CN105205245A - Direct-driven permanent-magnetic wind power generator multi-work-condition global efficiency optimum design method - Google Patents

Abstract

The invention discloses a direct-driven permanent-magnetic wind power generator multi-work-condition global efficiency optimum design method, which comprises the following steps of determining a system design parameter of a direct-driven permanent-magnetic wind power generator; building a design mathematical model by using the system constraint of a current converter and the control algorithm constraint as the constraint conditions and using the highest weighted average efficiency or smallest weighted average power loss of a plurality of work condition points as a target function on the basis of the system design parameter; setting a group of initial work condition values by aiming at the system design parameter; on the basis of the initial work condition values, using a simulated annealing algorithm for performing multi-work-condition iterative solving on the design mathematical model to obtain a plurality of groups of resolving results; selecting a group of optimum results from all solving results to be used as the optimum system design parameter obtained through design to be output. The method has the advantage that the multi-work-condition efficient operation of the direct-driven permanent-magnetic wind power generator can be realized under the conditions of variable speed, variable frequency, change of stator voltage along with the load and rotating speed change, and influence of operation characteristics by a current converter and a control strategy.

Description

A kind of direct-drive permanent magnet wind power generator multi-state global efficiency optimal-design method

Technical field

The present invention relates to the designing technique of direct-drive permanent magnet wind power generator, be specifically related to a kind of direct-drive permanent magnet wind power generator multi-state global efficiency optimal-design method.

Background technology

The general method for designing of current direct-drive permanent magnet wind power generator is in conjunction with the distinctive magnetic circuit design method of magneto on traditional synchronous generator method for designing basis, for single rated condition, the precondition run with voltage constant and constant-speed and constant-frequency designs, therefore exist can not meet that direct-drive permanent magnet wind power generator speed changing, frequency converting runs, stator voltage changes with load and rotation speed change, operation characteristic is by shortcomings such as current transformer and control strategy affect.

Summary of the invention

The technical problem to be solved in the present invention: for the problems referred to above of prior art, provides a kind of direct-drive permanent magnet wind power generator multi-state global efficiency optimal-design method of multi-state Effec-tive Function when can realize that direct-drive permanent magnet wind power generator changes with load and rotation speed change in speed changing, frequency converting, stator voltage, operation characteristic affects by current transformer and control strategy.

In order to solve the problems of the technologies described above, the technical solution used in the present invention is:

A kind of direct-drive permanent magnet wind power generator multi-state global efficiency optimal-design method, step comprises:

1) system design parameters of direct-drive permanent magnet wind power generator is determined;

2) be constrained to constraint condition with the constraint of the current/voltage of current transformer with control algolithm, the highest or weighted mean power loss is minimum for objective function with the weighted average efficiency of multiple operating point, set up design mathematic model based on described system design parameters;

3) obtain one group of initial operating mode value of system design parameters based on rated point design, based on described initial operating mode value, adopt simulated annealing to carry out multi-state iterative to described design mathematic model, obtain organizing solving result more;

4) from all solving results, select one group of optimal result to export as designing the optimal system design parameter obtained.

Preferably, described step 1) in the system design parameters of direct-drive permanent magnet wind power generator determined comprise frame bore D _i1, stator core length L _s, the close amplitude B of stator yoke magnetic _ysm, stator slot height h _s, stator groove width b _s, permanent magnet height h _m, permanent magnet width b _m, to pole span τ _p, MgO-ZrO_2 brick q.

Preferably, described step 2) in current/voltage constraint such as formula shown in (1);

$\left\{\begin{array}{c}u\≤U_{\lim }\\ I\≤I_{\lim }\end{array}\right.---\left(1\right)$

In formula (1), u is current transformer voltage, i is current transformer electric current, U _limfor voltage binding occurrence, I _limfor restriction of current value.

Preferably, described step 2) in control algolithm constraint such as formula intrafascicular any one of three treaties (2) Suo Shi;

$\left\{\begin{array}{c}i\→\min \\ u=u_c\\ Q_s=0\end{array}\right.---\left(2\right)$

In formula (2), i is current transformer electric current, and → min represents that optimal current controls, and u is current transformer voltage, u _cfor constant voltage binding occurrence, Q _sfor unit power factor.

