CN103312249A - Self-adaptation excitation control method for synchronous generator - Google Patents

Abstract

A kind of adaptive excitation control method of synchronous generator, it is characterised in that: firstly, primarily determining the stable region of each control parameter according to stability principle; Secondly, multi-Agent Genetic Algorithm MAGA and Cerebellar Model Articulation Controller are combined with conventional PID controller, using the global optimization ability of multi-Agent Genetic Algorithm in determining control parameter

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Optimizing is carried out in stable region; According to certain Controlling object function minimum principle, optimal control parameter combination is obtained, and then realizes the adaptive excitation con-trol of synchronous generator. Present invention employs the thoughts and technology of one-dimensional MAS control system distributed controll, can coordinate and optimize control parameter, and it is determining to solve the problems, such as that intelligent multi-parameter coupling, more control parameters are difficult to; Realize the control of synchronous generator exciting self-adapting intelligent; Accelerate the operation and analysis of control system; It can guarantee to ensure power grid security, stabilization, economy, reliable operation while synchronous generator economical operation.

Description

A kind of synchronous generator self adaptation excitation control method

Technical field

The present invention relates to a kind of synchronous generator exciting control method.

Background technology

Excitation Control System of Synchronization Generator is the complex control system of a strong nonlinearity, and therefore, system has highly uncertain, non-linear and the characteristics such as parameter time varying, close coupling; Interconnected along with large capacity unit and large-scale power system had higher requirement to the control quality of synchronous generator system, and conventional PID control is difficult to obtain desirable control effect.Seek a kind of adaptation energy property strong, the control method that robustness and real-time are good is urgent problem in the synchronous generator exciting control.Cerebellar Model Articulation Controller (CMAC) has the advantages such as processing is non-linear, self study, certain generalization ability.What comparatively commonly use in the industrial production is the feedfoward control of CMAC and the compound control that fixed gain feedback PID controller combines and consists of, but the control parameter that this control structure will be set is many, interacts between the parameter, very trouble; On the other hand, it is improper to choose such as the parameter setting of controller, not only can not obtain good control effect, also wild effect can occur.The PD controller that adopts at present fixed gain estimates at partially and random disturbances can only realize that Local Bounded stablizes when existing at CMAC, also someone proposes to adopt genetic algorithm that fixed gain PID controller is carried out optimal design, but can only realize the part optimizations between three of the PID control parameter, not relate to and how to process influencing each other and the problem of multi-parameter decoupling zero between pid control parameter and the CMAC control parameter.

Summary of the invention

The object of the present invention is to provide a kind of synchronous generator self adaptation excitation control method, overcome the synchronous generator exciting multi-parameter coupling difficulty and the poor shortcoming of adaptive control are set.

A kind of synchronous generator self adaptation excitation control method is characterized in that: at first, tentatively determine the stable region of each control parameter according to stability principle; Secondly, multi-Agent Genetic Algorithm MAGA and Cerebellar Model Articulation Controller are combined with conventional PID controller, utilize the global optimization ability of multi-Agent Genetic Algorithm in definite control parameter K _P, K _I, K _D, η carries out optimizing in the α stable region; According to certain Controlling object function minimum principle, obtain the optimal control parameter combination, and then realize the control of synchronous generator self adaptation excitation.

Further, the present invention includes following concrete steps:

The first step is tentatively determined parameter and stable region thereof: according to stability of control system, tentatively determine 5 optimised parameter stability territory scopes, the i.e. K of PID controller _P, K _I, K _DLearning rate η and the moment of inertia α general scope with the CMAC controller;

Second step, agent encoding: PID and CMAC composite controller have a plurality of control parameters to adjust, and these control parameters intercouple, this coded system is with the K of PID controller _P, K _I, K _DForm a gene of intelligent body with learning rate η, the α of CMAC controller, a represents an intelligent body, and S represents the search volume of controller parameter optimization problem;

a＝(K _P,K _i,K _d,η,α)∈S

The 3rd step, intelligent body living environment and adaptive value: the real-time that strengthens the control of CMAC composite controller in order to reduce computing cost, adopt the one dimension multiple agent, namely be fixed in the one dimension grid each intelligent body only with its neighborhood in about two neighbours interact, other intelligent bodies in each intelligent body and its neighborhood are selected, are mixed and intersect and the operation such as self adaptation variation by competition, and purpose is by himself adaptive value of intelligent body evolution raising;

