CN103107534B - A kind of double-fed induction electricity generation system optimizing power forecast Control Algorithm - Google Patents

Abstract

The invention discloses a kind of double-fed asynchronous wind generator system coordination optimization power prediction control method.By dual-feed asynchronous wind power generator and net side and rotor-side converter Mathematical Modeling based on, sliding-model control is carried out to it, the situation of change of controlled volume during prediction applying different controlling behavior, target setting function selects optimal control behavior, realize that dual feedback wind power generation system is gained merit, reactive power independent, effectively control.Consider in real system to exist and control delay effect, reach delay compensation by two-staged prediction.Adopt the present invention can realize the optimal control effect of dual feedback wind power generation system, have conventional vector control and based on switch list direct Power Control not available for following advantage: control structure is very simple, without the need to complicated coordinate transform and attitude conirol, judge without the need to Hysteresis control and sector, without the need to considering the system of selection of vector, and there is fast dynamic response and excellent steady-state behaviour.

Description

A kind of double-fed induction electricity generation system optimizing power forecast Control Algorithm

Technical field

The present invention relates to a kind of wind power generation field internal speed-changing constant frequency double-fed asynchronous wind generator system net side, rotor-side voltage source converter PWM control method, particularly one double-fed asynchronous wind generator system coordination optimization power prediction control method.

Background technology

Modern large-scale wind powered generation syst mainly adopts double-fed asynchronous generator (DFIG) and magneto alternator two type, for improving generating efficiency, all adopts variable speed constant frequency generator operational mode.Wherein, at most, technology is the most ripe, is current mainstream model in DFIG application.Double-fed wind generating scheme can realize variable speed constant frequency and control, and reduces the capacity of converter, also can realize gaining merit, idle uneoupled control, and can export corresponding perception or capacitive reactive power according to the requirement of electrical network, the flexibility of this idle control is highly beneficial to electrical network.Vector control (VC) and direct Power Control (DPC) are the control strategies of dual feedback wind power generation system main flow always.Vector control can realize the independent regulation of active power and reactive power, obtains good steady-state behaviour, but needs comparatively complicated synchronous speed rotating coordinate transformation and the phase information of line voltage, and the parameter tuning of pi regulator is complicated, and dynamic property is slightly poor.Traditional direct Power Control (LUT-DPC) based on switch list is without the need to synchronous speed rotating coordinate transformation, control structure is simple, dynamic response is fast, but need carry out stagnant chain rate comparatively and Stator flux linkage sectors judge, comparatively greatly, the steady-state behaviour of the method haves much room for improvement in system power fluctuation.Above control method or have certain defect in system dynamic response or in steady operation, can not reach comparatively ideal effect simultaneously, therefore has realistic meaning to the further investigation that double-fed induction electricity generation system controls.

Summary of the invention

The object of the invention is to for the deficiencies in the prior art, a kind of double-fed asynchronous wind generator system coordination optimization power prediction control method is provided.The inventive method compares traditional control method, and without the need to increasing additional hardware, and control structure is very simple, can reach good dynamic property and stable state accuracy.

Technical solution of the present invention, a kind of double-fed asynchronous wind generator system coordination optimization power prediction control method, comprises double-fed asynchronous wind generator system rotor side converter RSC(1) optimizing power forecast Control Algorithm and grid side converter GSC(12) optimizing power forecast Control Algorithm; Described rotor-side converter RSC(1) optimizing power forecast Control Algorithm and grid side converter GSC(12) optimizing power forecast Control Algorithm is within a kth sampling period, in the microprocessor it is processed after sampled signal discretization, to obtain the rotor-side converter switches signal S that kth+1 sampling period planted agent exports _a1(k+1), S _b1(k+1), S _c1and grid side converter switching signal S (k+1) _a2(k+1), S _b2(k+1), S _c2(k+1); If first sampling period internal rotor side converter switching signal S _a1(1), S _b1(1), S _c1and grid side converter switching signal S (1) _a2(1), S _b2(1), S _c2(1) output is 0;

Described RSC(1) optimizing power forecast Control Algorithm, comprise the following steps:

(1) set start time in this sampling period as t _k, utilize single-phase voltage Hall element (17) to gather DC side busbar voltage V _dc; Three voltage hall sensors (14) are utilized to gather dual-feed asynchronous wind power generator DFIG(18) threephase stator voltage signal U _sabc; The first three-phase current Hall element (13-1) is utilized to gather threephase stator current signal I _sabc, utilize the second three-phase current Hall element (13-2) to gather three-phase rotor current signal I _rabc;

(2) the threephase stator voltage signal U collected _sabcthe angular frequency obtaining electrical network or stator voltage is detected through phase-locked loop (6) _s; Adopt encoder (10) to detect the rotor position of DFIG simultaneously _r, then calculate rotational speed omega through differentiator (7) _r; What calculated by subtracter obtains slippage angular frequency _slip=ω _s-ω _r;

(3) the threephase stator voltage signal U will collected _sabcwith threephase stator current signal I _sabcthrough the first static three-phase to two-phase coordinate transformation module (5-1), obtain t _kstator voltage vector U under moment stator coordinate _{s α β}(k) and stator current vector I _{s α β}(k); By the three-phase rotor current signal I collected _rabcaccording to rotor position _rrotate to two-phase static coordinate conversion module (9) through two-phase, obtain t _krotor current vector I under moment stator coordinate _{r α β}(k);

(4) according to rotor position _rand the rotor converter switching signal S exported in this sampling period (a kth sampling period) calculated in the upper sampling period (-1 sampling period of kth) _a1(k), S _b1(k), S _c1k (), calculates t through module (8) _krotor voltage vector V under the stator coordinate that moment rotor-side converter applies _{r α β}(k);

(5) by the t obtained that samples _kthe stator voltage vector U in moment _{s α β}(k), stator current vector I _{s α β}(k), rotor current vector I _{r α β}(k), slippage angular frequency _slipand t _kthe rotor voltage vector V that moment applies _{r α β}k (), predicts next sampling period (+1 sampling period of kth) start time (t by rotor-side optimizing power prediction module (4-1) _k+1moment) stator voltage vector U _{s α β}(k+1), rotor current vector I _{r α β}and stator meritorious, the reactive power signals P that export (k+1) _s(k+1), Q _s(k+1);

