CN110058096A - Multifactor senile experiment method, system and device based on regional feature - Google Patents
Multifactor senile experiment method, system and device based on regional feature Download PDFInfo
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Abstract
The present invention provides a kind of multifactor senile experiment method based on regional feature, the synoptic model of objective area is determined by obtaining the meteorological data of objective area, apply different multifactor senile experiment parameters according to the synoptic model situation in different target area, to obtain based on regional composite insulator ageing results.The present invention also provides a kind of multifactor senile experiment system and device based on regional feature.The present invention is based on regional features to carry out multifactor senile experiment to composite insulator, it is easy to operate, it is with strong points, it provides more accurately to identify the operation time limit in different regions composite insulator as a result, the service life for raising composite insulator in each department provides certain help.
Description
Technical field
The present invention relates to high voltage external insulation fields, and in particular to a kind of multifactor senile experiment side based on regional feature
Method, the multifactor senile experiment system based on regional feature and the multifactor senile experiment device based on regional feature are used for base
Multifactor senile experiment is carried out to composite insulator in regional feature.
Background technique
Composite insulator plays the weight of electric insulation and mechanical support as one of device widely used in transmission system
It acts on, the quality of operation conditions is directly related to the stability and security of transmission system.Have more than 800 in electric system at present
Ten thousand composite insulator charging operations account for insulator uses total amount 55%.But with the increase of the operation time limit, problem of aging
It gets worse, has affected the normal operation of composite insulator.
A large amount of research has been carried out for composite insulator long-term ageing problem both at home and abroad.It is compound exhausted in these researchs
The aging of edge has focused largely on single factor test aging, and the research of multifactor aging more lacks.In IEC 61109:1992 for the first time
Propose the multifactor aging testing method of 5000h.Many tissues also attempt to propose the different multifactorial experiment sides 5000h simultaneously
Method.Such as ENEL 5000h multifactorial experiment, EPRL 5000h multifactorial experiment, FGH 5000h multifactorial experiment.But IEC
Degradation does not consider the recovery and forfeiture of the hydrophobicity of composite insulator.Salt fog is overweight in ENEL degradation, no solubility
Salt.EPRL is designed for U.S. environment.FGH degradation salt fog excessive cycle, ultraviolet time are partially short.Above-mentioned 5000h
Environmental parameter in multifactorial experiment differs more with the environmental parameter in China, cannot be directly used to the compound inslation of Domestic Environment
Son.On the other hand, the environmental parameter of different geographical is widely different, leads to the composite insulator ageing state difference pole of different geographical
Greatly, if therefore for different geographical using identical multifactor degradation, it will cause result inaccuracy, thus extreme influence
Judgement to composite insulator aging.Therefore need to propose a kind of 5000h multifactorial experiment design method for different geographical.
Summary of the invention
In view of problem above, the present invention proposes a kind of multifactor senile experiment method, system and dress based on regional feature
It sets, provides the experimental method of the composite insulator aging under different geographical environment, improve composite insulator aging judgement
Accuracy rate, for apply the composite insulator of different geographical the operation time limit judgement give a hand.
The first aspect of the application provides a kind of multifactor senile experiment method based on regional feature, for compound exhausted
Edge carries out multifactor senile experiment, which comprises
Creation aging action and the mapping table of synoptic model are simultaneously stored, wherein in the mapping table,
The synoptic model includes fine day mode, cloudy mode and rainy day mode, and the corresponding aging action of the fine day mode includes
Heating and ultraviolet light, the corresponding aging action of cloudy mode includes humidity and salt fog, and the corresponding aging action of rainy day mode includes
Rainfall；
The meteorological data for obtaining objective area one to December each moon, according to the division of the meteorological data of each moon
Four seasons of objective area simultaneously determine each mid-season feature month；
The meteorological data of every day in the feature month in each season is obtained, and according to the meteorological number of described every day
According to the synoptic model of every day in the determination feature month, wherein the synoptic model include fine day mode, cloudy mode with
And rainy day mode；
Point of every kind of synoptic model in the feature month is counted according to the synoptic model of every day in the feature month
Cloth situation, and calculate total number of days accounting in the total number of days of all synoptic models of every kind of synoptic model in the feature month
Than；
Total hourage of all synoptic models in each feature month is calculated, and total hourage of all synoptic models is removed
With aging accelerated factor, total hourage of each feature month in multifactor senile experiment is obtained, then according to every kind described
The accounting of synoptic model calculates total hourage of every kind of synoptic model in multifactor senile experiment, and according to every kind of weather
The distribution situation of mode carries out time distribution to each synoptic model in multifactor senile experiment；
The synoptic model distribution condition in seasonal characteristic month each in multifactor senile experiment is repeated into preset times, it is described
The value of preset times is equal to the value of total months in the season, obtains annual synoptic model in multifactor senile experiment；
According to the corresponding relationship of the aging action and synoptic model, according to synoptic model in the multifactor senile experiment
Distribution condition apply corresponding aging action, multifactor burn-in test is carried out to composite insulator to be tested.
Preferably, the meteorological data of the objective area each moon includes each monthly mean temperature, each come basis using warm therapy is waited
The monthly mean temperature of the moon divides four seasons of the objective area, determines each season in four seasons of the objective area
Feature month method are as follows: summer selection is characterized part temperature highest month, and autumn in spring and winter selection temperature are minimum
Be characterized month in month.
Preferably, the meteorological data of the objective area each moon further includes each monthly mean rainfall, is determined in each season
The method in feature month further include: in each season, when monthly mean temperature difference is less than preset value, select average precipitation
Most months are characterized month.
Preferably, the meteorological data of every day includes intra day ward and sunshine time in the feature month, the same day
Synoptic model is determined according to the intra day ward and sunshine time on the same day, wherein true according to the intra day ward on the same day and sunshine time
The method of settled day synoptic model includes:
Judge whether the intra day ward on the same day is greater than preset precipitation threshold value；
If intra day ward is greater than the precipitation threshold value, it is determined that the synoptic model on the same day is rainy day mode；
If intra day ward is less than the precipitation threshold value, it is default further to judge whether the sunshine time on the same day is greater than
Sunshine-duration threshold value；
If the sunshine time on the same day is greater than the sunshine-duration threshold value, it is determined that same day synoptic model is fine day；
If the sunshine time on the same day is less than the sunshine-duration threshold value, it is determined that same day synoptic model is the cloudy day.
