CN114021300A - Dynamic capacity-increasing probabilistic prediction method for overhead transmission line - Google Patents
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Abstract
The invention discloses a dynamic capacity-increasing probabilistic prediction method for an overhead transmission line, which is used for improving the power transmission capability of the built transmission line. The method is based on a thermal balance equation considering the rainfall cooling effect, the influence of each meteorological factor on the heat capacity of the power transmission line is quantitatively analyzed, and the most main meteorological factor is selected for dynamic capacity-increasing probabilistic prediction; establishing a time-space regression prediction model of main meteorological factors according to all meteorological data sources to obtain expected values and standard deviations predicted by the meteorological factors every hour day ahead; based on a thermal balance equation and Taylor series expansion, firstly, an expected value and a standard deviation of each span heat capacity prediction of the power transmission line are obtained, and then, the final distribution of the heat capacity of the whole line and a corresponding percentile value are obtained based on multivariate truncation Gaussian distribution, so that a day-ahead hourly dynamic capacity-increasing prediction value with high confidence is provided for safe and stable operation of a power transmission system.
Description
Technical Field
The invention provides a probabilistic prediction method for dynamic capacity increase of an overhead transmission line, and belongs to the technical field of power system automation.
Background
Many wind and solar power plants located in remote areas require transmission lines to deliver power to the areas of consumption. At present, a static power transmission line rated value with a conservative value is generally adopted, so that the power transmission capability of the existing line is limited, and the power transmission line is possibly blocked. And the construction of a new transmission line not only consumes a large amount of resources, but also has negative effects on the environment. The dynamic capacity increasing technology of the power transmission line timely adjusts the power transmission capacity of the power transmission line according to measured or predicted meteorological conditions, generally provides higher power transmission capacity, and does not affect the safety of a system. The high-precision dynamic capacity-increasing prediction can be combined with the problems of unit combination, power flow analysis and the like in the power system market at the day before, and the economic benefit is improved. Therefore, the probability dynamic capacity increase prediction with high percentage value or confidence level is significant for the power transmission line. Over the past decades, many research advances have been made in the technical field of dynamic capacity increase of power transmission lines, but the uncertainty of meteorological prediction and the multi-pitch topology of long-distance power transmission lines increase the complexity of dynamic capacity increase, often resulting in that the estimated dynamic power transmission capacity may exceed the actual heat capacity of the line, and causing unsafe and thermal overload of the power system.
Disclosure of Invention
The invention provides a probabilistic prediction method for dynamic capacity increase of an overhead transmission line, which can effectively solve the key problem in the background technology. On the basis of the influence level analysis, main meteorological factors influencing the heat capacity of the power transmission line are selected, and a time-space regression prediction model for carrying out meteorological prediction based on various meteorological data is established. The problem of power line spatial topology and meteorological uncertainty is solved by modeling the line rating as a random variable, i.e. the minimum value of the selected span heat capacity. And then the distribution of the dynamic transmission capacity and the corresponding percentile value are obtained with lower calculation requirements and higher modeling precision. The numerical test results of the short-distance and long-distance power transmission lines are consistent, and the method provides higher and safer rated power transmission capacity for the overhead power transmission line, and is expected to bring considerable economic benefits for power grid related enterprises.
In order to achieve the purpose, the invention adopts the following technical scheme:
a dynamic capacity increase probabilistic prediction method for an overhead transmission line comprises the following steps:
the method comprises the steps that firstly, based on a thermal balance equation considering a rainfall cooling effect, the influence of each meteorological factor on the transmission capacity of a power transmission line is quantitatively analyzed by adopting an influence level method, and main meteorological factors for dynamic capacity increase prediction are selected, wherein the main meteorological factors comprise the environmental temperature, the wind speed, the wind direction angle, the solar radiation heat density and the precipitation rate;
secondly, acquiring and acquiring historical measured values and day-ahead hourly predicted values of all meteorological factors through meteorological sensors arranged on the power transmission line and meteorological stations near the line based on the main meteorological factors determined in the first step;
thirdly, establishing a time-space regression prediction model of the meteorological factors based on the data of the meteorological factors acquired in the second step, and calculating expected values and standard deviations of the day-ahead hourly prediction data of the meteorological factors at each span position of the power transmission line;
fourthly, based on the predicted value of the main meteorological factors obtained in the third step, calculating the day-ahead hourly predicted expected value and standard deviation of the heat capacity of each span of the power transmission line through a heat balance equation and Taylor series expansion;
and step five, based on the expected value and the standard deviation of the heat capacity prediction of each span acquired in the step four, considering the topological structure of the transmission line and multivariate truncation Gaussian distribution, and calculating the distribution of the heat capacity prediction of the transmission of the whole line and the corresponding high-confidence percentile numerical value.
