CN109190815A - The transmission line of electricity anti-icing accurate prediction technique of ice-melt ultra-short term online - Google Patents
The transmission line of electricity anti-icing accurate prediction technique of ice-melt ultra-short term online Download PDFInfo
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Abstract
A kind of transmission line of electricity anti-icing accurate prediction technique of ice-melt ultra-short term online.It is predicted from ice-melt conducting wire on-line monitoring equipment.Icing weight data is acquired on simulation self-control thermal wire, wind speed, rainfall intensity meteorological data are acquired on meteorological observation equipment.Prediction technique includes data acquisition, building restriction on the parameters model, parametric prediction model, the prediction of icing weight, prediction data Credibility judgement and the prediction of ice covering thickness value added.The prediction of icing weight is to make thermal wire weight gain, rainfall intensity, mean wind speed by oneself by collected simulation to calculate liquid water fugacity in restriction on the parameters model.By parametric prediction model model prediction rainfall intensity, mean wind speed, liquid water fugacity and icing weight and ice covering thickness are predicted by above-mentioned parameter.The present invention provides ultra-short term to power delivery circuit future ice coating state and precisely analyzes, and provides the following icing for anti-icing ice-melt control and predicts accurate information, and burst weather disaster is effectively prevent to cause heavy losses to transmission line of electricity.
Description
(1) technical field
The invention belongs to the online ice-melt scope of power transmission line, in particular to anti-icing ice-melt is ultrashort online for a kind of transmission line of electricity
Phase accurate prediction technique.
(2) background technique
With the development of the social economy, in the environment of being continuously increased electric load application, to exposed power line
Road requires higher and higher.And in cold winter, the route in many areas can all freeze, and cause the damage of route.It is super when freezing
When crossing the endurance of route, the major accidents such as broken string will occur.So the power transmission line deicing in winter be it is essential,
It is highly important.In the prior art, de-icing technology is being continuously improved.Application number CN201610867150.1 is " a kind of from ice-melt
Conductor and ice-melting device " and application number CN201810370549.8 " the self-control heat conductor of insertion insulating heat-conduction material and heating
Equipment and its implementation " the online de-icing method of two distinct types of transmission line of electricity is disclosed, and ice-melt effect has than before
It increases substantially.Application number CN201810886319.7 " the transmission line of electricity anti-icing ice-melt calorimeter online based on self-control thermal wire
Calculation method " disclose heat Calculation method of the self-control thermal wire in anti-icing, heating in ice-melt, ice-melt, insulating process.But
It in Anti-icing of Transmission Lines deicing processes, needs to be monitored the operating condition of transmission line of electricity, for transmission line of electricity control and in advance
It surveys and reference is provided.By the present inventor application application No. is 201810952699.x " insertion heating material it is online from ice-melt conducting wire
Monitoring device and method " and application number 201810952697.0 " insertion insulating materials is from ice-melt conducting wire on-line monitoring equipment and side
Method " provide through simulator, simulate transmission pressure, simulate control process, to the anti-icing ice-melt of self-control thermal wire control into
Row anticipation and analysis.And to realize being precisely controlled for Anti-icing of Transmission Lines ice-melt, it is also necessary to provide Anti-icing of Transmission Lines ice-melt
Ultra-short term is precisely predicted.
The invention proposes the accurate prediction techniques of Anti-icing of Transmission Lines ice-melt ultra-short term.Utilize simulator and meteorological number
According to by realizing the anti-icing essence with ice-melt of the following transmission line of electricity real-time online to the increased accurate prediction of ultra-short term icing weight
Quasi- control.
