CN105825002B - A kind of wind power plant dynamic equivalent modeling method based on dynamic Gray Association Analysis - Google Patents
A kind of wind power plant dynamic equivalent modeling method based on dynamic Gray Association Analysis Download PDFInfo
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Abstract
The wind power plant dynamic equivalent modeling method based on dynamic Gray Association Analysis that the present invention relates to a kind of, when surveying operation data pretreatment, it is investigated using autocorrelation analysis to surveying any time data value of operation data and the relevance of historical data, excavate out the implicit rule in data, select rational time span, and then determine that the small data sample at K moment is inputted as simulation model according to different wind speed scales in span, reduce because the very few or complicated redundancy of data sample calculates simulation model precision and analysis the influence of duration.On the other hand, the present invention establishes degree of association matrix as clustering target using dynamic Gray Association Analysis, relevance and grey majorized model complicated between each running of wind generating set situation are taken into account in group of planes cluster process, so that group of planes cluster result is more reasonable, the accuracy of wind power plant dynamic equivalent model is substantially increased.
Description
Technical field
The present invention relates to regenerative resource interconnection technology fields, more specifically to one kind based on dynamic gray relative point
The wind power plant dynamic equivalent modeling method of analysis method.
Background technology
Wind-power electricity generation is due to big with installed capacity growth space, and cost declines fast, and it is excellent that safety, the energy are never exhausted etc.
Gesture is increasingly paid attention to by countries in the world.However wind energy has the characteristics that randomness, intermittence and unstability, with wind
The continuous expansion of electric capacity of power unit and wind power plant scale, influence of the wind-electricity integration to stability of power system are more notable.
According to incompletely statistics, 193 Wind turbines off-grid accidents just have occurred within only 2011, wherein the rule together that China in 2011 occurs
The maximum 2.24 Jiuquan large-scale wind power off-grid accident of mould causes 598 Wind turbines off-grids altogether, and loss, which is contributed, to be reached
840.43MW, northwest major network frequency is most down to 49.854Hz.
Therefore, it in order to accurately analyze and evaluate the interaction and influence between high-capacity wind power plant and electric system, grinds
Study carefully and seeks suitable wind power plant Dynamic Equivalence to the power system dynamic stability simulation analysis containing wind power plant with important
Meaning.
In the prior art, wind power plant Dynamic Equivalence is broadly divided into single machine equivalent method and multimachine equivalent method.
Wherein, single machine equivalent method modeling process is simple, usually assumes that the input wind speed of all Wind turbines is identical, will be entire
Wind power plant equivalence is a Wind turbines, but for large-scale wind power plant, due to topography and geomorphology and wake effect and
The influence of time lag, wind speed profile is uneven in wind power plant, and the wind speed of Wind turbines differs greatly, usual using single machine equivalence method
There can be large error.
The most frequently used and simplest method is to carry out classification polymerization to wind power plant according to arrangement position in multimachine equivalence, often
A wind energy conversion system will be equivalent to exhaust blower, it is contemplated that the difference of fan operation situation between difference row reduces wind power plant list
The error that motor-driven state Equivalent Model is brought, however in practical wind power plant, even if it is poor to be also likely to be present larger wind speed with exhaust blower
It is different.
In the prior art, related Equivalent Model is to all data sample equivalent processes, and input vector is more, data sample is complicated
Redundancy is unfavorable for analyzing, this can all cause certain harmful effect to simulation accuracy, simulation analysis time；On the other hand, existing
The related Equivalent Model of technology is generally using the monitoring project amount of certain independent quantity of state of each Wind turbines or whole as gathering
Class model input carries out unit equivalence division, and the relevance and grey majorized model between each running of wind generating set situation, which lack, to be considered, and
The operating status of each Wind turbines be different by the influence degree of many factors such as weather, grid operating conditions, temperature and
It is non-deterministic, it is a complicated non-linear process.
