CN109271658A - A kind of evaluation method of the extreme meteorologic parameter design basis of nuclear power plant - Google Patents
A kind of evaluation method of the extreme meteorologic parameter design basis of nuclear power plant Download PDFInfo
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Abstract
The invention belongs to nuclear safety assessment technique fields, are related to a kind of evaluation method of extreme meteorologic parameter design basis of nuclear power plant.The evaluation method is based on distribution function inspection, includes the following steps: that (1) constructs data ordered sequence；(2) graph visualization is examined；(3) different distributions Function Fitting goodness is examined；(4) distribution function is selected；(5) design basis is determined.Utilize the evaluation method of the extreme meteorologic parameter design basis of nuclear power plant of the invention, it can be in the engineering design of the large scale industry facility for the relevant designs benchmark such as nuclear power plant and the extreme meteorology of other needs assessments, more accurately determine relevant design basic parameter, it reduces design basis and determines not conservative bring security risk, or solve the problems, such as that the overly conservative bring project cost of design basis is promoted.
Description
Technical field
The invention belongs to nuclear safety assessment technique fields, are related to a kind of evaluation of extreme meteorologic parameter design basis of nuclear power plant
Method.
Background technique
In design of nuclear power plant, in order to guarantee the safety normal operation of building and the economy of construction, it usually needs right
Some extreme meteorologic parameters are assessed, and it is horizontal to calculate the corresponding recurrence of different outcross probabilities (or reproduction interval year), from
And according to nuclear power plant's difference structures design requirement, selecting corresponding design basis, (antidetonation I class such as relevant to nuclear safety is constructed
The design basis wind of object is 3 seconds a-hundred-year extreme winds, and the design basis wind of BOP correlation structures is usually to meet for 50 years one
10 minutes mean wind speeds).
In addition, the external event risk of nuclear power plant is in widespread attention after Fukushima, Japan nuclear accident, State Bureau of Nuclear Safety will
External event probabilistic safety analysis is classified as one of the work that nuclear power plant needs to carry out for a long time.According to external event to nuclear facilities safety
Importance, its design basis is evaluated, for the external event that cannot be screened out, needs to be considered as probability theory method
External event disaster curve is established, using the input as design, external disaster probabilistic safety analysis (PSA) and contingency plan etc..
The selection of statistical distribution functions is to determine the design basis value such as meteorologic parameter, calculate meteorological external event in PSA
An important factor for disaster curve.Different distribution functions can obtain different design basis, sometimes even can be to nuclear power plant
Safety and project cost bring great influence.Thus, when evaluating design basis, selected distribution function should be examined
It tests, specifies the applicability for the extreme meteorologic parameter that the distribution function considers site of nuclear power plant.Nuclear power plant of China is in work at present
In journey design, Geng Beier or PIII Function Fitting is mostly used for extreme meteorologic parameter, and is distributed letter used by paying little attention to
The applicability of several pairs of fitting sample overall distributions.
In addition, the parameters such as the extreme temperature of different regions, extreme wind speeds and Extreme Precipitation are by physics such as Different Weathers
The influence of process may statistically defer to the different regularities of distribution.Nuclear safety guide rule HAD101/10 " site of nuclear power plant choosing
The extreme weather event selected " in point out: the evaluation for extreme wind, in most of places, Geng Beier distribution is general to be applicable in, but
By the coastal area of Tropical System high wind and the medium wind of temperate zone system, Geng Beier, Fu Leixie and mixing Fu Leixie distribution
It is applied successfully.Geng Beier distribution or Fu Leixie or lognormal point can be used in evaluation for design basis accumulated snow
Cloth.Extreme temperature generally follows Geng Beier distribution, but also has exception.Thus, in commenting for extreme meteorologic parameter design basis
In valence, analysis of testing to the degree of fitting or applicability of selected statistical distribution functions is needed, selection is best suited for data
The statistical distribution of group evaluates average reproduction gap value.
Summary of the invention
The object of the present invention is to provide a kind of evaluation methods of the extreme meteorologic parameter design basis of nuclear power plant, to be used for core
It, can be more quasi- when the engineering design of the large scale industry facility of the relevant designs benchmark such as power plant and the extreme meteorology of other needs assessments
It really determines relevant design basic parameter, reduces design basis and determine not conservative bring security risk, or solve design basis
The problem of overly conservative bring project cost is promoted.
