CN104141591B - Improved self-adaptive torque control method for wind power generating maximum power point tracking - Google Patents

Abstract

The invention provides an improved self-adaptive torque control method for realizing wind power generating maximum power point tracking. The traditional self-adaptive torque control is realized by a maximum power point tracking control method which is based on a self-adaptive searching algorithm and is provided for improving the fan tracking performance, and the searching target value is the optimum torque gain coefficient at the corresponding wind speed. According to the method, through the interference by the wind speed change, the searching value deviates from the optimum torque gain coefficient, and the maximum wind energy catching efficiency at the corresponding wind speed cannot be obtained. The method provided by the invention aims at problems and provides the improved self-adaptive torque control method, and the searching range of the torque gain coefficient is set according to the statistic relationship of the optimum torque gain coefficient and the wind speed conditions, so that abnormal values deviating from the optimum torque gain coefficient in the searching process are eliminated, the output result is more similar to the optimum value, and higher wind energy catching efficiency can be obtained.

Description

The improvement self adaptation method for controlling torque that maximum power point of wind electric power generation is followed the tracks of

Technical field

The invention belongs to wind power generation field, the improvement self adaptation that particularly a kind of maximum power point of wind electric power generation is followed the tracks of turns Square control method.

Background technology

In order to improve the Wind energy extraction efficiency interval less than rated wind speed, speed-variable frequency-constant wind-driven generator group is general to be adopted High-power point follows the tracks of (maximum power point tracking, mppt) control strategy.Power curve method (power Signal feedback, psf) it is one of most widely used mppt control method.However, power curve method is steady based on system State designs and have ignored the dynamic process that blower fan system is followed the tracks of between different steady operation points, and this leads to apply the method One of the reason blower fan is unable to high efficiency capture wind energy.

In order to improve the problems referred to above, l.j.fingersh and p.w.carlin of National Renewable Energy laboratory is first The secondary improved though proposing using electromotor electromagnetic braking torque help blower fan acceleration or deceleration；On this basis, johnson Et al. k.e. propose reduction gain of torque (decreased torque gain, dtg) to control.This control method not only by Reduce electromagnetic braking torque and improve the acceleration when following the tracks of crescendo fitful wind for the blower fan, more first Application is to abandon partly low The rotating-speed tracking effect of wind speed section exchanges the control thought of the high Wind energy extraction efficiency of high wind speed section for；Further it is contemplated that dtg Control adopt constant gain of torque coefficient it is difficult to according to change wind friction velocity dynamic adjust gain coefficient higher to obtain Capture rate, for this johnson k.e. et al. design again self adaptation direct torque (adaptive torque control, At control, referred to as at), using the statistical data of adaptive algorithm and history run operating mode, iterative search is simultaneously repaiied online Positive torque gain coefficient, to respond the change of the wind friction velocity in iteration cycle time scale.But, the method but can be subject to wind speed The impact of change and calculate abnormal gain of torque coefficient, lead to Wind energy extraction efficiency not rise anti-fall.Grinding for this problem Study carefully and be only limitted to phenomenon analysis and Exploration on mechanism, there is not yet being correspondingly improved method.

In sum, the studies above work be mainly adjusted by electromagnetic braking torque come to improve blower fan dynamic property and Tracking effect, breaches conventional power curve method and ignores the dynamic limitation of tracking.However, search in self adaptation direct torque Abnormal problem remains the major reason of impact MPPT maximum power point tracking control effect.Thus, to self adaptation direct torque The abnormal situation of gain of torque factor search carry out improving very necessary, but there is no associated description in prior art.

Content of the invention

In order to solve existing the problems referred to above, the present invention provides the improvement self adaptation torque control that maximum power point of wind electric power generation is followed the tracks of Method processed, arranges the hunting zone of this parameter according to the statistical relationship of best torque gain coefficient and wind friction velocity, with tradition certainly Based on adapting to direct torque, electromagnetic braking gain of torque coefficient is optimized using self-adaptive search algorithm.