Preferably, the aging action in multifactor senile experiment includes temperature, ultraviolet light, salt fog, humidity, rainfall and electricity
Pressure, the method also includes carrying out parameter setting to the aging action for being applied to tested composite insulator, wherein aging action
In temperature parameter setting are as follows: when summer, temperature setting under fine day mode is the objective area max. daily temperature plus pre-
It is Daily minimum temperature when winter if floating value, remaining time is normal max. daily temperature；The value of ultraviolet light is set as the target
Regional day maximum radiation degree；Salt haze value is the filthy concentration value of the objective area；Humidity value is the objective area day highest
Humidity value；The numerical value of the rainfall is set as preset numerical value, and the preset numerical value is that can wash away pollution severity of insulators
Rainfall numerical quantity；The numerical value of the voltage is the retting-flax wastewater of composite insulator to be tested, wherein the retting-flax wastewater of insulator
For the ratio between the root-mean-square valve of highest operating voltage carried on the creepage distance and the insulator of insulator.
The second aspect of the application provides a kind of multifactor senile experiment system based on regional feature, the system packet
It includes:
Setup module, for creating the mapping table of aging action and synoptic model and being stored, wherein described
In mapping table, the synoptic model includes that fine day mode, cloudy mode and rainy day mode, the fine day mode are corresponding
Aging action includes heating and ultraviolet light, and the corresponding aging action of cloudy mode includes humidity and salt fog, and rainy day mode is corresponding
Aging action includes rainfall；
Feature month determining module, for obtaining the meteorological data of objective area one to December each moon, according to described
The meteorological data of each moon divides four seasons of the objective area and determines each mid-season feature month；
Synoptic model determining module, the meteorological data of every day in the feature month for obtaining each season, and
The synoptic model of every day in the feature month is determined according to the meteorological data of described every day, wherein the synoptic model
Including fine day mode, cloudy mode and rainy day mode；
Computing module, for counting in the feature month every kind according to the synoptic model of every day in the feature month
The distribution situation of synoptic model, and the total number of days for calculating every kind of synoptic model in the feature month is total in all synoptic models
Accounting in number of days；
Distribution module, for calculating total hourage of all synoptic models in each feature month, and by all weather moulds
Total hourage of formula obtains total hourage of each feature month in multifactor senile experiment, so divided by aging accelerated factor
Total hourage of every kind of synoptic model in multifactor senile experiment is calculated according to the accounting of every kind of synoptic model afterwards, and is pressed
Time distribution is carried out to each synoptic model in multifactor senile experiment according to the distribution situation of every kind of synoptic model；
Annual synoptic model determining module, for by the synoptic model in seasonal characteristic month each in multifactor senile experiment
Distribution condition repeats preset times, and the value of the preset times is equal to the value of total months in the season, obtains multifactor aging
Annual synoptic model in experiment；And
Control module, for the corresponding relationship according to the aging action and synoptic model, according to the multifactor aging
The distribution condition of synoptic model applies corresponding aging action in experiment, carries out multifactor aging to composite insulator to be tested
Test.
Third of the present invention, which facilitates, provides a kind of multifactor senile experiment device based on regional feature, for compound inslation
Son carries out senile experiment, and described device includes: aging action bringing device, real for applying multifactor aging to composite insulator
Aging action in testing；Processor；And memory, multiple program modules, the multiple program are stored in the memory
Module is loaded by the processor and executes the multifactor senile experiment method based on regional feature as previously described.
Multifactor senile experiment method in the present invention based on regional feature, is arranged different based on different regional features
Multifactor senile experiment parameter, thus obtain being based on regional composite insulator ageing results, it is easy to operate, it is with strong points,
The operation time limit for identification in this area's composite insulator provides more accurate structure, is to improve composite insulator in each department
Service life provides help.
Detailed description of the invention
Fig. 1 is the multifactor senile experiment method flow diagram based on regional feature that an embodiment of the present invention provides.
Fig. 2 is the multifactor senile experiment system schematic based on regional feature that an embodiment of the present invention provides.
Fig. 3 is the multifactor senile experiment schematic device based on regional feature that an embodiment of the present invention provides.
Specific embodiment
To better understand the objects, features and advantages of the present invention, with reference to the accompanying drawing and specific real
Applying example, the present invention will be described in detail.It should be noted that in the absence of conflict, embodiments herein and embodiment
In feature can be combined with each other.
In the following description, numerous specific details are set forth in order to facilitate a full understanding of the present invention, described embodiment is only
It is only a part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill
Personnel's every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
Unless otherwise defined, all technical and scientific terms used herein and belong to technical field of the invention
The normally understood meaning of technical staff is identical.Term as used herein in the specification of the present invention is intended merely to description tool
The purpose of the embodiment of body, it is not intended that in the limitation present invention.
Referring to Fig. 1, the multifactor aging method process based on regional feature provided for one embodiment of the present invention
Figure.The sequence of step can change in the flow chart according to different requirements, and certain steps can be omitted.For ease of description,
Only parts related to embodiments of the present invention are shown.
As shown in Figure 1, the multifactor senile experiment method of the composite insulator based on regional feature includes the following steps.
Step S1, it creates the mapping table of aging action and synoptic model and is stored, wherein in the corresponding pass
It is in table, the synoptic model includes but is not limited to that fine day mode, cloudy mode and rainy day mode, the fine day mode are corresponding
Aging action be heating and ultraviolet light, the corresponding aging action of cloudy mode is humidity and salt fog, and rainy day mode is corresponding old
Change factor is rainfall.
In present embodiment, referring to the multifactor senile experiment method of 5000h in IEC 61109:1992, by aging action
It is set as temperature, humidity, ultraviolet light, filth, rainfall, this electric 6 factors.
Step S2, the meteorological data for obtaining objective area one to December each moon, according to the meteorological data of each moon
It divides four seasons of the objective area and determines each mid-season feature month.
The objective area is the area that composite insulator to be tested is installed and used, for example, to be tested multiple when needing
Insulator use is closed at Hainan Province, then the objective area can choose a city in Hainan Province, such as Hainan Province
Haikou City.It should be noted that the objective area can be any area using composite insulator.
The meteorological data of objective area each moon includes each monthly mean temperature and each monthly mean rainfall.The present invention one
In embodiment, the meteorological data of objective area each moon is each monthly average meteorological data year after year.In other embodiments,
The meteorological data of objective area each moon is also possible to each monthly average meteorological data in a certain year.
