CN104268316B - Off-line calculation and online matching based doubly-fed wind power station probability equivalent modeling method - Google Patents
Off-line calculation and online matching based doubly-fed wind power station probability equivalent modeling method Download PDFInfo
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Abstract
The invention discloses an off-line calculation and online matching based doubly-fed wind power station probability equivalent modeling method. The off-line calculation and online matching based doubly-fed wind power station probability equivalent modeling method comprises obtaining electrical parameters and wind speed historical data of a DFIG (Doubly Fed Induction Generator) wind power station to be equivalent and determining a doubly-fed wind power station clustering principle through off-line calculation; performing equivalent calculation on the wind speed historical data of the wind power station and generating an equivalent model initial sample set; extracting representative parameter values of various equivalent machines and generating various representative scenes of the wind power station through combination; matching out a plurality of representative scenes which are matched with the current scene according to the actually measured power of the wind power station; applying a doubly-fed wind power station probability equivalent model and performing analysis and calculation on a system according to needs. According to the off-line calculation and online matching based doubly-fed wind power station probability equivalent modeling method, the defects that only a single static operation point is considered in the existing doubly-fed wind power station equivalent modeling method and the model adaptability is poor are overcome, the amount of calculation is greatly reduced, the model is high in accuracy, and the off-line calculation and online matching based doubly-fed wind power station probability equivalent modeling method is particularly suitable for rapid analysis of statistical characteristics of the tide and the stability of a large doubly-fed wind power station.
Description
Technical Field
The invention belongs to the technical field of power grids, relates to a wind power plant dynamic equivalence technical method, and particularly relates to a doubly-fed wind power plant probability equivalence modeling method based on offline calculation and online matching.
Background
With the rapid development of wind power generation technology, Doubly-fed induction wind generators (DFIGs) are widely used due to their good technical and economic performance. The dynamic model of the DFIG is complex, and the installed amount of a large wind power base is often as high as thousands of units due to the relatively small single-machine capacity of the DFIG. The grid-connected wind power plant has important influence on the power angle and the voltage stability of the system, so detailed analysis is needed. In order to reduce the order of the system and improve the analysis and calculation speed, dynamic equivalence processing needs to be carried out on the doubly-fed wind power plant, and then an equivalence model is applied to carry out stable analysis on the system. When the wind power plant actually runs, the wind speed is random, the running scene of the wind power plant continuously migrates, and the static running point of the wind power plant continuously changes, so that the equivalent model of the wind power plant also has randomness. Taking 10 wind farms as an example, if the wind speed of each wind farm is 10, the possible scenes of the wind farm will reach 100 hundred million (10)10). The modeling method of the doubly-fed wind power plant in the prior art has the following defects: 1. the equivalence calculation is performed based on a certain static operating point, and dynamic equivalence processing cannot be realized. Such as: the variable speed double-fed wind power plant clustering equivalent method based on mechanical and electrical dynamic characteristics disclosed in chinese patent CN201210087133.8 is performed for wind power plant equivalent modeling at a specific wind speed. 2. For the analysis of multi-scenario problems, the common method is: 1) monte carlo method: a large number of samples are generated through random sampling, then analysis and calculation are carried out one by one, and statistical analysis is carried out on the calculation results. The calculation amount is too large to be implemented for a large wind farm. 2) The method for solving the joint probability distribution density function by combining the semi-invariant and the Gram-Charlier series expansion has low analysis precision.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a doubly-fed wind power plant probability equivalence modeling method based on off-line calculation and on-line matching.
The technical scheme adopted by the invention for solving the technical problems is as follows: a doubly-fed wind power plant probability equivalence modeling method based on off-line calculation and on-line matching is characterized in that the method further comprises the following steps:
(1) determining a double-fed wind power plant grouping criterion through offline calculation;
(2) performing equivalence calculation on historical wind speed data of the wind power plant to generate an equivalence model initial sample set;
(3) extracting representative parameter values of various equivalence machines, and combining to generate various representative scenes of the wind power plant;
(4) matching a plurality of representative scenes most consistent with the current scene according to the actually measured power of the wind power plant;
(5) and (3) analyzing and calculating the system according to the requirement by applying the doubly-fed wind power plant probability equivalent model.
The step (1) determines the double-fed wind power plant clustering criterion through off-line calculation, and the process is as follows:
dividing the DFIG in the wind power plant into 3 groups according to the injection wind speed of the DFIG and the impedance of a Common access Point (PCC), wherein the grouping scheme is determined according to the wind speed of the DFIG and the position of a Point corresponding to the impedance of the PCC on a parameter plane, and when the Point is located in different areas, the DFIG respectively presents three different dynamic characteristics; for a given wind farm, the boundaries between the various zones of the parameter plane can be determined in particular by off-line numerical calculations.