(6) according to the t predicted in (5) step _k+1the stator voltage vector U in moment _{s α β}(k+1), rotor current vector I _{r α β}(k+1) what, stator exported gains merit, reactive power signals P _s(k+1), Q _sand 8 the rotor voltage vector V likely applied in kth+1 sampling period (k+1) _{r α β}(k+1), predicted by rotor-side second step prediction module (3-1) and applying these rotor voltage vector V respectively _{r α β}under the start time (t in+2 sampling periods of kth _k+2moment) all possible meritorious, reactive power signals P _s(k+2), Q _s(k+2); 8 groups of meritorious, reactive power signals P can be obtained _s(k+2), Q _s(k+2);

(7) t will predicted in (6) step _k+2moment 8 groups gains merit, reactive power signals P _s(k+2), Q _sand t (k+2) _k+2meritorious, the reactive power reference signal P of moment given rotor-side _s ^*, Q _s ^*, the rotor-side converter switches signal S of kth+1 sampling period planted agent output is obtained through the minimization of object function control module (2-1) _a1(k+1), S _b1(k+1), S _c1(k+1);

(8) by rotor-side converter switches signal S that the kth that obtains+1 sampling period planted agent exports _a1(k+1), S _b1(k+1), S _c1(k+1) at t _k+1moment through driver module drive IGBT realize RSC(1) optimizing power PREDICTIVE CONTROL.

Described GSC(12) optimizing power forecast Control Algorithm, comprise the following steps:

(1) set start time in this sampling period as t _k, utilize the 3rd three-phase current Hall element (13-3) Gather and input GSC(12) the three phase network current signal I flowing through filter inductance (15) _gabc;

(2) the three phase network current signal I collected _gabcthrough the second static three-phase to two-phase coordinate transformation module (5-2), obtain t _kgSC(12 under moment rest frame) power network current vector I _{g α β}(k);

(3) by this sampling period (a kth sampling period) the interior grid side converter switching signal S calculated in the upper sampling period (-1 sampling period of kth) _a2(k), S _b2(k), S _c2k (), calculates t through module (11) _kcontrol voltage vector V under the rest frame that moment grid side converter applies _{c α β}(k);

(4) by the t obtained that samples _kmoment line voltage vector U _{g α β}(k), power network current vector I _{g α β}(k) and t _kthe control voltage vector V that moment applies _{c α β}k (), predicts next sampling period (+1 sampling period of kth) start time (t by net side optimizing power prediction module (4-2) _k+1moment) line voltage vector U _{g α β}and input grid side converter is meritorious, reactive power signals P (k+1) _g(k+1), Q _g(k+1); Here, t _kthe line voltage vector U in moment _{g α β}k () is stator voltage vector U _{s α β}(k);

(5) predict according in (4) step the t obtained _k+1the line voltage vector U in moment _{g α β}(k+1), input that grid side converter is meritorious, reactive power signals P _g(k+1), Q _gand 8 the control voltage vector V likely applied in kth+1 sampling period (k+1) _{c α β}(k+1), these control voltage vector V are being applied respectively by net side second step prediction module (3-2) prediction _{c α β}(k+1)+2 start time in the sampling period (t of the kth under _k+2moment) all possible meritorious, reactive power signals P _g(k+2), Q _g(k+2); 8 groups of meritorious, reactive power signals P can be obtained _g(k+2), Q _g(k+2);

(6) t will predicted in (5) step _k+2moment 8 groups gains merit, reactive power signals P _g( _k+ ₂), Q _gand t (k+2) _k+2meritorious, the reactive power reference signal P of moment given net side _g ^*, Q _g ^*, obtain grid side converter switching signal S in+1 sampling period of kth through the minimization of object function control module (2-2) _a2(k+1), S _b2(k+1), S _c2(k+1).

(7) by grid side converter switching signal S that the kth that obtains+1 sampling period planted agent exports _a2(k+1), S _b2(k+1), S _c2(k+1) at t _k+1moment through driver module drive IGBT realize GSC(12) optimizing power PREDICTIVE CONTROL.

The invention has the beneficial effects as follows, can improve conventional vector control with based on the defect in each comfortable dynamic property of direct Power Control of switch list or steady-state behaviour.This method control structure is very simple, without the need to coordinate transform and the attitude conirol of complexity, judge without the need to Hysteresis control and sector, without the need to considering the system of selection of vector, the impact of Systematical control delay on control effects can be eliminated, reduce inverter power fluctuation, reduce the harmonic content of electric current, there is dynamic property fast, to the change of net side filter inductance parameter, there is comparatively strong robustness simultaneously.Consider in real system to exist and control delay effect, reach delay compensation by two-staged prediction, effectively improve the operation control performance of double-fed asynchronous wind generator system.

Accompanying drawing explanation

Fig. 1 is the schematic diagram of dual-feed asynchronous wind power generator group;

Fig. 2 is the schematic diagram that double-fed asynchronous wind generator system coordination optimization power prediction controls;

Fig. 3 (a) is the desirable control principle drawing without controlling in time delay situation, (b) be actual have control time delay situation under adopt two-staged prediction method to compensate schematic diagram;

Fig. 4 is the direct Power Control active reactive current waveform simulated effect figure of rotor-side tradition based on switch list;

Fig. 5 is that rotor-side is considered system delay and do not add active reactive current waveform simulated effect figure in compensation of delay situation;

Fig. 6 is that rotor-side considers system delay and active reactive current waveform simulated effect figure under adding compensation of delay situation.

Embodiment

Below in conjunction with accompanying drawing, the present invention is further described, and object of the present invention and effect will be more obvious.

Fig. 1 is the schematic diagram of dual-feed asynchronous wind power generator group, comprises wind turbine, gear box, dual-feed asynchronous wind power generator DFIG18, rotor-side converter RSC1, grid side converter GSC12, filter inductance 15, dc-link capacitance 16 and step-up transformer.

Fig. 2 is the schematic diagram of a kind of double-fed asynchronous wind generator system coordination optimization power prediction control method that the present invention proposes.For a 2KW variable speed constant frequency DFIG, adopt the double-fed asynchronous wind generator system coordination optimization power prediction control method that the present invention proposes, being wherein the optimizing power PREDICTIVE CONTROL of RSC1 in left-hand broken line frame, is the optimizing power PREDICTIVE CONTROL of GSC12 in the dotted line frame of right side.

