CN109190815B - Ultra-short-term accurate prediction method for online anti-icing and de-icing of power transmission line - Google Patents
Ultra-short-term accurate prediction method for online anti-icing and de-icing of power transmission line Download PDFInfo
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Abstract
An online anti-icing and de-icing ultra-short-term accurate prediction method for a power transmission line. And predicting on the self-melting ice wire online monitoring equipment. The ice coating weight data is collected on the simulated self-made thermal conductor, and the wind speed and rainfall intensity meteorological data is collected on meteorological observation equipment. The prediction method comprises the steps of data acquisition, parameter constraint model construction, parameter prediction model, icing weight prediction, prediction data reliability judgment and icing thickness increase value prediction. The icing weight prediction is that a liquid-water factor is calculated in a parameter constraint model through the collected simulated homemade heating wire weight increase value, rainfall intensity and average wind speed. And forecasting rainfall intensity, average wind speed and liquid water factor through the parameter forecasting model, and forecasting icing weight and icing thickness through the parameters. The method provides ultra-short-term accurate analysis for the future icing state of the power transmission circuit, provides accurate information for predicting future icing for ice-proof and ice-melting control, and effectively prevents great loss of the power transmission line caused by sudden weather disasters.
Description
(I) technical field
The invention belongs to the field of online ice melting of power transmission lines, and particularly relates to an online anti-icing and ice-melting ultra-short-term accurate prediction method for a power transmission line.
(II) background of the invention
With the development of social economy, the requirements for exposed power lines are higher and higher in the environment of increasing the application of power loads. In cold winter, the lines in many areas are frozen, and the lines are damaged. When the icing exceeds the bearing capacity of the line, serious accidents such as line breakage and the like can occur. Therefore, deicing of power transmission lines in winter is indispensable and very important. In the prior art, ice melting technology is continuously improving. The application number CN201610867150.1 'a self-melting ice conductor and melting ice device', and the application number CN201810370549.8 'self-made heat conductor and heating device embedded in insulating heat conduction material and implementation method thereof', disclose two different types of online melting ice methods for power transmission lines, and the melting ice effect is greatly improved compared with the prior art. Application number CN201810886319.7, "method for calculating heat of ice-melting and ice-preventing on-line of transmission line based on self-made thermal conductor", discloses a method for calculating heat of self-made thermal conductor during temperature rising, ice melting and heat preservation in the process of ice-melting and ice-preventing. However, in the process of ice prevention and ice melting of the power transmission line, the operation condition of the power transmission line needs to be monitored, and reference is provided for control and prediction of the power transmission line. The application number of 201810952699.x, embedded heating material self-melting ice wire online monitoring equipment and method and the application number of 201810952697.0, embedded insulating material self-melting ice wire online monitoring equipment and method, applied by the inventor provide that the anti-icing and ice-melting control of the self-made heating wire is predicted and analyzed through a simulation device, a power transmission wire is simulated and a control process is simulated. In order to realize the accurate control of the anti-icing and de-icing of the power transmission line, the ultra-short-term accurate prediction of the anti-icing and de-icing of the power transmission line also needs to be provided.
The invention provides an ultra-short-term accurate prediction method for ice prevention and ice melting of a power transmission line. By utilizing the simulation device and meteorological data, the real-time online anti-icing and de-icing accurate control of the future power transmission line is realized through the accurate prediction of the ultra-short-term icing weight increase.