And (2) performing equivalence calculation on wind power plant wind speed historical data to generate an equivalence model initial sample set, wherein the process is as follows:
n is provided for wind power plantSGroup wind speed history data, i.e. nSEach scene is one by one for each scenePerforming clustering equivalence calculation; dividing DFIG into 3 groups for polymerization; taking the i (i ═ 1,2,3) th group as an example, if the model numbers of the DFIG units are the same, the equivalent machine is equal to the unit value of the individual DFIG parameter (capacity based on the self-capacity of the generator), and the equivalent machine capacity S is equal to the unit valueequ_iEqual to the sum of the capacities of the single machines; the output mechanical power of the equivalent DFIG is equal to the sum of the output mechanical power of each machine in the group, and the wind speed upsilon of the equivalent machine can be solved according to the power-rotating speed characteristic of the equivalent machineequ_i(ii) a The direct current capacitance value of a converter link of the equivalent DFIG is the sum of single DFIG capacitors in the group;
considering DFIG as transient reactanceIn which Xs,Xr,XmThe DFIG stator reactance, the rotor reactance and the excitation reactance are respectively. The equivalent connection impedance Z between the equivalent DFIG and the PCC can be obtained according to the Thevenin theoremequ_i(ii) a Definition Ci＝[Sequ_i υequ_i Zequ_i]Is a parameter vector of the i-th group DFIG equivalent machine, C ═ C1 C2 C3]Is a parameter vector of the equivalent model of the doubly-fed wind power plantIs the initial sample set of the ith group of equals machines, and { C(k),k＝1,…,nSAnd constructing an initial sample set of the equivalent model of the doubly-fed wind power plant.
And (3) extracting representative parameter values of various equivalent machines, and combining to generate various representative scenes of the wind power plant, wherein the process comprises the following steps:
parameter C of numerical machine such as i (i is 1,2,3) th group DFIGiWhen different values are taken, the difference between the reactive power and the voltage of the PCC points of the wind power plant is very small, and the main difference is that the PCC points output the amplitude of the active power; dividing the variation range of the output power of the i-th group of iso-machines into miA representative parameter is used for all samples with output power in the same subintervalTo approximate the depiction;
in parameter takingWhen the output power of the DFIG equivalent machine is within the sub-interval, the deviation between the output power of the DFIG equivalent machine and the expected value of the output power of all samples within the sub-interval is minimum; thereby, m of the i-th group of equivalence machines is extractediA representative parameterIs used for approximately describing various possible dynamic characteristics of the ith group of isomachines; combining the representative values of three types of equivalent machines contained in the equivalent model of the wind power plant to form m1×m2×m3A representative scene is described, the corresponding representative scene set isRemoving some invalid combinations which do not meet the principle of total capacity invariance to obtain nrA valid representative scenario;
counting each representative scene according to the initial sample setCumulative probability p ofkFinally, the probability equivalent model of the doubly-fed wind power plant is obtained <math>
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And (4) matching a plurality of representative scenes most consistent with the current scene according to the measured power of the wind power plant, wherein the process is as follows:
setting the field actual measurement of the output power of the wind power plant as PPCC,mCurrent scene CcurrAnd representative scenesThe matching degree is as follows: <math>
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</math> wherein,representative scenario for wind farmThe corresponding output power; degree of matching KkIs actually measured PPCC,mAnd the inverse exponential function of the deviation between the output power of the representative scene and the occurrence probability p of the representative scenekIs in direct proportion; if the deviation of the two is larger than the deviation, the matching degree is considered to be zero; the capacity of each DFIG can be 4-5 times of the preset value generally; further on KkNormalization processing is carried out to obtain CcurrAndthe matching probability of (2); and sequencing according to the matching probability to obtain a few representative scenes which are most consistent with the current scene.
The step (5) applies a double-fed wind power plant probability equivalent model, and carries out analysis and calculation on the system according to the requirement, wherein the process is as follows:
according to wind farm representative scenarioCalculating the probability load flow of the wind power system according to the probability distribution; and (3) analyzing the probability stability of electromechanical oscillation of the system by utilizing a few representative scenes matched by real-time information, and carrying out rapid online analysis.