The technical problem to be solved is achieved through the following technical solutions:

The improvement self adaptation method for controlling torque that a kind of maximum power point of wind electric power generation is followed the tracks of is it is characterised in that adopt certainly Adapt to method for controlling torque, maximum is realized by the electromagnetic braking gain of torque coefficient that self-adaptive search algorithm optimizes electromotor Power points tracing control, formula used by described self adaptation method for controlling torque is:

$j\stackrel{\·}{\mathrm{\ω}}=t_m(v,\mathrm{\ω})-t_e\left(\mathrm{\ω}\right)---\left(1\right)$

$t_m(v,\mathrm{\ω})=\frac{0.5\mathrm{\ρ\π}{r}^5{c}_p\left(\mathrm{\λ}\right)}{{\mathrm{\λ}}^3}{\mathrm{\ω}}^2---\left(2\right)$

$t_e\left(\mathrm{\&omega;}\right)=\left\{\begin{array}{cc}0& \mathrm{\&omega;}<{\mathrm{\&omega;}}_{\mathrm{bgn}}\\ {\mathrm{\&rho;m}}_{k+1}{\mathrm{\&omega;}}^2& \mathrm{\&omega;}\&greaterequal;{\mathrm{\&omega;}}_{\mathrm{bgn}}\end{array}\right.---\left(3\right)$

In above formula, j is rotary inertia, t_mFor the pneumatic actuation torque of wind wheel, t_eFor electromagnetic braking torque, v is wind speed, ω For the angular velocity of wind wheel,For wind wheel angular acceleration, ρ is atmospheric density, and r is wind wheel radius, c_pFor power coefficient, λ= ω r/v is tip speed ratio, ω_bgnInitiate rotating speed for initial generating rotating speed, m is gain of torque, k is iterationses, m_k+1For kth The gain of torque of+1 iterative search acquisition, wherein m_k+1Periodicity adjustment comprise the following steps:

S1, determine gain of torque coefficient k_dSpan and MPPT maximum power point tracking wind speed range in all optimal turns Meansigma methodss k of square gain coefficient_avg；

S2, initializing torque gain, first to gain of torque update cycle t_rSampling period t with wind speed_cIt is configured, so After make k=0, gain of torque initial value m₀It is set to give tacit consent to gain of torque m_d, then adjust electromagnetic braking torque as follows,

$t_e\left(\mathrm{\&omega;}\right)=\left\{\begin{array}{cc}0& \mathrm{\&omega;}<{\mathrm{\&omega;}}_{\mathrm{bgn}}\\ {\mathrm{\&rho;m}}_0{\mathrm{\&omega;}}^2& \mathrm{\&omega;}\&greaterequal;{\mathrm{\&omega;}}_{\mathrm{bgn}}\end{array}\right.---\left(4\right),$

Average wind energy utilization η is calculated at the end of this cycle_avg, circular is as follows:

$\left\{\begin{array}{c}{\mathrm{\&eta;}}_{\mathrm{avg}}=\frac{\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&sigma;}}}{p}_{\mathrm{cap}}\left(i\right)}{\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&sigma;}}}{p}_{\mathrm{wy}}\left(i\right)}\\ p_{\mathrm{cap}}=t_e\left(\mathrm{\&omega;}\right)\mathrm{\&omega;}+\mathrm{j\&omega;}\stackrel{.}{\mathrm{\&omega;}}\\ p_{\mathrm{wy}}=0.5\mathrm{\&rho;\&pi;}{r}^2{v}^3{\cos }^3\mathrm{\&psi;}\end{array}\right.---\left(5\right);$

Wherein, n is the update cycle t of each iteration_rInterior wind-speed sample number of times；ψ is yaw error angle, p_capFor reality Power, p_wyFor power optimized value；

Then given gain of torque initial gain amount △ m then,₁, and the gain of torque in next cycle is calculated according to equation below,

m_k+1=m_k+△m_k+1(6)；

S3, make k=k+1, kth time iteration cycle is entered with the gain of torque updating, and is pressed with the gain of torque after updating Formula (3) adjusts electromagnetic braking torque, terminates back-pushed-type (5) and calculates corresponding average wind energy utilization in this cycle；

S4, calculate gain of torque m in next cycle according to formula (7)_k+1, wherein η_avgkFlat for kth time iteration cycle All wind energy utilizations, γ_△mFor the regulation coefficient of gain of torque, △ m_k+1It is the step-size in search for adjusting gain of torque

