CN115510382B - Comprehensive output coefficient calculation method based on discrete time-varying relation function set - Google Patents

Comprehensive output coefficient calculation method based on discrete time-varying relation function set Download PDF

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CN115510382B
CN115510382B CN202211395950.XA CN202211395950A CN115510382B CN 115510382 B CN115510382 B CN 115510382B CN 202211395950 A CN202211395950 A CN 202211395950A CN 115510382 B CN115510382 B CN 115510382B
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time
varying
discrete time
comprehensive
output coefficient
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CN115510382A (en
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聂强
张明波
缪益平
朱成涛
黄光伦
徐长江
欧阳硕
成建军
郑钰
秦凯
宋涛
杜涛
史东华
唐圣钧
熊世川
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Hubei Yifang Technology Development Co ltd
Bureau of Hydrology Changjiang Water Resources Commission
Yalong River Hydropower Development Co Ltd
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Hubei Yifang Technology Development Co ltd
Bureau of Hydrology Changjiang Water Resources Commission
Yalong River Hydropower Development Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F17/10—Complex mathematical operations
    • G06F17/15—Correlation function computation including computation of convolution operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention provides a comprehensive output coefficient calculation method based on a discrete time-varying relation function set, which comprises the steps of establishing a comprehensive output coefficient historical state set, analyzing time-varying sensitivity based on the historical state set, constructing a comprehensive influence factor discrete time-varying relation, establishing a discrete time-varying relation function, checking and screening the time-varying relation function; according to historical operation data, multiple influence factors and power generation plan compilation requirements are synthesized, two types of comprehensive output coefficient historical state sets, namely a necessary state variable set and a supplementary state variable set, are constructed, and a discrete time-varying relation function set based on a historical state is fitted, so that the simplicity and the applicability of the comprehensive output coefficient calculation method are improved, the calculation precision of simulated dispatching output is improved, and technical support is provided for improving power generation plan compilation efficiency and precision, optimizing watershed water resource comprehensive utilization benefits and improving water resource comprehensive benefits.

Description

Comprehensive output coefficient calculation method based on discrete time-varying relation function set
Technical Field
The invention relates to the field of power generation dispatching of watershed cascade hydropower stations, in particular to a comprehensive output coefficient calculation method based on a discrete time-varying relation function set.
Background
Complex coefficient of hydroelectric power plant
Figure 576727DEST_PATH_IMAGE001
The comprehensive index is a comprehensive index reflecting the generated energy, the generated water consumption and the average water head of a hydropower plant, and is obtained by calculating the comprehensive efficiency of the hydropower station by multiplying 9.81, the comprehensive efficiency of the hydropower station is determined by the efficiency of a water turbine and the efficiency of a generator, and the comprehensive coefficient of the hydropower station is usually 8.5-8.8 in the design of the hydropower station. Along with the gradual construction and operation of a plurality of large hydropower bases at the upper reaches of Yangtze river in China, the comprehensive output coefficient of the large hydropower station can often reach over 9.0 according to the accumulated kinetic energy index data of the actual operation of the controllable reservoir and the hydropower station, and is related to various factors such as the regulation and storage of the upper reservoir, the water level in front of the dam, the natural incoming water and the like. Taking the first-class and second-beach water power stations of the Yangtze river creeper as an example, the designed comprehensive output coefficient is only 8.5, and from the operation data of recent years, the comprehensive output coefficient of 9-10 months in one year exceeds 8.8.
At present, in order to overcome the defect that the comprehensive output coefficient in the traditional output calculation takes a fixed value, refined output calculation and variation are mostly adopted in domestic and foreign researches
Figure 310328DEST_PATH_IMAGE001
The value output calculation method comprises the steps that refined output calculation does not use a comprehensive output coefficient to calculate the output of a hydropower station, but determines the optimal output combination of a unit according to factors such as the running state of a unit group in the comprehensive station, the running condition of the hydropower station and the like, and is usually used for short-term power generation dispatching output calculation in the daytime; become and/or>
Figure 391416DEST_PATH_IMAGE001
Value output calculation method usually combines historical operation data, considers key factors such as water head, power generation flow and warehousing flow and the like and combines the key factors>
Figure 210468DEST_PATH_IMAGE001
The value influence is more in application in the medium-and-long-term power generation scheduling research, but has a plurality of influence factors, a certain method is only suitable for the limitation of a certain type of hydropower station, the technical method is complex, and the applicability in the actual power generation planning needs to be further researched.
The problems existing in the prior art are as follows:
in the prior art, when the influence factors of the comprehensive output coefficient are considered, specific influence factor combinations are selected from various factors such as scheduling period, water head, power generation flow and warehousing flow according to demands, but the factors such as power grid maintenance, unit maintenance and upstream reservoir regulation and storage are considered less, and the limitation that the method is only suitable for a certain type of hydropower station exists.
