CN113839398B - Variable droop coefficient control method for double-fed fans participating in primary frequency modulation of power grid - Google Patents
Variable droop coefficient control method for double-fed fans participating in primary frequency modulation of power grid Download PDFInfo
- Publication number
- CN113839398B CN113839398B CN202111013634.7A CN202111013634A CN113839398B CN 113839398 B CN113839398 B CN 113839398B CN 202111013634 A CN202111013634 A CN 202111013634A CN 113839398 B CN113839398 B CN 113839398B
- Authority
- CN
- China
- Prior art keywords
- coefficient
- formula
- wind
- doubly
- frequency
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 29
- 241000282461 Canis lupus Species 0.000 claims abstract description 40
- 230000004044 response Effects 0.000 claims abstract description 25
- 238000005457 optimization Methods 0.000 claims abstract description 22
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 19
- 238000007665 sagging Methods 0.000 claims abstract description 10
- 238000012544 monitoring process Methods 0.000 claims abstract description 4
- 239000013598 vector Substances 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 8
- 241000282421 Canidae Species 0.000 claims description 6
- 102000016550 Complement Factor H Human genes 0.000 claims description 3
- 108010053085 Complement Factor H Proteins 0.000 claims description 3
- 230000003247 decreasing effect Effects 0.000 claims description 3
- 238000012804 iterative process Methods 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000004088 simulation Methods 0.000 description 8
- 230000001360 synchronised effect Effects 0.000 description 7
- 101100499229 Mus musculus Dhrsx gene Proteins 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000010248 power generation Methods 0.000 description 4
- 230000001105 regulatory effect Effects 0.000 description 4
- 230000001276 controlling effect Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 230000003828 downregulation Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
- H02J3/241—The oscillation concerning frequency
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/48—Controlling the sharing of the in-phase component
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P9/00—Arrangements for controlling electric generators for the purpose of obtaining a desired output
- H02P9/007—Control circuits for doubly fed generators
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P9/00—Arrangements for controlling electric generators for the purpose of obtaining a desired output
- H02P9/10—Control effected upon generator excitation circuit to reduce harmful effects of overloads or transients, e.g. sudden application of load, sudden removal of load, sudden change of load
- H02P9/105—Control effected upon generator excitation circuit to reduce harmful effects of overloads or transients, e.g. sudden application of load, sudden removal of load, sudden change of load for increasing the stability
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
Abstract
The invention relates to a control method for a variable droop coefficient of a doubly-fed fan participating in primary frequency modulation of a power grid, which comprises the following steps: step 1) collecting real-time wind speed, and monitoring a power grid frequency deviation signal in real time; step 2) adding a custom droop characteristic unit into a traditional rotor active controller, and setting droop control difference adjustment coefficients of the doubly fed fans participating in primary frequency adjustment of the power grid at different wind speeds; step 3) optimizing the difference adjustment coefficient of the sagging control based on a gray wolf optimization algorithm; and 4) obtaining additional active power increment according to the power grid frequency deviation signal and the droop control adjustment difference coefficient after setting and optimizing, and participating in frequency modulation according to the active power increment. The beneficial effects are as follows: the frequency secondary drop caused by excessive response of the wind turbine generator set due to the excessively small setting of the difference adjustment coefficient is avoided; the control precision is improved, the self-adaptive capacity is better, the available capacity of the real-time unit can be fully utilized, and the frequency response capacity of the unit is improved.
Description
Technical Field
The invention belongs to the technical field of wind power generation, and particularly relates to a variable droop coefficient control method for a doubly-fed wind turbine participating in primary frequency modulation of a power grid.
Background
The wind power generation technology is mature, the construction period is short, and the cost is low, so that the wind power generation technology becomes one of new energy sources for the vigorous development of various countries in the world. The wind power plant is used as renewable energy, and has certain value in the aspects of energy conservation, emission reduction and power structure optimization, but the wind in the nature has instability, large and small wind speed, and has strong randomness and uncontrollability. Therefore, the large-scale grid connection of the wind power is liable to have great influence on the aspect of system frequency stability.
