CN105048451B - A kind of Interval Power Flow computational methods based on generation of electricity by new energy amount interval prediction - Google Patents
A kind of Interval Power Flow computational methods based on generation of electricity by new energy amount interval prediction Download PDFInfo
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- 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 discloses a kind of Interval Power Flow computational methods based on generation of electricity by new energy amount interval prediction, comprise the following steps (1) acquisition parameter data generated energy historical data；(2) two type fuzzy logic system of section is constructed；(3) initial parameter is input in system, and output obtains section two patterns paste prediction sets；(4) Interval Power Flow computation model is built；(5) will gather as the first iteration section of Interval Power Flow computation model, Krawczyk Moore operators are calculated, and sought common ground with Krawczyk Moore operators and initial section, obtain new section and as the initial section of second of iteration, judge whether the interval width meets the condition of convergence, the output interval if meeting, if being unsatisfactory for return to step (4) carries out next iteration.The present invention avoids artificially providing not restraining for the interval iteration caused by initial section, and convergence is too fast or excessively slow problem, so as to improve the accuracy of calculating.
Description
Technical field
The present invention relates to a kind of Interval Power Flow computational methods based on generation of electricity by new energy amount interval prediction, belong to new energy hair
Electrical domain.
Background technology
With petering out for fossil energy, environmental pollution and material impact caused by climatic deterioration, solar energy with
And the development of the new-generation technology such as regenerative resource such as wind-powered electricity generation, photovoltaic, renewable new energy power generation, which becomes, meets load growth
Demand, a kind of effective way for reducing environmental pollution, improving comprehensive utilization rate of energy source and power supply reliability, obtain in power grid
It is widely applied.Renewable energy power generation is mainly using solar energy, biomass energy, wind energy, water energy, wave energy etc., by ground manage bar
The influence of the factors such as part, weather condition and external environment condition, the generated energy output of these renewable power supplies is with intermittent and random
Property, it is difficult to accurately exported as a result, generated energy forecast interval can be obtained by fuzzy prediction.Thus, traditional electric power
Load flow calculation becomes the Load flow calculation of the amount containing section, i.e. Interval Power Flow calculates.
The content of the invention
In view of the deficienciess of the prior art, it is an object of the present invention to provide a kind of based on generation of electricity by new energy amount interval prediction
Interval Power Flow computational methods, using the ability of two type fuzzy logic processes uncertain problem of section, obtain generation of electricity by new energy amount
Section two pattern paste prediction sets, wherein, two pattern of section paste prediction sets, specify that the waving interval scope of generated energy,
The section can be the primary iteration section during Interval Power Flow calculates, and avoid artificially providing the interval iteration caused by initial section
Do not restrain, convergence it is too fast, the problems such as slow was restrained, so as to improve the accuracy of calculating.
To achieve these goals, the present invention is to realize by the following technical solutions：
A kind of Interval Power Flow computational methods based on generation of electricity by new energy amount interval prediction of the present invention, including following step
Suddenly：(1) (by taking photovoltaic generating system as an example, supplemental characteristic is intensity of illumination to the supplemental characteristic of collection distributed generation system, environment
Temperature, humidity) and distributed power generation amount historical data (by taking photovoltaic generating system as an example, historical data for history intensity of illumination,
History environment temperature, history humidity and history photo-voltaic power supply generated energy)；(2) using the supplemental characteristic in step (1) as input,
Distributed power generation amount historical data in step (1) constructs two type fuzzy logic system of section, and set the area as output
Between two type fuzzy logic systems initial parameter (by taking photovoltaic generating system as an example, initial parameter have intensity of illumination, environment temperature,
Humidity)；(3) the training two type fuzzy logic system of section, the real-time parameter data that will be collected are (with photovoltaic generating system
Exemplified by, using real-time lighting intensity, real time environment temperature, real-time humidity) as initial parameter (initial intensity of illumination, initial environment
Temperature, initial humidity) it is input in constructed two type fuzzy logic system of section, output obtains distributed generation resource generated energy area
Between two patterns paste prediction sets；(4) Interval Power Flow computation model is built；(5) the distributed generation resource generated energy for obtaining step (3)
Section two patterns paste prediction sets as the first iteration section of Interval Power Flow computation model, expand by the section for obtaining Jacobian matrix
Exhibition, Krawczyk-Moore operators are calculated according to Krawczyk-Moore operator definitions, and with the Krawczyk-
Moore operators and primary iteration section seek common ground, and obtain new section and as the initial section of second of iteration, then
Judge whether the interval width meets the condition of convergence, if meeting the condition of convergence, output interval, is returned if the condition of convergence is unsatisfactory for
Return step (4) and carry out next iteration.