$\left\{\begin{array}{c}{m}_{k+1}=m_k+{\mathrm{\&delta;m}}_{k+1}\\ {\mathrm{\&delta;m}}_{k+1}={\mathrm{\&gamma;}}_{\mathrm{\&delta;m}}\mathrm{sign}\left[{\mathrm{\&delta;m}}_k\right]\mathrm{sign}\left[{\mathrm{\&delta;\&eta;}}_{\mathrm{avg\; k}}\right]\sqrt{\left|{\mathrm{\&delta;\&eta;}}_{\mathrm{avg\; k}}\right|}\\ {\mathrm{\&delta;m}}_k=m_k-m_{k-1}\\ {\mathrm{\&delta;\&eta;}}_{\mathrm{avg\; k}}={\mathrm{\&eta;}}_{\mathrm{avg\; k}}-{\mathrm{\&eta;}}_{\mathrm{avg\; k}-1}\end{array}\right.---\left(7\right);$

S5, according to formula (8) calculating torque gain coefficient k_d, judge whether it meets k_dmin≤k_d≤k_dmaxIf meeting, Then using the current gain of torque as next cycle calculating and obtaining, it is then back to step s3, otherwise skips to step s6,

$\left\{\begin{array}{c}{k}_d=m_{k+1}/m_d\\ m_d=\frac{0.5\mathrm{\&pi;}{r}^5{c}_p^{\max }}{{\mathrm{\&lambda;}}_{\mathrm{opt}}^3}\end{array}\right.---\left(8\right)$

M in above formula_dFor giving tacit consent to gain of torque, λ_optFor optimum tip-speed ratio,For maximal wind-energy usage factor；

S6, make m_k+1=k_avg*m_d, with this m_k+1As the gain of torque in next cycle, return to step s3.

Further, described step s1 determines gain of torque coefficient k_dSpan and MPPT maximum power point tracking wind speed In the range of all best torque gain coefficients meansigma methodss k_avgComprise the following steps:

S11, according to residing for meteorological data determines blower fan the turbulent flow rank of wind field and the wind speed range of MPPT maximum power point tracking；

S12, emulated according to Wind turbines parameter, obtain wind friction velocity in MPPT maximum power point tracking wind speed range with The statistical relationship of best torque gain coefficient；

S13, according in step s12 obtain statistical relationship obtain most preferably turning in the wind speed range of MPPT maximum power point tracking The maximum k of square gain coefficient_dmaxWith minima k_dminAnd best torque gain system in the wind speed range of MPPT maximum power point tracking Meansigma methodss k of number_avg, and by k_dmaxIt is set to k_dHigher limit, k_dminIt is set to k_dLower limit, k_avgIt is set to k_dExceed during bound Value.

Preferably, the update cycle t of gain of torque m described in step s2_rFor 20 minutes, the sampling period t of wind speed_cFor 0.25 second～1 second.

The beneficial effect that the present invention is reached is: 1) present invention is carried out to the hunting zone of electromagnetic braking gain of torque coefficient Limit, improve the abnormal situation of this parameter search, further increase Wind energy extraction efficiency；2) present invention is according to specific Wind friction velocity arranges the hunting zone of gain of torque coefficient so as to hunting zone can preferably reflect that wind friction velocity and wind energy are divided Cloth characteristic；3) present invention adopts the optimum gain of torque coefficient of self-adaptive search algorithm search, can be according to wind speed in iteration cycle The change of condition obtains corresponding maximum capture rate；4) present invention does not need real-time calculation of wind speed in iterative search procedures to put down Average and turbulence intensity it is only necessary to obtain the information such as the weather statistical data of wind energy turbine set, algorithm information needed in algorithm initialization Few, very simple.

Brief description

The flow chart of the improvement self adaptation method for controlling torque that Fig. 1 follows the tracks of for maximum power point of wind electric power generation；

Fig. 2 is wind friction velocity and best torque gain coefficient k_doptStatistical relationship；

The simulation wind series that Fig. 3 adopts for embodiment；

Fig. 4 be improve before and after self adaptation direct torque gain of torque coefficient contrast (at represents self adaptation direct torque, Iat represents improvement self adaptation direct torque proposed by the present invention)；

Fig. 5 be improve before and after self adaptation direct torque Wind energy extraction efficiency contrast (at represents self adaptation direct torque, Iat represents improvement self adaptation direct torque proposed by the present invention)；

Fig. 6 is the improvement self adaptation method for controlling torque and additive method that the maximum power point of wind electric power generation of the present invention is followed the tracks of The contrast of Wind energy extraction efficiency (psf represents conventional power curve method, and dtg represents and reduces gain of torque and control, and at represents adaptive Answer direct torque, iat represents improvement self adaptation direct torque proposed by the present invention).