The 4th step, initial population: produce at random n intelligent body and form initial population P (0);

In the 5th step, individual fitness calculates: ask cost function value J and ask fitness function value f by 1/J by control output error corresponding to each intelligent body;

The 6th step, the intelligent body competition: in a dimension coordinate, a certain intelligent body agentL (i j)=(l ₁, l ₂, l ₃, L, l _n) with its neighborhood in about 2 neighbours' intelligent body individualities compete, the intelligent body of supposing its neighborhood endoadaptation value maximum is agentM (i j)=(m ₁, m ₂, m ₃, L, m _n), if the adaptive value of agentL (i j) greater than agentM (i j), then agentL (i j) continues survival, otherwise agentL (i j) is by new agentNew (i j)=(e ₁, e ₂, e ₃..., e _n) substitute;

The 7th step, at the new intelligent body that produces with P _cMix intersection, crossover location also is to determine at random, such as intelligent body agentA=(a ₁, a ₂, a ₃..., a _n) and intelligent body agentB=(b ₁, b ₂, b ₃..., b _n) mix two filial generations of intersection generation at the k point, wherein β is the random value in [0,1];

The 8th step is with random chance P _mRealize the self adaptation mutation operation;

In the 9th step, each intelligent body in the Agent Grid is realized the self study behavior in the sN neighborhood;

In the tenth step, replace the poorest individuality of this generation adaptation value with the individuality that adaptation value is the highest in the successive dynasties, so obtain population P of new generation (t+1);

In the 11 step, end condition is judged: if obtain desired indicator or reach setting evolutionary generation G, and end loop, the intelligent body of adaptive value maximum is the optimization control parameter combination in the Agent Grid, otherwise turns the circulation of the 5th step.

The present invention has adopted the thought of one dimension MAS control system distribution control and has utilized the one dimension multi-agent Technology, can control parameter by coordination optimization, solved intelligent multi-parameter coupling, multi-control difficult parameters with definite, conventional optimized algorithm convergence rate reaches the problem that easily is absorbed in local extremum slowly; Realized the control of synchronous generator exciting self-adapting intelligent; Computing and the analysis of control system have been accelerated; Guarantee power grid security, stable, economic, reliably operation when can guarantee the synchronous generator economical operation.

Description of drawings

CMAC and PID multiplex control system figure that Fig. 1 optimizes based on the one dimension multi-Agent Genetic Algorithm;

Fig. 2 is exciter control system figure of the present invention;

Fig. 3 is excitation control algolithm flow chart of the present invention;

Fig. 4 is excitation control algolithm emulation comparison diagram of the present invention;

Fig. 5 is excitation control algolithm error comparison diagram of the present invention.

Embodiment

Describe embodiments of the present invention in detail below in conjunction with accompanying drawing:

Fig. 1 is based on CMAC and the PID multiplex control system figure that the one dimension multi-Agent Genetic Algorithm is optimized, and PID controller and synchronous generator consist of FEEDBACK CONTROL, and CMAC realizes feedfoward control, the input V of CMAC _RefAnd e=V _Ref-V _t, represent respectively generator voltage desired output and system keeps track voltage error.

The PID of coordination optimization and the CMAC intelligent synchronization generator excitation controller course of work are divided into control and learn two stages.The weights of CMAC are initialized as 0 in when beginning control, and learning rate and the moment of inertia can very little numbers of initialization; Three control parameters of PID controller only need to be chosen according to traditional PI D parameter tuning method and universal experience in its stable region scope, need not accurate Calculation.At control stage, V _RefAnd e=V _Ref-V _tBe input among the CMAC as the address by after quantizing, in memory, find C corresponding with it unit, and the weights addition of these unit is obtained the output U of CMAC _n(k), then export U with corresponding PID controller _p(k) addition obtains corresponding exciting voltage U _f(k).

$U_n\left(k\right)=\underset{j=1}{\overset{C}{\mathrm{\Σ}}}{\mathrm{\ω}}_j\left(k\right)a_j\left(k\right)$ （1）

（2）

U _f(k)＝U _n(k)+U _p(k)

After each control cycle finished, system entered study and parameter optimization stage, and this moment, the one dimension multi-Agent Genetic Algorithm was according to the K of performance objective function (3) to the PID controller _P, K _I, K _DBe optimized with 5 control such as learning rate η, the α parameter of CMAC controller, to obtain next control cycle optimal control parameter.For fear of overshoot, adopt penalty, this moment, optimum index was (4) formula.