The doubly-fed wind power generator is a main stream type of wind power generation. Because the rotor of the doubly-fed wind power generator is connected with the power grid through the converter, the rotor rotating speed of the fan is completely decoupled from the system frequency, and the change of the system frequency cannot be responded. Therefore, after large-scale wind power is integrated into a power grid, the frequency modulation capability of the system is weakened, and the stability of the system is affected. Moreover, the frequency modulation capability of the wind turbine is closely related to the current wind speed, in a low wind speed section, the load shedding reserve of the wind turbine is less, the frequency modulation capability is limited, and if the load shedding reserve energy and the rotor kinetic energy of the wind turbine are excessively utilized, the stall of the fan is easily caused to be out of operation; and under the condition of high wind speed, the load shedding reserve of the wind turbine generator is sufficient, the available frequency modulation power is more, and the frequency modulation capability is stronger.
At present, some domestic and foreign expert scholars research a control method for participation of a doubly-fed wind turbine generator set in primary frequency modulation of a power grid based on a variable droop control coefficient. However, in the existing researches, the fluctuation and uncertainty of natural wind are ignored, and simulation checking calculation is performed only at a fixed wind speed. The control rule of the doubly-fed wind turbine generator is not considered, and only a fixed droop coefficient is simply adopted. If the droop coefficient is set to be smaller, excessive response of the wind turbine generator is caused, and the system frequency is caused to drop secondarily; if the droop coefficient is set to be larger, the frequency response capability of the wind turbine generator cannot be fully exerted. Only a few wind speed points are selected, the value of the corresponding wind speed down-regulation difference coefficient is obtained, the obtained data are combined into a sagging control curve, the control precision is insufficient, and a large lifting space is still provided.
Disclosure of Invention
Aiming at the defects of the prior art, the problems that a droop control curve is synthesized by a small amount of data, the control precision is insufficient, the frequency secondary drop is caused by the excessive response of a wind turbine generator set due to the excessively small setting of a difference adjustment coefficient, and the frequency response capability of the wind turbine generator set cannot be fully exerted due to the excessively large setting of the difference adjustment coefficient are solved, the invention provides a droop coefficient changing control method for a doubly fed fan to participate in primary frequency modulation of a power grid, and the droop coefficient changing control method is implemented by the following scheme:
the control method for the sag coefficient of the doubly-fed wind turbine participating in primary frequency modulation of the power grid comprises the following steps:
step 1) collecting real-time wind speed, and simultaneously monitoring a power grid frequency deviation signal delta f in real time;
step 2) adding a custom droop characteristic unit into a traditional rotor active controller, and setting droop control difference adjustment coefficients of the doubly fed fans participating in primary frequency adjustment of the power grid through the custom droop characteristic unit;
step 3) optimizing the droop control difference adjustment coefficient based on a gray wolf optimization algorithm, so that the doubly-fed wind turbine can automatically select the optimal droop control difference adjustment coefficient according to the current wind speed;
and 4) obtaining additional active power increment according to the grid frequency deviation signal delta f and the droop control adjustment difference coefficient after setting and optimizing, and participating in frequency modulation according to the active power increment.
The control method for the sag-changing coefficient of the doubly-fed wind turbine participating in the primary frequency modulation of the power grid is further designed in that the self-defined sag-changing characteristic unit in the step 2) completes the adjustment of the sag-controlling difference-adjusting coefficient of the doubly-fed wind turbine participating in the primary frequency modulation of the power grid according to the formula (1),
in formula (1), Δf 0 Is set to be 0.2Hz, f N Rated for 50Hz, P of the power grid del ' total reserve power for load shedding of n doubly-fed fans in wind power plant, P WN Rated active power of the wind power plant;
calculating total reserve power P of load shedding of n doubly-fed fans in wind power plant according to (2) del '：
in the formula ,Pdel Reserve power for load shedding of single doubly-fed wind generator, wherein ρ is air density, d is load shedding coefficient, and C p，max And v is the wind speed, and is the maximum wind energy capture coefficient when tracking the maximum wind power.
