CN102678192B - Optimized design method for nozzle number of nozzle sets considering turbine actual operation binding - Google Patents

Abstract

The invention discloses an optimized design method for nozzle number of nozzle sets considering turbine actual operation binding and relates to an optimized design method for the nozzle number of a turbine. The optimized design method aims at solving the problem of large throttle loss and relative reduced inner efficiency caused by small valve opening degree of several working load points under the condition of a plurality of normally used working load points of the existing units. The method includes: constructing a nozzle number optimizing model; calculating actual flow of each load point; calculating theoretical flow of each designated load points; constructing constraint conditions of the nozzle number optimizing model; and obtaining a corresponding optimized nozzle number combination when the comprehensive deviation degree Y is minimum under the designated load point based on the genetic algorithm theory. The optimized design method ensures that when the unit reaches the load points, the adjusting-level valves can be in the full-opening or full-closing state, so that the maximum valve nozzle number combination can be optimized, and the unit has optimal economy under the condition that the throttling reduction of an adjusting level of the turbine is reduced maximumly.

Description

Consider the number of nozzle Optimization Design of each nozzle sets of steam turbine actual motion constraint

Technical field

The present invention relates to a kind of number of nozzle Optimization Design of steam turbine.

Background technique

Steam turbine is a kind of is power with steam, the energy of steam is changed into the rotating machinery of mechanical work, is one of most important prime mover in the world, has apply extremely widely in industries such as power plant, industrial production, naval vessels.Therefore, be optimized transformation to steam turbine increasing economic efficiency and in energy saving, having very large actual application value.

The start and stop of steam turbine and the change of power are the changes of the aperture by modulating valve, thus change and enter that the steam flow of steam turbine or steam parameter realize.Current existing distribution way of steam has nozzle governing, vapour is joined in throttling, vapour is joined in sliding pressure, full electricity adjusts " management valve " formula to join vapour and bypass joins vapour.Wherein nozzle governing is that the static cascade of governing stage is separated into several air inlet segmental arc, each air inlet segmental arc is made up of the nozzle of some, different governing stage valves to each group of independent steam supply of nozzle, according to the needs of load by changing the aperture of governing stage valve and opening the throttle flow that number changes steam turbine.Because nozzle governing is little in the admission restriction loss of governing stage valve place, efficiency is high, and it is most widely used a kind of regulative mode.Existing large steam turbine is mostly adopted and is changed steam turbine throttle flow in this way.Known each valve can cause very large restriction loss when not standard-sized sheet, is the restriction loss reducing Control Stage of Steam Turbine, should ensures each valve wide open or full cut-off as far as possible.When the given multiple load of reality, for reaching required operation point, corresponding valve generally can not ensure standard-sized sheet or full-shut position, very large restriction loss can be caused, cause the long-term fallback of steam turbine, waste mass efficient heat energy, cost of electricity-generating is increased.The conventional loading point of domestic unit is about 70% ~ 80% of rated power, even restriction loss is eliminated for reducing, we imagine the conventional working load point according to priori, the number of nozzle corresponding to each valve of governing stage is optimized design, more conventional working load point can be combined into when ensureing each valve wide open or full cut-off thus, adapt to the economical operation of unit.In process of optimization, consider the actual motion constraint conditio of steam turbine, the application of optimum results in real work is had great importance.Steam turbine is run under varying duty condition often, and in actual motion, the operation frequency of different conventional loading point is not quite similar, the probability that some loading point are run is higher, it is relatively low that some loading point run probability, using the operation frequency of each loading point as one of constraint; The pressure of unit main steam in actual motion is generally according to sliding pressure curve setting, steam turbine pitch standard-sized sheet (or keep suitable aperture constant), main steam flow and pressure (steam temperature remains unchanged substantially) is regulated to regulate load by boiler, before this just makes the governing stage of steam turbine, main air flow amount and pressure are not constant, in nozzle sets design, add sliding pressure operation curve, therefore can improve governing stage efficiency under single valve runs, effect of optimization can be improved further.Concrete grammar is then, when inputting main steam pressure, sliding pressure curve is made look-up-table function, and the loading point different according to unit, draws corresponding main steam pressure.Using sliding pressure operation mode as constraint two.Consider above-mentioned two kinds of constraint conditios, design the optimum valve number of nozzle combination of Control Stage of Steam Turbine by real work load, the basis closer to actual motion condition can be improved the overall efficiency of steam turbine effectively, create economic benefit.