The invention has the following advantages and beneficial effects:
1. according to the method, through analysis of statistical rules of various equivalent machine parameters, the dynamic characteristics and the statistical rules of a large number of scenes of the doubly-fed wind power plant are approximately described by a few representative scenes and probability distribution of the representative scenes, and the defect that the existing equivalent method of the doubly-fed wind power plant only considers a single static operating point and is poor in model adaptability is overcome;
2. according to the method, the representative scene and the probability thereof are determined according to the historical scene set, the current scene is matched according to the actually measured power, the calculated amount is greatly reduced, the model has higher precision, and the method is particularly suitable for the rapid analysis of the statistical characteristics of the load flow and the stability of the large double-fed wind power plant.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 shows three typical dynamic characteristics of DFIG observed from the Point of Common Coupling (PCC) Point.
FIG. 3 considers the double-fed wind farm clustering criterion of the injected wind speed and the impedance value with the PCC point.
FIG. 4 is a diagram of an equivalent model structure of a doubly-fed wind power plant. Wherein: DFIGequ_iI is 1,2 and 3 are three types of equivalent double-fed wind motors which respectively pass through equivalent impedance zequ_iAnd a transformer TPCCIs connected to a PCC point with a grid connection impedance ZF,υequ_iIs the equivalent wind speed.
FIG. 5 is a block diagram of an exemplary system. Wherein: the double-fed wind generator DFIG1 is connected to a bus 9 of a left-side IEEE3 machine 9 node system through a 575v/20kv transformer, a line 12-11, a 20kv/230kv transformer and a line 10-9; the access mode of other double-fed wind motors in the wind power plant is similar to that of the other double-fed wind motors in the wind power plant.
FIG. 6 is a comparison chart of probability power flow of the lines 9-10 in the initial scene and the probability equivalent model. Wherein: the "solid line" is the initial scene and the "×" is the probabilistic equivalence model.
FIG. 7 is a probability distribution diagram of damping coefficients of different generators and different oscillation modes. Fig. 7(a) and 7(b) show probability distributions of damping coefficients of the generator G2 and the generator G3 in this order. Wherein: "ooo" corresponds to an f-2.2 Hz mode of oscillation, and "+++" corresponds to an f-1.4 Hz mode of oscillation.
Detailed Description
According to the method for modeling the probability equivalence of the doubly-fed wind power plant based on the offline calculation and the online matching, according to the characteristics of the DFIG, the equivalent model of the doubly-fed wind power plant is composed of 3 types of DFIG equivalent machines with different dynamic characteristics, the equivalent model structure is kept unchanged in various scenes, and characteristic parameters such as the injection wind speed, the rated capacity and the impedance value between a PCC point and each type of equivalent machine change along with the scene migration of the wind power plant, so that the method has randomness. The dynamics of the same kind of equivalent machines are similar, and the main difference is the magnitude of the output power. According to the historical wind speed data, an initial sample set of the equivalent model can be calculated. And then, depicting the dynamic characteristics of each class of equivalent machine by using a plurality of representative parameter values, combining the representative models of the 3 classes of equivalent machines, removing invalid samples in the representative models to obtain all representative values of the characteristic parameters of the wind power plant, and counting the cumulative probability of the representative values. In the real-time operation, a few representative scenes most consistent with the current scene can be matched further according to the PCC point injection power measured by a Wide Area Measurement System (WAMS).
The technical solution of the present invention is further explained with reference to the drawings and the embodiments.
A doubly-fed wind power plant probability equivalence modeling method based on off-line calculation and on-line matching is characterized in that the method further comprises the following steps:
(1) determining a double-fed wind power plant grouping criterion through offline calculation;
(2) performing equivalence calculation on historical wind speed data of the wind power plant to generate an equivalence model initial sample set;
(3) extracting representative parameter values of various equivalence machines, and combining to generate various representative scenes of the wind power plant;
(4) matching a plurality of representative scenes most consistent with the current scene according to the actually measured power of the wind power plant;
(5) and (3) analyzing and calculating the system according to the requirement by applying the doubly-fed wind power plant probability equivalent model.
The step (1) determines the double-fed wind power plant clustering criterion through off-line calculation, and the process is as follows:
dividing the DFIG in the wind power plant into 3 groups according to the injection wind speed of the DFIG and the impedance of a Common access Point (PCC), wherein the grouping scheme is determined according to the wind speed of the DFIG and the position of a Point corresponding to the impedance of the PCC on a parameter plane, and when the Point is located in different areas, the DFIG respectively presents three different dynamic characteristics; for a given wind farm, the boundaries between the various zones of the parameter plane can be determined in particular by off-line numerical calculations.