In present embodiment, four season of the objective area is divided according to the monthly mean temperature of each moon using warm therapy is waited
Section.In waiting warm therapy, with time (every five days are a time) temperature on average less than 10 DEG C for winter, waiting temperature on average to be greater than 22 DEG C is the summer
Wait respectively spring and autumn of the temperature on average between 10 DEG C -22 DEG C in season.The time temperature on average refers to continuous 5 days days
Temperature on average weighted average, it is to wait the important indicator that the four seasons are divided in warm therapy.
In present embodiment, the method that determines the feature month in each season in four seasons of the objective area are as follows:
Summer selection is characterized part temperature highest month, autumn in spring and winter selection temperature it is minimum be characterized month in month.
Further, it is determined that in each season feature month method further include: in each season, work as monthly mean temperature
Difference be less than preset value (such as 0.5 DEG C) when, select average precipitation it is most be characterized month in month.
For example, by taking Haikou City, Hainan Province as an example, the following table 1 is Haikou city each monthly mean temperature and average drop year after year
Water.According to each monthly mean temperature utilizes the time warm therapy it can be concluded that dividing in the season of Haikou City year after year in following table are as follows: summer
For the 4-11 month, spring, autumn are the 12-3 month.In summer, temperature highest and temperature on average difference be less than preset value (0.5 DEG C) the moon
Part is 6, July, and wherein the precipitation in June is greater than the precipitation in July, then the feature month that will be determined as summer June.?
In spring, autumn, the temperature in January is minimum, it is determined that January is the feature month in spring, autumn.
| Month | Each monthly total precipitation (0.1mm) year after year | Each monthly mean temperature (0.1 DEG C) year after year |
| 1 | 195 | 177 |
| 2 | 350 | 187 |
| 3 | 506 | 218 |
| 4 | 1002 | 252 |
| 5 | 1814 | 274 |
| 6 | 2270 | 285 |
| 7 | 2181 | 286 |
| 8 | 2356 | 281 |
| 9 | 2441 | 272 |
| 10 | 2244 | 253 |
| 11 | 813 | 223 |
| 12 | 349 | 190 |
Table 1
In present embodiment, the meteorological data of objective area each moon be can be from Chinese meteorological data net, Chinese state
What border switching station terrestrial climate standard value moon Value Data collection, the official website US National Aeronautics and Space Administration NASA etc. obtained.
Step S3, the meteorological data of every day in the feature month in each season is obtained, and according to described every day
Meteorological data determine the synoptic model of every day in the feature month, wherein the synoptic model includes fine day mode, yin
Day mode and rainy day mode.
In first embodiment of the invention, in the feature month meteorological data of every day include intra day ward and
Sunshine time, the synoptic model on the same day are according to determined by the intra day ward on the same day and sunshine time.
Specifically, include: according to the method that the intra day ward on the same day and sunshine time determine same day synoptic model
1) judge whether the intra day ward on the same day is greater than preset precipitation threshold value (such as 15 millimeters)；
2) if intra day ward is greater than the precipitation threshold value, it is determined that the synoptic model on the same day is rainy day mode；
It wherein, is more than the ultraviolet influence to composite insulator, institute to the effect of composite insulator due to Rainfall simulator erosion filth
With daily rainfall be greater than 15 millimeters when, based on rainy day mode；
If 3) intra day ward is less than the precipitation threshold value, it is pre- further to judge whether the sunshine time on the same day is greater than
If sunshine-duration threshold value (such as 6 hours)；
If the sunshine time on the same day is greater than the sunshine-duration threshold value, it is determined that same day synoptic model is fine day；
If the sunshine time on the same day is less than the sunshine-duration threshold value, it is determined that same day synoptic model is the cloudy day.
For example, in the case of precipitation threshold value is 15 millimeters, sunshine-duration threshold value is 6 small, according to the same day
The method that intra day ward and sunshine time determine same day synoptic model can be indicated by the following table 2.
Table 2
Further, the method also includes: judge whether the meteorological data of every day exceeds preset normal range (NR), if
Beyond normal range (NR), it is determined that the meteorological data of this day is wrong, then excludes the meteorological data of this day, without weather mould
The judgement of formula carries out the judgement of synoptic model if without departing from normal range (NR) to this day.Wherein, the preset normal range (NR)
It can be and preset and be stored in preset memory locations.For example, the normal range (NR) of the meteorological data can be day drop
Water is at 0-3000 millimeters, if intra day ward is more than 3000 millimeters, it is determined that the meteorological data of this day is wrong, without day
The judgement of gas mode.
In one embodiment of the present invention, the intra day ward and sunshine time can be the feature moon described in a certain time
Part meteorological data, such as in June, 2018 every day meteorological data.In other embodiments of the present invention, the day
Precipitation and sunshine time can be the average value for taking the meteorological data of every day in feature month in multiple historical years, such as mistake
Remove the average value of the meteorological data of every day in June in 10 years.
It continues with and the present invention is illustrated by taking Haikou City, Hainan Province as an example.As previously mentioned, in Haikou City summer
In June be characterized month, the meteorological data (including intra day ward and sunshine time) of every day and according to 6 in June
The synoptic model that the weather data of every day determines in month is as shown in table 3 below, wherein and " 1 " represents fine day in synoptic model,
" 2 " represent the cloudy day, and " 3 " represent the rainy day, and it is wrong that "/" represents meteorological data.For example, in the meteorological data on June 1, intra day ward
It is 3.3 millimeters, sunshine time is 9.3 hours, and according to the judgment rule in table 2, intra day ward is less than 15 millimeters of precipitation threshold value
And sunshine time is greater than 6 hours, it is determined that June 1 was fine day.And in the meteorological data on June 22, intra day ward is 22.7 millis
Rice is greater than 15 millimeters of the intra day ward threshold value, and the determining June 22 is rainy day mode, and so on, determine that June is every
One day synoptic model.And for June 19 and this two days meteorological data on June 28, because intra day ward is 3270 millis
Rice, has exceeded 3000 millimeters of the preset normal range (NR), thus this two days weather data is denoted as it is wrong, without weather
The determination of mode.
Table 3
Similarly, according to foregoing method also it can be concluded that the Haikou City spring, autumn the January feature moon in every day
Synoptic model.
