CN108181556A - Porcelain insulator zero value detection method based on chapeau de fer temperature difference time series analysis - Google Patents
Porcelain insulator zero value detection method based on chapeau de fer temperature difference time series analysis Download PDFInfo
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- CN108181556A CN108181556A CN201711369321.9A CN201711369321A CN108181556A CN 108181556 A CN108181556 A CN 108181556A CN 201711369321 A CN201711369321 A CN 201711369321A CN 108181556 A CN108181556 A CN 108181556A
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- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01R31/1227—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
Porcelain insulator zero value detection method based on chapeau de fer temperature difference time series analysis, is related to a kind of Faulty insulator diagnosis prediction method.Running insulator long-term work is in complex environment, with the increase of running time, its insulation performance and mechanical performance can slowly decline.Present invention employs a kind of Forecasting Methodologies based on time series method, using the temperature difference of chapeau de fer as characteristic quantity, the time series of the different interval extracted carries out auto-regressive analysis by ARIMA models, when the temperature difference of chapeau de fer reaches the numerical value in directive/guide, it is possible to predict the low null states of insulator.The technical program Universal insulator chapeau de fer temperature profile and time series method carry out Faulty insulator diagnosis prediction, and there is no check frequencies, can improve infrared thermal imagery method Faulty insulator Detection accuracy.
Description
Technical field
The present invention relates to a kind of Faulty insulator diagnosis prediction method more particularly to based on chapeau de fer temperature difference time series analysis
Porcelain insulator zero value detection method.
Background technology
Suspension disc insulator (calling insulator in the following text) is the important insulating element in power grid, while plays mechanical support
Effect.The complex environment that running insulator long-term work coexists in highfield, mechanical stress, filth and harsh weather etc.
In, with the increase of running time, being acted on by electromechanical combination, the insulation performance and mechanical performance of insulator can slowly decline, from
And generate low value (10~500) or zero resistance insulator (0~10).Low value or zero resistance insulator are referred to as Faulty insulator.If one
There is Faulty insulator appearance in insulator string, then it represents that SI semi-insulation is short-circuited, and correspondingly increases probability of flashover.If there is bad
The insulator chain for changing insulator occurs power frequency flashover or is struck by lightning, and has very high current and flows through interior insulator, powerful electricity
The raw fuel factor of miscarriage often causes insulator cap to burst or disengage, and falls string, conducting wire landing etc. so as to insulator chain occur
Major accident.Therefore carrying out effective prediction for the operating status of insulator just has the important meaning of reality.
The temperature change of charging equipment state is a complicated process, not only related with the property of each component itself, but also with
The environment of charging equipment influences related；The element of one influence insulator health status may display at once, it is also possible to
Just reflect into the presentation that can be observed after for a long time, thus one can to for a long time, the side that be analyzed of high-dimensional time series
Method is essential.
Invention content
The technical problem to be solved in the present invention and the technical assignment proposed are prior art to be improved with being improved,
The porcelain insulator zero value detection method based on chapeau de fer temperature difference time series analysis of offer, has insulator operating status to reach
Imitate the purpose of prediction.For this purpose, the present invention takes following technical scheme.
Porcelain insulator zero value detection method based on chapeau de fer temperature difference time series analysis, includes the following steps：
1) infrared thermogram for treating diagnosing insulation substring of different intervals section is acquired, extracts the infrared heat of insulator chain
As the chapeau de fer temperature of every insulator in collection of illustrative plates is as characteristic quantity, the pretreatment work of data is completed；
2) environmental information measured in the substation according to where insulator, calculates insulator position environmental element
Presumed value；
3) the chapeau de fer temperature difference that insulator is treated using ARIMA models is fitted, and associated environmental element is believed
Breath is added in the form of transfer function model in model；Test to the residual error of model, for residual error it is apparent abnormal when
It carves and attempts to intervene variate model, enhance fitting precision；
4) next insulator chain chapeau de fer temperature distribution history will be predicted after fitting to be compared with standard curve respectively, look for
Go out distortion point, and Faulty insulator judgement is carried out according to following criterion：
A, there are local low point, insulator chain chapeau de fer temperature distribution history identical bits in insulator chain disk temperature distribution history
It puts and local high spot occurs, judge the position for low resistance insulator；
B, there are local low point, insulator chain chapeau de fer temperature distribution history identical bits in insulator chain disk temperature distribution history
It puts and local low point occurs, judge the position for zero resistance insulator；
C, insulator chain disk temperature distribution history does not occur local low point, insulator chain chapeau de fer temperature distribution history occurs
Local high spot judges that insulator chain chapeau de fer temperature distribution history apparent local high spot position occurs for low resistance insulator；
D, insulator chain disk temperature distribution history does not occur local low point, insulator chain chapeau de fer temperature distribution history occurs
Apparent local low point or metapole, judge the insulator chain without Faulty insulator；
5) according to judging result, prediction result is exported.