And (2) performing equivalence calculation on wind power plant wind speed historical data to generate an equivalence model initial sample set, wherein the process is as follows:
n is provided for wind power plantSGroup wind speed history data, i.e. nSEach scene is subjected to clustering equivalence calculation one by one; dividing DFIG into 3 groups for polymerization; taking the i (i ═ 1,2,3) th group as an example, if the model numbers of the DFIG units are the same, the equivalent machine is equal to the unit value of the individual DFIG parameter (capacity based on the self-capacity of the generator), and the equivalent machine capacity S is equal to the unit valueequ_iEqual to the sum of the capacities of the single machines; the output mechanical power of the equivalent DFIG is equal to the sum of the output mechanical power of each machine in the group, and the wind speed upsilon of the equivalent machine can be solved according to the power-rotating speed characteristic of the equivalent machineequ_i(ii) a The direct current capacitance value of a converter link of the equivalent DFIG is the sum of single DFIG capacitors in the group;
considering DFIG as transient reactanceIn which Xs,Xr,XmThe DFIG stator reactance, the rotor reactance and the excitation reactance are respectively. The equivalent connection impedance Z between the equivalent DFIG and the PCC can be obtained according to the Thevenin theoremequ_i(ii) a Definition Ci＝[Sequ_i υequ_i Zequ_i]Is a parameter vector of the i-th group DFIG equivalent machine, C ═ C1 C2 C3]Is a parameter vector of the equivalent model of the doubly-fed wind power plantIs the initial sample set of the ith group of equals machines, and { C(k),k＝1,…,nSAnd constructing an initial sample set of the equivalent model of the doubly-fed wind power plant.
And (3) extracting representative parameter values of various equivalent machines, and combining to generate various representative scenes of the wind power plant, wherein the process comprises the following steps:
parameter C of numerical machine such as i (i is 1,2,3) th group DFIGiWhen different values are taken, the difference between the reactive power and the voltage of the PCC points of the wind power plant is very small, and the main difference is that the PCC points output the amplitude of the active power; dividing the variation range of the output power of the i-th group of iso-machines into miA representative parameter is used for all samples with output power in the same subintervalTo approximate the depiction;
in parameter takingWhen the output power of the DFIG equivalent machine is within the sub-interval, the deviation between the output power of the DFIG equivalent machine and the expected value of the output power of all samples within the sub-interval is minimum; thereby, m of the i-th group of equivalence machines is extractediA representative parameterIs used for approximately describing various possible dynamic characteristics of the ith group of isomachines; combining the representative values of three types of equivalent machines contained in the equivalent model of the wind power plant to form m1×m2×m3A representative scene is described, the corresponding representative scene set isRemoving some invalid combinations which do not meet the principle of total capacity invariance to obtain nrA valid representative scenario;
counting each representative scene according to the initial sample setCumulative probability p ofkFinally, the probability equivalent model of the doubly-fed wind power plant is obtained <math>
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And (4) matching a plurality of representative scenes most consistent with the current scene according to the measured power of the wind power plant, wherein the process is as follows:
setting the field actual measurement of the output power of the wind power plant as PPCC,mCurrent scene CcurrAnd representative scenesThe matching degree is as follows: <math>
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</math> wherein,representative scenario for wind farmThe corresponding output power; degree of matching KkIs actually measured PPCC,mAnd the inverse exponential function of the deviation between the output power of the representative scene and the occurrence probability p of the representative scenekIs in direct proportion; if the deviation of the two is larger than the deviation, the matching degree is considered to be zero; the capacity of each DFIG can be 4-5 times of the preset value generally; further on KkNormalization processing is carried out to obtain CcurrAndthe matching probability of (2); and sequencing according to the matching probability to obtain a few representative scenes which are most consistent with the current scene.
The step (5) applies a double-fed wind power plant probability equivalent model, and carries out analysis and calculation on the system according to the requirement, wherein the process is as follows:
according to wind farmRepresentative scenesCalculating the probability load flow of the wind power system according to the probability distribution; and (3) analyzing the probability stability of electromechanical oscillation of the system by utilizing a few representative scenes matched by real-time information, and carrying out rapid online analysis.
