CN104454346A - Maximum power tracking control method for small permanent-magnet direct-drive wind power generation system - Google Patents
Maximum power tracking control method for small permanent-magnet direct-drive wind power generation system Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 84
- 238000010248 power generation Methods 0.000 title claims abstract description 20
- 241000254158 Lampyridae Species 0.000 claims abstract description 57
- 239000013598 vector Substances 0.000 claims description 81
- 239000000523 sample Substances 0.000 claims description 24
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- 238000012549 training Methods 0.000 claims description 10
- 239000003623 enhancer Substances 0.000 claims description 5
- 238000005457 optimization Methods 0.000 claims description 5
- 230000015572 biosynthetic process Effects 0.000 claims description 3
- 238000012706 support-vector machine Methods 0.000 abstract description 6
- 230000001276 controlling effect Effects 0.000 description 15
- 239000011159 matrix material Substances 0.000 description 5
- 238000012417 linear regression Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
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- CYCGRDQQIOGCKX-UHFFFAOYSA-N Dehydro-luciferin Natural products OC(=O)C1=CSC(C=2SC3=CC(O)=CC=C3N=2)=N1 CYCGRDQQIOGCKX-UHFFFAOYSA-N 0.000 description 1
- BJGNCJDXODQBOB-UHFFFAOYSA-N Fivefly Luciferin Natural products OC(=O)C1CSC(C=2SC3=CC(O)=CC=C3N=2)=N1 BJGNCJDXODQBOB-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention relates to a maximum power tracking control method for a small permanent-magnet direct-drive wind power generation system. According to the method, on the basis of collecting a lot of actual samples of wind speed vector-rotating speed-power, a firefly support vector machine regression prediction model of the wind speed is established, and wind speed estimation is performed by utilizing the model; next, the optimal rotating speed, corresponding to the maximum power point, of a draught fan is predicted through the optimum tip speed ratio method; afterwards, the rotating speed of the draught fan is adjusted to the predicted optimum rotating speed of the draught fan, and the maximum power of the draught fan is tracked based on the perturbation and observation method at a set perturbation step length with the rotating speed as an initial value. According to the maximum power tracking control method, wind speed estimation without sensors is achieved, the control cost of a power generation system is greatly reduced, the speed and accuracy for looking for the maximum power point are enhanced, and power loss during perturbation is lowered.
Description
Technical field
The present invention relates to a kind of method for controlling minitype permanent magnetism directly-driving wind power generation system, belonging to technical field of power generation.
Background technique
Along with the continuous aggravation of global energy crisis, renewable energy sources is more and more subject to people's attention.Wind-power electricity generation has become one of Main way of current generation of electricity by new energy because having many advantages.Miniature wind power generation system (being less than 15kW) is lower because of its cost, the place of electric power support cannot be provided to have competitive ability at bulk power grid such as remote mountain area and island.Therefore, correlative study is carried out to miniature wind power generation system, have important practical significance.
The output power of wind-driven generator is relevant with external environment and loading condition, and the maximum power point (MPP) of existence anduniquess under certain external environment and loading condition.In order to improve generating efficiency, need between wind-driven generator and load, arrange maximal power tracing (MPPT) circuit.Current wind-power generating system maximum power tracking and controlling method has a variety of, and the most frequently used has tip speed ratio control methods, disturbance observation method (P & O), neural network (ANN) etc.The pluses and minuses of these methods are as follows:
Tip speed ratio control methods: under a certain fixing wind speed, as long as ensure that wind energy conversion system rotating speed is a certain numerical value, namely keeps tip speed ratio to be optimum tip-speed ratio, namely can realize maximal power tracing.The method is comparatively simple, but needs to measure effective wind speed.Because the area of wind wheel is comparatively large, and wind speed has certain wave properties and randomness, and the accurate measurement being carried out effective wind speed by air velocity transducer is comparatively difficult.In addition, the introducing of air velocity transducer not only increases the manufacture cost of power generation system, and can reduce the reliability of whole system.