When containing Faulty insulator in suspension disc insulator string, the Voltage Distribution meeting of insulator chain will be led to
It changes, so as to further change the temperature distributing rule of insulator, wherein the temperature of insulator disk is with insulator dielectric
The reduction of resistance and reduce, the temperature of insulator cap presents with the reduction of insulator dielectric resistance and first increases becoming of reducing afterwards
Gesture.However, under actual environment, when ambient humidity is inadequate, embodiment of the low zero in insulator temperature change is not
It is particularly evident, therefore the technical program employs a kind of Forecasting Methodology based on time series method, characteristic quantity is in value sometime
Be considered as this feature amount the value measured before when the state of environment or equipment itself changes it is changed as a result,
When with data before there are correlation in physical significance, therefore the time series of different interval can be expressed as, and then passed through
Between sequence analysis extract useful information.Using the temperature difference of chapeau de fer as characteristic quantity, the time sequence of the different interval extracted
Row carry out auto-regressive analysis by ARIMA models, when the temperature difference of chapeau de fer reaches the numerical value in directive/guide, it is possible to predict insulation
The low null states of son.
As further improving and supplementing to above-mentioned technical proposal, the invention also includes following additional technical features.
As optimization technique means：In step 3), using the insulator chain chapeau de fer temperature of extraction as characteristic quantity, with when
Between elapse, often external disturbance is influenced, therefore the corresponding time by past state and in the past simultaneously for the quantity of state at a certain moment
Existing MA of sequence has AR again, and the arma modeling for adding calculus of differences is referred to as autoregression synthesis moving average model, with
XtRepresent the temperature of a certain moment chapeau de fer, then wtRepresent the temperature difference of chapeau de fer, this model becomes one steadily after difference several times
Sequence：
wt=ΔdXt=(1-L)dXt
Wherein ΔdXt=(1-L)dXtFor d order difference operators；
W at this timetIt can be fitted with ARMA：
wt=φ1wt-1+φ2wt-2+......+φpwt-p+δ+ut+θ1ut-1+θ2ut-2+......+θput-p
An operator L for representing lag is introduced, enables LXt=Xt-1
Then φL=1- φ1L-φ2L2-......-φpLp=0
Wherein δ is constant, and φ is known as autoregressive coefficient；utFor white noise, the external disturbance suffered by current time is represented；
θ is sliding average coefficient.
As optimization technique means：In step 2), by Spatial Interpolation Method, calculating insulator position environment will
The presumed value of element.
Advantageous effect：Universal insulator chapeau de fer temperature profile and time series method of the present invention carry out Faulty insulator diagnosis
Prediction, there is no check frequencies, can improve infrared thermal imagery method Faulty insulator Detection accuracy.
Description of the drawings
Fig. 1 is flow chart of the present invention.
Fig. 2 insulator chain steel cap standard temperature curve figures
Fig. 3 treats diagnosing insulation substring steel cap temperature profile
Fig. 4 treats diagnosing insulation substring disk temperature profile
Specific embodiment
Technical scheme of the present invention is described in further detail below in conjunction with Figure of description.
As shown in Figure 1, of the present invention, detection method includes the following steps：
S01:Acquire the infrared thermogram for treating diagnosing insulation substring of different intervals section；
S02:The chapeau de fer temperature of every insulator in insulator chain infrared thermogram is extracted as characteristic quantity, completes data
Pretreatment work.