In the prior art, when a problem is solved by calculating output, a complex optimization method is often adopted, multiple parameters such as a water head and a power generation flow are selected to carry out optimization and calibration on a comprehensive output coefficient, and the water head and the power generation flow are mutually iterated with the operation state of a hydropower station, so that the situation that the mutual iteration exists between the water head and the power generation flow needs to be determined
Figure 707308DEST_PATH_IMAGE001
And complicated optimization calculation is performed before the value, so that the requirement of actual power generation planning is difficult to adapt.
Disclosure of Invention
The invention aims to provide a comprehensive output coefficient calculation method based on a discrete time-varying relation function set aiming at the defects of the prior art, so that the generation planning efficiency and precision are improved, and the comprehensive utilization benefit of watershed water resources is optimized.
In order to realize the purpose, the invention adopts the following technical scheme:
the invention provides a method for calculating a comprehensive output coefficient based on a discrete time-varying relation function set, which comprises the following steps of:
s1, establishing a comprehensive output coefficient historical state set;
extracting comprehensive output coefficient according to historical water and electricity operating data
Figure 685628DEST_PATH_IMAGE002
As target status variable->
Figure 45940DEST_PATH_IMAGE003
Hydro head->
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As a requisite status variable->
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And the water level before the dam of the hydropower station>
Figure 485646DEST_PATH_IMAGE006
Downstream water level->
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And the warehouse-in flow->
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And the power generation flow is greater or less>
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And/or a force->
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Is supplemented status variable->
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Establishing said integrated force contribution factor->
Figure 40572DEST_PATH_IMAGE012
Time-segment-wise historical state set->
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；
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Wherein the content of the first and second substances,
Figure 386778DEST_PATH_IMAGE015
the number of time periods is historical data;
s2, time-varying sensitivity analysis based on historical state set
Analyzing the comprehensive output coefficient from the historical operation data of the hydropower station
Figure 279648DEST_PATH_IMAGE016
The time-dependent change trend identifies the integrated force change time scale->
Figure 460094DEST_PATH_IMAGE017
Trend->
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And degree->
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Is based on the combined force variation characteristic indicator->
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(ii) a Necessary for matching analysisStatus->
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Complementary status variable>
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And the combined force coefficient->
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Synchronous change rate of (1), extracting key influence factor
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Set, constructing a library of time-varying sensitivity analyses>
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；
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Wherein, the first and the second end of the pipe are connected with each other,
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is a key influence factor; />
Figure 109753DEST_PATH_IMAGE028
Is the number of key influencing factors.
Further, still include:
s3, constructing a discrete time-varying relation of the comprehensive influence factors;
analyzing the key influence factors at different time scales
Figure 701271DEST_PATH_IMAGE029
Collecting and comparing said combined contribution factor>
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Influence of a change in relation to the integrated force change characteristic criterion->
Figure 250381DEST_PATH_IMAGE031
Determining discrete time step length by combining with power generation scheduling application precision requirement
Figure 702222DEST_PATH_IMAGE032
And a number of epochs within a scheduling period>
Figure 831852DEST_PATH_IMAGE033
Thereby constructing a discrete time step->
Figure 177383DEST_PATH_IMAGE034
In the following, the key influencing factor->
Figure 671949DEST_PATH_IMAGE035
Collecting and combining said integrated force contribution factor->
Figure 673403DEST_PATH_IMAGE036
The influence relationship of a change->
Figure 403462DEST_PATH_IMAGE037
；
Figure 977400DEST_PATH_IMAGE038
Wherein the content of the first and second substances,
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is->
Figure 990673DEST_PATH_IMAGE040
Individual key influencing factor and integrated force contribution factor->
Figure 665368DEST_PATH_IMAGE041
A set of correlation coefficients of (a); />
Figure 923174DEST_PATH_IMAGE042
Is->
Figure 618597DEST_PATH_IMAGE040
A key influencing factor>
Figure 532327DEST_PATH_IMAGE043
And the integrated output coefficient->
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The correlation coefficient of (2).
Further, still include:
s4, establishing a discrete time-varying relation function
Fitting to form a reflection
Figure 716500DEST_PATH_IMAGE045
Discrete time-varying relation function of relation
Figure 989350DEST_PATH_IMAGE046
Wherein the content of the first and second substances,
Figure 983851DEST_PATH_IMAGE047
selecting a linear curve, a power curve, a polynomial curve, an exponential curve and a logarithmic curve for fitting the curve type number; />
Figure 62665DEST_PATH_IMAGE048
To represent a library of discrete time-varying relationships.
Further, the polynomial curve is: :
Figure 731282DEST_PATH_IMAGE049
wherein the content of the first and second substances,
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is a first->
Figure 656829DEST_PATH_IMAGE051
A historical data period>
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And the fifth->
Figure 95081DEST_PATH_IMAGE053
Key influencing factor->
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A discrete time-varying relation function of (a); />
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、/>
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、/>
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Is a parameter of a polynomial relationship function; />
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In cases of 1 to->
Figure 250174DEST_PATH_IMAGE059
Number of or>
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The relationship (2) is a comprehensive value obtained by a weighting method.