(3) summary of the invention
The object of the present invention is to provide a kind of accurate prediction techniques of ultra-short term, to overhead transmission line future 10-60 minutes
The accurate prediction of interior icing weight gain, by realizing the following transmission line of electricity to the increased accurate prediction of ultra-short term icing weight
Anti-icing being precisely controlled with ice-melt of real-time online.Prevent or reduce burst weather disaster bring loss.Used in the present invention
Unit: average rainfall intensity: millimeter is per minute；Mean wind speed: metre per second (m/s)；Simulation self-control thermal wire weight gain: gram；Simulation
Transmission pressure iced insulator: gram per cubic centimeter；Simulation self-control thermal conductivity line length is L: centimetre；The increasing of analog conducting wire ice covering thickness
It is value added: centimetre；Time: minute；
The purpose of the present invention is what is reached in this way:
The anti-icing accurate prediction technique of ice-melt ultra-short term is from ice-melt conducting wire on-line monitoring equipment to this transmission line of electricity online
It is predicted, on-line monitoring equipment sensing device containing field wires and analog conducting wire monitoring system, in analog conducting wire monitoring system
Thermal wire is made by oneself containing simulation, and simulation self-control thermal wire and transmission line of electricity self-control thermal wire use identical model.Its feature exists
In: the acquisition icing weight data on simulation self-control thermal wire；Wind speed, rainfall intensity meteorology number are acquired on meteorological observation equipment
According to；Prediction technique includes data acquisition, building parametric prediction model and restriction on the parameters model, the prediction of icing weight, prediction data
Credibility judgement and the prediction of ice covering thickness value added；
The data acquisition:
Data acquisition is acquisition simulation self-control thermal wire weight gain, acquisition meteorological data；Simulation self-control thermal wire weight
Measuring value added is the weight gain of analog conducting wire in T minutes；Acquire meteorological data data are as follows: in T minutes, average rainfall
Intensity；Mean wind speed；According to restriction on the parameters relationship, liquid water fugacity is calculated；
Data acquisition is primary for every T minutes acquisition, acquires M times in total；
The parameter that the building parametric prediction model is the data using acquisition and is calculated, is established at the beginning of including sequence
Beginning data calculate the cumulative sequence A of measurement, calculate equal value sequence Z, form structure matrix B, and structure matrix B is (M-1) × 2 matrix,
Measurement vector Y is formed, measurement vector Y is M-1 dimensional vector, and design factor vector C, coefficient vector C are 2 dimensional vectors, are calculated
Restore cumulative data: Das (i) is known as restoring cumulative data, and calculate restoring data: Ds (i) is known as restoring data, calculates prediction number
According to prediction data is divided into the supplemental characteristic prediction model of prediction cumulative data Dar (i) and prediction data Dr (i)；
The restriction on the parameters model refers to that liquid water fugacity, icing weight, rainfall intensity, the constraint between mean wind speed are closed
System, liquid water fugacity is calculated with icing weight, rainfall intensity, mean wind speed.
Parameter prediction is N number of data after measurement data, and interval time is T minutes between data, and prediction data is divided into pre-
Survey cumulative data Dar (i) and prediction data prediction data Dr (i), i=1,2,3,4 ... ..., N；
The icing weight prediction is predicted according to restriction on the parameters relationship icing weight using institute's Prediction Parameters::
The prediction data Credibility judgement is judged using the Credibility judgement factor；The ice covering thickness value added
Every in T minutes, the value added of analog conducting wire ice covering thickness is judged for prediction.