Disturbance observation method: its principle applies periodic disturbance to rotation speed of fan, then determine that next step perturbation direction is to realize maximal power tracing according to the change of output power.The method implements relatively easily, but the operation point that searches out of the method can only near MPP oscillatory operation, cause wind energy not to be fully utilized.In addition, the initial value of rotation speed of fan and disturbance step-length have larger impact to the precision of following the tracks of and speed, sometimes also misjudgment phenomenon can occur, have impact on the control effects of this method.
Neural network ANN: the method predicts maximum power point (MPP) by training BP neural network model, can to avoid in disturbance observation method the power loss that disturbance repeatedly causes.But, by the impact of crossing study or deficient study occurred in sample narrow limitation and training process, between the maximum power point predicted value of model and actual value, there is certain error unavoidably.
In sum, there is the shortcomings such as cost is high, tracking poor accuracy in existing wind-power generating system controlling method, limits the development of wind-power electricity generation, be therefore necessary to be improved.
Summary of the invention
The object of the invention is to the drawback for prior art, a kind of low, that tracking accuracy is high minitype permanent magnetism directly-driving wind power generation system maximum power tracking and controlling method of controlling cost is provided.
Problem of the present invention realizes with following technical proposals:
A kind of minitype permanent magnetism directly-driving wind power generation system maximum power tracking and controlling method, described method is on the basis of actual sample gathering a large amount of wind velocity vector-rotating speed-power, set up the firefly Support vector regression forecasting model of wind speed and utilize this model to carry out wind estimation, then the optimum rotation speed of fan corresponding to maximum power point is doped by optimum tip-speed ratio method, then by the optimum rotation speed of fan of the rotational speed regulation of blower fan to prediction, and with this rotating speed for initial value, adopt disturbance observation method to follow the tracks of the peak output of blower fan with the disturbance step-length of setting.
Above-mentioned minitype permanent magnetism directly-driving wind power generation system maximum power tracking and controlling method, said method comprising the steps of:
A. firefly Support vector regression forecasting model is set up:
1. will
nindividual air velocity transducer is installed on front side of draught fan impeller respectively, the circular flat inswept with impeller coaxial and circle centre position in equal-sized circular flat and the distance center of circle
1/ (n-1)radius,
2/ (n-1)radius ..., 1 times of radius, records wind velocity vector
v=[
v 1 ,
v 2 ...,
v n ]
t;
2. remember that the wind velocity vector under a certain wind speed environments is
v (i)=[
v 1 (i),
v 2 (i)...,
v n (i)]
t, in the wind-power generating system corresponding with this wind velocity vector, the VDC of rectifier output end is
v oc (i), direct current (d.c.) is
i oc (i), rotation speed of fan is
ω oc (i), utilization records
v oc (i)with
i oc (i)calculate output power this moment
p oc (i)=V oc (i) I oc (i), obtain one group of sample (
v (i),
p oc (i),
ω oc (i)); By collecting the sample group under different wind speed environments, formation sample set (
v (i),
p oc (i),
ω oc (i));
3. utilize sample set (
v (i),
p oc (i),
ω oc (i)) training firefly Support vector regression forecasting model, wherein mode input be
p oc (i),
ω oc (i), model exports and is
v (i);
B. firefly Support vector regression forecasting model is utilized to carry out wind estimation:
At a time, voltage transducer, current sensor is utilized to gather the VDC that in wind-power generating system, rectifier exports respectively
v oc with direct current (d.c.)
i oc , utilize speed probe to gather rotation speed of fan
ω oc , and calculate output power this moment
p oc =V oc i oc ; Will
ω oc ,
p oc as the input of firefly Support vector regression forecasting model, model exports the estimated value for current wind velocity vector
, and calculate the estimated value of effective wind speed according to the following formula
:
（1）
C. utilize optimum tip-speed ratio method, calculate the estimated value of effective wind speed
corresponding best rotation speed of fan predicted value
ω ref :
（2）
Wherein,
λfor tip speed ratio;
rfor impeller radius;
D. passing ratio integral control method regulates the rotating speed of blower fan, makes it to reach best rotation speed of fan predicted value
ω ref ; Then with
ω ref for initial value, adopt disturbance observation method with the disturbance step delta of setting
ωfollow the tracks of the peak output of blower fan;
If the power difference e. before and after the disturbance of trying to achieve of disturbance observation method is more than or equal to setting threshold value
t r , illustrate that wind speed there occurs sudden change, repeat step b to step e; Otherwise, continue to adopt disturbance observation method to follow the tracks of the peak output of blower fan with the disturbance step-length of setting.