Thus, the correlation of above-mentioned unit equivalence division methods operating status between Wind turbines consider it is not comprehensive enough with
Obviously, the reasonability of cluster result can be influenced and reduce simulation accuracy.Therefore, how by the pass between each running of wind generating set situation
Connection property and grey majorized model take into account based on actual measurement operation data and combine be in the wind power plant dynamic equivalent modeling of clustering algorithm need to be into
One step solves the problems, such as.
Invention content
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of wind-powered electricity generations based on dynamic Gray Association Analysis
Field dynamic equivalent modeling method is reduced because data sample is very few or complicated redundancy, and reduction is very few or complicated superfluous because data sample
The remaining influence for calculating simulation model precision and analysis duration；Degree of association matrix is established as poly- using dynamic Gray Association Analysis
Class index takes into account relevance and grey majorized model complicated between each running of wind generating set situation in group of planes cluster process so that machine
Group's cluster result is more reasonable.
Technical scheme is as follows：
A kind of wind power plant dynamic equivalent modeling method based on dynamic Gray Association Analysis, steps are as follows：
Step 1) determines the small data sample in wind power plant actual operating data sample according to wind speed scale；
Step 2) establishes degree of association matrix of the wind-powered electricity generation group of planes operating status based on dynamic Gray Association Analysis, and as cluster
Index；
Step 3) carries out group of planes cluster using K-means clustering algorithms, obtains group of planes cluster result；
It is equivalent that step 4) uses the typhoon motor grouping machine group in a group of planes to carry out, and establishes the dynamic equivalent mould of wind power plant
Type.
Preferably, in step 1), wind power plant actual measurement operation data is pre-processed using autocorrelation analysis, specifically
For：
1.1) setting in wind power plant has the total n platforms of Wind turbines, chooses the actual measurement operation that wind power plant is incorporated into the power networks in period T
Data are as data sample, sampling number i, sample frequency f；
1.2) using in period T wind speed survey operation data as time series, calculate the time series and its at any time
Between the auto-correlation coefficient ρ of sequence that obtains after Δ t translationΔt：
Wherein, ytFor the wind series of t moment, yt+ΔtFor the sequence obtained after t moment wind series at any time Δ t translations
Row；
1.3) using selected Δ t as time span, wind speed changes maximum in default wind speed range in interception time section T
Actual measurement operation data sample chooses K according to preset wind speed scale in the data sample using Δ t as time span of interception
The small data sample at a moment.
Preferably, in step 1.2), after more than Δ t time spans, the auto-correlation coefficients of wind series drops to default
Below coefficient.
Preferably, in step 2), degree of association square of the wind-powered electricity generation group of planes operating status based on dynamic Gray Association Analysis is established
Battle array be specially：
2.1) it includes m platform Wind turbines to set wind power plant, acquires n continuous quantity monitoring project data, specially：
{xij∈(Si, Xj, Tt)}m*n*N；
Wherein, i=1,2 ..., m；J=1,2 ..., n；T=1,2 ..., N；xij(t) t-th of the i-th typhoon of moment is indicated
J-th of continuous quantity monitoring project data of machine；SiIndicate the operation conditions of i-th Wind turbines in certain moment wind power plant, XjIt indicates
J-th selected of continuous quantity monitoring project, TtIndicate t-th selected of moment；N is moment number；
2.2) A is defined1,A2,...,AnFor the continuous quantity monitoring project information square of different moments running of wind generating set situation
Gust, the data for including in matrix are the small data sample selected in step 1, specially：
2.3) Largest Mean on the continuous quantity monitoring project time dimension at calculating each moment is：
2.4) to small data sample selected in step 1 without guiding principle quantification treatment, specially：
2.5) the comparison sequence of certain moment separate unit running of wind generating set situation is：
Si(t)={ x 'ij(t)}m*n*N=[x 'i1(t),x′i2(t),...,x′in(t)]；
2.6) the dynamic reference sequence of all Wind turbines integrated operation situations is：
2.7) calculate indicate each moment, each running of wind generating set situation comparison sequence Si(t) with frame of reference S0
(T) grey relational grade between is：
Wherein, γ0i(j) it is grey incidence coefficient, it is as follows：
Wherein, two-stage lowest differenceTwo-stage maximum differenceρ is resolution ratio,
Δ0i(j)=| xj(T)-x′ij(t)|。
2.8) grey relational grade matrix is：
Wherein, i represents i-th Wind turbines, and t represents t-th of time cross-section, γtiIt represents in t moment the i-th typhoon motor
Gray relation grades between group operation conditions and all Wind turbines integrated operation situations, yiIt represents in Δ t time spans with wind speed
The gray relation grades change sequence of i-th Wind turbines operation conditions of change of rank.