Specific embodiment

In order to further describe technical characterstic and the effect of the present invention, below in conjunction with the drawings and specific embodiments to this Bright it is described further.

With reference to Fig. 1, initially with simulation wind series, maximum power point of wind electric power generation of realizing proposed by the present invention is followed the tracks of The effectiveness of improvement self adaptation method for controlling torque be analyzed, then pass through the statistical analysiss of 100 groups of simulation example, then will The improvement self adaptation method for controlling torque realizing maximum power point of wind electric power generation tracking proposed by the present invention and conventional power curve Method, dtg control and self adaptation direct torque is compared, to verify effectiveness of the invention and superiority.

First, the phantom of embodiment

1) simplify the parameter of blower fan model

The major parameter of blower fan model is set to: fan capacity 1.0mw, rotor diameter 52.67m, rotary inertia 1.1204 ×10⁶kgm².The maximum c of blower fan_pValueFor 0.4109, optimum tip-speed ratio λ_optFor 8.0.

2) parameter setting of the embodiment of the present invention

Illustrate that in conjunction with Fig. 2 the parameter realizing the improvement self adaptation method for controlling torque of maximum power point of wind electric power generation tracking sets Put.If the turbulent flow rank of wind energy turbine set is b level, the wind speed range of MPPT maximum power point tracking is 4.0-7.0m/s, then can obtain wind Fast condition and best torque gain coefficient k_doptStatistical relationship as shown in Fig. 2 its corresponding numerical value is listed in table 1.According to this Statistical relationship, can arrange k_dmin、k_dmaxAnd k_avgValue, as shown in table 2.Additionally, the improvement that maximum power point of wind electric power generation is followed the tracks of The iteration cycle t of self adaptation method for controlling torque_rIt is set to 20 minutes, wind-speed sample cycle t_cIt is set to 0.25s, the tune of gain of torque Integral coefficient γ_△m=2.3529 × 10⁴.

Table 1 wind friction velocity and the statistical relationship of best torque gain coefficient

Table 2 k_dmin、k_dmaxAnd k_avgSetting

3) construction of wind series

Illustrate to simulate the building method of wind series in conjunction with Fig. 3.Using autoregressive moving average (auto-regressive Moving-average, arma) Wind speed model, construction mean wind speed is incremented to 7m/s, each flat with 0.5m/s for step-length from 4m/s Totally 56 sections of wind speed periods that average is repeated 8 times, the persistent period is 20 minutes, as original wind series, then according to wind Fast meansigma methodss carry out permutation and combination to the above-mentioned original wind speed period, and the rise/fall constructing mean wind speed replaces slope (as schemed Shown in 3), with the long-term wind series (continuing total duration for 56*20 minute) constructing high degree of fluctuation.In simulation calculation, The turbulence intensity of each wind speed period is set to the turbulent flow grade b rank of wind energy turbine set.

4) the Realization of Simulation of the improvement self adaptation method for controlling torque that maximum power point of wind electric power generation is followed the tracks of is given by the present invention The step going out arranges gain of torque m of each iteration_k+1, and this be can achieve according to the adjustment electromagnetic braking torque of this gain of torque The improvement self adaptation method for controlling torque that invention proposes.Specific as follows:

A kind of maximum power point of wind electric power generation of the present invention follow the tracks of improvement self adaptation method for controlling torque it is characterised in that Using self adaptation method for controlling torque, the electromagnetic braking gain of torque coefficient of electromotor is optimized Lai real by self-adaptive search algorithm Existing MPPT maximum power point tracking controls, and formula used by described self adaptation method for controlling torque is:

$j\stackrel{\&centerdot;}{\mathrm{\&omega;}}=t_m(v,\mathrm{\&omega;})-t_e\left(\mathrm{\&omega;}\right)---\left(1\right)$