In step (2), the building method of the two type fuzzy logic system of section is as follows：(2-1) designs fuzzy device：It is fuzzy
Device obtains fuzzy interval by a main membership function, which is made of upper and lower membership function, described
The primary membership of distributed generation resource generated energy section two patterns paste prediction sets chooses the uncertain Gaussian function of mean square deviation,
Upper and lower membership function is shown below, and model has three inputs, an output,
Wherein,It is input exact value,It is mean square deviation variation range, K=1,2 ... p are to input dimension, xk∈X
It is system input；
(2-2) constructs rule base：The forward and backward part of rule all chooses distributed generation resource generated energy section two patterns paste forecast set
Close, primary membership is the uncertain Gaussian function of average, rule format such as following formula：
WhereinIt is regular former piece set, y ∈ Y are regular output,It is consequent set, l=1,2 ... M, M are regular total
Number；
(2-3) constructive inference machine：Reasoning process such as following formula, participate in calculating is two pattern of distributed generation resource generated energy section
Paste the upper and lower membership function of prediction sets
Wherein：* it is t- norms, takes minimal operator,It is the upper and lower degree of membership letter of consequent set respectively
Number,It is the upper and lower membership function of activation set respectively,
(2-4) obtains the inference pattern of a plurality of fuzzy rule system of this multiple input single output by step (2-2) and (2-3)
For：
η is taken, u, v are the supplemental characteristic of distributed generation system, and y is prediction generated energy,Supplemental characteristic
Set,For generate electricity duration set,It is distributed generation resource generated energy section two patterns paste prediction sets,
Wherein, due to using center collection drop type method,Elect as and gathered with the section represented by barycenter, thenExpression formula can be written as
A following interval numbers：
(2-5) designs drop type device：Using center collection drop type method, each rule power generation duration set is replaced with barycenter, so
The weighted average of barycenter is sought afterwards, finally obtains barycenter section, and expression is：
In formula：It is the Lower and upper bounds of each rule power generation duration set barycenter respectively,It is activation set respectively
Lower and upper bounds, L, R are threshold values.
In step (4), the construction method of the Interval Power Flow computation model is as follows：
For the interval extension of Jacobian matrix,The range format of voltage phase angle is represented,Represent voltage magnitude
Range format,The voltage phase angle lower bound upper bound is represented,It is the lower bound upper bound of voltage magnitude,It is Jacobian matrix member
The range format of element, represents voltage phase angle to active influence,It is the range format of Jacobian matrix element, represents voltage
Amplitude on active influence,It is the range format of Jacobian matrix element, represents voltage phase angle to idle influence,It is refined
Gram than matrix element range format, represent voltage magnitude to idle influenceIt is above-mentioned respectively
The lower bound of the range format of four Jacobian matrix elements and the upper bound.；
WithAnalytic explanation is carried out exemplified by submatrix,
Wherein,Represent at node i, the offset of active power,The voltage phase angle range format at node i is represented,Represent voltage magnitude range format at node i,Represent the voltage magnitude range format at node j, GijRepresent node i, j
Between impedance real part,Represent the range format of phase angle difference between node i and node j, BijThe void of impedance between node i, j
Portion,Represent the voltage phase angle range format at node j.
It can be seen thatWithIt is independent to consider that interval range is increased with strong correlation, therefore：
In step (5), the computational methods of an iteration Krawczyk-Moore operators are as follows：
By XcosAs initial section X0
Wherein
I is unit battle array,It is the range format of independent variable, m is the middle point function for taking interval number midpoint, PiAt node i
Active power, QiFor the reactive power at node i.
