CN114240025A - Distribution line fault probability evaluation method based on weather information - Google Patents

Distribution line fault probability evaluation method based on weather information Download PDF

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CN114240025A
CN114240025A CN202111300239.7A CN202111300239A CN114240025A CN 114240025 A CN114240025 A CN 114240025A CN 202111300239 A CN202111300239 A CN 202111300239A CN 114240025 A CN114240025 A CN 114240025A
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distribution line
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李丰君
王子欣
冯光
孙芊
徐恒博
郭剑黎
吴豫
牛荣泽
李宗峰
徐铭铭
王鹏
陈明
张建宾
谢芮芮
董轩
彭磊
姚福星
苗世洪
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Huazhong University of Science and Technology
State Grid Corp of China SGCC
State Grid Henan Electric Power Co Ltd
Electric Power Research Institute of State Grid Henan Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Henan Electric Power Co Ltd
Electric Power Research Institute of State Grid Henan Electric Power Co Ltd
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Abstract

The invention discloses a distribution line fault probability evaluation method based on weather information, which is characterized by establishing a multi-factor distribution line aging fault probability model based on distribution line aging influence factors; calculating a lead induced overvoltage peak value based on a simplified Rusck formula, considering the withstand voltage level of an insulator, calculating the probability of occurrence of lightning peak current when lightning stroke occurs, and determining the lightning stroke influence level; finally, a distribution line overvoltage fault probability model caused by lightning stroke is constructed through a Poisson regression model; considering weather factors and environment temperature, establishing the influence levels of different wind speeds and rainfall levels on the air gap discharge fault of the wire, and constructing a distribution line air gap discharge fault probability model caused by wind and rain according to a Poisson regression model; and finally, establishing a distribution line total fault probability model. The weather-based fault probability assessment method provided by the invention is more in line with the operation mechanism of the power system and has practical significance.

Description

Distribution line fault probability evaluation method based on weather information
Technical Field
The invention belongs to the field of distribution line state evaluation, and particularly relates to a distribution line fault probability evaluation method based on weather information.
Background
The distribution line is an important component of the power distribution network, according to statistics, the weather of strong convection in summer is one of the main reasons of power distribution network fault power failure, wherein the fault power failure of the 10kV distribution line caused by weather accounts for about 15%, and is mainly concentrated in Huang-Huai-Si cities in Henan; in addition, the rural distribution network in the area is relatively weak, the power failure influence range caused by strong convection weather is large, and the fault recovery time is long. Therefore, a reasonable distribution line fault model is selected for fault state analysis, and the method has important significance for carrying out fault prevention and maintenance work on the distribution line.
In the existing research, certain research accumulation is performed on the aspects of distribution line fault analysis, extreme weather state perception, weak link identification and the like, but the research accumulation mainly focuses on the aspects of regional early warning of extreme weather, weak link vulnerability index construction and the like, does not deeply excavate the association relation between strong convection weather and distribution line faults, and lacks consideration on specific factors such as distribution line channel environment, tree obstacles and the like. In order to make the distribution line fault probability assessment more consistent with the actual operation condition of the power grid, further improvement needs to be made for the problems.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a distribution line fault probability evaluation method based on weather information, which is used for respectively constructing a multi-factor distribution line aging fault probability model, a distribution line overvoltage fault probability model caused by lightning stroke and a distribution line air gap discharge fault probability model caused by wind and rain, and finally forming a distribution line overall fault probability model.
The invention adopts the following technical scheme.
A distribution line fault probability assessment method based on weather information comprises the following steps:
step A, establishing a multi-factor distribution line aging fault probability model based on distribution line aging influence factors;
step B, calculating a lead induced overvoltage peak value based on a simplified Rusck formula, considering the withstand voltage level of an insulator, calculating the probability of occurrence of lightning peak current when lightning stroke occurs, and determining the lightning stroke influence level; finally, a distribution line overvoltage fault probability model caused by lightning stroke is constructed through a Poisson regression model;
step C, considering weather factors and environment temperature, establishing the influence levels of different wind speeds and rainfall levels on the air gap discharge fault of the wire, and constructing a distribution line air gap discharge fault probability model caused by wind and rain according to the Poisson regression model;
and D, finally, establishing a distribution line overall fault probability model.
