CN102777878A - Main steam temperature PID control method of ultra supercritical unit based on improved genetic algorithm - Google Patents

Abstract

The invention relates to a main steam temperature PID (Proportion Integration Differentiation) control method of an ultra supercritical unit for boiler power generation in a thermal power plant based on an improved genetic algorithm. The method comprises the improved genetic algorithm. The improved genetic algorithm is improved on the basis of a simple genetic algorithm, optimal sequencing retaining selection, non-uniform linear intersection and Gauss mutation operators are introduced, a uniform design method is adopted to construct an initial population, and the intersection and variation probability is adjusted by Sigmoid function adaptability; and a PID controller orienting a DCS (distributed control system) is adopted, and the parameter set of the controller under a part load section is optimally obtained through the improved genetic algorithm. The optimizing speed and precision of the genetic algorithm are improved, the control method can replace an engineering adjusting method with complex engineering and low accuracy, the workload of on-site debugging personnel is reduced, the purpose of intelligent main steam temperature control is achieved, the application of an intelligent optimization technology under the DCS is realized, and the need of stably controlling the main steam temperature of the ultra supercritical unit in deep peak regulation is met.

Description

A kind of based on the ultra supercritical unit master steam temperature PID control method of improving genetic algorithm

Technical field

The present invention relates to the control method of a kind of heat power plant boiler generating set master steam temperature PID, especially relate to a kind of based on the heat power plant boiler generating ultra supercritical unit master steam temperature PID control method of improving genetic algorithm.

Background technology

Heat power plant boiler generating set main steam temperature is a direct current cooker important parameter that needs supervision in service, has complicated dynamic and static characteristic and wide influence factor.For the main control of steam temperature under the spray desuperheating disturbance; Usually adopt cascade PID control strategy in power plant's practical application with feedforward compensation; Do not consider its influence that model changes when varying duty fully; Need manual operations and Supervised Control when operating mode is complicated, when object model changes, often do not reach ideal effect.

For adapting to thermal power plant load down main steam temperature large time delay, non-linear, the uncertain characteristics of model on a large scale, Chinese scholars is incorporated into various control methods in the main control of steam temperature.Existing PID controller, advanced controller and the advanced control structures etc. improved are introduced in thermal power plant's thermal process, have implemented reliable and stable The field or l-G simulation test.There is the scholar to propose composite control method such as PID control, fuzzy self-adaption Predictive function control, state variable-PREDICTIVE CONTROL, nerve immunity FEEDBACK CONTROL based on immune genetic algorithm and neutral net; Be used for the field control of ultra supercritical unit master steam temperature, obtained good control quality.Also have the scientific research personnel to design a kind of omnidistance control of steam temperature system based on auto-disturbance rejection technology, result of the test shows, this system has and controls quality preferably, and system's output can be followed the tracks of preferably to optimize and start curve when the operating mode wide variation.These researchs have good facilitation for the main control of steam temperature method of exploring under the varying duty on a large scale; But all exist problems such as complex structure, parameter tuning rule is more; It is lacked aspect the software and hardware realization effectively support that particularly the application to actual field DCS layer has caused certain difficulty.

Summary of the invention

Technical problem to be solved by this invention; Just provide a kind of ultra supercritical unit master steam temperature PID control method based on the improvement genetic algorithm towards the DCS system; Can reduce field adjustable personnel's workload, and make system have stronger stability and robustness preferably.

For solving the problems of the technologies described above, the present invention adopts following technical scheme:

A kind of based on the heat power plant boiler generating ultra supercritical unit master steam temperature PID control method of improving genetic algorithm; Adopt PID controller control in the method towards the DCS system; The parameter set of said PID controller under minute load section obtains through improved genetic algorithm optimizing; Include the improvement genetic algorithm in the method, described improvement genetic algorithm may further comprise the steps:

Step1: set up pid parameter problem of tuning Mathematical Modeling, comprise the object function constraints;

Object function is relevant with the purpose that reaches control, and constraints is relevant with the main steam temperature characteristic of boiler of power plant, so the design of different this two aspect of designer of different power plant maybe be different.The fitness function of genetic algorithm is write according to object function, and constraints limit the computer capacity of genetic algorithm, all to set up according to actual conditions.

