CN106684905B - A kind of wind power plant Dynamic Equivalence considering wind-powered electricity generation uncertainty in traffic - Google Patents
A kind of wind power plant Dynamic Equivalence considering wind-powered electricity generation uncertainty in traffic Download PDFInfo
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- H—ELECTRICITY
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Abstract
The invention discloses a kind of wind power plant Dynamic Equivalences for considering wind-powered electricity generation uncertainty in traffic, this method comprises: determining the probability density function and parameter of wind speed and power error in wind-powered electricity generation prediction, more scene sampling are carried out using method of inverting, obtain the sample for obeying Wind turbines prediction probability of error distribution；Establish the sample frequency that every Fans are counted using air speed error and power error as the Two-dimensional Statistical grid of coordinate in statistics grid；Based on improved KL distance, blower is carried out using k-means clustering algorithm and divides group；It is equivalent that single machine is carried out to Wind turbines and collector system in each group；Invention considers wind speed by way of more scenes sampling based on probability distribution and power prediction error is uncertain, there is preferable accuracy in the case where error occurs in wind-powered electricity generation prediction, it can be calculated with better services in the early warning of electric power system dispatching, there is certain engineering application value.
Description
Technical field
The present invention relates to wind power plant equivalence method research fields, and in particular, to a kind of consideration wind-powered electricity generation uncertainty in traffic
Wind power plant Dynamic Equivalence.
Background technique
Large-scale wind power is accessed to bring challenges to all various aspects of operation of power networks, for the dynamic characteristic of wind-electricity integration system
It carries out research power grid operation personnel can be helped to carry out risk to estimate and science decision, and then improve the permeability of wind-powered electricity generation.Big
In the wind farm grid-connected emulation of type, if modeled to every Wind turbines, not only workload is great, but also will affect calculating
Speed, precision and convergence, therefore, it is necessary to study its dynamic equivalent model under the premise of guaranteeing wind power plant output accuracy.
In existing research, main wind power plant Dynamic Equivalence has single machine equivalent method, half equivalent method and multimachine equivalent method etc..It is single
Machine equivalent method refers to that by Wind turbines equivalences all in wind power plant be 1 machine；Half equivalent method refers to the wind-force for retaining wind turbine
Its generator model equivalence is 1 machine by machine part；Multimachine equivalent method refers to Wind turbines according to operating point equivalence Cheng Duotai
Machine.Wherein, multimachine equivalent method is because its precision is high, easy-operating advantage is widely adopted.
Wind turbines in wind power plant are divided into multiple groups according to operation characteristic first by multimachine equivalence, then to the wind in each group
It is equivalent that motor group carries out single machine.The index that can reflect its operating status, wind speed need to be selected when wind turbine component group, fan rotor turns
Speed, Wind turbines state variable, running of wind generating set control area, Wind turbines wind speed, revolving speed, stator voltage, q axis stator electricity
Stream and active power, wind speed, revolving speed and propeller pitch angle overall target etc. have been proposed as wind turbine component group index.However,
Current wind power plant dynamic equivalent research is based on deterministic data, it is believed that exact figures subject to data used by wind turbine component group
According to that is, there is no errors.However in the early warning of electric power system dispatching calculating, wind power plant multimachine Equivalent Model divides group's data will
From wind power prediction data, error is inevitable and has randomness.In the case where wind-powered electricity generation predicts accurate scene, use
Traditional multimachine Equivalent Model based on deterministic data has higher accuracy；However, most of scenes of wind-powered electricity generation prediction are
Inaccuracy, if wind power plant can give simulation calculation band still using the multimachine equivalence method for dividing group based on deterministic data at this time
Carry out large error.
In conclusion present inventor has found above-mentioned technology extremely during realizing the present application technical solution
It has the following technical problems less:
Conventionally, as most of scenes of wind-powered electricity generation prediction are inaccurate, if wind power plant still uses base at this time
Divide the multimachine equivalence method of group in deterministic data, then can bring large error, therefore existing wind power plant etc. to simulation calculation
Value method there are accuracys poor, the technical problem for causing simulation calculation error larger.