Above-mentioned minitype permanent magnetism directly-driving wind power generation system maximum power tracking and controlling method, described firefly Support vector regression forecasting model is the supporting vector machine model utilizing glowworm swarm algorithm to carry out parameter optimization, and concrete Optimization Steps is:
I. the parameter of initialization glowworm swarm algorithm: initialization firefly number
n, fluorescein volatility coefficient
, fluorescein enhancer
γ, sensing range
r s (i.e. penalty factor to be optimized in Support vector regression forecasting model
cwith nuclear parameter
excursion), neighborhood variance ratio
β, neighbours' threshold value
n t , moving step length
s, initial fluorescence element
l 0 with maximum iteration time;
II. penalty factor to be optimized in Support vector regression forecasting model
cwith nuclear parameter
span in, stochastic generation one group of numerical value is as the initial position of firefly individuality, and fitness function is for utilizing firefly present position (a certain penalty factor
cwith nuclear parameter
) negative value of the average relative error of Support vector regression forecasting model on test samples of training, average relative error
representation is:
；
Wherein,
n 1for test samples number,
for the true value of wind velocity vector,
for the wind velocity vector estimated value that regression model exports;
III. calculate the fluorescein concentration of the fluorescein concentration of every firefly, decision domain scope and neighbours, determine the movement direction of each firefly by the fluorescein concentration of neighbours and move forward;
IV. judge whether glowworm swarm algorithm reaches end condition, if reach, then choosing the highest firefly position of fitness is the parameter of supporting vector machine model, and the support vector recorded in corresponding regression model is in order to using; Otherwise, go to step III.
Above-mentioned minitype permanent magnetism directly-driving wind power generation system maximum power tracking and controlling method, the end condition of described glowworm swarm algorithm is the average relative error of Support vector regression forecasting model on test samples
3% or iterations exceed maximum iteration time.
Beneficial effect of the present invention is:
(1) the present invention realizes, without sensor wind estimation, greatly saving controlling cost of power generation system, improves the speed and degree of accuracy of finding maximum power point;
(2) when external environment changes, directly working speed can be adjusted to best rotation speed of fan predicted value by means of forecasting model
ω ref near, avoid the process that disturbance observation method is progressively soundd out, thus improve tracking velocity;
(3) with best rotation speed of fan predicted value
ω ref for initial value carry out disturbance observe time, due to best rotation speed of fan predicted value
ω ref very close to the best rotation speed of fan of reality, therefore less disturbance step-length can be set, thus effectively reduce the power loss of perturbation process;
(4) after the characteristic of blower fan changes, can, by again collecting sample, train new forecasting model to ensure precision of prediction.
Accompanying drawing explanation
Below in conjunction with accompanying drawing, the invention will be further described.
Fig. 1 is hardware structure diagram of the present invention;
Fig. 2 is the schematic diagram utilizing firefly Support vector regression forecasting model to carry out wind velocity vector estimation.
Fig. 3 is tip speed ratio and power coefficient graph of a relation;
Fig. 4 is the algorithm flow chart of disturbance observation method (P & O);
Fig. 5 is the flow chart of maximum power tracking method provided by the invention;
In figure, each label is: 1-air velocity transducer; 2-permanent magnet direct-driving aerogenerator; 3-speed probe; 4-rectifier; 5-voltage transducer; 6-first electric capacity; 7-second electric capacity; 8-current sensor.