Summary of the invention

The present invention is directed to existing unit under multiple conventional working load point condition, to wherein some working load point valve opening is little causes larger restriction loss, the problem that internal efficiency ratio reduces, under the consideration probability of multiple loading point operating time and the actual motion constraint conditio of steam turbine sliding pressure operation mode, propose a kind of method that the number of nozzle of Control Stage of Steam Turbine nozzle sets is optimized.

The present invention solves the problems of the technologies described above the technological scheme taked to be: the method for the invention realizes in accordance with the following steps:

Step one, structure number of nozzle Optimized model:

Y＝W ₁(xgz ₁-Ge ₁) ²+W ₂(xgz ₂-Ge ₂) ²+......+W _l(xgz _l-Ge _l) ² (2-10)

W ₁, W ₂... W _lrepresent each loading point percentage working time;

Xgz ₁, xgz ₂... xgz _lrepresent the actual flow of each given loading point;

Ge ₁, Ge ₂... Ge _lrepresent the theoretical delivery of each given loading point;

The comprehensive departure degree Y of actual flow and theoretical delivery represents;

Step 2, calculate the actual flow of each given loading point:

The actual flow (haveing nothing to do with given loading point) of 1......l given loading point is calculated by the valve opening of each valve nozzle number and correspondence thereof:

${\mathrm{xgz}}_{\mathrm{kj}}=\frac{0.648A_{\mathrm{nk}}}{\sqrt{\frac{p_{0k}}{{\mathrm{\ρ}}_{0k}}}}\frac{{\mathrm{\β}}_{1k}}{\frac{p_2}{p_{0k}}}{p}_2=A_k{\mathrm{\μ}}_k{p}_2---(1-1)$

In formula: j is the aperture combining form of valve, β _1kfor the governing stage flow-rate ratio of a kth nozzle sets, A _nkfor each nozzle throat sectional area sum of a kth nozzle sets, p _0kfor the pressure (before the nozzle sets that namely modulating valve is corresponding) after a kth modulating valve valve, ρ _0kfor the density (before the nozzle sets that namely modulating valve is corresponding) after a kth modulating valve valve, p _1kfor the nozzle back pressure of a kth nozzle sets, p ₂for the pressure in steam chest after governing stage;

In Practical Calculation process, p _1kwith p ₂value not etc., nozzle sets flow equation is changed into:

${\mathrm{xgz}}_{\mathrm{kj}}^{\′}=\frac{0.648A_{\mathrm{nk}}}{\sqrt{\frac{p_{0k}}{{\mathrm{\ρ}}_{0k}}}}\frac{{\mathrm{\β}}_{2k}{\mathrm{\λ}}_k}{\frac{p_2}{p_{0k}}}{p}_2---(1-2),$

In formula: β _2kfor the flow-rate ratio of governing stage, λ _kit is the function of pressure ratio before and after governing stage;

P is calculated by dichotomy ₂: pressure p after steps A, a given nozzle _1k; Step B, enthalpy h according to thermodynamic computing formulae discovery delivery nozzle outlet port steam ₁, steam speed c ₁with the relative velocity w of movable vane inlet steam ₁, then the rate of discharge G of a nozzle steam is calculated by formula (1-2) _n; The pressure of step C, then a hypothesis movable vane outlet vapor, i.e. pressure p after governing stage ₂; Step D, drawn the enthalpy h of movable vane outlet port steam by thermodynamic computing formula ₂, outlet port steam density p ₂, outlet vapor relative velocity w ₂with absolute velocity c ₂, then the flow G of movable vane outlet vapor is gone out by formulae discovery _b; If step e G _b≠ G _n, be back to step C and continue to calculate, until obtain G _b=G _n, draw pressure p after governing stage ₂; G _b=ρ ₂w ₂a _b, in formula: ρ ₂---movable vane outlet actual density; A _b---movable vane outlet throat area;