The step of building parametric prediction model, is:
1) sequence primary data is measurement data or calculating parameter；
X=x (1), x (2) ..., x (M) } (3-2)
2) it calculates and measures cumulative sequence A,
A=a (1), a (2) ..., a (M) }
3) equal value sequence Z is calculated,
Z=z (2), z (3), z (4) ..., z (M) }
4) structure matrix B is formed, structure matrix B is (M-1) × 2 matrix
5) measurement vector Y is formed, measurement vector Y is M-1 dimensional vector
6) design factor vector C, coefficient vector C are 2 dimensional vectors, contain c1, two members of c2:
7) calculate reduction cumulative data: Das (i) is known as restoring cumulative data,
8) calculate restoring data: Ds (i) is known as restoring data
9) calculate prediction data: prediction data is N number of data after measurement data, and interval time is T minutes between data,
Prediction data is divided into prediction cumulative data and prediction data, and prediction cumulative data is indicated with Dar (i), prediction data Dr (i) table
Show, i=1,2,3,4 ... ..., N；Calculation method is respectively
The step of icing weight gain is predicted is:
(1) average rainfall intensity P (i), mean wind speed V (i), simulation self-control thermal wire weight gain Dg (i), i are acquired
=1,2,3 ..., M；
(2) liquid water fugacity W (i): i=1,2,3 is calculated according to formula (3-1) ..., M
(3) rainfall intensity, the prediction data of mean wind speed, liquid water fugacity are calculated according to parametric prediction model；Rainfall intensity
Prediction data indicates that mean wind speed prediction data is indicated with Vr (i), liquid water fugacity prediction data Wr (i) is indicated, i with Pr (i)
=1,2,3 ..., N；
(4) calculating simulation self-control thermal wire weight gain prediction data is indicated with Drg (i), i=1,2,3 ... ..., N；
(5) according to parametric prediction model calculate rainfall intensity, mean wind speed, liquid water fugacity restoring data；Rainfall intensity
Restoring data indicates that mean wind speed restoring data is indicated with Vs (i), liquid water fugacity restoring data Ws (i) is indicated, i with Ps (i)
=2,3 ..., M；
(6) calculating simulation makes thermal wire weight gain restoring data, self-control thermal wire weight gain calculated by oneself
Restoring data Dsg (i) expression, i=2,3 ... ..., M
The prediction data Credibility judgement is judged using the Credibility judgement factor
Reliability judges the factor:
K is more than or equal to 0.9, and prediction data is credible,
K is more than or equal to 0.8 less than 0.9, and prediction data is substantially credible,
For k less than 0.8, prediction data is insincere.
The ice covering thickness value added prediction is every in T minutes, and the value added of analog conducting wire ice covering thickness is predicted；
Ice covering thickness value added refers to that the value added of analog conducting wire ice covering thickness is indicated with Drh (i) every in T minutes,
Unit be centimetre, i=, 2 ... ..., N；If simulation transmission pressure iced insulator is ρi, unit: gram per cubic centimeter；
Then
The process that liquid water fugacity is calculated in building restriction on the parameters model is:
If simulation self-control thermal conductivity line length be L, unit: centimetre, outer diameter R, unit: centimetre, simulation self-control thermal wire survey
Amount starting weight be G0, unit, gram；
Simulation self-control thermal wire weight gain refers to that in T minutes, the weight gain of analog conducting wire, simulation heats certainly
Dg (i) expression of wire weight value added, unit: gram, i=1,2,3 ... ..., M；
Liquid water fugacity W (i):
The acquisition meteorological data data are as follows:: in T minutes, average rainfall intensity P (i), unit: millimeter is per minute；It is average
Wind speed V (i), unit, metre per second (m/s).
The positive effect of the present invention is: in request for utilization CN201610867150.1, " one kind is from ice-melt conductor and melts
Icing equipment " and application number CN201810370549.8 " the self-control heat conductor and heating equipment and in fact of insertion insulating heat-conduction material
Existing method " publicity the online anti-icing de-icing technology in real time of transmission line of electricity during, power delivery circuit future ice coating state is provided super
Short-term precisely analysis, provides the following icing for anti-icing ice-melt control and predicts accurate information, prevents or reduces burst weather disaster pair
Transmission line of electricity causes heavy losses.