Described rotor-side converter RSC1 optimizing power forecast Control Algorithm and grid side converter GSC12 optimizing power forecast Control Algorithm are within a kth sampling period, in the microprocessor it is processed after sampled signal discretization, to obtain the rotor-side converter switches signal S that kth+1 sampling period planted agent exports _a1(k+1), S _b1(k+1), S _c1and grid side converter switching signal S (k+1) _a2(k+1), S _b2(k+1), S _c2(k+1); If first sampling period internal rotor side converter switching signal S _a1(1), S _b1(1), S _c1and grid side converter switching signal S (1) _a2(1), S _b2(1), S _c2(1) output is 0;

Described RSC1 optimizing power forecast Control Algorithm, comprises the steps:

(1) set start time in this sampling period as t _k, utilize single-phase voltage transducer 17 to measure DC bus-bar voltage signal V _dc; Three-phase voltage Hall element 14 is utilized to gather the threephase stator voltage signal U of stator side _sabc; The first three-phase current Hall element 13-1 is utilized to gather stator side threephase stator current signal I _sabc; The second three-phase current Hall element 13-2 is utilized to gather rotor-side three-phase rotor current signal I _rabc;

(2) the threephase stator voltage signal U will collected _sabcthe angular frequency obtaining stator voltage is detected through phase-locked loop 6 _s; Encoder 10 is simultaneously adopted to detect the rotor position of DFIG _r, then calculate rotational speed omega through differentiator 7 _r; And calculated by subtracter obtain slippage angular frequency _slip=ω _s-ω _r;

(3) the threephase stator voltage signal U will collected _sabcwith threephase stator current signal I _sabcthrough the first static three-phase to two-phase coordinate transformation module 5-1, obtain t _kstator voltage vector U under moment stator coordinate _{s α β}(k) and stator current vector I _{s α β}(k); By the three-phase rotor current signal I collected _rabcaccording to rotor position _rrotate to two-phase static coordinate conversion module 9 through three-phase, obtain t _krotor current vector I under moment stator coordinate _{r α β}(k); For stator voltage, static three-phase to the expression formula of two-phase coordinate transform such as formula described in 1; For rotor current, three-phase rotates to the expression formula of two-phase static coordinate conversion as shown in Equation 2;

$\left[\begin{array}{c}{U}_{\mathrm{s\α}}\left(k\right)\\ U_{\mathrm{s\β}}\left(k\right)\end{array}\right]=\frac{2}{3}\left[\begin{array}{ccc}1& -\frac{1}{2}& -\frac{1}{2}\\ 0& \frac{\sqrt{3}}{2}& -\frac{\sqrt{3}}{2}\end{array}\right]\left[\begin{array}{c}{U}_{\mathrm{sa}}\\ U_{\mathrm{sb}}\\ U_{\mathrm{sc}}\end{array}\right]---\left(1\right)$

$\left[\begin{array}{c}{I}_{\mathrm{r\α}}\left(k\right)\\ I_{\mathrm{r\β}}\left(k\right)\end{array}\right]=\frac{2}{3}\left[\begin{array}{ccc}{\cos \mathrm{\θ}}_r& \cos ({\mathrm{\θ}}_r+\frac{2}{3}\mathrm{\π})& \cos ({\mathrm{\θ}}_r-\frac{2}{3}\mathrm{\π})\\ {\sin \mathrm{\θ}}_r& \sin ({\mathrm{\θ}}_r+\frac{2}{3}\mathrm{\π})& \sin ({\mathrm{\θ}}_r-\frac{2}{3}\mathrm{\π})\end{array}\right]\left[\begin{array}{c}{I}_{\mathrm{ra}}\\ I_{\mathrm{rb}}\\ I_{\mathrm{rc}}\end{array}\right]---\left(2\right)$

In formula, U _{s α}(k), U _{s β}k () is respectively t _kthe stator voltage vector U in moment _{s α β}the α phase of (k) and β phase component; U _sa, U _sb, U _scbe respectively threephase stator voltage signal U _sabca phase, b phase, c phase component; I _{r α}(k), I _{r β}k () is respectively t _kthe rotor current vector I in moment _{r α β}the α phase of (k) and β phase component; I _ra, I _rb, I _rcbe respectively three-phase rotor current signal I _rabca phase, b phase, c phase component;

(4) according to rotor position _r, and the rotor-side converter switches signal S exported in this sampling period (a kth sampling period) calculated in the upper sampling period (-1 sampling period of kth) _a1(k), S _b1(k), S _c1k (), calculates t _krotor voltage vector V under the stator coordinate that moment rotor-side converter applies _{r α β}(k); Computing formula as shown in Equation 3;

$\left[\begin{array}{c}{V}_{\mathrm{r\α}}\left(k\right)\\ V_{\mathrm{r\β}}\left(k\right)\end{array}\right]=\frac{2}{3}{V}_{\mathrm{dc}}\left[\begin{array}{ccc}{\cos \mathrm{\θ}}_r& \cos ({\mathrm{\θ}}_r+\frac{2}{3}\mathrm{\π})& \cos ({\mathrm{\θ}}_r-\frac{2}{3}\mathrm{\π})\\ {\sin \mathrm{\θ}}_r& \sin ({\mathrm{\θ}}_r+\frac{2}{3}\mathrm{\π})& \sin ({\mathrm{\θ}}_r-\frac{2}{3}\mathrm{\π})\end{array}\right]\left[\begin{array}{c}{S}_{a1}\left(k\right)\\ S_{b1}\left(k\right)\\ S_{c1}\left(k\right)\end{array}\right]---\left(3\right)$

In formula, V _{r α}(k), V _{r β}k () is respectively t _kthe rotor voltage vector V in moment _{r α β}the α phase of (k) and β phase component;

(5) by t _kthe stator voltage vector U in moment _{s α β}with stator current vector I _{s α β}calculate t _kmeritorious, the reactive power signals P that moment stator exports _s(k), Q _s(k); Meritorious, reactive power calculates formula as shown in Equation 4;

$P_s\left(k\right)=-\frac{3}{2}[U_{\mathrm{s\α}}\left(k\right)I_{\mathrm{s\α}}\left(k\right)+U_{\mathrm{s\β}}\left(k\right)I_{\mathrm{s\β}}\left(k\right)]---\left(4\right)$

$Q_s\left(k\right)=-\frac{3}{2}[-U_{\mathrm{s\α}}\left(k\right)I_{\mathrm{s\β}}\left(k\right)+U_{\mathrm{s\β}}\left(k\right)I_{\mathrm{s\β}}\left(k\right)]$

(6) by t _kmeritorious, the reactive power signals P that moment stator exports _s(k), Q _s(k), stator voltage vector U _{s α β}(k) and t _kthe rotor voltage vector V that moment applies _{r α β}(k), the start time (t of the next sampling period (+1 sampling period of kth) of prediction _k+1moment) stator meritorious, the reactive power signals P that export _s(k+1), Q _s(k+1); Prediction expression as shown in Equation 5;