According to pressure p after the governing stage that dichotomy calculates ₂be updated in formula (1-2) and calculate actual flow xgz;

Step 3, calculate the theoretical delivery of each given loading point:

Ge _h=Ge ξ _h, wherein Ge _hfor the theoretical delivery of loading point, Ge is the rated flow of steam turbine, ξ _hfor accounting for the percentage of rated power in loading point tubine operate power, h=1,2 ... l, l are the number of working load point;

The constraint conditio of step 4, structure number of nozzle Optimized model:

X ₁+X ₂+X ₃+X ₄＝X _z，X _z＝const (2-2)

X _min≤X _i≤X _max，i＝1，2，3，4；X _min＝const，X _max＝const (2-3)

X ₁, X ₂, X ₃, X ₄represent the nozzle number of four nozzle sets, X _zfor total nozzle number, const represents definite value;

Step 5, obtain given loading point based on Genetic Algorithms Theory under the combination of optimum number of nozzle corresponding when making actual flow and the comprehensive departure degree Y of theoretical delivery minimum, detailed process is as follows:

Step 5 (one), initial population set:

Adopting floating-point encoding, is [X between code area _min, X _max],

$\mathrm{Chrom}=\left[\begin{array}{c}{x}_{11},x_{12},x_{13}\\ x_{21},x_{22},x_{23}\\ ..........\\ x_{m1},x_{m2},x_{m3}\end{array}\right],$ x _ij∈R ₊，i＝1，2，...，m，j＝1，2，3.(2-4)

In formula, the individual amount of m representative coding;

For body one by one, its each chromosome x _i1, x _i2, x _i3be respectively the nozzle sets number of nozzle X that first three regulating valve is corresponding ₁, X ₂, X ₃coding, its corresponding relation is:

X _ij＝round(x _ij)，i＝1，2...m，j＝1，2，3(2-5)

Round represents round, calculates X ₁, X ₂, X ₃after, X ₄for:

X _i4＝X _z-(X _i1+X _i2+X _i3) (2-7)

Step 5 (two), structure fitness function ObjV: calculated by fitness, realize individual optimum choice, make the 4th nozzle sets number of nozzle in optimum results meet constraint conditio (2-3) formula simultaneously, the individuality (2-8) not meeting constraint conditio given up in iteration:

X _i4＞X _max ORX _i4＜X _min (2-8)

If fitness function is:

ObjV＝OBJ_func(Ge，Fa，X ₁，X ₂，X ₃，X ₄，others)

Above-mentioned fitness function does not have explicit mathematic(al) representation, and the mapping relations of above-mentioned fitness function are as follows:

Input:

1), the flow Ge of all given loading point ₁, Ge ₂... Ge _l, and each loading point percentage working time W ₁, W ₂... W _l;

2), valve opening (12 kind 0,1 combination): [0000; 0011; 0101; 1001; 0110; 1100; 1010; 1110; 1101; 1011; 0111; 1111];

3), valve nozzle group number of nozzle combination X ₁, X ₂, X ₃, X ₄, produced by the coding of body one by one;

4), the thermodynamic parameter of variable condition calculation:

Jet expansion steam flow rate c ₁, movable vane import relative velocity w ₁, jet expansion enthalpy h ₁, absolute velocity c ₂, movable vane outlet relative velocity w ₂, movable vane outlet enthalpy h ₂, wheel efficiency η _u;

Governing stage characteristic curve: μ-ε, λ-ε, Ω _m-ε and η _u-x _a;

The peripheral velocity of u---movable vane;

λ---coefficient,

Ω _m---the average degree of reaction of level;

ε---stage pressure ratio;

η---wheel efficiency;

X _a---speed ratio;

μ---coefficient,

Geometric parameter: each nozzle throat sectional area A _nk, movable vane discharge area A _b;

Sliding pressure operation law curve: power-main steam pressure;

Map:

1), by valve nozzle number of combinations X ₁, X ₂, X ₃, X ₄with the thermodynamic parameter (c of variable condition calculation ₁, w ₁, h ₁, c ₂, w ₂, h ₂, η _u), governing stage characteristic curve (μ-ε, λ-ε, Ω _m-ε and η-x _a.), geometric parameter (each nozzle throat sectional area A _nk, movable vane discharge area A _b), sliding pressure operation law curve (power-main steam pressure), add 12 kinds of valve openings, substitute into variable condition calculation function described in step one to four;

The process of step one to four represents with following formula,

[Xgz，Xgz1，Xgz2，Xgz3，Xgz4，Xnri，Fux，Fuy]＝Changingstate(Ge ₁，Fa，X ₁，X ₂，X ₃，X ₄，others)

, obtain 12 flow value [Xgz ₁, Xgz ₂... .Xgz ₁₂];

2) flow value, calculating valve opening corresponding to each loading point and calculate; Check l loading point, then the calculated flow rate xgz of h the loading point of governing stage valve under standard-sized sheet or complete shut-down condition successively _hfor:

Xgz _h=Xgz _jmake | Xgz _j-Ge _h|=m1n (| Xgz ₁-Ge _h|, | Xgz ₂-Ge _h| ..., | Xgz ₁₂-Ge _h|) (2-9)

h＝1，2，...，l；j＝1，2，...，12

Obtain l flow xgz ₁, xgz ₂... xgz _l

Export:

Formula 2-10 characterizes the comprehensive effect of unit under different load point runs of this Nozzle combination, and Y is less, illustrates that this Nozzle combination is better;

Y＝W ₁(xgz ₁-Ge ₁) ²+W ₂(xgz ₂-Ge ₂) ²+......+W _l(xgz _l-Ge _l) ²(2-10)

Finally being expressed as of fitness function:

Work as X _min≤ X ₄≤ X _maxtime:

$\mathrm{ObjV}=\frac{1}{Y}---(2-11)$

Work as X ₄> X _maxoRX ₄< X _mintime:

$\mathrm{ObjV}=\frac{1}{Y}/e^{\mathrm{\&alpha;\&delta;}}---(2-12)$

Show not meet the reduced e of its fitness value of Nozzle combination of constraint 2-3 in formula 2-12 ^{α δ}doubly;

$\mathrm{\&delta;}=\left\{\begin{array}{cc}\frac{X_4-X_{\max }}{X_{\max }},\mathrm{if}& X_4>X_{\max }\\ \frac{X_{\min }-X_4}{X_{\min }},\mathrm{if}& X_4<X_{\min }\end{array}\right.---(2-13)$

α is coefficient of reduction;

Step 5 (three), complete step after, then carry out based on the selection of traditional genetic algorithm, intersection, mutation process; When genetic algebra reach end condition N for time, genetic process stops, export meet actual flow minimum with the comprehensive departure degree Y of theoretical delivery time corresponding optimum number of nozzle combination.

The invention has the beneficial effects as follows:

According to the inventive method, by the conventional working load point of the given steam turbine set of user, when ensureing that unit reaches each loading point, the each valve of governing stage can be in standard-sized sheet or full-shut position, under reducing the condition of the restriction loss of Control Stage of Steam Turbine to greatest extent, the valve nozzle number of combinations of optimization optimum, makes unit have best Economy.The different frequency that conventional loading point is run is taken in simultaneously, using the form of similar weighting in optimization by this factor as the condition optimized, and consider steam turbine sliding pressure operation rule, to reach the effect of optimization more adapting to actual set operating conditions.

Innovative point of the present invention is in particular in the following aspects:

1, utilize the inventive method, user according to the conventional working load point of the given steam-turbine unit of actual conditions oneself, can be applicable to all kinds of unit;

2, the inventive method considers the varying duty condition operating conditions of steam turbine, the operation frequency of conventional loading point has been carried out weighting process, in conjunction with actual motion condition under the condition ensureing the minimal restriction loss of governing stage, the number of nozzle combination of optimization optimum;

3, the sliding pressure operation condition of steam turbine reality is carried out the number optimization of nozzle sets by the inventive method as constraint;

4, the inventive method considers the actual motion constraint of steam turbine set, is optimized transformation, improves the performance of modulating valve, restriction loss is dropped to minimum, improve the Economy of unit to Control Stage of Steam Turbine valve nozzle.