(4) specific embodiment
The anti-icing accurate prediction technique of ice-melt ultra-short term is in the Shen applied by the present inventor to transmission line of electricity of the invention online
It please be number for 201810952699.x " insertion heating material from ice-melt conducting wire on-line monitoring equipment and method " and application number
It is carried out in the on-line monitoring equipment of 201810952697.0 " being embedded in insulating materials from ice-melt conducting wire on-line monitoring equipment and method "
's.On-line monitoring equipment sensing device containing field wires and analog conducting wire monitor system, contain mould in analog conducting wire monitoring system
Quasi- self-control thermal wire, simulation self-control thermal wire and transmission line of electricity self-control thermal wire use identical model.
This method acquires icing weight data on simulation self-control thermal wire, and wind speed, drop are acquired on meteorological observation equipment
Raininess degree meteorological data.The microclimate station of the present embodiment uses the model of Wuhan Zhong Kenenghui development in science and technology Co., Ltd production
The miniature automatic meteorological station of NHQXZ607.
Prediction technique includes data acquisition, building parametric prediction model and restriction on the parameters model, the prediction of icing weight, prediction
Data reliability judgement and the prediction of ice covering thickness value added.The interval time T that the present invention uses, acquisition data bulk M, prediction number
Data bulk N, need to be according to specific requirements when using by testing and emulating determination.
Data acquisition is acquisition simulation self-control thermal wire weight gain, acquisition meteorological data；Simulation self-control thermal wire weight
Measuring value added is the weight gain of analog conducting wire in T minutes；Acquire meteorological data data are as follows: in T minutes, average rainfall
Intensity；Mean wind speed.
According to restriction on the parameters relationship, liquid water fugacity is calculated；If simulation self-control thermal conductivity line length be L, unit: centimetre, outer diameter
For R, unit: centimetre, simulation self-control thermal wire measurement starting weight is G0, unit, gram；
Simulation self-control thermal wire weight gain refers to that in T minutes, the weight gain of analog conducting wire, simulation heats certainly
Dg (i) expression of wire weight value added, unit: gram, i=1,2,3 ... ..., M；
Icing weight, rainfall intensity, there is the constraint relationship as shown in formula (3-1) between mean wind speed in liquid water fugacity；Root
Liquid water fugacity is calculated according to formula (3-1)；
Liquid water fugacity W (i):
The acquisition meteorological data data are as follows: in T minutes, average rainfall intensity P (i), unit: millimeter is per minute；It is average
Wind speed V (i), unit, metre per second (m/s).
The acquisition meteorological data data are as follows: in T minutes, average rainfall intensity P (i), unit: millimeter is per minute；It is average
Wind speed V (i), unit, metre per second (m/s).
The building parametric prediction model is that the method foundation of the data using acquisition and the parameter being calculated includes
Sequence primary data calculates and measures cumulative sequence A, calculates equal value sequence Z, forms structure matrix B, structure matrix B be (M-1) ×
2 matrixes, form measurement vector Y, and measurement vector Y is M-1 dimensional vector, design factor vector C, coefficient vector C be 2 dimensions arrange to
Amount, calculate reduction cumulative data: Das (i) is known as restoring cumulative data, and calculate restoring data: Ds (i) is known as restoring data, meter
Prediction data is calculated, prediction data is divided into the supplemental characteristic prediction model of prediction cumulative data Dar (i) and prediction data Dr (i).
Parameter prediction is N number of data after measurement data, and interval time is T minutes between data, and prediction data is divided into pre-
Survey cumulative data Dar (i) and prediction data prediction data Dr (i), i=1,2,3,4 ... ..., N.
The step of constructing parametric prediction model is:
1) sequence primary data is measurement data.
