CN111191887A - Fitting method and system for time distribution characteristics of power transmission line meteorological disaster faults - Google Patents
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Abstract
The invention discloses a method and a system for fitting time distribution characteristics of power transmission line meteorological disaster faults, wherein the method comprises the following steps: the method comprises the steps of taking monthly distribution of trip times of a power transmission line in a certain region in the same period of history as an object, and respectively counting frequency distribution of meteorological disaster failure rates of the power transmission line in different months; carrying out cloud transformation fitting on the frequency distribution of the meteorological disaster failure rate of the power transmission line in different months to obtain a plurality of corresponding normal cloud models; and respectively calculating expected values, entropy values and amplitude coefficients of the comprehensive cloud concepts by a plurality of normal cloud models and adopting a jump method of the cloud models, and fitting the fault rate distribution of the meteorological disasters of the power transmission line. The method can establish a fault rate distribution function in the longitudinal time direction all year around to obtain a time-dependent fault rate mathematical model which is used for reflecting the fault time distribution rule of the power transmission line in different areas, different voltage grades and different meteorological environments and realizing the panoramic analysis of the power grid disaster and the meteorological disaster.
Description
Technical Field
The invention relates to the technical field of power transmission line protection, in particular to a cloud model-based method and system for fitting time distribution characteristics of power transmission line meteorological disaster faults.
Background
The transmission line network is distributed all over China and has the characteristics of multiple points, long line, wide area and the like. At present, most of power transmission equipment is located in the field of the wasteland, the difference of the landform and the landform of the area is large, the weather change is multiple, and the natural disaster is serious. Extreme meteorological disasters such as strong wind, ice disasters and heavy rain can cause a plurality of transmission line faults in a short time, and the power grid tide transfer induces incorrect actions of a relay protection device, so that the chain tripping of the line can be accelerated, and even large-area power failure accidents can be caused. Therefore, the method accurately recognizes the law of the meteorological disasters on the power transmission line, fully utilizes meteorological forecast data to perform fault risk early warning on the overhead power transmission line, and becomes a support means needed urgently for work such as power grid dispatching operation, operation and maintenance, emergency rescue and the like.
The failure rate of the transmission line is changed along with time, and different regions have different failure rate time distribution characteristics due to the difference of the geographical positions and the layout of the transmission network. Therefore, on the basis of obtaining the historical failure rate of each month in the same period, if the time change characteristic of the annual failure rate can be obtained through simulation, the method can be used for making a power grid operation and maintenance strategy. However, the power grid has the climate characteristic of being clear in four seasons, spring, summer, autumn and winter, the transmission line faults are generally distributed in a month-by-month time mode and have peak-valley-peak-valley characteristic, the peak-valley period is difficult to adapt only by adjusting the period coefficient of a fitting model, and the fitting effect of the traditional Fourier function and Gaussian function on a multi-peak periodic curve is poor.
Therefore, a fault rate distribution function in the longitudinal time direction of the whole year needs to be established to reflect the distribution rule of the fault time of the power transmission line in different areas, different voltage levels and different meteorological environments, so as to realize the panoramic analysis of the power grid disaster and the meteorological disaster.
Disclosure of Invention
The invention provides a fitting method and a fitting system for time distribution characteristics of meteorological disaster faults of a power transmission line, which are used for solving the technical problems that the time distribution characteristics of fault rates of the power transmission line are complex, so that the peak-valley period is difficult to adapt by only adjusting the periodic coefficients of a fitting model, and the fitting effect of the traditional Fourier function and the Gaussian function on a multi-peak periodic curve is poor.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
a fitting method for time distribution characteristics of power transmission line meteorological disaster faults comprises the following steps:
s1: the method comprises the steps of taking monthly distribution of trip times of a power transmission line in a certain region in the same period of history as an object, and respectively counting frequency distribution of meteorological disaster failure rates of the power transmission line in different months;
s2: carrying out cloud transformation fitting on the frequency distribution of the meteorological disaster failure rate of the power transmission line in different months to obtain a plurality of corresponding normal cloud models;
s3: and respectively calculating expected values, entropy values and amplitude coefficients of the comprehensive cloud concepts by a plurality of normal cloud models and adopting a jump method of the cloud models, and fitting the fault rate distribution of the meteorological disasters of the power transmission line.
Preferably, step S1 includes:
acquiring the frequency distribution of a meteorological disaster failure rate sequence: dividing the power transmission line meteorological disaster fault rate sequence of the area into 12 intervals according to months, respectively counting the number of the power transmission line meteorological disaster fault rate sequence in each month, and solving the k-th month line trip time sequence delta PkObtaining a frequency distribution function f (x) of the power transmission line meteorological disaster failure rate.
