CN105162159A - Differential evolutionary algorithm based parameter identifying method for controller of photovoltaic grid-connected inverter - Google Patents

Abstract

The invention discloses a differential evolutionary algorithm based parameter identifying method for a controller of a photovoltaic grid-connected inverter. The differential evolutionary algorithm is adopted for identifying parameters such as the proportionality coefficient and integer coefficient of a current d-axis control system and a current q-axis control system of the photovoltaic grid-connected inverter, the output limit amplitude of an integrator, the output limit amplitude of the controller and the like. Initial values of all solution vectors in an initial group are determined through a theory calculation method, the whole search domain is covered effectively, the search range of the differential evolutionary algorithm is narrowed and the speed at which the to-be-identified parameters converge to real values is accelerated. A staging identification process for PI adjuster parameters and limit amplitude link parameters is created for reducing parameter identification dimensions, so that the controller parameter identification efficiency is improved. The limit amplitude link parameters of the controller can be identified accurately, so that the failure transient characteristics of a photovoltaic power source are reflected accurately. Shortcomings in a prior controller parameter identifying method are made up and the method has a good application prospect.

Description

Based on the photovoltaic grid inverter controller parameter identification method of differential evolution algorithm

Technical field

The present invention relates to power system modeling technical field, be specifically related to a kind of photovoltaic grid inverter controller parameter identification method based on differential evolution algorithm.

Background technology

Photovoltaic power generation technology has not by regional condition restriction, scaleable, the advantage such as safe and reliable, pollution-free, and thus obtain in China and develop rapidly and promote, grid connection capacity is growing.But; fault current characteristics and the feature of exerting oneself that is intermittent, randomness of photo-voltaic power supply complexity also bring huge challenge to traditional protection philosophy and fault detection method; cause it may the position of failure judgement exactly, cause relay fail or malfunction.

We know; the basic task of relaying protection is according to the electric parameters produced after electric network fault (or non-electric quantity) variation characteristic; differentiate and isolated fault equipment or element, therefore, electric network fault specificity analysis is the basic content of relaying protection research field.

Because photo-voltaic power supply is by combining inverter and grid interface, the response characteristic of combining inverter during electric network fault determines the fault current characteristics of photo-voltaic power supply, and the fault response characteristics of photovoltaic combining inverter then depends on the selection of its controller parameter.Therefore; the controller parameter (comprising the proportionality coefficient of proportional and integral controller, integral coefficient and amplitude limit link parameter etc.) of accurate recognition photovoltaic combining inverter; for building, the photo-voltaic power supply equivalent model of high confidence level is significant, for research establishes solid foundation containing the electric network fault current characteristics of photo-voltaic power supply access and protection philosophy research.

At present, controller parameter discrimination method about the converter of photovoltaic combining inverter or other types is all intended to proportionality coefficient and the integral coefficient of identification controller inner and outer rings proportional and integral controller, the not parameter of the amplitude limit link such as the output violent change value of identification integrator and controller output violent change value, be specifically described as follows

As shown in Figure 1, grid-connected photovoltaic power supply is made up of photovoltaic array, DC bus capacitor, combining inverter, LC filter, and under two-phase synchronous rotary dq coordinate system, the mathematical equation of photovoltaic combining inverter is as follows,

$\left\{\begin{array}{c}{u}_d=L_1{\mathrm{pi}}_d+R_1{i}_d-{\mathrm{\ω}}_s{L}_1{i}_q+u_{gd}\\ u_q=L_1{\mathrm{pi}}_q+R_1{i}_q+{\mathrm{\ω}}_s{L}_1{i}_d+u_{gq}\end{array}\right.$

Wherein, u _d, u _qfor d, q axle component of brachium pontis output voltage, u _gd, u _gqfor d, q axle component of line voltage, i _d, i _qfor d, q axle component of inverter output current, ω ₁for synchronous angular velocity, L ₁for AC filter inductance, R ₁for the equivalent resistance of filter inductance;

Adopt grid voltage orientation vector control, d axle is oriented to line voltage vector, and during the control mode taking feedforward compensation and PI to regulate, as shown in Figure 2, as can be seen from Figure 2, system have employed double closed-loop control system to the double-loop control block diagram of photovoltaic combining inverter, outer shroud is power voltage ring, inner ring is electric current loop, in order to the unity power factor realizing photo-voltaic power supply runs, generally gets Q ^*=0.Outer shroud instruction obtains current inner loop instruction compared with value of feedback, through the PI controller of overpower-outer voltage with with compared with feedback current, through the PI controller of current inner loop, and after carrying out feedforward compensation, coordinate transform, obtain the three-phase voltage control command needed for inverter with

When a grid fault occurs, because trouble duration is general shorter, and photo-voltaic power supply is generally configured with energy storage device, therefore, can think photovoltaic combining inverter DC side electric current capacitance voltage U _dcsubstantially remain unchanged between age at failure.Meanwhile, in order to the response speed of photo-voltaic power supply during improving electric network fault, by the locking of power voltage outer shroud, current inner loop directly can be controlled.Therefore, in fault transient analysis, only need consider the impact of current inner loop controller operation characteristic, in the double-loop control block diagram of the photovoltaic combining inverter shown in Fig. 2, not consider the output current restriction of photovoltaic combining inverter.But due to the impact by factor such as inverter self capacity and overcurrent protection etc., the output current of photovoltaic combining inverter must be restricted.Therefore, in control strategy specific implementation process, the output violent change to controller is needed.When carrying out amplitude limit to controller output, if when grid collapses causes the command value of current inner loop to occur a larger step value, controller output can be subject to saturated restriction, this will make the hydraulic performance decline of actual closed-loop system, as overshoot increase, stabilization time increases, the even destruct limit stability of a system under serious conditions.This phenomenon causing system closed loop response to be deteriorated due to controller output violent change, is called windup phenomenon.In order to avoid the appearance of controller windup phenomenon, effective method the most directly perceived designs anti-windup controller exactly, the saturated phenomenon of inhibitory control device, and makes system can exit saturation region as early as possible.Wherein, the simplest anti-windup controller design method carries out amplitude limit to the output of the integral element in PI controller.

For d axle, after considering controller output violent change and anti-windup design (integrator output violent change), the specific implementation link of current inner loop control system as shown in Figure 3, due to the existence of amplitude limit link, after grid collapses the controller of photovoltaic combining inverter input, export between relation cannot describe with analytical expression.

By foregoing description, when a grid fault occurs, the disturbance of line voltage may make the controller of photovoltaic combining inverter enter nonlinear element, and affect integrator and export and controller output violent change, this will affect the fault transient characteristic of photo-voltaic power supply greatly.Therefore, the equivalent model of the photo-voltaic power supply utilizing the controller parameter discrimination method of conventional photovoltaic combining inverter to construct, accurately cannot reflect the fault transient characteristic of photo-voltaic power supply, how to make up the deficiency of existing controller parameter identification method, accurate recognition goes out the parameter of controller amplitude limit link, is current urgent problem.

Summary of the invention

Technical problem solved by the invention is the equivalent model overcoming the photo-voltaic power supply utilizing the controller parameter discrimination method of traditional photovoltaic combining inverter to construct, and accurately cannot reflect the problem of the fault transient characteristic of photo-voltaic power supply.

In order to achieve the above object, the technical solution adopted in the present invention is:

Based on a photovoltaic grid inverter controller parameter identification method for differential evolution algorithm, it is characterized in that: comprise the following steps,

Step (1), arranges population scale NP=50, obtains the evolution colony of moment t1, t2 according to formula (1) and formula (2) respectively with

$P_{P1}^{t1}=\left[\begin{array}{cc}{x}_{1,1}^{t1}& x_{1,2}^{t1}\\ x_{2,1}^{t1}& x_{2,2}^{t1}\\ \begin{array}{c}\·\\ \·\\ \·\end{array}& \begin{array}{c}\·\\ \·\\ \·\end{array}\\ x_{50,1}^{t1}& x_{50,2}^{t1}\end{array}\right]---\left(1\right)$

$P_{Lim}^{t2}=\left[\begin{array}{cccc}{x}_{1,3}^{t2}& x_{1,4}^{t2}& x_{1,5}^{t2}& x_{1,6}^{t2}\\ x_{2,3}^{t2}& x_{2,4}^{t2}& x_{2,5}^{t2}& x_{2,6}^{t2}\\ \begin{array}{c}\·\\ \·\\ \·\end{array}& \begin{array}{c}\·\\ \·\\ \·\end{array}& \begin{array}{c}\·\\ \·\\ \·\end{array}& \begin{array}{c}\·\\ \·\\ \·\end{array}\\ x_{50,3}^{t2}& x_{50,4}^{t2}& x_{50,5}^{t2}& x_{50,6}^{t2}\end{array}\right]---\left(2\right)$

Wherein, distinguish the proportionality coefficient k of d, q shaft current inner ring pi regulator in i-th population of corresponding t1 moment _p, integral coefficient k _i, i=1,2 ..., NP; with d, q shaft current inner ring integrator output violent change value L in i-th population of corresponding t2 moment respectively _{int_up}, L _{int_low}, and current inner loop controller output violent change value L _{out_up}, L _{out_low}, i=1,2 ..., NP;

Make t1=0, t2=0, initial population is set at interval [k _{p_1}/ 5,5k _{p_2}] interior value, at interval [k _{i_1}/ 5,5k _{i_2}] interior value, interval (0,2.0] interior value, in interval [-2.0,0] interior value, according to formula (3), calculate k _{p_1}, k _{p_2}, k _{i_1}and k _{i_2}value,

$\{\begin{array}{c}{k}_{P\_1}={\mathrm{\ω}}_c{L}_1\\ k_{I\_1}={\mathrm{\ω}}_c{R}_1\\ k_{P\_2}=2{\mathrm{\ξ\ω}}_n{L}_1\\ k_{I\_2}={\mathrm{\ω}}_n^2{L}_1\end{array}---\left(3\right)$

ω _cfor cut-off angular frequency, value is 10 ω _s, ω _sfor synchronous rotary angular speed; ξ is damping ratio, ω _nfor natural frequency of oscillation, ξ=0.707, ω _n=10 ω _s; L ₁for the filter inductance of photovoltaic combining inverter AC, R ₁for filter inductance L ₁internal resistance;

According to formula (4), list correspondence function

$J(x_{i\_PI}^{t1},x_{i\_Lim}^{t2})=\frac{1}{5N}\{\underset{k=1}{\overset{5N}{\Σ}}{\[i_d\left(k\right)-{\tilde{i}}_d\left(k\right)\]}^2+\underset{k=1}{\overset{5N}{\Σ}}{\[i_q\left(k\right)-{\tilde{i}}_q\left(k\right)\]}^2\}---\left(4\right)$

Wherein, i _d(k) and i _qk () is in current inner loop command value situation, the actual output of photovoltaic combining inverter current inner loop controller; with for in identical current inner loop command value situation, the output of photovoltaic combining inverter current inner loop controller trace model; $x_{i\_PI}^{t1}={\left[\begin{array}{cc}{x}_{i,1}^{t1}& x_{i,2}^{t1}\end{array}\right]}^T,x_{i\_Lim}^{t2}={\left[\begin{array}{cccc}{x}_{i,3}^{t2}& x_{i,4}^{t2}& x_{i,5}^{t2}& x_{i,6}^{t2}\end{array}\right]}^T$ For solution vector; N=48 is the sampling number of a power frequency period;

Grid-connected point voltage is set and drops to 0.85pu from 1.0pu, make current inner loop controller be operated in the range of linearity, and perform step (2);

Step (2), if t1>NG, NG are maximum evolutionary generation, then the proportionality coefficient of pi regulator and the identification process of integral coefficient terminate, population the minimum solution vector of middle correspondence function value is proportionality coefficient and the integral coefficient of the pi regulator picked out, and performs step (7); Otherwise, perform step (3);

Step (3), utilizes correspondence function to population in each solution vector evaluate, if population middle exist the satisfied matching condition as shown in formula (5) of solution vector, then parameter identification success,

$J(X_{i\_PI}^{t1},X_{i\_Lim}^{t2})\≤\mathrm{\ϵ}---\left(5\right)$

Wherein, ε value is 0.01; Corresponding solution vector is proportionality coefficient and the integral coefficient of the pi regulator picked out, and performs step (7); If population in each solution vector all meet matching condition as shown in formula (5), then perform step (4);

Step (4), to population in solution vector carry out mutation operation according to formula (6), obtain variation vector $v_{i\_PI}^{t1+1}={\left[\begin{array}{cc}{v}_{i,1}^{t1+1}& v_{i,2}^{t1+1}\end{array}\right]}^T,$

$v_{i\_PI}^{t1+1}=x_{r3\_PI}^{t1}+F*(x_{r1\_PI}^{t1}-x_{r2\_PI}^{t1})---\left(6\right)$

Wherein, r1, r2, r3 ∈ 1,2,3 ..., NP}, is all not equal to i, and unequal mutually between two; F is zoom factor, and span is [0,1];

Step (5), by population in solution vector with variation vector carry out the interlace operation as shown in formula (7), produce trial vector $u_{i\_PI}^{t1+1}={\left[\begin{array}{cc}{u}_{i,1}^{t1+1}& u_{i,2}^{t1+1}\end{array}\right]}^T,$

$u_{i,j}^{t1+1}=\left\{\begin{array}{cccc}{v}_{i,j}^{t1+1},& rand\left(j\right)\≤CR& or& j=randn\left(i\right)\\ x_{i,j}^{t1},& rand\left(j\right)>CR& or& j\≠randn\left(i\right)\end{array}\right.---\left(7\right)$

Wherein, rand (j) ∈ [0,1] is equally distributed random number; { 1,2}, represents a jth variable to j ∈; { 1,2} is the dimension variable index of Stochastic choice to randn (i) ∈; CR is crossover probability constant, and span is [0,1];

Step (6), the trial vector produced after variation with interlace operation with population in solution vector carry out the competition as shown in formula (8), and select vector that correspondence function value is less as filial generation, obtain the population in t1+1 moment

$x_{i\_PI}^{t1+1}=\left\{\begin{array}{cc}{u}_{i\_PI}^{t1+1},& J(u_{i\_PI}^{t1+1},x_{i\_Lim}^{t2})<(x_{i\_PI}^{t1},x_{i\_Lim}^{t2})\\ x_{i\_PI}^{t1},& J(u_{i\_PI}^{t1+1},x_{i\_Lim}^{t2})\&GreaterEqual;(x_{i\_PI}^{t1},x_{i\_Lim}^{t2})\end{array}\right.---\left(8\right)$

Make t1=t1+1, return step (2);

Step (7), arranges grid-connected point voltage and drops to 0.4pu from 1.0pu, make current inner loop controller be operated in nonlinear area, and perform step (8);

Step (8), if t2>NG, then amplitude limit link parameter identification process terminates, population the minimum solution vector of middle correspondence function value is the amplitude limit link parameter picked out; Otherwise, perform step (9);

Step (9), if population middle exist the satisfied matching condition as shown in formula (5) of solution vector, then amplitude limit link parameter identification success, and identification process terminates, and corresponding solution vector is the amplitude limit link parameter picked out; If population in each solution vector all meet as Suo Shi formula (5) matching condition, then perform step (10);

Step (10), to population in solution vector carry out mutation operation according to formula (9), obtain variation vector $v_{i\_Lim}^{t2+1}={\left[\begin{array}{cccc}{v}_{i,3}^{t2+1}& v_{i,4}^{t2+1}& v_{i,5}^{t2+1}& v_{i,6}^{t2+1}\end{array}\right]}^T,$

$v_{i\_Lim}^{t2+1}=x_{r3\_Lim}^{t2}+F*(x_{r1\_Lim}^{t2}-x_{r2\_Lim}^{t2})---\left(9\right)$

Wherein, r1, r2, r3 ∈ 1,2,3 ..., NP}, is all not equal to i, and unequal mutually between two; F is zoom factor, and span is [0,1];

Step (11), by population in solution vector with variation vector carry out the interlace operation as shown in formula (10), produce trial vector $u_{i\_Lim}^{t2+1}={\left[\begin{array}{cccc}{u}_{i,3}^{t2+1}& u_{i,4}^{t2+1}& u_{i,5}^{t2+1}& u_{i,6}^{t2+1}\end{array}\right]}^T,$

$u_{i,j}^{t2+1}=\left\{\begin{array}{cccc}{v}_{i,j}^{t2+1},& rand\left(j\right)\&le;CR& or& j=randn\left(i\right)\\ x_{i,j}^{t2},& rand\left(j\right)>CR& or& j\&NotEqual;randn\left(i\right)\end{array}\right.---\left(10\right)$

Wherein, rand (j) ∈ [0,1] is equally distributed random number, and { 3,4,5,6}, represents a jth variable to j ∈; { 3,4,5,6} is the dimension variable index of Stochastic choice to randn (i) ∈; CR is crossover probability constant, and span is [0,1];

Step (12), the trial vector produced after variation with interlace operation with population in solution vector carry out the competition as shown in formula (11), and select vector that correspondence function value is less as filial generation, obtain the population in t2+1 moment

$x_{i\_Lim}^{t2+1}=\left\{\begin{array}{cc}{x}_{i\_Lim}^{t2+1},& J(x_{i\_PI}^{t1},u_{i\_Lim}^{t2+1})<J(x_{i\_PI}^{t1},u_{i\_Lim}^{t2})\\ x_{i\_Lim}^{t2},& J(x_{i\_PI}^{t1},u_{i\_Lim}^{t2+1})\&GreaterEqual;J(x_{i\_PI}^{t1},u_{i\_Lim}^{t2})\end{array}\right.---\left(11\right)$

Make t2=t2+1, return step (8).

The aforesaid photovoltaic grid inverter controller parameter identification method based on differential evolution algorithm, is characterized in that: step (2), the maximum evolutionary generation NG of step (8) are 1000.

The aforesaid photovoltaic grid inverter controller parameter identification method based on differential evolution algorithm, is characterized in that: the preferred value of step (4), step (10) zoom factor F is 0.5.

The aforesaid photovoltaic grid inverter controller parameter identification method based on differential evolution algorithm, is characterized in that: the preferred value of step (5), step (11) crossover probability constant CR is 0.75.

The invention has the beneficial effects as follows: adopt differential evolution algorithm to carry out identification to parameters such as the proportionality coefficient of photovoltaic combining inverter current inner loop d axle control system and q axle control system proportional and integral controller and integral coefficient, integrator output violent change value and controller output violent change values, by the initial value of each solution vector in theoretical calculation method determination initial population, effectively cover and wholely search prime field, reduce the hunting zone of differential evolution algorithm, accelerate the speed that parameter to be identified converges to actual value; Establish the identification flow process stage by stage of pi regulator parameter and amplitude limit link parameter, to reduce parameter identification dimension, improve controller parameter identification efficiency.Accurate recognition goes out the parameter of controller amplitude limit link, accurately to reflect the fault transient characteristic of photo-voltaic power supply, makes up the deficiency of existing controller parameter identification method, has a good application prospect.

Accompanying drawing explanation

Fig. 1 is the system block diagram of grid-connected photovoltaic power supply.

Fig. 2 is the control block diagram of photovoltaic combining inverter double-loop control.

Fig. 3 is the control block diagram of current inner loop d axle control system.

Fig. 4 is the schematic diagram of the photovoltaic grid inverter controller parameter identification method based on differential evolution algorithm of the present invention.

Embodiment

Below in conjunction with Figure of description, the present invention is further illustrated.

Photovoltaic grid inverter controller parameter identification method based on differential evolution algorithm of the present invention, differential evolution algorithm is adopted to carry out identification to parameters such as the proportionality coefficient of photovoltaic combining inverter current inner loop d axle control system and q axle control system proportional and integral controller and integral coefficient, integrator output violent change value and controller output violent change values, by the initial value of each solution vector in theoretical calculation method determination initial population, effectively cover and wholely search prime field, reduce the hunting zone of differential evolution algorithm, accelerate the speed that parameter to be identified converges to actual value; Establish the identification flow process stage by stage of pi regulator parameter and amplitude limit link parameter, to reduce parameter identification dimension, improve controller parameter identification efficiency.Accurate recognition goes out the parameter of controller amplitude limit link, so that the accurately fault transient characteristic of reflection photo-voltaic power supply, make up the deficiency of existing controller parameter identification method, schematic diagram as shown in Figure 4, according to the difference of the output of photovoltaic combining inverter actual under grid fault conditions and the output of photovoltaic combining inverter current inner loop control system trace model, by differential evolution algorithm, trace model is constantly revised, thus pick out controller parameter, specifically comprise the following steps

Step (1), arranges population scale NP=50, obtains the evolution colony of moment t1, t2 according to formula (1) and formula (2) respectively with

$P_{P1}^{t1}=\left[\begin{array}{cc}{x}_{1,1}^{t1}& x_{1,2}^{t1}\\ x_{2,1}^{t1}& x_{2,2}^{t1}\\ \begin{array}{c}\&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\end{array}& \begin{array}{c}\&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\end{array}\\ x_{50,1}^{t1}& x_{50,2}^{t1}\end{array}\right]---\left(1\right)$

$P_{Lim}^{t2}=\left[\begin{array}{cccc}{x}_{1,3}^{t2}& x_{1,4}^{t2}& x_{1,5}^{t2}& x_{1,6}^{t2}\\ x_{2,3}^{t2}& x_{2,4}^{t2}& x_{2,5}^{t2}& x_{2,6}^{t2}\\ \begin{array}{c}\&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\end{array}& \begin{array}{c}\&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\end{array}& \begin{array}{c}\&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\end{array}& \begin{array}{c}\&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\end{array}\\ x_{50,3}^{t2}& x_{50,4}^{t2}& x_{50,5}^{t2}& x_{50,6}^{t2}\end{array}\right]---\left(2\right)$

Wherein, (i=1,2 ..., NP) the respectively proportionality coefficient k of d, q shaft current inner ring pi regulator in i-th population of corresponding t1 moment _p, integral coefficient k _i; with (i=1,2 ..., NP) and distinguish d, q shaft current inner ring integrator output violent change value L in i-th population of corresponding t2 moment _{int_up}, L _{int_low}, and current inner loop controller output violent change value L _{out_up}, L _{out_low};

Make t1=0, t2=0, initial population is set at interval [k _{p_1}/ 5,5k _{p_2}] interior value, at interval [k _{i_1}/ 5,5k _{i_2}] interior value, interval (0,2.0] interior value, in interval [-2.0,0] interior value, according to formula (3), calculate k _{p_1}, k _{p_2}, k _{i_1}and k _{i_2}value,

$\{\begin{array}{c}{k}_{P\_1}={\mathrm{\&omega;}}_c{L}_1\\ k_{I\_1}={\mathrm{\&omega;}}_c{R}_1\\ k_{P\_2}=2{\mathrm{\&xi;\&omega;}}_n{L}_1\\ k_{I\_2}={\mathrm{\&omega;}}_n^2{L}_1\end{array}---\left(3\right)$

ω _cfor cut-off angular frequency, value is 10 ω _s, ω _sfor synchronous rotary angular speed; ξ is damping ratio, ω _nfor natural frequency of oscillation, ξ=0.707, ω _n=10 ω _s; L ₁for the filter inductance of photovoltaic combining inverter AC, R ₁for filter inductance L ₁internal resistance;

According to formula (4), list correspondence function

$J(x_{i\_PI}^{t1},x_{i\_Lim}^{t2})=\frac{1}{5N}\{\underset{k=1}{\overset{5N}{\&Sigma;}}{\&lsqb;i_d\left(k\right)-{\tilde{i}}_d\left(k\right)\&rsqb;}^2+\underset{k=1}{\overset{5N}{\&Sigma;}}{\&lsqb;i_q\left(k\right)-{\tilde{i}}_q\left(k\right)\&rsqb;}^2\}---\left(4\right)$

Wherein, i _d(k) and i _qk () is in current inner loop command value situation, the actual output of photovoltaic combining inverter current inner loop controller; with for in identical current inner loop command value situation, the output of photovoltaic combining inverter current inner loop controller trace model; $x_{i\_PI}^{t1}={\left[\begin{array}{cc}{x}_{i,1}^{t1}& x_{i,2}^{t1}\end{array}\right]}^T,x_{i\_Lim}^{t2}={\left[\begin{array}{cccc}{x}_{i,3}^{t2}& x_{i,4}^{t2}& x_{i,5}^{t2}& x_{i,6}^{t2}\end{array}\right]}^T$ For solution vector; N=48 is the sampling number of a power frequency period;

Grid-connected point voltage is set and drops to 0.85pu from 1.0pu, make current inner loop controller be operated in the range of linearity, and perform step (2);

Step (2), if t1>NG, NG are maximum evolutionary generation, then the proportionality coefficient of pi regulator and the identification process of integral coefficient terminate, population the minimum solution vector of middle correspondence function value is proportionality coefficient and the integral coefficient of the pi regulator picked out, and performs step (7); Otherwise, perform step (3);

Step (3), utilizes correspondence function to population in each solution vector evaluate, if population middle exist the satisfied matching condition as shown in formula (5) of solution vector, then parameter identification success,

$J(x_{i\_PI}^{t1},x_{i\_Lim}^{t2})\&le;\mathrm{\&epsiv;}---\left(5\right)$

Wherein, ε value is 0.01; Corresponding solution vector is proportionality coefficient and the integral coefficient of the pi regulator picked out, and performs step (7); If population in each solution vector all meet matching condition as shown in formula (5), then perform step (4);

Step (4), to population in solution vector carry out mutation operation according to formula (6), obtain variation vector $v_{i\_PI}^{t1+1}={\left[\begin{array}{cc}{v}_{i,1}^{t1+1}& v_{i,2}^{t1+1}\end{array}\right]}^T,$

$v_{i\_PI}^{t1+1}=x_{r3\_PI}^{t1}+F*(x_{r1\_PI}^{t1}-x_{r2\_PI}^{t1})---\left(6\right)$

Wherein, r1, r2, r3 ∈ 1,2,3 ..., NP}, is all not equal to i, and unequal mutually between two; F is zoom factor, and span is [0,1];

Step (5), by population in solution vector with variation vector carry out the interlace operation as shown in formula (7), produce trial vector $u_{i\_PI}^{t1+1}={\left[\begin{array}{cc}{u}_{i,1}^{t1+1}& u_{i,2}^{t1+1}\end{array}\right]}^T,$

$u_{i,j}^{t1+1}=\left\{\begin{array}{cccc}{v}_{i,j}^{t1+1},& rand\left(j\right)\&le;CR& or& j=randn\left(i\right)\\ x_{i,j}^{t1},& rand\left(j\right)>CR& or& j\&NotEqual;randn\left(i\right)\end{array}\right.---\left(7\right)$

Wherein, rand (j) ∈ [0,1] is equally distributed random number; { 1,2}, represents a jth variable to j ∈; { 1,2} is the dimension variable index of Stochastic choice to randn (i) ∈; CR is crossover probability constant, and span is [0,1];

Step (6), the trial vector produced after variation with interlace operation with population in solution vector carry out the competition as shown in formula (8), and select vector that correspondence function value is less as filial generation, obtain the population in t1+1 moment

$x_{i\_PI}^{t1+1}=\left\{\begin{array}{cc}{u}_{i\_PI}^{t1+1},& J(u_{i\_PI}^{t1+1},x_{i\_Lim}^{t2})<(x_{i\_PI}^{t1},x_{i\_Lim}^{t2})\\ x_{i\_PI}^{t1},& J(u_{i\_PI}^{t1+1},x_{i\_Lim}^{t2})\&GreaterEqual;(x_{i\_PI}^{t1},x_{i\_Lim}^{t2})\end{array}\right.---\left(8\right)$

Make t1=t1+1, return step (2);

Step (7), arranges grid-connected point voltage and drops to 0.4pu from 1.0pu, make current inner loop controller be operated in nonlinear area, and perform step (8);

Step (8), if t2>NG, then amplitude limit link parameter identification process terminates, population the minimum solution vector of middle correspondence function value is the amplitude limit link parameter picked out; Otherwise, perform step (9);

Step (9), if population middle exist the satisfied matching condition as shown in formula (5) of solution vector, then amplitude limit link parameter identification success, and identification process terminates, and corresponding solution vector is the amplitude limit link parameter picked out; If population in each solution vector all meet as Suo Shi formula (5) matching condition, then perform step (10);

Step (10), to population in solution vector carry out mutation operation according to formula (9), obtain variation vector $v_{i\_Lim}^{t2+1}={\left[\begin{array}{cccc}{v}_{i,3}^{t2+1}& v_{i,4}^{t2+1}& v_{i,5}^{t2+1}& v_{i,6}^{t2+1}\end{array}\right]}^T,$

$v_{i\_Lim}^{t2+1}=x_{r3\_Lim}^{t2}+F*(x_{r1\_Lim}^{t2}-x_{r2\_Lim}^{t2})---\left(9\right)$

Wherein, r1, r2, r3 ∈ 1,2,3 ..., NP}, is all not equal to i, and unequal mutually between two; F is zoom factor, and span is [0,1];

Step (11), by population in solution vector with variation vector carry out the interlace operation as shown in formula (10), produce trial vector $u_{i\_Lim}^{t2+1}={\left[\begin{array}{cccc}{u}_{i,3}^{t2+1}& u_{i,4}^{t2+1}& u_{i,5}^{t2+1}& u_{i,6}^{t2+1}\end{array}\right]}^T,$

$u_{i,j}^{t2+1}=\left\{\begin{array}{cccc}{v}_{i,j}^{t2+1},& rand\left(j\right)\&le;CR& or& j=randn\left(i\right)\\ x_{i,j}^{t2},& rand\left(j\right)>CR& or& j\&NotEqual;randn\left(i\right)\end{array}\right.---\left(10\right)$

Wherein, rand (j) ∈ [0,1] is equally distributed random number; { 3,4,5,6}, represents a jth variable to j ∈; { 3,4,5,6} is the dimension variable index of Stochastic choice to randn (i) ∈; CR is crossover probability constant, and span is [0,1];

Step (12), the trial vector produced after variation with interlace operation with population in solution vector carry out the competition as shown in formula (11), and select vector that correspondence function value is less as filial generation, obtain the population in t2+1 moment

$x_{i\_Lim}^{t2+1}=\left\{\begin{array}{cc}{x}_{i\_Lim}^{t2+1},& J(x_{i\_PI}^{t1},u_{i\_Lim}^{t2+1})<J(x_{i\_PI}^{t1},u_{i\_Lim}^{t2})\\ x_{i\_Lim}^{t2},& J(x_{i\_PI}^{t1},u_{i\_Lim}^{t2+1})\&GreaterEqual;J(x_{i\_PI}^{t1},u_{i\_Lim}^{t2})\end{array}\right.---\left(11\right)$

Make t2=t2+1, return step (8).

More than show and describe general principle of the present invention and principal character and advantage of the present invention.The technical staff of the industry should understand; the present invention is not restricted to the described embodiments; what describe in above-described embodiment and specification just illustrates principle of the present invention; without departing from the spirit and scope of the present invention; the present invention also has various changes and modifications, and these changes and improvements all fall in the claimed scope of the invention.Application claims protection range is defined by appending claims and equivalent thereof.

Claims (4)

1., based on the photovoltaic grid inverter controller parameter identification method of differential evolution algorithm, it is characterized in that: comprise the following steps,

Step (1), arranges population scale NP=50, obtains the evolution colony of moment t1, t2 according to formula (1) and formula (2) respectively with

$P_{\mathrm{PI}}^{t1}=\left[\begin{array}{cc}{x}_{1,1}^{t1}& x_{1,2}^{t1}\\ x_{2,1}^{t1}& x_{2,2}^{t1}\\ \begin{array}{c}\&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\end{array}& \begin{array}{c}\&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\end{array}\\ x_{50,1}^{t1}& x_{50,2}^{t1}\end{array}\right]---\left(1\right)$

$P_{Lim}^{t2}=\left[\begin{array}{cccc}{x}_{1,3}^{t2}& x_{1,4}^{t2}& x_{1,5}^{t2}& x_{1,6}^{t2}\\ x_{2,3}^{t2}& x_{2,4}^{t2}& x_{2,5}^{t2}& x_{2,6}^{t2}\\ \begin{array}{c}\&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\end{array}& \begin{array}{c}\&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\end{array}& \begin{array}{c}\&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\end{array}& \begin{array}{c}\&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\end{array}\\ x_{50,3}^{t2}& x_{50,4}^{t2}& x_{50,5}^{t2}& x_{50,6}^{t2}\end{array}\right]---\left(2\right)$

Wherein, distinguish the proportionality coefficient k of d, q shaft current inner ring pi regulator in i-th population of corresponding t1 moment _p, integral coefficient k _i, i=1,2 ..., NP; with d, q shaft current inner ring integrator output violent change value L in i-th population of corresponding t2 moment respectively _{int_up}, L _{int_low}, and current inner loop controller output violent change value L _{out_up}, L _{out_low}, i=1,2 ..., NP;

Make t1=0, t2=0, initial population is set at interval [k _{p_1}/ 5,5k _{p_2}] interior value, at interval [k _{i_1}/ 5,5k _{i_2}] interior value, interval (0,2.0] interior value, in interval [-2.0,0] interior value, according to formula (3), calculate k _{p_1}, k _{p_2}, k _{i_1}and k _{i_2}value,

$\{\begin{array}{c}{k}_{P\_1}={\mathrm{\&omega;}}_c{L}_1\\ k_{I\_1}={\mathrm{\&omega;}}_c{R}_1\\ k_{P\_2}=2{\mathrm{\&xi;\&omega;}}_n{L}_1\\ k_{I\_2}={\mathrm{\&omega;}}_n^2{L}_1\end{array}---\left(3\right)$

ω _cfor cut-off angular frequency, value is 10 ω _s, ω _sfor synchronous rotary angular speed; ξ is damping ratio, ω _nfor natural frequency of oscillation, ξ=0.707, ω _n=10 ω _s; L ₁for the filter inductance of photovoltaic combining inverter AC, R ₁for filter inductance L ₁internal resistance;

According to formula (4), list correspondence function

$J(x_{i\_PI}^{t1},x_{i\_Lim}^{t2})=\frac{1}{5N}\{\underset{k=1}{\overset{5N}{\&Sigma;}}{\&lsqb;i_d\left(k\right)-{\tilde{i}}_d\left(k\right)\&rsqb;}^2+\underset{k=1}{\overset{5N}{\&Sigma;}}{\&lsqb;i_q\left(k\right)-{\tilde{i}}_q\left(k\right)\&rsqb;}^2\}---\left(4\right)$

Wherein, i _d(k) and i _qk () is in current inner loop command value situation, the actual output of photovoltaic combining inverter current inner loop controller; with for in identical current inner loop command value situation, the output of photovoltaic combining inverter current inner loop controller trace model; $x_{i\_PI}^{t1}={\left[\begin{array}{cc}{x}_{i,1}^{t1}& x_{i,2}^{t1}\end{array}\right]}^T,x_{i\_Lim}^{t2}={\left[\begin{array}{cccc}{x}_{i,3}^{t2}& x_{i,4}^{t2}& x_{i,5}^{t2}& x_{i,6}^{t2}\end{array}\right]}^T$ For solution vector; N=48 is the sampling number of a power frequency period;

Grid-connected point voltage is set and drops to 0.85pu from 1.0pu, make current inner loop controller be operated in the range of linearity, and perform step (2);

Step (2), if t1>NG, NG are maximum evolutionary generation, then the proportionality coefficient of pi regulator and the identification process of integral coefficient terminate, population the minimum solution vector of middle correspondence function value is proportionality coefficient and the integral coefficient of the pi regulator picked out, and performs step (7); Otherwise, perform step (3);

Step (3), utilizes correspondence function to population in each solution vector evaluate, if population middle exist the satisfied matching condition as shown in formula (5) of solution vector, then parameter identification success,

$J(X_{i\_PI}^{t1},X_{i\_Lim}^{t2})\&le;\mathrm{\&epsiv;}---\left(5\right)$

Wherein, ε value is 0.01; Corresponding solution vector is proportionality coefficient and the integral coefficient of the pi regulator picked out, and performs step (7); If population in each solution vector all meet matching condition as shown in formula (5), then perform step (4);

Step (4), to population in solution vector carry out mutation operation according to formula (6), obtain variation vector $v_{i\_PI}^{t1+1}={\left[\begin{array}{cc}{v}_{i,1}^{t1+1}& v_{i,2}^{t1+1}\end{array}\right]}^T,$

$v_{i\_PI}^{t1+1}=x_{r3\_PI}^{t1}+F*(x_{r1\_PI}^{t1}-x_{r2\_PI}^{t1})---\left(6\right)$

Wherein, r1, r2, r3 ∈ 1,2,3 ..., NP}, is all not equal to i, and unequal mutually between two; F is zoom factor, and span is [0,1];

Step (5), by population in solution vector with variation vector carry out the interlace operation as shown in formula (7), produce trial vector $u_{i\_PI}^{t1+1}={\left[\begin{array}{cc}{u}_{i,1}^{t1+1}& u_{i,2}^{t1+1}\end{array}\right]}^T,$

$u_{i,j}^{t1+1}=\left\{\begin{array}{cccc}{v}_{i,j}^{t1+1},& rand\left(j\right)\&le;CR& or& j=randn\left(i\right)\\ x_{i,j}^{t1},& rand\left(j\right)>CR& or& j\&NotEqual;randn\left(i\right)\end{array}\right.---\left(7\right)$

Wherein, rand (j) ∈ [0,1] is equally distributed random number; { 1,2}, represents a jth variable to j ∈; { 1,2} is the dimension variable index of Stochastic choice to randn (i) ∈; CR is crossover probability constant, and span is [0,1];

Step (6), the trial vector produced after variation with interlace operation with population in solution vector carry out the competition as shown in formula (8), and select vector that correspondence function value is less as filial generation, obtain the population in t1+1 moment

$x_{i\_PI}^{t1+1}=\left\{\begin{array}{cc}{u}_{i\_PI}^{t1+1},& J(u_{i\_PI}^{t1+1},x_{i\_Lim}^{t2})<(x_{i\_PI}^{t1},x_{i\_Lim}^{t2})\\ x_{i\_PI}^{t1},& J(u_{i\_PI}^{t1+1},x_{i\_Lim}^{t2})\&GreaterEqual;(x_{i\_PI}^{t1},x_{i\_Lim}^{t2})\end{array}\right.---\left(8\right)$

Make t1=t1+1, return step (2);

Step (7), arranges grid-connected point voltage and drops to 0.4pu from 1.0pu, make current inner loop controller be operated in nonlinear area, and perform step (8);

Step (8), if t2>NG, then amplitude limit link parameter identification process terminates, population the minimum solution vector of middle correspondence function value is the amplitude limit link parameter picked out; Otherwise, perform step (9);

Step (9), if population middle exist the satisfied matching condition as shown in formula (5) of solution vector, then amplitude limit link parameter identification success, and identification process terminates, and corresponding solution vector is the amplitude limit link parameter picked out; If population in each solution vector all meet as Suo Shi formula (5) matching condition, then perform step (10);

Step (10), to population in solution vector carry out mutation operation according to formula (9), obtain variation vector $v_{i\_Lim}^{t2+1}={\left[\begin{array}{cccc}{v}_{i,3}^{t2+1}& v_{i,4}^{t2+1}& v_{i,5}^{t2+1}& v_{i,6}^{t2+1}\end{array}\right]}^T,$

$v_{i\_Lim}^{t2+1}=x_{r3\_Lim}^{t2}+F*(x_{r1\_Lim}^{t2}-x_{r2\_Lim}^{t2})---\left(9\right)$

Wherein, r1, r2, r3 ∈ 1,2,3 ..., NP}, is all not equal to i, and unequal mutually between two; F is zoom factor, and span is [0,1];

Step (11), by population in solution vector with variation vector carry out the interlace operation as shown in formula (10), produce trial vector

$u_{i\_Lim}^{t2+1}={\left[\begin{array}{cccc}{u}_{i,3}^{t2+1}& u_{i,4}^{t2+1}& u_{i,5}^{t2+1}& u_{i,6}^{t2+1}\end{array}\right]}^T,$

$u_{i,j}^{t2+1}=\left\{\begin{array}{cccc}{v}_{i,j}^{t2+1},& rand\left(j\right)\&le;CR& or& j=randn\left(i\right)\\ x_{i,j}^{t2},& rand\left(j\right)>CR& or& j\&NotEqual;randn\left(i\right)\end{array}\right.---\left(10\right)$

Wherein, rand (j) ∈ [0,1] is equally distributed random number, and { 3,4,5,6}, represents a jth variable to j ∈; { 3,4,5,6} is the dimension variable index of Stochastic choice to randn (i) ∈; CR is crossover probability constant, and span is [0,1];

Step (12), the trial vector produced after variation with interlace operation with population in solution vector carry out the competition as shown in formula (11), and select vector that correspondence function value is less as filial generation, obtain the population in t2+1 moment

$x_{i\_Lim}^{t2+1}=\left\{\begin{array}{cc}{x}_{i\_Lim}^{t2+1},& J(x_{i\_PI}^{t1},u_{i\_Lim}^{t2+1})<J(x_{i\_PI}^{t1},u_{i\_Lim}^{t2})\\ x_{i\_Lim}^{t2},& J(x_{i\_PI}^{t1},u_{i\_Lim}^{t2+1})\&GreaterEqual;J(x_{i\_PI}^{t1},u_{i\_Lim}^{t2})\end{array}\right.---\left(11\right)$

Make t2=t2+1, return step (8).

2. the photovoltaic grid inverter controller parameter identification method based on differential evolution algorithm according to claim 1, is characterized in that: step (2), the maximum evolutionary generation NG of step (8) are 1000.

3. the photovoltaic grid inverter controller parameter identification method based on differential evolution algorithm according to claim 1, is characterized in that: the preferred value of step (4), step (10) zoom factor F is 0.5.

4. the photovoltaic grid inverter controller parameter identification method based on differential evolution algorithm according to claim 1, is characterized in that: the preferred value of step (5), step (11) crossover probability constant CR is 0.75.