum temperature difference value in the sample is 0.1695 ℃, according to the analysis of an infrared detection blind area, the following conclusion is obtained:
1. when the temperature difference sample is less than or equal to 0.2 ℃, the proportion of the resistance value of the insulation resistor in the range of 1-13M omega is 100%, the proportion of the zero value insulator is 100%, and the probability of the occurrence of the fitting curve is 2.18%. Therefore, 0.2 ℃ is used as a slight blind area to be degraded, and power failure replacement is not needed;
2. when the temperature difference sample is less than or equal to 0.4 ℃, the percentage of the resistance value of the insulation resistor in the range of 1-13M omega is 67.93%, the percentage of the zero-value insulator is 54.83%, and the probability of the occurrence of the fitting curve is 35.04%. Therefore, the temperature difference of 0.4 ℃ is used as the general slight deterioration, and the power failure replacement can be carried out according to the requirement;
3. when the temperature difference sample is less than or equal to 0.6 ℃, the percentage of the resistance value of the insulation resistor in the range of 1-13M omega is 35.24%, the percentage of the zero value insulator is 56.72%, and the probability of the occurrence of the fitting curve is 56.74%. Therefore, the temperature difference of 0.6 ℃ is used as general deterioration, and power failure replacement is needed;
4. when the temperature difference sample is less than or equal to 0.8 ℃, the percentage of the resistance value of the insulation resistor in the range of 1-13M omega is 18.57%, the percentage of the zero value insulator is 51.67%, and the probability of the occurrence of the fitting curve is 74.06%. Therefore, the temperature difference of 0.8 ℃ is regarded as general serious deterioration, and power failure replacement is needed;
5. when the temperature difference sample is less than or equal to 1 ℃, the percentage of the resistance value of the insulation resistor in the range of 1-13M omega is 10.24%, the percentage of the zero value insulator is 60.83%, and the probability of the occurrence of the fitting curve is 86.06%. Therefore, the temperature difference of 1 ℃ is regarded as serious deterioration, and power failure replacement is required immediately.
In this embodiment, the method is applied to an infrared detection project of a certain power transmission line, and taking a sample collected by a 1 string zero-valued insulator string under the large-size side of a line ii of a certain base tower as an example, the specific process is as follows:
(1) an infrared thermal imager is used for acquiring an infrared image, the image is processed, and then the temperature of each insulator cap is taken as a value, which is specifically shown in fig. 2.
(2) The insulator string is subjected to insulation resistance test by using the power failure maintenance opportunity, and the detection result is shown in the following table:
| insulator position numbering | Infrared charged detection result | Measured resistance/ |
| 1 | Is normal | 9300 |
| 2 | Is normal | 12234 |
| … | … | … |
| 24 | Is normal | 13670 |
| 25 | Zero |
2 |
| 26 | Is normal | 13955 |
| … | … | … |
| 47 | Is normal | 7900 |
| 48 | Is normal | 9975 |
(3) The 25 th insulator is calculated by using the formula of the step S2, and the embodiment sums up the long-term data of the infrared detection item of a certain transmission line to obtain 127 samples, which is specifically shown in fig. 3.
(4) Fitting the sample and solving the parameters according to the formula in the step S3, and calculating to obtain the following results: and K is 0.3908, sigma is 0.2191, mu is 0.4635, the fitting model is subjected to K-S test, and h is calculated to be 0, which indicates that the sample is subjected to the generalized extreme value distribution.
(5) According to the rule of sample distribution, the sample fitting model and the analysis of the infrared detection blind area in the figures 3, 4 and 5, the following infrared detection insulator temperature difference criterion is obtained:
1. when the temperature difference is more than or equal to 0 ℃ and less than or equal to 0.2 ℃, the deterioration is a slight blind area;
2. when the temperature difference is less than or equal to 0.4 ℃ at 0.2 ℃, the deterioration is generally slight;
3. when the temperature difference is less than or equal to 0.6 ℃ at 0.4 ℃, the deterioration is general;
4. when the temperature difference is less than or equal to 0.8 ℃ and less than 0.6 ℃, the deterioration is generally serious;
5. when the temperature difference is more than 0.8 ℃, the deterioration is serious.
The embodiment also provides a porcelain insulator infrared detection threshold value optimization system based on the generalized extreme value theory, which comprises a network interface, a memory and a processor; the network interface is used for receiving and sending signals in the process of receiving and sending information with other external network elements; a memory for storing computer program instructions executable on the processor; a processor for performing the steps of the inventive method when executing said computer program instructions.