$J=\underset{0}{\overset{\∞}{\text{\∫}}}\left(w_1\right|e\left(t\right)|+w_2{u}^2\left(t\right))\mathrm{dt}+w_3{t}_u$ （3）

if(ey(t)＜0) $J=\underset{0}{\overset{\∞}{\∫}}\left(w_1\right|e\left(t\right)|+w_2{u}^2\left(t\right)+w_4\mathrm{ey}\left(t\right))\mathrm{dt}+w_3{t}_u$ （4）

E in the formula (t) system tracking error; U (t) is the master control amount of controller output; t _uBe the rise time; w ₁, w ₂, w ₃, w ₄Weights for target function.w ₄＞＞w ₁Ey (t)=y (t)-y (t-1); Y (t) is system's output.

After control cycle finished, system entered learning phase.At this moment adopt the desired output of control system and the difference correction weights of actual output, enter the CMAC learning phase according to following formula (5).

$w_i(k+1)=w_i\left(k\right)+\mathrm{\η}\frac{e\left(k\right)}{C}+\mathrm{\α}(w_i\left(k\right)-w_i(k-1))$ e(k)＝V _ref(k)-V _t(k) （5）

W in the formula _i(k) be the weighted value of CMAC; C is extensive constant; η is the learning rate after optimizing; E (k) is the system keeps track voltage error.

Fig. 2 is the exciter control system figure that optimizes multi-parameters optimization based on the one dimension multi-Agent Genetic Algorithm of the present invention, and top is used for Excitation Control System of Synchronization Generator based on one dimension multi-Agent Genetic Algorithm optimization multi-parameters optimization CMAC and PID controller.

Excitation control based on one dimension multi-Agent Genetic Algorithm multi-parameters optimization is achieved as follows:

(1) agent encoding

PID and CMAC composite controller have a plurality of control parameters to adjust, and these control parameters intercouple.Because various parameters all are real numbers, therefore, adopt decimal coded.This coded system is with the K of PID controller _P, K _I, K _DForm a gene of intelligent body with learning rate η, the α of CMAC controller.A represents an intelligent body, and S represents the search volume of controller parameter optimization problem.

a＝(K _P,K _i,K _d,η,α)∈S

(2) intelligent body living environment and adaptive value

Strengthen the real-time of CMAC composite controller control in order to reduce computing cost, here adopt the one dimension multiple agent, namely be fixed in the one dimension grid each intelligent body only with its neighborhood in about two neighbours interact, other intelligent bodies in each intelligent body and its neighborhood by the competition selection, mix and intersect and the operation such as self adaptation variation, the target function of estimating is such as (3) formula and (4) formula, and purpose is to evolve by intelligent body to improve the adaptive value of himself.

(3) synchronous generator excited system modeling

Control designs because algorithm of the present invention is for generator excitation, so generator model can replace with first order inertial loop G1 (s).In order to reflect that creep may occur control object generator model parameter, here undergo mutation to simulate with two parameter K, T on the inertial element, actuator is that rectifier bridge can equivalence come equivalent for first order inertial loop G2 (s) in excitation control, it is equivalent that detection also uses first order inertial loop G3 (s) to come, so the model of synchronous generator excited system is that the Generalized Control object can represent with son (6).

$\left\{\begin{array}{c}G1\left(s\right)=\frac{K}{1+\mathrm{Ts}}\\ G2\left(s\right)=\frac{1}{1+0.3s}\\ G3\left(s\right)=\frac{1}{1+0.02s}\end{array}\right.$ （6）

0.7≤K≤1.0,1.0≤T≤2.0 generally speaking wherein.

(4) algorithm performing step

Fig. 3 is multiple agent self adaptation excitation control algolithm flow chart of the present invention, and the below is the specific implementation step of algorithm.