As a further preferable scheme, the specific steps in the step one are as follows:
1) the formula of the wire heat balance equation considering the rainfall cooling effect is calculated as follows:
wherein I2R(Tc) Joule heat generated for current carrying capacity I, resistance R is wire temperature TcFunction of, the conductor absorbs the solar radiation heat QsAccording to solar radiation heat density QseComputing and wire radiation heat dissipation QrFrom the temperature T of the wirecAnd the ambient temperature TaDetermination of heat dissipation by air convection QcIs Tc、TaWind speed V and wind direction angle phi, rain and snow fall evaporation heat dissipation QeIs the precipitation rate PrRelative humidity RH and air pressure Pa, MCpIs the thermal capacity of the wire;
calculating the heat capacity of the transmission line under stable conditions, i.e. assuming TcReach equilibrium, dTcWith/dt at zero, meteorological data (Q) over a given spanse,Ta,V,φ,Pr,RH,Pa) And maximum allowable temperature T of wirec maxMaximum allowable current ImaxThe calculation formula is as follows:
2) the influence level of various meteorological factors on the transmission capacity of the span transmission line is quantitatively analyzed, the influence level is defined as the relative percentage change of the maximum transmission capacity and the minimum transmission capacity of the overhead line when one meteorological factor is changed, and the calculation formula is as follows:
and selecting meteorological factors with obvious influence levels to be brought into a heat balance equation to carry out next prediction.
As a further preferable scheme, the calculation formula of the time-space regression prediction model in step three is as follows:
wherein the index m represents the meteorological factors considered, including temperature, adjusted wind speed, wind angle, solar radiation intensity and precipitation rate, the index k represents the selected span position, beta represents the synergistic coefficient, and epsilon is the residual error;
determining meteorological factors w at time t for target span kk,m(t) three weather stations n to be adjacent1Historical measured values w of (1,2,3)k,m(t-24) as part of interpreting variables in the regression model;
predicting a meteorological factor predicted value or interpolation value of a target gear pitch k at the moment t; to better capture spatial features and achieve predictions where there are no measurement points, a set of geographical covariates s is usedn2Making a prediction comprising n2(1,2,3,4)4 explanatory variables, namely latitude, longitude, distance to valley and distance to coast;
in the time-space regression prediction model, the wind speed of the nearby meteorological station
Height h of measuring point0Often inconsistent with the height of the power transmission line tower, the calculation formula of the wind speed at the height h of the tower is as follows:
wherein the parameter a is obtained by fitting historical wind speed data;
in the time-space regression prediction model, weather forecast values for the target location are obtained if not from the weather monitoring device
Then the inverse distance square weighting method is used to predict the number of nearby weather stationsAccording to the interpolation, the calculation formula is as follows:
where N is the number of weather stations considered, wn,mIs the predicted data of the mth meteorological factor of the nth nearby meteorological station, dn,kIs the distance between the nth nearby weather station and the kth location;
the coefficient in the time-space regression prediction model can be estimated and calculated through a least square method; when new data is input into the model, the expected value and standard deviation of each meteorological factor prediction can be directly calculated.