The further design of the control method of the variable droop coefficient of the doubly-fed wind turbine participating in primary frequency modulation of the power grid is that the difference adjustment coefficient based on the gray wolf optimization algorithm optimization droop control in the step 3) specifically comprises the following steps:
step 3-1) randomly defining and generating a group of gray wolves based on a difference adjustment coefficient formula aiming at different wind speeds, wherein the step uses the most initial searching of a local possible optimal solution range in a gray wolf optimization algorithm, see formula (3);
wherein ,is the distance between the individual gray wolf and the prey, t is the number of iterations, ++> and />Is a coefficient vector, ++>Is the position of the prey after t iterations, < >>Is the position of the gray wolf after t iterations;
step 3-2) controlling the minimum of the objective function after the optimization in the constraint condition so as to achieve the optimal power point tracking, and constructing the objective function according to the formula (4):
wherein f (x) is the frequency overshoot, Δf max Is the maximum frequency deviation in the frequency response process;
the constraint of constructing the objective function according to the formulas (5) to (12) is:
v in ＜v＜v out (5)
wherein v is wind speed, v in To cut in wind speed v out To cut out wind speed;
Δf 0 ≤0.2Hz (6)
in the formula ,Δf0 Is a frequency deviation allowable value;
ω min ≤ω ref ≤ω max (7)
in the formula ,ωmin Is the minimum value of the rotating speed omega ref For the rotation speed reference value omega max Is the most of the rotating speedA large value;
β min ≤β ref ≤β max (8)
in the formula ,βmin Is the minimum value of pitch angle beta ref As pitch angle reference value, beta max Is the maximum value of the pitch angle;
in the formula ,Pm Is the mechanical power of the doubly-fed fan, C p For wind energy capture coefficient, C p，max For maximum wind energy capture coefficient at maximum wind power tracking, v n Is the rated wind speed;
in the formula ,ωr Is the rotation speed;
in the formula ,PG Is the output power of the doubly-fed fan, K C For MPPT coefficient omega r,n Is rated rotation speed;
wherein f is frequency and t' is time, and the constraint condition ensures that frequency secondary drop does not occur in the frequency response process;
according to the simultaneous equation of the formula (13), based on a gray wolf optimization algorithm, the optimal difference adjustment coefficient under the corresponding wind speed is searched, so that the frequency overshoot f (x) is minimum under the constraint of each constraint condition.
Step 3-3) constructing a generalized objective function according to equation (14):
F(x)＝f(x)+δ(t)H(x) (14)
wherein f (x) is an original objective function, delta (t) H (x) is a penalty term, delta (t) is penalty force, and H (x) is a penalty factor; step 3-4) respectively calculating penalty factors of all constraint conditions of each individual gray wolf, calculating the fitness value of each individual gray wolf according to a formula (14), and recording the optimal fitness value and the corresponding position;
step 3-5), judging whether the penalty factor H (x) meets the precision requirement or the maximum iteration number, if so, ending the algorithm, and outputting an optimal solution; otherwise, executing the step 3-6);
step 3-6) respectively marking the individual positions of the gray wolves with the first three positions of the fitness value arrangement asAs a decision layer, other individuals and +.>Updating the position of each individual gray wolf according to formulas (16) - (17), and returning to step 3-4);
in the formula Respectively representing the distances between alpha, beta and delta and other individuals in the wolf group; />Current positions of α, β and δ are represented, respectively; />Is a coefficient vector, and t is the iteration number.
Equation (16) defines the direction and distance that omega individuals in the wolf group advance toward α, β and δ, respectively, and equation (17) represents the final position of omega individuals in the wolf group.
The method for controlling the sag coefficient of the doubly-fed wind turbine participating in primary frequency modulation of the power grid is further designed in that the coefficient in the step 3-1) is calculated by the following formula (18):
in the formula ,linearly decreasing from 2 to 0, # in an iterative process> and />Respectively [0,1 ]]Random vectors within.
The beneficial effects of the invention are that
According to the invention, the difference adjustment coefficient of the sagging control of the doubly fed wind turbine participating in the primary frequency adjustment of the power grid is optimized based on the gray wolf optimization algorithm, the difference adjustment coefficient of the sagging control can be adjusted according to different wind speeds, and the phenomenon that the frequency secondary drop is caused by excessive response of the wind turbine due to the fact that the difference adjustment coefficient is set too small is avoided; and the problem that the frequency response capability of the wind turbine generator cannot be fully exerted due to overlarge setting of the difference adjustment coefficient is avoided. Meanwhile, the difference adjustment coefficient is optimized based on the gray wolf optimization algorithm, so that the control precision is improved, the self-adaptive capacity is better, the available capacity of the real-time unit can be fully utilized, the frequency response capacity of the unit is improved, and the problems of sag control curve fitting by a small amount of data and insufficient control precision are solved.