Step2: genetic parameter is set, comprises population size, evolutionary generation, selection pressure, crossover probability scope, variation probable range;

Step3: adopt encode pid parameter

and the suitable parameters upper and lower limit is set of real coding mode;

The bound of parameter is the constraints among the step1, and is relevant with the characteristic of main steam temperature in the boiler of each power plant.Have a kind of method be with field adjustable personnel's empirical value 1/10 as lower limit, 10 times of empirical value as the upper limit.

Step4: set up uniform designs table, and according to even table to initialization of population, produce initial population Pini;

Step5: judge whether fitness restrains, and restrains then terminator, do not restrain then getting into next step;

Step6: calculate each for fitness individual in the population

Step7: adopt linear ordering operator and elite's selection strategy to select excellent individual;

Step8: adopt Sigmoid function (be the neuronic nonlinear interaction function of f (x)=1/ (1+e-x), below formula be that principle inventor oneself according to the sigmoid function releases) formula to calculate crossover probability P _c: probability adjustment curve adopts the Sigmoid function, and its computing formula does

$P_c^t=P_{c1}-\frac{P_{c1}-P_{c2}}{1+e^{-\mathrm{\β}(t/T-\mathrm{Ns})}}$

In the formula, P _C1Be initial crossover probability; P _C2For stopping crossover probability; β is a form factor, is set at 20; Ns is a separation, is set at 0.25;

Step9: adopt the two non-uniform linear crossover operator to produce new gene;

Step10: adopt the Sigmoid function formula to calculate variation probability P m: probability adjustment curve adopts the Sigmoid function, and its computing formula is (this formula is also own to be released according to the sigmoid function)

$P_m^t=P_{m1}+\frac{P_{m2}-P_{m1}}{1+e^{-\mathrm{\β}(t/T-\mathrm{Ns})}}$

In the formula, P _M1Be initial variation probability; P _M2For stopping the variation probability; β is a form factor, is set at 20; Ns is a separation, is set at 0.25;

Step11: adopt the Gaussian mutation operator to produce new gene;

Step12: record is the most individual, optimal adaptation degree and progeny population information;

Step13: judge whether to reach maximum evolutionary generation, then returned for the 6th step if not, repeat above calculating process up to reaching maximum evolutionary generation;

Step14: obtain optimal solution, stop the genetic algorithm optimization program.

Described step Step4 is specially: the structure to uniform designs table has adopted the grid point method, and tests its uniformity through the L2 deviation method, and this step Step4 further comprises following substep:

Step4-1: the number of levels n of given uniform designs table when n is odd number, generates one group by positive integer vector H _n={ h ₁, h ₂..., h _m, wherein h is littler than n, and their grand duke's factor is 1; When n is even number, construct uniform designs table earlier

With vectorial H _N+1

Step4-2: j row in the structure uniform designs table, its individual computing formula is χ _Ij=(i*h _j) (mod (n)), wherein mod representes the congruence computing, i=1, and 2 ..., n, j=1,2 ..., m, the congruence computing can guarantee that individuality drops on interval [1, n], thereby has obtained uniform designs table

U _{N * m}Matrix.When n is even number, produce matrix U _{(n+1) * m}Step4-3: adopt L2-departure function CD ₂(P) test its uniformity, select uniformity and do best s row formation uniform designs table

Its functional equation does

${\mathrm{CD}}_2\left(P\right)=[{\left(\frac{13}{12}\right)}^s-\frac{2}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\underset{j=1}{\overset{s}{\mathrm{\Π}}}(2+|x_{\mathrm{ij}}-\frac{1}{2}|+$