Further, in the step a, based on weibull distribution and considering the influence of operating temperature, establishing a distribution line aging fault probability model as follows:
Figure BDA0003338091850000021
wherein L islIs a coefficient of wire length, betalAs a shape parameter, TlFor the wire at a certain constant temperature thetaH0Run time ofeqConverting the operating time of the wire to theta at different operating temperaturesH0The equivalent run time of.
Further, said TlThe calculation process is as follows:
operating temperature theta of wire per unit lengthlComprises the following steps:
Figure BDA0003338091850000022
wherein, CpIs the specific heat capacity of the wire; m is the mass of the wire, qsIs the amount of solar heat absorbed by a unit length of wire, q1For the heat generated by a wire of a unit length at a nominal voltage, qcThe heat dissipated to the external environment for the lead with unit length;
the percentage loss of tensile strength of the wire per unit length W is:
W＝Wa{1-exp{-exp[A1+(B1/θ1)lnt+C1/θ1+D1 ln(R1/80)]}}
wherein, WaPercent tensile strength loss of the wire in the fully annealed condition; t is the temperature theta of the wirelThe duration of time of operation; a. thel、Bl、Cl、DlAnd RlIs a constant related to the material properties of the wire;
when the wire is at a certain constant temperature thetaH0Operate such that W is WmaxThen T can be equated to TlAnd then:
Figure BDA0003338091850000023
wherein when W reaches a maximum value WmaxThe wire life is considered to be terminated.
Further, said TeqThe calculation process is as follows:
Figure BDA0003338091850000031
further, in the step B, a distribution line overvoltage fault probability model caused by lightning stroke is constructed through a poisson regression model:
PL1＝m1 exp(n1L)
wherein m is1、n1And L is a dimensionless coefficient obtained by fitting historical fault data, and is a lightning stroke influence grade.
Further, the lightning impact level L calculation process:
L＝k1ρc[P(I0)]-b1
wherein k is1、b1The non-dimensional coefficient is obtained by fitting historical fault data and related insulator performance; rhocFor the influence of maintenance on the lightning resistance of the insulator, the value can be obtained by fitting the influence of a historical maintenance plan on the probability of lightning stroke fault, P (I)0) The probability of occurrence of lightning peak current when lightning stroke occurs.
Further, calculating the maximum induced overvoltage peak value U of the wiremax：
Figure BDA0003338091850000032
Wherein, U is an induced overvoltage peak value; h is the height of the wire; i is0Is the lightning peak current; sminThe direct lightning strike attraction distance;
let the critical breakdown voltage of the insulator be U50％If U is presentmax<U50％If so, the lightning stroke fault probability is 0; otherwise, a failure may occur due to lightning strike; the probability of occurrence of the lightning peak current when a lightning stroke occurs is as follows:
Figure BDA0003338091850000033
further, in the step C, the probability of the wire fault caused by wind and rain is constructed according to the poisson regression model as follows:
PL2＝m2 exp(n2h(v,Qy,T))
wherein m is2、n2For dimensionless coefficients obtained by fitting historical fault data, h (v, Q)yAnd T) is the influence level of the wind speed and rainfall intensity level on the air gap discharge fault of the wire.
Further, the influence levels of the wind speed and the rainfall intensity level on the air gap discharge fault of the wire are as follows:
Figure BDA0003338091850000041
wherein σvThe influence level of the wind speed on the discharge fault of the lead is shown; lambdavj、Nvj、MvjIs the jth dimensionless coefficient obtained by fitting historical fault statistical data under the wind speed grade v, wherein v is the wind speed and QyThe amount of rainfall is.
Further, in the step D, a distribution line total fault probability model is constructed:
Figure BDA0003338091850000042
compared with the prior art, the weather-based fault probability assessment method better conforms to the operation mechanism of the power system and has practical significance for the problems in the existing distribution line fault probability assessment.
The distribution line fault probability evaluation method based on weather information is based on a lead fault mechanism, and combines operating conditions such as sunshine, load rate, operating age, ambient temperature and wind speed to establish lead fault probability models under different weather conditions. The validity and the rationality of the probability model are proved by example analysis, and a reference basis can be provided for system operation risk assessment and maintenance plan formulation.
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FIG. 1 is a flow chart of a distribution line fault probability evaluation method based on weather information according to the present invention;
FIG. 2 is a comparison graph of the true and fitted fault probabilities of a 10kV power distribution conductor;
FIG. 3 is a graph of wind speed versus rainfall for air gap discharge fault probability;
fig. 4 is a comparison graph of calculated and true values of the failure probability of a wire.