Further, still include:
s5, checking and screening time-varying relation functions
Dividing the discrete time step
Figure 4820DEST_PATH_IMAGE061
The operation data of the average water head and the output of the lower time interval are brought into a discrete time-varying relation function>
Figure 23592DEST_PATH_IMAGE062
And calculating and fitting the comprehensive output coefficient->
Figure 842643DEST_PATH_IMAGE063
Sequence, and selecting error characteristic value as evaluation index, and selecting discrete time-varying relation function->
Figure 401800DEST_PATH_IMAGE064
The middle fitting relation function is tested, and finally the time-interval-by-time screening can reflect the current key influence factor and the comprehensive output coefficient->
Figure 317804DEST_PATH_IMAGE065
Changed fifth/or fifth>
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Optimal time-varying relation function->
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。
Further, the error characteristic value includes an absolute error
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Relative error->
Figure 117821DEST_PATH_IMAGE069
Standard deviation, root mean square error
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And a certainty factor->
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。
Further, the absolute error and the root mean square error are taken as error characteristic values:
if it is
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Figure 532436DEST_PATH_IMAGE073
，
If it is
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/>
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Wherein the content of the first and second substances,
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、/>
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is a first->
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Each key influence factor corresponds to>
Figure 294911DEST_PATH_IMAGE079
Absolute error, root mean square error, of the fitting process>
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、/>
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Are respectively based on>
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Absolute error, root mean square error of the fitting process;
if it is first
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Quasi-curve, fifth or fifth curve>
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The variety key influence factor corresponds to->
Figure 704343DEST_PATH_IMAGE085
The fitting process is optimal, will >>
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Is selected as>
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Optimal time-varying relation function->
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；
The optimal time-varying relation functions are combined time-wise to finally form a discrete time-varying relation function set
Figure 832574DEST_PATH_IMAGE089
,
Figure 125015DEST_PATH_IMAGE090
Figure 247692DEST_PATH_IMAGE091
。
Further, the integrated output change time scale
Figure 879662DEST_PATH_IMAGE092
Including season, month, ten days, day; the integrated output change time scale->
Figure 62381DEST_PATH_IMAGE093
Greater than historical data period number>
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。
Further, the discrete time step size
Figure 378273DEST_PATH_IMAGE095
The method is a single time scale of seasons, months, ten days and days, and can be expanded into a response relation set with multiple time step lengths according to needs.
The invention has the beneficial effects that: according to historical operation data, multiple influence factors and power generation plan compilation requirements are synthesized, two types of comprehensive output coefficient historical state sets, namely a necessary state variable set and a supplementary state variable set, are constructed, and a discrete time-varying relation function set based on a historical state is fitted, so that the simplicity and the applicability of the comprehensive output coefficient calculation method are improved, the calculation precision of simulated dispatching output is improved, and technical support is provided for improving power generation plan compilation efficiency and precision, optimizing watershed water resource comprehensive utilization benefits and improving water resource comprehensive benefits.
The established historical state set of the comprehensive output coefficient can comb historical operation data with a multilevel relation in the comprehensive output calculation into a three-layer state structure of the essential-supplement-target through classification of three types of state variables, namely target state variables, essential state variables, supplement state variables and the like.
The time-varying relation concept can couple the complex relation of multi-factor influence in the water head output relation to the time-varying relation of the unified comprehensive influence factor, can comprehensively reflect the influence of other factors such as upstream reservoir regulation and storage, hydropower station operation rule, unit maintenance, power grid maintenance and the like besides the water head and the power generation flow, can effectively reduce the variables in the comprehensive output calculation, and increases the generalization capability and the fitting capability of the invention.
The constructed discrete time-varying relation function set combines the multi-factor influence effect and the analysis result of the power generation dispatching application precision and the time-varying sensitivity to refine the dispatching period into a dispatching period
Figure 989383DEST_PATH_IMAGE096
In each time period, the calculation precision of the output of the analog dispatching is improved, and the conciseness and the applicability of the calculation method of the comprehensive output coefficient are also considered.