${|x_{\mathrm{ij}}-\frac{1}{2}|}^2)+\frac{1}{n^2}\underset{k=1}{\overset{n}{\mathrm{\Σ}}}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\underset{j=1}{\overset{s}{\mathrm{\Π}}}(1+\frac{1}{2}|x_{\mathrm{kj}}-\frac{1}{2}|$

$+|\frac{1}{2}{x}_{\mathrm{ij}}-\frac{1}{2}|-\frac{1}{2}|x_{\mathrm{kj}}-x_{\mathrm{ij}}|){]}^{\frac{1}{2}}$

Step4-4: produce the genetic algorithm initial population through uniform designs table; Wherein s is a number of parameters, need set according to the parameter search problem, and the population size is n; Therefore can construct the uniform designs table of a s factor n level, make the individuality in the initial population be evenly distributed on the s dimension space.

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

The present invention improves simple generic algorithm, has improved the optimizing speed and the precision of genetic algorithm; The method of using the optimizing of improvement genetic algorithm to obtain PID controller parameter set under minute load section that proposes can replace the not high engineering setting method of engineering complicacy and accuracy; Reduced field adjustable personnel's workload; Reach the purpose of main steam temperature Based Intelligent Control; Realize the application of intelligent optimization technology under the DCS system, satisfied the stable demand for control of ultra supercritical unit master steam temperature when degree of depth peak regulation.

Description of drawings

Fig. 1 is the optimizing flow chart that improves genetic algorithm among the present invention.

The specific embodiment

Combine accompanying drawing that technical scheme of the present invention is described further through embodiment below.

The present invention is employed in widely used PID controller on the DCS layer to the main steam temperature system of ultra supercritical unit, and the parameter set of this PID controller under minute load section obtains through improved genetic algorithm optimizing.

What the present invention proposed is a kind of based on the ultra supercritical unit master steam temperature PID control method of improving genetic algorithm, and the concrete steps of its improved genetic algorithm optimizing are following:

Step1: set up pid parameter problem of tuning Mathematical Modeling, comprise the object function constraints;

Step2: genetic parameter is set, comprises population size, evolutionary generation, selection pressure, crossover probability scope, variation probable range;

Step3: adopt encode pid parameter

and the suitable parameters upper and lower limit is set of real coding mode;

Step4: set up uniform designs table

And according to even table generation initial population P _IniWherein U representes uniform designs table, and n representes number of levels or experiment number, and s representes the factor number; A mistake! Do not find Reference source.If need the parameter of identification to have m, be n if set the population size, then can construct the uniform designs table of a horizontal m factor of n, the initial population individuality is evenly distributed in the m-dimensional space with the some form of loosing;

This specific embodiment has adopted the grid point method to the structure of uniform designs table, and has passed through L ₂-deviation method is tested its uniformity.This step further comprises following substep:

Step4-1: the number of levels n of given uniform designs table when n is odd number, generates one group by positive integer vector H _n={ h ₁, h ₂..., h _m, wherein h is littler than n, and their grand duke's factor is 1; When n is even number, construct uniform designs table earlier With vectorial H _N+1

Step4-2: j row in the structure uniform designs table, its individual computing formula is χ _Ij=(i*h _j) (mod (n)), wherein mod representes the congruence computing, i=1, and 2 ..., n, j=1,2 ..., m, the congruence computing can guarantee that individuality drops on interval [1, n], thereby has obtained uniform designs table U _{N * m}Matrix.When n is even number, produce matrix U _{(n+1) * m}Step4-3: adopt L ₂-departure function CD ₂(P) test its uniformity, select uniformity and do best s row formation uniform designs table Its functional equation does

${\mathrm{CD}}_2\left(P\right)=[{\left(\frac{13}{12}\right)}^s-\frac{2}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\underset{j=1}{\overset{s}{\mathrm{\Π}}}(2+|x_{\mathrm{ij}}-\frac{1}{2}|+$