Detailed Description
The present application 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 application is not limited thereby.
As shown in fig. 1, the distribution line fault probability evaluation method based on weather information according to the present invention includes the following steps:
step A, establishing a multi-factor distribution line aging fault probability model based on distribution line aging influence factors;
distribution lines ageing influence factor, mainly relevant temperature, wire operating temperature mainly depends on operating conditions such as sunshine, load factor, operation age, ambient temperature and wind speed, and above-mentioned operating conditions can divide into 3 kinds of factors that influence wire operating temperature and change, the heat that absorbs from external environment promptly, the heat that the wire itself generates heat and the heat that loses to external environment.
a, heat absorbed from the external environment;
the heat absorbed by the wire from the external environment is usually mainly solar heat, and the solar heat q absorbed by the wire per unit length (1km) iss(unit J) is:
qs＝αsQsD sinη (1)
wherein alpha issAlpha of bare overhead conductor for absorption of sunlight by the conductorsGenerally does not change along with the operation age, and is set as a constant value here; eta is an included angle (unit is DEG) between the sunlight direction and the lead, and in order to obtain the maximum failure probability of the equipment, the severe sunlight environment is considered, and eta is taken to be 90 DEG; d is the wire diameter (in m); qsThe solar heat (in J) obtained for the wire taking into account direct and diffuse conditions.
12:00 am at maximum day of sunshine all year round given by IEEEQsThe calculation formula (c) is as follows:
Figure BDA0003338091850000051
Figure BDA0003338091850000052
wherein HsIs the solar altitude (unit is degree);
Figure BDA0003338091850000053
the latitude (in °) at which the wire is located.
Q is shown by the formulas (2) and (3)sOnly with
Figure BDA0003338091850000054
In relation to the provincial range, the latitude of the lead is not greatly different, so that the heat absorbed by the lead with the same type and unit length in the provincial power grid from the outside can be set as a constant.
b, the heat generated by the wire itself;
within the temperature allowable range, the heat q generated by the wire in the IEEE standard1(in J) is approximately Joule heat. Rated voltage UeLower q1Can be expressed as:
Figure BDA0003338091850000055
wherein, KlIs the load factor; pmaxIs the maximum operating power of the wire (in W); ζ is the power factor angle; rcIs the resistance of the wire (in omega) whose value is related to the operating age and can be obtained by measurement.
c, heat dissipated to the external environment;
the wire heat dissipation mode mainly includes radiation heat dissipation and convection heat dissipation. Radiation heat dissipation accounts for only a small fraction of the total heat dissipation, and the invention ignores the heat dissipation effects of this approach. Heat q lost in convection heat dissipationc(unit ofJ) and the ambient temperature T (in ℃) and the wind speed vfThe unit is m/s, the surface dirt of the lead is related to the wind direction, and the main heat dissipation mode of the lead is provided.
The surface of the external lead is dirty and overhauled qcIs corrected to rho, and q is the maximum failure probability, assuming that the wind direction is perpendicular to the wirecThe expression of (a) is:
Figure BDA0003338091850000061
where ρ isfIs the air density (unit is kg/m)3)，μfIs the air viscosity coefficient, λfIs the thermal conductivity of air, A3)；θlThe operating temperature of the wire itself (in degrees c).
Operating temperature theta of unit length of wire according to IEEE standardlCan be expressed as:
Figure BDA0003338091850000062
wherein, CpThe specific heat capacity of the wire (in J/(kg. DEG C)); m is the mass of the wire (in kg).
The tensile strength of the wire is related to the operating temperature, and the empirical formula between the two is as follows:
W＝Wa{1-exp{-exp[A1+(B1/θ1)lnt+C1/θ1+D1 ln(R1/80)]}} (7)
wherein W is the percentage of tensile strength loss of the wire per unit length; waPercent tensile strength loss of the wire in the fully annealed condition; t is the temperature theta of the wirelThe duration of time of operation; a. thel、Bl、Cl、DlAnd RlIs a constant related to the material properties of the wire.
The life of the wire can be approximated by estimating the loss of tensile strength of the wire when W reaches a maximum value WmaxWhen it is, the service life of the wire is consideredAnd (5) ending the service.