Preferably, described step 2) in objective function such as formula shown in (3) or formula (4);

$\stackrel{\‾}{\mathrm{\η}}=\frac{{\mathrm{\Σ\η}}_i\left(x\right)W_i}{{\mathrm{\ΣW}}_i}\→max---\left(3\right)$

In formula (3), for the weighted average efficiency of all operating points, η _ix () is the av eff of i-th operating point, i is more than or equal to 3, W _ibe the weight factor of i-th operating point, → max represents the constraint condition that weighted average efficiency is the highest;

$\stackrel{\‾}{p}=\frac{{\mathrm{\Σp}}_i\left(x\right)W_i}{{\mathrm{\ΣW}}_i}\→min---\left(4\right)$

In formula (4), for the weighted mean power loss of all operating points, p _ix () is the power loss of i-th operating point, i is more than or equal to 3, W _ibe the weight factor of i-th operating point, → min represents the constraint condition that weighted mean power loss is minimum.

Preferably, described step 2) the design mathematic model set up is such as formula shown in (5);

$\left\{\begin{array}{c}\underset{X\∈D}{max}:{\mathrm{\η}}_i\left(X\right)/\underset{X\∈D}{min}:p_i\left(X\right)\\ D=\left\{X\right|g_i\left(X\right)\≤0,i=1,2,...,m\}\\ X={\[D_{i1},L_s,B_{ysm},h_s,b_s,h_m,b_m,{\mathrm{\τ}}_p,q\]}^T\end{array}\right.---\left(5\right)$

In formula (5), D is the total quantity of system design parameters to be asked, η _i(X) be the av eff of i-th operating point, p _i(X) be the power loss of i-th operating point, i is more than or equal to 3, the av eff power loss that is maximum or i-th operating point being i-th operating point is minimum, g _i(X) be constraint condition, m is the quantity of constraint condition, and X is the matrix that system design parameters to be asked is formed, D _i1for frame bore, L _sfor stator core length, B _ysmfor the close amplitude of stator yoke magnetic, h _sfor stator slot is high, b _sfor stator groove width, h _mfor permanent magnet height, b _mfor permanent magnet width, τ _pfor being MgO-ZrO_2 brick to pole span, q.

Preferably, described step 3) in detailed step as follows:

3.1) one group of initial operating mode value of system design parameters is obtained based on rated point design;

3.2) based on described initial operating mode value, simulated annealing is adopted to produce an initial solution x at random to described design mathematic model ₀, calculate initial solution x ₀target function value initialization iterations L _k, the initial temperature T of initialization simulated annealing ₀, accept to separate number of times m before temperature variation number of times k and tempering; Redirect performs step 3.3) carry out iteration;

3.3) make random perturbation to current optimum solution, shown in through type (6), function expression generates the new explanation of current iteration;

$\left\{\begin{array}{c}{x^j}_{k+1}=x_k^j+y^j(B_j-A_j)\\ y^j=\mathrm{sgn}(u^j-\frac{1}{2})T_k\[{(1+1/T_k)}^{\left|2u^j-1\right|}-1\]\\ T_k=T_0\exp (-{\mathrm{ck}}^{1/D})\end{array}\right.---\left(6\right)$

In formula (6), x ^j _k+1for the new explanation of current iteration, x ^j _kfor the new explanation of last iteration, y ^jfor the disturbance of a jth system design parameters to be asked, 1≤j≤D, D is the total quantity of system design parameters to be asked, B ^jfor the maximal value that a jth system design parameters to be asked may be got, A ^jfor the minimum value that a jth system design parameters to be asked may be got, u ^jfor the random value between 0 and 1, T _kfor the annealing temperature of current iteration, T ₀for the initial temperature of simulated annealing, c is the temperature control coefrficient of simulated annealing, and k is current temperature variation time numerical value;

3.4) the new explanation x of current iteration is calculated ^j _k+1corresponding target function value calculating target function value relatively last target function value between target function value increment

3.5) target function value increment is judged whether be less than 0 to set up, if set up, then accepting the new optimum solution produced is current optimum solution; If be false, then judge whether that accepting the new optimum solution produced is current optimum solution with the probability P of specifying; If accepting the new optimum solution produced is current optimum solution, then accept to separate number of times m before upgrading tempering;

3.6) accept to separate number of times m before judging to upgrade tempering and whether reach predetermined threshold value, if reach predetermined threshold value, then carry out the temper of simulated annealing, and accept reset tempering after temper before to separate number of times m;

3.7) iterations L is upgraded _kvalue, judge new iterations L _kwhether reach predetermined threshold value, if not yet reach predetermined threshold value, redirect performs step 3.3), otherwise redirect performs step 3.8);

3.8) if continuously the N number of new explanation of specified quantity is not all accepted or the cost function value of simulated annealing is less than set-point between one 0 to 1, then judge the end condition surely reaching simulated annealing, redirect performs step 4); Otherwise, reset iterations L _kif annealing process slowly, carries out the Quenching Treatment of simulated annealing, and redirect performs step 3.3).