In figure neutralization literary composition, each symbol is:
v oc (i)for the VDC of rectifier output end in wind-power generating system,
i oc (i)for the direct current (d.c.) of rectifier output end in wind-power generating system,
ω oc (i)for rotation speed of fan,
p oc (i)for blower fan output power,
n 1for test samples number,
for the true value of wind velocity vector,
for the estimated value of wind velocity vector,
for the estimated value of effective wind speed,
ω ref for best rotation speed of fan predicted value,
λfor tip speed ratio,
rfor impeller radius, Δ
ωfor disturbance step-length,
nfor initialization firefly number,
for fluorescein volatility coefficient,
γfor fluorescein enhancer,
r s for sensing range,
cfor penalty factor,
for nuclear parameter,
βfor neighborhood variance ratio,
n t for neighbours' threshold value,
sfor the moving step length of firefly,
l 0 for initial fluorescence element,
for the average relative error of Support vector regression forecasting model on test samples,
k(
x i ,
x) be kernel function,
for Lagrange coefficient,
y i for linear regression function output vector,
the Nonlinear Mapping matrix from the input space to high-dimensional feature space,
x i for input vector,
wfor weight matrix,
bfor bias vector,
j (x)for fitness function,
l i (t)for
tmoment
ithe fluorescein concentration of firefly.
Embodiment
Below in conjunction with accompanying drawing, preferred embodiment is elaborated.It should be emphasized that following explanation is only exemplary, instead of in order to limit the scope of the invention and apply.
Fig. 1 is the hardware structure diagram of the embodiment of the present invention, and wherein, the major parameter of minitype permanent magnetism direct wind-driven generator 2 is: rotor diameter is 1.2m, rated power is 300W, and voltage rating is 24V, rated speed 800r/min, threshold wind velocity 1m/s, rated wind speed 10m/s, survival wind speed 25m/s; The major parameter of maximum power tracking device is: MPPT controller adopts dsPIC33FJ06GS101 single-chip microcomputer, DC-DC converter adopts Boost circuit, driver module selects MCP14E3, voltage transducer 5 selects LV28-P, LA25-NP selected by current sensor 8, speed probe 3 adopts voltage zero-cross to detect formula frequency meter, the first electric capacity C1=10uF, the second electric capacity C2=100uF.
This minitype permanent magnetism directly-driving wind power generation system maximum power tracking and controlling method comprises the following steps:
Step 1: set up firefly Support vector regression forecasting model:
Step 101: will
nindividual air velocity transducer is installed on front side of draught fan impeller respectively, coaxial with impeller circular flat, parallel and circle centre position in equal-sized circular flat and the distance center of circle
1/ (n-1)radius,
2/ (n-1)radius ..., 1 times of radius, then can record wind velocity vector
v=[
v 1 ,
v 2 ...,
v n ]
t; In embodiment, get n=4, therefore can wind velocity vector be recorded
v=[
v 1 ,
v 2 ...,
v 4 ]
t;
Step 102: remember that the wind velocity vector under a certain wind speed environments is
v (i)=[
v 1 (i),
v 2 (i)...,
v n (i)]
t, now in wind-power generating system, the VDC of rectifier output end is
v oc (i), direct current (d.c.) is
i oc (i), rotation speed of fan is
ω oc (i), calculate output power this moment
p oc (i)=V oc (i) I oc (i), then can form one group of sample (
v (i),
p oc (i),
ω oc (i)); By collecting the sample group under different wind speed environments, formation sample set (
v (i),
p oc (i),
ω oc (i)); In embodiment, utilize blower, frequency variator and straight length to form small test wind-tunnel, control blower by regulating the frequency setting value of frequency variator and exert oneself, and then create different wind speed environments.Setting frequency converter frequency, from 10Hz, is that interval rises to 60Hz with 2Hz, can provides 31 kinds of wind speed environments, namely
v (i)=[
v 1 (i),
v 2 (i),
v 3 (i),
v 4 (i)]
t,
i=1 ..., 31}.Under each wind speed environments, by regulating the dutycycle of DC-DC converter pwm signal, controlling rotation speed of fan from 300r/min, is that interval is raised to 1500r/min with 30r/min, totally 41 kinds of rotating speeds, the VDC of rectifier output end corresponding under recording each rotating speed
v oc with direct current (d.c.)
i oc , and calculate corresponding output power
p oc =V oc i oc , then add up to obtain 1271 groups of samples.