In step (5), Krawcyzk-Moore operators and initial section x are utilized0Seek common ground, obtain new section x1：
Wherein, k represents iterations, xk+1,xkThe section of kth+1 and k independent variable, K are represented respectivelykRepresent kth time
K-M operators.
In step (5), the condition of convergence isAnd
Wherein, ω represents convergence coefficient,The section upper bound of kth+1 and k independent variable is represented respectively
And lower bound.
The beneficial effects of the present invention are：Compared with the existing probabilistic Load flow calculation of consideration, section used in the present invention
Measure to describe the more realistic situation of Uncertainty, obtain primary iteration section using two type fuzzy logic system of section, keep away
Exempt to think rule of thumb come a series of problems of iteration convergence caused by setting initial section, and saved iteration time,
It is more suitable for large scale system；Being applicable in for Krawczyk-Moore operators, has global convergence, it does not only give a section
Solution, and consider evaluated error；The combined use of two type fuzzy logic system of section and Interval Iterative Methodss, can not only succeed
Obtain consider probabilistic Load flow calculation solution, and solve some shortcomings of Interval Iterative Methodss in itself, have stronger
Engineering actually uses meaning.
Brief description of the drawings
Fig. 1 is the Interval Power Flow computational methods work flow diagram based on generation of electricity by new energy amount interval prediction of the present invention；
Fig. 2 is that the generated energy based on two type fuzzy logic system of section predicts structure chart.
Embodiment
To make the technical means, the creative features, the aims and the efficiencies achieved by the present invention easy to understand, with reference to
Embodiment, the present invention is further explained.
Referring to Fig. 1, a kind of Interval Power Flow computational methods based on generation of electricity by new energy amount interval prediction of the invention, including with
Under several steps：
(1) multiple input single output mode is used, gathers the supplemental characteristic and distributed power generation amount of distributed generation system
Historical data；
(2) two type fuzzy logic system of section is constructed, system is set as output using supplemental characteristic as input, generated energy
The initial parameter of system；
(3) training two type fuzzy logic system of section, the real-time parameter data collected are input to as initial parameter
In the two type fuzzy logic system of section constructed, output obtains distributed generation resource generated energy section two patterns paste prediction sets；
(4) Interval Power Flow computation model is built；
(5) two pattern of distributed generation resource generated energy section obtained step (3) pastes prediction sets as Interval Power Flow meter
The first iteration section of model is calculated, the interval extension of Jacobian matrix is obtained, Krawczyk-Moore operators is calculated, are used in combination
Krawczyk-Moore operators and initial section seek common ground, and obtain new section and as the original area of second of iteration
Between, judge whether the interval width meets the condition of convergence, the output interval if meeting, if being unsatisfactory for return to step (4) progress
Next iteration.
Referring to Fig. 2, the building method of two type fuzzy logic system of section is as follows：
A designs fuzzy device：Fuzzy device will input exact value and be converted into section two patterns paste prediction sets, fully to handle electricity
It is strong uncertain possessed by power load.The primary membership of section two patterns paste prediction sets chooses the uncertain height of mean square deviation
This function, upper and lower membership function are shown below.Model has 3 inputs, 1 output.
Wherein,It is input exact value,It is mean square deviation variation range, K=1,2 ... p are input dimensions.B is constructed
Rule base：The forward and backward part of rule all chooses section two patterns paste prediction sets, and primary membership is the uncertain Gaussian function of average
Number, rule format such as following formula：
Wherein xk∈ X are system inputs,It is that regular former piece set y ∈ Y are regular output,It is consequent set.L=1,
2 ... M, M are regular sums.
C constructive inference machines：For two type Mamdani fuzzy models of section, reasoning process such as following formula, participate in calculating is collection
The upper and lower membership function closed.
Wherein：* it is t- norms, takes minimal operator,It is the upper and lower degree of membership letter of consequent set respectively
Number,It is the upper and lower membership function of activation set respectively.