Summary of the invention
The present invention provides a kind of wind power plant Dynamic Equivalences for considering wind-powered electricity generation uncertainty in traffic, solve existing
That there are accuracys is poor for wind power plant equivalence method, and the technical problem for causing simulation calculation error larger is realized in wind power
Predict have preferable accuracy can be more preferable in the case where considering wind-powered electricity generation uncertainty in traffic under the scene for error occur
Serve the technical effect that the early warning of electric power system dispatching calculates.
In order to solve the above technical problems, this application provides a kind of wind power plant dynamics etc. for considering wind-powered electricity generation uncertainty in traffic
Value method includes the following steps: it is improved in that the method is to divide group based on wind-powered electricity generation uncertainty in traffic
A, the probability distribution and its parameter for determining wind speed and power error in wind-powered electricity generation prediction carry out more scenes using method of inverting
Sampling obtains the sample for obeying Wind turbines prediction probability of error distribution；
B, it establishes using air speed error and power error as the Two-dimensional Statistical grid of coordinate, counts every Fans in statistics grid
Interior sample frequency；
C, improved KL distance, i.e., improved KL divergence (Kullback-Leibler Divergence), KL divergence are based on
It is called relative entropy (Relative Entropy), carries out blower using k-means (k- mean value) clustering algorithm and divide group；
Kullback-Leibler is English name-to, and the country is referred to as KL divergence,
D, single machine equivalence is carried out to Wind turbines parameter in each group and network parameter.
Further, the step A the following steps are included:
A1, according to priori knowledge or historical forecast error information, determine wind speed error delta in every Fans wind-powered electricity generation prediction
vwWith power error Δ PwCumulative distribution function F (Δ vw)、F(ΔPw)；
A2, N is generated for every FanssRandom number c between a (0,1), i.e. probability, wherein the wind of same Fans
Speed is identical with the random number c of power sample；
A3, equation F (Δ v is solvedw)=c, F (Δ Pw)=c, every available N of FanssGroup includes air speed error Δ vw
With power error Δ PwTwo-dimensional array namely each group of two-dimensional array in, include the wind being calculated by the same random number c
Fast error delta vwWith power error Δ Pw。
Further, the step B includes the following steps:
B1, it is directed to air speed error Δ vwWith power error Δ Pw, its variation range is averagely reasonably divided into Mv、MPIt is a
Section, respectively with Δ vwWith Δ PwAs abscissa and ordinate, an available two-dimensional latticed interval range, section
It include M in rangev×MPA grid counts the N of every Fans respectivelysSample frequency of the group two-dimensional array in each grid, i-th
Sample frequency of the two-dimensional array of Fans in first of grid is Ei(l), l ∈ [1, Mv×MP]；
B2, judge whether the sample frequency in each grid is 0, for example 0 one minimum ε of superposition is carried out down if being not 0
One step.
Further, the step C includes the following steps:
C1, select k Fans as initial cluster center；
C2, to any one Fans, calculate its improved KL distance for arriving k cluster centre, which be grouped into apart from most
Group where small cluster centre, the improved KL distance calculation formula between the i-th Fans and jth Fans are as follows:
dKL(Ei,Ej)=[DKL(Ei||Ej)+DKL(Ej||Ei)]/2 (1)
Wherein, dKL(Ei,Ej) improved KL distance between the i-th Fans and jth Fans, DKL(Ei||Ej) it is i-th
Blower is to the KL distance between jth Fans, DKL(Ej||Ei) it is jth Fans to the KL distance between the i-th Fans, the two
Calculation formula are as follows:
Wherein, Ei(l) sample frequency for the two-dimensional array of the i-th Fans in first of grid, EjIt (l) is jth typhoon
Sample frequency of the two-dimensional array of machine in first of grid, l ∈ [1, Mv×MP]。
C3, average sample frequency of each blower in statistics grid in group is calculated, it is assumed that there is catwalk blower in k-th group,
Average sample frequency in first of grid are as follows:
Wherein, Er(l) sample frequency for the two-dimensional array of r Fans in first of grid, l ∈ [1, Mv×MP]。
And so on, available catwalk blower is in Mv×MPAverage sample frequency E in a gridav_k(l), as new cluster
Central value calculates its average sample frequency to k group, obtains k new cluster centre values；
C4, judgement: if all cluster centre values remain unchanged or update times reach the upper limit, turn C5, otherwise return
C2；
C5, output cluster result.