$\left[\begin{array}{c}{P}_s(k+1)\\ Q_s(k+1)\end{array}\right]=\left[\begin{array}{cc}1-{\mathrm{\δT}}_s{L}_r{R}_s& {-\mathrm{\ω}}_{\mathrm{slip}}{T}_s\\ {\mathrm{\ω}}_{\mathrm{slip}}{T}_s& 1-{\mathrm{\δT}}_s{L}_r{R}_s\end{array}\right]\left[\begin{array}{c}{P}_s\left(k\right)\\ Q_s\left(k\right)\end{array}\right]+$

$\frac{{3\mathrm{\δT}}_s{L}_m}{2}\left[\begin{array}{cc}{-U}_{\mathrm{s\α}}\left(k\right)& {-U}_{\mathrm{s\β}}\left(k\right)\\ {-U}_{\mathrm{s\β}}\left(k\right)& U_{\mathrm{s\α}}\left(k\right)\end{array}\right]\left[\begin{array}{c}{V}_{\mathrm{r\α}}\left(k\right)-R_r{I}_{\mathrm{r\α}}\left(k\right)\\ V_{\mathrm{r\β}}\left(k\right)-R_r{I}_{\mathrm{r\β}}\left(k\right)\end{array}\right]+---\left(5\right)$

$\frac{{3\mathrm{s\δT}}_s{L}_r}{2}\left[\begin{array}{c}{\left|U\right|}^2\\ 0\end{array}\right]$

In formula, R _sfor stator resistance, R _rfor rotor resistance, L _sfor stator inductance, L _rfor inductor rotor, L _mfor magnetizing inductance, T _sfor the sampling period, $\mathrm{\δ}=\frac{1}{{{\mathrm{\σL}}_m}^2},$ $\mathrm{\σ}=1-\frac{L_s{L}_r}{{L_m}^2},$ $\left|U\right|=\sqrt{U_{\mathrm{s\α}}{\left(k\right)}^2+U_{\mathrm{s\β}}{\left(k\right)}^2};$

(7) according to desirable electrical network voltage conditions, by t _kthe stator voltage vector U in moment _{s α β}k () predicts t _k+1the stator voltage vector U in moment _{s α β}(k+1); Prediction expression is such as formula described in 6;

U _sα(k+1)=U _sα(k)cos(ω _sT _s)-U _sβ(k)sin(ω _sT _s)（6）

U _sβ(k+1)=U _sβ(k)cos(ω _sT _s)+U _sβ(k)sin(ω _sT _s)

In formula, U _{s α}(k+1), U _{s β}(k+1) t is respectively _k+1the stator voltage vector U in moment _{s α β}(k+1) α phase and β phase component;

(8) by t _kthe stator voltage vector U in moment _{s α β}(k), stator current vector I _{s α β}(k), rotor current vector I _{r α β}(k) and rotor voltage vector V _{r α β}(k), prediction t _k+1the rotor current vector I in moment _{r α β}(k+1); Prediction expression as shown in Equation 7;

$\left[\begin{array}{c}{I}_{\mathrm{r\α}}(k+1)\\ L_{\mathrm{r\β}}(k+1)\end{array}\right]=-{\mathrm{\δT}}_s{L}_m\left[\begin{array}{c}{U}_{\mathrm{s\α}}\left(k\right)\\ U_{\mathrm{s\β}}\left(k\right)\end{array}\right]+{\mathrm{\δT}}_s{L}_s\left[\begin{array}{c}{V}_{\mathrm{r\α}}\left(k\right)\\ V_{\mathrm{r\β}}\left(k\right)\end{array}\right]+\left[\begin{array}{cc}1-{\mathrm{\δT}}_s{L}_s{R}_r& {-\mathrm{\δT}}_s{L}_s{L}_r{\mathrm{\ω}}_r\\ {\mathrm{\δT}}_s{L}_s{L}_r{\mathrm{\ω}}_r& 1-{\mathrm{\δT}}_s{L}_s{R}_r\end{array}\right]\left[\begin{array}{c}{I}_{\mathrm{r\α}}\left(k\right)\\ I_{\mathrm{r\β}\left(k\right)}\end{array}\right]+---\left(7\right)$

${\mathrm{\δT}}_s{L}_m\left[\begin{array}{cc}{R}_s& -L_s{\mathrm{\ω}}_r\\ L_s{\mathrm{\ω}}_r& R_s\end{array}\right]\left[\begin{array}{c}{I}_{\mathrm{s\α}}\left(k\right)\\ I_{\mathrm{s\β}}\left(k\right)\end{array}\right]$

In formula, I _{r α}(k+1), I _{r β}(k+1) t is respectively _k+1the rotor current vector I in moment _{r α β}(k+1) α phase and β phase component;

(9) predict by front 3 steps the t obtained _k+1the stator voltage vector U in moment _{s α β}(k+1), rotor current vector I _{r α β}(k+1) what, stator exported gains merit, reactive power signals P _s(k+1), Q _sand 8 the rotor voltage vector V likely applied in kth+1 sampling period (k+1) _{r α β}(k+1), predict and applying these rotor voltage vector V respectively _{r α β}(k+1) start time (t in+2 sampling periods of kth under _k+2moment) all possible meritorious, reactive power signals P _s(k+2), Q _s(k+2); Prediction expression as shown in Equation 8;

$\left[\begin{array}{c}{P}_s(k+2)\\ Q_s(k+2)\end{array}\right]=\left[\begin{array}{cc}1-{\mathrm{\δT}}_s{L}_r{R}_s& {-\mathrm{\ω}}_{\mathrm{slip}}{T}_s\\ {\mathrm{\ω}}_{\mathrm{slip}}{T}_s& 1-{\mathrm{\δT}}_s{L}_r{R}_s\end{array}\right]\left[\begin{array}{c}{P}_s(k+1)\\ Q_s(k+1)\end{array}\right]+$

$\frac{{3\mathrm{\δT}}_s{L}_m}{2}\left[\begin{array}{cc}{-U}_{\mathrm{s\α}}(k+1)& {-U}_{\mathrm{s\β}}(k+1)\\ {-U}_{\mathrm{s\β}}(k+1)& U_{\mathrm{s\α}}(k+1)\end{array}\right]\left[\begin{array}{c}{V}_{\mathrm{r\α}}(k+1)-R_r{I}_{\mathrm{r\α}}(k+1)\\ V_{\mathrm{r\β}}(k+1)-R_r{I}_{\mathrm{r\β}}(k+1)\end{array}\right]+---\left(8\right)$