Drawings
FIG. 1 is a flowchart of a method for calculating a comprehensive output coefficient based on a discrete time-varying relationship function set.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a method for calculating a comprehensive output coefficient based on a discrete time-varying relation function set, which comprises the following steps of:
s1, establishing a comprehensive output coefficient historical state set;
extracting comprehensive output coefficient according to historical water and electricity operation data
Figure 546266DEST_PATH_IMAGE002
As target status variable->
Figure 219824DEST_PATH_IMAGE003
Water head->
Figure 215462DEST_PATH_IMAGE004
As a requisite status variable->
Figure 618762DEST_PATH_IMAGE005
And the water level before the dam of the hydropower station>
Figure 517185DEST_PATH_IMAGE006
Downstream water level->
Figure 68252DEST_PATH_IMAGE007
And the warehouse-in flow->
Figure 477368DEST_PATH_IMAGE008
And the power generation flow is greater or less>
Figure 735174DEST_PATH_IMAGE009
And/or a force->
Figure 430598DEST_PATH_IMAGE010
Is supplemented status variable->
Figure 344327DEST_PATH_IMAGE011
Establishing said integrated force contribution factor->
Figure 619451DEST_PATH_IMAGE012
Time interval by time intervalHistory status set->
Figure 528501DEST_PATH_IMAGE013
；
Figure 801350DEST_PATH_IMAGE014
Wherein, the first and the second end of the pipe are connected with each other,
Figure 795851DEST_PATH_IMAGE015
the number of time segments is historical data;
s2, time-varying sensitivity analysis based on historical state set
Analyzing the comprehensive output coefficient from the historical operation data of the hydropower station
Figure 874665DEST_PATH_IMAGE016
On the time-dependent change trend, a combined force change time scale is identified>
Figure 277703DEST_PATH_IMAGE017
Trend->
Figure 580508DEST_PATH_IMAGE018
And degree->
Figure 265567DEST_PATH_IMAGE019
Is based on the combined force variation characteristic indicator->
Figure 554597DEST_PATH_IMAGE020
(ii) a Match analysis prerequisite status>
Figure 703819DEST_PATH_IMAGE021
Based on the status variable>
Figure 52892DEST_PATH_IMAGE022
And the combined force coefficient->
Figure 225247DEST_PATH_IMAGE023
Synchronous rate of change ofExtracting key influencing factors
Figure 442602DEST_PATH_IMAGE024
Collection, construction of a library of time-varying sensitivity assays>
Figure 321696DEST_PATH_IMAGE025
；
Figure 966304DEST_PATH_IMAGE026
Wherein the content of the first and second substances,
Figure 625956DEST_PATH_IMAGE027
is a key influence factor; />
Figure 755323DEST_PATH_IMAGE028
Is the number of key influencing factors.
Further comprising:
s3, constructing a discrete time-varying relation of the comprehensive influence factors;
analyzing the key influence factors at different time scales
Figure 879137DEST_PATH_IMAGE029
Collecting and comparing the integrated output coefficient>
Figure 304433DEST_PATH_IMAGE030
Influence of a change in relation to the integrated force change characteristic criterion->
Figure 451381DEST_PATH_IMAGE031
Determining discrete time step length by combining with power generation scheduling application precision requirement
Figure 10538DEST_PATH_IMAGE032
And the number of time periods within the scheduling period>
Figure 598646DEST_PATH_IMAGE033
Thereby constructing a discrete time step->
Figure 850635DEST_PATH_IMAGE034
In the following, the key influencing factor->
Figure 891404DEST_PATH_IMAGE035
Collecting and combining said integrated force contribution factor->
Figure 191935DEST_PATH_IMAGE036
The influence relationship of a change->
Figure 24762DEST_PATH_IMAGE037
；
Figure 821554DEST_PATH_IMAGE038
Wherein the content of the first and second substances,
Figure 474253DEST_PATH_IMAGE039
is->
Figure 578475DEST_PATH_IMAGE040
Individual key influencing factor and integrated force contribution factor->
Figure 141174DEST_PATH_IMAGE041
A set of correlation coefficients of (a); />
Figure 469387DEST_PATH_IMAGE042
Is->
Figure 750327DEST_PATH_IMAGE040
A key influencing factor>
Figure 189399DEST_PATH_IMAGE043
And the integrated output coefficient->
Figure 934501DEST_PATH_IMAGE044
The correlation coefficient of (2). />
Further comprising:
s4, establishing a discrete time-varying relation function
Fitting to form a reflection
Figure 308982DEST_PATH_IMAGE045
Discrete time-varying relation function of relation
Figure 201851DEST_PATH_IMAGE046
Wherein the content of the first and second substances,
Figure 818515DEST_PATH_IMAGE047
selecting a linear curve, a power curve, a polynomial curve, an exponential curve and a logarithmic curve for fitting the curve type number; />
Figure 214861DEST_PATH_IMAGE048
To represent a library of discrete time-varying relationship functions.
The polynomial curve is: :
Figure 88139DEST_PATH_IMAGE049
wherein the content of the first and second substances,
Figure 343671DEST_PATH_IMAGE050
is a first->
Figure 390125DEST_PATH_IMAGE051
A historical data period>
Figure 844240DEST_PATH_IMAGE052
And the fifth->
Figure 826102DEST_PATH_IMAGE053
Key influencing factor->
Figure 631247DEST_PATH_IMAGE054
Discrete time-varying relation functions of (a); />
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、/>
Figure 727696DEST_PATH_IMAGE056
、/>
Figure 739515DEST_PATH_IMAGE057
Is a parameter of a polynomial relationship function; />
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Is taken according to the ratio of 1 to->
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Is/are>
Figure 19558DEST_PATH_IMAGE060
The relationship (c) is a comprehensive value obtained by a weighting method.