S03:The environmental information measured in substation where obtaining insulator；
S04：Environmental information is pre-processed；
S05)：Environmental information carries out space interpolation；
S06)：Calculate the presumed value of insulator position environmental element；
S07)：ARIMA is modeled, and the chapeau de fer temperature difference that insulator is treated using ARIMA models is fitted, and will be associated
Environmental element information added in model in the form of transfer function model；For residual error it is significantly abnormal at the time of attempt to intervene
Variate model enhances fitting precision；
S08):Residual error is calculated by ARIMA models；
Using the insulator chain chapeau de fer temperature of extraction as characteristic quantity, as time goes by, the quantity of state at a certain moment is often
By past state and in the past, external disturbance is influenced simultaneously, therefore existing MA of corresponding time series has AR again, adds
The arma modeling of calculus of differences is referred to as autoregression synthesis moving average model, with XtRepresent the temperature of a certain moment chapeau de fer, then wt
Represent the temperature difference of chapeau de fer, this model can become a stable sequence after difference several times：
wt=ΔdXt=(1-L)dXt
Wherein ΔdXt=(1-L)dXtFor d order difference operators.
W at this timetIt can be fitted with ARMA：
wt=φ1wt-1+φ2wt-2+......+φpwt-p+δ+ut+θ1ut-1+θ2ut-2+......+θput-pIntroduce a table
Show the operator L of lag, enable LXt=Xt-1
Then φL=1- φ1L-φ2L2-......-φpLp=0
Wherein δ is constant, and φ is known as autoregressive coefficient.utFor white noise, the external disturbance suffered by current time is represented.
S09) LSTM predict, will be predicted after fitting come insulator chain chapeau de fer temperature distribution history respectively with standard curve
It is compared, finds out distortion point, and Faulty insulator judgement is carried out according to following criterion：
A, there are local low point, insulator chain chapeau de fer temperature distribution history identical bits in insulator chain disk temperature distribution history
It puts and local high spot occurs, judge the position for low resistance insulator.
B, there are local low point, insulator chain chapeau de fer temperature distribution history identical bits in insulator chain disk temperature distribution history
It puts and local low point occurs, judge the position for zero resistance insulator.
C, insulator chain disk temperature distribution history does not occur local low point, insulator chain chapeau de fer temperature distribution history occurs
Local high spot judges that insulator chain chapeau de fer temperature distribution history apparent local high spot position occurs for low resistance insulator.
D, insulator chain disk temperature distribution history does not occur local low point, insulator chain chapeau de fer temperature distribution history occurs
Apparent local low point or metapole, judge the insulator chain without Faulty insulator.
S10 prediction result) is exported.
Example：
Certain 220kV transmission circuit insulator string applies the suspension disc insulator online test method based on infrared thermal imagery
Carry out detection, per insulator string containing 14 with model insulator.It is diagnosis process below：
(1) infrared thermogram for treating diagnosing insulation substring of different intervals section is acquired, extraction insulator chain is infrared
The chapeau de fer temperature of every insulator completes the pretreatment work of data as characteristic quantity in Thermal imaging spectrum.Utilize time series method
Prediction result is obtained by flow chart as shown in Figure 1.
(2) extraction (1) the chapeau de fer temperature and disk for treating every insulator in diagnosing insulation substring infrared thermogram
Temperature, it is as shown in the table：
| Position Number | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| Disk temperature (DEG C) | 9.4 | 9.3 | 9.2 | 8.9 | 9.1 | 9.1 | 9 |
| Chapeau de fer temperature (DEG C) | 11 | 10. | 9.8 | 9.9 | 9.3 | 9.1 | 9 |
| Position Number | 8 | 9 | 10 | 11 | 12 | 13 | 14 |
| Disk temperature (DEG C) | 9 | 9.1 | 9.1 | 9.1 | 9.1 | 9.2 | 9.3 |
| Chapeau de fer temperature (DEG C) | 9.1 | 9.2 | 9.4 | 9.5 | 9.6 | 9.8 | 8.7 |
(3) generation treats diagnosing insulation substring disk temperature curve as shown in figure 3, diagnosing insulation substring chapeau de fer temperature is treated in generation
Curve is as shown in Figure 4.
(4) insulator chain steel cap standard temperature curve will be as shown in Fig. 2, diagnosing insulation substring disk temperature distribution history will be treated
Discovery is compared with standard curve respectively with chapeau de fer temperature distribution history：Treat diagnosing insulation substring disk temperature distribution history position
It puts 4 and local low point occurs, treat that local high spot occurs in diagnosing insulation substring chapeau de fer temperature distribution history same position, judge position 4
For low resistance insulator.
