CN106292291A - A kind of electrical network automatic electricity generation control system controller parameter optimization method - Google Patents
A kind of electrical network automatic electricity generation control system controller parameter optimization method Download PDFInfo
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- CN106292291A CN106292291A CN201610900460.9A CN201610900460A CN106292291A CN 106292291 A CN106292291 A CN 106292291A CN 201610900460 A CN201610900460 A CN 201610900460A CN 106292291 A CN106292291 A CN 106292291A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Abstract
The invention discloses a kind of electrical network automatic electricity generation control system controller parameter optimization method, relate to power system automation field, solve because controller parameter arranges improper, cause the technical problem that the dynamic control performance of electrical network automatic electricity generation control system is relatively low.This controller parameter optimization method includes: set up electrical network automatic electricity generation control system phantom, and electrical network automatic electricity generation control system phantom includes controller；According to automatic electricity generation control system phantom, set up the optimization of controller parameter and adjust model；Adjust model according to optimization, by microhabitat bacterial foraging algorithm, it is thus achieved that the initial optimization result of controller parameter；According to initial optimization result, pass through pattern search algorithm, it is thus achieved that the final optimization pass result of controller parameter.The present invention is applied to optimize the controller parameter of electrical network automatic electricity generation control system.
Description
Technical field
The present invention relates to power system automation field, particularly relate to a kind of electrical network automatic electricity generation control system controller
Parameter optimization method.
Background technology
Frequency is one of most important parameter in Operation of Electric Systems, reflects the Real-time Balancing shape of system active power
State.System frequency is controlled, is content very important in safe operation of power system.In order to maintain system operation
In frequency departure within the scope of admissible, modern power network is mainly by Automatic Generation Control (Automatic
Generation Control, AGC) technology constantly changes the meritorious of frequency modulation unit and exerts oneself, and carrys out the random fluctuation of balanced load, its
In, the parameter of controller has material impact to the dynamic control performance of electrical network automatic electricity generation control system.
Fuzzy logic control technology has been widely used for various industrial control system as a ripe control technology.Logical
Crossing online updating controller parameter, fuzzy logic control technology can significantly improve robustness and the controlling of closed-loop control system
Energy.There are some researches show, the scale factor of fuzzy controller (proportional-integral derivative controller) is compared to other factors pair
The control performance impact of electrical network automatic electricity generation control system is bigger.Electrical network can be improved automatic by the value of adjustment proportional factor
The dynamic control performance of power-generating control system, and inappropriate scale factor possibly cannot ensure electrical network automatic electricity generation control system
Control target.Therefore, it is necessary to propose a kind of controller parameter optimization method, it is used for optimizing electrical network automatic electricity generation control system
Controller parameter, to improve the dynamic control performance of electrical network automatic electricity generation control system.
Summary of the invention
It is an object of the invention to provide a kind of electrical network automatic electricity generation control system controller parameter optimization method, for excellent
Change the controller parameter of electrical network automatic electricity generation control system, to improve the dynamic control performance of electrical network automatic electricity generation control system.
For reaching above-mentioned purpose, the present invention adopts the following technical scheme that
This electrical network automatic electricity generation control system controller parameter optimization method includes:
Setting up electrical network automatic electricity generation control system phantom, described electrical network automatic electricity generation control system phantom includes
Controller；
According to described electrical network automatic electricity generation control system phantom, set up the optimization of controller parameter and adjust model；
Adjust model according to described optimization, by microhabitat bacterial foraging algorithm, it is thus achieved that described controller parameter preliminary
Optimum results；
According to described initial optimization result, pass through pattern search algorithm, it is thus achieved that the final optimization pass knot of described controller parameter
Really.
Compared with prior art, the electrical network automatic electricity generation control system controller parameter optimization method that the present invention provides has
Following beneficial effect:
In the electrical network automatic electricity generation control system controller parameter optimization method that the present invention provides, automatically send out according to electrical network
Electric control system phantom, establishes the optimization of electrical network automatic electricity generation control system simulation model controller parameter and adjusts model
Afterwards, first pass through microhabitat bacterial foraging algorithm, it is thus achieved that the initial optimization result of this controller parameter, obtain initial optimization
After result, continue to use pattern search algorithm, the initial optimization result of this controller parameter is optimized further, thus
Obtain the final optimization pass result of the controller parameter more optimized.By microhabitat bacterial foraging algorithm is calculated with pattern search
Method combines, and substantially increases the optimization precision of the optimum results of controller parameter, thus improves electrical network Automatic Generation Control
The dynamic control performance of system, it is ensured that the safe operation of power system.