As a further preferred variant, the heat capacity I of the span in step fourkAssumed to be a truncated Gaussian distribution, denoted N (. mu.) (k,σk 2；ak,bk) In which μkIs the mean value, σkIs the standard deviation, parameter akAnd bkIs IkUpper and lower limits determined in extreme cases; taylor series-based expected value point prediction in meteorological factors
Performing a first order expansion to obtain a heat capacity I at a span kkThe calculation formula is as follows:
wherein
Is the derivative of a function f, RkIs a residual term;
further obtaining expected gear distance value E (I)k) The calculation formula is as follows:
span standard deviation Var (I)k) The approximate calculation formula of (2) is:
wherein Var (w)m,k) Is the variance of meteorological factor m at span k, Cov (w), obtained from a space-time meteorological regression prediction modeli,k,wj,k) Representing the covariance of the two meteorological factors.
As a further preferable scheme, in the step five, for the whole power transmission line considering multiple spans, the rated value of the heat capacity of the power transmission is determined by the heat capacity of the critical span, and the calculation formula is as follows:
wherein, IkIs the thermal capacity at span K, Y is the thermal capacity of the entire overhead transmission line, K is the total number of spans considered; distribution function F of heat capacity of whole overhead transmission lineYThe calculation formula is as follows:
wherein,
is the cumulative density function of truncated Gaussian distribution, and the other items are the cumulative density functions of truncated Gaussian variable combined distribution;
the calculation formula of the percentage bit value (p) for predicting the heat capacity of the power transmission line is as follows:
advantageous effects
According to the method, on the basis of carrying out quantitative analysis on the influence of each meteorological factor on the heat capacity of the power transmission line, the most main meteorological factor is selected for dynamic capacity increase probabilistic prediction. And establishing a space-time regression prediction model of meteorological factors based on various meteorological data sources, and calculating a meteorological predicted value and a standard deviation considering the time-space characteristics of the power transmission line. Based on the thermal balance equation and Taylor series expansion, expected values and standard deviations of the prediction data of the heat capacities of all spans of the power transmission line can be obtained. The method comprises the steps of modeling a rated value of a line as a random variable, namely selecting the minimum value of the span heat capacity, obtaining the distribution of the heat capacity of the whole power transmission line and the corresponding percentile number based on multivariate truncation Gaussian distribution, and simultaneously solving the important problems of uncertainty of spatial topology and meteorological factors of the power transmission line. The distribution of the dynamic power transmission capacity and the corresponding high-confidence-degree percentile numerical value are calculated with lower calculation requirements and higher modeling precision. The numerical test results of the short-distance power transmission line and the long-distance power transmission line show that the method provides a higher and safer heat capacity predicted value for the overhead power transmission line, has more unique advantages compared with other methods, provides guarantee for safe and stable operation of a power transmission system, has better engineering practical value, and is expected to bring remarkable economic benefits for power transmission enterprises.
Drawings
FIG. 1 is a flow chart of overhead transmission line dynamic capacity increase prediction;
FIG. 2 is an illustration of the effect of various meteorological factors on span transmission capacity;
FIG. 3 is a meteorological data source associated with an overhead transmission line;
fig. 4 is a diagram of the percentile prediction result of the experimental transmission line.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The method comprises the steps that firstly, based on a thermal balance equation considering a rainfall cooling effect, the influence of all meteorological factors on the transmission capacity of a power transmission line is quantitatively analyzed by adopting an influence level method, and the most main meteorological factors for dynamic capacity increase prediction are selected, wherein the main meteorological factors comprise the environmental temperature, the wind speed, the wind direction angle, the solar radiation heat density and the precipitation rate;
acquiring historical measured values and day-ahead hourly preset values of all meteorological factors through meteorological sensors arranged on the power transmission line and meteorological stations near the line based on the main meteorological factors determined in the step one;
step three, establishing a time-space regression prediction model of meteorological factors by adopting the multiple meteorological data sources obtained in the step two, and obtaining expected values and standard deviations of the meteorological factors predicted every hour before each span position of the power transmission line;
fourthly, calculating the day-ahead hourly predicted expected values and standard deviations of the heat capacities of all spans of the power transmission line through a heat balance equation and Taylor series expansion on the basis of the predicted values of the main meteorological factors obtained in the third step;
and step five, based on the expected value and the standard deviation of the heat capacity prediction of each span obtained in the step four, obtaining the distribution of the heat capacity prediction of the whole line and the corresponding high-confidence percentile numerical value by considering the topological structure of the power transmission line and the multivariate truncated Gaussian distribution.