X=x (1), x (2) ..., x (M) } (3-2)
2) it calculates and measures cumulative sequence A,
A=a (1), a (2) ..., a (M) }
3) equal value sequence Z is calculated,
Z=z (2), z (3), z (4) ..., z (M) }
4) structure matrix B is formed, structure matrix B is (M-1) × 2 matrix
5) measurement vector Y is formed, measurement vector Y is M-1 dimensional vector
6) design factor vector C, coefficient vector C are 2 dimensional vectors, contain c1, two members of c2:
7) calculate reduction cumulative data: Das (i) is known as restoring cumulative data,
8) calculate restoring data: Ds (i) is known as restoring data
9) calculate prediction data: prediction data is N number of data after measurement data, and interval time is T minutes between data,
Prediction data is divided into prediction cumulative data and prediction data, and prediction cumulative data is indicated with Dar (i), prediction data Dr (i) table
Show, i=1,2,3,4 ... ..., N；Calculation method is respectively
Restriction on the parameters model refers to liquid water fugacity, icing weight, rainfall intensity, the constraint relationship between mean wind speed, uses
Icing weight, rainfall intensity, mean wind speed calculate liquid water fugacity.
The process that liquid water fugacity is calculated in building restriction on the parameters model is:
If simulation self-control thermal conductivity line length be L, unit: centimetre, outer diameter R, unit: centimetre, simulation self-control thermal wire survey
Amount starting weight be G0, unit, gram；
Simulation self-control thermal wire weight gain refers to that in T minutes, the weight gain of analog conducting wire, simulation heats certainly
Dg (i) expression of wire weight value added, unit: gram, i=1,2,3 ... ..., M；
Liquid water fugacity W (i):
The acquisition meteorological data data are as follows: in T minutes, average rainfall intensity P (i), unit: millimeter is per minute；It is average
Wind speed V (i), unit, metre per second (m/s).
The prediction of icing weight is to be predicted using the method for parameter prediction icing weight: making collected simulation by oneself thermal conductivity
Line weight gain, meteorological data numerical value, the liquid water fugacity being calculated are predicted in parametric prediction model, further according to weight
Measure value added, meteorological data numerical value, liquid water fugacity predictor calculation icing weight gain value:
The step of icing weight gain is predicted is:
(1) average rainfall intensity P (i), mean wind speed V (i), simulation self-control thermal wire weight gain Dg (i), i are acquired
=1,2,3 ..., M；
(2) liquid water fugacity W (i): i=1,2,3 is calculated according to formula (3-1) ..., M
(3) rainfall intensity, the prediction data of mean wind speed, liquid water fugacity are calculated according to parametric prediction model；Rainfall intensity
Prediction data indicates that mean wind speed prediction data is indicated with Vr (i), liquid water fugacity prediction data Wr (i) is indicated, i with Pr (i)
=1,2,3 ..., N；
(4) calculating simulation makes thermal wire weight gain prediction data, simulation self-control thermal wire weight gain prediction by oneself
Data Drg (i) expression, i=1,2,3 ... ..., N；
(5) according to parametric prediction model calculate rainfall intensity, mean wind speed, liquid water fugacity restoring data；Rainfall intensity
Restoring data indicates that mean wind speed restoring data is indicated with Vs (i), liquid water fugacity restoring data Ws (i) is indicated, i with Ps (i)
=2,3 ..., M；
(6) calculating simulation makes thermal wire weight gain restoring data, self-control thermal wire weight gain calculated by oneself
Restoring data Dsg (i) expression, i=2,3 ... ..., M
Prediction data Credibility judgement is judged using the Credibility judgement factor
Reliability judges the factor:
K is more than or equal to 0.9, and prediction data is credible,
K is more than or equal to 0.8 less than 0.9, and prediction data is substantially credible,
For k less than 0.8, prediction data is insincere.
The prediction of ice covering thickness value added is: judging every in T minutes the value added of analog conducting wire ice covering thickness.
Ice covering thickness value added refers to that the value added of analog conducting wire ice covering thickness is indicated with Drh (i) every in T minutes,
Unit be centimetre, i=, 2 ... ..., N；If simulation transmission pressure iced insulator is ρi, unit: gram per cubic centimeter；
Then
According to in-site measurement, the factor is judged according to reliability:Judgement, confirmed k and be greater than
Equal to 0.9, prediction data is credible, and k is more than or equal to 0.8, prediction data is substantially credible, and k is less than 0.8, prediction data less than 0.9
Incredible judgement.