Drawings
FIG. 1 is a block diagram of a doubly-fed wind turbine generator system droop coefficient control.
FIG. 2 is a graph of the relationship between the difference coefficient and wind speed for the down-regulation of the gray wolf optimization algorithm.
Fig. 3 is a simulation model diagram built in the PSCAD.
FIG. 4 is a graph showing simulation results obtained by setting droop control slip coefficients of 2%,3.47% and 5% in PSCAD, respectively, at a wind speed of 11m/s.
FIG. 5 is a graph showing simulation results obtained by setting droop control slip coefficients to 2%,5% in PSCAD, respectively, and not adding a droop control link, when the wind speed is 13m/s.
FIG. 6 is a graph showing the pitch angle change when the sag control slip factor is set to 2%,5% and no sag control link is added, respectively, in PSCAD at a wind speed of 13m/s.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings.
The invention relates to a control method for a variable droop coefficient of a doubly-fed fan participating in primary frequency modulation of a power grid, which comprises the following steps:
and 1) collecting real-time wind speed, and simultaneously monitoring the power grid frequency deviation signal delta f in real time.
And 2) adding a custom droop characteristic unit into the traditional rotor active controller, and setting droop control difference adjustment coefficients of the doubly fed fans participating in primary frequency adjustment of the power grid through the custom droop characteristic unit.
And 3) optimizing the droop control difference adjustment coefficient based on the gray wolf optimization algorithm, so that the doubly-fed wind turbine can automatically select the optimal droop control difference adjustment coefficient according to the current wind speed.
And 4) obtaining additional active power increment according to the grid frequency deviation signal delta f and the droop control adjustment difference coefficient after setting and optimizing, and participating in frequency modulation according to the active power increment.
In the step 2), a custom droop characteristic unit is added on the basis of a traditional rotor active controller, and the droop control coefficient is automatically set according to the current wind speed. The control block diagram of the variable droop coefficient of the double-fed wind turbine generator set is shown in fig. 1, and the specific steps are as follows:
step 2-1) when simulating the droop characteristics of a traditional synchronous generator, Δp=kΔf, where:
in the formula ,KG Regulating power, K, for units of conventional synchronous generators G * Regulating the power per unit value, P, for a conventional synchronous generator unit GN Rated active power for traditional synchronous generator, f N For a nominal frequency of 50Hz, delta G The delta f is the system frequency variation and is the traditional synchronous generator difference adjustment coefficient.
Step 2-2) therefore, the doubly-fed wind generator can be obtained by simulating the expression of the conventional synchronous generator by the following expression:
wherein K is a sagging control coefficient, delta w Is the difference adjustment coefficient, P, of the doubly-fed wind generator WN The rated active power of the doubly-fed wind power generator is achieved.
Step 2-3) thus has:
from the above, the droop control coefficient K and the doubly-fed wind generator adjustment coefficient delta w Related to。
Step 2-4) defining the differential regulating coefficient of the variable droop control of the doubly-fed wind turbine according to a defining formula of the differential regulating coefficient of the traditional synchronous generator as follows:
in the formula ,Δf0 Is set to be 0.2Hz, f N For the rated frequency of 50Hz of the power grid, considering the equivalent value of a single machine, P del ’ Total reserve power for load shedding of n doubly-fed fans in wind power plant, P WN Is rated active power of the wind power plant.
in the formula ,Pdel The reserve power for load shedding of a single doubly-fed wind generator is represented by ρ, the air density, and d, the load shedding coefficient, and the load shedding level herein is 20%. C (C) p，max And v is the wind speed, and is the maximum wind energy capture coefficient when tracking the maximum wind power.
The difference adjustment coefficient based on the gray wolf optimization algorithm in the step 3) for optimizing the sagging control specifically comprises the following steps:
step 3-1) randomly defining and generating a group of gray wolves based on a difference adjustment coefficient formula aiming at different wind speeds, wherein the step uses the most initial searching of a local possible optimal solution range in a gray wolf optimization algorithm, see formula (3);
wherein ,is a gray wolf individual and huntingDistance between objects, t is the number of iterations, +.> and />Is a coefficient vector, ++>Is the position of the prey after t iterations, < >>Is the position of the wolf over t iterations.
