CN106780134A - The determination method and system of the wind power generating algorithm apoplexy electricity condition number upper limit - Google Patents
The determination method and system of the wind power generating algorithm apoplexy electricity condition number upper limit Download PDFInfo
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Abstract
The present invention relates to a kind of determination method and system of the wind power status number upper limit in wind power generating algorithm based on MCMC, the method is collected and arranges history wind power output data first, and it is divided into different number of state of exerting oneself, state transfer frequency and state transition probability matrix that different numbers are exerted oneself under state between each wind power output data point are calculated respectively, and Chi-square Test is carried out to it, the critical status number of exerting oneself of Markov switching characteristic is determined for compliance with, as the status number upper limit of exerting oneself that can be divided.For theoretical foundation has been established in the further investigation of wind power output time series modeling.The present invention can determine the maximum wind status number for needing to divide by the chi square distribution method of inspection, and the precision that MCMC methodology generates wind power sequence is lifted to greatest extent.
Description
Technical field
The present invention relates to technical field of new energy power generation, and in particular to one kind is based on MCMC wind power generating algorithm apoplexy
The determination method and system of the electrical power status number upper limit.
Background technology
Markov chain Monte-Carlo method (Markov Chain Monte Carlo, MCMC) method is a kind of simple and practical
The random generation method of wind power output time series.The wind power output stochastic variable generated using the method disclosure satisfy that it is being determined
Transition probability requirement between the different conditions of justice, while MCMC methodology can make to generate to obtain wind power output time series reservation original
The average of beginning data, standard deviation, probability density function (Probability Density Function, PDF) and auto-correlation
Coefficient (Autocorrelation Function, ACF), therefore have practical value higher.
But MCMC methods are only used for the discrete state point of generation, in each state the value of wind power be it is arbitrary,
Typically it is overlapped using equally distributed random number.After taking this processing mode, the wind power output time series of generation
Characteristic is closely bound up with the selection of status number.Status number defines more, then generate the characteristic and original of wind power output time series
Beginning wind-powered electricity generation sequence closer to.But excessive status number can cause the transfer between wind power output state without Markov property, disobey
Carried on the back the theoretical foundation of MCMC methodology, thus the selection MCMC methodology of status number a major issue.
The content of the invention
To solve above-mentioned deficiency of the prior art, it is an object of the invention to provide a kind of MCMC wind powers generating algorithm
The determination method and system of the middle wind power status number upper limit, for the modeling accuracy for lifting MCMC methodology to greatest extent is provided
Theoretical foundation.
The purpose of the present invention is realized using following technical proposals：
The present invention also provides a kind of determination method of the wind power generating algorithm apoplexy electricity condition number upper limit based on MCMC,
It thes improvement is that the determination method comprises the steps：
Step 1：Collect and finishing time length is that 1 year resolution ratio is the wind-powered electricity generation station wind power output history of 15min
Data；
Step 2：Wind-powered electricity generation station historical data is collected and normalized；
Step 3：Determine Markov Transition Probabilities matrix；
Step 4：Determine that the critical condition of Markov distribution is obeyed in wind power transfer.
Further, in the step 2, calculate wind-powered electricity generation station history wind power output time series and exist with installed capacity of wind-driven power
The ratio of correspondence time data value, obtains normalized history wind power output value, and normalized history wind power output value calculates public
Formula is as follows：
Wherein：PsIt is normalized history wind power output value, PtIt is history power generating value, PinstallIt is installed capacity.
Further, wind-powered electricity generation station historical data is collected with after normalized, defines the difference of wind power output
Exert oneself state, by the possibility span discretization of wind power output, each power interval represents a state of wind power, if
Status number is N, if normalized history wind power output value PsMeet following formula (2), then normalized history wind power output value PsBelong to
State i；
Wherein：I represents state, i=1,2 ... N, normalized history wind power output value PsRepresent the history of wind power output
Moment is state i.
Further, the step 3 includes：Calculate the state-transition matrix P under the conditions of different conditions number N, the state
Transfer matrix P is a matrix of N × N, as shown in formula (3)
Each element p in state-transition matrix PijValue to represent wind power output current time be state i, turn in subsequent time
The probability of state j is moved on to, each element pijCalculated by following formula (4)：
pij=Pr(xn=j | xn-1=i) (4)
Wherein：xnAnd xn-1The state at n and n-1 moment is represented respectively.
Further, in the step 4, when sample number is more than 10000, the calculated value of equation (5) is to submit to freedom
Spend is (n-1)2Chi square distribution, the value of calculation equation (5) is as follows：
Wherein：χ is (n-1) to submit to the free degree2Chi square distribution calculated value；fijIt is the transfer frequency between state,
p·jIt is marginal probability, is calculated by following formula：
Wherein：I represents state, i=1,2 ... N, N are status number.