Step 103: utilize sample set (
v (i),
p oc (i),
ω oc (i)) training firefly Support vector regression forecasting model, wherein mode input be
,
ω oc (i)), model exports and is
v (i); In embodiment, randomly draw 900 groups of training samples as model in 1271 groups of samples, all the other 371 groups of test samples as model, for test model precision.
The input space is mapped to the feature space of higher-dimension by Support vector regression SVR by Nonlinear Mapping, utilize a linear function collection to carry out regression estimates.Firefly support vector machine of the present invention is multi output support vector machine, given sample set
(
for input vector,
for corresponding output vector, N is number of samples, and n is input vector dimension, and m is output vector dimension), multi output support vector machine SVR linear regression function used is:
(3)
Wherein:
y i for linear regression function output vector;
it is the Nonlinear Mapping matrix from the input space to high-dimensional feature space;
x i for input vector;
wfor weight matrix;
bfor bias vector.
Weight matrix
wand bias vector
bby minimizing following formula to calculate:
(4)
In formula:
cfor penalty factor;
for the slack variable vector introduced;
Set up Lagrange's equation according to (4), can solve linear regression function is:
(5)
In formula:
k(
x i ,
x) be kernel function,
;
for Lagrange coefficient, non-vanishing
corresponding vector
x i be called support vector.
Select multi-form kernel function can generate different support vector machine, conventional kernel function has: polynomial function, Gaussian function, Sigmoid function etc.It is kernel function that the present invention chooses Gaussian function, that is:
(6)
Wherein,
for the width parameter of gaussian kernel function.
The precision of described Support vector regression forecasting model depends primarily on two major parameters
c,
, penalty factor
cfor the compromise of Controlling model complexity and approximate error; Nuclear parameter
reflect distribution or the range property of training sample data.In order to obtain high-precision regressive prediction model, the present invention adopts glowworm swarm algorithm to be optimized these two parameters.
Glowworm swarm algorithm is the novel colony intelligence optimized algorithm of one that India scholar Krishnanand and Ghose proposed in 2005.Glowworm swarm algorithm simulating nature circle firefly, by the luminous behavior attracting companion to seek a spouse or look for food, has good ability of searching optimum, has been successfully applied to the problem such as function optimization, sensor configuration.
In GSO algorithm, every firefly is distributed in the definition space of objective function, they self the brightness with luciferin relevant with the objective function fitness value on oneself position, brighter firefly represents that the position at its place is better.Suppose
x i (t)represent
tmoment
ithe position of firefly,
j (x)for fitness function,
l i (t)represent
tmoment
ithe fluorescein concentration of firefly, then
(7)
In formula (7):
for fluorescein volatility coefficient;
for fluorescein enhancer, i.e. fitness withdrawal ratio.
Firefly finds neighborhood in the decision region of region, and brighter firefly has higher attraction force and goes to attract firefly around to move toward this direction.The size of region decision region can by the impact of neighbours' quantity, and when neighbours' density is lower, the decision-making radius of firefly can strengthen and be beneficial to find more neighbours; Otherwise decision-making radius reduces.Decision domain scope more new formula is
(8)
In formula (8):
for
t+1 moment
ithe decision region of individual firefly;
r s for sensing range;
for neighborhood variance ratio;
n t for neighbours' threshold value;
n i (
t) be
tmoment
ithe neighborhood of firefly.
(9)
In formula (9),
represent
xnorm.Firefly is in movement process, and the fluorescein concentration according to firefly each in its neighborhood decides its movement direction.
p ij (
t) represent
tmoment
ifirefly is in its neighborhood
jthe probability of firefly movement, then
(10)
The is selected according to movement probability by wheel disc method
ithe direction of firefly movement, then
ifirefly exists
tthe position in+1 moment is
(11)
In formula (11),
for the moving step length of firefly.