Example (b):
the doubly-fed wind farm probability equivalent modeling method based on offline calculation and online matching is applied to an example system shown in FIG. 5. The calculation system is formed by expanding an IEEE three-machine nine-node system, wherein IEEE standard parameters are taken by the IEEE three-machine nine-node part, a bus 1 is a balance node, and a bus 9 is a PCC (point of common control). The wind power plant is composed of 60 DFIG wind driven generators, and the DFIG is connected to a power grid through lines 9-10 after being boosted by a two-stage transformer. The rated capacity of the DFIG is 1.5MVA, and the rated voltage is 575V; DFIG parameters: [ R ]s,XS,Rr,Xr,Xm]＝[0.007,0.171,0.005,0.156,2.9]p.u.，H＝5.04s，F＝0.01。
Wind power plant network parameters: the transformation ratio of the 1 st step-up transformer is 0.575/20kV, and the capacity is 2MVA, XT10.05 p.u.; the transformation ratio of the 2 nd step-up transformer is 20/230kV, and the capacity is 100MVA, XT2＝0.05p.u.。Z9-10＝0.001+0.0058j，Z11-12＝0.0084+0.0495j,Z11-13＝2×(0.0084+0.0495j),Z11-14＝3×(0.0084+0.0495j),Z11-15＝4×(0.0084+0.0495j),Z11-16＝5×(0.0084+0.0495j),Z11-176 × (0.0084+0.0495j) (the line parameters are per unit, and the reference capacity is 100 MVA). The sample system was simulated by using Matlab/Simulink software.
2008 year-round observation data of a wind power plant in a certain place in Jiangsu is taken as the historical wind speed data of the wind power plant in the example, a group of wind speed data is recorded every 15 minutes, and 35040 scenes are recorded all the year round. The scenes are analyzed one by one, and the clustering criterion shown in figure 3 is firstly adoptedDividing the DFIG in the wind power plant into 3 groups, replacing the groups with three equivalent machines, solving the capacity, the wind speed and the impedance to a PCC point of each equivalent machine according to a fan equivalent theory, and obtaining a parameter vector C under each scene1、C2、C3And C, 35040 parameter vector samples are calculated from the initial scene. According to the active power change condition output by the equivalent DFIG, the equivalent machine 1 is divided into seven types: 1) is absent; 2) if the equivalent wind speed is too low, the fan is not started; 3) p e (0, 1.5)]；4)P∈(1.5,3.0]；5)P∈(3.0,5.0]；6)P∈(5.0,7.5](ii) a 7) P is more than 7.5. Similarly, equivalence machines 2 are classified into six classes: 1) is absent; 2) p is less than or equal to 7.5; 3) p e (7.5, 10)]；4)P∈(10,15]；5)P∈(15,25](ii) a 6) P is more than 25. The equivalence machines 3 are divided into five classes: 1) is absent; 2) p is less than or equal to 15; 3) p e (15, 22)]；4)P∈(22,30](ii) a 5) P is more than 30. And calculating the output expected value of all the iso-machine samples belonging to each subclass, and selecting the sample with the minimum deviation from the expected output value as the representative value of the subclass. Taking 7 representative values of the equivalent machine 16 representative values of the equivalence machine 2And 5 representative values of the equivalent machine 3Combining 210 representative scenes, removing invalid scenes which do not meet the total capacity invariance principle to obtain 135 effective representative scenes, and counting the cumulative probability p of the 135 effective representative sceneskEstablishing a probability equivalent model of an arithmetic wind power plant <math>
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The model is used for calculating the probability load flow of the system, the load flow distribution of the lines 10-9 under the initial scene and the probability equivalent model is shown in figure 6, and the probability distribution curves of the lines are basically consistent. Further calculation shows that the expectation and variance of the output power in the initial scene are 8.6282MW and 7.6922 MW, respectively, and when a probability equivalent model is used, the corresponding values are 8.3703MW and 7.7684MW, respectively, the error before and after the equivalence is small, and the static characteristic of the system is well maintained.
And analyzing the dynamic characteristics of the system by using the wind power plant probability model. Assuming that three phase-to-earth short circuit faults occur in the bus 9 within 1 second, and the faults are removed after 0.1 second, the power angle oscillation curves of the generators G2 and G3 are recorded, and the oscillation modes and the damping coefficients of the generators are analyzed by applying a Prony algorithm. Research shows that the power-angle oscillations of the generators G2 and G3 both include two oscillation modes, f is 2.2Hz and f is 1.4Hz, the damping coefficient of the oscillation mode, f is 2.2Hz, is greater than that of the oscillation mode, f is 1.4Hz, the damping coefficient of the generator G2 is greater than that of the generator G3 under the same scene, and the probability distribution of the system damping coefficient is shown in fig. 7. In summary, the wind power system is stable, but in some scenes, the minimum damping of the system is lower than 3%, so that the problem of insufficient damping exists, and further control measures need to be taken.