Further, in the step 4, for level of significance α=0.05 for giving, following formula (7) is solved, it is resulting
The value of N is status number higher limit, the critical condition as tried to achieve,Value by being looked into known chi-square distribution table
；
Wherein：χαChi-square distribution table under given level of significance α.
The present invention also provides a kind of system for determining the wind power generating algorithm apoplexy electricity condition number upper limit based on MCMC,
It thes improvement is that the system includes：
Collection module：It is that the wind-powered electricity generation station wind-powered electricity generation that 1 year resolution ratio is 15min goes out for collecting simultaneously finishing time length
Power historical data；
Processing module：For being collected and normalized to wind-powered electricity generation station historical data；
Computing module：For determining Markov Transition Probabilities matrix；
Critical condition determining module：Determine that the critical condition of Markov distribution is obeyed in wind power transfer.
Further, the processing module, is additionally operable to：Wind-powered electricity generation station history wind power output time series is calculated to be filled with wind-powered electricity generation
Machine capacity obtains normalized history wind power output value in the ratio of correspondence time data value.
Further, the computing module, is additionally operable to：Calculate the state-transition matrix P under the conditions of different conditions number N, institute
It is a matrix of N × N to state state-transition matrix P, and matrix form is as follows：
Each element p in state-transition matrix PijValue to represent wind power output current time be state i, turn in subsequent time
Move on to the probability of state j.
Further, the critical condition determining module, is additionally operable to：Determine that wind power turns using chi square distribution inspection
Move the critical condition for obeying Markov distribution.
In order to some aspects to the embodiment for disclosing have a basic understanding, simple summary is shown below is.Should
Summarized section is not extensive overview, nor to determine key/critical component or describe the protection domain of these embodiments.
Its sole purpose is that some concepts are presented with simple form, in this, as the preamble of following detailed description.
Compared with immediate prior art, the excellent effect that the technical scheme that the present invention is provided has is：
The invention provides a kind of determination of the wind power status number upper limit in wind power generating algorithm based on MCMC
Method and system, the method is collected and arranges history wind power output data first, and is divided into different number of shape of exerting oneself
State, calculate respectively different numbers exert oneself state transfer frequency under state between each wind power output data point and state transfer it is general
Rate matrix, and carry out Chi-square Test to it, is determined for compliance with the critical status number of exerting oneself of Markov switching characteristic, used as can divide
The status number upper limit of exerting oneself, be that theoretical foundation has been established in the further investigation of wind power output time series modeling.The present invention is by card
Square distribution inspection method, can determine the maximum wind status number for needing to divide, and MCMC methodology generation wind is lifted to greatest extent
The precision of electrical power sequence.
Brief description of the drawings
Fig. 1 is the determination of the wind power status number upper limit in the wind power generating algorithm based on MCMC that the present invention is provided
The flow chart of method.
Specific embodiment
Specific embodiment of the invention is described in further detail below in conjunction with the accompanying drawings.
The following description and drawings fully show specific embodiments of the present invention, to enable those skilled in the art to
Put into practice them.Other embodiments can include structure, logic, it is electric, process and it is other changes.Embodiment
Only represent possible change.Unless explicitly requested, otherwise single component and function are optional, and the order for operating can be with
Change.The part of some embodiments and feature can be included in or replace part and the feature of other embodiments.This hair
The scope of bright embodiment includes the gamut of claims, and all obtainable of claims is equal to
Thing.Herein, these embodiments of the invention can individually or generally be represented that this is only with term " invention "
For convenience, and if in fact disclosing the invention more than, it is not meant to automatically limit the scope of the application to appoint
What single invention or inventive concept.
The selection of wind-powered electricity generation status number is depended on using the precision of MCMC methodology generation wind power time series, status number is fixed
It is adopted must be more, then generate characteristic and the original wind-powered electricity generation sequence of wind power output time series closer to.But excessive status number can lead
Cause the transfer between wind power output state not have Markov property, run counter to the theoretical foundation of MCMC methodology, therefore status number
Choose a major issue of MCMC methodology.The present invention can determine the maximum for needing to divide by the chi square distribution method of inspection
Wind-powered electricity generation status number, lifts the precision that MCMC methodology generates wind power sequence to greatest extent.