$t_m(v,\mathrm{\&omega;})=\frac{0.5\mathrm{\&rho;\&pi;}{r}^5{c}_p\left(\mathrm{\&lambda;}\right)}{{\mathrm{\&lambda;}}^3}{\mathrm{\&omega;}}^2---\left(2\right)$

$t_e\left(\mathrm{\&omega;}\right)=\left\{\begin{array}{cc}0& \mathrm{\&omega;}<{\mathrm{\&omega;}}_{\mathrm{bgn}}\\ {\mathrm{\&rho;m}}_{k+1}{\mathrm{\&omega;}}^2& \mathrm{\&omega;}\&greaterequal;{\mathrm{\&omega;}}_{\mathrm{bgn}}\end{array}\right.---\left(3\right)$

In above formula, j is rotary inertia, t_mFor the pneumatic actuation torque of wind wheel, t_eFor electromagnetic braking torque, v is wind speed, ω For the angular velocity of wind wheel,For wind wheel angular acceleration, ρ is atmospheric density, and r is wind wheel radius, c_pFor power coefficient, λ= ω r/v is tip speed ratio, ω_bgnInitiate rotating speed for initial generating rotating speed, m is gain of torque, k is iterationses, m_k+1For kth The gain of torque of+1 iterative search acquisition, wherein m_k+1Periodicity adjustment comprise the following steps:

S1, determine gain of torque coefficient k_dSpan and MPPT maximum power point tracking wind speed range in all optimal turns Meansigma methodss k of square gain coefficient_avg；

S2, initializing torque gain, first to gain of torque update cycle t_rSampling period t with wind speed_cIt is configured, so After make k=0, gain of torque initial value m₀It is set to give tacit consent to gain of torque m_d, then adjust electromagnetic braking torque as follows,

$t_e\left(\mathrm{\&omega;}\right)=\left\{\begin{array}{cc}0& \mathrm{\&omega;}<{\mathrm{\&omega;}}_{\mathrm{bgn}}\\ {\mathrm{\&rho;m}}_0{\mathrm{\&omega;}}^2& \mathrm{\&omega;}\&greaterequal;{\mathrm{\&omega;}}_{\mathrm{bgn}}\end{array}\right.---\left(4\right),$

Average wind energy utilization η is calculated at the end of this cycle_avg, circular is as follows:

$\left\{\begin{array}{c}{\mathrm{\&eta;}}_{\mathrm{avg}}=\frac{\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&sigma;}}}{p}_{\mathrm{cap}}\left(i\right)}{\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&sigma;}}}{p}_{\mathrm{wy}}\left(i\right)}\\ p_{\mathrm{cap}}=t_e\left(\mathrm{\&omega;}\right)\mathrm{\&omega;}+\mathrm{j\&omega;}\stackrel{.}{\mathrm{\&omega;}}\\ p_{\mathrm{wy}}=0.5\mathrm{\&rho;\&pi;}{r}^2{v}^3{\cos }^3\mathrm{\&psi;}\end{array}\right.---\left(5\right);$

Wherein, n is the update cycle t of each iteration_rInterior wind-speed sample number of times；ψ is yaw error angle, p_capFor reality Power, p_wyFor power optimized value；

Then given gain of torque initial gain amount △ m then,₁, and the gain of torque in next cycle is calculated according to equation below,

m_k+1=m_k+△m_k+1(6)；

S3, make k=k+1, kth time iteration cycle is entered with the gain of torque updating, and is pressed with the gain of torque after updating Formula (3) adjusts electromagnetic braking torque, terminates back-pushed-type (5) and calculates corresponding average wind energy utilization in this cycle；

S4, calculate gain of torque m in next cycle according to formula (7)_k+1, wherein η_avgkAverage for kth time iteration cycle Wind energy utilization, γ_△mFor the regulation coefficient of gain of torque, △ m_k+1It is the step-size in search for adjusting gain of torque,

$\left\{\begin{array}{c}{m}_{k+1}=m_k+{\mathrm{\&delta;m}}_{k+1}\\ {\mathrm{\&delta;m}}_{k+1}={\mathrm{\&gamma;}}_{\mathrm{\&delta;m}}\mathrm{sign}\left[{\mathrm{\&delta;m}}_k\right]\mathrm{sign}\left[{\mathrm{\&delta;\&eta;}}_{\mathrm{avg\; k}}\right]\sqrt{\left|{\mathrm{\&delta;\&eta;}}_{\mathrm{avg\; k}}\right|}\\ {\mathrm{\&delta;m}}_k=m_k-m_{k-1}\\ {\mathrm{\&delta;\&eta;}}_{\mathrm{avg\; k}}={\mathrm{\&eta;}}_{\mathrm{avg\; k}}-{\mathrm{\&eta;}}_{\mathrm{avg\; k}-1}\end{array}\right.---\left(7\right);$

