CN102235305A - Wind power generation vane-change fuzzy control method - Google Patents
Wind power generation vane-change fuzzy control method Download PDFInfo
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- CN102235305A CN102235305A CN2011100973055A CN201110097305A CN102235305A CN 102235305 A CN102235305 A CN 102235305A CN 2011100973055 A CN2011100973055 A CN 2011100973055A CN 201110097305 A CN201110097305 A CN 201110097305A CN 102235305 A CN102235305 A CN 102235305A
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Abstract
The invention relates to a wind power generation vane-change fuzzy control method, which solves the problems of low control quality and low steady-state performance of a wind power generation unit system and belongs to the technical field of application of wind power generation. The technical scheme comprises the following steps of: adjusting the magnitude of a modifying factor according to a membership function of a fuzzy object to achieve an adjustment control rule; introducing a fragmentation biquadratic Lagrange interpolation algorithm of a binary function into a self-setting fuzzy controller to convert discrete control variables into continuous control variables, so that the output power of a wind power generation unit can be relatively accurately and quickly controlled; therefore, the adjusting dead zone and the offset of the controller are eliminated, and the control quality of the system is ensured. In the wind power generation unit, due to the application of a control algorithm in which the self-setting fuzzy controller is combined with the Lagrange interpolation algorithm, wind power can be more accurately controlled, so that the wind power generation unit can better meet the requirements of power grids; and intelligent control over the wind power generation unit has become one of mainstream development directions of the current wind power generation technologies.
Description
Technical field:
The present invention relates to a kind of wind generating variable-propeller fuzzy control method, solve the controlling performance and the steady-state behaviour problem of wind-driven generator group system, belong to the wind-power electricity generation applied technical field.
Background technique:
Along with the develop rapidly of automatic control theory and technology, obtained using widely based on the modern control theory of state-variable description, but they still depend on system's precise math model, are difficult in the effect that is well controlled in the electric control system.
Summary of the invention:
The object of the invention provides a kind of wind generating variable-propeller fuzzy control method, do not rely on the mathematical models of controlled device, can overcome effect of non-linear, parameter variation to controlled plant has stronger robustness, dynamic response is good, can suppress overshoot well, solve the problems referred to above that exist in the background technique.
Technological scheme of the present invention is: a kind of wind generating variable-propeller fuzzy control method, and adjust the size of modifying factor according to the membership function of fuzzy object, thereby reach the adjustment control law; In certainly the fuzzy controller of adjusting, introduce the burst biquadratic Lagrange's interpolation algorithm of binary function. realized of the conversion of discrete controlled variable to the continuous control variable. thereby the control to the wind power generating set output power is comparatively accurate and rapid, thereby has eliminated the adjusting dead band and the static difference of controller. guaranteed system's controlling performance.
The major function of the present invention in wind power generating set:
Rotating speed control: rotating speed control is to make wind wheel accomplish steady raising speed in start-up course, is having when interference to make stabilization of speed within the specific limits.
Power control: power mainly is to control by feather, makes wind power generating set maintain operation under the rated power all the time.
The present invention is based on the TMS320F2812 chip, the F2812 chip instruction cycle is 6.67ns(150MHz), the flash program storage of 128K in the sheet, the external memory of support 1M; Two event manager module EVA and EVB; WatchDog Timer module (WDT); Programme separately or multiplexing general I/O pin (GPIO); 16 12 analog-digital conversion module of channel (ADC), eCAN (Enhanced Controller Area Network) bus can conveniently realize controlling requirement.
Control section and power supply/signal plate adopts isolation method, avoids signal interference problem; Adopted heat radiation, system in package that electromagnetic wave shielding is good simultaneously, guaranteed the operation that controller secure is stable from hardware.
Signals such as the wind speed of collection blower fan, wind direction, voltage and current are transferred among the master controller TMS320F2812 by communication unit during the analog acquisition unit; Data and the fuzzy control rule contrast of master controller by collecting controlled for the operation that becomes oar actuator according to result calculated, reaches the control requirement.
The invention has the beneficial effects as follows: wind power generating set is by increasing the utilization of the control algorithm that combines from adjust fuzzy controller and Lagrange's interpolation, guaranteed system's controlling performance, can control more accurate to wind-powered electricity generation, make wind power generating set satisfy the electrical network requirement better, the wind power generating set of intelligent control has become one of mainstream development direction of current wind generating technology.