The specific steps in the first step are as follows:
1) the formula of the wire heat balance equation considering the rainfall cooling effect is calculated as follows:
wherein I2R(Tc) Joule heat generated for current carrying capacity I, resistance R is wire temperature TcFunction of, the conductor absorbs the solar radiation heat QsAccording to solar radiation heat density QseComputing and wire radiation heat dissipation QrFrom the temperature T of the wirecAnd the ambient temperature TaDetermination of heat dissipation by air convection QcIs Tc、TaWind speed V and wind direction angle phi, rain and snow fall evaporation heat dissipation QeIs the precipitation rate PrRelative humidity RH and air pressure Pa, MCpIs the heat capacity of the wire；
Calculating the heat capacity of the transmission line under stable conditions, i.e. assuming TcReach equilibrium, dTcWith/dt at zero, meteorological data (Q) over a given spanse,Ta,V,φ,Pr,RH,Pa) And maximum allowable temperature T of wirec maxMaximum allowable current ImaxThe calculation formula is as follows:
2) the influence level of various meteorological factors on the transmission capacity of the span transmission line is quantitatively analyzed, and is defined as the relative percentage change of the maximum transmission capacity and the minimum transmission capacity of the overhead line when one meteorological factor is changed, and the calculation formula is as follows:
the relationship between each meteorological factor and the change of the heat capacity of the power transmission line is shown in fig. 2. When one meteorological factor is analyzed, the rest meteorological factors are fixed on the static capacity calculation reference value of the power transmission line shown in the table 1. Of all the meteorological factors given, wind speed is the most influential meteorological factor. Therefore, table 2 calculates the influence levels of other meteorological factors on the capacity of the transmission line at five different wind speeds. It can be seen that wind direction angle, ambient temperature, solar radiation and precipitation rate are the main factors affecting the heat capacity of the transmission line. Two factors, namely the relative humidity and the atmospheric pressure, which have small influence on the heat capacity of the transmission line, can be fixed reference values, and the uncertainty of the factors is not considered in the dynamic capacity increasing system. Therefore, meteorological factors with significant influence levels are selected and are brought into a heat balance equation to be predicted next.
TABLE 1 reference values for various meteorological factors
TABLE 2 influence level (%) of each meteorological factor on the calculation of the heat capacity of the transmission line
In the second step, the meteorological factor data are acquired and obtained through the meteorological sensors arranged on the power transmission line and the meteorological stations near the line as shown in fig. 3.
The time-space regression prediction model in the third step has the calculation formula as follows:
wherein the index m represents the meteorological factors considered, including temperature, adjusted wind speed, wind angle, solar radiation intensity and precipitation rate, the index k represents the selected span position, beta represents the synergistic coefficient, and epsilon is the residual error;
determining meteorological factors w at time t for target span kk,m(t) three weather stations n to be adjacent1Historical measured values w of (1,2,3)k,m(t-24) as part of interpreting variables in the regression model;
predicting a meteorological factor predicted value or interpolation value of a target gear pitch k at the moment t; to better capture spatial features and achieve predictions where there are no measurement points, a set of geographical covariates s is usedn2Is composed of n2(1,2,3,4)4 explanatory variables, namely latitude, longitude, distance to valley and distance to coast;
in the time-space regression prediction model, the wind speed of the nearby meteorological station
Height h of measuring point0Often inconsistent with the height of the power transmission line tower, the calculation formula of the wind speed at the height h of the tower is as follows:
wherein the parameter a is obtained by fitting historical wind speed data;
in the time-space regression prediction model, if the meteorological predicted value of the target position cannot be obtained from the meteorological monitoring device
Then, the data of the nearby weather station is interpolated by using an inverse distance square weighting method, and the calculation formula is as follows:
where N is the number of weather stations considered, wn,mIs the predicted data of the mth meteorological factor of the nth nearby meteorological station, dn,kIs the distance between the nth nearby weather station and the kth location;
the coefficients in the spatio-temporal regression prediction model may be calculated by least squares estimation. When new input data comes, the expected value and standard deviation of each meteorological factor prediction can be directly obtained.