Accompanying drawing explanation
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, embodiment will be described below
The accompanying drawing used required in is briefly described, it should be apparent that, the accompanying drawing in describing below is only some of the present invention
Embodiment, for those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to attached according to these
Figure obtains other accompanying drawing.
The automatic electricity generation control system controller parameter optimization method flow chart that Fig. 1 provides for the embodiment of the present invention；
The structural representation of the electrical network automatic electricity generation control system phantom that Fig. 2 provides for the embodiment of the present invention；
The structural representation of the fuzzy controller that Fig. 3 provides for the embodiment of the present invention；
The basic step flow chart of the microhabitat bacterial foraging algorithm that Fig. 4 provides for the embodiment of the present invention；
The basic step flow chart of the pattern search algorithm that Fig. 5 provides for the embodiment of the present invention；
Each convergence of algorithm curve chart that Fig. 6 provides for the embodiment of the present invention；
The dynamic time-domain response curve figure of the frequency departure of the first area that Fig. 7 provides for the embodiment of the present invention；
The dynamic time-domain response curve figure of the frequency departure of the second area that Fig. 8 provides for the embodiment of the present invention；
During the dominant eigenvalues deviation between first area and second area that Fig. 9 provides for the embodiment of the present invention dynamic
Domain response curve chart.
Description of reference numerals:
1 fuzzy controller, 2 speed regulators, 3 steam turbines,
11 differentiators, 12 integrators.
Detailed description of the invention
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Describe, it is clear that described embodiment is a part of embodiment of the present invention rather than whole embodiments wholely.Based on this
Embodiment in bright, the every other enforcement that those of ordinary skill in the art are obtained under not making creative work premise
Example, broadly falls into the scope of protection of the invention.
Embodiment one
The embodiment of the present invention provides a kind of electrical network automatic electricity generation control system controller parameter optimization method, as it is shown in figure 1,
This electrical network automatic electricity generation control system controller parameter optimization method includes:
Step S1, set up electrical network automatic electricity generation control system phantom, electrical network automatic electricity generation control system phantom
Including controller.
Exemplarily, the electrical network automatic electricity generation control system phantom containing fuzzy controller can be set up, specifically set up
The method of the electrical network automatic electricity generation control system phantom containing fuzzy controller and existing foundation are containing fuzzy-adaptation PID control
The method of the electrical network automatic electricity generation control system phantom of device is identical, and those skilled in the art can refer to existing method and set up
Electrical network automatic electricity generation control system phantom, the embodiment of the present invention is not defined.
Step S2, according to electrical network automatic electricity generation control system phantom, set up the optimization of controller parameter and adjust model.
Exemplarily, the conventional integral square error index of control system, time can be used to take advantage of integral square error index
Or the time take advantage of Error Absolute Value integration index etc. to adjust as the optimization of controller noted above parameter the object function of model, this area
Technical staff can be arranged according to practical situation, and the embodiment of the present invention is not defined.
Step S3, model of adjusting according to optimization, by microhabitat bacterial foraging algorithm, it is thus achieved that controller parameter the most excellent
Change result.
Step S4, according to initial optimization result, pass through pattern search algorithm, it is thus achieved that the final optimization pass of controller parameter knot
Really.
In the technical scheme of the present embodiment, according to electrical network automatic electricity generation control system phantom, establish electrical network certainly
The optimization of dynamic power-generating control system simulation model controller parameter is adjusted after model, first passes through microhabitat bacterial foraging algorithm,
Obtain the initial optimization result of this controller parameter, after obtaining initial optimization result, continue to use pattern search algorithm,
The initial optimization result of this controller parameter is optimized further, thus obtains the controller parameter that more optimizes
Whole optimum results, by being combined with pattern search algorithm by microhabitat bacterial foraging algorithm, substantially increases controller parameter
The optimization precision of optimum results, thus improve the dynamic control performance of electrical network automatic electricity generation control system, it is ensured that electric power
The safe operation of system.