S5, checking and screening time-varying relation functions
Step the discrete time
Figure 343223DEST_PATH_IMAGE061
The operation data of the average water head and the output of the lower time interval are brought into a discrete time-varying relation function>
Figure 857381DEST_PATH_IMAGE062
In (1), evaluating a fit of the combined contribution factor +>
Figure 783749DEST_PATH_IMAGE063
Sequence, and selecting error characteristic value as evaluation index, and selecting discrete time-varying relation function->
Figure 270225DEST_PATH_IMAGE064
The middle fitting relation function is tested, and finally the time-interval-by-time screening can reflect the current key influence factor and the comprehensive output coefficient->
Figure 623846DEST_PATH_IMAGE065
Changed first->
Figure 625300DEST_PATH_IMAGE066
Optimal time-varying relation function->
Figure 496304DEST_PATH_IMAGE067
。
The error characteristic value comprises an absolute error
Figure 696341DEST_PATH_IMAGE068
Relative error->
Figure 424126DEST_PATH_IMAGE069
Standard deviation, root mean square error->
Figure 349094DEST_PATH_IMAGE070
And a certainty factor->
Figure 617265DEST_PATH_IMAGE071
。
Taking absolute error and root mean square error as error characteristic values:
if it is
Figure 875071DEST_PATH_IMAGE072
Figure 711439DEST_PATH_IMAGE073
，
If it is
Figure 749803DEST_PATH_IMAGE074
Figure 759347DEST_PATH_IMAGE075
/>
Wherein, the first and the second end of the pipe are connected with each other,
Figure 74922DEST_PATH_IMAGE076
、/>
Figure 941247DEST_PATH_IMAGE077
is the first->
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Corresponding to each key influence factor>
Figure 889928DEST_PATH_IMAGE079
Absolute error, root mean square error, of the fitting process>
Figure 184643DEST_PATH_IMAGE080
、/>
Figure 861350DEST_PATH_IMAGE081
Are respectively based on>
Figure 546409DEST_PATH_IMAGE082
Absolute error, root mean square error of the fitting process;
if it is first
Figure 960073DEST_PATH_IMAGE083
Quasi-curve, th->
Figure 984661DEST_PATH_IMAGE084
The variety key influence factor corresponds to->
Figure 458367DEST_PATH_IMAGE085
The fitting process is optimal, then will->
Figure 630723DEST_PATH_IMAGE085
Is selected as>
Figure 989023DEST_PATH_IMAGE086
Optimal time-varying relation function->
Figure 868117DEST_PATH_IMAGE087
；
The optimal time-varying relation functions are combined time-wise to finally form a discrete time-varying relation function set
Figure 178953DEST_PATH_IMAGE089
,
Figure 872103DEST_PATH_IMAGE090
Figure 605704DEST_PATH_IMAGE091
。
Said integrated output change time scale
Figure 421213DEST_PATH_IMAGE092
Including season, month, ten days and day; said integrated force change time scale>
Figure 568160DEST_PATH_IMAGE093
Greater than historical data period number>
Figure 268263DEST_PATH_IMAGE094
。
The discrete time step
Figure 981004DEST_PATH_IMAGE095
The method is a single time scale of seasons, months, ten days and days, and can be expanded into a response relation set with multiple time step lengths according to needs.
Example one
(1) Typical reservoir and data time scale selection
The method takes the Yazhenjiang basin outlet controlled reservoir second beach hydropower station as an embodiment, analyzes the calculation method of the second beach hydropower station comprehensive output coefficient according to a calculation method flow of the hydropower station comprehensive output coefficient based on a discrete time-varying relation function set by using hydropower station daily scale series data, obtains the second beach hydropower station discrete time-varying relation function capable of reflecting the change of the key influence factor set and the comprehensive output coefficient, and expresses the effect achieved by the method.
(2) Discrete time-varying relation function set solving step
The method comprises the following steps: and establishing a comprehensive output coefficient historical state set.
The comprehensive output coefficient of the elegant rice hulling second beach hydropower station in the design stage is 8.5, the comprehensive output coefficient is gradually increased to 8.8 in recent years after more than 20 years of operation practice since the hydropower station is built and put into operation in 2000. According to data analysis in the last 3 years, the comprehensive output coefficient of the second beach is greatly increased compared with the design and scheduling regulation stage, wherein the maximum comprehensive output coefficient is in 3 months and is 9.21 at the maximum, and the minimum comprehensive output coefficient is in 10 months and 12 months and is 8.55 at the minimum. The comprehensive output coefficient of the average month in the last three years is higher than 9.00 in 1-3 months, wherein the minimum output coefficient is 8.60 in 12 months; the maximum value is 9.13 in 3 months, and is related to various factors such as upstream reservoir regulation, dam front water level, natural incoming water and the like.