The inference pattern that D is obtained a plurality of fuzzy rule system of this multiple input single output by process (2) and (3) is：
X, u are taken, v is supplemental characteristic, and y is prediction generated energy,Supplemental characteristic set,For the quantity set that generates electricity
Close.It is section two patterns paste prediction sets, wherein due to using center collection drop type methodElect as and use matter
Section set represented by the heart, thenExpression formula be：
E designs drop type device：Using center collection drop type method, each rule power generation duration set is replaced with barycenter, Ran Houqiu
The weighted average of barycenter, finally obtains barycenter section.The simplification proposed in calculating using Karnik and Mendel, is embodied
Formula is：
In formula：It is the Lower and upper bounds of each rule power generation duration set barycenter respectively,It is activation set respectively
Lower and upper bounds, L, R are threshold values.
In step (4), the construction method of Interval Power Flow computation model is as follows：
For the interval extension of Jacobian matrix；
WithAnalytic explanation is carried out exemplified by submatrix.
It can be seen thatWithIt is independent to consider that interval range is increased with strong correlation, therefore：
In step (5), the computational methods of an iteration Krawczyk-Moore operators are as follows：
By XcosAs initial section x0
Wherein
I is unit battle array.
In step (5), Krawcyzk-Moore operators and initial section x are utilized0Seek common ground, obtain new section X：
In step (5), the condition of convergence isAnd
In the present embodiment, for supplemental characteristic using the uncertain Gaussian function of mean square deviation as its upper and lower degree of membership letter
Number, the exact value of input is obscured and turns to the conjunction of monodrome type-2 fuzzy sets.The rule of two type fuzzy logic system of section uses " IF-
THEN " forms, Mamdani inference patterns.Activation set is produced by input and regular former piece, then by activation set and consequent set
Output is calculated, participate in calculating is the upper and lower membership function of each set.Reasoning process includes calculating collocation degree, asks excitation strong
Spend, seek effective consequent membership function and seek total output membership function.Using center collection drop type method, each rule is generated electricity
Duration set is replaced with barycenter, then seeks the weighted average of barycenter, finally obtains barycenter section.Tide is described using section amount
Uncertainty in stream calculation, simply gears to actual circumstances, and the barycenter section hereinbefore obtained can be used as Interval Power Flow meter
The initial section calculated.According to the calculation formula of Newton method, and network topology structure, it is each to try to achieve Jacobian matrix in Load flow calculation
The Extending of element.Jacobian matrix interval extension according to Interval Iterative Methodss and above, and initial section above,
Obtain Krawczyk-Moore operators, then obtain the intersection of K-M operators and initial section, as second iteration just
Beginning section, and judge whether to meet the condition of convergence.
Since the generated energy of distributed generator is difficult to set up accurate mathematical model, predict that generated energy exists very exactly
Big difficulty.The present invention describes Uncertainty using section, simple direct, and more tallies with the actual situation than Probabilistic Load Flow, counts
Calculation amount smaller.Two type fuzzy logic system of section of the present invention need not establish accurate mathematical model, utilize linguistic form
Rule base unascertained information logic is described, according to membership function fog-level constructive inference machine, be adapted to distributed generation resource hair
The characteristics of electricity is uncertain.The present invention uses the Interval Iterative Methodss based on Newton method, has preferable robustness, and can be complete
Office's convergence；, as initial value, avoid caused by initial-value problem to a point problem, simplify using from two pattern of section paste prediction sets
Convergence process.Interval Power Flow computational methods proposed by the present invention based on two type fuzzy logic system of section, it is accurate to carry out
Interval Power Flow calculate, and solve the problems, such as the convergence of Interval Iterative Methodss in itself it is too fast, it is excessively slow, do not restrain, with higher
Practical value.
The basic principles, main features and the advantages of the invention have been shown and described above.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the above embodiments and description only describe this
The principle of invention, without departing from the spirit and scope of the present invention, various changes and modifications of the present invention are possible, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appended claims and its
Equivalent thereof.