Exemplarily, electrical network automatic electricity generation control system phantom as shown in Figure 2, this electrical network automatic generation can be set up
Control System Imitation model includes all including in first area and second area, first area and second area fuzzy-adaptation PID control
Device 1, speed regulator 2, steam turbine 3 etc..
In figure, Δ f1For the frequency departure of first area, Δ f2Frequency departure for second area；ΔPt12It it is first area
Flow to the dominant eigenvalues deviation of second area；B1For the frequency bias coefficient of first area, B2Frequency departure for second area
Coefficient；ACE1For the district control deviation of first area, ACE2District control deviation for second area；u1For first area
The output of fuzzy controller, u2Output for the fuzzy controller of second area；R1Poor for the tune of unit in first area
Coefficient, R2For the difference coefficient of unit in second area；TG1For speed regulator time constant in first area, TG2For in second area
Speed regulator time constant；TT1For steam turbine time constant in first area, TT2For steam turbine time constant in second area；Δ
PG1For unit output variable quantity in first area, Δ PG2For unit output variable quantity in second area；ΔPT1For in first area
Steam turbine valve variable quantity, Δ PT2For steam turbine valve variable quantity in second area；ΔPL1For load variations in first area
Amount, Δ PL2For load variations amount in second area；KPS1Gain merit frequency transform coefficient for system in first area, KPS2It it is the secondth district
In territory, system is gained merit frequency transform coefficient；TPS1For first area system time constant, TPS2For second area system time constant；
T12Interconnection synchronization factor for first area Yu second area；α12Dominant eigenvalues for first area with second area turns
Change coefficient, when two zonal basis power are identical, α12=-1.
Specifically, the structure of above-mentioned fuzzy controller 1 is as it is shown on figure 3, this fuzzy controller includes K1、K2、K3、
K4Four scale factors (i.e. the controller parameter of this fuzzy controller), differentiator 11 and integrator 12, wherein, fuzzy
The district control deviation (ACE) that input is above-mentioned control system of controller；x1And x2The input being respectively fuzzy logic control becomes
Amount, y1And y2It is respectively the output variable of fuzzy logic control；The regulation that output u is above-mentioned control system of fuzzy controller
Power.
Exemplarily, the time can be used to take advantage of Error Absolute Value integration index to imitate as above-mentioned electrical network automatic electricity generation control system
The optimization of true mode middle controller parameter is adjusted the object function of model.Specifically, the optimization of the parameter of this controller is adjusted mould
Type includes object function and inequality constraints condition:
Object function is:
Wherein, T is the simulation time of electrical network automatic electricity generation control system phantom, Δ f1Frequency for first area is inclined
Difference, Δ f2For the frequency departure of second area, Δ Pt12The dominant eigenvalues deviation of second area is flowed to for first area.
Inequality constraints condition includes:
Wherein, K1、K2、K3And K4It is controller parameter.It should be noted that " min " in the embodiment of the present invention refers both to
Be minima, " max " refers to maximum.
Exemplarily, step S3 is adjusted model according to optimization, by microhabitat bacterial foraging algorithm, it is thus achieved that controller is joined
The concrete steps of the initial optimization result of number include:
Step S31, obtain the initial value of many group controllers parameter.
Step S32, adjust according to optimization model and the initial value of every group controller parameter, it is thus achieved that optimize mesh in model of adjusting
The initial value of scalar functions.
Exemplarily, above-mentioned electrical network automatic electricity generation control system emulation mould can be built in Matlab/Simulink software
Type, for each group of (K1,K2,K3,K4) initial value, be assigned in phantom corresponding controller module, passed through
Matlab/Simulink simulation calculation goes out corresponding Δ f1、Δf2、ΔPt12Value, and then calculate the initial of object function J
Value.
Step S33, by microhabitat bacterial foraging algorithm, the initial value of combined objective function, to often organizing scale factor
Initial value is iterated calculating, it is thus achieved that initial optimization result.
Exemplarily, the basic step of above-mentioned microhabitat bacterial foraging algorithm as shown in Figure 4, look for food calculation by this microhabitat antibacterial
The basic step of method includes:
Step 1: initialize the relevant parameter of microhabitat bacterial foraging algorithm.
