CN110428164A - A kind of operation of power networks state monitoring method, device, equipment and readable storage medium storing program for executing - Google Patents
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Abstract
The invention discloses a kind of operation of power networks state monitoring methods: using the input probability model established by NPNT algorithm by sample point transformation each in the sample dot matrix obtained in advance to original domain, obtain each initial air speed value and each original negative charge values, and certainty Load flow calculation network is inputted, obtain initial calculation of tidal current；The sample point for generating preset quantity in probability space is not represented in sample dot matrix, and transforms to original domain, obtains each newly-increased air speed value and each newly-increased load value；Calculation of tidal current after being updated using ILHS algorithm；If the not up to condition of convergence, the sample point that preset quantity is generated in probability space is not represented in sample dot matrix；If reaching, the monitoring to operation of power networks state is completed.The balance of power grid Probabilistic Load Flow analysis precision and efficiency is realized, realizes effective monitoring to the operating status of power grid.The invention also discloses a kind of operation of power networks state monitoring apparatus, equipment and storage mediums, have relevant art effect.
Description
Technical field
The present invention relates to technical field of power systems, more particularly to a kind of operation of power networks state monitoring method, device, set
Standby and computer readable storage medium.
Background technique
As more and more VSC-MTDC systems are put into operation, power grid scale will constantly be expanded, uncertain source
Quantity also can sharp increase.These uncertain source distribution are in wide geographical space and may be by Various Complex factor
Influence (for example, geographical conditions, meteorologic factor etc.), may obey Arbitrary distribution and distribution between there are correlations.Therefore, institute
There are the abilities of correlation between the input probability model of foundation needs to have processing stochastic variable obedience Arbitrary distribution and is distributed.So
And in probabilistic load flow, uncertain source is usually assumed that common distribution, such as load are generally seen as normal state
Normal distribution, wind speed are often distributed to simulate with Wei Buer Weibull.Meanwhile it being established between different distributions using NATAF transformation
Correlativity.In a small amount of Run-time scenario, above-mentioned input probability model may have certain validity.But practical electricity
Wind speed and load might not obey common distribution in net, and very likely obey non-common distribution.So, it is based on common distribution
Input probability model will lead to Probabilistic Load Flow analysis and generate huge error.
Common Probabilistic Load Flow algorithm includes three classes: analytic method, approximation method and Monte Carlo Method (Monte Carlo
Simulation, MCS).The computational efficiency of analytic method is high, but nonlinear model is linearized and can not be located by most of analytic method
The correlation between stochastic variable is managed, causes its computational accuracy unsatisfactory.The basic thought of approximation method is by selected
The sample point of input probability distribution is with approximation input distribution, but approximation method can not directly obtain the general of Probabilistic Load Flow analysis result
Rate density function (probability density function, PDF).This, which will lead to, is difficult to carry out deeply Probabilistic Load Flow result
Enter analysis.
Monte Carlo Method usually can provide the simulation result of " correct ", be usually used in verifying the effective of other algorithms
Property.But the Probabilistic Load Flow analysis based on Monte Carlo Method is extremely time-consuming.Traditional Latin based on Monte Carlo Method
Hypercube sampling technique (conventional Latin hypercube sampling, CLHS) is generated by way of layering
Sample point, can the distribution of larger range of covering input probability, achieve the purpose that promote Probabilistic Load Flow analysis efficiency and precision.Together
When, traditional Latin Hypercube Sampling technology has the ability of output square information and probability density function.However, traditional Latin is super
The shortcomings that cube sampling technique is that the sample point set of highly structural makes it be difficult to directly increase additional sample point.Such as
Fruit tradition Latin Hypercube Sampling technology directly increases sample point, then forming new sample point set (comprising original sample point
With newly-increased sample point) it will be difficult to keep original hierarchical nature, cause traditional Latin Hypercube Sampling technology computational efficiency to reduce.
Meanwhile because traditional Latin Hypercube Sampling technology is difficult to increase sample point, using traditional Latin Hypercube Sampling technology to electricity
Net carries out that a problem will be drawn when Probabilistic Load Flow analysis, i.e., it is suitable for selecting how many sample point to carry out analysis to power grid on earth
, it is well known that if sample size is very few, the precision that will lead to Probabilistic Load Flow analysis result is undesirable, and sample size is excessive, will lead
Cause reduce Probabilistic Load Flow analysis efficiency, cannot the operating status to power grid effectively monitored.
It cannot be to power grid Probabilistic Load Flow point in conclusion how to efficiently solve existing operation of power networks state analysis mode
Analysis precision and efficiency are balanced, to be current this field the problems such as cannot effectively be monitored to the operating status of power grid
Technical staff's urgent problem.
Summary of the invention
The object of the present invention is to provide a kind of operation of power networks state monitoring method, this method realizes the analysis of power grid Probabilistic Load Flow
The balance of precision and efficiency, to realize effective monitoring to the operating status of power grid；It is a further object of the present invention to provide
A kind of operation of power networks state monitoring apparatus, equipment and computer readable storage medium.
In order to solve the above technical problems, the invention provides the following technical scheme:
A kind of operation of power networks state monitoring method, comprising:
When receiving the instruction of operation of power networks status monitoring, acquisition is uniformly distributed middle initial number sample point, obtains sample
This dot matrix, and become the sample point each in the sample dot matrix using the input probability model established based on NPNT algorithm
Original domain is changed to, each initial air speed value and each original negative charge values of obeying Arbitrary distribution are obtained；Wherein, the input probability model
For what is established based on wind speed historical data and demand history data；
Each initial air speed value and each original negative charge values are inputted into certainty Load flow calculation network, obtain initial tide
Stream calculation result；
The sample point that preset quantity is generated in probability space is not represented in the sample dot matrix using ILHS algorithm,
Newly-generated each sample point is transformed into the original domain using the input probability model, obtains obeying Arbitrary distribution
Each newly-increased air speed value and each newly-increased load value；
Whole air speed values and whole load values are input to the certainty Load flow calculation network, trend meter after being updated
Calculate result；
The initial calculation of tidal current and calculation of tidal current after the update are compared, according to obtained comparison
As a result judge whether to reach the preset condition of convergence；
If it is not, then repeating described raw in probability space in not represented for the sample dot matrix using ILHS algorithm
At preset quantity sample point the step of, until reach the condition of convergence；
If so, completing the monitoring to operation of power networks state according to calculation of tidal current after the update.