Preferably, step 3) is specially：
3.1) using grey relational grade matrix as the input data of K-means Clustering Models, wherein every Wind turbines fortune
The gray relation grades change sequence of row situation is an input vector：
Y=(y1 y2 ... yi...ym)=(γti)N×m；
3.2) h sample is chosen as initial clustering point；
3.3) center of gravity for calculating each class, as cluster centre：
3.4) other samples are included into the class minimum with its distance；
3.5) step 3.2), 3.3) is repeated, until all samples cannot all distribute；
3.6) profile value S (i) is calculated, specially：
Wherein：A is sample i and with the average distance between other samples of cluster；B is a vector, and element is sample i
From the average distance between sample in the cluster of different clusters；
The threshold value of predetermined S (i) is chosen initial clustering point and is clustered again first if S (i) cannot meet condition, until
S (i) meets condition；If all initial clustering points cannot meet, h values are re-entered, are clustered.
Preferably, step 4) is specially：It is a Wind turbines by the Wind turbines equivalence in same group, and uses capacity
Weighting method calculates the parameter of equivalent Wind turbines, establishes the dynamic equivalent model of wind power plant.
Preferably, in step 1), actual measurement operation data exports to obtain connection amount monitoring item mesh number by SCADA system
According to.
Preferably, SCADA system connection amount monitoring project data include：Generator drive end end bearing temperature, gear
Case input shaft temperature, cabin cabinet temperature, net survey A phase voltages, generator anti-drive end end bearing temperature, gearbox output shaft temperature
Degree, bottom of tower cabinet temperature, net survey A phase currents, generator windings temperature, gear-box oil temperature, wind speed, active power, generator cooling
Air temperature, gear-box base bearing temperature, mean wind speed, mains frequency, generator speed, cabin temperature, environment temperature in 5 minutes
One or more.
Beneficial effects of the present invention are as follows：
Wind power plant dynamic equivalent modeling method of the present invention based on dynamic Gray Association Analysis runs number in actual measurement
When Data preprocess, carried out using autocorrelation analysis to surveying any time data value of operation data and the relevance of historical data
It investigates, excavates out the implicit rule in data, select rational time span, and then true according to different wind speed scales in span
The small data sample for determining K moment is inputted as simulation model, reduces the very few or complicated redundancy because of data sample to emulation
Model accuracy and analysis calculate the influence of duration.
On the other hand, the present invention establishes degree of association matrix as clustering target, Jiang Gefeng using dynamic Gray Association Analysis
Complicated relevance and grey majorized model take into account in group of planes cluster process between motor group operation conditions so that group of planes cluster result more closes
Reason, substantially increases the accuracy of wind power plant dynamic equivalent model.
Description of the drawings
Fig. 1 is the wind power plant dynamic equivalent modeling method flow chart based on dynamic Gray Association Analysis；
Fig. 2 is certain practical detailed simulation model of wind power plant of embodiment；
Fig. 3 is the IEEE39 node systems of embodiment；
When Fig. 4 is gust disturbances, wind power plant using single machine Equivalent Model, the present invention is based on the wind of dynamic Gray Association Analysis
Active power dynamic response curve comparison diagram when electric field dynamic equivalent model and detailed model；
When Fig. 5 is gust disturbances, wind power plant using single machine Equivalent Model, the present invention is based on the wind of dynamic Gray Association Analysis
Reactive power dynamic response curve comparison diagram when electric field dynamic equivalent model and detailed model；
When Fig. 6 is that three-phase ground short trouble occurs for system side, wind power plant using single machine Equivalent Model, the present invention is based on dynamic
Active power dynamic response curve comparison diagram when the wind power plant dynamic equivalent model and detailed model of state Gray Association Analysis；
When Fig. 7 is that three-phase ground short trouble occurs for system side, wind power plant using single machine Equivalent Model, the present invention is based on dynamic
Reactive power dynamic response curve comparison diagram when the wind power plant dynamic equivalent model and detailed model of state Gray Association Analysis.