${|x_{\mathrm{ij}}-\frac{1}{2}|}^2)+\frac{1}{n^2}\underset{k=1}{\overset{n}{\mathrm{\Σ}}}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\underset{j=1}{\overset{s}{\mathrm{\Π}}}(1+\frac{1}{2}|x_{\mathrm{kj}}-\frac{1}{2}|$

$+|\frac{1}{2}{x}_{\mathrm{ij}}-\frac{1}{2}|-\frac{1}{2}|x_{\mathrm{kj}}-x_{\mathrm{ij}}|){]}^{\frac{1}{2}}$

Step4-4: produce the genetic algorithm initial population through uniform designs table; Wherein s is a number of parameters, need set according to the parameter search problem, and the population size is n; Therefore can construct the uniform designs table of a s factor n level, make the individuality in the initial population be evenly distributed on the s dimension space.

Step5: judge whether fitness restrains, and restrains then terminator, do not restrain then getting into circulation next time;

Step6: calculate each for fitness

individual in the population

Step7: adopt line preface ordering operator and elite's selection strategy to select excellent individual: its individual probability calculation formula does

$\mathrm{Ps}\left(a_j\right)=\frac{1}{M}({\mathrm{\η}}^{+}-\frac{{\mathrm{\η}}^{+}-{\mathrm{\η}}^{-}}{M-1}(j-1))$

In the formula, M is a group size; J is the descending sequence number; η ⁺Be optimized individual expectation number; η ^-Be the poorest individual expectation number, η ⁺+ η-=2.

Step8: calculate crossover probability P _c: probability adjustment curve adopts the Sigmoid function, and its computing formula does

$P_c^t=P_{c1}-\frac{P_{c1}-P_{c2}}{1+e^{-\mathrm{\β}(t/T-\mathrm{Ns})}}$

In the formula, P _C1Be initial crossover probability; P _C2For stopping crossover probability; β is a form factor, is set at 20; Ns is a separation, is set at 0.25;

Step9: use of non-uniform linear crossover operator generates new gene; located parent individuals is

and

then the offspring is generated after crossing

$\left\{\begin{array}{c}{X}_1^{t+1}=c\·X_1^t+(1-c)\·X_2^t\\ X_2^{t+1}=(1-c)\·X_1^t+c\·X_2^t\end{array}\right.$

In the formula, c is a scale factor, and its value produces between (0,1) at random;

Step10: calculate the variation probability P _m: probability adjustment curve adopts the Sigmoid function, and its computing formula does

$P_m^t=P_{m1}+\frac{P_{m2}-P_{m1}}{1+e^{-\mathrm{\β}(t/T-\mathrm{Ns})}}$

In the formula, P _M1Be initial variation probability; P _M2For stopping the variation probability; β is a form factor, is set at 20; Ns is a separation, is set at 0.25;

Step11: adopt the Gaussian mutation operator to produce new gene: the computing formula of offspring individual does

X ^t+1＝X ^t+N(0,σ)

In the formula, and N (0, σ) be Gaussian distribution; σ is a variance.Wherein, σ is set at dynamic parameter σ (t), promptly for the variation step-length is reduced with the increase of genetic algebra

$\mathrm{\σ}\left(t\right)=1-0.9\·\frac{g}{G}$

In the formula, g is current evolutionary generation; G is a genetic algebra;

Step12: record is the most individual, optimal adaptation degree and progeny population information;

Step13: judge whether to reach maximum evolutionary generation, then return Step6 if not, repeat above calculating process up to reaching maximum evolutionary generation;

Step14: obtain optimal solution, stop the genetic algorithm optimization program.