Disclosure of the invention
The invention aims to provide an ultra-short-term accurate prediction method, which can accurately predict the ice coating weight increment of an overhead transmission line within 10-60 minutes in the future and realize the accurate control of real-time online ice prevention and ice melting of the transmission line in the future through the accurate prediction of the ultra-short-term ice coating weight increment. Prevent or reduce the loss that sudden weather disaster brought. The unit used in the present invention: average rainfall intensity: millimeters per minute; average wind speed: meters per second; simulating the weight increase value of the self-made heat conducting wire: g; simulating the icing density of the transmission conductor: grams per cubic centimeter; the length of the simulated self-control heat conducting wire is L: centimeters; simulating the increase value of the ice coating thickness of the lead: centimeters; time: the method comprises the following steps of (1) taking minutes;
the purpose of the invention is achieved by the following steps:
the online anti-icing and deicing ultra-short-term accurate prediction method for the power transmission line is characterized in that prediction is carried out on online monitoring equipment of a self-icing wire, the online monitoring equipment comprises a field wire sensing device and a simulation wire monitoring system, the simulation wire monitoring system comprises a simulation self-made heating wire, and the simulation self-made heating wire and the power transmission line self-made heating wire are of the same model. The method is characterized in that: collecting ice coating weight data on a simulated self-made thermal conductor; acquiring wind speed and rainfall intensity meteorological data on meteorological observation equipment; the prediction method comprises the steps of data acquisition, parameter prediction model and parameter constraint model construction, icing weight prediction, prediction data reliability judgment and icing thickness increase value prediction;
the data acquisition:
the data acquisition is to collect weight increment values of simulated self-made heat conducting wires and collect meteorological data; simulating the weight increase value of the self-made heat conducting wire within T minutes; the collected meteorological data is as follows: average rainfall intensity within T minutes; average wind speed; calculating a liquid water factor according to the parameter constraint relation;
the data acquisition is carried out once every T minutes, and M times are acquired in total;
the parameter prediction model is constructed by utilizing acquired data and calculated parameters, and comprises sequence initial data, a calculation measurement accumulation sequence A, a calculation mean value sequence Z and a calculation coefficient vector C, wherein the structure matrix B is an (M-1) x 2 matrix and forms a measurement vector Y, the measurement vector Y is an M-1-dimensional column vector and a 2-dimensional column vector, the reduction accumulation data is calculated, and das (i) is called reduction accumulation data and calculates the reduction data: ds (i) a parametric data prediction model called reduction data, which calculates prediction data divided into prediction accumulation data dar (i) and prediction data dr (i);
the parameter constraint model is a constraint relation among a liquid water factor, an icing weight, rainfall intensity and average wind speed, and the liquid water factor is calculated by the icing weight, the rainfall intensity and the average wind speed.
The parameter prediction is N data after the measurement data, the interval time between the data is T minutes, the prediction data is divided into prediction accumulated data dar (i) and prediction data Dr (i), wherein i is 1,2,3,4, … … and N;
the ice coating weight prediction is to predict the ice coating weight according to the parameter constraint relation by using the predicted parameters: :
the reliability judgment of the prediction data is carried out by using a reliability judgment factor; and predicting the icing thickness increment, and simulating the icing thickness increment of the lead to judge every T minutes.
The step of constructing the parameter prediction model is as follows:
1) the sequence initial data is measurement data or calculation parameters;
X＝{x(1),x(2),…,x(M)} (3-2)
2) a measurement accumulation sequence a is calculated,
A＝{a(1),a(2),……,a(M)}
3) the sequence of mean values Z is calculated,
Z＝{z(2),z(3),z(4),……,z(M)}
4) forming a structural matrix B which is an (M-1) x 2 matrix
5) Forming a measurement vector Y, wherein the measurement vector Y is an M-1 dimensional column vector
6) Calculating a coefficient vector C, wherein the coefficient vector C is a 2-dimensional column vector and comprises two elements of C1 and C2:
7) calculating the restored accumulated data, das (i) called restored accumulated data,
8) calculating reduction data: ds (i) referred to as reduced data
9) Calculating prediction data: the predicted data is N data after the measured data, the interval time between the data is T minutes, the predicted data is divided into predicted accumulated data and predicted data, the predicted accumulated data is represented by dar (i), the predicted data is represented by Dr (i), and i is 1,2,3,4, … … and N; the calculation methods are respectively
The steps for predicting the increase of the icing weight are as follows:
(1) collecting average rainfall intensity P (i) and average wind speed V (i), and simulating the weight increase value Dg (i) of the homemade heat conducting wire, wherein i is 1,2,3, … … and M;
(2) calculating the liquid water factor W (i) according to the formula (3-1): 1,2,3, … …, M
(3) Calculating the prediction data of rainfall intensity, average wind speed and liquid water factor according to the parameter prediction model; rainfall intensity prediction data is represented by pr (i), average wind speed prediction data is represented by vr (i), liquid water factor prediction data is represented by wr (i), i is 1,2,3, … …, N;
(4) calculating and simulating weight increase value prediction data of the homemade heating wire, wherein the weight increase value prediction data is represented by Drg (i), and i is 1,2,3, … … and N;
(5) calculating reduction data of rainfall intensity, average wind speed and liquid water factor according to the parameter prediction model; rainfall intensity reduction data is represented by Ps (i), average wind speed reduction data is represented by Vs (i), liquid water factor reduction data is represented by Ws (i), and i is 2,3, … … and M;
(6) calculating weight gain reduction data of the simulated homemade heating wire, wherein the weight gain reduction data of the calculated homemade heating wire is represented by Dsg (i), i is 2,3, … …, M
The reliability judgment of the prediction data is carried out by using a reliability judgment factor
Reliability judgment factor:
k is more than or equal to 0.9, the prediction data is credible,
k is less than 0.9 and more than or equal to 0.8, the prediction data is basically credible,
k is less than 0.8, and the prediction data is not credible.