The present embodiment also provides a computer storage medium storing a computer program that when executed by a processor can implement the method described above. The computer-readable medium may be considered tangible and non-transitory. Non-limiting examples of a non-transitory tangible computer-readable medium include a non-volatile memory circuit (e.g., a flash memory circuit, an erasable programmable read-only memory circuit, or a mask read-only memory circuit), a volatile memory circuit (e.g., a static random access memory circuit or a dynamic random access memory circuit), a magnetic storage medium (e.g., an analog or digital tape or hard drive), and an optical storage medium (e.g., a CD, DVD, or blu-ray disc), among others. The computer program includes processor-executable instructions stored on at least one non-transitory tangible computer-readable medium. The computer program may also comprise or rely on stored data. The computer programs may include a basic input/output system (BIOS) that interacts with the hardware of the special purpose computer, a device driver that interacts with specific devices of the special purpose computer, one or more operating systems, user applications, background services, background applications, and the like.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, 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, CD-ROM, 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.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Claims (8)
1. A porcelain insulator infrared detection threshold value optimization method based on a generalized extreme value theory is characterized by comprising the following steps: the method comprises the following steps:
s1: testing the insulation resistance value of the deteriorated insulator detected by infrared, setting a sample range of the insulation resistance value, and taking the deteriorated insulator positioned in the sample range as a sample set;
s2: solving temperature difference data of the deteriorated insulator in the sample set;
s3: fitting the deteriorated insulators in the sample set by using generalized extreme value distribution, carrying out maximum likelihood estimation according to a function expression of the generalized extreme value distribution to obtain distributed parameter values, and solving a deteriorated insulator temperature difference mathematical model according to the temperature difference data obtained in the step S2;
s4: obtaining the distribution of a fitting probability density function according to a mathematical model of the temperature difference of the deteriorated insulator, obtaining the probability value that the average temperature difference of the distribution is greater than a set temperature value by calculating the area of a shadow part according to the definition of the probability density function and the area of a fitting curve and an abscissa of the probability density function is the occurrence probability of corresponding data, and obtaining the temperature difference threshold value of the deteriorated insulator through the analysis of an infrared detection blind area according to the minimum temperature difference value in the deteriorated insulator in a sample set.
2. The porcelain insulator infrared detection threshold value optimization method based on the generalized extreme value theory as claimed in claim 1, wherein: the sample range of the insulation resistance value in the step S1 is equal to or less than 300M Ω.
3. The porcelain insulator infrared detection threshold value optimization method based on the generalized extreme value theory as claimed in claim 1, wherein: the step S3 specifically includes:
the probability distribution function of the generalized extremum distribution is:
the probability density function of the generalized extremum distribution is:
wherein k is a shape parameter, and k is a shape parameter,μ is a position parameter and σ is a scale parameter, and the obtained sample X ═ X1，x2，…，xn) And carrying out maximum likelihood estimation on parameters of the generalized extreme value distribution, wherein the log likelihood function is as follows:
order to
And (5) solving a mathematical model of the temperature difference of the degraded insulator.
4. The porcelain insulator infrared detection threshold value optimization method based on the generalized extreme value theory as claimed in claim 1, wherein: and after the step S3 is executed, performing goodness-of-fit inspection on the samples in the sample set by adopting K-S inspection to inspect whether the samples accord with distribution.
5. The porcelain insulator infrared detection threshold optimization method based on the generalized extreme value theory as claimed in claim 4, wherein: the goodness-of-fit test specifically comprises the following steps:
constructing a statistic:
KS＝max(|Fn(x)-G(x)|)
wherein Fn(x) And G (x) is an empirical distribution function obtained by a sample, a significant level value is set for the specified distribution function, and after K-S test, if a K-S test return value h is calculated to be 0, the sample data is proved to be in accordance with the generalized extreme value distribution by accepting the original hypothesis.
6. The porcelain insulator infrared detection threshold value optimization method based on the generalized extreme value theory as claimed in claim 1, wherein: the step S2 uses the formula
Finding out temperature difference data of deteriorated insulator, wherein TnRepresenting the deteriorated insulatorThe nth piece of degraded insulator cap temperature in the string.
7. The utility model provides a porcelain insulator infrared detection threshold value optimizing system based on generalized extreme value theory which characterized in that: the system comprises a network interface, a memory and a processor; wherein the content of the first and second substances,
the network interface is used for receiving and sending signals in the process of receiving and sending information with other external network elements;
the memory to store computer program instructions operable on the processor;
the processor is used for executing the steps of the porcelain insulator infrared detection threshold value optimization method based on the generalized extreme value theory according to any one of claims 1 to 6 when the computer program instructions are executed.
8. A computer storage medium, characterized in that: the computer storage medium stores a program of the porcelain insulator infrared detection threshold optimization method based on the generalized extreme value theory, and the program of the porcelain insulator infrared detection threshold optimization method based on the generalized extreme value theory is executed by at least one processor to realize the steps of the porcelain insulator infrared detection threshold optimization method based on the generalized extreme value theory according to any one of claims 1 to 6.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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