Thermal capacity of step four intermediate span IkAssumed to be a truncated Gaussian distribution, denoted N (. mu.) (k,σk 2；ak,bk) In which μkIs the mean value, σkIs the standard deviation, parameter akAnd bkIs IkUpper and lower limits determined in extreme cases; taylor series-based expected value point prediction in meteorological factors
Performing first-order expansion to obtain heat capacity I at span kkThe calculation formula is as follows:
wherein
Is the derivative of a function f, RkIs a residual term.
Convective cooling effect QcCannot be calculated directly because of QcA max function is involved:
QC＝max(qc1,qc2)
to calculate the partial derivatives, the maximum function is rewritten as an absolute value function as:
then to QcAfter derivation, the following can be obtained:
further, a desired gear distance E (I) is obtainedk) The approximate calculation formula of (2) is:
span standard deviation Var (I)k) The approximate calculation formula of (2) is:
wherein Var (w)m,k) Is the variance of the weather factor m at span k obtained from the spatio-temporal regression model. Since the weather factors can be regarded as independent random variables, the covariance Cov (w) of every two weather factorsi,k,wj,k) Is zero.
And fifthly, considering the whole power transmission line with multiple spans, wherein the rated value of the heat capacity of power transmission is determined by the heat capacity of the most critical span, and the calculation formula is as follows:
wherein, IkIs the thermal capacity at span K, Y is the thermal capacity of the entire overhead transmission line, and K is the number of spans considered. Distribution function F of heat capacity of whole overhead transmission lineYThe calculation formula is as follows:
wherein,
is a cumulative density function of truncated gaussian distribution, and the calculation formula is:
where Φ is the cumulative density function of a standard gaussian distribution.
FYThe remaining terms of (a) are cumulative density functions of the truncated gaussian variable joint distribution. In order to reduce the calculation complexity, only the correlation between the heat capacities of the continuous pitches is considered, and the calculation formula is as follows:
from the above, the predicted cumulative density function of the heat capacity of the power transmission line is calculated. Further, a calculation formula for obtaining the predicted percentage bit value (p) of the heat capacity of the power transmission line is as follows:
typically, low percentages, such as 2 nd, 1 st and 0.1 th percentile transmission line heat capacity prediction values are used as safety limits, which means that the probability that the actual transmission line heat capacity will not exceed the prediction values is 98%, 99% and 99.9%.
The invention adopts a time-space meteorological prediction model and a multi-span transmission line capacity probability model to predict dynamic capacity increase. Quantitatively analyzing the influence of each meteorological factor on the heat capacity of the power transmission line based on a thermal balance equation considering the rainfall cooling effect, and selecting main meteorological factors for dynamic capacity increase probabilistic prediction; establishing a space regression prediction model of main meteorological factors based on various meteorological data to obtain expected values and standard deviations predicted by each meteorological factor every hour before the day; based on a thermal balance equation and Taylor series expansion, firstly, an expected value and a standard deviation of the heat capacity prediction of each span of the power transmission line are obtained, and then the final distribution of the heat capacity of the whole line and a corresponding percentile numerical value are obtained based on multivariate truncation Gaussian distribution, so that a day-ahead hourly dynamic capacity increase prediction value with high confidence level is provided for the safe and stable operation of a power transmission system. The numerical experiment result shows that the method has higher prediction precision and calculation efficiency. For example, in experiments, the invention was applied to a long 230kV transmission line between a substation and a power plant in colorado, usa, which line comprises 6 sections with 68 spans. As shown in fig. 4, the predicted value of transmission capacity in the 2 nd percentile is higher than the static transmission capacity currently in use and does not exceed the transmission capacity that the conductor can actually carry. Obviously, the method can provide accurate, effective, safe and high-quality dynamic capacity-increasing predicted values for the power transmission line.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be able to cover the technical scope of the present invention, the technical solutions and the inventive concepts of the present invention with equivalent or modified alternatives and modifications within the technical scope of the present invention.