Combining with external conditions of other hydropower station construction in a drainage basin, regional power grid construction and the like, selecting a two-beach hydropower station actual operation day-by-day series with stable external boundary in 2018 and 2019 as data, and establishing a comprehensive output coefficient time-duration state set
Figure 905098DEST_PATH_IMAGE097
. Respectively comprising an integrated power factor>
Figure 975560DEST_PATH_IMAGE098
Water head->
Figure 72829DEST_PATH_IMAGE099
The water level before the dam is greater or less than>
Figure 108918DEST_PATH_IMAGE100
And a downstream water level->
Figure 876017DEST_PATH_IMAGE101
And the warehouse-in flow->
Figure 59873DEST_PATH_IMAGE102
And the power generation flow is greater or less>
Figure 836200DEST_PATH_IMAGE103
And/or a force->
Figure 461216DEST_PATH_IMAGE104
。
The supplementary state variables include, but are not limited to, the hydropower station operating state variables described above, and other relevant operating state variables may be supplemented according to the actual conditions of the study object.
Step two: time-varying sensitivity analysis based on a set of historical states;
analyzing the comprehensive output coefficient from the historical operation data of water and electricity
Figure 789429DEST_PATH_IMAGE105
The time-dependent change trend identifies the integrated force change time scale->
Figure 70369DEST_PATH_IMAGE106
Trend->
Figure 775020DEST_PATH_IMAGE107
And degree->
Figure 520122DEST_PATH_IMAGE108
Is based on the combined force variation characteristic indicator->
Figure 393138DEST_PATH_IMAGE109
(ii) a Match analysis prerequisite status>
Figure 286007DEST_PATH_IMAGE110
And the supplementation status variable>
Figure 404136DEST_PATH_IMAGE111
And the combined force coefficient->
Figure 3745DEST_PATH_IMAGE112
Extracts the key influencing factor->
Figure 939340DEST_PATH_IMAGE113
Collection, construction of a library of time-varying sensitivity assays>
Figure 929292DEST_PATH_IMAGE114
；
Figure 913429DEST_PATH_IMAGE115
(2.1) time-varying feature index extraction
Analyzing the change trend of the comprehensive output coefficient along with the time from historical data, and identifying the time scale of the change of the comprehensive output
Figure 429861DEST_PATH_IMAGE116
Trend->
Figure 146144DEST_PATH_IMAGE117
And degree->
Figure 13606DEST_PATH_IMAGE118
In combination with a characteristic indicator of the change in force->
Figure 801433DEST_PATH_IMAGE119
(2.2) time-varying sensitivity analysis
Analyzing the comprehensive output coefficient in sequence according to the established historical state set of the comprehensive output coefficient of the second hydropower station
Figure 546273DEST_PATH_IMAGE120
And the water head->
Figure 823671DEST_PATH_IMAGE121
The water level before the dam is greater or less than>
Figure 522636DEST_PATH_IMAGE122
And a downstream water level->
Figure 114155DEST_PATH_IMAGE123
And the warehouse-in flow->
Figure 339600DEST_PATH_IMAGE124
And the electricity generation flow rate>
Figure 663265DEST_PATH_IMAGE125
Output of the power
Figure 443002DEST_PATH_IMAGE126
Synchronous rate of change of operating state variables.
The comprehensive output coefficient has strong correlation with the synchronous change of output, generated flow and water head and weak correlation with other variables. And the output of the hydropower station in the conventional power generation dispatching operation is calculated by the comprehensive output coefficient, so that the discrete time-varying relation function established in the example is concentrated and does not contain the output factor.
Determining head by combining the above analysis
Figure 369370DEST_PATH_IMAGE127
And the power generation flow is greater or less>
Figure 590267DEST_PATH_IMAGE128
As a key influencing factor->
Figure 209467DEST_PATH_IMAGE129
Centralizing major factors, establishing a time-varying sensitivity analysis bank>
Figure 210921DEST_PATH_IMAGE130
。
Step three: and constructing a discrete time-varying relation of the comprehensive influence factors.