In order to achieve this, the present invention provides a kind of extreme meteorologic parameter design of nuclear power plant in the embodiment on basis
The evaluation method of benchmark, the evaluation method are based on distribution function inspection, include the following steps:
(1) it constructs data ordered sequence: determining the extreme meteorologic parameter to be evaluated, collect gas in site of nuclear power plant region
As the relative meteorological factors of the station, data ordered sequence is constituted；
(2) graph visualization is examined: a variety of distribution functions are respectively adopted to data ordered sequence and are fitted, for every kind
The fitting analog result of distribution function is tested using graph visualization method from different perspectives；
(3) different distributions Function Fitting goodness is examined: the goodness of fit using quantitative statistics index to different distributions function
Carry out quantitative testing；
(4) distribution function is selected: comparative pattern visualizes inspection result and quantitative testing as a result, selecting the goodness of fit most
Good distribution function is as finally selected distribution function；
(5) it determines design basis: using finally selected distribution function, calculating different reproduction spacing value, and combine related
The requirement that standard directive guide determines design basis determines the design basis of corresponding extreme meteorologic parameter.
Evaluation method of the invention, can be with for solving the problems, such as the extreme meteorologic parameter design basis accurate evaluation of nuclear power plant
The typical data group collected in the region of factory site is fitted using different distributions function, and uses a variety of distribution function inspections
Index examines the selected distribution function to the suitability of data group, between selecting the optimal distribution function evaluation of degree of fitting is average to reproduce
Every value, to more accurately determine relevant design benchmark, the economy for safety and the construction of design of nuclear power plant provides guarantor
Barrier.
In a preferred embodiment, the present invention provides a kind of evaluation of extreme meteorologic parameter design basis of nuclear power plant
Method wherein in step (1), is collected the relevant weather of meteorological station at least 30 years or longer period in site of nuclear power plant region and is wanted
Element determines an extreme value year for each meteorology, constitutes one group of data sequence.
In a preferred embodiment, the present invention provides a kind of evaluation of extreme meteorologic parameter design basis of nuclear power plant
Method wherein in step (1), carries out ascending order to data sequence and arranges to obtain the required ordered sequence of maximum fitting；Or logarithm
Descending, which is carried out, according to sequence arranges to obtain the required ordered sequence of minimum fitting.
In a preferred embodiment, the present invention provides a kind of evaluation of extreme meteorologic parameter design basis of nuclear power plant
Method, wherein in step (2), a variety of distribution functions include Geng Beier distribution function, generalized extreme value distribution function,
Gamma distribution function etc..
In a preferred embodiment, the present invention provides a kind of evaluation of extreme meteorologic parameter design basis of nuclear power plant
Method wherein in step (2), for the fitting analog result of every kind of distribution function, uses graph visualization from 4 different angles
Method is tested,
First angle: the data sequence for being n for the sample size by sequence calculates the data for being located at i-th bit
Empirical cumulative frequency F*(xi)=i/ (n+1)；Using the distribution function of hypothesis, the parameter of distribution function is estimated using data sample,
Further calculate simulation cumulative frequency corresponding with ordered samples value；Empirical cumulative frequency and simulation cumulative frequency are made in one
It opens on figure, referred to as probability graph, for scatterplot closer to diagonal line, degree of fitting is better；
Second angle: using the distribution function and estimation parameter value assumed, F is calculated*(xi) the corresponding analogue value, with phase
The measured data sequence answered is made on a figure, referred to as quantile plot, and for scatterplot closer to diagonal line, degree of fitting is better；
Third angle: for given reproduction interval or not past probability, joined using the distribution function and estimation of hypothesis
Numerical value calculates different reproduction interval or the analogue value corresponding not past probability, the analogue value and reproduction interval is drawn with curve mode
It is drawn on same figure on a figure, and by measured value with corresponding empirical cumulative frequency scatterplot, referred to as recurrence level view,
For simulation curve closer to actual measurement scatterplot, degree of fitting is better；
4th angle: measured data is grouped, the frequency of every group of appearance is counted, corresponding probability density is calculated, with column
Shape figure is shown；Phase is calculated for given extreme meteorologic parameter observation scope using the distribution function and estimation parameter value of hypothesis
The analog probability density answered, is superimposed upon on histogram；Comparative simulation probability density curve and actual measurement probability density, the two kiss
It is better to close, and degree of fitting is more excellent.