Embodiment one
The invention provides a kind of determination of the wind power status number upper limit in wind power generating algorithm based on MCMC
Method.The method is collected and arranges history wind power output data first, and is divided into different number of state of exerting oneself, respectively
State transfer frequency and state transition probability matrix that different numbers are exerted oneself under state between each wind power output data point are calculated,
And Chi-square Test is carried out to it, the critical status number of exerting oneself of Markov switching characteristic is determined for compliance with, as exerting oneself for can dividing
The status number upper limit.For theoretical foundation has been established in the further investigation of wind power output time series modeling.As shown in figure 1, specific implementation
Step is as follows：
Step 1：Collect and finishing time length is that 1 year resolution ratio is the wind-powered electricity generation station wind power output history of 15min
Data；
Step 2：Wind-powered electricity generation station historical data is collected and normalized；
Step 3：Determine Markov Transition Probabilities matrix；
Step 4：Determine that the critical condition of Markov distribution is obeyed in wind power transfer.
Specifically, in the step 2, calculating wind-powered electricity generation station history wind power output time series with installed capacity of wind-driven power right
The ratio of moment data value is answered, normalized history wind power output value, normalized history wind power output value computing formula is obtained
It is as follows：
Wherein：PsIt is normalized history wind power output value, PtIt is history power generating value, PinstallIt is installed capacity.
Specifically, being collected to wind-powered electricity generation station historical data with after normalized, the difference for defining wind power output goes out
Power state, by the possibility span discretization of wind power output, each power interval represents a state of wind power, if shape
State number is N, if normalized history wind power output value PsMeet following formula (2), then normalized history wind power output value PsBelong to shape
State i；
Wherein：I represents state, i=1,2 ... N, normalized history wind power output value PsRepresent the history of wind power output
Moment is state i.
Specifically, the step 3 includes：The state-transition matrix P under the conditions of different conditions number N is calculated, the state turns
It is a matrix of N × N to move matrix P, as shown in formula (3)
Each element p in state-transition matrix PijValue to represent wind power output current time be state i, turn in subsequent time
The probability of state j is moved on to, each element pijCalculated by following formula (4)：
pij=Pr(xn=j | xn-1=i) (4)
Wherein：xnAnd xn-1The state at n and n-1 moment is represented respectively.
Specifically, in the step 4, when sample number is more than 10000, the calculated value of equation (5) is to submit to the free degree
It is (n-1)2Chi square distribution, the value of calculation equation (5) is as follows：
Wherein：χ is (n-1) to submit to the free degree2Chi square distribution calculated value；fijIt is the transfer frequency between state,
p·jIt is marginal probability, is calculated by following formula：
Wherein：I represents state, i=1,2 ... N, N are status number.
Specifically, in the step 4, for level of significance α=0.05 for giving, solving following formula (7), resulting N
Value be status number higher limit, the critical condition as tried to achieve,Value by being looked into known chi-square distribution table
；
Wherein：χαChi-square distribution table under given level of significance α.
Embodiment two
Based on same design, the present invention also provides wind-powered electricity generation shape in a kind of wind power generating algorithm of the determination based on MCMC
The system of the state number upper limit, including：
Collection module：It is that the wind-powered electricity generation station wind-powered electricity generation that 1 year resolution ratio is 15min goes out for collecting simultaneously finishing time length
Power historical data；
Processing module：For being collected and normalized to wind-powered electricity generation station historical data；
Computing module：For determining Markov Transition Probabilities matrix；
Critical condition determining module：Determine that the critical condition of Markov distribution is obeyed in wind power transfer.
The processing module, is additionally operable to：Wind-powered electricity generation station history wind power output time series is calculated to exist with installed capacity of wind-driven power
The ratio of correspondence time data value, obtains normalized history wind power output value.
The computing module, is additionally operable to：Calculate the state-transition matrix P under the conditions of different conditions number N, the state transfer
Matrix P is a matrix of N × N, and matrix form is as follows：
Each element p in state-transition matrix PijValue to represent wind power output current time be state i, turn in subsequent time
Move on to the probability of state j.
The critical condition determining module, is additionally operable to：Determine that horse is obeyed in wind power transfer using chi square distribution inspection
The critical condition of Er Kefu distributions.
Embodiment three
A specific example is given below, the data for using are the Jiangsu part wind power plant data of a year, time resolution
Rate is 15min, and data point is 35040, be can be calculated as N=112 using formula (1) to formula (6), is its critical condition.
The above embodiments are merely illustrative of the technical solutions of the present invention rather than its limitations, although with reference to above-described embodiment pair
The present invention has been described in detail, and those of ordinary skill in the art can still enter to specific embodiment of the invention
Row modification or equivalent, these are applying without departing from any modification of spirit and scope of the invention or equivalent
Within pending claims of the invention.