When the wire is at a certain constant temperature thetaH0Operate such that W is WmaxThen, T in the formula (7) can be equivalent to the expected lifetime TlThen equation (7) can be rewritten as:
Figure BDA0003338091850000063
but the wire does not always run at thetaH0Then, the operating time corresponding to the fluctuating operating temperature curve of the wire needs to be converted into thetaH0The equivalent run time of. N small regions can be divided according to the operating temperature of the wire, and the operating temperature theta of the wire in each small regionliConstant, tliIs a wire at thetaliThe running time of the wires at different running temperatures can be converted to thetaH0Equivalent running time T ofeqAs shown in formula (9).
Figure BDA0003338091850000071
The power distribution line aging fault related probability model has the characteristics that Weibull distribution fully conforms to the bathtub curve variation trend, is widely applied to failure modeling of power distribution equipment, and is established based on the Weibull distribution and considering the influence of operating temperature as follows:
Figure BDA0003338091850000072
wherein L islIs the wire length factor; beta is alAs a shape parameter, can be obtained by fitting historical data of the faults caused by wire aging.
Step B, calculating a lead induced overvoltage peak value based on an IEEE simplified Rusck classical formula, considering the withstand voltage level of an insulator to prevent lightning, calculating the probability of occurrence of lightning peak current when lightning stroke occurs, and determining the lightning stroke influence level; finally, a distribution line overvoltage fault probability model caused by lightning stroke is constructed through a Poisson regression model;
the peak value of the induced overvoltage of the wire can be obtained by utilizing an IEEE simplified Rusck classical formula as follows:
Figure BDA0003338091850000073
wherein, U is the peak value of the induced overvoltage (the unit is kV); h is the height of the wire, and is generally 5-15 m; i is0Is the lightning peak current (in kA); s is the vertical distance (in m) of the lightning strike point from the wire.
Definition of SminFor direct lightning attraction distance, when S>SminThen the thunder and lightning will not be attracted by the wire to become direct lightning, SminThe calculation formula of (2) is as follows:
Figure BDA0003338091850000074
generally, the height of the wire is 10m, and the maximum induced overvoltage peak value is as follows:
Figure BDA0003338091850000075
the 10kV lead is not generally provided with a lightning conductor, if the action of other lightning arresters is not considered, the line is mainly protected against lightning by improving the withstand voltage level of the insulator. Let the critical breakdown voltage of the insulator be U50％If U is presentmax|I0,h<U50％If so, the lightning stroke fault probability is 0; otherwise, a failure may occur due to lightning strike. The probability of occurrence of the lightning peak current when a lightning stroke occurs is as follows:
Figure BDA0003338091850000081
as can be seen from equation (13), the magnitude of the induced overvoltage peak depends on I0Therefore P (I)0) Characterised by the influence of induced lightning on the lineThe frequency is used as a classification standard of the lightning strike influence level L, as shown in formula (15).
Figure BDA0003338091850000082
Wherein k is1、b1The non-dimensional coefficient is obtained by fitting historical fault data and related insulator performance; rhocIn order to examine and repair the influence on the lightning-resistant performance of the insulator, the value can be obtained by fitting the influence of a historical examination and repair plan on the probability of lightning faults.
The poisson regression model is a statistical model for describing independent and discrete distribution of variables and determining quantitative relations among 2 or more variables, and is commonly used for counting data and modeling of a list table. Furthermore, the poisson regression model satisfies exponential modeling and is often applied to non-negative integer cases where the dependent variable is of a limited range. Intuitively, faults occurring in a power distribution system are a counting process, and the distribution of the lightning strike influence grade and the lightning strike fault probability basically meeting an exponential form is found by counting the relation between the lightning strike influence grade and the lightning strike fault probability, so that the distribution line overvoltage lightning strike fault probability constructed according to a Poisson regression model is as follows:
PL1＝m1 exp(n1L) (16)
wherein m is1、n1Is a dimensionless coefficient obtained by fitting historical fault data.
Step C, considering the influence of weather factors and environment temperature, establishing the influence levels of different wind speeds and rainfall levels on the air gap discharge fault of the wire, and constructing a distribution line air gap discharge fault probability model caused by wind and rain according to the Poisson regression model;
probabilistic model of wire failure due to wind and rain, UbIncreases with increasing ambient temperature T, when T>22 ℃ or T<At 11 ℃ T to UbThe influence of (A) is negligible; when T is more than or equal to 11 ℃ and less than or equal to 22 ℃, T and UbSubstantially linear.