A) according to traditional PI D parameter tuning method and universal experience, at first determine 5 about scopes of optimised parameter, i.e. [min, max] and parameter is carried out decimal coded according to formula (7) determines the parameters such as iterations, Population Size, competition neighborhood simultaneously;

K＝min+(max-min)×rand （7）

In the formula: rand is a random number that meets even probability distribution in (0,1) scope, (K _P, K _I, K _D, η, α) and be that AGENT is individual.

B) produce at random n intelligent body and form initial population P (0);

C) adaptive value of intelligent computing agent is asked cost function value J and is asked fitness function value f by 1/J by formula (4) with the control output error that each intelligent body is corresponding, if e (t)≤0 is by (formula (4) is asked cost function value J;

D) each intelligent body in the Agent Grid is adopted competitive behavior in its effect neighborhood;

In a dimension coordinate, a certain intelligent body agentL (i j)=(l ₁, l ₂, l ₃, L, l _n) compete (only having 2 neighbours' intelligent bodies about it here) with the individuality in its neighborhood, the intelligent body of supposing its neighborhood endoadaptation value maximum is agentM (i j)=(m ₁, m ₂, m ₃, L, m _n), if the adaptive value of agentL (i j) greater than agentM (i j), then agentL (i j) continues survival, otherwise agentL (i j) is generated new agentNew (i j)=(e with (8) ₁, e ₂, e ₃..., e _n) substitute.

$e_k=\left\{\begin{array}{cc}\underset{\‾}{x_k}& (m_k+U(-\mathrm{1,1})\×(m_k-l_k))\≤\underset{\‾}{x_k}\\ \stackrel{\‾}{x_k}& (m_k+U(-\mathrm{1,1})\×(m_k-l_k))\≤x_k\\ m_k+U(-\mathrm{1,1})\×(m_k-l_k)& \mathrm{else}\end{array}\right.$ （8）

E) at the new intelligent body that produces with P _cMix intersection, crossover location also is to determine at random, Crossover Strategy is undertaken by (9) formula;

$\left\{\begin{array}{c}\mathrm{agentA}=(a_1,a_2,a_3,...,{a_k}^{\′},b_{k+1}...,b_n)\\ \mathrm{agentB}=(b_1,b_2,b_3,...,{b_k}^{\′},a_{k+1}...,a_n)\\ {a_k}^{\′}=a_k+\mathrm{\β}(b_k-a_k)\\ {b_k}^{\′}=v_k+\mathrm{\β}(u_k-v_k)\end{array}\right.$ （9）

Intelligent body agentA=(a ₁, a ₂, a ₃..., a _n) and intelligent body agentB=(b ₁, b ₂, b ₃..., b _n) mix two filial generations of intersection generation at the k point, wherein β is the random value in [0,1].

F) with random chance P _mRealize the self adaptation mutation operation according to formula (10);

$e_k=\left\{\begin{array}{cc}{1}_k& U\left(\mathrm{0,1}\right)\≤P_m\\ 1_k+G(\mathrm{0,1}/t)& \mathrm{else}\end{array}\right.$ k＝12....n （10）

G) each intelligent body in the Agent Grid is realized the self study behavior in the sN neighborhood;

H) replace the poorest individuality of this generation adaptation value with the individuality that adaptation value is the highest in the successive dynasties, so obtain population P of new generation (t+1);

I) end condition is judged.If obtain desired indicator or reach setting evolutionary generation G, end loop, the intelligent body of adaptive value maximum is the optimization control parameter combination in the Agent Grid, otherwise turns (c);

(5) simulation example

The generator model parameter is K=0.8, T=1.5, and conventional fixed gain PID controller parameter is set as Kp=117, Kd=7.0, Ki=11.0, the setting parameter of CMAC are η=0.1, α=0.04, C=5, N=100; The CMAC composite controller Kp scope [80,130] that one dimension multi-Agent Genetic Algorithm and standard genetic algorithm are optimized, Ki is [4,10], and Kd is [6,14], and η is [0,1], and α is [0,1], C=5, N=100, P _c=0.1, P _m=0.05, Population Size Size=30, algebraically G=1000, target function is such as (3) and (4) formula, wherein weights are got ω 1=0.999, ω 2=0.001, ω 3=2.0, ω 4=100, sampling period T=1ms, emulation machine Pentium(R) 2.8GHz, internal memory 3G, environment Matlab2009a.When t=3.5s is arrived in emulation, the model of generator is changed to K=1.0, T=2.0, and also U=5.0 is disturbed in stack on controlled quentity controlled variable, Fig. 4 is multiple agent self adaptation excitation control algolithm emulation comparison diagram of the present invention, reflected among the figure based on the obvious PID controller of superior and routine or genetic algorithm optimization of the self adaptation excitation of multiple agent control, control precision is high and robustness good, Fig. 5 is multiple agent self adaptation excitation control algolithm error comparison diagram of the present invention, and this figure has reflected the error change process in the simulation process.