Claims (10)
1. a kind of determination method of the wind power generating algorithm apoplexy electricity condition number upper limit based on MCMC, it is characterised in that institute
Determination method is stated to comprise the steps：
Step 1：Collect and finishing time length is that 1 year resolution ratio is the wind-powered electricity generation station wind power output historical data of 15min；
Step 2：Wind-powered electricity generation station historical data is collected and normalized；
Step 3：Determine Markov Transition Probabilities matrix；
Step 4：Determine that the critical condition of Markov distribution is obeyed in wind power transfer.
2. it is as claimed in claim 1 to determine method, it is characterised in that in the step 2, calculate wind-powered electricity generation station history wind-powered electricity generation and go out
Power time series and installed capacity of wind-driven power obtain normalized history wind power output value in the ratio of correspondence time data value, return
The one history wind power output value computing formula changed is as follows：
Wherein：PsIt is normalized history wind power output value, PtIt is history power generating value, PinstallIt is installed capacity.
3. it is as claimed in claim 2 to determine method, it is characterised in that wind-powered electricity generation station historical data is collected and normalization
After treatment, the difference for defining wind power output is exerted oneself state, by the possibility span discretization of wind power output, each power interval
A state of wind power is represented, if status number is N, if normalized history wind power output value PsMeet following formula (2), then return
The one history wind power output value P for changingsBelong to state i；
Wherein：I represents state, i=1,2 ... N, normalized history wind power output value PsRepresent the historical juncture of wind power output
It is state i.
4. it is as claimed in claim 1 to determine method, it is characterised in that the step 3 includes：Calculate different conditions number N conditions
Under state-transition matrix P, the state-transition matrix P be a matrix of N × N, as shown in formula (3)：
Each element p in state-transition matrix PijValue to represent wind power output current time be state i, be transferred in subsequent time
The probability of state j, each element pijCalculated by following formula (4)：
pij=Pr(xn=j | xn-1=i) (4)
Wherein：xnAnd xn-1The state at n and n-1 moment is represented respectively.
5. it is as claimed in claim 1 to determine method, it is characterised in that in the step 4, when sample number is more than 10000, etc.
The calculated value of formula (5) is to submit to the free degree for (n-1)2Chi square distribution, the value of calculation equation (5) is as follows：
Wherein：χ is (n-1) to submit to the free degree2Chi square distribution calculated value；fijIt is the transfer frequency between state, p·jFor
Marginal probability, is calculated by following formula：
Wherein：I represents state, i=1,2 ... N, N are status number.
6. it is as claimed in claim 5 to determine method, it is characterised in that in the step 4, for the level of significance α for giving
=0.05, following formula (7) is solved, the value of resulting N is status number higher limit, the critical condition as tried to achieve,Value by being checked in known chi-square distribution table；
Wherein：χαChi-square distribution table under given level of significance α.
7. a kind of system that determination is based on the wind power generating algorithm apoplexy electricity condition number upper limit of MCMC, it is characterised in that institute
The system of stating includes：
Collection module：It is that the wind-powered electricity generation station wind power output that 1 year resolution ratio is 15min is gone through for collecting simultaneously finishing time length
History data；
Processing module：For being collected and normalized to wind-powered electricity generation station historical data；
Computing module：For determining Markov Transition Probabilities matrix；
Critical condition determining module：Determine that the critical condition of Markov distribution is obeyed in wind power transfer.
8. system as claimed in claim 7, it is characterised in that the processing module, is additionally operable to：Calculate wind-powered electricity generation station history wind
Electricity is exerted oneself the ratio of time series and installed capacity of wind-driven power in correspondence time data value, obtains normalized history wind power output
Value.
9. system as claimed in claim 7, it is characterised in that the computing module, is additionally operable to：Calculate different conditions number N bars
State-transition matrix P, the state-transition matrix P under part are a matrixes of N × N, and matrix form is as follows：
Each element p in state-transition matrix PijValue to represent wind power output current time be state i, be transferred in subsequent time
The probability of state j.
10. system as claimed in claim 7, it is characterised in that the critical condition determining module, is additionally operable to：Using card side
Distribution inspection come determine wind power transfer obey Markov distribution critical condition.
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| CN113935250A (en) * | 2021-11-25 | 2022-01-14 | 华北电力大学（保定） | New energy cluster modeling method based on comprehensive probability model and Markov matrix |
| CN113935250B (en) * | 2021-11-25 | 2024-04-23 | 华北电力大学（保定） | New energy cluster modeling method based on comprehensive probability model and Markov matrix |
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