It is understood that in other embodiments of the present invention, it is each in each seasonal characteristic month of the acquisition
It meteorological data can also directly include the weather predicted conditions of every day, for example, pre- by the weather obtained in weather site
When weather is fine in report, synoptic model is determined as fine day mode；When weather is yin in weather forecast, determine synoptic model for yin
Day mode；When weather in weather forecast is sleet, determine that synoptic model is rainy day mode.
Step S4, every kind of weather mould in the feature month is counted according to the synoptic model of every day in the feature month
The distribution situation of formula, and calculate accounting of the total number of days of every kind of synoptic model in the total number of days of all synoptic models.
Specifically, total number of days of every kind of synoptic model in the feature month is first calculated, every kind of synoptic model is recycled
Total number of days divided by all synoptic models in feature month total number of days, so that every kind of synoptic model be calculated in the entire feature moon
Accounting in part.Wherein, due to that may have several days meteorological datas in feature month because being determined beyond normal range (NR)
To be wrong, in calculating process, these are confirmed as wrong number of days and are deducted.
For example, continue to be illustrated with the meteorological data in foregoing Haikou City June, a total of 30 days in June,
Wherein being determined the wrong number of days of precipitation record is 2 days, this 2 days wrong data are deducted, therefore all synoptic models
Total number of days is 28 days.Wherein total number of days of fine day mode is 25 days, i.e., fine day mode accounting is 25/28；Total day of cloudy mode
Number is 2 days, i.e., cloudy mode accounting is 2/28；Total number of days of rainy day mode is 1 day, i.e., rainy day mode accounting is 1/28.Similarly
The accounting of every kind of synoptic model in 1 month feature month in spring and autumn can be calculated.
Step S5, total hourage of all synoptic models in each feature month is calculated, and by the total of all synoptic models
Hourage obtains total hourage of each feature month in multifactor senile experiment, then basis divided by aging accelerated factor
The accounting of every kind of synoptic model calculates total hourage of every kind of synoptic model in multifactor senile experiment, and according to described
The distribution situation of every kind of synoptic model carries out time distribution to each synoptic model in multifactor senile experiment.
Wherein, the numerical value of the aging accelerated factor can need to be configured according to experiment, and the present invention, which does not do this, to be had
Body limits.In the present embodiment, the numerical value of the aging accelerated factor is 16.It is described in other embodiments of the present invention
Aging accelerated factor is also possible to other numerical value.
Continue by taking foregoing Haikou City as an example, in the present embodiment, June feature month summer all weather moulds
Total number of days of formula is 28 days, then total hourage of all synoptic models is 28*24=672 (hour), then by all weather moulds
Total hourage 672 of formula obtains total hour of the feature month of summer in multifactor senile experiment divided by aging accelerated factor 16
Number is 42 hours, wherein in this 42 hours, the accounting of fine day mode is 25/28, and it is real in multifactor aging can to obtain fine day mode
Total hourage in testing is 37.5 hours, and it is 3 hours that cloudy mode, which is similarly calculated, and the rainy day is 1.5 hours.Referring to June
Every kind of synoptic model distribution situation, the result being allocated to each synoptic model can simplify are as follows: undergo the fine day of 37.5h
Afterwards, it experienced the cloudy day of 1.5h, then after the rainy day of experience 1.5h, then undergo the cloudy day of 1.5h.
Step S6, the synoptic model distribution condition in seasonal characteristic month each in multifactor senile experiment is repeated default time
Number, the value of the preset times are equal to the value of total months in the season, to obtain annual weather in multifactor senile experiment
Mode.
For example, foregoing Haikou City summer total months are 9 months, and total months in spring and autumn are 3 months,
So the synoptic model distribution condition in June feature month summer is repeated 9 times, that is, by after the fine day for undergoing 37.5h, undergo
The cloudy day of 1.5h then after the rainy day of experience 1.5h, then undergoes this cloudy process of 1.5h to be repeated 9 times, similarly, then by the spring
The synoptic model distribution condition in the January in feature month in season and autumn was repeated 3 times to get a year and a day into multifactor senile experiment
Synoptic model distribution condition.
Step S7, according to the corresponding relationship of the aging action and synoptic model, according in the multifactor senile experiment
The distribution condition of synoptic model applies corresponding aging action, carries out multifactor burn-in test to composite insulator to be tested.
Continuation is illustrated by taking foregoing Haikou City as an example, according in the multifactor senile experiment determined in step S5
The distribution condition of each synoptic model applies corresponding aging action, such as corresponding aging in June to composite insulator to be tested
Factor can be according to being applied in the following table 4: the corresponding aging action of fine day mode is for heating and ultraviolet light, duration
37.5 hours, then heating device and ultraviolet light bringing device 37.5 hours are opened in multifactor senile experiment, it is fine to simulate
It when composite insulator ambient condition；It is then turned on the corresponding salt fog of the corresponding aging action-salt fog of cloudy mode and applies dress
Set 1.5 hours, come the ambient condition of composite insulator when simulating the cloudy day, be then turned on the corresponding aging action of rainy day mode ---
The corresponding rainfall bringing device of rainfall 1.5 hours finally opens cloudy mould come the ambient condition of composite insulator when simulating the rainy day
The corresponding salt fog bringing device of the corresponding aging action-salt fog of formula 1.5 hours, come the environment of composite insulator when simulating the cloudy day
State.In present embodiment, referring to the multifactor senile experiment method of 5000h in IEC 61109:1992, aging action is set
It is set to temperature, humidity, ultraviolet light, filth, rainfall, voltage this 6 factors.Wherein voltage and humidity are in the entire experiment process
It is continuously applied, such as voltage and humidity are continuously applied 42 hours in June corresponding senile experiment." -- " indicates not in following table
Apply aging action.