Preferably, described step 3.4) in judge whether to accept the new optimum solution produced with the probability P of specifying be that current optimum solution specifically refers to: produce one first at random [0,1] equally distributed random number ζ on interval, then always calculating probability P is carried out with Metropolis criterion formula (7) Suo Shi, if probability P is greater than random number ζ, then accepting the new optimum solution produced is current optimum solution, otherwise abandons accepting the new optimum solution produced being current optimum solution;

$P=\exp \[-\frac{\mathrm{\Δ}\stackrel{\‾}{\mathrm{\η}}}{T_k}\]---\left(7\right)$

In formula (7), P is probability to be calculated, for target function value increment, T _kfor the annealing temperature of current iteration.

Preferably, described step 3.6) in carry out the temper of simulated annealing detailed step comprise:

3.6.1) according to the gradient of each system design parameters in method of finite difference compute vector x formula (8) Suo Shi;

$s_j=\left|\frac{\stackrel{\‾}{\mathrm{\η}}(x_{opt}+e_j\mathrm{\δ})-\stackrel{\‾}{\mathrm{\η}}\left(x_{opt}\right)}{\mathrm{\δ}}\right|---\left(8\right)$

In formula (8), s _jrepresent the gradient of a jth system design parameters in vector x, for the weighted average efficiency of all operating points, x _optfor the optimum solution in the current all solutions found, e _jbe a dimension be the vector of D, D is the quantity of system design parameters, vectorial e _ja middle jth component is 1, all the other components be 0, δ are step-length;

3.6.2) for each system design parameters, if the gradient of this system design parameters equals default Grads threshold s _max, then redirect performs step 3.6.3); Otherwise exit temper;

3.6.3) annealing temperature T is upgraded according to formula (9) _kwith the value of temperature variation number of times k;

$\left\{\begin{array}{c}{T}_{k^{\′}}^{\′}=\frac{s_{max}}{s_j}{T}_k\\ k^{\′}={\[ln(T_0/T_{k^{\′}}^{\′})/c\]}^D\end{array}\right.---\left(9\right)$

In formula (9), for the annealing temperature after renewal, s _maxthe Grads threshold preset, s _jrepresent the gradient of a jth system design parameters in vector x, s _jrepresent the gradient of a jth system design parameters in vector x, T _kfor the annealing temperature before renewal; K' is the temperature variation number of times after upgrading, T ₀for the initial temperature of simulated annealing, c is the temperature control coefrficient of simulated annealing, and D is the quantity of system design parameters.

Preferably, described step 3.8) in Quenching Treatment specifically refer to according to formula (10) upgrade annealing temperature T _kvalue;

$T_k=T_0\exp (-{\mathrm{ck}}^{q_j/D})---\left(10\right)$

In formula (10), T _kfor the annealing temperature after renewal, T ₀for the initial temperature of simulated annealing, c is the temperature control coefrficient of simulated annealing, and k is temperature variation number of times, q _jfor the quenching factor of simulated annealing, D is the quantity of system design parameters.

Direct-drive permanent magnet wind power generator multi-state global efficiency optimal-design method of the present invention has following advantage: direct-drive permanent magnet wind power generator multi-state global efficiency optimal-design method of the present invention with rated point design result for starting condition, with current transformer electric current and voltage and control algolithm for constraint condition, with the weighted average efficiency of multiple operating point the highest (or weighted mean power loss is minimum) for target, based on global optimization approach, direct-drive permanent magnet wind power generator multi-state load design problem is solved, thus obtain the system design parameters of one group of optimum, direct-drive permanent magnet wind power generator can be realized at speed changing, frequency converting, stator voltage changes with load and rotation speed change, multi-state Effec-tive Function when operation characteristic affects by current transformer and control strategy.

Accompanying drawing explanation

Fig. 1 is the basic procedure schematic diagram of embodiment of the present invention method.

Fig. 2 is embodiment of the present invention step 3) schematic flow sheet.