$\frac{{3\mathrm{s\δT}}_s{L}_r}{2}\left[\begin{array}{c}{\left|U\right|}^2\\ 0\end{array}\right]$

In formula, rotor voltage vector V _{r α β}(k+1) by the S of rotor-side converter switches signal all possible within+1 sampling period of kth _a1(k+1), S _b1(k+1), S _c1(k+1) obtain, computing formula cotype 3; S _a1(k+1), S _b1(k+1), S _c1(k+1) 2 are had ³totally 8 kinds of combinations, therefore 8 kinds of rotor voltage vector V can be obtained _{r α β}(k+1), measurablely 8 t are obtained _k+2meritorious, the reactive power signals P that moment is possible _s(k+2), Q _s(k+2);

(10) 8 t obtained will be predicted _k+2meritorious, the reactive power signals P that moment is possible _s(k+2), Q _s(k+2) with given t _k+2meritorious, the reactive power reference signal P in moment _s ^*, Q _s ^*substitute in target function, obtain 8 target function values, compare its size, the rotor voltage vector V corresponding to wherein minimum target function value _{r α β}(k+1), t is _k+1the rotor voltage vector V that moment rotor converter should apply _{r α β}(k+1), its switching signal S _a1(k+1), S _b1(k+1), S _c1(k+1) as t _k+1the switching signal of moment rotor-side converter; Target function as shown in Equation 9;

$g=|{P_s}^{*}-P_s(k+2)|+|{Q_s}^{*}-Q_s(k+2)|---\left(9\right)$

(11) by rotor-side converter switches signal S that the kth that obtains+1 sampling period planted agent exports _a1(k+1), S _b1(k+1), S _c1(k+1) at t _k+1moment drives IGBT to realize the optimizing power PREDICTIVE CONTROL of RSC1 through driver module.

Described GSC12 optimizing power forecast Control Algorithm, comprises the following steps:

(1) set start time in this sampling period as t _kin the moment, the 3rd three-phase current Hall element 13-3 is utilized to gather the three phase network current signal I of net side _gabc;

(2) the three phase network current signal I will collected _gabcthrough the second static three-phase to two-phase coordinate transformation module 5-2, obtain t _kpower network current vector I under moment rest frame _{g α β}(k); Conversion expression formula cotype 1;

(3) by the grid side converter switching signal S in this sampling period (a kth sampling period) calculated in the upper sampling period (-1 sampling period of kth) _a2(k), S _b2(k), S _c2k () calculates t _kthe control voltage vector V that moment grid side converter applies _{c α β}(k); Computing formula as shown in Equation 10;

$\left[\begin{array}{c}{V}_{\mathrm{c\α}}\left(k\right)\\ V_{\mathrm{c\β}}\left(k\right)\end{array}\right]=\frac{2}{3}{V}_{\mathrm{dc}}\left[\begin{array}{ccc}1& -\frac{1}{2}& -\frac{1}{2}\\ 0& \frac{\sqrt{3}}{2}& -\frac{\sqrt{3}}{2}\end{array}\right]\left[\begin{array}{c}{S}_{a2}\left(k\right)\\ S_{b2}\left(k\right)\\ S_{c2}\left(k\right)\end{array}\right]---\left(10\right)$

In formula, V _{c α}(k), V _{c β}k () is respectively t _kthe control voltage vector V in moment _{c α β}the α phase of (k) and β phase component;

(4) by t _kthe line voltage vector U in moment _{g α β}with power network current vector I _{g α β}calculate t _kmeritorious, the reactive power signals P of moment input grid side converter _g(k), Q _g(k); Meritorious, reactive power calculates formula as shown in Equation 11;

$P_g\left(k\right)=\frac{3}{2}[U_{\mathrm{g\α}}\left(k\right)I_{\mathrm{g\α}}\left(k\right)+U_{\mathrm{g\β}}\left(k\right)I_{\mathrm{g\β}}\left(k\right)]---\left(11\right)$ $Q_g\left(k\right)=\frac{3}{2}[-U_{\mathrm{g\α}}\left(k\right)I_{\mathrm{g\β}}\left(k\right)+U_{\mathrm{g\β}}\left(k\right)I_{\mathrm{g\α}}\left(k\right)]$

In formula, t _kline voltage vector U in moment rest frame _{g α β}k () is stator voltage vector U _{s α β}(k); U _{g α}(k), U _{g β}k () is respectively t _kthe line voltage vector U in moment _{g α β}the α phase of (k) and β phase component;

(5) by t _kmoment gains merit, reactive power signals P _g(k), Q _g(k), line voltage vector U _{g α β}(k), and t _kthe control voltage vector V that moment applies _{c α β}(k), the start time (t of the next sampling period (+1 sampling period of kth) of prediction _k+1moment) meritorious, the reactive power signals P of input grid side converter _g(k+1), Q _g(k+1), prediction expression as shown in Equation 12;

$\left[\begin{array}{c}{P}_g(k+1)\\ Q_g(k+1)\end{array}\right]=\left[\begin{array}{cc}1& {-\mathrm{\ω}}_s{T}_s\\ {\mathrm{\ω}}_s{T}_s& 1\end{array}\right]\left[\begin{array}{c}{P}_g\left(k\right)\\ Q_g\left(k\right)\end{array}\right]-\frac{{3T}_s}{{2L}_g}\left[\begin{array}{cc}{-U}_{\mathrm{g\α}}\left(k\right)& {-U}_{\mathrm{g\β}}\left(k\right)\\ {-U}_{\mathrm{g\β}}\left(k\right)& U_{\mathrm{g\α}}\left(k\right)\end{array}\right]\left[\begin{array}{c}{V}_{\mathrm{c\α}}\left(k\right)\\ V_{\mathrm{c\β}}\left(k\right)\end{array}\right]----\left(12\right)$

$\frac{{3T}_s}{{2L}_g}\left[\begin{array}{c}{\left|U\right|}^2\\ 0\end{array}\right]$

In formula, L _gfor grid side converter filter inductance;

(6) by predicting the t obtained before _k+1the line voltage vector U in moment _{g α β}, input grid side converter meritorious, reactive power signals P _g(k+1), Q _gand 8 the control voltage vector V likely applied in kth+1 sampling period (k+1) _{c α β}(k+1), predict and applying these control voltage vector V respectively _{c α β}(k+1) start time (t in+2 sampling periods of kth under _k+2moment) all possible meritorious, reactive power signals P _g(k+2), Q _g(k+2); Here, t _k+1the line voltage vector U in moment _{g α β}be t _k+1the stator voltage vector U in moment _{s α β}(k+1); Prediction expression as shown in Equation 13;