In a kind of specific embodiment of the invention, in acquisition is uniformly distributed after initial number sample point, benefit
With the input probability model established based on NPNT algorithm by the sample point each in the sample dot matrix transform to original domain it
Before, further includes:
Each sample point is ranked up using cholesky decomposition algorithm, obtains initial ranking results；
Using the input probability model established based on NPNT algorithm by the sample point transformation each in the sample dot matrix
To original domain, comprising:
According to the initial ranking results, using the input probability model established based on NPNT algorithm by the sample
Each sample point transforms to the original domain in dot matrix；
The sample point that preset quantity is generated in probability space is not represented in the sample dot matrix using ILHS algorithm
Later, before newly-generated each sample point being transformed to the original domain using the input probability model, further includes:
It to all sample points and is resequenced using cholesky decomposition algorithm, sort knot after being updated
Fruit；
Newly-generated each sample point is transformed into the original domain using the input probability model, comprising:
According to sequence after the update as a result, using the input probability model by newly-generated each sample point transformation
To the original domain.
It, will using the input probability model established based on NPNT algorithm in a kind of specific embodiment of the invention
Each sample point transforms to the original domain in the sample dot matrix, comprising:
It will be each described in the sample dot matrix using the nine rank multinomial input probability models established based on NPNT algorithm
Sample point transforms to the original domain.
In a kind of specific embodiment of the invention, calculation of tidal current is completed to transport power grid after according to the update
After the monitoring of row state, further includes:
Obtain operation of power networks status monitoring result；
Efficiency assessment is carried out to the operation of power networks status monitoring result.
A kind of operation of power networks state monitoring apparatus, comprising:
Initial value obtains module, initial in being uniformly distributed for obtaining when receiving the instruction of operation of power networks status monitoring
Quantity sample point, obtains sample dot matrix, and using the input probability model established based on NPNT algorithm by the sample point
Each sample point transforms to original domain in matrix, obtains each initial air speed value and each original negative charge values of obeying Arbitrary distribution；
Wherein, the input probability model is to be established based on wind speed historical data and demand history data；
Initial results obtain module, for each initial air speed value and each original negative charge values input certainty is damp
Stream calculation network obtains initial calculation of tidal current；
Added value obtains module, for raw in probability space in not represented for the sample dot matrix using ILHS algorithm
At the sample point of preset quantity, newly-generated each sample point transformed to using the input probability model described original
Domain obtains each newly-increased air speed value for obeying Arbitrary distribution and each newly-increased load value；
Result obtains module after update, by whole air speed values and whole load values to be input to based on the certainty trend
Calculate network, calculation of tidal current after being updated；
Judgment module, for the initial calculation of tidal current and calculation of tidal current after the update to be compared,
Judged whether to reach the preset condition of convergence according to obtained comparing result, if it is not, then triggering the added value obtains module, if
It is, then triggering state monitoring modular；
The state monitoring module, for completing the prison to operation of power networks state according to calculation of tidal current after the update
It surveys.
In a kind of specific embodiment of the invention, further includes:
Ranking results obtain module, after the initial number sample point in acquisition is uniformly distributed, using being based on
Before the sample point each in the sample dot matrix is transformed to original domain by the input probability model that NPNT algorithm is established, utilize
Cholesky decomposition algorithm is ranked up each sample point, obtains initial ranking results；And in utilization ILHS algorithm in institute
State sample dot matrix not by represented in probability space generate preset quantity sample point after, utilize the input probability model
Before newly-generated each sample point is transformed to the original domain, using cholesky decomposition algorithm to all samples
It puts and resequences, ranking results after being updated；
It includes domain transformation submodule that the added value, which obtains module, and the domain transformation submodule is specially according to described initial
Ranking results, using the input probability model established based on NPNT algorithm by the sample point each in the sample dot matrix
Transform to the original domain, and according to sequence after the update as a result, using the input probability model by newly-generated each institute
State the module that sample point transforms to the original domain.
In a kind of specific embodiment of the invention, the domain transformation submodule is specially to build using based on NPNT algorithm
The sample point each in the sample dot matrix is transformed to the mould of the original domain by nine vertical rank multinomial input probability models
Block.
In a kind of specific embodiment of the invention, further includes:
Monitoring result obtain module, for after according to the update calculation of tidal current complete to operation of power networks state
After monitoring, operation of power networks status monitoring result is obtained；
Efficiency assessment module, for carrying out efficiency assessment to the operation of power networks status monitoring result.
A kind of operation of power networks condition monitoring device, comprising:
Memory, for storing computer program；
Processor realizes the step of operation of power networks state monitoring method as previously described when for executing the computer program
Suddenly.
A kind of computer readable storage medium is stored with computer program on the computer readable storage medium, described
The step of operation of power networks state monitoring method as previously described is realized when computer program is executed by processor.