Preferably, step S2 includes:
for a frequency distribution function f (x) of a given transmission line monthly fault rate time sequence, a peak-based cloud transformation algorithm is adopted, and the mathematical expression of the cloud transformation algorithm is as follows:
in the formula riIs an amplitude coefficient; k is the number of discrete concepts generated; c (Ex)i,Eni,Hei) Is one of the transformed cloud models;
the frequency distribution function of monthly fault rate data of the power transmission line on the domain of discourse is regarded as the superposition of a plurality of cloud models with normal distribution, so that the following results can be obtained:
wherein ε is a predefined maximum allowable error; f. ofi(x) A distribution function of each cloud model based on a probability density expectation function of a normal cloud, i.e., a frequency distribution function f (x).
Preferably, the peak-based cloud transform algorithm in step S2 includes:
(1) the maximum value point of the maximum amplitude position in the frequency distribution function f (x) of the monthly fault rate data of the power transmission line is taken as the center of the cloud concept, namely the expected value Exi；
(2) Calculate by ExiEntropy En for the desired cloud modeliObtaining a cloud model characterization function f separated based on the original transmission line month-by-month fault rate sequencei(x)；
(3) Subtracting the cloud model characterization function part from the frequency distribution function of the monthly fault rate of the original power transmission line, and searching a local maximum value point at the maximum amplitude; repeating the above process until the frequency of the residual data is lower than the preset threshold;
(4) and obtaining the distribution function of each cloud model for fitting the distribution function of the monthly fault rate of the power transmission line according to the known f (x), and solving the super-entropy value of each cloud concept by using a reverse cloud algorithm without certainty.
Preferably, the jump of the cloud model in step S3 is: and gradually merging two cloud concepts with the shortest distance in the cloud concepts to obtain the integrated comprehensive cloud concept.
Preferably, step S3 includes:
is provided with C1(Ex1,En1,He1) And C2(Ex2,En2,He2) Are two adjacent basic cloud concepts on the domain U, if Ex1≤Ex2Then C is1And C2Firstly, obtaining a comprehensive cloud concept C according to a concept jump method based on a 'soft or' mode3(Ex3,En3,He3) The numerical characteristic values of (a) are:
He3＝max(He1,He2)
through the operation, two adjacent basic cloud concepts are promoted into a comprehensive cloud concept;
respectively calculating expected values, entropy values and amplitude coefficients of the comprehensive cloud concept to obtain a fitting equation of the power transmission line meteorological disaster fault rate curve
The present invention also provides a computer system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the methods described above when executing the computer program.
The invention has the following beneficial effects:
the invention relates to a fitting method and a system for time distribution characteristics of power transmission line meteorological disaster faults, which are used for classifying all line tripping accidents according to regions and time by adopting a 'segmentation-statistics-fitting' method aiming at power transmission line meteorological disaster fault rates, counting the frequency of power values falling in each interval, performing function fitting on the tripping times-numerical values of lines in the same period of time in the same region, decomposing the frequency distribution of a power transmission line monthly fault rate sequence into a plurality of cloud models with normal distribution for superposition, and realizing the conversion from quantitative representation to qualitative concept. The fault rate distribution function in the longitudinal time direction of the whole year can be established, a time-dependent fault rate mathematical model is obtained, and the time-dependent fault rate mathematical model is used for reflecting the fault time distribution rules of the power transmission line in different areas, different voltage levels and different meteorological environments, so that the panoramic analysis of the power grid disaster and the meteorological disaster is realized. The method can better simulate the time-dependent fault rule of the power transmission line, and accordingly is used for predicting the fault rate of a certain period of time in the future and reducing the line tripping accidents.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The present invention will be described in further detail below with reference to the accompanying drawings.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic flow chart of a fitting method of time distribution characteristics of a power transmission line meteorological disaster fault according to a preferred embodiment 1 of the present invention;
FIG. 2 is a comparative example of preferred embodiment 2 of the present invention: a schematic diagram of a curve fitted by a Gaussian simulation function;
FIG. 3 is a comparative example of preferred embodiment 2 of the present invention: a schematic diagram of a Weibull function fitting curve;
fig. 4 is a schematic diagram of a cloud model fitting curve according to preferred embodiment 2 of the present invention.