Claims (2)

1. one kind based on the ultra supercritical unit master steam temperature PID control method of improving genetic algorithm, and it is characterized in that: comprise the improvement genetic algorithm in the described method, described improvement genetic algorithm may further comprise the steps:

Step1: set up pid parameter problem of tuning Mathematical Modeling, comprise the object function constraints;

Step2: genetic parameter is set, comprises population size, evolutionary generation, selection pressure, crossover probability scope, variation probable range;

Step3: adopt encode pid parameter

and the parameter upper and lower limit is set of real coding mode;

Step4: set up uniform designs table, and according to even table to initialization of population, produce initial population Pini;

Step5: judge whether fitness restrains, and restrains then terminator, do not restrain then getting into next step;

Step6: calculate each for fitness

individual in the population

Step7: adopt line preface ordering operator and elite's selection strategy to select excellent individual;

Step8: adopt the Sigmoid function formula to calculate crossover probability P _c: probability adjustment curve adopts the Sigmoid function, and its computing formula does

$P_c^t=P_{c1}-\frac{P_{c1}-P_{c2}}{1+e^{-\mathrm{\β}(t/T-\mathrm{Ns})}}$

In the formula, P _C1Be initial crossover probability, P _C2For stopping crossover probability, β is a form factor, is set at 20; Ns is a separation, is set at 0.25;

Step9: adopt the two non-uniform linear crossover operator to produce new gene;

Step10: adopt the Sigmoid function formula to calculate the variation probability P _m: probability adjustment curve adopts the Sigmoid function, and its computing formula does

$P_m^t=P_{m1}+\frac{P_{m2}-P_{m1}}{1+e^{-\mathrm{\β}(t/T-\mathrm{Ns})}}$

In the formula, P _M1Be initial variation probability; P _M2For stopping the variation probability; β is a form factor, is set at 20; Ns is a separation, is set at 0.25;

Step11: adopt the Gaussian mutation operator to produce new gene;

Step12: record is the most individual, optimal adaptation degree and progeny population information;

Step13: judge whether to reach maximum evolutionary generation, then return Step6 if not, repeat above calculating process up to reaching maximum evolutionary generation;

Step14: obtain optimal solution, stop the genetic algorithm optimization program.

2. according to claim 1 based on the ultra supercritical unit master steam temperature PID control method of improving genetic algorithm, it is characterized in that: described step Step4 comprises following substep:

Step4-1: the number of levels n of given uniform designs table when n is odd number, generates one group by positive integer vector H _n={ h ₁, h ₂..., h _m, wherein h is littler than n, and their grand duke's factor is 1; When n is even number, construct uniform designs table earlier

With vectorial H _N+1

Step4-2: j row in the structure uniform designs table, its individual computing formula is χ _Ij=(i*h _j) (mod (n)), wherein mod representes the congruence computing, i=1, and 2 ..., n, j=1,2 ..., m, the congruence computing can guarantee that individuality drops on interval [1, n], thereby has obtained uniform designs table

U _{N * m}Matrix when n is even number, produces matrix U _{(n+1) * m}Step4-3: adopt L ₂-departure function CD ₂(P) test its uniformity, select uniformity and do best s row formation uniform designs table

Its functional equation does

${\mathrm{CD}}_2\left(P\right)=[{\left(\frac{13}{12}\right)}^s-\frac{2}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\underset{j=1}{\overset{s}{\mathrm{\Π}}}(2+|x_{\mathrm{ij}}-\frac{1}{2}|+$

${|x_{\mathrm{ij}}-\frac{1}{2}|}^2)+\frac{1}{n^2}\underset{k=1}{\overset{n}{\mathrm{\Σ}}}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\underset{j=1}{\overset{s}{\mathrm{\Π}}}(1+\frac{1}{2}|x_{\mathrm{kj}}-\frac{1}{2}|$

$+|\frac{1}{2}{x}_{\mathrm{ij}}-\frac{1}{2}|-\frac{1}{2}|x_{\mathrm{kj}}-x_{\mathrm{ij}}|){]}^{\frac{1}{2}}$

Step4-4: produce the genetic algorithm initial population through uniform designs table; Wherein s is a number of parameters, need set according to the parameter search problem, and the population size is n; Can construct the uniform designs table of a s factor n level, make the individuality in the initial population be evenly distributed on the s dimension space.