$\left[\begin{array}{c}P(k+2)\\ Q(k+2)\end{array}\right]=\left[\begin{array}{cc}1& {-\mathrm{\ω}}_s{T}_s\\ {\mathrm{\ω}}_s{T}_s& 1\end{array}\right]\left[\begin{array}{c}P(k+1)\\ Q(k+1)\end{array}\right]-\frac{{3T}_s}{{2L}_g}\left[\begin{array}{cc}{-U}_{\mathrm{g\α}}(k+1)& {-U}_{\mathrm{g\β}}(k+1)\\ {-U}_{\mathrm{g\β}}(k+1)& U_{\mathrm{g\α}}(k+1)\end{array}\right]\left[\begin{array}{c}{V}_{\mathrm{c\α}}(k+1)\\ V_{\mathrm{c\β}}(k+1)\end{array}\right]----\left(13\right)$

$\frac{{3T}_s}{{2L}_g}\left[\begin{array}{c}{\left|U\right|}^2\\ 0\end{array}\right]$

In formula, control voltage vector V _{c α β}(k+1) by grid side converter switching signal S all possible within+1 sampling period of kth _a2(k+1), S _b2(k+1), S _c2(k+1) obtain, computing formula cotype 10; S _a2(k+1), S _b2(k+1), S _c2(k+1) 2 are had ³totally 8 kinds of combinations, therefore 8 kinds of control voltage vector V can be obtained _{c α β}(k+1), measurablely 8 t are obtained _k+2meritorious, the reactive power signals P that moment is possible _g(k+2), Q _g(k+2);

(7) 8 t obtained will be predicted _k+2meritorious, the reactive power signals P that moment is possible _g(k+2), Q _g(k+2) with given meritorious, reactive power reference signal substitute in target function, obtain 8 target function values, compare its size, the control voltage vector V corresponding to wherein minimum target function value _{c α β}(k+1), t is _k+1the control voltage vector V that moment grid side converter should apply _{c α β}(k+1), its switching signal S _a2(k+1), S _b2(k+1), S _c2(k+1) as t _k+1moment grid side converter switching signal; Target function as shown in Equation 14;

$g=|{P_g}^{*}-P_g(k+2)|+|{Q_g}^{*}-Q_g(k+2)|---\left(14\right)$

(8) kth that obtains+1 sampling period planted agent is exported grid side converter switching signal S _a2(k+1), S _b2(k+1), S _c2(k+1) at t _k+1moment drives IGBT to realize the optimizing power PREDICTIVE CONTROL of GSC12 through driver module.。

The actual value of power is represented, U with reference to Fig. 3, S (t) _i(i=1 ... 8) 8 kinds of different voltage vectors that inverter exports are represented, T _sthe sampling period of representative system, S (t) ^*represent the reference value of power, S _pi(t _k+1) (i=1 ... 8) for adopting voltage vector U _itime power predicted value.Principle is, according to the predicted value S of power _pi(t _k+1) (i=1 ... 8) select the controlling behavior making target function minimum, actual power can be made closest to given power.Can be found out at t by figure (a) _k+1moment S _p3(t _k) closest to S (t _k+1) ^*, so at t _kmoment is selected to apply U ₃, in like manner, at t _k+1moment is selected to apply U ₂, the rest may be inferred.In real system, sampling, the calculating of system cannot instantaneously complete, and time delay certainly exists, and needs to carry out compensation of delay.Suppose at t _kin the moment, determine to apply U by the prediction of back ₃, then can according to U ₃and the S (t recorded _k) to S _p3(t _k+1) carry out first step prediction, then according to S _p3(t _k+1) and U _ito S _pi(t _k+2) carry out second step prediction, visible t _k+2moment S _pi(t _k+2) closest to set-point, so at t _k+1moment is selected to apply U ₂, the rest may be inferred, that is: in current control period, select the voltage vector that next control cycle will apply.Through two-staged prediction, as long as complete control logic in a control cycle, the control time delay of system can be eliminated.

With reference to Fig. 4,5,6, can find out that the optimizing power forecast Control Algorithm after two-staged prediction delay compensation effectively can eliminate fluctuation that is meritorious, reactive power, reduce the harmonic content of electric current, there is the fast dynamic response performance identical with the direct Power Control based on switch list.

In sum, method control structure disclosed by the invention is very simple, without the need to carrying out complicated coordinate transform and attitude conirol, judge without the need to hystersis controller with to vector sector, also without the need to considering the system of selection of vector according to sector, to the change of net side filter inductance parameter, there is comparatively strong robustness simultaneously, can realize that dual-feed asynchronous wind power generator is gained merit, reactive power independent, effectively control, reach good dynamic response and steady-state behaviour control effects.

Claims (3)

1. a double-fed asynchronous wind generator system coordination optimization power prediction control method, it is characterized in that, the method comprises the optimizing power forecast Control Algorithm of double-fed asynchronous wind generator system rotor side converter RSC (1) and the optimizing power forecast Control Algorithm of grid side converter GSC (12); The optimizing power forecast Control Algorithm of described rotor-side converter RSC (1) optimizing power forecast Control Algorithm and grid side converter GSC (12) is within a kth sampling period, process after sampled signal discretization in the microprocessor, to obtain the rotor-side converter switches signal S that kth+1 sampling period planted agent exports _a1(k+1), S _b1(k+1), S _c1and grid side converter switching signal S (k+1) _a2(k+1), S _b2(k+1), S _c2(k+1); If first sampling period internal rotor side converter switching signal S _a1(1), S _b1(1), S _c1and grid side converter switching signal S (1) _a2(1), S _b2(1), S _c2(1) output is 0;

Described RSC (1) optimizing power forecast Control Algorithm, comprises the following steps:

1) set a kth start time in sampling period as t _k, utilize single-phase voltage Hall element (17) to gather DC side busbar voltage V _dc; Three voltage hall sensors (14) are utilized to gather dual-feed asynchronous wind power generator DFIG (18) threephase stator voltage signal U _sabc; The first three-phase current Hall element (13-1) is utilized to gather threephase stator current signal I _sabc, utilize the second three-phase current Hall element (13-2) to gather three-phase rotor current signal I _rabc;