The present invention provides a kind of operation of power networks state monitoring methods: when receiving the instruction of operation of power networks status monitoring,
Acquisition is uniformly distributed middle initial number sample point, obtains sample dot matrix, and general using the input established by NPNT algorithm
Rate model by sample point transformation each in sample dot matrix to original domain, obtain each initial air speed value for obeying Arbitrary distribution and it is each just
Beginning load value；Wherein, input probability model is to be established based on wind speed historical data and demand history data；By each initial wind speed
Value and each original negative charge values input certainty Load flow calculation network, obtain initial calculation of tidal current；Using ILHS algorithm in sample
This dot matrix is not represented the sample point that preset quantity is generated in probability space, will be newly-generated each using input probability model
Sample point transforms to original domain, obtains each newly-increased air speed value for obeying Arbitrary distribution and each newly-increased load value；By whole air speed values
Certainty Load flow calculation network, calculation of tidal current after being updated are input to whole load values；By initial Load flow calculation knot
Fruit compares with calculation of tidal current after update, is judged whether to reach the preset condition of convergence according to obtained comparing result；
The sample that preset quantity is generated in probability space is not represented in sample dot matrix using ILHS algorithm if it is not, then repeating
The step of point, until reaching the condition of convergence；If so, completing the prison to operation of power networks state according to calculation of tidal current after update
It surveys.
According to the above-mentioned technical solution, by being based on wind speed historical data and demand history number using by NPNT algorithm
According to the input probability model of foundation, the sample point that will acquire transforms to original domain, to obtain obeying the wind speed of Arbitrary distribution
Value and load value, can be directly based upon wind speed historical data and demand history data establish input probability model, not need in advance
Acquisition probability density function significantly improves power grid Probabilistic Load Flow analysis precision, can pass through iteration meter using ILHS algorithm
Calculation adaptively evaluates reach the preset condition of convergence needed for sample point quantity, significantly improve power grid Probabilistic Load Flow point
Efficiency is analysed, the balance of power grid Probabilistic Load Flow analysis precision and efficiency is realized, to realize to the effective of the operating status of power grid
Monitoring.
Correspondingly, the embodiment of the invention also provides operations of power networks corresponding with above-mentioned operation of power networks state monitoring method
State monitoring apparatus, equipment and computer readable storage medium, have above-mentioned technique effect, and details are not described herein.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 is a kind of implementation flow chart of operation of power networks state monitoring method in the embodiment of the present invention；
Fig. 2 is another implementation flow chart of operation of power networks state monitoring method in the embodiment of the present invention；
Fig. 3 (a) is the distribution map for the sample point being initially generated in the embodiment of the present invention；
Fig. 3 (b) is the schematic diagram in the underlapped section being divided into the embodiment of the present invention；
Fig. 3 (c) is that remaining section shows after deleting the section represented in sample dot matrix in the embodiment of the present invention
It is intended to；
Fig. 3 (d) be in the embodiment of the present invention after the section that will have been represented in sample dot matrix is deleted in remaining section
Generate the schematic diagram of new sample point；
Fig. 3 (e) is that the remaining section after generation new sample point is put back to the signal after sample matrix in the embodiment of the present invention
Figure；
Fig. 4 shows for the convergent tendency of Probabilistic Load Flow algorithm used by the embodiment of the present invention and existing Probabilistic Load Flow algorithm
It is intended to；
Fig. 5 is that Probabilistic Load Flow algorithm used by the embodiment of the present invention and existing Probabilistic Load Flow algorithm are iterated calculating
The schematic diagram of duration used；
Fig. 6 is a kind of structural block diagram of operation of power networks state monitoring apparatus in the embodiment of the present invention；
Fig. 7 is a kind of structural block diagram of operation of power networks condition monitoring device in the embodiment of the present invention.
Specific embodiment
In order to enable those skilled in the art to better understand the solution of the present invention, with reference to the accompanying drawings and detailed description
The present invention is described in further detail.Obviously, described embodiments are only a part of the embodiments of the present invention, rather than
Whole embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not making creative work premise
Under every other embodiment obtained, shall fall within the protection scope of the present invention.
Embodiment one:
Referring to Fig. 1, Fig. 1 is a kind of implementation flow chart of operation of power networks state monitoring method in the embodiment of the present invention, the party
Method may comprise steps of:
S101: when receiving the instruction of operation of power networks status monitoring, acquisition is uniformly distributed middle initial number sample point, obtains
To sample dot matrix, and utilizes and arrived sample point transformation each in sample dot matrix based on the input probability model that NPNT algorithm is established
Original domain obtains each initial air speed value and each original negative charge values of obeying Arbitrary distribution.
Wherein, input probability model is to be established based on wind speed historical data and demand history data.
When needing to be monitored operation of power networks state, operation of power networks can be sent to operation of power networks status monitoring center
Status monitoring instruction, operation of power networks status monitoring center receives the instruction of operation of power networks status monitoring, and obtains in being uniformly distributed
Initial number sample point obtains sample dot matrix.It can be in advance based on according to wind speed historical data and demand history data
NPNT algorithm establishes input probability model, and the process for establishing input probability model may include according to wind speed historical data and load
Historical data estimates nine rank multinomial coefficients, and it is high to calculate standard according to corresponding nine rank multinomial of nine obtained rank multinomial coefficients
The correlation matrix in this domain.After establishing input probability model, using input probability model by sample point each in sample dot matrix
Original domain is transformed to, detailed process may include that the sample point for the initial number being uniformly distributed that will acquire is converted into mark
Quasi- Gaussian Profile domain, the correlation matrix based on the standard gaussian domain obtained calculate the sample point in standard gaussian distribution, base
The sample point of Gaussian Profile is converted to original domain in nine rank multinomials, to obtain obeying each initial air speed value of Arbitrary distribution
With each original negative charge values.
It should be noted that initial number can be set and be adjusted according to the actual situation, the embodiment of the present invention is to this
Without limitation.
S102: each initial air speed value and each original negative charge values are inputted into certainty Load flow calculation network, obtain initial trend
Calculated result.
It, can be by each initial air speed value and each original negative charge values after obtaining each initial air speed value and each original negative charge values
Inputting certainty Load flow calculation network can be by each initial air speed value and each original negative charge values such as AC-DC hybrid power grid
It is input to AC/VSC-MTDC serial-parallel power grid certainty Load flow calculation network, obtains initial calculation of tidal current.