Introducing a correlation analysis method to intensively analyze key influence factors under different time scales
Figure 314881DEST_PATH_IMAGE131
Paired comprehensive output coefficient->
Figure 514918DEST_PATH_IMAGE132
Influence of the change is based on the characteristic indicator of the integrated output change>
Figure 180386DEST_PATH_IMAGE133
In the example study, the month is taken as the time step, and the comprehensive output coefficient is fitted>
Figure 669136DEST_PATH_IMAGE134
And the water head->
Figure 202885DEST_PATH_IMAGE135
And the power generation flow is greater or less>
Figure 132795DEST_PATH_IMAGE136
Constructing a month-scale key influence factor(s) based on a month-to-month time-varying relation function of the key influence factors>
Figure 297060DEST_PATH_IMAGE137
Collecting and comprehensive force coefficient->
Figure 69844DEST_PATH_IMAGE138
Changing response relation>
Figure 17072DEST_PATH_IMAGE139
。
Figure 394963DEST_PATH_IMAGE140
Step four, establishing a discrete time-varying relation function
In the example study, a polynomial is taken as an example, and a discrete time-varying relation function of the comprehensive output coefficient and the key influence factor with the month as a time scale is formed through fitting.
Figure 369610DEST_PATH_IMAGE141
Figure 629690DEST_PATH_IMAGE142
For fitting the curve type number, linear curves, power curves, polynomial curves, exponential curves, and logarithmic curves may be selected and are not limited, and polynomials are used as examples here:
Figure 115030DEST_PATH_IMAGE143
step five: time-varying relationship function testing and screening
In the example research, three types of parameters such as absolute errors, standard deviations, certainty coefficients and the like are selected as evaluation indexes for error characteristic values, the monthly discrete time-varying relation functions established in the previous four steps are compared and analyzed one by one, and calculation results are shown in table 1 by taking months 1, months 6 and months 12 as examples.
TABLE 1 discrete time-varying relation function evaluation index inspection table
Figure 347428DEST_PATH_IMAGE144
Wherein the selected function is selected comprehensively in 1 month and 6 months
Figure 650233DEST_PATH_IMAGE145
、/>
Figure 272976DEST_PATH_IMAGE146
Calculating; comprehensive 12-month selection function>
Figure 889902DEST_PATH_IMAGE147
。
The optimal time-varying relation functions are combined time-wise to finally form a discrete time-varying relation function set
Figure 773544DEST_PATH_IMAGE148
。
Figure 886731DEST_PATH_IMAGE149
/>
Figure 855824DEST_PATH_IMAGE150
(3) To summarize
According to the results after the technical scheme is implemented, the method is constructedThe water head output time-varying relation function set combines the multi-factor influence effect, combines the accuracy requirement of power generation dispatching application and time-varying sensitivity analysis result, and refines the dispatching period into
Figure 10862DEST_PATH_IMAGE151
In each time period, the complex relation of multi-factor influence in the waterhead output relation can be coupled and unified to the discrete time-varying relation of the comprehensive influence factors, so that the calculation precision of the simulated dispatching output is improved, and the simplicity and the applicability of the calculation method of the comprehensive output coefficient are also considered. Therefore, the discrete time-varying relation function set obtained by the invention can meet the actual operation management requirement of the cascade hydropower station and has certain engineering practicability.
The above-mentioned embodiments only express the embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (5)

1. A comprehensive output coefficient calculation method based on a discrete time-varying relation function set is characterized by comprising the following steps:
s1, establishing a comprehensive output coefficient historical state set;
extracting comprehensive output coefficient according to historical water and electricity operation data
Figure QLYQS_1
As target status variable->
Figure QLYQS_3
Hydro head->
Figure QLYQS_4
As a requisite status variable->
Figure QLYQS_5
And the water level before the dam of the hydropower station>
Figure QLYQS_6
And a downstream water level->
Figure QLYQS_7
And the warehouse-in flow->
Figure QLYQS_9
And the electricity generation flow rate>
Figure QLYQS_2
And/or a force->
Figure QLYQS_8
Is supplemented status variable->
Figure QLYQS_10
Establishing said combined contribution factor>
Figure QLYQS_11
Time-segment-wise historical state set->
Figure QLYQS_12
；
Figure QLYQS_13
Wherein, the first and the second end of the pipe are connected with each other,
Figure QLYQS_14
the number of time segments is historical data;
s2, time-varying sensitivity analysis based on historical state set
Analyzing the comprehensive output coefficient from the historical operation data of the hydropower station
Figure QLYQS_16
The variation trend along with time, and the comprehensive output variationTime scale->
Figure QLYQS_17
Trend->
Figure QLYQS_20
And degree->
Figure QLYQS_21
Is based on the combined force variation characteristic indicator->
Figure QLYQS_22
(ii) a Matching analysis requisite states
Figure QLYQS_23
Based on the status variable>
Figure QLYQS_24
And the combined force coefficient->
Figure QLYQS_15
Extracts the key influencing factor->
Figure QLYQS_18