Table 4
In the present embodiment, before step S7, the method also includes to being applied to tested composite insulator
Aging action carry out parameter setting the step of, wherein in aging action temperature parameter setting are as follows: when summer, fine day mode
Lower temperature setting is that max. daily temperature adds default floating value (such as the default floating value is 11 degree), and when winter is that day is minimum
Temperature, remaining time are normal max. daily temperature.The value of ultraviolet light is set as objective area day maximum radiation degree；Salt haze value is mesh
Mark the filthy concentration value in area.Humidity value is the objective area day highest humidity value.The numerical value of the rainfall is set as default
Numerical value, the preset numerical value is the rainfall numerical quantity that can be washed away pollution severity of insulators, reach flushing effect.The electricity
The numerical value of pressure is the retting-flax wastewater of composite insulator to be tested, wherein the retting-flax wastewater of insulator is the creepage distance of insulator
With the ratio between the root-mean-square valve of highest operating voltage carried on the insulator.Wherein, the max. daily temperature of the objective area, day
The data such as minimum temperature, day maximum radiation degree, filthy situation, day highest humidity can be from meteorological department's database of objective area
Middle acquisition, can also be by being measured from.The setting rule of design parameter is referred to such as the following table 5:
Table 5
Multifactor senile experiment method based on regional feature of the invention, is arranged different based on different regional features
Multifactor senile experiment parameter, thus obtain being based on regional composite insulator ageing results, it is easy to operate, it is with strong points,
The operation time limit for identification in this area's composite insulator provides more accurate structure, is to improve composite insulator in each department
Service life provides certain help.
Fig. 2 is the structure chart for the multifactor senile experiment system based on regional feature that an embodiment of the present invention provides.
In some embodiments, the multifactor senile experiment system 200 based on regional feature may include multiple
The functional module as composed by program code segments.It is each in the multifactor senile experiment system 200 based on regional feature
The program code of program segment can store in the memory of computer installation, and be handled by least one of computer installation
Performed by device, to realize the multifactor senile experiment function of composite insulator based on regional feature.
With reference to Fig. 2, in present embodiment, the multifactor senile experiment system 200 based on regional feature is according to performed by it
Function, can be divided into multiple functional modules, each functional module for execute Fig. 1 correspond to it is each in embodiment
A step, to realize the multifactor senile experiment function of composite insulator based on regional feature.It is described to be based in present embodiment
The functional module of the multifactor senile experiment system 200 of regional feature includes: setup module 201, feature month determining module
202, synoptic model determining module 203, computing module 204, distribution module 205, annual synoptic model determining module 206, control
Module 207.The function of each functional module will be described in detail in the following embodiments.
The setup module 201 is used for step S1, creates the mapping table of aging action and synoptic model and deposited
Storage, wherein in the mapping table, the synoptic model includes but is not limited to fine day mode, cloudy mode and rainy day
Mode, the corresponding aging action of the fine day mode are heating and ultraviolet light, the corresponding aging action of cloudy mode be humidity with
Salt fog, the corresponding aging action of rainy day mode are rainfall.
The feature month determining module 202 is used to obtain the meteorological data of objective area one to December each moon, root
Four seasons of the objective area are divided according to the meteorological data of each moon and determine each mid-season feature month.
The objective area is the area that composite insulator to be tested is installed and used, for example, to be tested multiple when needing
Insulator use is closed at Hainan Province, then the objective area can choose a city in Hainan Province, such as Hainan Province
Haikou City.It should be noted that the objective area can be any area using composite insulator.
The meteorological data of objective area each moon includes each monthly mean temperature and each monthly mean rainfall.The present invention one
In embodiment, the meteorological data of objective area each moon is each monthly average meteorological data year after year.In other embodiments,
The meteorological data of objective area each moon is also possible to each monthly average meteorological data in a certain year.
In present embodiment, four season of the objective area is divided according to the monthly mean temperature of each moon using warm therapy is waited
Section.In waiting warm therapy, with time (every five days are a time) temperature on average less than 10 DEG C for winter, waiting temperature on average to be greater than 22 DEG C is the summer
Wait respectively spring and autumn of the temperature on average between 10 DEG C -22 DEG C in season.The time temperature on average refers to continuous 5 days days
Temperature on average weighted average, it is to wait the important indicator that the four seasons are divided in warm therapy.
In present embodiment, the feature month determining module 202 determines each in four seasons of the objective area
The method in the feature month in season are as follows: summer selection is characterized part temperature highest month, and autumn in spring and winter select temperature
Minimum month is characterized month.
Further, the method that the feature month determining module 202 determines feature month in each season further include:
In each season, when monthly mean temperature difference is less than preset value (such as 0.5 DEG C), month for selecting average precipitation most for
Feature month.
For example, by taking Haikou City, Hainan Province as an example, Haikou city is got year after year after each monthly mean temperature, determines and exists
Temperature on average is waited 4-11 month greater than 22 DEG C, it is determined that 4-11 month is summer；Determine wait in 12-3 month temperature on average between
10 DEG C -22 DEG C, it is determined that Haikou City spring, autumn are the 12-3 month.And in summer, temperature highest and temperature on average difference are less than pre-
If the month for being worth (0.5 DEG C) is 6, July, wherein the precipitation in June is greater than the precipitation in July, then will be determined as the summer in June
The feature month in season.In spring, autumn, the temperature in January is minimum, it is determined that January is the feature month in spring, autumn.
In present embodiment, the meteorological data of objective area each moon be can be from Chinese meteorological data net, Chinese state
What border switching station terrestrial climate standard value moon Value Data collection, the official website US National Aeronautics and Space Administration NASA etc. obtained.
The synoptic model determining module 203 is used to obtain the meteorological number of every day in the feature month in each season
According to, and determine according to the meteorological data of described every day the synoptic model of every day in the feature month, wherein the weather
Mode includes fine day mode, cloudy mode and rainy day mode.
In first embodiment of the invention, in the feature month meteorological data of every day include intra day ward and
Sunshine time, the synoptic model on the same day are according to determined by the intra day ward on the same day and sunshine time.
Specifically, the synoptic model determining module 203 ought be according to the intra day ward and sunshine time determination on the same day everyday
Gas mode includes:
1) judge whether the intra day ward on the same day is greater than preset precipitation threshold value (such as 15 millimeters)；
2) if intra day ward is greater than the precipitation threshold value, it is determined that the synoptic model on the same day is rainy day mode；
It wherein, is more than the ultraviolet influence to composite insulator, institute to the effect of composite insulator due to Rainfall simulator erosion filth
With daily rainfall be greater than 15 millimeters when, based on rainy day mode；
If 3) intra day ward is less than the precipitation threshold value, it is pre- further to judge whether the sunshine time on the same day is greater than
If sunshine-duration threshold value (such as 6 hours)；
If the sunshine time on the same day is greater than the sunshine-duration threshold value, it is determined that same day synoptic model is fine day；
If the sunshine time on the same day is less than the sunshine-duration threshold value, it is determined that same day synoptic model is the cloudy day.