Glowworm swarm algorithm is utilized to the concrete steps that support vector machine is optimized to be:
1) parameter of initialization glowworm swarm algorithm.Initialization firefly number
n, fluorescein volatility coefficient
, fluorescein enhancer
γ, sensing range
r s , neighborhood variance ratio
β, neighbours' threshold value
n t , moving step length
s, initial fluorescence element
l 0 with maximum iteration time, sensing range
r s for parameter (penalty factor to be optimized in Support vector regression forecasting model
cwith nuclear parameter
) excursion.In embodiment, arrange
n=20,
=0.4,
β=0.08,
n t =5,
s=0.03, initial fluorescence element
l 0 =5, maximum iteration time is 1000, penalty factor
cspan be [10
-1, 10
4], nuclear parameter
span be [10
-2, 10
4];
2) in parameter to be optimized
cwith
span in, stochastic generation one group of numerical value is as the initial position of firefly individuality.Fitness function is for utilizing firefly present position (a certain
cwith
) negative value of the average relative error of Support vector regression forecasting model on test samples of training.Average relative error
representation is:
(12)
Wherein,
n 1for test samples number,
for the true value of wind velocity vector,
for the wind velocity vector estimated value that regression model exports.Known, the average relative error of model on test samples is less, and fitness is higher;
3) according to formula (7)-(11), calculate the fluorescein concentration of the fluorescein concentration of every firefly, decision domain scope and neighbours, determine the movement direction of each firefly by the fluorescein concentration of neighbours and move forward;
4) judge whether glowworm swarm algorithm reaches end condition.If reach, then choosing the highest firefly position of fitness is the parameter of supporting vector machine model, and the support vector recorded in corresponding regression model is in order to using; Otherwise, turn 3).In the present embodiment, the end condition of setting is the average relative error of Support vector regression forecasting model on test samples
3% or iterations exceed maximum iteration time.Through 128 iteration, optimizing process terminates, and the supporting vector machine model parameter that the fitness of acquisition is the highest is
c=486.2,
=69.54, this model comprises 89 support vectors altogether.
Utilize the Support vector regression forecasting model obtained, can estimate known power and rotating speed
p oc ,
ω oc operating mode under wind velocity vector, as shown in Figure 2.
Step 2: utilize firefly Support vector regression forecasting model to carry out wind estimation:
p oc (i)=V oc (i) I oc (i)
At a time, the VD after rectifier in voltage transducer, current sensor collection wind-power generating system is utilized respectively
v oc with output current
i oc , utilize speed probe to gather rotation speed of fan
ω oc , and calculate output power this moment
p oc =V oc i oc ; Will
p oc ,
ω oc as the input of firefly Support vector regression forecasting model, model exports the estimated value for current wind velocity vector
, and draw the estimated value of effective wind speed according to the following formula
v m :
In the present embodiment, the VD after the rectifier that a certain moment collects
v oc for 15V, output current
i oc for 12A, then output power is this moment 180W, the rotary speed information that speed probe collects
ω oc for 600r/min, by { 180W, the 600r/min } input as firefly Support vector regression forecasting model, the estimated value exporting wind velocity vector is
v=[8.92,9.14,8.94,9.02]
t, then the estimated value of effective wind speed
v m =9.0m/s.
Step 3: according to optimum tip-speed ratio method, draw wind speed
v m corresponding best rotation speed of fan predicted value
ω ref :
Wherein,
for tip speed ratio;
rfor impeller radius; Tip speed ratio be blade tip tangential velocity with wind wheel before the ratio of wind speed, according to the formula of wind energy utilization, when different wind speed, have fixing tip speed ratio to make wind energy utilization be fixed on maximum value, as shown in Figure 3.In embodiment, get
=6.7, when effective wind speed is 9m/s,
rduring=0.6m, can show that best rotation speed of fan predicted value is 961r/min.
Step 4: passing ratio integral control method regulates the rotating speed of blower fan, makes it to reach best rotation speed of fan predicted value
ω ref ; And with
ω ref for initial value, disturbance observation method is adopted to follow the tracks of the peak output of blower fan with the disturbance step delta ω of setting; In embodiment, get disturbance step delta ω=5r/min.
Fig. 4 is the flow chart of disturbance observation method (P & O), its principle be periodically disturbance blower fan rotating speed (
ω+Δ
ω), then compare the changed power before and after its disturbance, if output power increases, then represent that perturbation direction is correct, continue in the same direction (
+Δ
ω) disturbance; If output power reduces, then towards contrary (-Δ
ω) direction disturbance.In embodiment, the A/D module due to dsPIC33FJ06GS101 completes once sampling only needs 0.5
us, when measuring VD, the average anode current of blower fan, in order to eliminate the signal jitter that the copped wave of DC-DC converter medium-high frequency causes, making A/D module change 10 times continuously and averaging as measured value.