Embodiment

As shown in Figure 1, the step of the present embodiment direct-drive permanent magnet wind power generator multi-state global efficiency optimal-design method comprises:

1) system design parameters of direct-drive permanent magnet wind power generator is determined;

2) be constrained to constraint condition with the constraint of the current/voltage of current transformer with control algolithm, the highest or weighted mean power loss is minimum for objective function with the weighted average efficiency of multiple operating point (referring to the running parameter of generator under a certain specified conditions), sets up design mathematic model based on system design parameters;

3) obtain one group of initial operating mode value of system design parameters based on rated point design, based on initial operating mode value, adopt simulated annealing to carry out multi-state iterative to design mathematic model, obtain organizing solving result more;

4) from all solving results, select one group of optimal result to export as designing the optimal system design parameter obtained.

In the present embodiment, step 1) in the system design parameters of direct-drive permanent magnet wind power generator determined comprise frame bore D _i1, stator core length L _s, the close amplitude B of stator yoke magnetic _ysm, stator slot height h _s, stator groove width b _s, permanent magnet height h _m, permanent magnet width b _m, to pole span τ _p, MgO-ZrO_2 brick q.

In the present embodiment, step 2) in current/voltage constraint such as formula shown in (1);

$\left\{\begin{array}{c}u\≤U_{\lim }\\ I\≤I_{\lim }\end{array}\right.---\left(1\right)$

In formula (1), u is current transformer voltage, i is current transformer electric current, U _limfor voltage binding occurrence, I _limfor restriction of current value.

In the present embodiment, step 2) in control algolithm constraint such as formula intrafascicular any one of three treaties (2) Suo Shi;

$\left\{\begin{array}{c}i\→\min \\ u=u_c\\ Q_s=0\end{array}\right.---\left(2\right)$

In formula (2), i is current transformer electric current, and → min represents that optimal current controls, and u is current transformer voltage, u _cfor constant voltage binding occurrence, Q _sfor unit power factor.

In the present embodiment, step 2) in objective function such as formula shown in (3) or formula (4);

$\stackrel{\‾}{\mathrm{\η}}=\frac{{\mathrm{\Σ\η}}_i\left(x\right)W_i}{{\mathrm{\ΣW}}_i}\→max---\left(3\right)$

In formula (3), for the weighted average efficiency of all operating points, η _ix () is the av eff of i-th operating point, i is more than or equal to 3, W _ibe the weight factor of i-th operating point, → max represents the constraint condition that weighted average efficiency is the highest; Because direct-drive permanent magnet wind power generator global efficiency optimal design Problem is a max problem, therefore can adopt objective function shown in formula (3) as required, convert thereof into (namely ) solve.

$\stackrel{\‾}{p}=\frac{{\mathrm{\Σp}}_i\left(x\right)W_i}{{\mathrm{\ΣW}}_i}\→min---\left(4\right)$

In formula (4), for the weighted mean power loss of all operating points, p _ix () is the power loss of i-th operating point, i is more than or equal to 3, W _ibe the weight factor of i-th operating point, → min represents the constraint condition that weighted mean power loss is minimum.Certainly, also can adopt objective function shown in formula (4) as required, convert thereof into solve.

Direct-drive permanent magnet wind power generator global efficiency optimal design Problem belongs to non-linear, multiple goal Solve problems, and its solution is not unique.Adopt simulated annealing to carry out multi-state iterative to design mathematic model in the present embodiment, speed of convergence is very fast, and the solving result finally obtained is globally optimal solution.For step 3) set one group of initial operating mode value for system design parameters, based on initial operating mode value, simulated annealing is adopted to carry out multi-state iterative to design mathematic model, obtain the execution part of many group solving results, visual c++ is adopted to be written as direct-drive permanent magnet wind power generator energy characteristics calculation procedure QE.exe in the present embodiment, perform step 3) then call and perform QE.exe, can greatly simplified design step.

In the present embodiment, step 2) the design mathematic model set up is such as formula shown in (5);

$\left\{\begin{array}{c}\underset{X\∈D}{max}:{\mathrm{\η}}_i\left(X\right)/\underset{X\∈D}{min}:p_i\left(X\right)\\ D=\left\{X\right|g_i\left(X\right)\≤0,i=1,2,...,m\}\\ X={\[D_{i1},L_s,B_{ysm},h_s,b_s,h_m,b_m,{\mathrm{\τ}}_p,q\]}^T\end{array}\right.---\left(5\right)$

In formula (5), D is the total quantity of system design parameters to be asked, η _i(X) be the av eff of i-th operating point, p _i(X) be the power loss of i-th operating point, i is more than or equal to 3, the av eff power loss that is maximum or i-th operating point being i-th operating point is minimum, g _i(X) be constraint condition, m is the quantity of constraint condition, and X is the matrix that system design parameters to be asked is formed, D _i1for frame bore, L _sfor stator core length, B _ysmfor the close amplitude of stator yoke magnetic, h _sfor stator slot is high, b _sfor stator groove width, h _mfor permanent magnet height, b _mfor permanent magnet width, τ _pfor being MgO-ZrO_2 brick to pole span, q.Shown in formula (5), design mathematic model comprises the mapping relations between system design parameters from energy characteristics under different operating mode.