Step 3-2) controlling the minimum of the objective function after the optimization in the constraint condition so as to achieve the optimal power point tracking, and constructing the objective function according to the formula (4):
wherein f (x) is the frequency overshoot, Δf max Is the maximum frequency deviation in the frequency response process;
the constraint of constructing the objective function according to the formulas (5) to (12) is:
v in ＜v＜v out (5)
wherein v is wind speed, v in To cut in wind speed v out To cut out wind speed;
Δf 0 ≤0.2Hz (6)
in the formula ,Δf0 Is a frequency deviation allowable value;
ω min ≤ω ref ≤ω max (7)
in the formula ,ωmin Is the minimum value of the rotating speed omega ref For the rotation speed reference value omega max Is the maximum value of the rotating speed;
β min ≤β ref ≤β max (8)
in the formula ,βmin Is the minimum value of pitch angle beta ref As pitch angle reference value, beta max Is the maximum value of the pitch angle;
in the formula ,Pm Is the mechanical power of the doubly-fed fan, C p For wind energy capture coefficient, C p，max For maximum wind energy capture coefficient at maximum wind power tracking, v n Is the rated wind speed;
in the formula ,ωr Is the rotation speed;
in the formula ,PG Is the output power of the doubly-fed fan, K C For MPPT coefficient omega r,n Is rated rotation speed;
where f is the frequency and t' is the time, the constraint ensures that no secondary drop in frequency occurs during the frequency response.
According to the simultaneous equation of the formula (13), based on a gray wolf optimization algorithm, the optimal difference adjustment coefficient under the corresponding wind speed is searched, so that the frequency overshoot f (x) is minimum under the constraint of each constraint condition.
Step 3-3) constructing a generalized objective function according to equation (14):
F(x)＝f(x)+δ(t)H(x) (14)
wherein f (x) is the original objective function, delta (t) H (x) is the penalty term, delta (t) is the penalty force, and H (x) is the penalty factor. And 3-4) respectively calculating penalty factors of all constraint conditions of each individual gray wolf, calculating the fitness value of each individual gray wolf according to a formula (14), and recording the optimal fitness value and the corresponding position.
Step 3-5), judging whether the penalty factor H (x) meets the precision requirement or the maximum iteration number, if so, ending the algorithm, and outputting an optimal solution; otherwise, step 3-6) is performed.
Step 3-6) respectively marking the individual positions of the gray wolves with the first three positions of the fitness value arrangement asAs a decision layer, other individuals and +.>And updating the position of each individual gray wolf according to equations (16) - (17), returning again to step 3-4).
in the formula Respectively representing the distances between alpha, beta and delta and other individuals in the wolf group; />Current positions of α, β and δ are represented, respectively; />Is a coefficient vector, and t is the iteration number.
Equation (16) defines the direction and distance that omega individuals in the wolf group advance toward α, β and δ, respectively, and equation (17) represents the final position of omega individuals in the wolf group.
The coefficients in step 3-1) are calculated by the following formula (18):
in the formula ,linearly decreasing from 2 to 0, # in an iterative process> and />Respectively [0,1 ]]Random vectors within.
The relation diagram of the optimized difference adjustment coefficient and the wind speed is shown in fig. 2.
In order to verify the effectiveness of the frequency modulation strategy, the PSCAD software is adopted to build a simulation model, and the model is shown in figure 3. In the model, a wind power plant consists of 10 fans with rated capacity of 2.5MW, and is connected with a 110kV system through 35kV collecting lines in a 35/110kV boosting mode, and rated wind speed is 11m/s. The 110kV system simulates the P-f sagging characteristic, is initially connected with a 20MW load, is simulated to be operated for 6s, and is connected with a 5MW load. The input frequency of the simulation load is reduced, and the fan participates in frequency modulation. And setting the doubly-fed wind power generator to run with 20% load shedding.
The invention also provides two specific embodiments, wherein 2 typical representative wind speeds are respectively 11m/s and 13m/s. Since the frequency response time scale of the invention is around 18s, it is assumed that the wind speed is constant during the frequency response.