Step 5: the power difference before and after the disturbance that disturbance observation method is tried to achieve is more than or equal to setting threshold value
t r time, illustrate that wind speed there occurs sudden change, repeat step 2 to step 5; Otherwise, continue to adopt disturbance observation method to follow the tracks of the peak output of blower fan with the disturbance step-length of setting, as shown in Figure 5.In embodiment, get
t r =15W.
Above-mentioned maximal power tracing (MPPT) method is passed through
clISP program LISP write control chip dsPIC33FJ06GS101, output pwm signal drives DC-DC converter, can realize maximal power tracing function.
The correctness of extracting method in order to verify, itself and conventional disturbance is observed (P & O) by hardware platform of the present invention and compares.Be specially: under same wind speed environments (frequency converter frequency arranging wind-tunnel is 40Hz), controlling blower fan initial speed is ω
0=600r/min, compares the tracking velocity of two kinds of methods and the average power of steady-state process respectively.
The tracing process that P & O method (adjustment cycle T=5s, step delta ω=20r/min) is observed in conventional disturbance is: after the continuous 17 postive direction disturbances of clapping, start at maximum power point MPP(ω
opt =950r/min) left and right vibration, namely enter steady-state process, needed time is about 85 seconds altogether.
The tracing process of the method for the invention is, first basis power is this moment 180W, and rotating speed is 600r/min, estimates wind velocity vector to be by SVR model
v=[8.92,9.14,8.94,9.02]
t, then the estimated value of effective wind speed
v m =9.0m/s.Maximum power point rotor speed forecast value ω is obtained according to optimum tip-speed ratio method
ref after=961r/min, directly working speed ω is adjusted to 961r/min by PI controller, then starts to carry out little step-length disturbance and observe P & O(adjustment cycle T=5s, step delta ω=5r/min), due to ω
ref itself is maximum power point MPP(ω closely
opt =950r/min), after the continuous 3 negative direction disturbances of clapping, enter steady-state process, needed time is about 15 seconds altogether.It can thus be appreciated that the tracking velocity of institute of the present invention extracting method will apparently higher than conventional disturbance observation method.
After entering steady-state process, calculate the average power of 60 seconds steady-state processs respectively, show that method is 209 watts in conventional disturbance observation (P & O), and the method for the invention is 222 watts, this illustrates and adopts the method for the invention to effectively reduce power loss.
The above; be only the present invention's preferably embodiment, but protection scope of the present invention is not limited thereto, is anyly familiar with within the scope of core technology that those skilled in the art disclose in the present invention; the change that can guess or replacement, all should be encompassed within protection scope of the present invention.
Claims (4)
1. a minitype permanent magnetism directly-driving wind power generation system maximum power tracking and controlling method, it is characterized in that, described method is on the basis of actual sample gathering a large amount of wind velocity vector-rotating speed-power, set up the firefly Support vector regression forecasting model of wind speed and utilize this model to carry out wind estimation, then the optimum rotation speed of fan corresponding to maximum power point is doped by optimum tip-speed ratio method, then by the optimum rotation speed of fan of the rotational speed regulation of blower fan to prediction, and with this rotating speed for initial value, disturbance observation method is adopted to follow the tracks of the peak output of blower fan with the disturbance step-length of setting.