For example, in the case of precipitation threshold value is 15 millimeters, sunshine-duration threshold value is 6 small, according to the same day
The method that intra day ward and sunshine time determine same day synoptic model can be indicated by following table.
Further, the synoptic model determining module 203 is also used to: it is pre- to judge whether the meteorological data of every day exceeds
If normal range (NR), if exceeding normal range (NR), it is determined that the meteorological data of this day is wrong, then arranges the meteorological data of this day
It removes, without the judgement of synoptic model, carries out the judgement of synoptic model to this day if without departing from normal range (NR).Wherein, institute
It states preset normal range (NR) and can be and preset and be stored in preset memory locations.For example, the meteorological data is just
Normal range can be intra day ward at 0-3000 millimeters, if intra day ward is more than 3000 millimeters, it is determined that the meteorology of this day
Data are wrong, the judgement without synoptic model.
In one embodiment of the present invention, the intra day ward and sunshine time can be the feature moon described in a certain time
Part meteorological data, such as in June, 2018 every day meteorological data.In other embodiments of the present invention, the day
Precipitation and sunshine time can be the average value for taking the meteorological data of every day in feature month in multiple historical years, such as mistake
Remove the average value of the meteorological data of every day in June in 10 years.
It is understood that in other embodiments of the present invention, it is each in each seasonal characteristic month of the acquisition
It meteorological data can also directly include the weather predicted conditions of every day, for example, pre- by the weather obtained in weather site
When weather is fine in report, synoptic model is determined as fine day mode；When weather is yin in weather forecast, determine synoptic model for yin
Day mode；When weather in weather forecast is sleet, determine that synoptic model is rainy day mode.
The computing module 204 is used to count the feature month according to the synoptic model of every day in the feature month
In every kind of synoptic model distribution situation, and calculate total number of days of every kind of synoptic model in the total number of days of all synoptic models
Accounting.
Specifically, the computing module 204 first calculates total number of days of every kind of synoptic model in the feature month, recycles
Total number of days of every kind of synoptic model divided by all synoptic models in feature month total number of days, so that every kind of weather mould be calculated
Accounting of the formula in entire feature month.Wherein, due to that may have several days meteorological datas in feature month because beyond just
Normal range and be confirmed as wrong, in calculating process, these are confirmed as wrong number of days and are deducted.
For example, by taking the meteorological data in Haikou City June as an example, a total of 30 days in June, wherein being determined precipitation note
Recording wrong number of days is 2 days, this 2 days wrong data are deducted, therefore total number of days of all synoptic models is 28 days.It is wherein fine
Total number of days of day mode is 25 days, i.e., fine day mode accounting is 25/28；Total number of days of cloudy mode is 2 days, i.e., cloudy mode accounts for
Than being 2/28；Total number of days of rainy day mode is 1 day, i.e., rainy day mode accounting is 1/28.Spring and autumn can similarly be calculated
1 month feature month in every kind of synoptic model accounting.
The experimental period distribution module 205 is used to calculate total hourage of all synoptic models in each feature month,
And total hourage of all synoptic models is obtained into each feature month in multifactor senile experiment divided by aging accelerated factor
Total hourage, every kind of synoptic model is then calculated in multifactor senile experiment according to the accounting of every kind of synoptic model
Total hourage, and according to the distribution situation of every kind of synoptic model in multifactor senile experiment each synoptic model carry out when
Between distribute.
Wherein, the numerical value of the aging accelerated factor can need to be configured according to experiment, and the present invention, which does not do this, to be had
Body limits.In the present embodiment, the numerical value of the aging accelerated factor is 16.It is described in other embodiments of the present invention
Aging accelerated factor is also possible to other numerical value.
Continue by taking foregoing Haikou City as an example, in the present embodiment, June feature month summer all weather moulds
Total number of days of formula is 28 days, then total hourage of all synoptic models is 28*24=672 (hour), then by all weather moulds
Total hourage 672 of formula obtains total hour of the feature month of summer in multifactor senile experiment divided by aging accelerated factor 16
Number is 42 hours, wherein in this 42 hours, the accounting of fine day mode is 25/28, and it is real in multifactor aging can to obtain fine day mode
Total hourage in testing is 37.5 hours, and it is 3 hours that cloudy mode, which is similarly calculated, and the rainy day is 1.5 hours.Referring to June
Every kind of synoptic model distribution situation, the result being allocated to each synoptic model can simplify are as follows: undergo the fine day of 37.5h
Afterwards, it experienced the cloudy day of 1.5h, then after the rainy day of experience 1.5h, then undergo the cloudy day of 1.5h.
The whole year synoptic model determining module 206 is used for the day in seasonal characteristic month each in multifactor senile experiment
Gas mode distribution condition repeats preset times, and the value of the preset times is equal to the value of total months in the season, to obtain
Annual synoptic model in multifactor senile experiment.
For example, if Haikou City summer total months are 9 months, total months in spring and autumn are 3 months, then by the summer
The synoptic model distribution condition in Ji Tezheng June in month is repeated 9 times, similarly, then by the day in spring and the January in feature month in autumn
Gas mode distribution condition is repeated 3 times to get the synoptic model distribution condition of a year and a day into multifactor senile experiment.
The control module 207 is used for according to the corresponding relationship of the aging action and synoptic model, according to it is described mostly because
The distribution condition of synoptic model controls the aging action bringing device and applies to composite insulator to be tested in plain senile experiment
Add corresponding aging action, multifactor burn-in test is carried out to composite insulator to be tested.
In the present embodiment, the control module 207 is also used to the aging to tested composite insulator is applied to
Factor carries out parameter setting, wherein the temperature parameter setting in aging action are as follows: when summer, temperature setting is under fine day mode
Max. daily temperature adds default floating value (such as the default floating value is 11 degree), is Daily minimum temperature when winter, remaining when
Between be normal max. daily temperature.The value of ultraviolet light is set as objective area day maximum radiation degree；Salt haze value is the dirt of objective area
Dirty concentration value.Humidity value is the objective area day highest humidity value.The numerical value of the rainfall is set as preset numerical value, described
Preset numerical value is the rainfall numerical quantity that can be washed away pollution severity of insulators, reach flushing effect.The numerical value of the voltage is
The retting-flax wastewater of composite insulator to be tested, wherein the retting-flax wastewater of insulator is the creepage distance and the insulator of insulator
The ratio between the root-mean-square valve of highest operating voltage of upper carrying.Wherein, the max. daily temperature, Daily minimum temperature of the objective area,
The data such as day maximum radiation degree, filthy situation, day highest humidity can be obtained from meteorological department's database of objective area,
It can be by being measured from.