S5, according to formula (8) calculating torque gain coefficient k_d, judge whether it meets k_dmin≤k_d≤k_dmaxIf meeting, Then using the current gain of torque as next cycle calculating and obtaining, it is then back to step s3, otherwise skips to step s6,

$\left\{\begin{array}{c}{k}_d=m_{k+1}/m_d\\ m_d=\frac{0.5\mathrm{\&pi;}{r}^5{c}_p^{\max }}{{\mathrm{\&lambda;}}_{\mathrm{opt}}^3}\end{array}\right.---\left(8\right)$

M in above formula_dFor giving tacit consent to gain of torque, m for the wind-driven generator of certain fixing model_dFor constant, λ_optFor optimal Tip speed ratio,For maximal wind-energy usage factor；

S6, make m_k+1=k_avg*m_d, with this m_k+1As the gain of torque in next cycle, return to step s3.

Further, described step s1 determines gain of torque coefficient k_dSpan and MPPT maximum power point tracking wind speed In the range of all best torque gain coefficients meansigma methodss k_avgComprise the following steps:

S11, according to residing for meteorological data determines blower fan the turbulent flow rank of wind field and the wind speed range of MPPT maximum power point tracking；

S12, emulated according to Wind turbines parameter, obtain wind friction velocity in MPPT maximum power point tracking wind speed range with The statistical relationship of best torque gain coefficient；

S13, according in step s12 obtain statistical relationship obtain best torque in the wind speed range of MPPT maximum power point tracking The maximum k of gain coefficient_dmaxWith minima k_dminAnd best torque gain coefficient in the wind speed range of MPPT maximum power point tracking Meansigma methodss k_avg, and by k_dmaxIt is set to k_dHigher limit, k_dminIt is set to k_dLower limit, k_avgIt is set to k_dExceed during bound Value.

Preferably, the update cycle t of gain of torque m described in step s2_rFor 20 minutes, the sampling period t of wind speed_cFor 0.25 second～1 second.

2nd, effectiveness of the invention analysis

In conjunction with Fig. 4 and Fig. 5, this section passes through the effectiveness of Example Verification improved method proposed by the present invention.Using upper State the building method of simulation wind speed, construct high turbulent flow wind series, respectively application self adaptation direct torque and the present invention Improve self adaptation direct torque, obtain their gain of torque coefficient as shown in figure 4, corresponding average wind energy utilization such as Fig. 5 Shown.As shown in Figure 4, the corresponding optimal k of this section of wind series_dIt is between 0.75 to 0.9, improvement proposed by the present invention is adaptive Answer the k of direct torque (iat)_dValue is all around optimal k_dValue changes, fluctuation range is less, and the k of self adaptation direct torque (at)_d Value deviates considerably from optimum, or even the situation more than 1.And, proposed by the present invention improve the flat of self adaptation direct torque All wind energy utilizations apparently higher than self adaptation direct torque, as shown in figure 5, demonstrating effectiveness of the invention.

3rd, the relative analyses of the Wind energy extraction efficiency of multiple methods

Contrast the Wind energy extraction efficiency of above-mentioned 4 kinds of methods in conjunction with Fig. 6.The present invention is according to above-mentioned wind speed building method to wind speed Be simulated, for each wind series, respectively application conventional power curve method (psf), reduce gain of torque control (dtg), Self adaptation direct torque (at) and the improvement self adaptation direct torque side realizing maximum power point of wind electric power generation tracking of the present invention Method, and repeat 100 groups of examples, then pass through the average wind energy utilization η of each method of statistical analysiss_avgTo compare said method. Specific analytical method is as follows:

When above-mentioned 4 kinds of methods are applied to the wind series comprising 56 wind speed periods, can calculate corresponding to every respectively The method of kind and the η of this wind series_avgMeansigma methodss, be designated asShown as the following formula:

${\stackrel{\&overbar;}{\mathrm{\&eta;}}}_{\mathrm{avg}}=\underset{i=1}{\overset{56}{\mathrm{\&sigma;}}}{\mathrm{\&eta;}}_{\mathrm{avg}}^i/56---\left(9\right)$

Further, can calculate what 100 groups of simulation example obtainedMeansigma methodss, be designated asAs shown in Figure 6.By Fig. 6 is visible, the improvement self adaptation method for controlling torque that maximum power point of wind electric power generation proposed by the present invention is followed the tracks of(the 4th Block diagram) improve 0.73% than conventional power curve method, control than dtg and improve 0.24%, carry than self adaptation direct torque High by 0.27%.Demonstrate the superior of the improvement self adaptation method for controlling torque that maximum power point of wind electric power generation of the present invention is followed the tracks of Property.

Above-described embodiment does not limit the present invention in any form, all takes equivalent or the form of equivalent transformation to be obtained Technical scheme, be within the scope of the present invention.

Claims (3)

1. the improvement self adaptation method for controlling torque that maximum power point of wind electric power generation is followed the tracks of is it is characterised in that turned using self adaptation Square control method, realizes maximum power point by the electromagnetic braking gain of torque coefficient that self-adaptive search algorithm optimizes electromotor Tracing control, formula used by described self adaptation method for controlling torque is:

$j\stackrel{\&centerdot;}{\mathrm{\&omega;}}=t_m(v,\mathrm{\&omega;})-t_e\left(\mathrm{\&omega;}\right)---\left(1\right)$

$t_m(v,\mathrm{\&omega;})=\frac{0.5\mathrm{\&rho;\&pi;}{r}^5{c}_p\left(\mathrm{\&lambda;}\right)}{{\mathrm{\&lambda;}}^3}{\mathrm{\&omega;}}^2---\left(2\right)$

$t_e\left(\mathrm{\&omega;}\right)=\left\{\begin{array}{cc}0& \mathrm{\&omega;}<{\mathrm{\&omega;}}_{\mathrm{bgn}}\\ {\mathrm{\&rho;m}}_{k+1}{\mathrm{\&omega;}}^2& \mathrm{\&omega;}\&greaterequal;{\mathrm{\&omega;}}_{\mathrm{bgn}}\end{array}\right.---\left(3\right)$

In above formula, j is rotary inertia, t_mFor the pneumatic actuation torque of wind wheel, t_eFor electromagnetic braking torque, v is wind speed, and ω is wind The angular velocity of wheel,For wind wheel angular acceleration, ρ is atmospheric density, and r is wind wheel radius, c_pFor power coefficient, λ=ω r/v It is tip speed ratio, ω_bgnInitiate rotating speed for initial generating rotating speed, m is gain of torque, k is iterationses, m_k+1For kth+1 time The gain of torque that iterative search obtains, wherein m_k+1Periodicity adjustment comprise the following steps:

S1, determine gain of torque coefficient k_dSpan and MPPT maximum power point tracking wind speed range in all best torques increase Meansigma methodss k of beneficial coefficient_avg；

S2, initializing torque gain, first to gain of torque update cycle t_rSampling period t with wind speed_cIt is configured, then make k =0, gain of torque initial value m₀It is set to give tacit consent to gain of torque m_d, then adjust electromagnetic braking torque as follows,

$t_e\left(\mathrm{\&omega;}\right)=\left\{\begin{array}{cc}0& \mathrm{\&omega;}<{\mathrm{\&omega;}}_{\mathrm{bgn}}\\ {\mathrm{\&rho;m}}_0{\mathrm{\&omega;}}^2& \mathrm{\&omega;}\&greaterequal;{\mathrm{\&omega;}}_{\mathrm{bgn}}\end{array}\right.---\left(4\right),$

Average wind energy utilization η is calculated at the end of this cycle_avg, circular is as follows:

$\left\{\begin{array}{c}{\mathrm{\&eta;}}_{\mathrm{avg}}=\frac{\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&sigma;}}}{p}_{\mathrm{cap}}\left(i\right)}{\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&sigma;}}}{p}_{\mathrm{wy}}\left(i\right)}\\ p_{\mathrm{cap}}=t_e\left(\mathrm{\&omega;}\right)\mathrm{\&omega;}+\mathrm{j\&omega;}\stackrel{.}{\mathrm{\&omega;}}\\ p_{\mathrm{wy}}=0.5\mathrm{\&rho;\&pi;}{r}^2{v}^3{\cos }^3\mathrm{\&psi;}\end{array}\right.---\left(5\right);$