In a preferred embodiment, the present invention provides a kind of evaluation of extreme meteorologic parameter design basis of nuclear power plant
Method, wherein in step (3), the quantitative statistics index of use includes poor fit standard, Andrei Kolmogorov statistic and Pierre
Inferior method of inspection, these quantitative statistics indexs are smaller, show that degree of fitting is better.
In a kind of more preferred embodiment, the present invention provides a kind of extreme meteorologic parameter design basis of nuclear power plant
Evaluation method, wherein in step (4), what selection was examined by Pearson came, and fit standard difference and Andrei Kolmogorov statistic
Small as far as possible, the best distribution function of the goodness of fit is as finally selected distribution function.
The beneficial effects of the present invention are utilize the evaluation side of the extreme meteorologic parameter design basis of nuclear power plant of the invention
Method, can be in the engineering of the large scale industry facility for the relevant designs benchmark such as nuclear power plant and the extreme meteorology of other needs assessments
When design, relevant design basic parameter is more accurately determined, reduce design basis and determine not conservative bring security risk, or solution
The problem of certainly overly conservative bring project cost of design basis is promoted.
Evaluation method of the invention is fitted data sample using different distributions function, distinguishes fitting result
Determine that the goodness of fit, selection are best suited for data sample by the way of graph visualization inspection and quantitative statistics index test
The fitting distribution function of group calculates the design basis of the extreme meteorologic parameter of nuclear power plant.Such method can more precisely really
The design basis for determining the extreme meteorologic parameter of nuclear power plant has the safety and economy that ensure nuclear power plant project design important
Effect.
Detailed description of the invention
Fig. 1 is the flow chart of the evaluation method of the illustrative extreme meteorologic parameter design basis of nuclear power plant of the invention.
Fig. 2 is the probability graph of Geng Beier fitting of distribution Extreme Maximum Temperature in specific embodiment citing, quantile plot, returns
Return level view and probability density figure.
Fig. 3 be in specific embodiment citing generalized extreme value distribution be fitted the probability graph of Extreme Maximum Temperature, quantile plot,
Return level view and probability density figure.
Fig. 4 is the probability graph of Gamma fitting of distribution Extreme Maximum Temperature in specific embodiment citing, quantile plot, returns
Return level view and probability density figure.
Specific embodiment
A specific embodiment of the invention is further illustrated below in conjunction with attached drawing.
The process of the evaluation method of the illustrative extreme meteorologic parameter design basis of nuclear power plant of the invention as shown in Figure 1,
Include the following steps.
(1) data ordered sequence is constructed
Determine the extreme meteorologic parameter to be evaluated, meteorological station at least 30 years or longer period in collection factory site region
Relative meteorological factors determine an extreme value year for each meteorology, constitute one group of data sequence.Ascending order row is carried out to data sequence
Column, ordered sequence needed for obtaining maximum fitting；Or descending arrangement is carried out to data sequence, obtaining has needed for minimum fitting
Sequence sequence.
(2) graph visualization is examined
To this group of data ordered sequence be respectively adopted a variety of distribution functions (such as Geng Beier distribution, generalized extreme value distribution,
Gamma distribution etc.) it is fitted.It is visual using figure from 4 different angles for the fitting analog result of every kind of distribution function
Change method is tested.
First angle: the data sequence for being n for the sample size by sequence calculates the data for being located at i-th bit
Empirical cumulative frequency F*(xi)=i/ (n+1).Using the distribution function of hypothesis, the parameter of distribution function is estimated using data sample,
Further calculate simulation cumulative frequency corresponding with ordered samples value.Empirical cumulative frequency and simulation cumulative frequency are made in one
It opens on figure, referred to as probability graph.For scatterplot closer to diagonal line, degree of fitting is better.
Second angle: using the distribution function and estimation parameter value assumed, F is calculated*(xi) the corresponding analogue value, with phase
The measured data sequence answered is made on a figure, referred to as quantile plot.For scatterplot closer to diagonal line, degree of fitting is better.