Claims (5)
1. A dynamic capacity-increasing probabilistic prediction method for an overhead transmission line is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the steps that firstly, based on a thermal balance equation considering a rainfall cooling effect, the influence of all meteorological factors on the transmission capacity of a power transmission line is quantitatively analyzed by adopting an influence level method, and main meteorological factors for dynamic capacity increase prediction are selected, wherein the main meteorological factors comprise the environmental temperature, the wind speed, the wind direction angle, the solar radiation heat density and the precipitation rate;
secondly, acquiring and acquiring historical measured values and day-ahead hourly predicted values of all meteorological factors through meteorological sensors arranged on the power transmission line and meteorological stations near the line based on the main meteorological factors determined in the first step;
thirdly, establishing a time-space regression prediction model of the meteorological factors based on the data of the meteorological factors acquired in the second step, and calculating expected values and standard deviations of the meteorological factors in the day-ahead hourly prediction data of each span position of the power transmission line;
fourthly, based on the predicted value of the main meteorological factors obtained in the third step, calculating the day-ahead hourly predicted expected value and standard deviation of the heat capacity of each span of the power transmission line through a heat balance equation and Taylor series expansion;
and step five, based on the expected value and the standard deviation of the heat capacity prediction of each span acquired in the step four, considering the topological structure of the power transmission line and multivariate truncation Gaussian distribution, and calculating the distribution of the heat capacity prediction of the power transmission of the whole line and the corresponding high-confidence percentile numerical value.
2. The overhead transmission line dynamic capacity increase probabilistic prediction method according to claim 1, characterized in that: the specific steps in the first step are as follows:
1) the formula of the wire heat balance equation considering the rainfall cooling effect is calculated as follows:
wherein I2R(Tc) Joule heat generated for current carrying capacity I, resistance R is wire temperature TcFunction of, the conductor absorbs the solar radiation heat QsAccording to solar radiation heat density QseComputing and wire radiation heat dissipation QrFrom the temperature T of the wirecAnd the ambient temperature TaDetermination of heat dissipation by air convection QcIs Tc、TaFunction of wind speed V and wind direction angle phi, rainfall and snowfall evaporation heat dissipation QeIs the precipitation rate PrRelative humidity RH and air pressure Pa, MCpIs the heat capacity of the wire;
calculating the heat capacity of the transmission line under stable conditions, i.e. assuming TcReach equilibrium, dTcWith/dt at zero, meteorological data (Q) over a given spanse,Ta,V,φ,Pr,RH,Pa) And maximum allowable temperature T of wirec maxMaximum allowable current ImaxThe calculation formula is as follows:
2) the influence level of various meteorological factors on the transmission capacity of the span transmission line is quantitatively analyzed, the influence level is defined as the relative percentage change of the maximum transmission capacity and the minimum transmission capacity of the overhead line when one meteorological factor is changed, and the calculation formula is as follows:
and selecting meteorological factors with obvious influence levels to be brought into a heat balance equation to carry out next prediction.