As shown in Figure 2, step 3) in detailed step as follows:

3.1) one group of initial operating mode value of system design parameters is obtained based on rated point design;

3.2) based on initial operating mode value, simulated annealing is adopted to produce an initial solution x at random to design mathematic model ₀, calculate initial solution x ₀target function value initialization iterations L _k, the initial temperature T of initialization simulated annealing ₀, accept to separate number of times m before temperature variation number of times k and tempering; Redirect performs step 3.3) carry out iteration; In the present embodiment, the initial temperature T of simulated annealing ₀initialization value be 1.0, the initialization value of temperature variation number of times k is 1;

3.3) make random perturbation to current optimum solution, shown in through type (6), function expression generates the new explanation of current iteration;

$\left\{\begin{array}{c}{x^j}_{k+1}=x_k^j+y^j(B_j-A_j)\\ y^j=\mathrm{sgn}(u^j-\frac{1}{2})T_k\[{(1+1/T_k)}^{\left|2u^j-1\right|}-1\]\\ T_k=T_0\exp (-{\mathrm{ck}}^{1/D})\end{array}\right.---\left(6\right)$

In formula (6), x ^j _k+1for the new explanation of current iteration, x ^j _kfor the new explanation of last iteration, y ^jfor the disturbance of a jth system design parameters to be asked, 1≤j≤D, D is the total quantity of system design parameters to be asked, B ^jfor the maximal value that a jth system design parameters to be asked may be got, A ^jfor the minimum value that a jth system design parameters to be asked may be got, u ^jfor the random value between 0 and 1, T _kfor the annealing temperature of current iteration, T ₀for the initial temperature of simulated annealing, c is the temperature control coefrficient of simulated annealing, and k is current temperature variation time numerical value;

3.4) the new explanation x of current iteration is calculated ^j _k+1corresponding target function value calculating target function value relatively last target function value between target function value increment

3.5) target function value increment is judged whether be less than 0 to set up, if set up, then accepting the new optimum solution produced is current optimum solution; If be false, then judge whether that accepting the new optimum solution produced is current optimum solution with the probability P of specifying; If accepting the new optimum solution produced is current optimum solution, then accept to separate number of times m before upgrading tempering;

3.6) accept to separate number of times m before judging to upgrade tempering and whether reach predetermined threshold value, if reach predetermined threshold value, then carry out the temper of simulated annealing, and accept reset tempering after temper before to separate number of times m;

3.7) iterations L is upgraded _kvalue, judge new iterations L _kwhether reach predetermined threshold value, if not yet reach predetermined threshold value, redirect performs step 3.3), otherwise redirect performs step 3.8);

3.8) if continuously the N number of new explanation of specified quantity is not all accepted or the cost function value of simulated annealing is less than set-point ε (0< ε <1) between one 0 to 1, then judge the end condition surely reaching simulated annealing, redirect performs step 4); Otherwise, reset iterations L _kif annealing process slowly, carries out the Quenching Treatment of simulated annealing, and redirect performs step 3.3).

In the present embodiment, step 3.4) in judge whether to accept the new optimum solution produced with the probability P of specifying be that current optimum solution specifically refers to: produce one first at random [0,1] equally distributed random number ζ on interval, then always calculating probability P is carried out with Metropolis criterion formula (7) Suo Shi, if probability P is greater than random number ζ, then accepting the new optimum solution produced is current optimum solution, otherwise abandons accepting the new optimum solution produced being current optimum solution;

$P=\exp \&lsqb;-\frac{\mathrm{\&Delta;}\stackrel{\&OverBar;}{\mathrm{\&eta;}}}{T_k}\&rsqb;---\left(7\right)$

In formula (7), P is probability to be calculated, for target function value increment, T _kfor the annealing temperature of current iteration.