Collection, construction of a library of time-varying sensitivity assays>
Figure QLYQS_19
；
Figure QLYQS_25
Wherein the content of the first and second substances,
Figure QLYQS_26
is a key influencing factor; />
Figure QLYQS_27
Is the number of key impact factors;
further comprising:
s3, constructing a discrete time-varying relation of the comprehensive influence factors;
analyzing the key influence factors at different time scales
Figure QLYQS_29
Collecting and comparing the integrated output coefficient>
Figure QLYQS_30
Influence of a change in relation to the integrated force change characteristic criterion->
Figure QLYQS_32
Determining a discrete time step->
Figure QLYQS_33
And the number of time periods within the scheduling period>
Figure QLYQS_34
Thereby constructing a discrete time step->
Figure QLYQS_35
In the following, the key influencing factor->
Figure QLYQS_36
Collecting and combining said integrated force contribution factor->
Figure QLYQS_28
The influence relationship of a change->
Figure QLYQS_31
；/>
Figure QLYQS_37
Wherein the content of the first and second substances,
Figure QLYQS_38
is->
Figure QLYQS_39
Individual key influencing factor and integrated force contribution factor->
Figure QLYQS_40
A set of correlation coefficients of (a); />
Figure QLYQS_41
Is->
Figure QLYQS_42
Key influencing factor->
Figure QLYQS_43
And the integrated output coefficient->
Figure QLYQS_44
The correlation coefficient of (a);
further comprising:
s4, establishing a discrete time-varying relation function
Fitting to form a reflection
Figure QLYQS_45
Discrete time-varying relation function of relation
Figure QLYQS_46
Wherein the content of the first and second substances,
Figure QLYQS_47
selecting a linear curve, a power curve, a polynomial curve, an exponential curve and a logarithmic curve for fitting the curve type number; />
Figure QLYQS_48
Representing a discrete time-varying relational function library;
the polynomial curve is:
Figure QLYQS_49
wherein the content of the first and second substances,
Figure QLYQS_51
is the first->
Figure QLYQS_52
A historical data period>
Figure QLYQS_54
And the fifth->
Figure QLYQS_55
Key influencing factor->
Figure QLYQS_56
Discrete time-varying relation functions of (a); />
Figure QLYQS_57
Parameters that are polynomial relational functions; />
Figure QLYQS_58
In cases of 1 to->
Figure QLYQS_50
Number of or>
Figure QLYQS_53
The relationship (2), a comprehensive value obtained by a weighting method;
further comprising:
s5, checking and screening time-varying relation functions
Step the discrete time
Figure QLYQS_59
The average head and output operation data of the lower time period are brought into a discrete time-varying relation function
Figure QLYQS_60
And calculating and fitting the comprehensive output coefficient->
Figure QLYQS_61
Sequence, and selecting error characteristic value as evaluation index, and selecting discrete time-varying relation function->
Figure QLYQS_62
The middle fitting relation function is tested, and finally the time-interval-by-time screening can reflect the current key influence factor and the comprehensive output coefficient->
Figure QLYQS_63
Changed fifth/or fifth>
Figure QLYQS_64
Optimal time-varying relationship function for periods>
Figure QLYQS_65
。
2. The method of claim 1, wherein the computing method of the integrated output coefficient based on the discrete time-varying relation function set comprises: the error characteristic value comprises an absolute error
Figure QLYQS_66
Relative error->
Figure QLYQS_67
Standard deviation, root mean square error->
Figure QLYQS_68
Determining coefficient>
Figure QLYQS_69
。
3. The method of claim 2, wherein the computing method of the integrated output coefficient based on the discrete time-varying relation function set comprises: taking absolute error and root mean square error as error characteristic values:
if it is
Figure QLYQS_70
，
If it is
Figure QLYQS_71
Wherein the content of the first and second substances,
Figure QLYQS_72
、/>
Figure QLYQS_73
is the first->
Figure QLYQS_74
Each key influence factor corresponds to>
Figure QLYQS_75
Absolute error, root mean square error of fitting procedure>
Figure QLYQS_76
、/>
Figure QLYQS_77
Are respectively based on>
Figure QLYQS_78
Absolute error, root mean square error of the fitting process;
if it is first
Figure QLYQS_79
Quasi-curve, th->
Figure QLYQS_80
Seed key influence factor corresponds to->
Figure QLYQS_81
Fitting process optimizationWill->
Figure QLYQS_82
Is selected as>
Figure QLYQS_83
Optimal time-varying relationship function for periods>
Figure QLYQS_84
；
The optimal time-varying relation functions are combined time-wise to finally form a discrete time-varying relation function set
Figure QLYQS_85
,
Figure QLYQS_86
。
4. The method of claim 1, wherein the computing method of the integrated output coefficient based on the discrete time-varying relation function set comprises: said integrated output change time scale
Figure QLYQS_87
Including season, month, ten days, day; said integrated output change time scale
Figure QLYQS_88
Greater than historical data period>
Figure QLYQS_89
。
5. The method of claim 1, wherein the computing method of the integrated output coefficient based on the discrete time-varying relation function set comprises: the discrete time step
Figure QLYQS_90
Is a single time scale of season, month, ten days and day according to requirementsTo be able to extend to a set of response relations for multiple time steps. />
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