Further, the step D includes the following steps:
D1, equivalence is carried out to the Wind turbines parameter in wind turbine group:
It is constant to Wind turbines parameter aggregation based on Wind turbines output characteristics before and after equivalence, to wind-powered electricity generation in wind turbine group
Wind speed v, wind sweeping area A, capacity S, active-power P, reactive power Q, the shafting inertia time constant H, axis rigidity system of unit
Number K and shafting damped coefficient D parameter carry out according to following formula equivalent respectively:
In formula: nwFor Wind turbines number in wind turbine group；veq、viWind turbines is total respectively in wind turbine group
The wind speed of wind speed and i-th Wind turbines；Aeq、AiTotal wind sweeping area of Wind turbines and i-th respectively in wind turbine group
The wind sweeping area of Wind turbines；Seq、SiThe appearance of the total capacity of Wind turbines and i-th Wind turbines respectively in wind turbine group
Amount；Peq、PiThe active power of total active power of Wind turbines and i-th Wind turbines respectively in wind turbine group；
D2, equivalence is carried out to network parameter:
Equivalence to line impedance is carried out based on the constant principle of voltage loss before and after equivalence, is calculated as follows:
In formula: nwFor Wind turbines number, n in groupfFor Wind turbines number, Z in trunk line type blower branch in wind power plantg
For g sections of branch impedances in trunk line type branch；
Equivalent admittance Y over the groundeqIt calculates as follows:
In formula: Y is admittance over the ground.
The excellent effect that technical solution provided by the invention has is:
Technical solution in above-mentioned the embodiment of the present application, at least have the following technical effects or advantages:
The wind power plant Dynamic Equivalence provided by the invention for considering wind-powered electricity generation uncertainty in traffic determines that wind-powered electricity generation predicts apoplexy
The probability distribution and its parameter of speed and power error carry out more scene sampling using method of inverting, and obtain obeying Wind turbines prediction
The sample of probability of error distribution；It establishes using air speed error and power error as the Two-dimensional Statistical grid of coordinate, counts every Fans
Sample frequency in statistics grid；Based on improved KL distance, blower is carried out using k-means clustering algorithm and divides group；According to point
Group is as a result, constant to Wind turbines parameter aggregation based on equivalent front and back Wind turbines output characteristics, based on equivalent front and back voltage damage
Consume constant principle network parameter is carried out it is equivalent, simulation result show wind power plant Dynamic Equivalence provided by the invention by
There is the field of error in consideration wind-powered electricity generation uncertainty in traffic (i.e. the randomness of wind-powered electricity generation prediction error), thus in wind power prediction
There is preferable accuracy under scape, can be calculated with better services in the early warning of electric power system dispatching, to raising wind power plant dynamic etc.
The security and stability of the accuracy and the operation of wind-electricity integration system that are worth model has certain meaning.
Detailed description of the invention
Attached drawing described herein is used to provide to further understand the embodiment of the present invention, constitutes one of the application
Point, do not constitute the restriction to the embodiment of the present invention；
Fig. 1 is the flow diagram that the wind power plant Dynamic Equivalence of wind-powered electricity generation uncertainty in traffic is considered in the application.
Specific embodiment
The present invention provides a kind of wind power plant Dynamic Equivalences for considering wind-powered electricity generation uncertainty in traffic, solve existing
That there are accuracys is poor for wind power plant equivalence method, and the technical problem for causing simulation calculation error larger is realized in wind power
Predict have preferable accuracy can be more preferable in the case where considering wind-powered electricity generation uncertainty in traffic under the scene for error occur
Serve the technical effect that the early warning of electric power system dispatching calculates.