In the present embodiment, step 3.6) in carry out the temper of simulated annealing detailed step comprise:

3.6.1) according to the gradient of each system design parameters in method of finite difference compute vector x formula (8) Suo Shi;

$s_j=\left|\frac{\stackrel{\&OverBar;}{\mathrm{\&eta;}}(x_{opt}+e_j\mathrm{\&delta;})-\stackrel{\&OverBar;}{\mathrm{\&eta;}}\left(x_{opt}\right)}{\mathrm{\&delta;}}\right|---\left(8\right)$

In formula (8), s _jrepresent the gradient of a jth system design parameters in vector x, for the weighted average efficiency of all operating points, x _optfor the optimum solution in the current all solutions found, e _jbe a dimension be the vector of D, D is the quantity of system design parameters, vectorial e _ja middle jth component is 1, all the other components be 0, δ are step-length;

3.6.2) for each system design parameters, if the gradient of this system design parameters equals default Grads threshold s _max, then redirect performs step 3.6.3); Otherwise exit temper;

3.6.3) annealing temperature T is upgraded according to formula (9) _kwith the value of temperature variation number of times k;

$\left\{\begin{array}{c}{T}_{k^{\&prime;}}^{\&prime;}=\frac{s_{max}}{s_j}{T}_k\\ k^{\&prime;}={\&lsqb;ln(T_0/T_{k^{\&prime;}}^{\&prime;})/c\&rsqb;}^D\end{array}\right.---\left(9\right)$

In formula (9), for the annealing temperature after renewal, s _maxthe Grads threshold preset, s _jrepresent the gradient of a jth system design parameters in vector x, s _jrepresent the gradient of a jth system design parameters in vector x, T _kfor the annealing temperature before renewal; K' is the temperature variation number of times after upgrading, T ₀for the initial temperature of simulated annealing, c is the temperature control coefrficient of simulated annealing, and D is the quantity of system design parameters.

In the present embodiment, step 3.8) in Quenching Treatment specifically refer to and upgrade annealing temperature T according to formula (10) _kvalue;

$T_k=T_0\exp (-{\mathrm{ck}}^{q_j/D})---\left(10\right)$

In formula (10), T _kfor the annealing temperature after renewal, T ₀for the initial temperature of simulated annealing, c is the temperature control coefrficient of simulated annealing, and k is temperature variation number of times, q _jfor the quenching factor of simulated annealing, D is the quantity of system design parameters.

The above is only the preferred embodiment of the present invention, protection scope of the present invention be not only confined to above-described embodiment, and all technical schemes belonged under thinking of the present invention all belong to protection scope of the present invention.It should be pointed out that for those skilled in the art, some improvements and modifications without departing from the principles of the present invention, these improvements and modifications also should be considered as protection scope of the present invention.

Claims (10)

1. a direct-drive permanent magnet wind power generator multi-state global efficiency optimal-design method, is characterized in that step comprises:

1) system design parameters of direct-drive permanent magnet wind power generator is determined;

2) be constrained to constraint condition with the constraint of the current/voltage of current transformer with control algolithm, the highest or weighted mean power loss is minimum for objective function with the weighted average efficiency of multiple operating point, set up design mathematic model based on described system design parameters;

3) obtain one group of initial operating mode value of system design parameters based on rated point design, based on described initial operating mode value, adopt simulated annealing to carry out multi-state iterative to described design mathematic model, obtain organizing solving result more;

4) from all solving results, select one group of optimal result to export as designing the optimal system design parameter obtained.

2. direct-drive permanent magnet wind power generator multi-state global efficiency optimal-design method according to claim 1, is characterized in that, described step 1) in the system design parameters of direct-drive permanent magnet wind power generator determined comprise frame bore D _i1, stator core length L _s, the close amplitude B of stator yoke magnetic _ysm, stator slot height h _s, stator groove width b _s, permanent magnet height h _m, permanent magnet width b _m, to pole span τ _p, MgO-ZrO_2 brick q.

3. direct-drive permanent magnet wind power generator multi-state global efficiency optimal-design method according to claim 2, is characterized in that, described step 2) in current/voltage constraint such as formula shown in (1);

$\{\begin{array}{c}u\&le;U_{\lim }\\ i\&le;I_{\lim }\end{array}---\left(1\right)$

In formula (1), u is current transformer voltage, i is current transformer electric current, U _limfor voltage binding occurrence, I _limfor restriction of current value.

4. direct-drive permanent magnet wind power generator multi-state global efficiency optimal-design method according to claim 3, is characterized in that, described step 2) in control algolithm constraint such as formula intrafascicular any one of three treaties (2) Suo Shi;

$\left\{\begin{array}{c}i\&RightArrow;min\\ u=u_c\\ Q_s=0\end{array}\right.---\left(2\right)$

In formula (2), i is current transformer electric current, and → min represents that optimal current controls, and u is current transformer voltage, u _cfor constant voltage binding occurrence, Q _sfor unit power factor.