Third angle: for given reproduction interval or not past probability, joined using the distribution function and estimation of hypothesis
Numerical value calculates different reproduction interval (or not past probability) corresponding analogue value, by the analogue value and reproduction interval with curve mode
It is drawn on a figure, and measured value is drawn on same figure with corresponding empirical cumulative frequency scatterplot, referred to as return horizontal
Figure.For simulation curve closer to actual measurement scatterplot, degree of fitting is better.
4th angle: measured data is grouped, the frequency of every group of appearance is counted, corresponding probability density is calculated, with column
Shape figure is shown.Phase is calculated for given extreme meteorologic parameter observation scope using the distribution function and estimation parameter value of hypothesis
The analog probability density answered, is superimposed upon on histogram.Comparative simulation probability density curve and actual measurement probability density, the two kiss
It is better to close, and degree of fitting is more excellent.
(3) different distributions Function Fitting goodness is examined
Quantitative testing is carried out to the goodness of fit of different distributions function using quantitative statistics index.Common quantitative statistics refer to
Indicate poor fit standard, Andrei Kolmogorov statistic and Pearson came method of inspection.These statistical indicators are smaller, then show degree of fitting
Better.
Fit standard difference σ:
Wherein XiFor i-th of ordered sample,For corresponding match value, n is sample size.
Andrei Kolmogorov statistic Dn: Dn=Max | F (Xi)-F*(Xi) |, i=1 ..., n
Wherein F (Xi) it is theoretic distribution function, F*(Xi) it is empirical distribution function, DnIt indicates to fit the theoretical distribution come
With the maximum deviation of experience distribution.
Pearson came method of inspection: being divided into k group for measured data sample, and the frequency in each group is mi, construct a statistic:
Wherein PiThe probability in i-th group is fallen in for data.For given level of significance α, if χ2(wherein less than k-1-l
L be assume distribution function in estimate parameter number) freedom degree χ2It is distributed 1- α quartile point value, then shows to examine by Pearson came
It tests, it is assumed that distribution function can be used to the overall distribution of approximate data sample.
(4) distribution function is selected
Comparative pattern visualization inspection result and quantitative testing are as a result, selection is examined by Pearson came, and fit standard
Difference and Andrei Kolmogorov statistic are small as far as possible, and the best distribution function of the goodness of fit is as finally selected distribution function.
(5) design basis is determined
Using finally selected distribution function, different reproduction spacing value is calculated, and combines relevant criterion directive/guide to design base
Accurately fixed requirement determines the design basis of corresponding extreme meteorologic parameter.
The applicating example of the evaluation method of the extreme meteorologic parameter design basis of the nuclear power plant of the invention of above-mentioned example is such as
Under (by taking the evaluation of the Extreme Maximum Temperature design basis of south China Coastal Nuclear Power Plant as an example).
(1) highest temperature data of several weather stations since building a station in the region of factory site has been collected, first with wherein a certain
For weather station, totally 53 years highest temperature data since build a station take a maximum, constituting one group includes 53 every year
The sequence of a year highest temperature carries out ascending order arrangement to the sequence, obtains the station Extreme Maximum Temperature sequence, be shown in Table 1.
Extreme maximum temperature sequence of the table 1 by sequence
(2) to this group of Extreme Maximum Temperature sequence, Geng Beier distribution, generalized extreme value distribution and Gamma distribution is respectively adopted
Function is fitted, and specific approximating method and method for parameter estimation can be with reference statistical pertinent texts (such as " climatic statistics principles
With method ", the chief editor such as Ma Kaiyu, Meteorology Publishing House；"An Introduction to Statistical Modeling of
Extreme Values ", Stuart Coles, Springer).For the fitting result of every kind of distribution function, make experience respectively
Cumulative frequency-simulation cumulative frequency probability graph, the quantile plot of the analogue value-measured value corresponding with empirical cumulative frequency, again
The recurrence level view of current-simulation regressand value, and the probability density figure of actual measurement and simulation.Fig. 2, Fig. 3 and Fig. 4 are set forth
Above-mentioned four figures being fitted using Geng Beier distribution, generalized extreme value distribution and Gamma distribution function.It can see Geng Beier
It is distributed obtained probability graph and quantile plot has all deviated considerably from diagonal line, from level view is returned as it can be seen that Geng Beier distribution simulation
Obtained longer corresponding extreme maximum temperature current again is deviated significantly from measured value, hence it is evident that has over-evaluated extreme maximum temperature.