S103: the sample that preset quantity is generated in probability space is not represented in sample dot matrix using ILHS algorithm
Point obtains each newly-increased wind for obeying Arbitrary distribution using input probability model by newly-generated each sample point transformation to original domain
Speed value and each newly-increased load value.
After the sample dot matrix that the sample point obtained by initial number is constituted, ILHS algorithm can use in sample point
Matrix is not represented the sample point that preset quantity is generated in probability space, and utilizes input probability model by newly-generated various kinds
This point transformation arrives original domain, obtains each newly-increased air speed value for obeying Arbitrary distribution and each newly-increased load value.
The core concept of ILHS algorithm is to choose a small amount of sample point first to carry out Probabilistic Load Flow analysis, then according to meter
It calculates required precision and the condition of convergence gradually increases sample point.Increase in the sample dot matrix being made of the sample point of initial number
Add new sample point.The relatively direct advantage for generating a new samples point set is the meter that can re-use the acquisition of available sample point
It calculates as a result, promoting computational efficiency in turn.This be for probabilistic load flow it is very significant, particularly with it is deterministic hand over it is straight
Serial-parallel power grid Load flow calculation is flowed, relatively pure AC network is more complicated and time-consuming.The receipts analyzed based on given Probabilistic Load Flow
Precision is held back, ILHS algorithm can adaptively evaluate required sample size, so that computational efficiency be substantially improved.In addition,
ILHS algorithm can also obtain the data square information (such as mean value and standard deviation) and PDF function of Probabilistic Load Flow analysis.
In order to further illustrate ILHS algorithm, Fig. 3 (a) to Fig. 3 (e) may refer to, it is equally distributed to define two obediences
Stochastic variable uma, umb.The hypothesis as shown in Fig. 3 (a) generates 3 sample points when initial.The purpose of ILHS algorithm is raw
At sample dot matrix (Fig. 3 (a)) in continue growing new sample point, and form a new sample point set.It is assumed that needing altogether
Generate 6 sample points.That is, needing to increase 3 sample points newly in Fig. 3 (a).The stochastic variable uma as shown in Fig. 3 (b),
Umb can be divided into 6 underlapped sections in equal size.In Fig. 3 (b), the space that the sample point generated represents is by item
Line part is covered.As shown in Fig. 3 (c), if the space represented in Fig. 3 (b) deleted, the space that remainder is not represented
(white space) just will appear.
As shown in Fig. 3 (d), the thought based on traditional Latin Hypercube Sampling technology CLHS is easily determined in white space
The position of three sample points.As shown in Fig. 3 (e), white space is put back in being uniformly distributed it can be found that inheriting sample point (circle
Point) and newly-increased sample point (square dot) constitute a new sample point set.This group of new sample point (6 groups of sample points) is relatively former
The sample point set (3 groups of sample points) come can more comprehensively cover the probability distribution of stochastic variable.It, can after obtaining new sample point
New sample point is converted into original probability distribution space, deterministic Load flow calculation network is then inputted.It is worth noting that,
Based on ILHS algorithm power grid Probabilistic Load Flow analysis can re-use the sample point obtained and its Probabilistic Load Flow as a result, from
And greatly improve computational accuracy and efficiency.
It should be noted that preset quantity can be set and be adjusted according to the actual situation, the embodiment of the present invention is to this
Without limitation, it can be such as adjusted according to preset convergence precision, it, can will be pre- when pre-set convergence precision is higher
If quantity setting is relatively larger, when preset convergence precision is lower, preset quantity can be arranged relatively smaller.
S104: whole air speed values and whole load values are input to certainty Load flow calculation network, trend after being updated
Calculated result.
After obtaining obeying each newly-increased air speed value and each newly-increased load value of Arbitrary distribution, can by whole air speed values and
Whole load values are input to certainty Load flow calculation network, again based on initial air speed value, original negative charge values, newly-increased air speed value and
Newly-increased load value carries out probabilistic load flow, calculation of tidal current after being updated.
S105: initial calculation of tidal current and calculation of tidal current after update are compared, according to obtained comparison knot
Fruit judges whether to reach the preset condition of convergence, if it is not, S103 is thened follow the steps, if so, thening follow the steps S106.
The condition of convergence can be preset, it, can be by initial Load flow calculation after being updated after calculation of tidal current
As a result it is compared with calculation of tidal current after update, is judged whether to reach preset convergence item according to obtained comparing result
Part can repeat sharp in step S103 if it is not, the calculation of tidal current difference for then illustrating that front and back obtains twice is also bigger
With ILHS algorithm sample dot matrix not by represented in probability space generate preset quantity sample point the step of, if so,
The calculation of tidal current difference for illustrating that front and back obtains twice is smaller, can continue to execute step S106.
The setting up procedure of the condition of convergence and convergence precision can be as follows:
It presets convergence precision β and is set as 5%；
The condition of convergence are as follows:
Wherein,For the data square information of kth time iteration posterior probability calculation of tidal current.
Any one square information of the first to the 9th rank square can be selected as the condition of convergence.
S106: the monitoring to operation of power networks state is completed according to calculation of tidal current after update.
After determination reaches the default condition of convergence, it can be completed according to calculation of tidal current after update to operation of power networks shape
The monitoring of state.
According to the above-mentioned technical solution, by being based on wind speed historical data and demand history number using by NPNT algorithm
According to the input probability model of foundation, the sample point that will acquire transforms to original domain, to obtain obeying the wind speed of Arbitrary distribution
Value and load value, can be directly based upon wind speed historical data and demand history data establish input probability model, not need in advance
Acquisition probability density function significantly improves power grid Probabilistic Load Flow analysis precision, can pass through iteration meter using ILHS algorithm
Calculation adaptively evaluates reach the preset condition of convergence needed for sample point quantity, significantly improve power grid Probabilistic Load Flow point
Efficiency is analysed, the balance of power grid Probabilistic Load Flow analysis precision and efficiency is realized, to realize to the effective of the operating status of power grid
Monitoring.