The present invention provides a kind of wind power plant Dynamic Equivalence for considering wind-powered electricity generation uncertainty in traffic, flow chart such as Fig. 1
It is shown, include the following steps:
A, the probability distribution and its parameter for determining wind speed and power error in wind-powered electricity generation prediction carry out more scenes using method of inverting
Sampling obtains the sample for obeying Wind turbines prediction probability of error distribution
A1, according to priori knowledge or historical forecast error information, determine wind speed error delta in every Fans wind-powered electricity generation prediction
vwWith power error Δ PwCumulative distribution function F (Δ vw)、F(ΔPw)；
A2, N is generated for every FanssRandom number c between a (0,1), i.e. probability, wherein the wind of same Fans
Speed is identical with the random number c of power sample；
A3, equation F (Δ v is solvedw)=c, F (Δ Pw)=c, every available N of FanssGroup includes air speed error Δ vw
With power error Δ PwTwo-dimensional array namely each group of two-dimensional array in, include the wind being calculated by the same random number c
Fast error delta vwWith power error Δ Pw。
B, it establishes using air speed error and power error as the Two-dimensional Statistical grid of coordinate, counts every Fans in statistics grid
Interior sample frequency
B1, it is directed to air speed error Δ vwWith power error Δ Pw, its variation range is averagely reasonably divided into Mv、MPIt is a
Section, respectively with Δ vwWith Δ PwAs abscissa and ordinate, an available two-dimensional latticed interval range, section
It include M in rangev×MPA grid counts the N of every Fans respectivelysSample frequency of the group two-dimensional array in each grid, i-th
Sample frequency of the two-dimensional array of Fans in first of grid is Ei(l), l ∈ [1, Mv×MP]；
B2, judge whether the sample frequency in each grid is 0, for example 0 one minimum ε of superposition is carried out down if being not 0
One step.
C, it is based on improved KL distance, blower is carried out using k-means clustering algorithm and divides group
C1, select k Fans as initial cluster center；
C2, to any one Fans, calculate its improved KL distance for arriving k cluster centre, which be grouped into apart from most
Group where small cluster centre, the improved KL distance calculation formula between the i-th Fans and jth Fans are as follows:
dKL(Ei,Ej)=[DKL(Ei||Ej)+DKL(Ej||Ei)]/2 (15)
Wherein, dKL(Ei,Ej) improved KL distance between the i-th Fans and jth Fans, DKL(Ei||Ej) it is i-th
Blower is to the KL distance between jth Fans, DKL(Ej||Ei) it is jth Fans to the KL distance between the i-th Fans, the two
Calculation formula are as follows:
Wherein, Ei(l) sample frequency for the two-dimensional array of the i-th Fans in first of grid, EjIt (l) is jth typhoon
Sample frequency of the two-dimensional array of machine in first of grid, l ∈ [1, Mv×MP].Each blower is in statistics grid in C3, calculating group
In average sample frequency, it is assumed that have catwalk blower in k-th group, the average sample frequency in first of grid are as follows:
Wherein, Er(l) sample frequency for the two-dimensional array of r Fans in first of grid, l ∈ [1, Mv×MP]。
And so on, available catwalk blower is in Mv×MPAverage sample frequency E in a gridav_k(l), as
New cluster centre value calculates its average sample frequency to k group, obtains k new cluster centre values；
C4, judgement: if all cluster centre values remain unchanged or update times reach the upper limit, turn C5, otherwise return
C2；
C5, output cluster result.
D, single machine equivalence is carried out to Wind turbines parameter in wind turbine group and network parameter
D1, equivalence is carried out to the Wind turbines parameter in wind turbine group:
It is constant to Wind turbines parameter aggregation based on Wind turbines output characteristics before and after equivalence, to wind-powered electricity generation in wind turbine group
Wind speed v, wind sweeping area A, capacity S, active-power P, reactive power Q, the shafting inertia time constant H, axis rigidity system of unit
Number K and shafting damped coefficient D parameter carry out according to following formula equivalent respectively:
In formula: nwFor Wind turbines number in wind turbine group；veq、viWind turbines is total respectively in wind turbine group
The wind speed of wind speed and i-th Wind turbines；Aeq、AiTotal wind sweeping area of Wind turbines and i-th respectively in wind turbine group
The wind sweeping area of Wind turbines；Seq、SiThe appearance of the total capacity of Wind turbines and i-th Wind turbines respectively in wind turbine group
Amount；Peq、PiThe active power of total active power of Wind turbines and i-th Wind turbines respectively in wind turbine group；
D2, equivalence is carried out to network parameter:
Equivalence to line impedance is carried out based on the constant principle of voltage loss before and after equivalence, is calculated as follows:
In formula: nwFor Wind turbines number, n in groupfFor Wind turbines number, Z in trunk line type blower branch in wind power plantg
For g sections of branch impedances in trunk line type branch；
Equivalent admittance Y over the groundeqIt calculates as follows:
In formula: Y is admittance over the ground.