The predication of the icing thickness increment value is to simulate the icing thickness increment value of the lead to carry out predication every T minutes;
the ice coating thickness increase value is the increase value of the ice coating thickness of the simulated lead every T minutes, and is expressed by Drh (i) with the unit of centimeter, i is 2, … … and N; ice coating density of simulation power transmission conductorDegree is rhoiThe unit: grams per cubic centimeter;
then
The process of calculating the liquid water factor in constructing the parameter constraint model is as follows:
setting the length of the simulated self-control heat conducting wire as L, unit: centimeter, outside diameter R, unit: centimeter, starting weight of G0, units, grams, measured by simulating a homemade heat conductor;
the weight increase value of the simulated homemade thermal wire is represented by dg (i) in a unit of: g, i ═ 1,2,3, … …, M;
liquid water factor w (i):
the collected meteorological data is as follows: : average rainfall p (i), units: millimeters per minute; average wind speed v (i), in meters per second.
The invention has the positive effects that: in the technical process of online real-time ice prevention and melting of the power transmission line disclosed by application No. CN201610867150.1 & lt & gt self-melting ice conductor and ice melting equipment & gt application No. CN201810370549.8 & lt & gt self-made thermal conductor and heating equipment embedded in insulating thermal conductive material & lt & gt implementation method & gt, ultra-short-term accurate analysis is provided for the future icing state of the power transmission line, accurate information for predicting future icing is provided for ice prevention and melting control, and great loss of the power transmission line caused by sudden weather disasters is prevented or reduced.
(IV) detailed description of the preferred embodiments
The online anti-icing and deicing ultra-short-term accurate prediction method for the power transmission line is carried out on online monitoring equipment with application number 201810952699.x, embedded heating material self-deicing wire online monitoring equipment and method and application number 201810952697.0, embedded insulating material self-deicing wire online monitoring equipment and method. The on-line monitoring equipment comprises a field lead sensing device and an analog lead monitoring system, wherein the analog lead monitoring system comprises an analog self-made hot lead, and the analog self-made hot lead and the transmission line self-made hot lead adopt the same model.
The method collects ice coating weight data on a simulated self-made thermal conductor and collects meteorological data of wind speed and rainfall intensity on meteorological observation equipment. The microclimate station of this embodiment adopts the small-size automatic weather station of model NHQXZ607 that wuhan zhong ke neng hui science and technology development limited company produced.
The prediction method comprises the steps of data acquisition, parameter prediction model and parameter constraint model construction, icing weight prediction, prediction data reliability judgment and icing thickness increase value prediction. The interval time T used by the invention, the collected data quantity M and the predicted data quantity N need to be determined by experiments and simulation according to the specific requirements in use.
The data acquisition is to collect weight increment values of simulated self-made heat conducting wires and collect meteorological data; simulating the weight increase value of the self-made heat conducting wire within T minutes; the collected meteorological data is as follows: average rainfall intensity within T minutes; the average wind speed.