It should be noted that based on the above embodiment one, the embodiment of the invention also provides be correspondingly improved scheme.Rear
Involved in continuous embodiment with can mutually be referred between same steps or corresponding steps in above-described embodiment one, corresponding beneficial effect
Can also be cross-referenced, it is no longer repeated one by one in improvement embodiment below.
Embodiment two:
Referring to fig. 2, Fig. 2 is another implementation flow chart of operation of power networks state monitoring method in the embodiment of the present invention, should
Method may comprise steps of:
S201: when receiving the instruction of operation of power networks status monitoring, acquisition is uniformly distributed middle initial number sample point, obtains
To sample dot matrix.
S202: it is sorted using cholesky decomposition algorithm to each sample point, obtains initial ranking results.
After getting the sample dot matrix being made of initial number sample point, it can use cholesky and decompose calculation
Method sorts to each sample point, obtains initial ranking results.
S203:, will using the nine rank multinomial input probability models established based on NPNT algorithm according to initial ranking results
Each sample point transformation obtains each initial air speed value for obeying Arbitrary distribution and each initial load to original domain in sample dot matrix
Value.
Wherein, input probability model is to be established based on wind speed historical data and demand history data.
After obtaining initial ranking results, nine established based on NPNT algorithm can be utilized according to initial ranking results
Sample point transformation each in sample dot matrix to original domain, detailed process be may include that will acquire by rank multinomial input probability model
To the sample point of the initial number being uniformly distributed be converted into independent Gaussian range of distribution, use ZindependentIt indicates to obey independent mark
The stochastic variable of quasi- Gaussian Profile, and the correlation matrix R between Gaussian Profile is acquired in advanceZ, wherein correlation matrix can indicate
Are as follows:
Rz=LLΤ；
Wherein, L indicates lower triangular matrix, is acquired by Cholesky decomposition and is sorted to obtain to each sample point.
Pass through formula:
Z=LZindependent；
Can calculate containing correlation is RZMultidimensional standardized normal distribution variable Z.By the multidimensional normal state containing correlation point
Cloth variable Z substitutes into corresponding nine rank multinomial:
Wherein, ai,kFor multinomial coefficient.
There is correlation and normalised Arbitrary distribution stochastic variable to obtain.Standardized stochastic variable is gone into standard
Change to get arriving with correlation and obeying the multiple random variables of Arbitrary distribution.
In the input probability model established using NPNT algorithm, the estimation procedure of multinomial coefficient may include:
Nine rank multinomials can indicate are as follows:
Wherein, xoIndicate the random variable of continuous type (such as wind speed) in actual electric network, μxAnd σxStochastic variable Biao Shi not inputted
xoMean value and standard deviation, x indicate by standardization after input stochastic variable.Z indicates to obey the random of standardized normal distribution
Variable, ak(k=1,2..., 9) obeys the stochastic variable of Arbitrary distribution for can be by the stochastic variable of obedience standardized normal distribution
Simulation.Data square is normally used for characterizing the probability characteristics of random data.The present invention uses probability right square (probability
Weighted moment, PWM) description power grid in stochastic source historical data probability characteristics.The method for calculating PWM is as follows:
Size sequence x is carried out to input stochastic variable1≤…≤xi…≤xn, then PWM can be acquired by following formula:
The coefficient of nine rank multinomials can be found out based on following two formula:
Wherein, Φ (z) andRespectively indicate the cumulative distribution function and probability density function of standardized normal distribution.It indicates a constant value, can be acquired by numerical integration.Based on linearisation as above
Formula can easily solve the coefficient of nine rank multinomials.
Estimation procedure to the related coefficient in standard normal space may include:
Correlation cannot be ignored between stochastic variable in the power grid of adjoining area.Assuming that x1And x2It is two obedience Arbitrary distributions
And pass through standardized stochastic variable.By above-mentioned nine rank multinomial it is found that simulating x with standardized normal distribution1And x2, can indicate
Are as follows:
Stochastic variable z1、z2Correlation coefficient ρ between (obeying standardized normal distribution)zWith stochastic variable x1、x2It (may obey
Arbitrary distribution) between correlation coefficient ρxFunctional relation can indicate are as follows:
Wherein, μrAnd σrRespectively indicate the mean value and standard deviation for obeying Arbitrary distribution stochastic variable.
E(x1x2) can be expressed as about ρzMultinomial, i.e. above formula can be write as:
Generally, stochastic variable x can be estimated by historical data1、x2Correlation between (Arbitrary distribution may be obeyed)
Coefficient ρx, stochastic variable z can be acquired by above formula using dichotomy1、z2Related coefficient between (obeying standardized normal distribution)
ρz。
The correlation matrix acquired based on stochastic variable historical data:
The correlation matrix for finding out standard normal space can be corresponded to
Since air speed value and load value are the data with correlation, thus by by independent Gaussian distribution transformation be containing
The Gaussian distributed random variable of correlation can be further improved power grid Probabilistic Load Flow analysis precision.
S204: each air speed value and each load value are inputted into certainty Load flow calculation network, obtain initial calculation of tidal current.
S205: the sample that preset quantity is generated in probability space is not represented in sample dot matrix using ILHS algorithm
Point.
S206: to whole sample points and being resequenced using cholesky decomposition algorithm, and sort knot after being updated
Fruit.
Sample dot matrix by do not represented in probability space generate preset quantity sample point after, can use
Cholesky decomposition algorithm is to whole sample points and resequences, ranking results after being updated.
S207: according to sequence after update as a result, using input probability model by newly-generated each sample point transformation to original
Domain obtains each newly-increased air speed value for obeying Arbitrary distribution and each newly-increased load value.
After being updated after ranking results, it can use input probability model and arrive newly-generated each sample point transformation
Original domain obtains each newly-increased air speed value for obeying Arbitrary distribution and each newly-increased load value.Newly-generated each sample point is transformed into
The process of original domain is referred to the associated description in step S203, does not repeat herein.