5. direct-drive permanent magnet wind power generator multi-state global efficiency optimal-design method according to claim 4, is characterized in that, described step 2) in objective function such as formula shown in (3) or formula (4);

$\stackrel{\&OverBar;}{\mathrm{\&eta;}}=\frac{{\mathrm{\&Sigma;\&eta;}}_i\left(x\right)W_i}{{\mathrm{\&Sigma;W}}_i}\&RightArrow;max---\left(3\right)$

In formula (3), for the weighted average efficiency of all operating points, η _ix () is the av eff of i-th operating point, i is more than or equal to 3, W _ibe the weight factor of i-th operating point, → max represents the constraint condition that weighted average efficiency is the highest;

$\stackrel{\&OverBar;}{p}=\frac{{\mathrm{\&Sigma;p}}_i\left(x\right)W_i}{{\mathrm{\&Sigma;W}}_i}\&RightArrow;min---\left(4\right)$

In formula (4), for the weighted mean power loss of all operating points, p _ix () is the power loss of i-th operating point, i is more than or equal to 3, W _ibe the weight factor of i-th operating point, → min represents the constraint condition that weighted mean power loss is minimum.

6. direct-drive permanent magnet wind power generator multi-state global efficiency optimal-design method according to claim 5, is characterized in that, described step 2) the design mathematic model set up is such as formula shown in (5);

$\left\{\begin{array}{c}\underset{X\&Element;D}{max}:{\mathrm{\&eta;}}_i\left(X\right)/\underset{X\&Element;D}{min}:p_i\left(X\right)\\ D=\left\{X\right|g_i\left(X\right)\&le;0,i=1,2,...,m\}\\ X={\&lsqb;D_{i1},L_s,B_{ysm}{h}_s,b_s,h_m,b_m,{\mathrm{\&tau;}}_p,q\&rsqb;}^T\end{array}\right.---\left(5\right)$

In formula (5), D is the total quantity of system design parameters to be asked, η _i(X) be the av eff of i-th operating point, p _i(X) be the power loss of i-th operating point, i is more than or equal to 3, the av eff power loss that is maximum or i-th operating point being i-th operating point is minimum, g _i(X) be constraint condition, m is the quantity of constraint condition, and X is the matrix that system design parameters to be asked is formed, D _i1for frame bore, L _sfor stator core length, B _ysmfor the close amplitude of stator yoke magnetic, h _sfor stator slot is high, b _sfor stator groove width, h _mfor permanent magnet height, b _mfor permanent magnet width, τ _pfor being MgO-ZrO_2 brick to pole span, q.

7., according to the direct-drive permanent magnet wind power generator multi-state global efficiency optimal-design method in claim 1 ~ 6 described in any one, it is characterized in that, described step 3) in detailed step as follows:

3.1) one group of initial operating mode value of system design parameters is obtained based on rated point design;

3.2) based on described initial operating mode value, simulated annealing is adopted to produce an initial solution x at random to described design mathematic model ₀, calculate initial solution x ₀target function value initialization iterations L _k, the initial temperature T of initialization simulated annealing ₀, accept to separate number of times m before temperature variation number of times k and tempering; Redirect performs step 3.3) carry out iteration;

3.3) make random perturbation to current optimum solution, shown in through type (6), function expression generates the new explanation of current iteration;

$\{\begin{array}{c}{x^j}_{k+1}=x_k^j+y^j(B_j-A_j)\\ y^j=sgn(u^j-\frac{1}{2})T_k\&lsqb;{(1+1/T_k)}^{|2u^j-1|}-1\&rsqb;\\ T_k=T_0\exp (-{\mathrm{ck}}^{1/D})\end{array}---\left(6\right)$

In formula (6), x ^j _k+1for the new explanation of current iteration, x ^j _kfor the new explanation of last iteration, y ^jfor the disturbance of a jth system design parameters to be asked, 1≤j≤D, D is the total quantity of system design parameters to be asked, B ^jfor the maximal value that a jth system design parameters to be asked may be got, A ^jfor the minimum value that a jth system design parameters to be asked may be got, u ^jfor the random value between 0 and 1, T _kfor the annealing temperature of current iteration, T ₀for the initial temperature of simulated annealing, c is the temperature control coefrficient of simulated annealing, and k is current temperature variation time numerical value;

3.4) the new explanation x of current iteration is calculated ^j _k+1corresponding target function value calculating target function value relatively last target function value between target function value increment

3.5) target function value increment is judged whether be less than 0 to set up, if set up, then accepting the new optimum solution produced is current optimum solution; If be false, then judge whether that accepting the new optimum solution produced is current optimum solution with the probability P of specifying; If accepting the new optimum solution produced is current optimum solution, then accept to separate number of times m before upgrading tempering;

3.6) accept to separate number of times m before judging to upgrade tempering and whether reach predetermined threshold value, if reach predetermined threshold value, then carry out the temper of simulated annealing, and accept reset tempering after temper before to separate number of times m;

3.7) iterations L is upgraded _kvalue, judge new iterations L _kwhether reach predetermined threshold value, if not yet reach predetermined threshold value, redirect performs step 3.3), otherwise redirect performs step 3.8);

3.8) if continuously the N number of new explanation of specified quantity is not all accepted or the cost function value of simulated annealing is less than set-point between one 0 to 1, then judge the end condition surely reaching simulated annealing, redirect performs step 4); Otherwise, reset iterations L _kif annealing process slowly, carries out the Quenching Treatment of simulated annealing, and redirect performs step 3.3).

8. direct-drive permanent magnet wind power generator multi-state global efficiency optimal-design method according to claim 7, it is characterized in that, described step 3.4) in judge whether to accept the new optimum solution produced with the probability P of specifying be that current optimum solution specifically refers to: produce one first at random [0, 1] equally distributed random number ζ on interval, then always calculating probability P is carried out with Metropolis criterion formula (7) Suo Shi, if probability P is greater than random number ζ, then accepting the new optimum solution produced is current optimum solution, otherwise abandoning accepting the new optimum solution produced is current optimum solution,

$P=\exp \&lsqb;-\frac{\mathrm{\&Delta;}\stackrel{\&OverBar;}{\mathrm{\&eta;}}}{T_k}\&rsqb;---\left(7\right)$

In formula (7), P is probability to be calculated, for target function value increment, T _kfor the annealing temperature of current iteration.

9. direct-drive permanent magnet wind power generator multi-state global efficiency optimal-design method according to claim 8, is characterized in that, described step 3.6) in carry out the temper of simulated annealing detailed step comprise:

3.6.1) according to the gradient of each system design parameters in method of finite difference compute vector x formula (8) Suo Shi;

$s_j=\left|\frac{\stackrel{\&OverBar;}{\mathrm{\&eta;}}(x_{opt}+e_j\mathrm{\&delta;})-\stackrel{\&OverBar;}{\mathrm{\&eta;}}\left(x_{opt}\right)}{\mathrm{\&delta;}}\right|---\left(8\right)$

In formula (8), s _jrepresent the gradient of a jth system design parameters in vector x, for the weighted average efficiency of all operating points, x _optfor the optimum solution in the current all solutions found, e _jbe a dimension be the vector of D, D is the quantity of system design parameters, vectorial e _ja middle jth component is 1, all the other components be 0, δ are step-length;

3.6.2) for each system design parameters, if the gradient of this system design parameters equals default Grads threshold s _max, then redirect performs step 3.6.3); Otherwise exit temper;

3.6.3) annealing temperature T is upgraded according to formula (9) _kwith the value of temperature variation number of times k;

$\left\{\begin{array}{c}{T}_{k^{\&prime;}}^{\&prime;}=\frac{s_{max}}{s_j}{T}_k\\ k^{\&prime;}={\&lsqb;ln(T_0/T_{k^{\&prime;}}^{\&prime;})/c\&rsqb;}^D\end{array}\right.---\left(9\right)$

In formula (9), T ' _{k '}for the annealing temperature after renewal, s _maxthe Grads threshold preset, s _jrepresent the gradient of a jth system design parameters in vector x, s _jrepresent the gradient of a jth system design parameters in vector x, T _kfor the annealing temperature before renewal; K' is the temperature variation number of times after upgrading, T ₀for the initial temperature of simulated annealing, c is the temperature control coefrficient of simulated annealing, and D is the quantity of system design parameters.

10. direct-drive permanent magnet wind power generator multi-state global efficiency optimal-design method according to claim 9, is characterized in that, described step 3.8) in Quenching Treatment specifically refer to according to formula (10) upgrade annealing temperature T _kvalue;

$T_k=T_0\exp (-{\mathrm{ck}}^{q_j/D})---\left(10\right)$

In formula (10), T _kfor the annealing temperature after renewal, T ₀for the initial temperature of simulated annealing, c is the temperature control coefrficient of simulated annealing, and k is temperature variation number of times, q _jfor the quenching factor of simulated annealing, D is the quantity of system design parameters.