Calculating a liquid water factor according to the parameter constraint relation; setting the length of the simulated self-control heat conducting wire as L, unit: centimeter, outside diameter R, unit: centimeter, starting weight of G0, units, grams, measured by simulating a homemade heat conductor;
the weight increase value of the simulated homemade thermal wire is represented by dg (i) in a unit of: g, i ═ 1,2,3, … …, M;
a constraint relation shown in a formula (3-1) exists among the liquid water factor, the icing weight, the rainfall intensity and the average wind speed; calculating the liquid water factor according to the formula (3-1);
liquid water factor w (i):
the collected meteorological data is as follows: average rainfall p (i), units: millimeters per minute; average wind speed v (i), in meters per second.
The collected meteorological data is as follows: average rainfall p (i), units: millimeters per minute; average wind speed v (i), in meters per second.
The parameter prediction model is constructed by utilizing collected data and a method of calculating obtained parameters, and comprises the steps of establishing sequence initial data, calculating a measurement accumulation sequence A, calculating a mean value sequence Z to form a structural matrix B, wherein the structural matrix B is an (M-1) x 2 matrix to form a measurement vector Y, the measurement vector Y is an M-1-dimensional column vector, calculating a coefficient vector C, the coefficient vector C is a 2-dimensional column vector, calculating reduction accumulation data, and Das (i) is called reduction accumulation data, and calculating reduction data: ds (i) is called reduction data, and calculates prediction data, which is divided into a parameter data prediction model of prediction accumulation data dar (i) and prediction data dr (i).
The parameter prediction is N data after the measured data, the interval time between the data is T minutes, the predicted data is divided into prediction accumulated data dar (i) and prediction data for Dr (i), wherein i is 1,2,3,4, … … and N.
The steps of constructing the parameter prediction model are as follows:
1) the sequence initial data is measurement data.
X＝{x(1),x(2),…,x(M)} (3-2)
2) A measurement accumulation sequence a is calculated,
A＝{a(1),a(2),……,a(M)}
3) the sequence of mean values Z is calculated,
Z＝{z(2),z(3),z(4),……,z(M)}
4) forming a structural matrix B which is an (M-1) x 2 matrix
5) Forming a measurement vector Y, wherein the measurement vector Y is an M-1 dimensional column vector
6) Calculating a coefficient vector C, wherein the coefficient vector C is a 2-dimensional column vector and comprises two elements of C1 and C2:
7) calculating the restored accumulated data, das (i) called restored accumulated data,
8) calculating reduction data: ds (i) referred to as reduced data
9) Calculating prediction data: the predicted data is N data after the measured data, the interval time between the data is T minutes, the predicted data is divided into predicted accumulated data and predicted data, the predicted accumulated data is represented by dar (i), the predicted data is represented by Dr (i), and i is 1,2,3,4, … … and N; the calculation methods are respectively
The parameter constraint model is a constraint relation among the liquid water factor, the icing weight, the rainfall intensity and the average wind speed, and the liquid water factor is calculated by the icing weight, the rainfall intensity and the average wind speed.
The process of calculating the liquid water factor in constructing the parameter constraint model is as follows:
setting the length of the simulated self-control heat conducting wire as L, unit: centimeter, outside diameter R, unit: centimeter, starting weight of G0, units, grams, measured by simulating a homemade heat conductor;
the weight increase value of the simulated homemade thermal wire is represented by dg (i) in a unit of: g, i ═ 1,2,3, … …, M;
liquid water factor w (i):
the collected meteorological data is as follows: average rainfall p (i), units: millimeters per minute; average wind speed v (i), in meters per second.