Example 1:
when the wind speed is 11m/s, delta is respectively taken for analyzing the influence of the sagging coefficient on the frequency response performance of the doubly-fed wind generating set w 2%,3.47% and 5%. Wherein 2% is a smaller setting value; 3.47% is the setting value obtained by the process of the invention; 5% is the set value, and the simulation is shown in FIG. 4.
It can be seen from FIG. 4 that when delta w Setting the frequency to be smaller (2%), and generating secondary drop of the power grid frequency in the frequency modulation process. When delta w Setting the maximum (5%) and setting the absolute value ratio delta of the maximum deviation of the dynamic frequency of the power grid in spite of no secondary drop of the power grid frequency in the frequency modulation process w The maximum deviation absolute value of the dynamic frequency is large when the dynamic frequency is set to be 3.47%, and the dynamic response effect of the frequency is poorer than that of the method. The method has the advantages that when the sag control coefficient changing method is used for frequency modulation, the absolute value of the maximum deviation of the dynamic frequency of the power grid can be reduced, the frequency fluctuation can be reduced, and the stability of the frequency of the power grid can be maintained.
Example 2:
at a wind speed of 13m/s, pitch angle control works to reserve capacity. To analyze the influence of sagging coefficient on the frequency response performance of the doubly-fed wind turbine generator system, delta is taken respectively w 2%,5%, and no sag control was added for comparison. Wherein 2% is the setting value obtained by the present invention; 5% is the set value, and the simulation is shown in FIG. 5.
As can be seen from FIG. 5, by adopting the droop coefficient setting method provided by the invention, the excessive response of the doubly-fed wind turbine generator can be avoided, and the dynamic response of the frequency can be effectively improved. Similar to the case of 8m/s wind speed, when delta w The setting is larger (5%), and the method provided by the invention has better frequency response capability.
As can be seen in connection with FIGS. 5 and 6, pitch angle control participates in the frequency modulation when the wind speed is 13m/s. When the setting value obtained by the method is adopted, the pitch angle response is quicker, and the frequency response capability improving effect is more obvious.
The above description is only of the preferred embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art who is skilled in the art to which the present invention pertains will appreciate that the technical scheme according to the present invention and the inventive concept thereof are equally substituted or changed within the scope of the present invention.
Claims (3)
1. A control method for a variable droop coefficient of a doubly-fed wind turbine participating in primary frequency modulation of a power grid is characterized by comprising the following steps:
step 1) collecting real-time wind speed, and simultaneously monitoring a power grid frequency deviation signal delta f in real time;
step 2) adding a custom droop characteristic unit into a traditional rotor active controller, and setting droop control difference adjustment coefficients of the doubly fed fans participating in primary frequency adjustment of the power grid through the custom droop characteristic unit;
step 3) optimizing the droop control difference adjustment coefficient based on a gray wolf optimization algorithm, so that the doubly-fed wind turbine can automatically select the optimal droop control difference adjustment coefficient according to the current wind speed;
step 4) obtaining additional active power increment according to the power grid frequency deviation signal delta f and the droop control adjustment difference coefficient after setting and optimization, and participating in frequency modulation according to the active power increment;
the difference adjustment coefficient based on the gray wolf optimization algorithm in the step 3) for optimizing the sagging control specifically comprises the following steps: step 3-1) randomly defining and generating a group of gray wolves based on a difference adjustment coefficient formula aiming at different wind speeds, wherein the step uses the most initial searching of a local possible optimal solution range in a gray wolf optimization algorithm, see formula (3);