In summary, the effect of wind speed and rainfall intensity level on the wire air gap discharge faultRank h (v, Q)y，T)。
Figure BDA0003338091850000091
Wherein σvThe influence level of the wind speed on the discharge fault of the lead is shown; lambdavj、Nvj、MvjAnd the j dimensionless coefficient is obtained by fitting historical fault statistical data under the wind speed level v.
Because the historical statistical result of the wind and rain influence level and the wire air gap discharge fault probability meets the exponential distribution, the probability of the wire fault caused by wind and rain is constructed according to the Poisson regression model as follows:
PL2＝m2 exp(n2h(v,Qy,T)) (18)
wherein m is2、n2Is a dimensionless coefficient obtained by fitting historical fault data.
And D, finally, establishing a distribution line overall fault probability model.
Considering that the fault factors of the distribution line are independent of each other, and by combining the above contents, the overall fault probability model of the distribution line is as follows:
Figure BDA0003338091850000092
the present invention will be described in further detail with reference to specific embodiments.
Step one, according to historical fault data of 10kV overhead bare conductors in 2013-2017 of a certain power distribution network and corresponding weather information, the historical fault data of 1/2 is randomly selected to serve as a model fitting sample, and in addition, the historical data of 1/2 serves as a verification sample. The distribution line fault data is shown in table 1, and the parameters to be evaluated are shown in table 2.
TABLE 1
Figure BDA0003338091850000093
Figure BDA0003338091850000101
TABLE 2
Figure BDA0003338091850000102
And step two, fitting calculation is carried out on the distribution line fault probability according to the evaluation parameters and the fault probability models of the distribution line under different conditions, and the obtained fitting fault probability is compared with the real fault probability. The real fault probability can be obtained by dividing the number of times of the type of fault of the equipment in the historical record by the total number of the type of equipment under the same operation condition; the fitting fault probability is calculated by various fault probability models of the equipment under corresponding operating conditions.
In order to reflect the advantages and disadvantages of the fitting method, a unitary multi-time model is introduced to perform fitting calculation simultaneously with the method provided by the invention, the obtained fitting fault probability is compared with the real fault probability, and the final result is shown in fig. 2. FIG. 3 is a graph of wind speed and rainfall effect on air gap discharge failure probability when ambient temperature is greater than 22 ℃. The failure probability calculation method is recorded as a scheme 1, the failure probability is calculated by using a Poisson regression model with the equipment running state grades (good, medium and poor) as variable values and recorded as a scheme 2, and the effect of the failure probability model built by the method is further tested. And (3) respectively and randomly selecting 10 overhead bare conductor fault scenes from equipment fault data of 2013-2017 years of a certain 10kV power distribution network. The failure probability of the wire is calculated by using 2 schemes respectively, and the calculation result is compared with the real failure rate, and the result is shown in fig. 4.
Compared with the prior art, the weather-based fault probability assessment method better conforms to the operation mechanism of the power system and has practical significance for the problems in the existing distribution line fault probability assessment.
The distribution line fault probability evaluation method based on weather information is based on a lead fault mechanism, and combines operating conditions such as sunshine, load rate, operating age, ambient temperature and wind speed to establish lead fault probability models under different weather conditions. The validity and the rationality of the probability model are proved by example analysis, and a reference basis can be provided for system operation risk assessment and maintenance plan formulation.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.

Claims (10)

1. A distribution line fault probability assessment method based on weather information is characterized by comprising the following steps:
step A, establishing a multi-factor distribution line aging fault probability model based on distribution line aging influence factors;
step B, calculating a lead induced overvoltage peak value based on a simplified Rusck formula, considering the withstand voltage level of an insulator, calculating the probability of occurrence of lightning peak current when lightning stroke occurs, and determining the lightning stroke influence level; finally, a distribution line overvoltage fault probability model caused by lightning stroke is constructed through a Poisson regression model;
step C, considering weather factors and environment temperature, establishing the influence levels of different wind speeds and rainfall levels on the air gap discharge fault of the wire, and constructing a distribution line air gap discharge fault probability model caused by wind and rain according to the Poisson regression model;
and D, finally, establishing a distribution line overall fault probability model.