Detailed Description
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
Example 1:
referring to fig. 1, the fitting method for time distribution characteristics of power transmission line meteorological disaster faults of the embodiment is provided. The method comprises the following steps:
s1: and acquiring the frequency distribution of the meteorological disaster failure rate sequence. The trip times of the power transmission lines in a certain area in the same period of history are distributed month by month as objects, and the frequency distribution of the meteorological disaster failure rate of the power transmission lines in different months is respectively counted.
Acquiring the frequency distribution of a meteorological disaster failure rate sequence: dividing the power transmission line meteorological disaster fault rate sequence of the area into 12 intervals according to months, respectively counting the number of the power transmission line meteorological disaster fault rate sequence in each month, and solving the k-th month line trip time sequence delta PkObtaining a frequency distribution function f (x) of the power transmission line meteorological disaster failure rate.
S2: cloud transformation of the frequency distribution. Namely, the frequency distribution of the power transmission line meteorological disaster failure rate in different months is subjected to cloud transformation fitting to obtain a plurality of corresponding normal cloud models.
Using a peak cloud transform-based idea for fitting the distribution function f of each cloud model of f (x)i(x) In that respect For a frequency distribution function f (x) of a given transmission line monthly fault rate time sequence, a peak-based cloud transformation algorithm is adopted, and the mathematical expression of the cloud transformation algorithm is as follows:
in the formula riIs an amplitude coefficient; k is the number of discrete concepts generated; c (Ex)i,Eni,Hei) Is one of the transformed cloud models. Considering the universality of the normal cloud model, the frequency distribution of the monthly fault rate data of the power transmission line on the domain of discourse can be regarded as the superposition of a plurality of cloud models with normal distribution, so that the following results can be obtained:
where ε is a predefined maximum allowable error, fi(x) Is a probability density expectation function based on normal cloud.
The specific implementation process based on peak cloud transformation comprises the following steps:
(1) firstly, a maximum value point at the maximum amplitude position in a distribution function f (x) of the monthly fault rate data frequency of the power transmission line is taken as the center of a cloud concept, namely an expected value Exi；
(2) Calculate by ExiEntropy En for the desired cloud modeliObtaining a cloud model characterization function f separated based on the original transmission line month-by-month fault rate sequencei(x)；
(3) And then, subtracting the cloud model characterization function part from the monthly fault rate frequency distribution of the original power transmission line, and searching a local maximum value point at the position with the maximum amplitude. Repeating the above process until the frequency of the residual data is lower than the preset threshold;
(4) and obtaining the distribution function of each cloud model for fitting the distribution function of the monthly fault rate of the power transmission line according to the known f (x), and solving the super-entropy value of each cloud concept by using a reverse cloud algorithm without certainty.
In the above cloud conversion, the peak value E is determinedxThen, the determination of the entropy En of the cloud model is important, and can be determined by using a heuristic-based method:
firstly, finding a peak value Ex, respectively taking n test points (the number of the test points can be selected through experiments) on the left side and the right side of the Ex, respectively finding the positions of a first trough and a first peak in the left-right range of the taken test points by taking the value of the Ex as the center, and calculating whether the difference between the first trough and the first peak is greater than a threshold value preset by a user. If yes, respectively marking the found left and right side points as xleftAnd xrightAnd compare Ex-xleftAnd xrightThe magnitude of the Ex value, the smaller one being set as Ex'. Then [ Ex-Ex ', Ex + Ex']Data points in the range are used as cloud droplets, and the value of En is calculated by using a reverse cloud algorithm. In actual operation, in order to obtain a cloud model better fitting a monthly fault rate probability distribution curve of a power transmission line, the En value is also required to be adjusted slightly according to different situations.
Based on the cloud transformation process, the monthly fault rate distribution of the power transmission line can be automatically decomposed according to the distribution condition of the power transmission line to obtain a plurality of cloud concepts, and then the fitted function expression of the meteorological disaster fault rate of the power transmission line can be obtained by superposing the probability density expectation functions of the cloud concepts.