Claims (6)
1. a kind of Interval Power Flow computational methods based on generation of electricity by new energy amount interval prediction, it is characterised in that including following
Step：
(1) supplemental characteristic and distributed power generation amount historical data of distributed generation system are gathered；
(2) using the supplemental characteristic in step (1) as inputting, the distributed power generation amount historical data in step (1) is used as output,
Two type fuzzy logic system of section is constructed, and the initial parameter of the two type fuzzy logic system of section is set；
(3) the training two type fuzzy logic system of section, the real-time parameter data that will be collected are inputted as initial parameter
Into the two type fuzzy logic system of section constructed, output obtains distributed generation resource generated energy section two patterns paste forecast set
Close；
(4) Interval Power Flow computation model is built；
(5) the distributed generation resource generated energy section two patterns paste prediction sets obtained step (3) calculate mould as Interval Power Flow
The first iteration section of type, obtains the interval extension of Jacobian matrix, is calculated according to Krawczyk-Moore operator definitions
Krawczyk-Moore operators, and sought common ground with the Krawczyk-Moore operators and primary iteration section, obtain new area
Between and as the initial section of second of iteration, then judge whether the interval width meets the condition of convergence, if meeting to receive
Condition is held back, then output interval, return to step (4) carries out next iteration if the condition of convergence is unsatisfactory for.
2. the Interval Power Flow computational methods according to claim 1 based on generation of electricity by new energy amount interval prediction, its feature exist
In in step (2), the building method of the two type fuzzy logic system of section is as follows：
(2-1) designs fuzzy device：Fuzzy device by a main membership function obtains fuzzy interval, the main membership function by
Upper and lower membership function is formed, and the primary membership of two pattern of the distributed generation resource generated energy section paste prediction sets is chosen
The uncertain Gaussian function of mean square deviation, upper and lower membership function are shown below, and model has three inputs, an output,
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(2-2) constructs rule base：The forward and backward part of rule all chooses distributed generation resource generated energy section two patterns paste prediction sets, main
Membership function is the uncertain Gaussian function of average, rule format such as following formula：
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WhereinIt is regular former piece set, y ∈ Y are regular output,It is consequent set, l=1,2...M, M is regular sum；
(2-3) constructive inference machine：Reasoning process such as following formula, participate in calculating is that distributed generation resource generated energy section two patterns paste is pre-
Survey the upper and lower membership function of set
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Wherein：* it is t- norms, takes minimal operator,It is the upper and lower membership function of consequent set respectively, f l
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The inference pattern that (2-4) is obtained a plurality of fuzzy rule system of this multiple input single output by step (2-2) and (2-3) is：
η is taken, u, v are the supplemental characteristic of distributed generation system, and y is prediction generated energy,Supplemental characteristic set,For generate electricity duration set,It is distributed generation resource generated energy section two patterns paste prediction sets, wherein, by
In use center collection drop type method,Elect as and gathered with the section represented by barycenter, thenExpression formula can be written as following one
A interval number：
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<mi>y</mi>
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</munder>
<mo>,</mo>
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</mover>
</mrow>
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<mrow>
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<mrow>
<mfrac>
<mrow>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>l</mi>
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<mn>1</mn>
</mrow>
<mi>L</mi>
</munderover>
<msup>
<mover>
<mi>f</mi>
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</mover>
<mi>l</mi>
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<mi>M</mi>
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<munder>
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</msup>
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</munder>
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</msup>
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</munder>
</mrow>
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<mn>1</mn>
</mrow>
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</munderover>
<msup>
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</msup>
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</munderover>
<munder>
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<mo>,</mo>
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</munderover>
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<msup>
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</mover>
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</mrow>
</mfrac>
</mrow>
<mo>&rsqb;</mo>
</mrow>
</mrow>
In formula：yl 、It is the Lower and upper bounds of each rule power generation duration set barycenter respectively,fl 、It is the upper and lower of activation set respectively
Boundary, M, L, R are threshold values.