2) the threephase stator voltage signal U collected _sabcthe angular frequency obtaining electrical network or stator voltage is detected through phase-locked loop (6) _s; Adopt encoder (10) to detect the rotor position of DFIG simultaneously _r, then calculate rotational speed omega through differentiator (7) _r; Slippage angular frequency is calculated by subtracter _slip=ω _s-ω _r;

3) the threephase stator voltage signal U will collected _sabcwith threephase stator current signal I _sabcthrough the first static three-phase to two-phase coordinate transformation module (5-1), obtain t _kstator voltage vector U under moment stator coordinate _{s α β}(k) and stator current vector I _{s α β}(k); By the three-phase rotor current signal I collected _rabcaccording to rotor position _rrotate to two-phase static coordinate conversion module (9) through two-phase, obtain t _krotor current vector I under moment stator coordinate _{r α β}(k);

4) according to rotor position _rand the rotor-side converter switches signal S exported in the kth sampling period calculated in-1 sampling period of kth _a1(k), S _b1(k), S _c1k (), calculates t through the first module (8) _krotor voltage vector V under the stator coordinate that moment rotor-side converter applies _{r α β}(k);

5) by the t obtained that samples _kthe stator voltage vector U in moment _{s α β}(k), stator current vector I _{s α β}(k), rotor current vector I _{r α β}(k), slippage angular frequency _slipand t _kthe rotor voltage vector V that moment applies _{r α β}k (), utilizes formula (5) to predict+1 start time in sampling period of kth and t by rotor-side optimizing power prediction module (4-1) _k+1the stator in moment exports meritorious, reactive power signals P _s(k+1), Q _s(k+1);

$\begin{array}{c}\left[\begin{array}{c}{P}_s(k+1)\\ Q_s(k+1)\end{array}\right]=\left[\begin{array}{cc}1-{\mathrm{\δT}}_s{L}_r{R}_s& -{\mathrm{\ω}}_{slip}{T}_s\\ {\mathrm{\ω}}_{slip}{T}_s& 1-{\mathrm{\δT}}_s{L}_r{R}_s\end{array}\right]\left[\begin{array}{c}{P}_s\left(k\right)\\ Q_s\left(k\right)\end{array}\right]+\\ \frac{3{\mathrm{\δT}}_s{L}_m}{2}\left[\begin{array}{cc}-U_{s\mathrm{\α}}\left(k\right)& -U_{s\mathrm{\β}}\left(k\right)\\ -U_{s\mathrm{\β}}\left(k\right)& U_{s\mathrm{\α}}\left(k\right)\end{array}\right]\left[\begin{array}{c}{V}_{r\mathrm{\α}}\left(k\right)-R_r{I}_{r\mathrm{\α}}\left(k\right)\\ V_{r\mathrm{\β}}\left(k\right)-R_r{I}_{r\mathrm{\β}}\left(k\right)\end{array}\right]\\ \frac{3{\mathrm{s\δT}}_s{L}_r}{2}\left[\begin{array}{c}{\left|U\right|}^2\\ 0\end{array}\right]\end{array}---\left(5\right)$

Wherein, P _s(k), Q _sk () is respectively t _kmeritorious, the reactive power signals that moment stator exports, computing formula is such as formula shown in (4);

$\begin{array}{c}{P}_s\left(k\right)=-\frac{3}{2}\[U_{s\mathrm{\α}}\left(k\right)I_{s\mathrm{\α}}\left(k\right)+U_{s\mathrm{\β}}\left(k\right)I_{s\mathrm{\β}}\left(k\right)\]\\ Q_s\left(k\right)=-\frac{3}{2}\[-U_{s\mathrm{\α}}\left(k\right)I_{s\mathrm{\β}}\left(k\right)+U_{s\mathrm{\β}}\left(k\right)I_{s\mathrm{\α}}\left(k\right)\]\end{array}---\left(4\right)$

In formula, R _sfor stator resistance, R _rfor rotor resistance, L _sfor stator inductance, L _rfor inductor rotor, L _mfor magnetizing inductance, T _sfor the sampling period, $\mathrm{\δ}=\frac{1}{{{\mathrm{\σL}}_m}^2},\mathrm{\σ}=1-\frac{L_s{L}_r}{{L_m}^2},\left|U\right|=\sqrt{U_{s\mathrm{\α}}{\left(k\right)}^2+U_{s\mathrm{\β}}{\left(k\right)}^2},$ S is revolutional slip, $s=\frac{{\mathrm{\ω}}_s-{\mathrm{\ω}}_r}{{\mathrm{\ω}}_s};$

6) according to desirable electrical network voltage conditions, by t _kthe stator voltage vector U in moment _{s α β}(k) prediction t _k+1the stator voltage vector U in moment _{s α β}(k+1); By t _kthe stator voltage vector U in moment _{s α β}(k), stator current vector I _{s α β}(k), rotor current vector I _{r α β}(k) and rotor voltage vector V _{r α β}(k), prediction t _k+1the rotor current vector I in moment _{r α β}(k+1);

7) according to the 5th)-6) t that predicts in step _k+1the stator voltage vector U in moment _{s α β}(k+1), rotor current vector I _{r α β}(k+1) what, stator exported gains merit, reactive power signals P _s(k+1), Q _sand 8 the rotor voltage vector V likely applied in kth+1 sampling period (k+1) _{r α β}(k+1), by rotor-side second step prediction module (3-1), utilize formula (8) to predict and applying these rotor voltage vector V respectively _{r α β}(k+1) start time in+2 sampling periods of kth under and t _k+2moment all possible meritorious, reactive power signals P _s(k+2), Q _s(k+2); Obtain 8 groups of meritorious, reactive power signals P _s(k+2), Q _s(k+2);

$\begin{array}{c}\left[\begin{array}{c}{P}_s(k+2)\\ Q_s(k+2)\end{array}\right]=\left[\begin{array}{cc}1-{\mathrm{\δT}}_s{L}_r{R}_s& -{\mathrm{\ω}}_{slip}{T}_s\\ {\mathrm{\ω}}_{slip}{T}_s& 1-{\mathrm{\δT}}_s{L}_r{R}_s\end{array}\right]\left[\begin{array}{c}{P}_s(k+1)\\ Q_s(k+1)\end{array}\right]+\\ \frac{3{\mathrm{\δT}}_s{L}_m}{2}\left[\begin{array}{cc}-U_{s\mathrm{\α}}(k+1)& -U_{s\mathrm{\β}}(k+1)\\ -U_{s\mathrm{\β}}(k+1)& U_{s\mathrm{\α}}(k+1)\end{array}\right]\left[\begin{array}{c}{V}_{r\mathrm{\α}}(k+1)-R_r{I}_{r\mathrm{\α}}(k+1)\\ V_{r\mathrm{\β}}(k+1)-R_r{I}_{r\mathrm{\β}}(k+1)\end{array}\right]\\ \frac{3{\mathrm{s\δT}}_s{L}_r}{2}\left[\begin{array}{c}{\left|U\right|}^2\\ 0\end{array}\right]\end{array}---\left(8\right)$