Above content is in conjunction with concrete preferred implementation further description made for the present invention, can not assert that implementation of the present invention is confined to these explanations.For the general technical staff of the technical field of the invention; make without departing from the inventive concept of the premise some alternative or obvious modification that are equal to; and performance or purposes are identical, then should be considered as belonging to the protection range that claims that the present invention submits to are determined.

Claims (2)

1. a synchronous generator self adaptation excitation control method is characterized in that: at first, tentatively determine the stable region of each control parameter according to stability principle; Secondly, multi-Agent Genetic Algorithm MAGA and Cerebellar Model Articulation Controller are combined with conventional PID controller, utilize the global optimization ability of multi-Agent Genetic Algorithm in definite control parameter K _P, K _I, K _D, η carries out optimizing in the α stable region; According to certain Controlling object function minimum principle, obtain the optimal control parameter combination, and then realize the control of synchronous generator self adaptation excitation.

2. synchronous generator self adaptation excitation control method according to claim 1, comprise following concrete steps: the first step, preliminary definite parameter and stable region thereof: according to stability of control system, tentatively determine 5 optimised parameter stability territory scopes, the i.e. K of PID controller _P, K _I, K _DLearning rate η and the moment of inertia α general scope with the CMAC controller;

Second step, agent encoding: PID and CMAC composite controller have a plurality of control parameters to adjust, and these control parameters intercouple, this coded system is with the K of PID controller _P, K _I, K _DForm a gene of intelligent body with learning rate η, the α of CMAC controller, a represents an intelligent body, and S represents the search volume of controller parameter optimization problem;

a＝(K _P,K _i,K _d,η,α)∈S

The 3rd step, intelligent body living environment and adaptive value: the real-time that strengthens the control of CMAC composite controller in order to reduce computing cost, adopt the one dimension multiple agent, namely be fixed in the one dimension grid each intelligent body only with its neighborhood in about two neighbours interact, other intelligent bodies in each intelligent body and its neighborhood are selected, are mixed and intersect and the operation such as self adaptation variation by competition, and purpose is by himself adaptive value of intelligent body evolution raising;

The 4th step, initial population: produce at random n intelligent body and form initial population P (0);

In the 5th step, individual fitness calculates: ask cost function value J and ask fitness function value f by 1/J by control output error corresponding to each intelligent body;

The 6th step, the intelligent body competition: in a dimension coordinate, a certain intelligent body agentL (i j)=(l ₁, l ₂, l ₃, L, l _n) with its neighborhood in about 2 neighbours' intelligent body individualities compete, the intelligent body of supposing its neighborhood endoadaptation value maximum is agentM (i j)=(m ₁, m ₂, m ₃, L, m _n), if the adaptive value of agentL (i j) greater than agentM (i j), then agentL (i j) continues survival, otherwise agentL (i j) is by new agentNew (i j)=(e ₁, e ₂, e ₃..., e _n) substitute;

The 7th step, at the new intelligent body that produces with P _cMix intersection, crossover location also is to determine at random, such as intelligent body agentA=(a ₁, a ₂, a ₃..., a _n) and intelligent body agentB=(b ₁, b ₂, b ₃..., b _n) mix two filial generations of intersection generation at the k point, wherein β is the random value in [0,1];

The 8th step is with random chance P _mRealize the self adaptation mutation operation;

In the 9th step, each intelligent body in the Agent Grid is realized the self study behavior in the sN neighborhood;

In the tenth step, replace the poorest individuality of this generation adaptation value with the individuality that adaptation value is the highest in the successive dynasties, so obtain population P of new generation (t+1);

In the 11 step, end condition is judged: if obtain desired indicator or reach setting evolutionary generation G, and end loop, the intelligent body of adaptive value maximum is the optimization control parameter combination in the Agent Grid, otherwise turns the circulation of the 5th step.

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