3. the Interval Power Flow computational methods according to claim 2 based on generation of electricity by new energy amount interval prediction, its feature exist
In in step (4), the construction method of the Interval Power Flow computation model is as follows：
<mrow>
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For the interval extension of Jacobian matrix,The range format of voltage phase angle is represented,Represent the section of voltage magnitude
Form,θ,The voltage phase angle lower bound upper bound is represented,U,It is the lower bound upper bound of voltage magnitude,It is the area of Jacobian matrix element
Between form, represent voltage phase angle to active influence,It is the range format of Jacobian matrix element, represents voltage magnitude to having
Work(influences,It is the range format of Jacobian matrix element, represents voltage phase angle to idle influence,It is Jacobian matrix member
The range format of element, represents voltage magnitude to idle influenceH, K, N, L,It is aforementioned four Jacobi square respectively
The lower bound of the range format of array element element and the upper bound；
WithAnalytic explanation is carried out exemplified by submatrix,
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<msub>
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<mi>c</mi>
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Wherein,Represent at node i, the offset of active power,The voltage phase angle range format at node i is represented,Generation
Voltage magnitude range format at table node i,Represent the voltage magnitude range format at node j, GijRepresent node i, j it
Between impedance real part,Represent the range format of phase angle difference between node i and node j, BijThe imaginary part of impedance between node i, j,Represent the voltage phase angle range format at node j；
It can be seen thatWithIt is independent to consider that interval range is increased with strong correlation, therefore：
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<msqrt>
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<mrow>
<mo>(</mo>
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δij=arctan (- Bij/Gij)。
4. the Interval Power Flow computational methods according to claim 3 based on generation of electricity by new energy amount interval prediction, its feature exist
In in step (5), the computational methods of an iteration Krawczyk-Moore operators are as follows：
By XcosAs initial section X0
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<msub>
<mi>G</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mi>sin</mi>
<mrow>
<mo>(</mo>
<mrow>
<mi>m</mi>
<mrow>
<mo>(</mo>
<msub>
<mover>
<mi>&theta;</mi>
<mo>~</mo>
</mover>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mi>m</mi>
<mrow>
<mo>(</mo>
<msub>
<mover>
<mi>&theta;</mi>
<mo>~</mo>
</mover>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>B</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mi>cos</mi>
<mrow>
<mo>(</mo>
<mrow>
<mi>m</mi>
<mrow>
<mo>(</mo>
<msub>
<mover>
<mi>&theta;</mi>
<mo>~</mo>
</mover>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mi>m</mi>
<mrow>
<mo>(</mo>
<msub>
<mover>
<mi>&theta;</mi>
<mo>~</mo>
</mover>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>,</mo>
</mrow>
<mrow>
<msup>
<mi>Y</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
<mo>=</mo>
<msup>
<mrow>
<mo>&lsqb;</mo>
<mi>m</mi>
<mrow>
<mo>(</mo>
<msup>
<mi>F</mi>
<mo>&prime;</mo>
</msup>
<mo>(</mo>
<mover>
<mi>x</mi>
<mo>~</mo>
</mover>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
<mo>&prime;</mo>
</msup>
<mo>,</mo>
</mrow>
I is unit battle array,It is the range format of independent variable, m is the middle point function for taking interval number midpoint, PiTo be active at node i
Power, QiFor the reactive power at node i.
5. the Interval Power Flow computational methods according to claim 4 based on generation of electricity by new energy amount interval prediction, its feature exist
In in step (5), utilizing Krawcyzk-Moore operators and initial section x0Seek common ground, obtain new section x1：
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msup>
<mover>
<mi>x</mi>
<mo>~</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</msup>
<mo>=</mo>
<msup>
<mover>
<mi>x</mi>
<mo>~</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
<mo>&cap;</mo>
<msup>
<mi>K</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
Wherein, k represents iterations,The section of kth+1 and k independent variable, K are represented respectively(k)Represent kth time
Krawcyzk-Moore operators.
6. the Interval Power Flow computational methods according to claim 5 based on generation of electricity by new energy amount interval prediction, its feature exist
In in step (5), the condition of convergence is |x k+1-x k| ＜ ω and
Wherein, ω represents convergence coefficient, x k+1,x kRepresent respectively kth+1 and k independent variable the section upper bound and under
Boundary.
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