Technical solution in above-mentioned the embodiment of the present application, at least have the following technical effects or advantages:
The wind power plant Dynamic Equivalence provided by the invention for considering wind-powered electricity generation uncertainty in traffic determines that wind-powered electricity generation predicts apoplexy
The probability distribution and its parameter of speed and power error carry out more scene sampling using method of inverting, and obtain obeying Wind turbines prediction
The sample of probability of error distribution；It establishes using air speed error and power error as the Two-dimensional Statistical grid of coordinate, counts every Fans
Sample frequency in statistics grid；Based on improved KL distance, blower is carried out using k-means clustering algorithm and divides group；According to point
Group is as a result, constant to Wind turbines parameter aggregation based on equivalent front and back Wind turbines output characteristics, based on equivalent front and back voltage damage
Consume constant principle network parameter is carried out it is equivalent, simulation result show wind power plant Dynamic Equivalence provided by the invention by
There is the field of error in consideration wind-powered electricity generation uncertainty in traffic (i.e. the randomness of wind-powered electricity generation prediction error), thus in wind power prediction
There is preferable accuracy under scape, can be calculated with better services in the early warning of electric power system dispatching, to raising wind power plant dynamic etc.
The security and stability of the accuracy and the operation of wind-electricity integration system that are worth model has certain meaning.
Although preferred embodiments of the present invention have been described, it is created once a person skilled in the art knows basic
Property concept, then additional changes and modifications may be made to these embodiments.So it includes excellent that the following claims are intended to be interpreted as
It selects embodiment and falls into all change and modification of the scope of the invention.
Obviously, various changes and modifications can be made to the invention without departing from essence of the invention by those skilled in the art
Mind and range.In this way, if these modifications and changes of the present invention belongs to the range of the claims in the present invention and its equivalent technologies
Within, then the present invention is also intended to include these modifications and variations.
Claims (5)
1. a kind of wind power plant Dynamic Equivalence for considering wind-powered electricity generation uncertainty in traffic, which is characterized in that the wind in the method
Motor group is to carry out a point group based on wind-powered electricity generation uncertainty in traffic, is included the following steps:
A, the probability distribution and its parameter for determining wind speed and power error in wind-powered electricity generation prediction carry out more scene pumpings using method of inverting
Sample obtains the sample for obeying Wind turbines prediction probability of error distribution；
B, it establishes using air speed error and power error as the Two-dimensional Statistical grid of coordinate, counts every Fans in statistics grid
Sample frequency；
C, it is based on improved KL distance, blower is carried out using k-means clustering algorithm and divides group；
D, single machine equivalence is carried out to Wind turbines parameter in each group and network parameter.
2. wind power plant Dynamic Equivalence as described in claim 1, which is characterized in that the step A the following steps are included:
A1, according to preset condition or historical forecast error information, determine wind speed error delta v in every Fans wind-powered electricity generation predictionwAnd function
Rate error delta PwCumulative distribution function F (Δ vw)、F(ΔPw)；
A2, N is generated for every FanssRandom number c between a 0 to 1, i.e. probability, wherein the wind speed and function of same Fans
The random number c of rate sample is identical；
A3, equation F (Δ v is solvedw)=c, F (Δ Pw)=c, every available N of FanssGroup includes air speed error Δ vwAnd power
Error delta PwTwo-dimensional array namely each group of two-dimensional array in, include the air speed error being calculated by the same random number c
ΔvwWith power error Δ Pw。
3. wind power plant Dynamic Equivalence as claimed in claim 2, which is characterized in that the step B includes the following steps:
B1, it is directed to air speed error Δ vwWith power error Δ Pw, its variation range is averagely divided into Mv、MPA section, respectively with
ΔvwWith Δ PwAs abscissa and ordinate, a two-dimensional latticed interval range is obtained, includes M in interval rangev×MP
A grid counts the N of every Fans respectivelysSample frequency of the group two-dimensional array in each grid, the two-dimensional array of the i-th Fans
Sample frequency in first of grid is Ei(l), l ∈ [1, Mv×MP]；
B2, judge whether the sample frequency in each grid is 0, if 0 one minimum ε of superposition, if not 0 carries out step C.