And setting the output power of the wind power plant actually measured on site to be 11.5MW, carrying out scene matching according to the method provided by the invention to obtain 26 most representative scenes, and calculating the matching probability of the scenes. Therefore, the statistical rule of the current equivalent model of the wind power plant is approximately expressed by a few scenes, and model preparation is provided for online controller parameter optimization. Only the 3 representative values with the highest match probability are listed in table 1, limited to space.
Table 13 representative scenarios with the highest wind farm match at Ppcc of 11.5MW
Claims (6)
1. A doubly-fed wind power plant probability equivalence modeling method based on off-line calculation and on-line matching is characterized in that the method further comprises the following steps:
(1) determining a double-fed wind power plant grouping criterion through offline calculation;
(2) performing equivalence calculation on historical wind speed data of the wind power plant to generate an equivalence model initial sample set;
(3) extracting representative parameter values of various equivalence machines, and combining to generate various representative scenes of the wind power plant;
(4) matching a plurality of representative scenes most consistent with the current scene according to the actually measured power of the wind power plant;
(5) and (3) analyzing and calculating the system according to the requirement by applying the doubly-fed wind power plant probability equivalent model.
2. The doubly-fed wind farm probability equivalent modeling method based on offline calculation and online matching as claimed in claim 1, wherein said step (1) determines the doubly-fed wind farm clustering criterion through offline calculation, and the process is as follows:
dividing the DFIGs in the wind power plant into 3 groups according to the injection wind speed of the DFIGs and the impedance to the PCC, determining the grouping scheme according to the positions of points corresponding to the wind speed of the DFIGs and the impedance to the PCC on a parameter plane, and respectively presenting three different dynamic characteristics when the points are located in different areas; for a given wind farm, the boundaries between the various zones of the parameter plane can be determined in particular by off-line numerical calculations.
3. The doubly-fed wind farm probability equivalent modeling method based on offline calculation and online matching as claimed in claim 1, wherein said step (2) performs equivalent calculation on wind farm wind speed historical data to generate an equivalent model initial sample set, and the process is as follows:
n is provided for wind power plantSGroup wind speed history data, i.e. nSEach scene is subjected to clustering equivalence calculation one by one; dividing DFIG into 3 groups for polymerization; taking group i as an example, wherein i is 1,2,3, and if the models of the DFIG units are the same, the equivalent machine is equal to the per unit value of the DFIG parameter, wherein the per unit value takes the self capacity of the generator as the reference capacity; equivalent machine capacity Sequ_iEqual to the sum of the capacities of the single machines; the output mechanical power of the equivalent DFIG is equal to the sum of the output mechanical power of each machine in the group, and the wind speed upsilon of the equivalent machine can be solved according to the power-rotating speed characteristic of the equivalent machineequ_i(ii) a The direct current capacitance value of a converter link of the equivalent DFIG is the sum of single DFIG capacitors in the group;
considering DFIG as transient reactanceIn which Xs,Xr,XmThe DFIG comprises a stator reactance, a rotor reactance and an excitation reactance respectively; the equivalent connection impedance Z between the equivalent DFIG and the PCC can be obtained according to the Thevenin theoremequ_i(ii) a Definition Ci＝[Sequ_i υequ_i Zequ_i]Is as followsiParameter vector of group DFIG equivalent machine, C ═ C1 C2 C3]Is a parameter vector of the equivalent model of the doubly-fed wind power plantIs the initial sample set of the ith group of equals machines, and { C(k),k＝1,…,nSAnd constructing an initial sample set of the equivalent model of the doubly-fed wind power plant.