Claims (6)
1. a kind of transmission line of electricity anti-icing accurate prediction technique of ice-melt ultra-short term online, enterprising from ice-melt conducting wire on-line monitoring equipment
Row prediction, on-line monitoring equipment sensing device containing field wires and analog conducting wire monitor system, contain in analog conducting wire monitoring system
There is simulation self-control thermal wire,
It is characterized by: simulation self-control thermal wire and transmission line of electricity self-control thermal wire use identical model,
Icing weight data is acquired on simulation self-control thermal wire；It is meteorological that wind speed, rainfall intensity are acquired on meteorological observation equipment
Data；Prediction technique includes data acquisition, building parametric prediction model and restriction on the parameters model, the prediction of icing weight, prediction number
It is predicted according to Credibility judgement and ice covering thickness value added；
The data acquisition:
Data acquisition is acquisition simulation self-control thermal wire weight gain, acquisition meteorological data；Simulation self-control thermal wire weight increases
Value added is the weight gain of analog conducting wire in T minutes；Acquire meteorological data data are as follows: in T minutes, average rainfall intensity；
Mean wind speed；According to restriction on the parameters relationship, liquid water fugacity is calculated；
Data acquisition is primary for every T minutes acquisition, acquires M times in total；
The parameter that the building parametric prediction model is the data using acquisition and is calculated, establishing includes sequence initial number
According to, calculate and measure cumulative sequence A, calculate equal value sequence Z；Structure matrix B is formed, structure matrix B is (M-1) × 2 matrix, is formed
Vector Y is measured, measurement vector Y is M-1 dimensional vector, and design factor vector C, coefficient vector C are 2 dimensional vectors, calculate reduction
Cumulative data: Das (i) is known as restoring cumulative data, and calculate restoring data: Ds (i) is known as restoring data, calculates prediction data,
Prediction data is divided into the supplemental characteristic prediction model of prediction cumulative data Dar (i) and prediction data Dr (i)；
Parameter prediction is N number of data after measurement data, and interval time is T minutes between data, and it is tired that prediction data is divided into prediction
Addend is according to Dar (i) and prediction data Dr (i), i=1,2,3,4 ... ..., N；
The restriction on the parameters model refers to liquid water fugacity, icing weight, rainfall intensity, the constraint relationship between mean wind speed, uses
Icing weight, rainfall intensity, mean wind speed calculate liquid water fugacity；
The icing weight gain prediction is predicted according to restriction on the parameters relationship icing weight using institute's Prediction Parameters；
The prediction data Credibility judgement is judged using the Credibility judgement factor；The ice covering thickness value added prediction
Every in T minutes, the value added of analog conducting wire ice covering thickness is judged.