In addition, the probability density of actual measurement and simulation also has significant deviation.From four figures as it can be seen that Geng Beier distribution function is to the pole at the station
Hold highest temperature degree of fitting poor, i.e., the station Extreme Maximum Temperature sample sequence does not meet Geng Beier distribution.For other two kinds
Distribution, from probability graph and quantile plot as it can be seen that scatterplot returns level view and show simulation curve and actual measurement scatterplot kiss close to diagonal line
It closes very well, the probability density histogram of measured value is corresponding with the probability density curve of the analogue value very well, shows generalized extreme value distribution
It is distributed with Gamma preferable to the degree of fitting of sample sequence.
(3) for the fitting result of three kinds of distribution functions, fit standard difference and Andrei Kolmogorov statistic are calculated separately.
Pearson came is examined, actual measurement sample sequence is divided into 34.5~35.5 DEG C, 35.5~36.5 DEG C, 36.5~37.5 DEG C, 37.5
~38.5 DEG C, 38.5~39.5 DEG C, 39.5~40.5 DEG C totally 6 groups, the actual frequency of each group sample is respectively 1,4,13,20,13
With 2.Calculate separately the theoretical frequency of three kinds of distribution functions, the theoretical frequency of honest and just Baire function is respectively 0.127,4.893,
16.079,15.879,9.067 and 4.120；The theoretical frequency of generalized extreme value distribution function are as follows: 0.558,4.055,12.851,
19.110,13.321,3.070；The theoretical frequency of Gamma distribution function are as follows: 0.384,3.752,13.726,19.860,
11.869 with 3.035.Thus the Pearson came test value point of honest and just Baire function, generalized extreme value distribution function and Gamma function is obtained
It Wei 10.5955,0.7737 and 1.5032.The degree of fitting inspection result of three kinds of distribution functions is shown in such as the following table 2.
The degree of fitting inspection result of 2 three kinds of distribution functions of table
The estimation number of parameters of Geng Beier distribution, generalized extreme value distribution and Gamma distribution is respectively 2,3,2.It is thus right
The χ answered2Being distributed freedom degree is respectively 3,2 and 3.For 0.05 significance, the corresponding χ of three distribution functions20.95 point of distribution
Point value (i.e. the critical value of Pearson came inspection) is respectively 7.8147,5.9915 and 7.8147.As it can be seen that except Geng Beier distribution is not led to
It crosses outside Pearson came inspection, in addition two kinds of distribution functions pass through inspection.
(4) in comparison diagram 2- Fig. 4 Graphics testing result and table 2 quantitative testing of three distribution functions as a result, generalized extreme value
The fit standard of distribution function is poor, Andrei Kolmogorov statistic and Pearson came test value are minimum, in three distribution functions
It is optimal to the degree of fitting of measured data, and passed through Pearson came inspection, thus select generalized extreme value distribution function should as calculating
The preferred distribution of different reproduction of standing interval extreme maximum temperature.
(5) generalized extreme value distribution function is used, 50 years one chances in the station, 100 years chances, 200 years chances and 500 is calculated
The Extreme Maximum Temperature that year one meets is respectively as follows: 39.9 DEG C, 40.0 DEG C, 40.1 DEG C and 40.3 DEG C.It is generally selected in nuclear power station design
Design basis of the a-hundred-year value as Extreme Maximum Temperature, it is contemplated that the actual measurement highest temperature occurs 40.2 DEG C, according to the world
The case where newest " nuclear facilities Site Evaluating " requirement of International Atomic Energy Agency is more than calculated value for measured value, to it is external from
The right conservative angle considered of event, design basis answer envelope measured value.Thus, the Extreme Maximum Temperature design basis in the factory site is answered
On the basis of comprehensively considering the Fitting Calculation result and history measured value of each weather station in factory site periphery, selection most envelope
Property or representative value.
Obviously, various changes and modifications can be made to the invention without departing from essence of the invention by those skilled in the art
Mind and range.If in this way, belonging to the model of the claims in the present invention and its equivalent technology to these modifications and changes of the present invention
Within enclosing, then the present invention is also intended to include these modifications and variations.Above embodiment only illustrates to of the invention
Bright, the present invention can also be implemented with other ad hoc fashions or other particular forms, without departing from the gist of the invention or originally
Matter feature.Therefore, the embodiment of description is regarded as illustrative and non-limiting in any way.Model of the invention
Enclosing should be illustrated by appended claims, and any variation equivalent with the intention and range of claim should also be included in the present invention
In the range of.