Specific implementation mode
The present invention is further described in detail with reference to the accompanying drawings and embodiments.
The present invention provides a kind of wind based on dynamic Gray Association Analysis to solve various deficiencies of the existing technology
Electric field dynamic equivalent modeling method, as shown in Figure 1, steps are as follows：
Step 1) determines the small data sample in wind power plant actual operating data sample according to wind speed scale；
Step 2) establishes degree of association matrix of the wind-powered electricity generation group of planes operating status based on dynamic Gray Association Analysis, and as cluster
Index；
Step 3) carries out group of planes cluster using K-means clustering algorithms, obtains group of planes cluster result；
It is equivalent that step 4) uses the typhoon motor grouping machine group in a group of planes to carry out, and establishes the dynamic equivalent mould of wind power plant
Type.
In step 1), wind power plant actual measurement operation data is pre-processed using autocorrelation analysis, specially：
1.1) setting in wind power plant has the total n platforms of Wind turbines, chooses the actual measurement fortune that wind power plant is incorporated into the power networks in certain period T
For row data as sample, sampling number is i, and sample frequency is 1 beat/min.It includes 19 energy consolidated statements to survey operation data
The SCADA system continuous quantity monitoring project of existing wind power plant real-time operating conditions, as shown in table 1.
Table 1：The SCADA system continuous quantity monitoring project of 19 energy general performance wind power plant real-time operating conditions
1.2) consider that wind speed variation is the basic reason of fan parameter variation, therefore use wind speed as preprocessing process
Survey the foundation of operation data by stages.Wind speed in period T is surveyed into operation data as time series, calculates the time
The auto-correlation coefficient ρ of the sequence obtained after sequence and its at any time Δ t translationsΔt：
Wherein, ytFor the wind series of t moment, yt+ΔtFor the sequence obtained after t moment wind series at any time Δ t translations
Row；
Due to being generally acknowledged that when auto-correlation coefficient is less than 0.3, the correlation of current data and data variation before time Δt
It is weaker.Therefore, the Δ t for taking certain selected is that time span data intercept sample analyzes wind power plant actual measurement operation data.It should
The condition that Δ t should meet is：After more than Δ t time spans, the auto-correlation coefficient of wind series drops to 0.3 or less；
1.3) using selected Δ t as time span, wind speed is in default wind speed range (3m/s~21m/ in interception time section T
S) the maximum actual measurement operation data sample of variation in, according to preset wind speed scale, in interception using Δ t as the number of time span
According to the small data sample for choosing K moment in sample.In the present embodiment, the division of wind speed scale is specially：Gentle breeze (3.4m/s~
5.4m/s) and wind (5.5m/s~7.9m/s), strong wind (8.0m/s~10.7m/s), high wind (10.8m/s~13.8m/s), disease
Wind (13.9m/s~17.1m/s) and strong wind (17.2m/s~20.7m/s).Then per typhoon in the small data sample at this K moment
The air speed data of motor group should include at least preceding 4 wind speed scales.