Fig. 3 is the functional module for the multifactor senile experiment device based on regional feature that an embodiment of the present invention provides
Schematic diagram.The multifactor senile experiment device 10 based on regional feature includes at least aging action bringing device 11, storage
Device 12, processor 13 and it is stored in the computer program 14 that can be run in the memory 12 and on the processor 13,
Such as the multifactor senile experiment program based on regional feature.The processor 13 is realized when executing the computer program 14
The step of stating the multifactor senile experiment method in embodiment of the method based on regional feature.Alternatively, the processor 13 executes institute
State the function that computer program 14 realizes each module/unit in the above system embodiment, such as the module 201-204 in Fig. 2.
Illustratively, the computer program 14 can be divided into one or more module/units, it is one or
Multiple module/units are stored in the memory 12, and are executed by the processor 13, to complete the present invention.Described one
A or multiple module/units can be the series of computation machine program instruction section that can complete specific function, which is used for
Implementation procedure of the computer program 14 in the multifactor senile experiment device 10 based on regional feature is described.Example
Such as, the computer program 14 can be divided into the module 201-207 in Fig. 2.
It will be understood by those skilled in the art that the schematic diagram 3 is only based on the multifactor senile experiment of regional feature
The example of device 10 does not constitute the restriction to the multifactor senile experiment device 10 based on regional feature, is based on regional feature
Multifactor senile experiment device 10 may include perhaps combining certain components or not than illustrating more or fewer components
Same component, such as the multifactor senile experiment device 10 based on regional feature can also be including input-output equipment etc..
The aging action bringing device 11 to composite insulator to be tested for applying under the control of processor 13
Aging action in multifactor senile experiment.Specifically, the aging action bringing device can include but is not limited to: for applying
The temperature bringing device of heating degree, the ultraviolet light bringing device for applying ultraviolet light, the salt fog for applying salt fog apply dress
It sets, the humidity bringing device for applying humidity, the rain making bringing device for applying rainfall and for applying voltage
Voltage application device.
Alleged processor 13 can be central processing unit (Central Processing Unit, CPU), can also wrap
Include other general processors, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor is also possible to any conventional processor
Deng the processor 13 is the control centre of the multifactor senile experiment device 10 based on regional feature, is connect using various
Mouthful and the entire multifactor senile experiment device 10 based on regional feature of connection various pieces.
The memory 12 can be used for storing the computer program 14 and/or module/unit, and the processor 13 passes through
Operation executes the computer program and/or module/unit being stored in the memory 12, and calls and be stored in memory
Data in 12 realize the various functions of the multifactor senile experiment device 10 based on regional feature.Memory 12 can be with
It also may include memory including exterior storage medium.In addition, memory 12 may include high-speed random access memory, may be used also
To include nonvolatile memory, such as hard disk, memory, plug-in type hard disk, intelligent memory card (Smart Media Card,
SMC), secure digital (Secure Digital, SD) blocks, and flash card (Flash Card), dodges at least one disk memory
Memory device or other volatile solid-state parts.
If the integrated module/unit of the multifactor senile experiment device 10 based on regional feature is with software function list
Member form realize and when sold or used as an independent product, can store in a computer-readable storage medium
In.Based on this understanding, the present invention realizes all or part of the process in above-described embodiment method, can also pass through computer
Program is completed to instruct relevant hardware, and the computer program can be stored in a computer readable storage medium, should
Computer program is when being executed by processor, it can be achieved that the step of above-mentioned each embodiment of the method.It should be noted that the meter
The content that calculation machine readable medium includes can carry out increase and decrease appropriate according to the requirement made laws in jurisdiction with patent practice,
It such as does not include electric carrier signal and telecommunications according to legislation and patent practice, computer-readable medium in certain jurisdictions
Signal.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included in the present invention.Any reference signs in the claims should not be construed as limiting the involved claims.This
Outside, it is clear that one word of " comprising " does not exclude other units or steps, and odd number is not excluded for plural number.It is stated in computer installation claim
Multiple units or computer installation can also be implemented through software or hardware by the same unit or computer installation.The
One, the second equal words are used to indicate names, and are not indicated any particular order.
Finally it should be noted that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although reference
Preferred embodiment describes the invention in detail, those skilled in the art should understand that, it can be to of the invention
Technical solution is modified or equivalent replacement, without departing from the spirit and scope of the technical solution of the present invention.
Claims (9)
1. a kind of multifactor senile experiment method based on regional feature, real for carrying out multifactor aging to composite insulator
It tests, which is characterized in that the described method includes:
Creation aging action and the mapping table of synoptic model are simultaneously stored, wherein described in the mapping table
Synoptic model includes fine day mode, cloudy mode and rainy day mode, and the corresponding aging action of the fine day mode includes heating
And ultraviolet light, the corresponding aging action of cloudy mode includes humidity and salt fog, and the corresponding aging action of rainy day mode includes rainfall；
The meteorological data for obtaining objective area one to December each moon divides the target according to the meteorological data of each moon
Four seasons in area simultaneously determine each mid-season feature month；
The meteorological data of every day in the feature month in each season is obtained, and true according to the meteorological data of described every day
The synoptic model of every day in the fixed feature month, wherein the synoptic model includes fine day mode, cloudy mode and rain
Day mode；
The distribution feelings of every kind of synoptic model in the feature month are counted according to the synoptic model of every day in the feature month
Condition, and calculate accounting of the total number of days of every kind of synoptic model in the feature month in the total number of days of all synoptic models；
Total hourage of all synoptic models in each feature month is calculated, and by total hourage of all synoptic models divided by old
Change accelerated factor, total hourage of each feature month in multifactor senile experiment is obtained, then according to every kind of weather
The accounting of mode calculates total hourage of every kind of synoptic model in multifactor senile experiment, and according to every kind of synoptic model
Distribution situation in multifactor senile experiment each synoptic model carry out time distribution；
The synoptic model distribution condition in seasonal characteristic month each in multifactor senile experiment is repeated into preset times, it is described default
The value of number is equal to the value of total months in the season, obtains annual synoptic model in multifactor senile experiment；
According to the corresponding relationship of the aging action and synoptic model, according to point of synoptic model in the multifactor senile experiment
Apply corresponding aging action with situation, multifactor burn-in test is carried out to composite insulator to be tested.