Claims (7)
1. a kind of evaluation method of the extreme meteorologic parameter design basis of nuclear power plant, which is characterized in that the evaluation method is based on
Distribution function inspection includes the following steps:
(1) it constructs data ordered sequence: determining the extreme meteorologic parameter to be evaluated, collect meteorological observatory in site of nuclear power plant region
The relative meteorological factors stood constitute data ordered sequence；(2) graph visualization is examined: data ordered sequence being respectively adopted more
Kind distribution function is fitted, and for the fitting analog result of every kind of distribution function, uses graph visualization side from different perspectives
Method is tested；
(3) different distributions Function Fitting goodness is examined: being carried out using quantitative statistics index to the goodness of fit of different distributions function
Quantitative testing；
(4) distribution function is selected: comparative pattern visualizes inspection result and quantitative testing as a result, the selection goodness of fit is best
Distribution function is as finally selected distribution function；
(5) it determines design basis: using finally selected distribution function, calculating different reproduction spacing value, and combine relevant criterion
The requirement that directive/guide determines design basis determines the design basis of corresponding extreme meteorologic parameter.
2. evaluation method according to claim 1, it is characterised in that: in step (1), collect gas in site of nuclear power plant region
As the station at least 30 years or the relative meteorological factors of longer period, an extreme value is determined in year for each meteorology, constitutes one group of number
According to sequence.
3. evaluation method according to claim 1, it is characterised in that: in step (1), carry out ascending order arrangement to data sequence
Ordered sequence needed for obtaining maximum fitting；Or descending is carried out to data sequence and arranges to obtain the required orderly sequence of minimum fitting
Column.
4. evaluation method according to claim 1, it is characterised in that: in step (2), a variety of distribution functions include
Geng Beier distribution function, generalized extreme value distribution function, Gamma distribution function.
5. evaluation method according to claim 1, it is characterised in that: the fitting in step (2), for every kind of distribution function
Analog result is tested from 4 different angles using graph visualization method,
First angle: the data sequence for being n for the sample size by sequence calculates the experience for being located at the data of i-th bit
Cumulative frequency F*(xi)=i/ (n+1)；Using the distribution function of hypothesis, using the parameter of data sample estimation distribution function, then into
One step calculates simulation cumulative frequency corresponding with ordered samples value；Empirical cumulative frequency and simulation cumulative frequency are made in a figure
On, referred to as probability graph, for scatterplot closer to diagonal line, degree of fitting is better；
Second angle: using the distribution function and estimation parameter value assumed, F is calculated*(xi) the corresponding analogue value, and it is corresponding
Measured data sequence is made on a figure, referred to as quantile plot, and for scatterplot closer to diagonal line, degree of fitting is better；
Third angle: for given reproduction interval or not past probability, using the distribution function and estimation parameter value of hypothesis,
Different reproduction interval or the analogue value corresponding not past probability are calculated, the analogue value and reproduction interval are drawn in one with curve mode
On figure, and measured value is drawn on same figure with corresponding empirical cumulative frequency scatterplot, referred to as recurrence level view, simulation is bent
For line closer to actual measurement scatterplot, degree of fitting is better；
4th angle: measured data is grouped, the frequency of every group of appearance is counted, corresponding probability density is calculated, with histogram
Display；Given extreme meteorologic parameter observation scope is calculated corresponding using the distribution function and estimation parameter value of hypothesis
Analog probability density, is superimposed upon on histogram；Comparative simulation probability density curve and actual measurement probability density, the two are coincide more
Good, degree of fitting is more excellent.
6. evaluation method according to claim 1, it is characterised in that: in step (3), the quantitative statistics index of use includes
Fit standard is poor, Andrei Kolmogorov statistic and Pearson came method of inspection, these quantitative statistics indexs are smaller, show that degree of fitting is got over
It is good.
7. evaluation method according to claim 6, it is characterised in that: in step (4), what selection was examined by Pearson came,
And fit standard difference and Andrei Kolmogorov statistic are small as far as possible, the best distribution function of the goodness of fit is as finally selected
Distribution function.
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