Specifically, the relevant parameter of above-mentioned microhabitat bacterial foraging algorithm includes: dimension p to be optimized, and the present invention is real
Execute in example, have four scale factors to be optimized, now, p=4, antibacterial number S (be taken scale factor group
Number), chemotactic times Nc, maximum travelling times Ns, breed times Nre, migrate times Ned, transition probability Ped, attracting factor daWith
ωa, rejection factor hrAnd ωr, microhabitat radius δ0With spacing distance L etc..
You need to add is that, in the embodiment of the present invention, initial flora position can be randomly generated.
Step 2: judge whether bacterial migration number of times reaches maximum and migrate number of times, the most then export optimum results；If it is not,
Then enter next step.
Step 3: judge whether flora breeding number of times reaches maximum breeding number of times, the most then forward step 8 to, enter and migrate
Operation；If it is not, then enter next step.
Step 4: judge whether antibacterial maximum chemotactic number of times reaches maximum chemotactic number of times, the most then forward step 6 to, enters
Breeding operation；If it is not, then enter next step.
Step 5: each organisms in flora is carried out chemotactic operation, then goes to step 4.
Step 6: the flora obtained after chemotactic is passed through health degree shared mechanism and its health degree of limit competition Developing Tactics.
Step 7: use roulette method choice a new generation bacterial population, then go to step 3.
Step 8: antibacterial is performed migration operation, then goes to step 2.
By above-mentioned microhabitat bacterial foraging algorithm, can increase the multiformity of population, the overall situation that improve this algorithm is sought
Excellent ability, it is to avoid problem that local optimum occurs.
Additionally, after obtaining above-mentioned initial optimization result, according to initial optimization result in above-mentioned steps S4, pass through pattern
Searching algorithm, it is thus achieved that the concrete steps of the final optimization pass result of controller parameter include:
Step S41, according to initial optimization result, it is thus achieved that the initial optimization value of a group controller parameter.
Step S42, initial optimization value according to controller parameter, it is thus achieved that optimize the most excellent of object function in model of adjusting
Change value.
Exemplarily, the concrete grammar of the initial optimization value of above-mentioned acquisition object function and step S32 obtain target letter
The concrete grammar of the initial value of number is similar to, and those skilled in the art can refer to obtain in step S32 the initial value of object function
Method obtains the initial optimization value of object function, the most no longer repeats.
Step S43, by pattern search algorithm, the initial optimization value of combined objective function, to controller noted above parameter
Initial optimization value is iterated calculating, it is thus achieved that final optimization pass result.
Exemplarily, above-mentioned pattern search algorithm basic step as it is shown in figure 5, in figure A represent by above-mentioned microhabitat
The initial optimization result that bacterial foraging algorithm obtains, " PS " is writing a Chinese character in simplified form of " pattern search " pattern search, " poll " table
Show structure dragnet Mesh centered by starting point, find the process of the point making object function decline.Specifically, this pattern search
The basic step of algorithm includes:
Step 1:
Given initial point x0, and calculate the target function value f (x of initial point0), given initialization step-size in search Δ0, expand
Factor lambda and contraction factor β, and set maximum iteration time gmax, make g=0.
Step 2:
Current search starting point x is setk。
Step 3:
Structural model vector ViAnd site Mk,i, i=1,2 ..., n.
Step 4:
Calculate the target function value f (M of each sitek,i)。
Step 5:
If searching any one some Mk,i, have f (Mk,i) ＜ f (xk), then poll success, makes xk+1=Mk,i, and press formula
Δk+1=λ ΔkUpdate Δk+1, wherein, λ ＞ 1；
If searching any one some Mk,i, have f (Mk,i) ＞ f (xk), then poll is unsuccessful, makes xk+1=xk, and press formula
Δk+1=β ΔkUpdate Δk+1, wherein, 0 ＜ β ＜ 1.
Step 6:
Iterations g=g+1, if g is ＜ gmax, forward step 2 to, enter next iteration；Otherwise, algorithm terminates, and exports excellent
Change result.
The present invention using the initial optimization result that obtained by microhabitat bacterial foraging algorithm as at the beginning of pattern search algorithm
Initial point, by performing fine search at microhabitat bacterial foraging algorithm, it is possible to carry near the initial optimization result that obtains further
The precision of the optimum results of high gained.
For the ease of skilled artisan understands that and implementing, below the embodiment of the present invention be given one use above-mentioned control
The concrete application example of device parameter optimization method.