4. The doubly-fed wind farm probability equivalence modeling method based on offline calculation and online matching according to claim 1, characterized in that:
and (2) performing equivalence calculation on wind power plant wind speed historical data to generate an equivalence model initial sample set, wherein the process is as follows:
n is provided for wind power plantSGroup wind speed history data, i.e. nSEach scene is subjected to clustering equivalence calculation one by one; dividing DFIG into 3 groups for polymerization; taking group i as an example, wherein i is 1,2,3, and if the models of the DFIG units are the same, the equivalent machine is equal to the per unit value of the DFIG parameter, wherein the per unit value takes the self capacity of the generator as the reference capacity; equivalent machine capacity Sequ_iEqual to the sum of the capacities of the single machines; the output mechanical power of the equivalent DFIG is equal to the sum of the output mechanical power of each machine in the group, and the wind speed upsilon of the equivalent machine can be solved according to the power-rotating speed characteristic of the equivalent machineequ_i(ii) a The direct current capacitance value of a converter link of the equivalent DFIG is the sum of single DFIG capacitors in the group;
treating DFIG as transient reactanceIs composed ofIn which Xs,Xr,XmThe DFIG comprises a stator reactance, a rotor reactance and an excitation reactance respectively; the equivalent connection impedance Z between the equivalent DFIG and the PCC can be obtained according to the Thevenin theoremequ_i(ii) a Definition Ci＝[Sequ_i υequ_i Zequ_i]Is as followsiParameter vector of group DFIG equivalent machine, C ═ C1C2C3]Is a parameter vector of the equivalent model of the doubly-fed wind power plantIs the initial sample set of the ith group of equals machines, and { C(k),k＝1,…,nSConstructing an initial sample set of the equivalent model of the doubly-fed wind power plant;
and (3) extracting representative parameter values of various equivalent machines, and combining to generate various representative scenes of the wind power plant, wherein the process comprises the following steps:
parameter C of i-th group DFIG equivalent machineiWhen different values are taken, wherein i is 1,2 and 3, the difference between the reactive power and the voltage of the PCC points of the wind power plant is very small, and the main difference is that the PCC points output the amplitude of the active power; dividing the variation range of the output power of the i-th group of iso-machines into miA representative parameter is used for all samples with output power in the same subintervalTo approximate the depiction;
in parameter takingWhen the output power of the DFIG equivalent machine is within the sub-interval, the deviation between the output power of the DFIG equivalent machine and the expected value of the output power of all samples within the sub-interval is minimum; thereby, m of the i-th group of equivalence machines is extractediA representative parameterIs used for approximately describing various possible dynamic characteristics of the ith group of isomachines; combining the representative values of three types of equivalent machines contained in the equivalent model of the wind power plant to form m1×m2×m3A representative scene is described, the corresponding representative scene set isRemoving some invalid combinations which do not meet the principle of total capacity invariance to obtain nrA valid representative scene；
Counting each representative scene according to the initial sample setCumulative probability p ofkFinally, the probability equivalent model of the doubly-fed wind power plant is obtained
5. The doubly-fed wind farm probability equivalence modeling method based on offline calculation and online matching according to claim 1, characterized in that:
and (2) performing equivalence calculation on wind power plant wind speed historical data to generate an equivalence model initial sample set, wherein the process is as follows:
n is provided for wind power plantSGroup wind speed history data, i.e. nSEach scene is subjected to clustering equivalence calculation one by one; dividing DFIG into 3 groups for polymerization; taking group i as an example, wherein i is 1,2,3, and if the models of the DFIG units are the same, the equivalent machine is equal to the per unit value of the DFIG parameter, wherein the per unit value takes the self capacity of the generator as the reference capacity; equivalent machine capacity Sequ_iEqual to the sum of the capacities of the single machines; the output mechanical power of the equivalent DFIG is equal to the sum of the output mechanical power of each machine in the group, and the wind speed upsilon of the equivalent machine can be solved according to the power-rotating speed characteristic of the equivalent machineequ_i(ii) a Converter link direct current capacitance value of equivalent DFIGThe sum of the single DFIG capacitors in the group;
considering DFIG as transient reactanceIn which Xs,Xr,XmThe DFIG comprises a stator reactance, a rotor reactance and an excitation reactance respectively; the equivalent connection impedance Z between the equivalent DFIG and the PCC can be obtained according to the Thevenin theoremequ_i(ii) a Definition Ci＝[Sequ_i υequ_i Zequ_i]Is a parameter vector of the i-th group DFIG equivalent machine, C ═ C1 C2 C3]Is a parameter vector of the equivalent model of the doubly-fed wind power plantIs the initial sample set of the ith group of equals machines, and { C(k),k＝1,…,nSConstructing an initial sample set of the equivalent model of the doubly-fed wind power plant;
and (3) extracting representative parameter values of various equivalent machines, and combining to generate various representative scenes of the wind power plant, wherein the process comprises the following steps:
parameter C of i-th group DFIG equivalent machineiWhen different values are taken, wherein i is 1,2 and 3, the difference between the reactive power and the voltage of the PCC points of the wind power plant is very small, and the main difference is that the PCC points output the amplitude of the active power; dividing the variation range of the output power of the i-th group of iso-machines into miA representative parameter is used for all samples with output power in the same subintervalTo approximate the depiction;