wherein ,is the distance between the individual gray wolf and the prey, t is the number of iterations, ++> and />Is a coefficient vector, ++>Is the position of the prey after t iterations, < >>Is the position of the gray wolf after t iterations;
step 3-2) controlling the minimum of the objective function after the optimization in the constraint condition so as to achieve the optimal power point tracking, and constructing the objective function according to the formula (4):
wherein f (x) is the frequency overshoot, Δf max Is the maximum frequency deviation in the frequency response process;
the constraint of constructing the objective function according to the formulas (5) to (12) is:
v in ＜v＜v out (5)
wherein v is wind speed, v in To cut in wind speed v out To cut out wind speed;
Δf 0 ≤0.2Hz (6)
in the formula ,Δf0 Is a frequency deviation allowable value;
ω min ≤ω ref ≤ω max (7)
in the formula ,ωmin Is the minimum value of the rotating speed omega ref For the rotation speed reference value omega max Is the maximum value of the rotating speed;
β min ≤β ref ≤β max (8)
in the formula ,βmin Is the minimum value of pitch angle beta ref As pitch angle reference value, beta max Is the maximum value of the pitch angle;
in the formula ,Pm Is the mechanical power of the doubly-fed fan, C p For wind energy capture coefficient, C p，max For maximum wind energy capture coefficient at maximum wind power tracking, v n Is the rated wind speed;
in the formula ,ωr Is the rotation speed;
in the formula ,PG Is the output power of the doubly-fed fan, K C For MPPT coefficient omega r,n Is rated rotation speed;
wherein f is frequency and t' is time, and the constraint condition ensures that frequency secondary drop does not occur in the frequency response process;
searching an optimal difference adjustment coefficient under a corresponding wind speed based on a gray wolf optimization algorithm according to a simultaneous equation of a formula (13), so that the frequency overshoot f (x) is minimum under the constraint of each constraint condition;
step 3-3) constructing a generalized objective function F (x) according to equation (14):
F(x)＝f(x)+δ(t)H(x) (14)
wherein f (x) is an original objective function, delta (t) H (x) is a penalty term, delta (t) is penalty force, and H (x) is a penalty factor; step 3-4) respectively calculating penalty factors of all constraint conditions of each individual gray wolf, calculating the fitness value of each individual gray wolf according to a formula (14), and recording the optimal fitness value and the corresponding position;
step 3-5), judging whether the penalty factor H (x) meets the precision requirement or the maximum iteration number, if so, ending the algorithm, and outputting an optimal solution; otherwise, executing the step 3-6);
step 3-6) respectively marking the individual positions of the gray wolves with the first three positions of the fitness value arrangement asAs a decision layer, other individuals and +.>Updating the position of each individual gray wolf according to formulas (16) - (17), and returning to step 3-4);
in the formula Respectively representing the distances between alpha, beta and delta and other individuals in the wolf group; />Current positions of α, β and δ are represented, respectively; />Is a coefficient vector, and t is the iteration number;
equation (16) defines the direction and distance that omega individuals in the wolf group advance toward α, β and δ, respectively, and equation (17) represents the final position of omega individuals in the wolf group.
2. The method for controlling the sag factor of the doubly-fed wind turbine participating in the primary frequency modulation of the power grid according to claim 1, wherein the customized sag characteristic unit in step 2) completes the adjustment of the sag control difference adjustment factor of the doubly-fed wind turbine participating in the primary frequency modulation of the power grid according to formula (1),
in formula (1), Δf 0 Is set to be 0.2Hz, f N Rated for 50Hz, P of the power grid del ' total reserve power for load shedding of n doubly-fed fans in wind power plant, P WN Rated active power of the wind power plant;
calculating total reserve power P of load shedding of n doubly-fed fans in wind power plant according to (2) del ：
in the formula ,Pdel Reserve power for load shedding of single doubly-fed wind generator, wherein ρ is air density, d is load shedding coefficient, and C p，max And v is the wind speed, and is the maximum wind energy capture coefficient when tracking the maximum wind power.