2. the transmission line of electricity as described in claim 1 anti-icing accurate prediction technique of ice-melt ultra-short term online, it is characterised in that: described
The step of constructing parametric prediction model is:
1) sequence primary data is measurement data or calculating parameter；
X=x (1), x (2) ..., x (M) } (3-2)
2) it calculates and measures cumulative sequence A,
A=a (1), a (2) ..., a (M) }
3) equal value sequence Z is calculated,
Z=z (2), z (3), z (4) ..., z (M) }
4) structure matrix B is formed, structure matrix B is (M-1) × 2 matrix
5) measurement vector Y is formed, measurement vector Y is M-1 dimensional vector
6) design factor vector C, coefficient vector C are 2 dimensional vectors, contain c1, two members of c2:
7) calculate reduction cumulative data: Das (i) is known as restoring cumulative data,
8) calculate restoring data: Ds (i) is known as restoring data
9) calculate prediction data: prediction data is N number of data after measurement data, and interval time is T minutes between data, prediction
Data are divided into prediction cumulative data and prediction data, and prediction cumulative data indicates that prediction data is indicated with Dr (i), i with Dar (i)
=1,2,3,4 ..., N；Calculation method is respectively
3. the transmission line of electricity as described in claim 1 anti-icing accurate prediction technique of ice-melt ultra-short term online, it is characterised in that: icing
The step of weight gain is predicted is:
(1) average rainfall intensity P (i) is acquired, unit: millimeter is per minute；, mean wind speed V (i), unit, metre per second (m/s), simulation is certainly
It heats wire weight value added Dg (i), unit: gram, i=1,2,3 ... ..., M；
(2) liquid water fugacity W (i): i=1,2,3 is calculated according to formula (3-1) ..., M
(3) rainfall intensity, the prediction data of mean wind speed, liquid water fugacity are calculated according to parametric prediction model；Rainfall intensity prediction
Data indicate that mean wind speed prediction data is indicated with Vr (i), liquid water fugacity prediction data Wr (i) is indicated, i=1 with Pr (i),
2,3,……,N；
(4) calculating simulation makes thermal wire weight gain prediction data by oneself,
Drg (i) expression of simulation self-control thermal wire weight gain prediction data, i=1,2,3 ... ..., N；
If simulation self-control thermal conductivity line length be L, unit: centimetre, outer diameter R, unit: centimetre,
(5) according to parametric prediction model calculate rainfall intensity, mean wind speed, liquid water fugacity restoring data；Rainfall intensity reduction
Data indicate that mean wind speed restoring data is indicated with Vs (i), liquid water fugacity restoring data Ws (i) is indicated, i=2 with Ps (i),
3,……,M；
(6) calculating simulation makes thermal wire weight gain restoring data, self-control thermal wire weight gain reduction calculated by oneself
Data Dsg (i) expression, i=2,3 ... ..., M
4. the transmission line of electricity as described in claim 1 anti-icing accurate prediction technique of ice-melt ultra-short term online, it is characterised in that: described
Prediction data Credibility judgement is judged using the Credibility judgement factor,
Reliability judges the factor:
K is more than or equal to 0.9, and prediction data is credible,
K is more than or equal to 0.8 less than 0.9, and prediction data is substantially credible,
For k less than 0.8, prediction data is insincere.
5. the transmission line of electricity as described in claim 1 anti-icing accurate prediction technique of ice-melt ultra-short term online, it is characterised in that: described
The prediction of ice covering thickness value added is: predicting every in T minutes the value added of analog conducting wire ice covering thickness；
Ice covering thickness value added refers to that the value added of analog conducting wire ice covering thickness is indicated, unit with Drh (i) every in T minutes
For centimetre, i=, 2 ... ..., N；If simulation transmission pressure iced insulator is ρi, unit: gram per cubic centimeter；
Then
6. the transmission line of electricity as described in claim 1 anti-icing accurate prediction technique of ice-melt ultra-short term online, it is characterised in that:
The process that liquid water fugacity is calculated in building restriction on the parameters model is:；
If simulation self-control thermal conductivity line length be L, unit: centimetre, outer diameter R, unit: centimetre, simulation self-control thermal wire measure
Starting weight amount be G0, unit, gram；
Simulation self-control thermal wire weight gain refers to that in T minutes, thermal wire is made in the weight gain of analog conducting wire, simulation by oneself
Weight gain Dg (i) expression, unit: gram, i=1,2,3 ... ..., M；
Liquid water fugacity W (i):
The acquisition meteorological data data are as follows: in T minutes, average rainfall intensity P (i), unit: millimeter is per minute；Mean wind speed V
(i), unit, metre per second (m/s).
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| CN201810965666.9A CN109190815B (en) | 2018-08-22 | 2018-08-22 | Ultra-short-term accurate prediction method for online anti-icing and de-icing of power transmission line |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110649505A (en) * | 2019-09-09 | 2020-01-03 | 国网北京市电力公司 | Three-dimensional monitoring method and device for icing of power transmission line |
Citations (9)
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