Wherein, n is the update cycle t of each iteration_rInterior wind-speed sample number of times；ψ is yaw error angle, p_capFor actual power, p_wyFor power optimized value；

Then given gain of torque initial gain amount △ m then,₁, and the gain of torque in next cycle is calculated according to equation below,

m_k+1=m_k+△m_k+1(6)；

S3, make k=k+1, kth time iteration cycle is entered with the gain of torque updating, and formula (3) is pressed with the gain of torque after updating Adjustment electromagnetic braking torque, terminates back-pushed-type (5) and calculates corresponding average wind energy utilization in this cycle；

S4, calculate gain of torque m in next cycle according to formula (7)_k+1, wherein η_avgkAverage wind energy for kth time iteration cycle Utilization rate,_γ△mFor the regulation coefficient of gain of torque, △ m_k+1It is the step-size in search for adjusting gain of torque,

$\left\{\begin{array}{c}{m}_{k+1}=m_k+{\mathrm{\&delta;m}}_{k+1}\\ {\mathrm{\&delta;m}}_{k+1}={\mathrm{\&gamma;}}_{\mathrm{\&delta;m}}\mathrm{sign}\left[{\mathrm{\&delta;m}}_k\right]\mathrm{sign}\left[{\mathrm{\&delta;\&eta;}}_{\mathrm{avg\; k}}\right]\sqrt{\left|{\mathrm{\&delta;\&eta;}}_{\mathrm{avg\; k}}\right|}\\ {\mathrm{\&delta;m}}_k=m_k-m_{k-1}\\ {\mathrm{\&delta;\&eta;}}_{\mathrm{avg\; k}}={\mathrm{\&eta;}}_{\mathrm{avg\; k}}-{\mathrm{\&eta;}}_{\mathrm{avg\; k}-1}\end{array}\right.---\left(7\right);$

S5, according to formula (8) calculating torque gain coefficient k_d, judge whether it meets k_dmin≤k_d≤k_dmaxIf meeting, to work as The front gain of torque as next cycle calculating acquisition, is then back to step s3, otherwise skips to step s6,

$\left\{\begin{array}{c}{k}_d=m_{k+1}/m_d\\ m_d=\frac{0.5\mathrm{\&pi;}{r}^5{c}_p^{\max }}{{\mathrm{\&lambda;}}_{\mathrm{opt}}^3}\end{array}\right.---\left(8\right)$

M in above formula_dFor giving tacit consent to gain of torque, λ_optFor optimum tip-speed ratio,For maximal wind-energy usage factor；

S6, make m_k+1=k_avg*m_d, with this m_k+1As the gain of torque in next cycle, return to step s3.

2. the improvement self adaptation method for controlling torque that maximum power point of wind electric power generation according to claim 1 is followed the tracks of, it is special Levy and be: described step s1 determines gain of torque coefficient k_dSpan and MPPT maximum power point tracking wind speed range in all Meansigma methodss k of best torque gain coefficient_avgComprise the following steps:

S11, according to residing for meteorological data determines blower fan the turbulent flow rank of wind field and the wind speed range of MPPT maximum power point tracking；

S12, emulated according to Wind turbines parameter, obtain wind friction velocity in MPPT maximum power point tracking wind speed range with optimal The statistical relationship of gain of torque coefficient；

S13, according in step s12 obtain statistical relationship obtain best torque gain in the wind speed range of MPPT maximum power point tracking The maximum k of coefficient_dmaxWith minima k_dminAnd in the wind speed range of MPPT maximum power point tracking best torque gain coefficient flat Average k_avg, and by k_dmaxIt is set to k_dHigher limit, k_dminIt is set to k_dLower limit, k_avgIt is set to k_dExceed value during bound.

3. the improvement self adaptation method for controlling torque that maximum power point of wind electric power generation according to claim 1 is followed the tracks of, it is special Levy and be: the update cycle t of gain of torque m described in step s2_rFor 20 minutes, the sampling period t of wind speed_cFor 0.25 second～1 Second.