S3: jump of the cloud model. Calculating expected values, entropy values and amplitude coefficients of the comprehensive cloud concepts respectively by a plurality of normal cloud models and adopting a cloud model jump method, fitting the fault rate distribution of the power transmission line meteorological disasters, and obtaining a fitting equation of a power transmission line meteorological disaster fault rate curve
The jump of the cloud model refers to gradually merging two cloud concepts which are closest to each other in the obtained basic cloud concepts to obtain a comprehensive cloud concept after the synthesis. The specific implementation process is as follows:
is provided with C1(Ex1,En1,He1) And C2(Ex2,En2,He2) Are two adjacent basic cloud concepts on the domain U, if Ex1≤Ex2Then C is1And C2Firstly, according to a concept jump method based on a 'soft or' mode given by the literature, obtaining a comprehensive cloud concept C3(Ex3,En3,He3) The numerical characteristic values of (a) are:
He3＝max(He1,He2)
let concept C1And C2The expected curve of probability density of (c) intersects at a point d (x)d,yd) Truncation entropy En1'and En'2Is calculated as follows:
it can be seen that the comprehensive cloud obtained by the cloud synthesis method is closer to the concept with larger entropy, but the influence of the amplitude coefficient on the combination is not considered. In practical application, the concept of large amplitude coefficient should be tilted, that is, the combined integrated cloud expectation should be closer to the expectation value of large amplitude coefficient. Let the amplitude coefficients of two adjacent cloud concepts be r respectively1And r2The improved concept jump algorithm is as follows:
calculating the amplitude coefficient of the merged cloud concept as follows:
through the soft or operation, two adjacent basic cloud concepts are promoted to be comprehensive cloud concepts, the expected value, the entropy value and the amplitude coefficient of the comprehensive cloud concepts are respectively calculated, and a fitting equation of the power transmission line meteorological disaster fault rate curve is obtained:
the steps are directed at the meteorological disaster failure rate of the power transmission line, a 'segmentation-statistics-fitting' method is adopted, all line tripping accidents are classified according to regions and time, the frequency of power values in all intervals is counted, and then function fitting is carried out on the tripping times-numerical value frequency of the lines in the same region in the same time period. In order to facilitate the analysis of the power transmission line meteorological disaster fault rate, the monthly distribution of the trip times of the power transmission line in a certain region in the same period of history is taken as an object, the frequency distribution of the power transmission line meteorological disaster fault rate in different months is respectively counted, and the distribution is subjected to cloud transformation fitting to obtain a plurality of corresponding normal cloud models so as to fit the power transmission line meteorological disaster fault rate distribution. The fault rate distribution function in the longitudinal time direction of the whole year can be established, a time-dependent fault rate mathematical model is obtained, and the time-dependent fault rate mathematical model is used for reflecting the fault time distribution rules of the power transmission line in different areas, different voltage levels and different meteorological environments, so that the panoramic analysis of the power grid disaster and the meteorological disaster is realized. The method can better simulate the time-dependent fault rule of the power transmission line, and accordingly is used for predicting the fault rate of a certain period of time in the future and reducing the line tripping accidents.
Example 2:
this example is an application of example 1. In this embodiment, the fault events of the power transmission line of 220kV and above related to the meteorological environment in 2010-2017 of the power grid in Hunan are used as samples, the fitting method of the time distribution characteristics of the meteorological disaster fault of the power transmission line in embodiment 1 is adopted, the monthly-by-monthly time distribution assumption of the fault rate represented by the function is carried out, and further, the function parameter fitting is carried out.
Comparative example: the fit curve of the gaussian simulation function is plotted in fig. 2, and the goodness of fit is: the determination coefficient R-square is 0.8989.
Comparative example: the fit curve of the weibull function is plotted in fig. 3, with the goodness of fit: the determination coefficient R-square is 0.8261.
The cloud model fitting curve of this embodiment is plotted in fig. 4, and the goodness of fit is: the determination coefficient R-square is 0.9954.
Therefore, by adopting the cloud model-based power transmission line meteorological disaster fault time distribution characteristic fitting method, compared with the traditional Gaussian function and Weibull function, the historical monthly fault rate time distribution in the same period can be better fitted, the monthly fault rate distribution function is obtained, the monthly fault rate distribution function is used for simulating the time-dependent fault rule of the power transmission line, and therefore the cloud model-based power transmission line meteorological disaster fault time distribution characteristic fitting method can be used for predicting the fault rate in a certain period.
Example 3:
the present embodiment provides a computer system, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above embodiments when executing the computer program.