Specifically, in Matlab/Simulink software, build electrical network automatic electricity generation control system emulation as shown in Figure 2
Model, for simplifying the analysis, it is assumed that two district system parameters are identical, and the parameter of fuzzy controller 1 is the most identical.Set:
K1、K2、K3、K4Span be [0,2].
The value of the initial parameter of microhabitat bacterial foraging algorithm is as follows:
P=4, S=20, Nc=8, Ns=3, Nre=4, Ned=2, Ped=0.25, da=0.01, ωa=0.04, hr=
0.01, ωr=10, δ0=1, L=0.1.
The value of the initial parameter of pattern search algorithm is as follows:
λ=2, β=0.5, Δ0=1, gmax=50.
The value of the initial parameter of grid control system is as follows:
TG1=TG2=0.08s, Tt1=Tt2=0.3s, TPS1=TPS2=20s, KPS1=KPS2=120Hz/p.u., T12=
0.545p.u./Hz, B1=B2=0.425p.u./Hz, R1=R2=2.4Hz/p.u., α12=-1.
Assume that first area load increases 0.1p.u., be respectively adopted pattern search algorithm, microhabitat bacterial foraging algorithm and
Algorithm microhabitat bacterial foraging algorithm and pattern search algorithm combined provided in the embodiment of the present invention, to fuzzy
The scale factor of controller is optimized, and maximum iteration time is all set to 50 times.Concrete optimization process is as follows:
Step 1: randomly generate 20 initial population in the range of search volume [0,2], the individual generation of each in population
One group of (K of table1,K2,K3,K4) value.
Step 2: the value of each individuality is updated in built electrical network automatic electricity generation control system phantom,
Each individual corresponding target function value is gone out by Matlab/Simulink simulation calculation.Exemplarily, simulation time can be set
It is set to 15 seconds.
Step 3: be respectively adopted in pattern search algorithm, microhabitat bacterial foraging algorithm and the embodiment of the present invention provide general
Population at individual is iterated calculating by the algorithm that microhabitat bacterial foraging algorithm and pattern search algorithm combine.
Step 4: judge whether to reach the condition of convergence.The most then export optimal result；If it is not, then forward under step 2 carries out
An iteration optimizing.
Step 5: calculate the electrical network automatic electricity generation control system response under optimal result.
As shown in Figure 6, " PS " in Fig. 6 represents pattern search algorithm to each convergence of algorithm curve, and " NBFO " represents your pupil
Border bacterial foraging algorithm, " hNBFO-PS " represent in the embodiment of the present invention provide microhabitat bacterial foraging algorithm and pattern are searched
The algorithm that rope algorithm combines, " ITAE " is to be multiplied by Error Absolute Value integration the time, and the optimization that i.e. embodiment of the present invention provides is whole
The object function of cover half type.It will be appreciated from fig. 6 that use microhabitat bacterial foraging algorithm and pattern search algorithm to be iterated excellent respectively
During change, after iteration 30 times, the most substantially enter convergence state.Therefore, in use, microhabitat bacterial foraging algorithm and pattern are searched
When the algorithm that rope algorithm combines is iterated optimizing, it is arranged on use microhabitat bacterial foraging algorithm and is iterated 30 and takes second place
After, proceed to use pattern search algorithm to proceed iteration optimization.
Use pattern search algorithm, microhabitat bacterial foraging algorithm and the embodiment of the present invention provide by microhabitat antibacterial
The algorithm that foraging algorithm and pattern search algorithm combine is iterated calculating optimum results such as table 1 institute of gained to population at individual
Show.
Table 1 is by the best proportion factor of the fuzzy controller of algorithms of different gained
| K1 | K2 | K3 | K4 | |
| Pattern search algorithm | 0.7317 | 0.6477 | 0.4509 | 0.5470 |
| Microhabitat bacterial foraging algorithm | 0.9393 | 0.4998 | 0.7191 | 0.8834 |
| Microhabitat bacterial foraging algorithm and pattern search algorithm combination algorithm | 1.5393 | 0.4998 | 0.8691 | 1.0834 |
The best proportion factor of the fuzzy controller of algorithms of different gained is applied to above-mentioned electrical network Automatic Generation Control
After in system, dynamic time-domain response curve such as Fig. 7 institute of the frequency departure of first area in this electrical network automatic electricity generation control system
Show, the dynamic time-domain response curve of the frequency departure of second area as shown in Figure 8, the contact between first area and second area
As it is shown in figure 9, wherein, " PS " in Fig. 7, Fig. 8, Fig. 9 all represents pattern search to the dynamic time-domain response curve of linear heat generation rate deviation
Algorithm, " NBFO " all represents microhabitat bacterial foraging algorithm, " hNBFO-PS " all represent in the embodiment of the present invention provide by little
The algorithm that habitat bacterial foraging algorithm and pattern search algorithm combine.