in parameter takingWhen the output power of the DFIG equivalent machine is within the sub-interval, the deviation between the output power of the DFIG equivalent machine and the expected value of the output power of all samples within the sub-interval is minimum; thereby, m of the i-th group of equivalence machines is extractediA representative parameterIs used for approximately describing various possible dynamic characteristics of the ith group of isomachines; combining the representative values of three types of equivalent machines contained in the equivalent model of the wind power plant to form m1×m2×m3A representative scene is described, the corresponding representative scene set isRemoving some invalid combinations which do not meet the principle of total capacity invariance to obtain nrA valid representative scene；
Counting each representative scene according to the initial sample setCumulative probability p ofkFinally, the probability equivalent model of the doubly-fed wind power plant is obtained <math>
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And (4) matching a plurality of representative scenes most consistent with the current scene according to the measured power of the wind power plant, wherein the process is as follows:
setting the field actual measurement of the output power of the wind power plant as PPCC,m，Current scene CcurrAnd representative scenesThe matching degree is as follows: <math>
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6. The doubly-fed wind farm probability equivalence modeling method based on offline calculation and online matching according to claim 1, characterized in that:
and (2) performing equivalence calculation on wind power plant wind speed historical data to generate an equivalence model initial sample set, wherein the process is as follows:
n is provided for wind power plantSGroup wind speed history data, i.e. nSEach scene is subjected to clustering equivalence calculation one by one; dividing DFIG into 3 groups for polymerization; taking group i as an example, wherein i is 1,2,3, and if the models of the DFIG units are the same, the equivalent machine is equal to the per unit value of the DFIG parameter, wherein the per unit value takes the self capacity of the generator as the reference capacity; equivalent machine capacity Sequ_iEqual to the sum of the capacities of the single machines; the output mechanical power of the equivalent DFIG is equal to the sum of the output mechanical power of each machine in the group, and the wind speed upsilon of the equivalent machine can be solved according to the power-rotating speed characteristic of the equivalent machineequ_i(ii) a The direct current capacitance value of a converter link of the equivalent DFIG is the sum of single DFIG capacitors in the group;
considering DFIG as transient reactanceIn which Xs,Xr,XmThe DFIG comprises a stator reactance, a rotor reactance and an excitation reactance respectively; the equivalent connection impedance Z between the equivalent DFIG and the PCC can be obtained according to the Thevenin theoremequ_i(ii) a Definition Ci＝[Sequ_i υequ_i Zequ_i]Is a parameter vector of the i-th group DFIG equivalent machine, C ═ C1 C2 C3]Is a parameter vector of the equivalent model of the doubly-fed wind power plantIs the initial sample set of the ith group of equals machines, and { C(k),k＝1,…,nSConstructing an initial sample set of the equivalent model of the doubly-fed wind power plant;
and (3) extracting representative parameter values of various equivalent machines, and combining to generate various representative scenes of the wind power plant, wherein the process comprises the following steps:
parameter C of i-th group DFIG equivalent machineiWhen different values are taken, wherein i is 1,2 and 3, the difference between the reactive power and the voltage of the PCC points of the wind power plant is very small, and the main difference is that the PCC points output the amplitude of the active power; dividing the variation range of the output power of the i-th group of iso-machines into miA representative parameter is used for all samples with output power in the same subintervalTo approximate the depiction;
in parameter takingWhen the output power of the DFIG equivalent machine is within the sub-interval, the deviation between the output power of the DFIG equivalent machine and the expected value of the output power of all samples within the sub-interval is minimum; thereby, m of the i-th group of equivalence machines is extractediA representative parameterIs used for approximately describing various possible dynamic characteristics of the ith group of isomachines; combining the representative values of three types of equivalent machines contained in the equivalent model of the wind power plant to form m1×m2×m3A representative scene is described, the corresponding representative scene set isRemoving some invalid combinations which do not meet the principle of total capacity invariance to obtain nrA valid representative scene；
Counting each representative scene according to the initial sample setCumulative probability p ofkFinally, the probability equivalent model of the doubly-fed wind power plant is obtained <math>
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And (4) matching a plurality of representative scenes most consistent with the current scene according to the measured power of the wind power plant, wherein the process is as follows:
setting the field actual measurement of the output power of the wind power plant as PPCC,m，Current scene CcurrAnd representative scenesThe matching degree is as follows: <math>
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the step (5) applies a double-fed wind power plant probability equivalent model, and carries out analysis and calculation on the system according to the requirement, wherein the process is as follows:
according to wind farm representative scenarioCalculating the probability load flow of the wind power system according to the probability distribution; and (3) analyzing the probability stability of electromechanical oscillation of the system by utilizing a few representative scenes matched by real-time information, and carrying out rapid online analysis.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
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