3. The method for controlling the sag factor of the doubly-fed wind turbine participating in primary frequency modulation of the power grid according to claim 1, wherein the factor in step 3-1) is calculated by the following formula (18):
in the formula ,linearly decreasing from 2 to 0, # in an iterative process> and />Respectively [0,1 ]]Random vectors within.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111013634.7A CN113839398B (en) | 2021-08-31 | 2021-08-31 | Variable droop coefficient control method for double-fed fans participating in primary frequency modulation of power grid |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111013634.7A CN113839398B (en) | 2021-08-31 | 2021-08-31 | Variable droop coefficient control method for double-fed fans participating in primary frequency modulation of power grid |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113839398A CN113839398A (en) | 2021-12-24 |
| CN113839398B true CN113839398B (en) | 2023-08-25 |
Family
ID=78961843
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111013634.7A Active CN113839398B (en) | 2021-08-31 | 2021-08-31 | Variable droop coefficient control method for double-fed fans participating in primary frequency modulation of power grid |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113839398B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101450147B1 (en) * | 2014-08-05 | 2014-10-13 | 전북대학교산학협력단 | Inertial control method of wind turbines |
| CN104343629A (en) * | 2014-09-25 | 2015-02-11 | 河海大学 | Control method for frequency response of doubly-fed generator |
| CN106998070A (en) * | 2017-04-19 | 2017-08-01 | 河海大学 | A kind of double-fed fan motor unit frequency droop coefficient modification method and its control system |
| CN110867894A (en) * | 2019-11-25 | 2020-03-06 | 上海电力大学 | Dynamic frequency division wind power generation system with autonomous inertia response |
-
2021
- 2021-08-31 CN CN202111013634.7A patent/CN113839398B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101450147B1 (en) * | 2014-08-05 | 2014-10-13 | 전북대학교산학협력단 | Inertial control method of wind turbines |
| CN104343629A (en) * | 2014-09-25 | 2015-02-11 | 河海大学 | Control method for frequency response of doubly-fed generator |
| CN106998070A (en) * | 2017-04-19 | 2017-08-01 | 河海大学 | A kind of double-fed fan motor unit frequency droop coefficient modification method and its control system |
| CN110867894A (en) * | 2019-11-25 | 2020-03-06 | 上海电力大学 | Dynamic frequency division wind power generation system with autonomous inertia response |
Non-Patent Citations (1)
| Title |
|---|
| 基于双馈风电机组的变下垂系数控制策略;潘文霞;全锐;王飞;;电力系统自动化(第11期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113839398A (en) | 2021-12-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109494769B (en) | Wind field participating frequency modulation method and system | |
| Wang et al. | Dynamic load frequency control for high-penetration wind power considering wind turbine fatigue load | |
| CN110768307A (en) | Wind power plant primary frequency modulation control method and system | |
| CN108365633A (en) | A kind of doubly-fed wind turbine hypervelocity virtual inertia control method of Control of decreasing load variable element | |
| CN106786677A (en) | A kind of interconnected electric power system distributed dynamic matrix frequency control method of meter and wind-powered electricity generation | |
| CN110417032A (en) | A kind of double-fed blower participates in the multiobjective optimization control method of system frequency modulation | |
| CN109004687A (en) | The intelligent inertia response control mehtod and system of wind power plant participation power grid frequency modulation | |
| CN105656034A (en) | Simulation modeling method for isolated island microgrid frequency control | |
| Malmir et al. | Controlling megawatt class WECS by ANFIS network trained with modified genetic algorithm | |
| CN109980660B (en) | Robust interval wind power scheduling method based on improved frequency response characteristic of generator | |
| CN113394813B (en) | Method for calculating unit power instruction value of offshore wind farm and distributed scheduling method | |
| Hosseini et al. | Intelligent pitch angle control based on gain-scheduled recurrent ANFIS | |
| Hemeyine et al. | Robust takagi sugeno fuzzy models control for a variable speed wind turbine based a DFI-generator | |
| Lee et al. | Operation scheme for a wind farm to mitigate output power variation | |
| CN113839398B (en) | Variable droop coefficient control method for double-fed fans participating in primary frequency modulation of power grid | |
| CN116937637A (en) | Energy storage capacity distribution robust planning method for providing frequency support through multi-energy complementation | |
| CN115811070A (en) | Flywheel energy storage self-adaptive capacity configuration method and system for assisting thermal power generating unit in frequency modulation | |
| CN115102228A (en) | Multi-target coordination frequency optimization method and device for wind power plant containing flywheel energy storage | |
| CN112634076B (en) | Distributed regulation and control method for wind power-containing multi-microgrid system considering flexibility reserve | |
| Bubshait et al. | Centralized power reserve algorithm of de-loaded wind farm for primary frequency regulation | |
| Ma et al. | Wind turbine down-regulation strategy for minimum wake deficit | |
| Lebioda et al. | Investigation of disposable reserve power in a large-scale wind farm | |
| Reddy et al. | Fuzzy Logic-Based Pitch Angle Control for VariableSpeed Wind Systems | |
| Wang et al. | An optimal over-frequency droop control for DFIG-based wind farm under unreliable communication | |
| HEMEYINE et al. | Takagi Sugeno Fuzzy Models for Wind Turbine Driving a DFI-Generator via Linear Matrix Inequalities |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |