CN105262145B - A kind of new forms of energy hybrid system controls the method for optimizing of parameter - Google Patents

Abstract

The invention discloses a kind of new forms of energy hybrid system and control the method for optimizing of parameter, for pid control parameter being carried out preferably in new forms of energy hybrid system.Set up model according to new forms of energy hybrid system, then set up with PID controller input and the output parameter object function as quantity of state according to this analogue system, use the method for optimizing of present invention design to solve object function and obtain optimum PID control parameter.The new forms of energy hybrid system of present invention design controls the method for optimizing of parameter, uses a kind of novel heuristic value optimization object function, can search more excellent target function value, and solving of obtaining represents more excellent pid control parameter.More excellent pid control parameter can make new forms of energy hybrid system frequency departure less, and faster, system response curve is more smooth for governing speed, and system fading margin quality is higher.

Description

A kind of new forms of energy hybrid system controls the method for optimizing of parameter

Technical field

The invention belongs to technical field of new energies, more particularly, to a kind of new forms of energy hybrid system control The method for optimizing of parameter processed, for comparative example integral differential (Proportion in new forms of energy hybrid system Integration Differentiation, PID) control parameter carry out preferably.

Background technology

Along with lifting and the impact of global warming considerations of energy demand, the new forms of energy such as wind energy, the sun Develop and become urgent needs with grid-connected.It is very big, for stabilizing new forms of energy that wind energy and the sun go out fluctuation Undulatory property of exerting oneself, general increase energy storage device and open and close generating set flexibly, constituting new energy therewith Source hybrid system, is exerted oneself by adjustment system, is allowed to respond system loading, so that system frequency is fast Speed is stable.

New forms of energy hybrid system typically uses PID control, including Traditional PID, Fractional Order PID and mould Stick with paste PID etc..The selection of control parameter of PID controller directly affects the control product of new forms of energy hybrid system Matter.Traditional pid control parameter method for optimizing includes grid search (Grid Search, GS), particle Colony optimization algorithm (Particle Swarm optimization, PSO) etc..Owing to PSO algorithm is in complexity Optimization problem exists precocity, is absorbed in the deficiencies such as local minimum, possibly cannot obtain the new of optimum Energy hybrid system controls parameter.

Summary of the invention

For the deficiency of traditional method, the present invention proposes a kind of new forms of energy hybrid system and controls parameter Method for optimizing, the method, based on novel heuristic value, can be effectively improved new forms of energy hybrid system Control platform, improves this energy mix system stability.

To achieve these goals, the invention provides a kind of new forms of energy hybrid system and control the excellent of parameter Choosing method, comprises the steps:

Step (1): set up the phantom of new forms of energy hybrid system, new forms of energy hybrid system such as Fig. 1 Shown in.Described new forms of energy hybrid system includes wind power generation system, solar heat power generation system, water power Solve groove, fuel cell, flywheel energy storage system, battery energy storage system, super capacitor, diesel-driven generator With micro electric Force system.Described new forms of energy hybrid system describes consideration wind energy and solar energy is random and Under having a rest property, and the factor such as the undulatory property of load, the energy management of micro electric Force system and control principle. The part that wind power generation system and solar heat power generation system entirety are exerted oneself is produced by driving water electrolyser Raw hydrogen, the hydrogen of generation is carried out noting after generating produces metastable exerting oneself by recycling fuel cell Entering miniature power system, remaining wind energy and solar energy are exerted oneself and are directly injected into micro electric Force system；Due to Wind energy and solar energy have randomness, and simultaneity factor load also has undulatory property, when system injecting power and After deviation occurs in load, micro electric Force system will produce frequency offset, and PID controller is inclined according to frequency Shifting amount produce regulation control signal, control flywheel energy storage system, battery energy storage system, super capacitor and Diesel-driven generator works, and eliminates rapidly miniature Power Systems and load deviation.Conventional controller bag Include, PID controller and the modified model PID such as Fractional Order PID Controller, fuzzy controller control Device processed.For convenience of description, the present invention is using PID controller as new forms of energy hybrid system controller.Adopt Functional arrangement is transmitted as shown in Figure 1 by the new forms of energy hybrid system of PID controller.K in PID controller_P、 K_IAnd K_DThe ratio of being respectively, integration and the differential gain, be the control parameter needing to adjust；

Step (2): set up the Optimization about control parameter object function of above-mentioned new forms of energy hybrid system.With frequency Controlled quentity controlled variable u of rate deviation delta f and controller output is quantity of state, and object function is:

$\min f\left(X\right)={\∫}_0^{T_{\max }}\left(w_1{\left(\mathrm{\Δ}f\right)}^2+w_2{(u-u_{ss})}^2\right)dt$

Wherein, optimized variable X=[K_P,K_I,K_D], Δ f is with the new forms of energy mixed stocker controlling Parameters variation System frequency departure, u is PID controller output；w₁And w₂It is weight coefficient, T_maxDuring for emulating total Between, u_ssFor steady state control signal, expression formula is u_ss=0.8H (t)+0.17H (t-40)+1.12H (t-80), Wherein H (t) is He Wei Saden jump function.Expression is:

$H\left(t\right)=\left\{\begin{array}{c}0,t<0\\ 0.5,t=0\\ 1,t>0\end{array}\right.$

Step (3): use object function in heuristic value solution procedure (2), it is thus achieved that Excellent control parameter.

Step 1: algorithm initialization: algorithm parameter is set, including population size N, total number of iterations T, Individual random search sum N_l, eliminate range coefficient σ, skip threshold p；Determine pid control parameter model Enclose, K_P∈[K_P,min,K_P,max], K_I∈[K_I,min,K_I,max], K_D∈[K_D,min,K_D,max], determine optimized variable Border [B_L,B_U], B_L=[K_P,min,K_I,min,K_D,min], B_U=[K_P,max,K_I,max,K_D,max], K_P,min,K_P,maxPoint Wei the minima of proportional control factor and maximum, K_I,min,K_I,maxIt is respectively integral control coefficient Little value and maximum, K_D,min,K_D,maxIt is respectively minima and the maximum of derivative control coefficient, in this district Between the position vector of all individualities in random initializtion colony, individual position vector X_i=[K_P,K_I,K_D], i=1 ..., N, represent one group and control parameter；Make current iteration number of times t=0；

Step 2: calculate the target function value F of each individuality_i ^t=f (X_i(t)), i=1 ..., N.Process is as follows: From individual i position vector X_iT () decoding obtains controlling parameter, wherein K_P、K_IAnd K_DBe respectively position to First, second, and third element in amount, will control parameter and substitute into new forms of energy mixing in step (1) System simulation platform, emulation obtains system state variables process over time.Obtain system frequency Deviation delta f and controller output u, obtain the object function of individual i according to object function in step (2) Value F_i ^t.Further, calculate target population function minimum, there is the individual true of minimum target functional value It is set to current optimum individual X_B(t)；

Step 3: to all individual X_i, i=1 ..., N carries out individual random search, calculates inertia vector

The individual searching times l=0 of Step 3.1: order；

Step 3.2: look around a positionCalculateI=1 ..., N:

$X_i^{play}\left(t\right)=X_i\left(t\right)+rand\&CenterDot;{\mathrm{\&epsiv;}}_{play}$

Rand is random number between (0,1), ε_playFor looking around step-length, ε_play=0.1 | | B_U-B_L||；

Step 3.3: calculate next current location

$\left\{\begin{array}{cc}{X}_i^{self}\left(t\right)=X_i\left(t\right)+rand\&CenterDot;\frac{X_i^{play}\left(t\right)-X_i\left(t\right)}{\left|\right|X_i^{play}\left(t\right)-X_i\left(t\right)\left|\right|}\&CenterDot;{\mathrm{\&epsiv;}}_{step}& iff\left(X_i^{play}\left(t\right)\right)<f\left(X_i\left(t\right)\right)\\ X_i^{self}\left(t\right)=X_i\left(t\right)& iff\left(X_i^{play}\left(t\right)\right)\&GreaterEqual;f\left(X_i\left(t\right)\right)\end{array}\right.$

Rand is random number between (0,1), ε_stepFor inertia step-length, ε_step=0.2 | | B_U-B_L||；

Step 3.4:l=l+1, if l is ＜ N_l, go to Step 3.2；Otherwise, Step 4 is gone to；

Step 4: calculate each individuality by current optimum individual calling vector

$\left\{\begin{array}{c}{X}_i^{bw}\left(t\right)=X_B\left(t\right)+c_2\&CenterDot;{\mathrm{\&delta;}}_i\\ {\mathrm{\&delta;}}_i=|c_1\&CenterDot;X_B\left(t\right)-X_i\left(t\right)|\end{array}\right.$

Wherein δ_iDistance vector with current optimum individual individual for middle i-th, random number c₁=2 rand, c₂=(2 rand-1) (1-t/T) is random number between (0,1)；It can thus be appreciated that c₁Between (0,2) Random number, represent the charisma of current optimum individual, work as c₁During ＞ 1, represent current optimum individual Power of influence strengthens, otherwise weakens；c₂For dynamic random number, so c₂Random scope the most linear by 1 It is decremented to 0；

Step 5: according to an individual location updating formula renewal body position:

$X_i(t+1)=2\&CenterDot;rand\&CenterDot;X_i^{bw}\left(t\right)+rand\&CenterDot;X_i^{self}\left(t\right)$

Step 6: judge individual the need of being eliminated and reinitializing:

Step 6.1: if i-th individuality meets formula, this individuality is eliminated and reinitializes:

$F_i^t>F_{ave}^t+\mathrm{\&omega;}\&CenterDot;(F_{ave}^t-F_{\min }^t),i=1,...,N$

Wherein,It is the t meansigma methods for population all individual goals functional value,It it is minimum mesh Offer of tender numerical value, ω be one with iterations the parameter of linear increment,Value model Enclose for [-σ, σ]；

Step 6.2: the individual initialization being eliminated:

X_i=rand (1, D) × (B_U-B_L)+B_L

Wherein, D is position vector dimension, D=3；

Step 7: judge whether that continuous p is not moved for current optimum individual position, if it is, Think population extinction, the population that inverting reconstruct is new according to the following formula:

$X_i=X_B+rand\&times;\frac{R^2}{{\mathrm{\&delta;}}_i},i=1,2,...,N$

Wherein R is radius of inversion, R=0.1 | | B_U-B_L||；Rand is random number between (0,1), p For skip threshold；

Step 8:t=t+1, if t > T, algorithm terminates, and exports current optimum individual position as whole solution； Otherwise, Step 2 is proceeded to.Current optimum individual position is optimal control parameter vector.

Compared with prior art, the present invention has the following advantages and effect:

The new forms of energy hybrid system of present invention design controls the method for optimizing of parameter, and employing one is novel to be opened Hairdo optimized algorithm optimization object function, can search more excellent target function value, and the Xie obtained represents More excellent pid control parameter.More excellent pid control parameter can make new forms of energy hybrid system frequency departure Less, faster, system response curve is more smooth for governing speed, and system fading margin quality is higher.

Accompanying drawing explanation

Fig. 1 is the schematic diagram of new forms of energy hybrid control system of the present invention；

Fig. 2 is the load variations figure of new forms of energy hybrid control system of the present invention；

Fig. 3 is the wind power generation variation diagram of new forms of energy hybrid control system of the present invention；

Fig. 4 is the solar energy thermal-power-generating variation diagram of new forms of energy hybrid control system of the present invention；

Fig. 5 is the controlled quentity controlled variable of new forms of energy hybrid control system controller of the present invention output；

Fig. 6 is the frequency of new forms of energy hybrid control system under the preferred method of the present invention and PSO optimized algorithm Rate side-play amount comparison diagram.

Detailed description of the invention

In order to make the purpose of the present invention, technical scheme and advantage clearer, below in conjunction with accompanying drawing And embodiment, the present invention is further elaborated.Should be appreciated that described herein specifically Embodiment only in order to explain the present invention, is not intended to limit the present invention.Additionally, it is disclosed below Just may be used as long as technical characteristic involved in each embodiment of the present invention does not constitutes conflict each other To be mutually combined.

The present invention relates to the Optimization about control parameter of a kind of new forms of energy hybrid system, this system is by wind power generation System, solar heat power generation system, water electrolyser, fuel cell, flywheel energy storage system, battery store up Energy system, super capacitor, diesel-driven generator and micro electric Force system composition, as shown in Figure 1.This Bright purpose is to propose the control parameter method for optimizing of this system a kind of, thus improves new forms of energy mixed stocker System Control platform, improves this energy mix system stability.

For effect of the present invention is described, right using a certain new forms of energy hybrid system as the enforcement of the present invention below As the inventive method is described in detail:

Step (1): new forms of energy hybrid system is as it is shown in figure 1, include wind-driven generator system, solar heat Electricity generation system, water electrolyser, fuel cell, flywheel energy storage system, battery energy storage system, super electricity Appearance, diesel-driven generator and micro electric Force system.

Described new forms of energy hybrid system describes consideration wind energy and solar energy is random and intermittent, and negative Under the factors such as the undulatory property of lotus, the energy management of micro electric Force system and control principle.Wind power generation system The part that system and solar heat power generation system entirety are exerted oneself produces hydrogen by driving water electrolyser, then Fuel cell is utilized to carry out the hydrogen of generation injecting miniature electric power after generating produces metastable exerting oneself System, remaining wind energy and solar energy are exerted oneself and are directly injected into micro electric Force system；Due to wind energy and the sun Can have randomness, simultaneity factor load also has undulatory property, when system injecting power and load occur partially After the recovery, micro electric Force system will produce frequency offset, and PID controller produces according to frequency offset and adjusts Joint control signal, controls flywheel energy storage system, battery energy storage system, super capacitor and diesel-driven generator Work, eliminates rapidly miniature Power Systems and load deviation.This system is carried out by PID controller Regulation controls.K_P、K_IAnd K_DThe ratio of being respectively, integration and the differential gain, K_P、K_IAnd K_DIt is Need preferred pid control parameter.

Set up the phantom of new forms of energy hybrid system, the various parameters of new forms of energy hybrid system be set, The parameter of various new forms of energy is as shown in table 1；The iteration step length of new forms of energy hybrid system is 0.05 second, always Simulation time is 120 seconds, and the value arranging each energy initial time is 0, and load initial time value is 1； The load fluctuation of new forms of energy hybrid system is as shown in Figure 2；In view of wind energy in new forms of energy hybrid system and The fluctuation of the unstability of solar energy, wind energy and solar energy is as shown in Figure 3, Figure 4；

The design parameter of the assembly of table 1 new forms of energy hybrid system

Step (2) sets up the Optimization about control parameter object function of system: with frequency error Δ f and controller Controlled quentity controlled variable u of output is input quantity, and object function is:

$\min f\left(X\right)={\&Integral;}_0^{T_{\max }}\left(w_1{\left(\mathrm{\&Delta;}f\right)}^2+w_2{(u-u_{ss})}^2\right)dt$

Wherein, optimized variable X=[K_P,K_I,K_D], Δ f is with the new forms of energy mixed stocker controlling Parameters variation System frequency departure, u is PID controller output, as shown in fig. 1, weight w₁And w₂All it is set to 1, T_maxFor 120 seconds total times, u_ssFor steady state control signal, expression formula For: u_ss=0.8H (t)+0.17H (t-40)+1.12H (t-80), wherein H (t) is He Wei Saden jump function.

Expression is:

$H\left(t\right)=\left\{\begin{array}{c}0,t<0\\ 0.5,t=0\\ 1,t>0\end{array}\right.$

Step (3): use heuristic value to calculate the optimization object function of new forms of energy hybrid system, Preferably go out optimum PID control parameter.

Step 1: novel heuristic value parameter is set: total number of iterations T=50, population size N=20, other parameter of algorithm is provided that individual random search number N_l=3, eliminate range coefficient σ=0.01, skip threshold p=300；Determine pid control parameter scope, K_P∈[K_P,min,K_P,max], K_I∈[K_I,min,K_I,max], K_D∈[K_D,min,K_D,max], determine optimized variable border [B_L,B_U], B_L=[K_P,min,K_I,min,K_D,min], B_U=[K_P,max,K_I,max,K_D,max]；B_L=[0.001,0.001,0.001], B_U=[3,3,3], K_P,min,K_P,maxIt is respectively minima and maximum, the K of proportional control factor_I,min,K_I,max It is respectively minima and maximum, the K of integral control coefficient_D,min,K_D,maxIt is respectively derivative control coefficient Minima and maximum, the position vector of all individualities, individual body position during at this, interval initializes colony Vector X_i=[K_P,K_I,K_D], i=1 ..., N, represent one group and control parameter；Make current iteration number of times t=0；

Step 2: calculate the target function value F of each individuality_i ^t=f (X_i(t)), i=1 ..., N.Process is as follows: From individual i position vector X_iT () decoding obtains controlling parameter, wherein K_P、K_IAnd K_DBe respectively position to First, second, and third element in amount, will control parameter and substitute into new forms of energy mixing in step (1) System simulation platform, emulation obtains system state variables process over time.Obtain system frequency Deviation delta f and controller output u, obtain the object function of individual i according to object function in step (2) Value F_i ^t.Further, calculate target population function minimum, there is the individual true of minimum target functional value It is set to current optimum individual X_B(t)；

Step 3: to all individual X_i, i=1 ..., N carries out individual random search, calculates inertia vector

The individual searching times l=0 of Step 3.1: order；

Step 3.2: look around a positionCalculateI=1 ..., N:

$X_i^{play}\left(t\right)=X_i\left(t\right)+rand\&CenterDot;{\mathrm{\&epsiv;}}_{play}$

Rand is random number between (0,1), ε_playFor looking around step-length, ε_play=0.1 | | B_U-B_L||；

Step 3.3: calculate next current location

$\left\{\begin{array}{cc}{X}_i^{self}\left(t\right)=X_i\left(t\right)+rand\&CenterDot;\frac{X_i^{play}\left(t\right)-X_i\left(t\right)}{\left|\right|X_i^{play}\left(t\right)-X_i\left(t\right)\left|\right|}\&CenterDot;{\mathrm{\&epsiv;}}_{step}& iff\left(X_i^{play}\left(t\right)\right)<f\left(X_i\left(t\right)\right)\\ X_i^{self}\left(t\right)=X_i\left(t\right)& iff\left(X_i^{play}\left(t\right)\right)\&GreaterEqual;f\left(X_i\left(t\right)\right)\end{array}\right.$

Rand is random number between (0,1), ε_stepFor inertia step-length, ε_step=0.2 | | B_U-B_L||；

Step 3.4:l=l+1, if l is ＜ N_l, go to Step 3.2；Otherwise, Step 4 is gone to；

Step 4: calculate each individuality by current optimum individual calling vector

$\left\{\begin{array}{c}{X}_i^{bw}\left(t\right)=X_B\left(t\right)+c_2\&CenterDot;{\mathrm{\&delta;}}_i\\ {\mathrm{\&delta;}}_i=|c_1\&CenterDot;X_B\left(t\right)-X_i\left(t\right)|\end{array}\right.$

δ_iDistance vector with current optimum individual individual for middle i-th, random number c₁=2 rand, c₂=(2 rand-1) (1-t/T) is random number between (0,1)；It can thus be appreciated that c₁Between (0,2) Random number, represent the charisma of current optimum individual, work as c₁During ＞ 1, represent current optimum individual Power of influence strengthens, otherwise weakens；c₂For dynamic random number, so c₂Random scope the most linear by 1 It is decremented to 0；

Step 5: according to an individual location updating formula renewal body position:

$X_i(t+1)=2\&CenterDot;rand\&CenterDot;X_i^{bw}\left(t\right)+rand\&CenterDot;X_i^{self}\left(t\right)$

Step 6: judge individual the need of being eliminated and reinitializing:

Step 6.1: if i-th individuality meets formula, this individuality is eliminated and reinitializes:

$F_i^t>F_{ave}^t+\mathrm{\&omega;}\&CenterDot;(F_{ave}^t-F_{\min }^t),i=1,...,N$

Wherein,It is the t meansigma methods for population all individual goals functional value,It it is minimum mesh Offer of tender numerical value, ω be one with iterations the parameter of linear increment,Value model Enclose for [-σ, σ]；

Step 6.2: the individual initialization being eliminated:

X_i=rand (1, D) × (B_U-B_L)+B_L

Wherein, D is position vector dimension, D=3；

Step 7: judge whether that continuous p is not moved for current optimum individual position, if it is, Think population extinction, the population that inverting reconstruct is new according to the following formula:

$X_i=X_B+rand\&times;\frac{R^2}{{\mathrm{\&delta;}}_i},i=1,2,...,N$

Wherein R is radius of inversion, R=0.1 | | B_U-B_L||；Rand is random number between (0,1)；

Step 8:t=t+1, if t > T, algorithm terminates, and exports current optimum individual position as whole solution； Otherwise, Step 2 is proceeded to.Current optimum individual position is optimal control parameter vector.

It is K that Optimization Solution obtains Zhongdao pid control parameter_P=1.823, K_I=0.797, K_D=0.945. After parameter optimization, PID controller output signal u of new forms of energy hybrid system is as shown in Figure 5.

For comparing the performance of the method for the invention, new forms of energy mixed stocker based on PSO algorithm with tradition System controls parameter method for optimizing and contrasts.The parameter of PSO algorithm is set as: Population Size N=20, Iterations T=50, maximum inertial factor W_max=0.9, minimum inertial factor W_min=0.1, inertia is weighed Weight W=0.5, self learning rate F_i=2, social learning rate F_g=2, the pid parameter obtained is: K_P=2.04, K_I=0.64, K_D=0.61.The pid control parameter present invention preferably obtained obtains with PSO algorithm The system response that pid control parameter is corresponding in new forms of energy hybrid system simulation model contrasts, system Frequency departure comparing result as shown in Figure 6.Comparing result shows, uses the method for the invention to obtain When new forms of energy hybrid system is controlled by the pid control parameter obtained, the system fading margin time faster, shakes Swinging number of times less, whole curve is the most smooth.

As it will be easily appreciated by one skilled in the art that and the foregoing is only presently preferred embodiments of the present invention, Not in order to limit the present invention, all made within the spirit and principles in the present invention any amendment, etc. With replacement and improvement etc., should be included within the scope of the present invention.

Claims (5)

1. the method for optimizing of a new forms of energy hybrid system control parameter, it is characterised in that described method Comprise the steps:

Step (1): set up the phantom of new forms of energy hybrid system, described new forms of energy hybrid system bag Include wind power generation system, solar heat power generation system, water electrolyser, fuel cell, flywheel energy storage system, Battery energy storage system, super capacitor, diesel-driven generator and micro electric Force system, wherein wind power generation system By driving water electrolyser to produce hydrogen more sharp with the part that solar heat power generation system entirety is exerted oneself Carry out injecting miniature power train after generating produces metastable exerting oneself by the hydrogen of generation with fuel cell System, remaining wind energy and solar energy are exerted oneself and are directly injected into micro electric Force system；When system injecting power is with negative After deviation occurs in lotus, micro electric Force system will produce frequency offset, and PID controller is according to frequency shift (FS) Amount produces regulation control signal, controls flywheel energy storage system, battery energy storage system, super capacitor and diesel oil Generator operation, eliminates miniature Power Systems and load deviation；

Step (2): set up the Optimization about control parameter object function of above-mentioned new forms of energy hybrid system, with frequency Controlled quentity controlled variable u of rate deviation delta f and controller output is quantity of state, and object function is:

$\min f\left(X\right)={\&Integral;}_0^{T_{\max }}(w_1{\left(\mathrm{\&Delta;}f\right)}^2+w_2{\left(u-u_{ss}\right)}^2)dt$

Wherein, optimized variable X=[K_P,K_I,K_D], K_P、K_IAnd K_DThe ratio of being respectively, integration and differential Gain, Δ f is that u is PID controller with the new forms of energy hybrid system frequency departure controlling Parameters variation Output；w₁And w₂It is weight coefficient, T_maxFor emulation total time, u_ssFor steady state control signal；

Step (3): use the object function in heuristic value solution procedure (2), it is thus achieved that Excellent control parameter；Wherein, described step (3) specifically includes following sub-step:

Step 1: algorithm initialization: algorithm parameter is set, including population size N, total number of iterations T, Individual random search sum N_l, eliminate range coefficient σ, skip threshold p；Determine pid control parameter model Enclose, K_P∈[K_P,min,K_P,max], K_I∈[K_I,min,K_I,max], K_D∈[K_D,min,K_D,max], determine optimized variable limit Boundary [B_L,B_U], B_L=[K_P,min,K_I,min,K_D,min], B_U=[K_P,max,K_I,max,K_D,max], K_P,min,K_P,maxRespectively For minima and the maximum of proportional control factor, K_I,min,K_I,maxIt is respectively the minimum of integral control coefficient Value and maximum, K_D,min,K_D,maxIt is respectively minima and the maximum of derivative control coefficient, interval at this The position vector of all individualities in random initializtion colony, individual position vector X_i=[K_P,K_I,K_D], i=1 ..., N, represent one group and control parameter；Make current iteration number of times t=0；

Step 2: calculate the target function value of each individualityAnd find colony Object function minima, the individuality with minimum target functional value is defined as current optimum individual X_B(t)；

Step 3: to all individual X_i, i=1 ..., N, carry out individual random search, calculate inertia vectorDescribed step Step 3 specifically includes following sub-step:

The individual searching times l=0 of Step 3.1: order；

Step 3.2: look around a positionCalculate

$X_i^{play}\left(t\right)=X_i\left(t\right)+rand\&CenterDot;{\mathrm{\&epsiv;}}_{play}$

Rand is random number between (0,1), ε_playFor looking around step-length；

Step 3.3: calculate next current location

$\left\{\begin{array}{ccc}{X}_i^{self}\left(t\right)=X_i\left(t\right)+rand\&CenterDot;\frac{X_i^{play}\left(t\right)-X_i\left(t\right)}{\left|\right|X_i^{play}\left(t\right)-X_i\left(t\right)\left|\right|}\&CenterDot;{\mathrm{\&epsiv;}}_{step}& if& f\left(X_i^{play}\left(t\right)\right)<f\left(X_i\left(t\right)\right)\\ X_i^{self}\left(t\right)=X_i\left(t\right)& if& f\left(X_i^{play}\left(t\right)\right)\&GreaterEqual;f\left(X_i\left(t\right)\right)\end{array}\right.$

Rand is random number between (0,1), ε_stepFor inertia step-length；

Step 3.4:l=l+1, if l is < N_l, go to Step 3.2；Otherwise, Step 4 is gone to；

Step 4: calculate each individuality current optimum individual calling vector

$\left\{\begin{array}{c}{X}_i^{bw}\left(t\right)=X_B\left(t\right)+c_2\&CenterDot;{\mathrm{\&delta;}}_i\\ {\mathrm{\&delta;}}_i=|c_1\&CenterDot;X_B\left(t\right)-X_i\left(t\right)|\end{array}\right.$

Wherein δ_iDistance vector with current optimum individual individual for i-th, random number c₁=2 rand, c₂=(2 rand-1) (1-t/T) is random number between (0,1)；

Step 5: according to an individual location updating formula renewal body position:

$X_i(t+1)=2\&CenterDot;rand\&CenterDot;X_i^{bw}\left(t\right)+rand\&CenterDot;X_i^{self}\left(t\right)$

Step 6: judge individual the need of being eliminated and reinitializing:

Step 6.1: if i-th individuality meets formula, this individuality is eliminated and reinitializes:

$F_i^t>F_{ave}^t+\mathrm{\&omega;}\&CenterDot;(F_{ave}^t-F_{\min }^t),i=1,...,N$

Wherein,It is the t meansigma methods for population all individual goals functional value,It it is minimum mesh Offer of tender numerical value, w be one with iterations the parameter of linear increment,Value model Enclose for [-σ, σ]；

Step 6.2: the individual initialization being eliminated:

X_i=rand (1, D) × (B_U-B_L)+B_L

Wherein, D is position vector dimension, D=3；

Step 7: judge whether that continuous p is not moved for current optimum individual position, if it is, Think population extinction, the population that inverting reconstruct is new according to the following formula:

$X_i=X_B+rand\&times;\frac{R^2}{{\mathrm{\&delta;}}_i},i=1,2,...,N$

Wherein R is radius of inversion, R=0.1 | | B_U-B_L||；Rand is random number between (0,1), and p is Skip threshold；

Step 8:t=t+1, if t > T, algorithm terminates, and exports current optimum individual position as whole solution, Current optimum individual position is optimal control parameter vector；Otherwise, Step 2 is proceeded to.

2. the method for claim 1, it is characterised in that calculate each in described step Step 2 The target function value of individualityParticularly as follows:

From individual i position vector X_iT () decoding obtains controlling parameter, wherein K_P、K_IAnd K_DIt is respectively position Put first, second, and third element in vector, parameter will be controlled and substitute into new forms of energy in step (1) Hybrid system simulation platform, emulation obtains system state variables process over time, obtains system frequency Rate deviation delta f and controller output u, obtain the target letter of individual i according to object function in step (2) Numerical value

3. method as claimed in claim 1 or 2, it is characterised in that described u_ssExpression formula be u_ss=0.8H (t)+0.17H (t-40)+1.12H (t-80), wherein H (t) is He Wei Saden jump function；Specifically Expression formula is:

$H\left(t\right)=\left\{\begin{array}{c}0,t<0\\ 0.5,t=0\\ 1,t>0\end{array}\right..$

4. the method for claim 1, it is characterised in that in Step 3.2 ε_play=0.1 | | B_U-B_L||。

5. the method for claim 1, it is characterised in that in Step 3.3 ε_step=0.2 | | B_U-B_L||。