S208: whole air speed values and whole load values are input to certainty Load flow calculation network, trend after being updated
Calculated result.
S209: initial calculation of tidal current and calculation of tidal current after update are compared, according to obtained comparison knot
Fruit judges whether to reach the preset condition of convergence, if it is not, S205 is thened follow the steps, if so, thening follow the steps S210.
S210: the monitoring to operation of power networks state is completed according to calculation of tidal current after update.
S211: operation of power networks status monitoring result is obtained.
By completing the monitoring to operation of power networks state according to calculation of tidal current after update, operation of power networks state prison is obtained
Survey result.
S212: efficiency assessment is carried out to operation of power networks status monitoring result.
After getting operation of power networks status monitoring result, validity can be carried out to operation of power networks status monitoring result
Assessment.
Referring to fig. 4, when Fig. 4 illustrates NPNT-ILHS and NPNT-CLHS algorithm using different sample sizes, what is obtained is straight
Flow the average value of busbar voltage FHSI.Obviously, the computational accuracy of NPNT-ILHS and NPNT-CLHS algorithm is almost the same.Work as sample
When amount is 3600,3700 and 3800, the average value of NPNT-CLHS and NPNT-ILHS algorithm DC bus-bar voltage FHSI is respectively
94.32%, 92.15% and 94.07% (NPNT-CLHS), 94.09%, 92.47% and 93.89% (NPNT-ILHS).From number
The actuarial precision that value result can be seen that two kinds of algorithms is very close.
Referring to Fig. 5, (increase by 100 every time) when sample size increases to 3800 from 100, NPNT-CLHS and NPNT-ILHS
The calculating time of algorithm.When sample size is respectively 100,200,300,400 and 500, NPNT-CLHS and NPNT-ILHS are calculated
The calculating time of method is respectively 21.71s, 42.36s, 65.98s, 85.75s and 103.89s (for NPNT-CLHS algorithm),
21.75s, 21.91s, 22.05s, 22.24s and 22.51s (for NPNT-ILHS algorithm).From figure 5 it can be seen that NPNT-
ILHS algorithm is respectively less than 40s in each iterative process evaluation time of falling into a trap, and the meter in NPNT-CLHS algorithm major part iterative process
Evaluation time is all larger than 40s.Obviously, Probabilistic Load Flow algorithm provided by the embodiment of the present invention can greatly improve alternating current-direct current mixed connection
The computational efficiency of power grid.The reason is that NPNT-ILHS algorithm can concentrate newly-increased sample point in existing sample point, and again sharp
With obtained probabilistic load flow as a result, its efficiency in probability analysis is substantially improved.And NPNT-CLHS algorithm
Do not have the ability of sample size needed for assessment AC-DC hybrid power grid Probabilistic Load Flow is analyzed, so it is required when calculating every time
It regenerates a completely new sample point set to lay equal stress on newly to power grid progress Probabilistic Load Flow analysis, computation burden is caused to aggravate.
Corresponding to above method embodiment, the embodiment of the invention also provides a kind of operation of power networks state monitoring apparatus,
Operation of power networks state monitoring apparatus described below can correspond to each other reference with above-described operation of power networks state monitoring method.
Referring to Fig. 6, Fig. 6 is a kind of structural block diagram of operation of power networks state monitoring apparatus in the embodiment of the present invention, the device
May include:
Initial value obtains module 61, for obtaining in being uniformly distributed just when receiving the instruction of operation of power networks status monitoring
Beginning quantity sample point, obtains sample dot matrix, and using the input probability model established based on NPNT algorithm by sample point square
Each sample point transformation obtains each initial air speed value and each original negative charge values of obeying Arbitrary distribution to original domain in battle array；Wherein, defeated
Entering probabilistic model is to be established based on wind speed historical data and demand history data；
Initial results obtain module 62, for each initial air speed value and each original negative charge values to be inputted certainty Load flow calculation
Network obtains initial calculation of tidal current；
Added value obtains module 63, for being generated using ILHS algorithm in not represented in probability space for sample dot matrix
The sample point of preset quantity obtains obeying any using input probability model by newly-generated each sample point transformation to original domain
Each newly-increased air speed value of distribution and each newly-increased load value；
Result obtains module 64 after update, for whole air speed values and whole load values to be input to certainty Load flow calculation
Network, calculation of tidal current after being updated；
Judgment module 65, for initial calculation of tidal current and calculation of tidal current after update to be compared, according to
To comparing result judge whether to reach the preset condition of convergence, if it is not, then trigger added value obtain module, if so, triggering
State monitoring module；
State monitoring module 66, for completing the monitoring to operation of power networks state according to calculation of tidal current after update.
According to the above-mentioned technical solution, by being based on wind speed historical data and demand history number using by NPNT algorithm
According to the input probability model of foundation, the sample point that will acquire transforms to original domain, to obtain obeying the wind speed of Arbitrary distribution
Value and load value, can be directly based upon wind speed historical data and demand history data establish input probability model, not need in advance
Acquisition probability density function significantly improves power grid Probabilistic Load Flow analysis precision, can pass through iteration meter using ILHS algorithm
Calculation adaptively evaluate reach preset condition needed for sample point quantity, significantly improve power grid Probabilistic Load Flow analysis effect
Rate realizes the balance of power grid Probabilistic Load Flow analysis precision and efficiency, to realize effective monitoring to the operating status of power grid.