In step 2), establishing degree of association matrix of the wind-powered electricity generation group of planes operating status based on dynamic Gray Association Analysis is specially：
2.1) wind power plant is set as a multisystem, multiple index evaluation system, i.e., wind power plant includes m platform Wind turbines, acquires n
A continuous quantity monitoring project data, specially：
{xij∈(Si, Xj, Tt)}m*n*N；
Wherein, i=1,2 ..., m；J=1,2 ..., n；T=1,2 ..., N；xij(t) t-th of the i-th typhoon of moment is indicated
J-th of continuous quantity monitoring project data of machine；SiIndicate the operation conditions of i-th Wind turbines in certain moment wind power plant, XjIt indicates
J-th of the continuous quantity monitoring project selected in step 1), TtIndicate t-th of the moment selected in step 1)；N is moment number；
2.2) A is defined1,A2,...,AnFor the SCADA system continuous quantity monitoring item of different moments running of wind generating set situation
Mesh information matrix, the data for including in matrix are the small data sample selected in step 1, specially：
2.3) Largest Mean on the SCADA system continuous quantity monitoring project time dimension at calculating each moment is：
2.4) to small data sample selected in step 1 without guiding principle quantification treatment, specially：
2.5) the comparison sequence of certain moment separate unit running of wind generating set situation is：
Si(t)={ x 'ij(t)}m*n*N=| x 'i1(t),x′i2(t),...,x′in(t)]；
2.6) the dynamic reference sequence of all Wind turbines integrated operation situations is：
2.7) calculate indicate each moment, each running of wind generating set situation comparison sequence Si(t) with frame of reference S0
(T) grey relational grade between is：
Wherein, γ0i(j) it is grey incidence coefficient, it is as follows：
Wherein, two-stage lowest differenceTwo-stage maximum differenceρ is resolution ratio,
In the present embodiment, ρ=0.5, Δ0i(j)=| xj(T)-x′ij(t)|。
2.8) grey relational grade matrix is：
Wherein, i represents i-th Wind turbines, and t represents t-th of time cross-section, γtiIt represents in t moment the i-th typhoon motor
Gray relation grades between group operation conditions and all Wind turbines integrated operation situations, yiIt represents in Δ t time spans with wind speed
The gray relation grades change sequence of i-th Wind turbines operation conditions of change of rank.
Step 3) is specially：
3.1) input data of the calculated grey relational grade matrix as K-means Clustering Models in selecting step 2,
In every Wind turbines operation conditions gray relation grades change sequence be an input vector：
Y=(y1 y2 ... yi...ym)=(γti)N×m；
3.2) h sample is chosen as initial clustering point；
3.3) center of gravity for calculating each class, as cluster centre：
3.4) other samples are included into the class minimum with its distance；
3.5) step 3.2), 3.3) is repeated, until all samples cannot all distribute；
3.6) profile value S (i) is calculated, specially：
Wherein：A is sample i and with the average distance between other samples of cluster；B is a vector, and element is sample i
From the average distance between sample in the cluster of different clusters；
The threshold value of predetermined S (i) is chosen initial clustering point and is clustered again first if S (i) cannot meet condition, until
S (i) meets condition；If all initial clustering points cannot meet, h values are re-entered, are clustered.
Step 4) is specially：It is a Wind turbines by the Wind turbines equivalence in same group, and uses capacity weighting method meter
The parameter for calculating equivalent Wind turbines, establishes the dynamic equivalent model of wind power plant.
Below for for practical wind power plant actual implementation：
To the present invention is based on the wind of dynamic Gray Association Analysis on electric system full digital trigger technique device (ADPSS) platform
Electric field dynamic equivalent modeling method carries out emulation explanation.As shown in Fig. 2, 22 Wind turbines are shared in the wind power plant, unit
Type is GE1.5MW, and wind power plant total installation of generating capacity is 33MW.
Choose 19 selected SDADA system continuous quantities in two weeks on October 15,1 day to 2014 October in 2014
Monitoring project actual measurement operation data is analyzed.With the actual measurement operation data of the period to Wind turbines in field using the present invention
Based on dynamic Gray Association Analysis wind power plant dynamic equivalent modeling the wind power plant is modeled.It analyzes gust disturbances and is
When three-phase ground short trouble occurs for system side, single machine Equivalent Model, the present invention is based on the wind power plants of dynamic Gray Association Analysis to move
The dynamic response characteristic of state Equivalent Model and detailed model.
Gust disturbances：Start if meeting to the fitful wind 4s of wind power plant, 8s terminates, and fitful wind maximum value is 3m/s, such as Fig. 4, Fig. 5 institute
Show.Can be found by comparison in the case where wind speed disturbs, wind power plant dynamic equivalent model that the present invention establishes and detailed model it is active
Power is closer to reactive power dynamic response curve.