Description of drawings:
Accompanying drawing 1 is the structural representation of pid parameter fuzzy controller of the present invention;
Accompanying drawing 2 is f (e) fair curve figure.
Embodiment:
Below in conjunction with accompanying drawing, the invention will be further described by embodiment.
In an embodiment, fuzzy control rule and question blank all are on the basis of artificial experience, design a kind of membership function according to fuzzy object and adjust the size of modifying factor, thereby reach the adjustment control law.In certainly the fuzzy controller of adjusting, introduce the burst biquadratic Lagrange's interpolation algorithm of binary function. realized of the conversion of discrete controlled variable to the continuous control variable. thereby the control to the wind power generating set output power is comparatively accurate and rapid, thereby has eliminated the adjusting dead band and the static difference of controller. guaranteed system's controlling performance.
The correction function f (e) that sets up deviation and be e and be the independent variable amount is when system deviation is big, influence to deviation gives bigger weight, improve speed of response, to eliminate deviation as early as possible: when system deviation hour, influence to deviation gives less weight, in order to avoid the overshoot of system makes system enter stable state as soon as possible.So correction function f (e) may be selected to be;
f(e)=?α=k︳e/R︱
P (1)
In the formula: e is a system deviation, e=r-y; R is the setting value of system; K, p are undetermined parameter.Set R, parameter k, p can regulate by computer keyboard and set.From the deviation e of the fuzzy controller of adjusting and the domain of deviation variation rate ec be
E(e)=｛-5.-4.-3.-2.-1.0.1.2.3.4.5｝
E(ec)=｛-5.-4.-3.-2.-1.0.1.2.3.4.5｝
Then the normalizing fuzzy quantity is
5sign(e)?︱e/R?︳≥0.8
4sign(e)?︱e/R?︳≥0.5
E?= 3sign(e)?︱e/R?︳≥0.3 (2)
2sign(e)?︱e/R?︳≥0.1
1sign(e)?︱e/R?︳≥0.03
0 ︱e/R?︳＜0.03
5sign(e)?︱e/R?︳≥0.3
4sign(e)?︱e/R?︳≥0.2
EC?= 3sign(e)?︱e/R?︳≥0.15 (3)
2sign(e)?︱e/R?︳≥0.08
1sign(e)?︱e/R?︳≥0.02
0 ︱e/R?︳＜0.02
By choose reasonable k, the value of p, correction function f (e) can adjust fuzzy control rule flexibly according to the variation of deviation e.The form of the fuzzy control rule of controlled system is chosen as:
﹤αE﹥ ︱E︱≥E
m
U?= ﹤αE+(1-α)EC﹥ E
m≤︱E︱ (4)
﹤αE+(1-α)EC+ ∑e﹥ E
W?≤︱E︱
By choose reasonable k, the p value, B is the deviation integration weight coefficient; E
m. E
wFor deviation threshold value (E
m＞E
w).Correction function and normalization deviation (e/R) and parameter k, the relation between the p as shown in Figure 2
In order to strengthen the effect of EC, getting parameter P ∈ ﹙ 0.5.3 ﹚. the span P of parameter k is relevant.The process response is accelerated when making the deviation initial value big, gets K＞1.But as ︱ E ︱=E
mThe time, should satisfy (1-α)＞0, so the span that can obtain by formula (1)
1≤K≤︱R/E
m︱
P (5)
In order to improve the control law that generates by (1)-(5), improve control accuracy, utilization formula (6), (7) are segmented meta-rule:
5sign(e)?︱e/R?︳≥0.8
︱5-(0.8-e/R?)/0.3︱sign(e)?︱e/R?︳≥0.5
e’= ︱4-(0.5-e/R?)/0.2︱sign(e)?︱e/R?︳≥0.3
︱3-(0.3-e/R?)/0.2︱sign(e)?︱e/R?︳≥0.1 (6)
︱2-(0.1-e/R?)/0.07︱sign(e)︱e/R?︳≥0.03
0 ︱e/R?︳＜0.03
5?sign(e)?︱e/R?︳≥0.3
︱5-(0.3-ec/R?)/0.1︱sign(e) ︱e/R?︳≥0.2
ec’=?︱4-(0.2-ec/R?)/0.05︱sign(e)?︱e/R?︳≥0.15
︱3-(0.15-ec/R?)/0.7︱sign(e)?︱e/R?︳≥0.08 (7)
︱2-(0.08-ec/R?)/0.6︱sign(e)?︱e/R?︳≥0.023
0 ︱e/R?︳＜0.02
When setting R, parameter k after p is definite, can generate fuzzy control table by formula (1)-(7), i.e. the function table of function U=f (E.EC).