8) by the 7th) t that predicts in step _k+2moment 8 groups gains merit, reactive power signals P _s(k+2), Q _sand t (k+2) _k+2meritorious, the reactive power reference signal P of moment given rotor-side _s ^*, Q _s ^*, the rotor-side converter switches signal S of kth+1 sampling period planted agent output is obtained through first object function minimization control module (2-1) _a1(k+1), S _b1(k+1), S _c1(k+1);

9) by rotor-side converter switches signal S that the kth that obtains+1 sampling period planted agent exports _a1(k+1), S _b1(k+1), S _c1(k+1) at t _k+1moment drives IGBT to realize the optimizing power PREDICTIVE CONTROL of RSC (1) through driver module;

Described GSC (12) optimizing power forecast Control Algorithm, comprises the following steps:

1) set start time in this sampling period as t _k, utilize the three phase network current signal I flowing through filter inductance (15) of the 3rd three-phase current Hall element (13-3) Gather and input GSC (12) _gabc;

2) the three phase network current signal I collected _gabcthrough the second static three-phase to two-phase coordinate transformation module (5-2), obtain t _kgSC (12) power network current vector I under moment rest frame _{g α β}(k);

3) by grid side converter switching signal S in the kth sampling period calculated in-1 sampling period of kth _a2(k), S _b2(k), S _c2k () calculates t through the second module (11) _kcontrol voltage vector V under the rest frame that moment grid side converter applies _{c α β}(k);

4) by the t obtained that samples _kmoment line voltage vector U _{g α β}(k), power network current vector I _{g α β}(k) and t _kthe control voltage vector V that moment applies _{c α β}k (), predicts+1 start time in sampling period of kth and t by net side optimizing power prediction module (4-2) _k+1the line voltage vector U in moment _{g α β}and input grid side converter is meritorious, reactive power signals P (k+1) _g(k+1), Q _g(k+1); Here, t _kthe line voltage vector U in moment _{g α β}k () is stator voltage vector U _{s α β}(k);

5) according to the 4th) predict the t obtained in step _k+1the line voltage vector U in moment _{g α β}(k+1), input that grid side converter is meritorious, reactive power signals P _g(k+1), Q _gand 8 the control voltage vector V likely applied in kth+1 sampling period (k+1) _{c α β}(k+1), by net side second step prediction module (3-2), utilize formula (13) to predict and applying these control voltage vector V respectively _{c α β}(k+1)+2 start time in the sampling period (t of the kth under _k+2moment) all possible meritorious, reactive power signals P _g(k+2), Q _g(k+2); Obtain 8 groups of meritorious, reactive power signals P _g(k+2), Q _g(k+2);

$\begin{array}{c}\left[\begin{array}{c}P(k+2)\\ Q(k+2)\end{array}\right]=\left[\begin{array}{cc}1& -{\mathrm{\ω}}_s{T}_s\\ {\mathrm{\ω}}_s{T}_s& 1\end{array}\right]\left[\begin{array}{c}P(k+1)\\ Q(k+1)\end{array}\right]-\frac{3T_s}{2L_g}\left[\begin{array}{cc}-U_{g\mathrm{\α}}(k+1)& -U_{g\mathrm{\β}}(k+1)\\ -U_{g\mathrm{\β}}(k+1)& U_{g\mathrm{\α}}(k+1)\end{array}\right]\left[\begin{array}{c}{V}_{c\mathrm{\α}}(k+1)\\ V_{c\mathrm{\β}}(k+1)\end{array}\right]-\\ \frac{3T_s}{2L_g}\left[\begin{array}{c}{\left|U\right|}^2\\ 0\end{array}\right]\end{array}---\left(13\right)$

In formula, L _gfor grid side converter filter inductance;

6) by the 5th) t that predicts in step _k+2moment 8 groups gains merit, reactive power signals P _g(k+2), Q _gand t (k+2) _k+2meritorious, the reactive power reference signal P of moment given net side _g ^*, Q _g ^*, obtain grid side converter switching signal S in+1 sampling period of kth through the second the minimization of object function control module (2-2) _a2(k+1), S _b2(k+1), S _c2(k+1);

7) by grid side converter switching signal S that the kth that obtains+1 sampling period planted agent exports _a2(k+1), S _b2(k+1), S _c2(k+1) at t _k+1moment drives IGBT to realize the optimizing power PREDICTIVE CONTROL of GSC (12) through driver module.

2. one according to claim 1 double-fed asynchronous wind generator system coordination optimization power prediction control method, it is characterized in that, rotor-side optimizing power prediction module (4-1) described in RSC (1) optimizing power forecast Control Algorithm, by t _kthe baseband signal of moment rotor-side, i.e. rotor-side stator voltage vector U _{s α β}(k), stator current vector I _{s α β}(k), rotor current vector I _{r α β}(k), slippage angular frequency _slip, and t _kthe control signal in moment, i.e. sided rotor voltage vector V _{r α β}(k), prediction subsequent time t _k+1meritorious, the reactive power signals P that stator exports _s(k+1), Q _s(k+1); Net side optimizing power prediction module (4-2) described in GSC (12) optimizing power forecast Control Algorithm, by t _kthe baseband signal of moment net side, namely nets side line voltage vector U _{g α β}(k), power network current vector I _{g α β}(k), and t _kthe control signal in moment, namely nets side control voltage vector V _{c α β}(k), prediction subsequent time t _k+1meritorious, the reactive power signals P of input grid side converter _g(k+1), Q _g(k+1).

3. one according to claim 1 double-fed asynchronous wind generator system coordination optimization power prediction control method, it is characterized in that RSC (1) optimizing power forecast Control Algorithm and the first object function minimization control module (2-1) described in GSC (12) optimizing power forecast Control Algorithm, second the minimization of object function control module (2-2), target function chooses g=|P*-P|+|Q*-Q|, after calculating the target function under the different control signal of applying, choose the control signal making target function value minimum, as the space vector of optimum, obtain t _k+1moment rotor-side converter switches signal S _a1(k+1), S _b1(k+1), S _c1and grid side converter switching signal S (k+1) _a2(k+1), S _b2(k+1), S _c2(k+1).