System side three-phase ground short trouble：In emulation experiment wind is used as using IEEE39 node systems are (as shown in Figure 3)
Electric field grid-connected system, 35 nodes of wind power plant interventional systems.If IEEE39 node systems, in t=1.00s, busbar 24 occurs three
Phase ground short circuit failure, Failure elimination when t=1.12s.As shown in Figure 6, Figure 7, it can find to occur three in system side by comparison
Under phase ground short circuit failure, the active power and reactive power of wind power plant dynamic equivalent model and detailed model that the present invention establishes
Dynamic response curve is closer to.
Wind speed is disturbed to be located in advance with system side three-phase ground short trouble disturbance simulation result explanation, data using the present invention
The small data sample that reason method obtains is reasonable and effective, can reduce the data sample of complicated redundancy, front and back to modeling
Dynamic response characteristic influences smaller.In addition, group of planes cluster is carried out using dynamic Gray Association Analysis proposed by the present invention, by wind-powered electricity generation
Complicated grey majorized model comprehensively takes into account in clustering target with relevance between unit operation situation, and energy is compared with single machine Equivalent Model
Enough effective accuracys for improving the modeling of wind power plant dynamic equivalent.
Above-described embodiment is intended merely to illustrate the present invention, and is not used as limitation of the invention.As long as according to this hair
Bright technical spirit is changed above-described embodiment, modification etc. will all be fallen in the scope of the claims of the present invention.
Claims (7)
1. a kind of wind power plant dynamic equivalent modeling method based on dynamic Gray Association Analysis, which is characterized in that steps are as follows：
Step 1) determines the small data sample in wind power plant actual operating data sample according to wind speed scale；
Step 2) establishes degree of association matrix of the wind-powered electricity generation group of planes operating status based on dynamic Gray Association Analysis, and refers to as cluster
Mark；
Step 3) carries out group of planes cluster using K-means clustering algorithms, obtains group of planes cluster result；
It is equivalent that step 4) uses the typhoon motor grouping machine group in a group of planes to carry out, and establishes the dynamic equivalent model of wind power plant；
In step 1), wind power plant actual measurement operation data is pre-processed using autocorrelation analysis, specially：
1.1) setting in wind power plant has the total n platforms of Wind turbines, chooses the actual measurement operation data that wind power plant is incorporated into the power networks in period T
As data sample, sampling number i, sample frequency f；
1.2) wind speed in period T is surveyed into operation data as time series, calculates the time series and its Δ t at any time
The auto-correlation coefficient ρ of the sequence obtained after translationΔt：
Wherein, ytFor the wind series of t moment, yt+ΔtFor the sequence obtained after t moment wind series at any time Δ t translations；
1.3) using selected Δ t as time span, wind speed changes maximum actual measurement in default wind speed range in interception time section T
Operation data sample, according to preset wind speed scale, when choosing K in the data sample using Δ t as time span of interception
The small data sample at quarter.
2. the wind power plant dynamic equivalent modeling method according to claim 1 based on dynamic Gray Association Analysis, feature
It is, in step 1.2), after more than Δ t time spans, the auto-correlation coefficient of wind series drops to predetermined coefficient or less.