In a kind of specific embodiment of the invention, which can also include:
Ranking results obtain module, after the initial number sample point in acquisition is uniformly distributed, using being based on
The input probability model that NPNT algorithm is established utilizes cholesky for before sample point transformation to original domain each in sample dot matrix
Decomposition algorithm sorts to each sample point, obtains initial ranking results；And probability space is not represented in sample dot matrix
It is middle generate preset quantity sample point after, using input probability model by newly-generated each sample point transformation to original domain it
Before, it to whole sample points and is resequenced using cholesky decomposition algorithm, ranking results after being updated；
It includes domain transformation submodule that added value, which obtains module 63, domain transformation submodule be specially according to initial ranking results,
Using the input probability model established based on NPNT algorithm by sample point transformation each in sample dot matrix to original domain, and according to more
Sequence after new is as a result, using input probability model by the module of newly-generated each sample point transformation to original domain.
In a kind of specific embodiment of the invention, domain transformation submodule is specially to utilize to establish based on NPNT algorithm
Nine rank multinomial input probability models are by the module of sample point transformation each in sample dot matrix to original domain.
In a kind of specific embodiment of the invention, which can also include:
Monitoring result obtains module, for monitoring of the calculation of tidal current completion to operation of power networks state after according to update
Later, operation of power networks status monitoring result is obtained；
Efficiency assessment module, for carrying out efficiency assessment to operation of power networks status monitoring result.
Corresponding to above method embodiment, referring to Fig. 7, Fig. 7 is that operation of power networks status monitoring provided by the present invention is set
Standby schematic diagram, the equipment may include:
Memory 71, for storing computer program；
Processor 72 can realize following steps when for executing the computer program of the above-mentioned storage of memory 71:
When receiving the instruction of operation of power networks status monitoring, acquisition is uniformly distributed middle initial number sample point, obtains sample
This dot matrix, and using the input probability model established by NPNT algorithm by sample point transformation each in sample dot matrix to original
Domain obtains each initial air speed value and each original negative charge values of obeying Arbitrary distribution；Wherein, input probability model is to be gone through based on wind speed
What history data and demand history data were established；Each initial air speed value and each original negative charge values are inputted into certainty Load flow calculation net
Network obtains initial calculation of tidal current；The sample point that preset quantity is generated in probability space is not represented in sample dot matrix,
Using input probability model by newly-generated each sample point transformation to original domain, each newly-increased air speed value for obeying Arbitrary distribution is obtained
With each newly-increased load value；Whole air speed values and whole load values are input to certainty Load flow calculation network using ILHS algorithm,
Calculation of tidal current after being updated；Initial calculation of tidal current and calculation of tidal current after update are compared, according to
To comparing result judge whether to reach the preset condition of convergence；If it is not, then repeating not represented in sample dot matrix
The step of sample point of preset quantity is generated in probability space, until reaching the condition of convergence；If so, according to trend meter after update
Calculate monitoring of the result completion to operation of power networks state.
Above method embodiment is please referred to for the introduction of equipment provided by the invention, this will not be repeated here by the present invention.
It is computer-readable the present invention also provides a kind of computer readable storage medium corresponding to above method embodiment
It is stored with computer program on storage medium, can realize following steps when computer program is executed by processor:
When receiving the instruction of operation of power networks status monitoring, acquisition is uniformly distributed middle initial number sample point, obtains sample
This dot matrix, and using the input probability model established by NPNT algorithm by sample point transformation each in sample dot matrix to original
Domain obtains each initial air speed value and each original negative charge values of obeying Arbitrary distribution；Wherein, input probability model is to be gone through based on wind speed
What history data and demand history data were established；Each air speed value and each load value are inputted into certainty Load flow calculation network, obtained just
Beginning calculation of tidal current；The sample point for generating preset quantity in probability space is not represented in sample dot matrix, utilizes input
Newly-generated each sample point transformation to original domain is obtained each newly-increased air speed value for obeying Arbitrary distribution and each newly-increased by probabilistic model
Load value；Whole air speed values and whole load values are input to certainty Load flow calculation network using ILHS algorithm, are updated
Calculation of tidal current afterwards；Initial calculation of tidal current and calculation of tidal current after update are compared, according to obtained comparison
As a result judge whether to reach the preset condition of convergence；Probability space is not represented in sample dot matrix if it is not, then repeating
The step of middle sample point for generating preset quantity, until reaching the condition of convergence；If so, complete according to calculation of tidal current after update
The monitoring of pairs of operation of power networks state.
The computer readable storage medium may include: USB flash disk, mobile hard disk, read-only memory (Read-Only
Memory, ROM), random access memory (Random Access Memory, RAM), magnetic or disk etc. is various to deposit
Store up the medium of program code.
Above method embodiment is please referred to for the introduction of computer readable storage medium provided by the invention, the present invention exists
This is not repeated them here.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with it is other
The difference of embodiment, same or similar part may refer to each other between each embodiment.For being filled disclosed in embodiment
It sets, for equipment and computer readable storage medium, since it is corresponded to the methods disclosed in the examples, so the comparison of description
Simply, reference may be made to the description of the method.
Used herein a specific example illustrates the principle and implementation of the invention, and above embodiments are said
It is bright to be merely used to help understand technical solution of the present invention and its core concept.It should be pointed out that for the common of the art
, without departing from the principle of the present invention, can be with several improvements and modifications are made to the present invention for technical staff, these
Improvement and modification are also fallen within the protection scope of the claims of the present invention.
Claims (10)
1. a kind of operation of power networks state monitoring method characterized by comprising
When receiving the instruction of operation of power networks status monitoring, acquisition is uniformly distributed middle initial number sample point, obtains sample point
Matrix, and utilize and transformed to the sample point each in the sample dot matrix based on the input probability model that NPNT algorithm is established
Original domain obtains each initial air speed value and each original negative charge values of obeying Arbitrary distribution；Wherein, the input probability model is base
It is established in wind speed historical data and demand history data；
Each initial air speed value and each original negative charge values are inputted into certainty Load flow calculation network, obtain initial trend meter
Calculate result；
The sample point for generating preset quantity in probability space is not represented in the sample dot matrix using ILHS algorithm, is utilized
Newly-generated each sample point is transformed to the original domain by the input probability model, obtains obeying each new of Arbitrary distribution
Increase air speed value and each newly-increased load value；
Whole air speed values and whole load values are input to the certainty Load flow calculation network, Load flow calculation knot after being updated
Fruit；
The initial calculation of tidal current and calculation of tidal current after the update are compared, according to obtained comparing result
Judge whether to reach the preset condition of convergence；
If it is not, then repeat it is described using ILHS algorithm the sample dot matrix do not represented generated in probability space it is pre-
If the step of sample point of quantity, until reaching the condition of convergence；
If so, completing the monitoring to operation of power networks state according to calculation of tidal current after the update.