3. The overhead transmission line dynamic capacity increase probabilistic prediction method according to claim 1, characterized in that: the time-space regression prediction model in the third step has the calculation formula as follows:
wherein the index m represents the meteorological factors considered, including temperature, adjusted wind speed, wind angle, solar radiation intensity and precipitation rate, the index k represents the selected span position, beta represents the synergistic coefficient, and epsilon is the residual error;
determining meteorological factors w at time t for target span kk,m(t) three weather stations n to be adjacent1Historical measured values w of (1,2,3)k,m(t-24) as part of interpreting variables in the regression model;
predicting a meteorological factor predicted value or interpolation value of the target span k at the moment t; to better capture spatial features and achieve predictions where there are no measurement points, a set of geographical covariates s is usedn2Making a prediction comprising n2(1,2,3,4)4 explanatory variables, namely latitude, longitude, distance to valley and distance to coast;
in the time-space regression prediction model, the wind speed of the nearby meteorological station
Height h of measuring point0Often inconsistent with the height of the power transmission line tower, the calculation formula of the wind speed at the height h of the tower is as follows:
wherein the parameter a is obtained by fitting historical wind speed data;
in the time-space regression prediction model, if the meteorological predicted value of the target position cannot be obtained from the meteorological monitoring equipment
Then the inverse distance square weighting method is used to predict the number of nearby weather stationsAccording to the interpolation, the calculation formula is as follows:
where N is the number of weather stations considered, wn,mIs the predicted data of the mth meteorological factor of the nth nearby meteorological station, dn,kIs the distance between the nth nearby weather station and the kth location;
the coefficient in the time-space regression prediction model can be estimated and calculated through a least square method; when new data is input into the model, the expected value and standard deviation of each meteorological factor prediction can be directly calculated.
4. The overhead transmission line dynamic capacity increase probabilistic prediction method according to claim 1, characterized in that: thermal capacity of step four intermediate span IkAssumed to be a truncated Gaussian distribution, denoted N (. mu.) (k,σk 2；ak,bk) In which μkIs the mean value, σkIs the standard deviation, parameter akAnd bkIs IkUpper and lower limits determined in extreme cases; taylor series-based expected value point prediction in meteorological factors
Performing a first order expansion to obtain a heat capacity I at a span kkThe calculation formula is as follows:
wherein
Is the derivative of a function f, RkIs a residual term;
further obtaining expected gear distance value E (I)k) The calculation formula is as follows:
span standard deviation Var (I)k) The approximate calculation formula of (2) is:
wherein Var (w)m,k) Is the variance of meteorological factor m at span k, Cov (w), obtained from a spatio-temporal meteorological regression prediction modeli,k,wj,k) Representing the covariance of the two meteorological factors.
5. The overhead transmission line dynamic capacity increase probabilistic prediction method according to claim 1, characterized in that: in the step five, for the whole power transmission line considering multiple spans, the rated value of the heat capacity of power transmission is determined by the heat capacity of the critical span, and the calculation formula is as follows:
wherein, IkIs the thermal capacity at span K, Y is the thermal capacity of the entire overhead transmission line, K is the total number of spans considered; distribution function F of heat capacity of whole overhead transmission lineYThe calculation formula is as follows:
wherein,
is the cumulative density function of truncated Gaussian distribution, and the other items are the cumulative density functions of truncated Gaussian variable combined distribution;
the calculation formula of the percentage bit value (p) for predicting the heat capacity of the power transmission line is as follows:
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114707426A (en) * | 2022-04-29 | 2022-07-05 | 广西电网有限责任公司南宁供电局 | Line dynamic capacity increasing method based on multi-source data fusion |
| CN115438591A (en) * | 2022-11-07 | 2022-12-06 | 国网湖北省电力有限公司信息通信公司 | Dynamic capacity increasing method, system and medium for balancing loss of multi-connection circuit |
| CN115496002A (en) * | 2022-11-16 | 2022-12-20 | 国网湖北省电力有限公司信息通信公司 | Multi-dimensional feature interactive line dynamic capacity increasing method, system and medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114707426A (en) * | 2022-04-29 | 2022-07-05 | 广西电网有限责任公司南宁供电局 | Line dynamic capacity increasing method based on multi-source data fusion |
| CN115438591A (en) * | 2022-11-07 | 2022-12-06 | 国网湖北省电力有限公司信息通信公司 | Dynamic capacity increasing method, system and medium for balancing loss of multi-connection circuit |
| CN115496002A (en) * | 2022-11-16 | 2022-12-20 | 国网湖北省电力有限公司信息通信公司 | Multi-dimensional feature interactive line dynamic capacity increasing method, system and medium |
| CN115496002B (en) * | 2022-11-16 | 2023-02-24 | 国网湖北省电力有限公司信息通信公司 | Multi-dimensional feature interactive line dynamic capacity increasing method, system and medium |
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