3. the wind power plant dynamic equivalent modeling method according to claim 1 based on dynamic Gray Association Analysis, feature
It is, in step 2), establishing degree of association matrix of the wind-powered electricity generation group of planes operating status based on dynamic Gray Association Analysis is specially：
2.1) it includes m platform Wind turbines to set wind power plant, acquires n continuous quantity monitoring project data, specially：
{xij∈(Si,Xj,Tt)}m*n*N；
Wherein, i=1,2 ..., m；J=1,2 ..., n；T=1,2 ..., N；xij(t) t-th the i-th Fans of moment are indicated
J-th of continuous quantity monitoring project data；SiIndicate the operation conditions of i-th Wind turbines in certain moment wind power plant, XjIndicate selected
J-th of continuous quantity monitoring project, TtIndicate t-th selected of moment；N is moment number；
2.2) A is defined1,A2,...,AnFor the continuous quantity monitoring project information matrix of different moments running of wind generating set situation, square
The data for including in battle array are the small data sample selected in step 1, specially：
2.3) Largest Mean on the continuous quantity monitoring project time dimension at calculating each moment is：
2.4) to small data sample selected in step 1 without guiding principle quantification treatment, specially：
2.5) the comparison sequence of certain moment separate unit running of wind generating set situation is：
Si(t)={ x'ij(t)}m*n*N=[x'i1(t),x'i2(t),...,x'in(t)]；
2.6) the dynamic reference sequence of all Wind turbines integrated operation situations is：
2.7) calculate indicate each moment, each running of wind generating set situation comparison sequence Si(t) with frame of reference S0(T) it
Between grey relational grade be：
Wherein, γ0i(j) it is grey incidence coefficient, it is as follows：
Wherein, two-stage lowest differenceTwo-stage maximum differenceρ is resolution ratio, Δ0i
(j)=| xj(T)-x'ij(t)|；
2.8) grey relational grade matrix is：
Wherein, i represents i-th Wind turbines, and t represents t-th of time cross-section, γtiIt represents in i-th Wind turbines fortune of t moment
Gray relation grades between row situation and all Wind turbines integrated operation situations, yiIt represents in Δ t time spans with wind speed scale
The gray relation grades change sequence of i-th Wind turbines operation conditions of variation.
4. the wind power plant dynamic equivalent modeling method according to claim 3 based on dynamic Gray Association Analysis, feature
It is, step 3) is specially：
3.1) using grey relational grade matrix as the input data of K-means Clustering Models, wherein every running of wind generating set shape
The gray relation grades change sequence of condition is an input vector：
Y=(y1 y2 ... yi...ym)=(γti)N×m；
3.2) h sample is chosen as initial clustering point；
3.3) center of gravity for calculating each class, as cluster centre：
3.4) other samples are included into the class minimum with its distance；
3.5) step 3.2), 3.3) is repeated, until all samples cannot all distribute；
3.6) profile value S (i) is calculated, specially：
Wherein：A is sample i and with the average distance between other samples of cluster；B is a vector, element be sample i with not
With the average distance between sample in the cluster of cluster；
The threshold value of predetermined S (i) is chosen initial clustering point and is clustered, until S (i) again first if S (i) cannot meet condition
Meet condition；If all initial clustering points cannot meet, h values are re-entered, are clustered.
5. the wind power plant dynamic equivalent modeling method according to claim 1 based on dynamic Gray Association Analysis, feature
It is, step 4) is specially：It is a Wind turbines by the Wind turbines equivalence in same group, and using capacity weighting method calculating etc.
It is worth the parameter of Wind turbines, establishes the dynamic equivalent model of wind power plant.
6. the wind power plant dynamic equivalent modeling method according to claim 1 based on dynamic Gray Association Analysis, feature
It is, in step 1), actual measurement operation data exports to obtain connection amount monitoring project data by SCADA system.
7. the wind power plant dynamic equivalent modeling method according to claim 6 based on dynamic Gray Association Analysis, feature
It is, SCADA system connection amount monitoring project data include：Generator drive end end bearing temperature, gearbox input shaft temperature,
Cabin cabinet temperature, net survey A phase voltages, generator anti-drive end end bearing temperature, gearbox output shaft temperature, bottom of tower cabinet temperature,
Net surveys A phase currents, generator windings temperature, gear-box oil temperature, wind speed, active power, generator cooling wind temperature, gear-box master
Bearing temperature, mean wind speed, mains frequency, generator speed, cabin temperature, the one or more of environment temperature in 5 minutes.
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| CN106640547B (en) * | 2016-11-24 | 2020-08-18 | 东北电力大学 | Method and system for monitoring state of wind turbine generator |
| CN107180391B (en) * | 2017-03-31 | 2023-03-24 | 中国电力科学研究院 | Wind power data span selection method and device |
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