Lagrange's interpolation: the controlled variable U of the fuzzy controller of adjusting certainly disperses.The adjusting dead band of controller may be caused, and steady-state error can't be eliminated.In order to guarantee the ageing of control system, select for use the burst biquadratic to catch value-based algorithm.
Behind the utilization interpolation algorithm.Be equivalent to the fuzzy control rule table that is generated by formula (1)～(7) has been replenished infinite control law new, through segmenting in the mode of linear interpolation, fundamentally eliminated
The adjusting dead band and the steady-state error of controller, thus overcome owing to deviation changes steady-state error and the stable state flutter phenomenon that causes. improved the stable state quality of system.
Claims (3)
1. a wind generating variable-propeller fuzzy control method is characterized in that adjusting according to the membership function of fuzzy object the size of modifying factor, thereby reaches the adjustment control law; In certainly the fuzzy controller of adjusting, introduce the burst biquadratic Lagrange's interpolation algorithm of binary function. realized of the conversion of discrete controlled variable to the continuous control variable. thereby the control to the wind power generating set output power is comparatively accurate and rapid, thereby has eliminated the adjusting dead band and the static difference of controller. guaranteed system's controlling performance.
2. the wind generating variable-propeller fuzzy control method according to claim 1 is characterized in that the present invention is based on the TMS320F2812 chip, and the F2812 chip instruction cycle is 6.67ns150MHz, the flash program storage of 128K in the sheet, the external memory of support 1M; Two event manager module EVA and EVB; WatchDog Timer module WDT; Programme separately or multiplexing general I/O pin GPIO; 12 analog-digital conversion module ADC of 16 channels, the eCAN bus can conveniently realize controlling requirement.
3. the wind generating variable-propeller fuzzy control method according to claim 2, signals such as the wind speed of collection blower fan, wind direction, voltage and current are transferred among the master controller TMS320F2812 by communication unit when it is characterized in that the analog acquisition unit; Data and the fuzzy control rule contrast of master controller by collecting controlled for the operation that becomes oar actuator according to result calculated, reaches the control requirement.
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| CN2011100973055A CN102235305B (en) | 2010-12-30 | 2011-04-19 | Wind power generation vane-change fuzzy control method |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107250531A (en) * | 2014-08-12 | 2017-10-13 | 蒋素芳 | A kind of wind power generation plant and system |
| CN111828246A (en) * | 2019-04-23 | 2020-10-27 | 新疆金风科技股份有限公司 | Wind generating set overspeed prevention control method and device and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0629968A2 (en) * | 1993-06-14 | 1994-12-21 | Motorola, Inc. | Method for continuous logic computation |
| CN101498282A (en) * | 2008-02-01 | 2009-08-05 | 北京能高自动化技术有限公司 | Yaw control method for large-sized wind-driven generator group |
| CN101576059A (en) * | 2009-04-02 | 2009-11-11 | 保定天威集团有限公司 | Variable-pitch controller for wind power generator |
| CN101598109A (en) * | 2009-05-21 | 2009-12-09 | 中国电力科学研究院 | A kind of intelligence control method of wind driven generator yaw system |
-
2011
- 2011-04-19 CN CN2011100973055A patent/CN102235305B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0629968A2 (en) * | 1993-06-14 | 1994-12-21 | Motorola, Inc. | Method for continuous logic computation |
| CN101498282A (en) * | 2008-02-01 | 2009-08-05 | 北京能高自动化技术有限公司 | Yaw control method for large-sized wind-driven generator group |
| CN101576059A (en) * | 2009-04-02 | 2009-11-11 | 保定天威集团有限公司 | Variable-pitch controller for wind power generator |
| CN101598109A (en) * | 2009-05-21 | 2009-12-09 | 中国电力科学研究院 | A kind of intelligence control method of wind driven generator yaw system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107250531A (en) * | 2014-08-12 | 2017-10-13 | 蒋素芳 | A kind of wind power generation plant and system |
| CN111828246A (en) * | 2019-04-23 | 2020-10-27 | 新疆金风科技股份有限公司 | Wind generating set overspeed prevention control method and device and storage medium |
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