2. operation of power networks state monitoring method according to claim 1, which is characterized in that initial in acquisition is uniformly distributed
After quantity sample point, using the input probability model established based on NPNT algorithm by the sample each in the sample dot matrix
Before this point transformation to original domain, further includes:
Each sample point is ranked up using cholesky decomposition algorithm, obtains initial ranking results；
The sample point each in the sample dot matrix is transformed into original using the input probability model established based on NPNT algorithm
Beginning domain, comprising:
According to the initial ranking results, using the input probability model established based on NPNT algorithm by the sample point square
Each sample point transforms to the original domain in battle array；
Using ILHS algorithm the sample dot matrix not by represented in probability space generate preset quantity sample point after,
Before newly-generated each sample point is transformed to the original domain using the input probability model, further includes:
It to all sample points and is resequenced using cholesky decomposition algorithm, ranking results after being updated；
Newly-generated each sample point is transformed into the original domain using the input probability model, comprising:
According to sequence after the update as a result, newly-generated each sample point is transformed to institute using the input probability model
State original domain.
3. operation of power networks state monitoring method according to claim 2, which is characterized in that established using based on NPNT algorithm
The input probability model sample point each in the sample dot matrix is transformed into the original domain, comprising:
Using the nine rank multinomial input probability models established based on NPNT algorithm by the sample each in the sample dot matrix
Point transformation is to the original domain.
4. operation of power networks state monitoring method according to any one of claims 1 to 3, which is characterized in that according to
After calculation of tidal current is completed to the monitoring of operation of power networks state after update, further includes:
Obtain operation of power networks status monitoring result；
Efficiency assessment is carried out to the operation of power networks status monitoring result.
5. a kind of operation of power networks state monitoring apparatus characterized by comprising
Initial value obtains module, for when receiving the instruction of operation of power networks status monitoring, acquisition to be uniformly distributed middle initial number
A sample point, obtains sample dot matrix, and using the input probability model established based on NPNT algorithm by the sample dot matrix
In each sample point transform to original domain, obtain obey Arbitrary distribution each initial air speed value and each original negative charge values；Wherein,
The input probability model is to be established based on wind speed historical data and demand history data；
Initial results obtain module, based on by each initial air speed value and each original negative charge values input certainty trend
Network is calculated, initial calculation of tidal current is obtained；
Added value obtains module, for being generated in advance using ILHS algorithm in not represented in probability space for the sample dot matrix
If the sample point of quantity, newly-generated each sample point is transformed into the original domain using the input probability model, is obtained
To each newly-increased air speed value and each newly-increased load value for obeying Arbitrary distribution；
Result obtains module after update, for whole air speed values and whole load values to be input to the certainty Load flow calculation net
Network, calculation of tidal current after being updated；
Judgment module, for the initial calculation of tidal current and calculation of tidal current after the update to be compared, according to
Obtained comparing result judges whether to reach the preset condition of convergence, if it is not, then triggering the added value obtains module, if so,
Then triggering state monitoring modular；
The state monitoring module, for completing the monitoring to operation of power networks state according to calculation of tidal current after the update.
6. operation of power networks state monitoring apparatus according to claim 5, which is characterized in that further include:
Ranking results obtain module, after the initial number sample point in acquisition is uniformly distributed, calculate using based on NPNT
Before the sample point each in the sample dot matrix is transformed to original domain by the input probability model that method is established, utilize
Cholesky decomposition algorithm is ranked up each sample point, obtains initial ranking results；And in utilization ILHS algorithm in institute
State sample dot matrix not by represented in probability space generate preset quantity sample point after, utilize the input probability model
Before newly-generated each sample point is transformed to the original domain, using cholesky decomposition algorithm to all samples
It puts and resequences, ranking results after being updated；
It includes domain transformation submodule that the added value, which obtains module, and the domain transformation submodule is specially according to the initial sequence
As a result, using the input probability model established based on NPNT algorithm by the sample point transformation each in the sample dot matrix
To the original domain, and according to sequence after the update as a result, using the input probability model by newly-generated each sample
This point transformation arrives the module of the original domain.
7. operation of power networks state monitoring apparatus according to claim 6, which is characterized in that the domain transformation submodule is specific
To utilize the nine rank multinomial input probability models established based on NPNT algorithm by the sample point each in the sample dot matrix
Transform to the module of the original domain.
8. according to the described in any item operation of power networks state monitoring apparatus of claim 5 to 7, which is characterized in that further include:
Monitoring result obtains module, for monitoring of the calculation of tidal current completion to operation of power networks state after according to the update
Later, operation of power networks status monitoring result is obtained；
Efficiency assessment module, for carrying out efficiency assessment to the operation of power networks status monitoring result.
9. a kind of operation of power networks condition monitoring device characterized by comprising
Memory, for storing computer program；
Processor realizes that operation of power networks state is supervised as described in any one of Claims 1-4 when for executing the computer program
The step of survey method.
10. a kind of computer readable storage medium, which is characterized in that be stored with computer on the computer readable storage medium
Program realizes the operation of power networks status monitoring as described in any one of Claims 1-4 when the computer program is executed by processor
The step of method.
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| CN112491056A (en) * | 2020-11-23 | 2021-03-12 | 广东电网有限责任公司电网规划研究中心 | Probabilistic load flow calculation method, device and medium for alternating current-direct current hybrid power grid |
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