The icing weight prediction is carried out by utilizing a parameter prediction method: predicting the collected weight increase value of the simulated self-made heat conducting wire, the meteorological data value and the calculated liquid water factor in a parameter prediction model, and calculating the value of the ice coating weight increase according to the predicted values of the weight increase value, the meteorological data value and the liquid water factor:
the steps for predicting the increase of the icing weight are as follows:
(1) collecting average rainfall intensity P (i) and average wind speed V (i), and simulating the weight increase value Dg (i) of the homemade heat conducting wire, wherein i is 1,2,3, … … and M;
(2) calculating the liquid water factor W (i) according to the formula (3-1): 1,2,3, … …, M
(3) Calculating the prediction data of rainfall intensity, average wind speed and liquid water factor according to the parameter prediction model; rainfall intensity prediction data is represented by pr (i), average wind speed prediction data is represented by vr (i), liquid water factor prediction data is represented by wr (i), i is 1,2,3, … …, N;
(4) calculating the weight increase value prediction data of the simulated homemade heat wire, wherein the weight increase value prediction data of the simulated homemade heat wire is represented by Drg (i), and i is 1,2,3, … … and N;
(5) calculating reduction data of rainfall intensity, average wind speed and liquid water factor according to the parameter prediction model; rainfall intensity reduction data is represented by Ps (i), average wind speed reduction data is represented by Vs (i), liquid water factor reduction data is represented by Ws (i), and i is 2,3, … … and M;
(6) calculating weight gain reduction data of the simulated homemade heating wire, wherein the weight gain reduction data of the calculated homemade heating wire is represented by Dsg (i), i is 2,3, … …, M
The reliability judgment of the prediction data is carried out by using a reliability judgment factor
Reliability judgment factor:
k is more than or equal to 0.9, the prediction data is credible,
k is less than 0.9 and more than or equal to 0.8, the prediction data is basically credible,
k is less than 0.8, and the prediction data is not credible.
The ice coating thickness increase value prediction is as follows: and judging the increase value of the ice coating thickness of the simulation lead every T minutes.
The ice coating thickness increase value is the increase value of the ice coating thickness of the simulated lead every T minutes, and is expressed by Drh (i) with the unit of centimeter, i is 2, … … and N; let the ice density of the simulation transmission conductor be rhoiThe unit: grams per cubic centimeter;
then
According to field measurement and a reliability judgment factor:
and (3) verifying that k is more than or equal to 0.9, the predicted data is credible, k is less than 0.9 and more than or equal to 0.8, the predicted data is basically credible, k is less than 0.8, and the predicted data is incredible.
Claims (1)
1. An online anti-icing and deicing ultra-short term accurate prediction method for a power transmission line is characterized in that prediction is carried out on online monitoring equipment of a self-icing wire, the online monitoring equipment comprises an on-site wire sensing device and an analog wire monitoring system, the analog wire monitoring system comprises an analog self-made heat wire,
the method is characterized in that: the simulated self-made hot wire and the transmission line self-made hot wire adopt the same model,
collecting ice coating weight data on a simulated self-made thermal conductor; acquiring wind speed and rainfall intensity meteorological data on meteorological observation equipment; the prediction method comprises the steps of data acquisition, parameter prediction model and parameter constraint model construction, icing weight increment prediction, prediction data reliability judgment and icing thickness increment prediction;
the data acquisition:
the data acquisition is to collect weight increment values of simulated self-made heat conducting wires and collect meteorological data; simulating the weight increase value of the self-made heat conducting wire within T minutes; the collected meteorological data is as follows: average rainfall intensity within T minutes; average wind speed; calculating a liquid water factor according to the parameter constraint relation;
the data acquisition is carried out once every T minutes, and M times are acquired in total; the parameter prediction model is constructed by utilizing the acquired data and the calculated parameters, and the sequence initial data, the calculation measurement accumulation sequence A and the calculation mean value sequence Z are established; forming a structural matrix B, wherein the structural matrix B is an (M-1) multiplied by 2 matrix, forming a measurement vector Y, the measurement vector Y is an M-1-dimensional column vector, calculating a coefficient vector C, the coefficient vector C is a 2-dimensional column vector, calculating reduction accumulated data, and Das (i) is called reduction accumulated data, and calculating the reduction data: ds (i) a parametric data prediction model called reduction data, which calculates prediction data divided into prediction accumulation data dar (i) and prediction data dr (i); the parameter is predicted to be N data after the data is measured, the interval time between the data is T minutes, and the predicted data is divided into predicted accumulated data dar (i) and predicted data Dr (i), wherein i is 1,2,3,4, … …, N;