The porcelain insulator zero value detection method based on chapeau de fer temperature difference time series analysis shown in figure 1 above is the present invention
Specific embodiment, embodied substantive distinguishing features of the present invention and progress, can be according to practical use needs, the present invention's
Under enlightenment, to its equivalent modifications, the row in the protection domain of this programme.
Claims (3)
1. the porcelain insulator zero value detection method based on chapeau de fer temperature difference time series analysis, it is characterised in that including following step
Suddenly：
1) infrared thermogram for treating diagnosing insulation substring of different intervals section is acquired, extracts insulator chain Infrared Thermogram
The chapeau de fer temperature of every insulator completes the pretreatment work of data as characteristic quantity in spectrum；
2) environmental information measured in the substation according to where insulator, calculates the presumption of insulator position environmental element
Value；
3) the chapeau de fer temperature difference that insulator is treated using ARIMA models is fitted, and by associated environmental element information with
The form of transfer function model is added in model；Test to the residual error of model, for residual error it is significantly abnormal at the time of adopt
With variate model is intervened, enhance fitting precision；
4) next insulator chain chapeau de fer temperature distribution history will be predicted after fitting to be compared with standard curve respectively, find out abnormal
Height, and carry out Faulty insulator judgement according to following criterion：
A, there is local low point in insulator chain disk temperature distribution history, insulator chain chapeau de fer temperature distribution history same position goes out
Existing local high spot, judges the position for low resistance insulator；
B, there is local low point in insulator chain disk temperature distribution history, insulator chain chapeau de fer temperature distribution history same position goes out
Existing local low point, judges the position for zero resistance insulator；
C, insulator chain disk temperature distribution history does not occur local low point, part occurs in insulator chain chapeau de fer temperature distribution history
High point judges that insulator chain chapeau de fer temperature distribution history apparent local high spot position occurs for low resistance insulator；
D, insulator chain disk temperature distribution history does not occur local low point, insulator chain chapeau de fer temperature distribution history occurs significantly
Local low point or metapole judge the insulator chain without Faulty insulator；
5) according to judging result, prediction result is exported.
2. the porcelain insulator zero value detection method according to claim 1 based on chapeau de fer temperature difference time series analysis,
It is characterized in that：In step 3), using the insulator chain chapeau de fer temperature of extraction as characteristic quantity, as time goes by, a certain moment
Quantity of state often external disturbance is influenced, therefore existing MA of corresponding time series and is had by past state and in the past simultaneously
AR, the arma modeling for adding calculus of differences is referred to as autoregression synthesis moving average model, with XtRepresent a certain moment iron
The temperature of cap, then wtRepresent the temperature difference of chapeau de fer, this model becomes a stable sequence after difference several times：
wt=ΔdXt=(1-L)dXt
Wherein ΔdXt=(1-L)dXtFor d order difference operators；
W at this timetIt can be fitted with ARMA：
wt=φ1wt-1+φ2wt-2+......+φpwt-p+δ+ut+θ1ut-1+θ2ut-2+......+θput-p
An operator L for representing lag is introduced, enables LXt=Xt-1
Then φL=1- φ1L-φ2L2-......-φpLp=0
Wherein δ is constant, and φ is known as autoregressive coefficient；utFor white noise, the external disturbance suffered by current time is represented；θ is slides
Dynamic mean coefficient.
3. the porcelain insulator zero value detection method according to claim 2 based on chapeau de fer temperature difference time series analysis,
It is characterized in that：In step 2), by Spatial Interpolation Method, the presumed value of insulator position environmental element is calculated.