In summary, in order to facilitate analysis of power transmission line meteorological disaster failure rate, the power transmission line monthly failure rate statistical method starts from the periodic characteristics of meteorological influence on a power grid, statistics is carried out on the power transmission line monthly failure rate according to the same month failure events in the past year, a cloud model characteristic fitting analysis method is introduced on the basis of obtaining the month failure rate samples, frequency distribution of a power transmission line monthly failure rate sequence is decomposed into a plurality of cloud models with normal distribution for superposition, and therefore conversion from quantitative representation to qualitative concept is achieved. The fault rate distribution function in the longitudinal time direction of the whole year can be established, a time-dependent fault rate mathematical model is obtained, and the time-dependent fault rate mathematical model is used for reflecting the fault time distribution rules of the power transmission line in different areas, different voltage levels and different meteorological environments, so that the panoramic analysis of the power grid disaster and the meteorological disaster is realized.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A fitting method for time distribution characteristics of power transmission line meteorological disaster faults is characterized by comprising the following steps:
s1: the method comprises the steps of taking monthly distribution of trip times of a power transmission line in a certain region in the same period of history as an object, and respectively counting frequency distribution of meteorological disaster failure rates of the power transmission line in different months;
s2: carrying out cloud transformation fitting on the frequency distribution of the meteorological disaster failure rate of the power transmission line in different months to obtain a plurality of corresponding normal cloud models;
s3: and respectively calculating expected values, entropy values and amplitude coefficients of the comprehensive cloud concepts by a plurality of normal cloud models and adopting a jump method of the cloud models, and fitting the fault rate distribution of the meteorological disasters of the power transmission line.
2. The method for fitting time distribution characteristics of power transmission line meteorological disaster faults according to claim 1, wherein the step S1 includes:
acquiring the frequency distribution of a meteorological disaster failure rate sequence: dividing the power transmission line meteorological disaster fault rate sequence of the area into 12 intervals according to months, respectively counting the number of the power transmission line meteorological disaster fault rate sequence in each month, and solving the k-th month line trip time sequence delta PkObtaining a frequency distribution function f (x) of the power transmission line meteorological disaster failure rate.
3. The method for fitting time distribution characteristics of power transmission line meteorological disaster faults according to claim 1, wherein the step S2 includes:
for a frequency distribution function f (x) of a given transmission line monthly fault rate time sequence, a peak-based cloud transformation algorithm is adopted, and the mathematical expression of the cloud transformation algorithm is as follows:
in the formula riIs an amplitude coefficient; k is the generated discrete conceptCounting; c (Ex)i,Eni,Hei) Is one of the transformed cloud models;
the frequency distribution function of monthly fault rate data of the power transmission line on the domain of discourse is regarded as the superposition of a plurality of cloud models with normal distribution, so that the following results can be obtained:
wherein ε is a predefined maximum allowable error; f. ofi(x) A distribution function of each cloud model based on a probability density expectation function of a normal cloud, i.e., a frequency distribution function f (x).
4. The method for fitting time distribution characteristics of power transmission line meteorological disaster faults according to claim 3, wherein the peak-based cloud transformation algorithm in the step S2 comprises:
(1) the maximum value point of the maximum amplitude position in the frequency distribution function f (x) of the monthly fault rate data of the power transmission line is taken as the center of the cloud concept, namely the expected value Exi；
(2) Calculate by ExiEntropy En for the desired cloud modeliObtaining a cloud model characterization function f separated based on the original transmission line month-by-month fault rate sequencei(x)；
(3) Subtracting the cloud model characterization function part from the frequency distribution function of the monthly fault rate of the original power transmission line, and searching a local maximum value point at the maximum amplitude; repeating the above process until the frequency of the residual data is lower than the preset threshold;
(4) and obtaining the distribution function of each cloud model for fitting the distribution function of the monthly fault rate of the power transmission line according to the known f (x), and solving the super-entropy value of each cloud concept by using a reverse cloud algorithm without certainty.
5. The fitting method for time distribution characteristics of power transmission line meteorological disaster faults according to claim 4, wherein the cloud model jumping method in the step S3 is as follows: and gradually merging two cloud concepts with the shortest distance in the cloud concepts to obtain the integrated comprehensive cloud concept.
6. The method for fitting time distribution characteristics of power transmission line meteorological disaster faults according to claim 5, wherein the step S3 includes:
is provided with C1(Ex1,En1,He1) And C2(Ex2,En2,He2) Are two adjacent basic cloud concepts on the domain U, if Ex1≤Ex2Then C is1And C2Firstly, obtaining a comprehensive cloud concept C according to a concept jump method based on a 'soft or' mode3(Ex3,En3,He3) The numerical characteristic values of (a) are:
He3＝max(He1,He2)
through the operation, two adjacent basic cloud concepts are promoted into a comprehensive cloud concept;
respectively calculating expected values, entropy values and amplitude coefficients of the comprehensive cloud concept to obtain a fitting equation of the power transmission line meteorological disaster fault rate curve
7. A computer system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any of the preceding claims 1 to 6 are performed when the computer program is executed by the processor.
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| CN112785138A (en) * | 2021-01-18 | 2021-05-11 | 内蒙古电力（集团）有限责任公司呼和浩特供电局 | Method for carrying out three-span line monitoring analysis early warning based on numerical weather |
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