the parameter constraint model is a constraint relation among a liquid water factor, an icing weight, rainfall intensity and average wind speed, and the liquid water factor is calculated by using the icing weight, the rainfall intensity and the average wind speed;
the ice coating weight increment prediction is to predict the ice coating weight increment by utilizing the predicted parameters according to the parameter constraint relation;
the reliability judgment of the prediction data is carried out by using a reliability judgment factor; predicting the icing thickness increment within T minutes, and simulating the icing thickness increment of the lead to judge;
the step of constructing the parameter prediction model is as follows:
1) the sequence initial data is measurement data or calculation parameters;
X＝{x(1),x(2),…,x(M)} (3-2)
2) a measurement accumulation sequence a is calculated,
A＝{a(1),a(2),……,a(M)}
3) the sequence of mean values Z is calculated,
Z＝{z(2),z(3),z(4),……,z(M)}
4) forming a structural matrix B which is an (M-1) x 2 matrix
5) Forming a measurement vector Y, wherein the measurement vector Y is an M-1 dimensional column vector
6) Calculating a coefficient vector C, wherein the coefficient vector C is a 2-dimensional column vector and comprises two elements of C1 and C2:
7) calculating the restored accumulated data, das (i) called restored accumulated data,
8) calculating reduction data: ds (i) referred to as reduced data
9) Calculating prediction data: the predicted data is N data after the measured data, the interval time between the data is T minutes, the predicted data is divided into predicted accumulated data and predicted data, the predicted accumulated data is represented by dar (i), the predicted data is represented by Dr (i), and i is 1,2,3,4, … … and N; the calculation methods are respectively
The steps for predicting the increase of the icing weight are as follows:
(1) average rainfall intensity p (i), unit: millimeter per minute, average wind speed v (i), unit, meter per second, simulated homemade heat wire weight gain value dg (i), unit: g, i ═ 1,2,3, … …, M;
(2) calculating the liquid water factor W (i) according to the formula (3-1): 1,2,3, … …, M;
(3) calculating the prediction data of rainfall intensity, average wind speed and liquid water factor according to the parameter prediction model; rainfall intensity prediction data is represented by pr (i), average wind speed prediction data is represented by vr (i), liquid water factor prediction data is represented by wr (i), i is 1,2,3, … …, N;
(4) calculating and simulating the weight increase value prediction data of the self-made heat conducting wire,
the simulated homemade heat wire weight increase value prediction data is represented by drg (i), i is 1,2,3, … …, N;
setting the length of the simulated self-control heat conducting wire as L, unit: centimeter, outside diameter R, unit: the length of each centimeter of the product is,
(5) calculating reduction data of rainfall intensity, average wind speed and liquid water factor according to the parameter prediction model; rainfall intensity reduction data is represented by Ps (i), average wind speed reduction data is represented by Vs (i), liquid water factor reduction data is represented by Ws (i), and i is 2,3, … … and M;
(6) calculating weight gain reduction data of the simulated homemade heating wire, wherein the weight gain reduction data of the calculated homemade heating wire is represented by Dsg (i), i is 2,3, … …, M
The reliability judgment of the prediction data is carried out by using a reliability judgment factor,
reliability judgment factor:
k is more than or equal to 0.9, the prediction data is credible,
k is less than 0.9 and more than or equal to 0.8, the prediction data is basically credible,
k is less than 0.8, and the predicted data is not credible; the ice coating thickness increase value prediction is as follows: predicting the increase value of the ice coating thickness of the simulation lead every T minutes;
the ice coating thickness increase value is the increase value of the ice coating thickness of the simulated lead every T minutes, and is expressed by Drh (i) with the unit of centimeter, i is 2, … … and N; let the ice density of the simulation transmission conductor be rhoiThe unit: grams per cubic centimeter;
then
The process of calculating the liquid water factor in constructing the parameter constraint model is as follows:
setting the length of the simulated self-control heat conducting wire as L, unit: centimeter, outside diameter R, unit: centimeter, starting weight of G0, units, grams, measured by simulating a homemade heat conductor;
the weight increase value of the simulated homemade thermal wire is represented by dg (i) in a unit of: g, i ═ 1,2,3, … …, M;
liquid water factor w (i):
the collected meteorological data is as follows: average rainfall p (i), units: millimeters per minute; average wind speed v (i), in meters per second.
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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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| 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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