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Cited By (10)
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| CN109142991A (en) * | 2018-07-05 | 2019-01-04 | 国网湖南省电力有限公司电力科学研究院 | A kind of infrared survey zero-temperature coefficient threshold determination method of porcelain insulator based on Burr distribution |
| CN109827662A (en) * | 2019-01-22 | 2019-05-31 | 江苏双汇电力发展股份有限公司 | Determination method based on dead wind area low resistance insulator infrared detection temperature threshold |
| CN111141996A (en) * | 2019-11-22 | 2020-05-12 | 国网江苏省电力有限公司电力科学研究院 | Porcelain insulator infrared detection threshold optimization method and system based on generalized extreme value theory and storage medium |
| CN111178553A (en) * | 2019-12-16 | 2020-05-19 | 北京航天智造科技发展有限公司 | Industrial equipment health trend analysis method and system based on ARIMA and LSTM algorithms |
| CN112070322A (en) * | 2020-09-28 | 2020-12-11 | 国网河北省电力有限公司雄安新区供电公司 | High-voltage cable line running state prediction method based on long-short term memory network |
| WO2021190056A1 (en) * | 2020-03-26 | 2021-09-30 | 国网湖北省电力有限公司电力科学研究院 | Infrared zero value diagnosis method and system for porcelain insulator string |
| CN114034997A (en) * | 2021-11-10 | 2022-02-11 | 国网江苏省电力有限公司检修分公司 | Insulator degradation degree prediction method and system based on multiple parameters |
| CN114724042A (en) * | 2022-06-09 | 2022-07-08 | 国网江西省电力有限公司电力科学研究院 | Automatic detection method for zero-value insulator in power transmission line |
| CN115062804A (en) * | 2022-06-29 | 2022-09-16 | 无锡物联网创新中心有限公司 | Maintenance method of textile equipment and related device |
| CN116596920A (en) * | 2023-07-12 | 2023-08-15 | 国网江西省电力有限公司电力科学研究院 | Real-time zero measurement method and system for long-string porcelain insulator unmanned aerial vehicle |
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109142991A (en) * | 2018-07-05 | 2019-01-04 | 国网湖南省电力有限公司电力科学研究院 | A kind of infrared survey zero-temperature coefficient threshold determination method of porcelain insulator based on Burr distribution |
| CN109827662A (en) * | 2019-01-22 | 2019-05-31 | 江苏双汇电力发展股份有限公司 | Determination method based on dead wind area low resistance insulator infrared detection temperature threshold |
| CN109827662B (en) * | 2019-01-22 | 2020-08-04 | 江苏双汇电力发展股份有限公司 | Method for judging infrared detection temperature threshold value of low-value insulator based on inverse Gaussian distribution |
| CN111141996A (en) * | 2019-11-22 | 2020-05-12 | 国网江苏省电力有限公司电力科学研究院 | Porcelain insulator infrared detection threshold optimization method and system based on generalized extreme value theory and storage medium |
| CN111178553A (en) * | 2019-12-16 | 2020-05-19 | 北京航天智造科技发展有限公司 | Industrial equipment health trend analysis method and system based on ARIMA and LSTM algorithms |
| WO2021190056A1 (en) * | 2020-03-26 | 2021-09-30 | 国网湖北省电力有限公司电力科学研究院 | Infrared zero value diagnosis method and system for porcelain insulator string |
| CN112070322A (en) * | 2020-09-28 | 2020-12-11 | 国网河北省电力有限公司雄安新区供电公司 | High-voltage cable line running state prediction method based on long-short term memory network |
| CN112070322B (en) * | 2020-09-28 | 2022-05-13 | 国网河北省电力有限公司雄安新区供电公司 | High-voltage cable line running state prediction method based on long-short term memory network |
| CN114034997A (en) * | 2021-11-10 | 2022-02-11 | 国网江苏省电力有限公司检修分公司 | Insulator degradation degree prediction method and system based on multiple parameters |
| CN114724042A (en) * | 2022-06-09 | 2022-07-08 | 国网江西省电力有限公司电力科学研究院 | Automatic detection method for zero-value insulator in power transmission line |
| CN115062804A (en) * | 2022-06-29 | 2022-09-16 | 无锡物联网创新中心有限公司 | Maintenance method of textile equipment and related device |
| CN116596920A (en) * | 2023-07-12 | 2023-08-15 | 国网江西省电力有限公司电力科学研究院 | Real-time zero measurement method and system for long-string porcelain insulator unmanned aerial vehicle |
| CN116596920B (en) * | 2023-07-12 | 2023-11-07 | 国网江西省电力有限公司电力科学研究院 | Real-time zero measurement method and system for long-string porcelain insulator unmanned aerial vehicle |
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