2. minitype permanent magnetism directly-driving wind power generation system maximum power tracking and controlling method according to claim 1, it is characterized in that, described method processes according to the following steps:
A. firefly Support vector regression forecasting model is set up:
1. will
nindividual air velocity transducer is installed on front side of draught fan impeller respectively, the circular flat inswept with impeller coaxial and circle centre position in equal-sized circular flat and the distance center of circle
1/ (n-1)radius,
2/ (n-1)radius ..., 1 times of radius, records wind velocity vector
v=[
v 1 ,
v 2 ...,
v n ]
t;
2. remember that the wind velocity vector under a certain wind speed environments is
v (i)=[
v 1 (i),
v 2 (i)...,
v n (i)]
t, in the wind-power generating system corresponding with this wind velocity vector, the VDC of rectifier output end is
v oc (i), direct current (d.c.) is
i oc (i), rotation speed of fan is
ω oc (i), utilization records
v oc (i)with
i oc (i)calculate output power this moment
p oc (i)=V oc (i) I oc (i), obtain one group of sample (
v (i),
p oc (i),
ω oc (i)); By collecting the sample group under different wind speed environments, formation sample set (
v (i),
p oc (i),
ω oc (i));
3. utilize sample set (
v (i),
p oc (i),
ω oc (i)) training firefly Support vector regression forecasting model, wherein mode input be
p oc (i),
ω oc (i), model exports and is
v (i);
B. firefly Support vector regression forecasting model is utilized to carry out wind estimation:
At a time, voltage transducer, current sensor is utilized to gather the VDC that in wind-power generating system, rectifier exports respectively
v oc with direct current (d.c.)
i oc , utilize speed probe to gather rotation speed of fan
ω oc , and calculate output power this moment
p oc =V oc i oc ; Will
ω oc ,
p oc as the input of firefly Support vector regression forecasting model, model exports the estimated value for current wind velocity vector
, and calculate the estimated value of effective wind speed according to the following formula
:
；
C. utilize optimum tip-speed ratio method, calculate the estimated value of effective wind speed
corresponding best rotation speed of fan predicted value
ω ref :
；
Wherein,
λfor tip speed ratio;
rfor impeller radius;
D. passing ratio integral control method regulates the rotating speed of blower fan, makes it to reach best rotation speed of fan predicted value
ω ref ; Then with
ω ref for initial value, adopt disturbance observation method with the disturbance step delta of setting
ωfollow the tracks of the peak output of blower fan;
If the power difference e. before and after the disturbance of trying to achieve of disturbance observation method is more than or equal to setting threshold value
t r , illustrate that wind speed there occurs sudden change, repeat step b to step e; Otherwise, continue to adopt disturbance observation method to follow the tracks of the peak output of blower fan with the disturbance step-length of setting.
3. minitype permanent magnetism directly-driving wind power generation system maximum power tracking and controlling method according to claim 1 and 2, it is characterized in that, described firefly Support vector regression forecasting model is the supporting vector machine model utilizing glowworm swarm algorithm to carry out parameter optimization, and concrete Optimization Steps is:
I. the parameter of initialization glowworm swarm algorithm: initialization firefly number
n, fluorescein volatility coefficient
, fluorescein enhancer
γ, sensing range
r s (i.e. penalty factor to be optimized in Support vector regression forecasting model
cwith nuclear parameter
excursion), neighborhood variance ratio
β, neighbours' threshold value
n t , moving step length
s, initial fluorescence element
l 0 with maximum iteration time;
II. penalty factor to be optimized in Support vector regression forecasting model
cwith nuclear parameter
span in, stochastic generation one group of numerical value is as the initial position of firefly individuality, and fitness function is for utilizing firefly present position (a certain penalty factor
cwith nuclear parameter
) negative value of the average relative error of Support vector regression forecasting model on test samples of training, average relative error
representation is:
；
Wherein,
n 1for test samples number,
for the true value of wind velocity vector,
for the wind velocity vector estimated value that regression model exports;
III. calculate the fluorescein concentration of the fluorescein concentration of every firefly, decision domain scope and neighbours, determine the movement direction of each firefly by the fluorescein concentration of neighbours and move forward;
IV. judge whether glowworm swarm algorithm reaches end condition, if reach, then choosing the highest firefly position of fitness is the parameter of supporting vector machine model, and the support vector recorded in corresponding regression model is in order to using; Otherwise, go to step III.
4. minitype permanent magnetism directly-driving wind power generation system maximum power tracking and controlling method according to claim 3, is characterized in that, the end condition of described glowworm swarm algorithm is the average relative error of Support vector regression forecasting model on test samples
3% or iterations exceed maximum iteration time.
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