As shown in Figure 7, Figure 8 and Figure 9, by what the optimization method provided in the use embodiment of the present invention obtained, there is ratio of greater inequality
After the controller of the example factor is applied to electrical network automatic electricity generation control system, it is possible to make this electrical network automatic electricity generation control system every
Dominant eigenvalues deviation between the frequency departure in individual region and two regions returns to zero soon, substantially increases electrical network
The dynamic time domain response performance of automatic electricity generation control system, it is ensured that the safe operation of power system.
The above, the only detailed description of the invention of the present invention, but protection scope of the present invention is not limited thereto, and any
Those familiar with the art, in the technical scope that the invention discloses, can readily occur in change or replace, should contain
Cover within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with described scope of the claims.
Claims (4)
1. an electrical network automatic electricity generation control system controller parameter optimization method, it is characterised in that including:
Setting up electrical network automatic electricity generation control system phantom, described electrical network automatic electricity generation control system phantom includes controlling
Device；
According to described electrical network automatic electricity generation control system phantom, set up the optimization of controller parameter and adjust model；
Adjust model according to described optimization, by microhabitat bacterial foraging algorithm, it is thus achieved that the initial optimization of described controller parameter
Result；
According to described initial optimization result, pass through pattern search algorithm, it is thus achieved that the final optimization pass result of described controller parameter.
Electrical network automatic electricity generation control system controller parameter optimization method the most according to claim 1, it is characterised in that institute
State electrical network automatic electricity generation control system phantom and include first area and second area, described first area and described secondth district
Territory all includes described controller；
The optimization of described controller parameter model of adjusting includes object function and inequality constraints condition:
Described object function is:Wherein, T is described electrical network Automatic Generation Control
The simulation time of system simulation model, Δ f1For the frequency departure of described first area, Δ f2Frequency for described second area is inclined
Difference, Δ Pt12The dominant eigenvalues deviation of described second area is flowed to for described first area；
Described inequality constraints condition includes:
Wherein, K1、K2、K3And K4It is described controller parameter.
Electrical network automatic electricity generation control system controller parameter optimization method the most according to claim 1, it is characterised in that root
Adjust model according to described optimization, by microhabitat bacterial foraging algorithm, it is thus achieved that the initial optimization result of described controller parameter
Concrete steps include:
Obtain the initial value organizing described controller parameter more；
Adjust model and often organize the initial value of described controller parameter according to described optimization, it is thus achieved that described optimization is adjusted mesh in model
The initial value of scalar functions；
By described microhabitat bacterial foraging algorithm, in conjunction with the initial value of described object function, to often organizing described controller parameter
Initial value be iterated calculate, it is thus achieved that the initial optimization result of described controller parameter.
Electrical network automatic electricity generation control system controller parameter optimization method the most according to claim 1, it is characterised in that root
According to described initial optimization result, pass through pattern search algorithm, it is thus achieved that the concrete step of the final optimization pass result of described controller parameter
Suddenly include:
According to described initial optimization result, it is thus achieved that the initial optimization value of controller parameter described in a group；
Initial optimization value according to described controller parameter, it is thus achieved that described optimization is adjusted the initial optimization of object function in model
Value；
By pattern search algorithm, in conjunction with the initial optimization value of described object function, the initial optimization to described controller parameter
Value is iterated calculating, it is thus achieved that described final optimization pass result.
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| CN110417031A (en) * | 2019-07-05 | 2019-11-05 | 国电南瑞科技股份有限公司 | A kind of automatic electricity generation control system frequency bias coefficient segmentation setting method |
| CN111181178A (en) * | 2020-01-14 | 2020-05-19 | 华电潍坊发电有限公司 | Thermal power plant synchronous generator excitation system optimal parameter setting method based on self-adaptive compass search algorithm |
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