2. The weather information-based distribution line failure probability evaluation method of claim 1,
in the step A, based on Weibull distribution and considering the influence of the operating temperature, establishing a distribution line aging fault probability model as follows:
Figure FDA0003338091840000011
wherein L islIs a coefficient of wire length, betalAs a shape parameter, TlFor the wire at a certain constant temperature thetaH0Run time ofeqConverting the operating time of the wire to theta at different operating temperaturesH0The equivalent run time of.
3. The weather information-based distribution line failure probability evaluation method according to claim 2,
the T islThe calculation process is as follows:
operating temperature theta of wire per unit lengthlComprises the following steps:
Figure FDA0003338091840000012
wherein, CpIs the specific heat capacity of the wire; m is the mass of the wire, qsIs the amount of solar heat absorbed by a unit length of wire, q1For the heat generated by a wire of a unit length at a nominal voltage, qcThe heat dissipated to the external environment for the lead with unit length;
the percentage loss of tensile strength of the wire per unit length W is:
W＝Wa{1-exp{-exp[A1+(B1/θ1)lnt+C1/θ1+D1ln(R1/80)]}}
wherein, WaPercent tensile strength loss of the wire in the fully annealed condition; t is the temperature theta of the wirelThe duration of time of operation; a. thel、Bl、Cl、DlAnd RlIs a constant related to the material property of the wireCounting;
when the wire is at a certain constant temperature thetaH0Operate such that W is WmaxThen T can be equated to TlAnd then:
Figure FDA0003338091840000021
wherein when W reaches a maximum value WmaxThe wire life is considered to be terminated.
4. The weather information-based distribution line failure probability evaluation method according to claim 3,
the T iseqThe calculation process is as follows:
Figure FDA0003338091840000022
5. the weather information-based distribution line failure probability evaluation method of claim 1,
in the step B, a distribution line overvoltage fault probability model caused by lightning stroke is constructed through a Poisson regression model:
PL1＝m1exp(n1L)
wherein m is1、n1And L is a dimensionless coefficient obtained by fitting historical fault data, and is a lightning stroke influence grade.
6. The weather information-based distribution line failure probability evaluation method according to claim 5,
and the lightning strike influence level L calculation process:
Figure FDA0003338091840000023
wherein k is1、b1The non-dimensional coefficient is obtained by fitting historical fault data and related insulator performance; rhocFor the influence of maintenance on the lightning resistance of the insulator, the value can be obtained by fitting the influence of a historical maintenance plan on the probability of lightning stroke fault, P (I)0) The probability of occurrence of lightning peak current when lightning stroke occurs.
7. The weather information-based distribution line failure probability evaluation method according to claim 6,
calculating the maximum induced overvoltage peak value U of the leadmax：
Figure FDA0003338091840000031
Wherein, U is an induced overvoltage peak value; h is the height of the wire; i is0Is the lightning peak current; sminThe direct lightning strike attraction distance;
let the critical breakdown voltage of the insulator be U50％If U is presentmax<U50％If so, the lightning stroke fault probability is 0; otherwise, a failure may occur due to lightning strike; the probability of occurrence of the lightning peak current when a lightning stroke occurs is as follows:
Figure FDA0003338091840000032
8. the weather information-based distribution line failure probability evaluation method of claim 1,
in the step C, the probability of the wire fault caused by wind and rain is established according to the Poisson regression model as follows:
PL2＝m2exp(n2h(v,Qy,T))
wherein m is2、n2For dimensionless coefficients obtained by fitting historical fault data, h (v, Q)yAnd T) is wind speed, rainfall intensity, etcThe level of impact on the wire air gap discharge fault.
9. The weather information-based distribution line failure probability assessment method according to claim 8,
the influence levels of the wind speed and the rainfall intensity level on the air gap discharge fault of the conducting wire are as follows:
Figure FDA0003338091840000033
wherein σvThe influence level of the wind speed on the discharge fault of the lead is shown; lambdavj、Nvj、MvjIs the jth dimensionless coefficient obtained by fitting historical fault statistical data under the wind speed grade v, wherein v is the wind speed and QyThe amount of rainfall is.
10. The weather information-based distribution line failure probability evaluation method of claim 1,
in the step D, a distribution line overall fault probability model is constructed:
Figure FDA0003338091840000041
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