4. wind power plant Dynamic Equivalence as claimed in claim 3, which is characterized in that the step C includes the following steps:
C1, select k Fans as initial cluster center；
C2, to any one Fans, calculate its arrive k cluster centre improved KL distance, which is grouped into apart from the smallest
Group where cluster centre, the improved KL distance calculation formula between the i-th Fans and jth Fans are as follows:
dKL(Ei,Ej)=[DKL(Ei||Ej)+DKL(Ej||Ei)]/2 (1)
Wherein, dKL(Ei,Ej) improved KL distance between the i-th Fans and jth Fans, DKL(Ei||Ej) it is the i-th Fans
To the KL distance between jth Fans, DKL(Ej||Ei) it is jth Fans to the KL distance between the i-th Fans, the meter of the two
Calculate formula are as follows:
Wherein, Ei(l) sample frequency for the two-dimensional array of the i-th Fans in first of grid, EjIt (l) is jth Fans
Sample frequency of the two-dimensional array in first of grid, l ∈ [1, Mv×MP]；
C3, average sample frequency of each blower in statistics grid in group is calculated, it is assumed that have catwalk blower in k-th group, the
Average sample frequency E in l gridav_kAre as follows:
Wherein, Er(l) sample frequency for the two-dimensional array of r Fans in first of grid, l ∈ [1, Mv×MP]；
And so on, catwalk blower is obtained in Mv×MPAverage sample frequency E in a gridav_k(l), as new cluster
Central value calculates its average sample frequency to k group, obtains k new cluster centre values；
C4, judgement: if all cluster centre values remain unchanged or update times reach the upper limit, turn C5, otherwise return to C2；
C5, output cluster result.
5. wind power plant Dynamic Equivalence as described in claim 1, which is characterized in that the step D includes the following steps:
D1, equivalence is carried out to the Wind turbines parameter in each group:
It is constant to Wind turbines parameter aggregation based on Wind turbines output characteristics before and after equivalence, to Wind turbines in wind turbine group
Wind speed v, wind sweeping area A, capacity S, active-power P, reactive power Q, shafting inertia time constant H, axis rigidity COEFFICIENT K and
Shafting damped coefficient D parameter carries out according to following formula equivalent respectively:
In formula: nwFor Wind turbines number in wind turbine group；veq、viTotal wind speed of Wind turbines respectively in wind turbine group
With the wind speed of i-th Wind turbines；Aeq、AiTotal wind sweeping area of Wind turbines and the i-th typhoon electricity respectively in wind turbine group
The wind sweeping area of unit；Seq、SiThe capacity of the total capacity of Wind turbines and i-th Wind turbines respectively in wind turbine group；
Peq、PiThe active power of total active power of Wind turbines and i-th Wind turbines respectively in wind turbine group；
D2, equivalence is carried out to network parameter:
Equivalence to line impedance is carried out based on the constant principle of voltage loss before and after equivalence, is calculated as follows:
In formula: nwFor Wind turbines number, n in groupfFor Wind turbines number, Z in trunk line type blower branch in wind power plantgIt is dry
G sections of branch impedances in wire type branch；
Equivalent admittance Y over the groundeqIt calculates as follows:
In formula: Y is admittance over the ground.
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| CN109146709B (en) * | 2018-09-12 | 2020-10-09 | 国网辽宁省电力有限公司 | Wind power measuring point identification method and device |
| CN109409575A (en) * | 2018-09-27 | 2019-03-01 | 贵州电网有限责任公司 | Wind power plant group of planes division methods based on Gap Statistic |
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