2. the multifactor senile experiment method based on regional feature as described in claim 1, which is characterized in that the target
The meteorological data of area's each moon includes each monthly mean temperature, divides the target according to the monthly mean temperature of each moon using warm therapy is waited
Four seasons in area, the method for determining the feature month in each season in four seasons of the objective area are as follows: summer choosing
Select and be characterized part temperature highest month, autumn in spring and winter selection temperature it is minimum be characterized month in month.
3. the multifactor senile experiment method based on regional feature as claimed in claim 2, which is characterized in that the target
The meteorological data of area's each moon further includes each monthly mean rainfall, the method for determining feature month in each season further include: every
In a season, when monthly mean temperature difference be less than preset value when, select average precipitation it is most be characterized month in month.
4. the multifactor senile experiment method based on regional feature as described in claim 1, which is characterized in that the feature moon
The meteorological data of every day includes intra day ward and sunshine time in part, and the synoptic model on the same day is according to the intra day ward on the same day
And sunshine time determines, wherein includes: according to the method that the intra day ward on the same day and sunshine time determine same day synoptic model
Judge whether the intra day ward on the same day is greater than preset precipitation threshold value；
If intra day ward is greater than the precipitation threshold value, it is determined that the synoptic model on the same day is rainy day mode；
If intra day ward is less than the precipitation threshold value, further judge whether the sunshine time on the same day is greater than preset day
According to time threshold；
If the sunshine time on the same day is greater than the sunshine-duration threshold value, it is determined that same day synoptic model is fine day；
If the sunshine time on the same day is less than the sunshine-duration threshold value, it is determined that same day synoptic model is the cloudy day.
5. the multifactor senile experiment method based on regional feature as claimed in claim 4, which is characterized in that the method is also
It include: to judge whether the meteorological data of every day exceeds preset range, if exceeding preset range, it is determined that the gas of this day
Image data is wrong, the meteorological data of this day is excluded, the judgement without synoptic model；If without departing from the preset range
The judgement of synoptic model is carried out to this day.
6. the multifactor senile experiment method based on regional feature as described in claim 1, which is characterized in that multifactor aging
Aging action in experiment includes temperature, ultraviolet light, salt fog, humidity, rainfall and voltage, and the method also includes to being applied to
The aging action of tested composite insulator carries out parameter setting, wherein the temperature parameter setting in aging action are as follows: summer
When, the temperature setting under fine day mode is that the objective area max. daily temperature adds default floating value, minimum for day when winter
Temperature, remaining time are normal max. daily temperature；The value of ultraviolet light is set as the objective area day maximum radiation degree；Salt haze value
For the filthy concentration value of the objective area；Humidity value is the objective area day highest humidity value；The numerical value of the rainfall is set
It is set to preset numerical value, the preset numerical value is the rainfall numerical quantity that can wash away pollution severity of insulators；The voltage
Numerical value is the retting-flax wastewater of composite insulator to be tested, wherein the retting-flax wastewater of insulator is the creepage distance of insulator and is somebody's turn to do
The ratio between root-mean-square valve of highest operating voltage carried on insulator.
7. the multifactor senile experiment method based on regional feature as described in claim 1, which is characterized in that the aging adds
The numerical value of the fast factor is 16.
8. a kind of multifactor senile experiment system based on regional feature, which is characterized in that the system comprises:
Setup module, for creating the mapping table of aging action and synoptic model and being stored, wherein in the correspondence
In relation table, the synoptic model includes fine day mode, cloudy mode and rainy day mode, the corresponding aging of the fine day mode
Factor includes heating and ultraviolet light, and the corresponding aging action of cloudy mode includes humidity and salt fog, the corresponding aging of rainy day mode
Factor includes rainfall；
Feature month determining module, for obtaining the meteorological data of objective area one to December each moon, according to each moon
Meteorological data divide four seasons of the objective area and determine each mid-season feature month；
Synoptic model determining module, the meteorological data of every day in the feature month for obtaining each season, and according to
The meteorological data of described every day determines the synoptic model of every day in the feature month, wherein the synoptic model includes
Fine day mode, cloudy mode and rainy day mode；
Computing module, for counting every kind of weather in the feature month according to the synoptic model of every day in the feature month
The distribution situation of mode, and total number of days of every kind of synoptic model in the feature month is calculated in the total number of days of all synoptic models
In accounting；
Distribution module, for calculating total hourage of all synoptic models in each feature month, and by all synoptic models
Total hourage obtains total hourage of each feature month in multifactor senile experiment, then root divided by aging accelerated factor
Total hourage of every kind of synoptic model in multifactor senile experiment is calculated according to the accounting of every kind of synoptic model, and according to institute
The distribution situation for stating every kind of synoptic model carries out time distribution to each synoptic model in multifactor senile experiment；
Annual synoptic model determining module, for distributing the synoptic model in seasonal characteristic month each in multifactor senile experiment
Situation repeats preset times, and the value of the preset times is equal to the value of total months in the season, obtains multifactor senile experiment
Middle whole year synoptic model；And
Control module, for the corresponding relationship according to the aging action and synoptic model, according to the multifactor senile experiment
The distribution condition of middle synoptic model applies corresponding aging action, carries out multifactor aging survey to composite insulator to be tested
Examination.
9. a kind of multifactor senile experiment device based on regional feature, special for carrying out senile experiment to composite insulator
Sign is that described device includes:
Aging action bringing device, for applying the aging action in multifactor senile experiment to composite insulator；
Processor；And
Memory, multiple program modules are stored in the memory, and the multiple program module is loaded simultaneously by the processor
Execute the multifactor senile experiment method based on regional feature as described in any one of claim 1-7.
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| CN117438012A (en) * | 2023-11-16 | 2024-01-23 | 国网山东省电力公司超高压公司 | Multi-factor aging test analysis system for composite insulator material |
| CN117438012B (en) * | 2023-11-16 | 2024-05-24 | 国网山东省电力公司超高压公司 | Multi-factor aging test analysis system for composite insulator material |
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