Economy of the present invention is: the inlet steam of steam turbine is many to be provided by boiler, by the Optimizing Reconstruction of the present invention to Control Stage of Steam Turbine, significantly can reduce steam throttling loss, effectively improve the overall efficiency of steam turbine, and then reduce the coal consumption of boiler.According to related data display, when often spending electricity and saving 2 grams of coal consumptions, the economic benefit that the unit of a 600MW is created for a year is by increase about 5,000,000 yuan.Below for the further economic value of the present invention of 250MW Turbo-generator Set and application prospect.

Test with Mudanjiang, Heilungkiang second power plant #6 unit for control group:

From accompanying drawing 3: the Y value of original Nozzle combination is 98, and namely the deviation of theoretical delivery and actual flow is 98t/h.If unit will be allowed in actual motion in given load place stable operation, then valve opening needs a larger change.And by the result display that genetic algorithm is optimized, the value of Y is optimized to 15 from initial close to 80, namely the difference of final theoretical delivery and actual flow only has 15t/h, then in actual motion, the change of valve opening is very little, and restriction loss is little.

The actual effect of number of nozzle optimization can be embodied by Fig. 2.Under the loading point of given 5 band temporal frequencies, the number of each nozzle sets of optimization.Higher than original number of nozzle through optimizing its governing stage efficiency of nozzle sets, in general, after optimizing, governing stage internal efficiency improves more than 5%.This illustrates from another angle, and optimizing process is rational, because the deviation of actual flow and theoretical delivery is less after optimizing, reduces restriction loss, therefore obtains in efficiency and promote significantly.

Technology contents general introduction of the present invention:

The present invention is under the condition of the given relevant parameter of user, first random generation one group of number of nozzle combination, then carrying out loop iteration, using the departure degree of calculated flow rate and theoretical delivery as measuring foundation, drawing the advanced technique number of combinations of the optimum under the given conventional load of user.Its BROAD SUMMARY is as follows:

1, the given steam-turbine unit of user commonly uses maximum, the minimum value of working load point and governing stage valve nozzle total number and each relevant parameter.

2, optimize number of nozzle: if to the given all loading point of user, corresponding modulating valve can be found under the nozzle sets condition of optimal design to open (standard-sized sheet) combination, then reach optimized object.In analytical calculation, using calculated flow rate when valve wide open or full cut-off and user to the departure degree between the theoretical delivery of constant load as measuring foundation, consider the probability of different conventional loading point in process, the operation frequency of each loading point is introduced in the mode of similar weighting.If departure degree is minimum, then result is optimum, and the number of nozzle combination of optimization is optimum combination.Specific implementation process is as follows:

First random generation one group of number of nozzle combination, calculating calculated flow rate in this case and the departure degree of theoretical delivery, then row iteration is combined into number of nozzle, find out under user's given nozzle total number range of condition, the minimum departure degree of calculated flow rate and theoretical delivery, the number of nozzle combination corresponding to it is optimal solution.

A, calculate theoretical flow: according to actual operation parameters and the sliding pressure operation law curve of steam turbine set, each theoretical delivery G of its correspondence can be calculated by given loading point _ei; Investigate actual power plant runnability, its conventional working load point 1, so given i=1 in the present invention, 2 ... 1;

B, by each valve nozzle number and corresponding valve opening thereof, according to rating curve calculated flow rate G _ij: produce one group of random number of nozzle combination, assuming that modulating valve is all in the optimum state of standard-sized sheet or full cut-off according to the number of nozzle scope that user is given.There is not according to actual conditions the situation that a valve list opens, thus the combination of each valve opening have 2^4-4=12 kind (j=1,2 ... 12).To each valve opening combination calculated flow rate XG _ij,

Make xG _i=XG _ijmake | XG _ij-G _ei|=min (| XG _i1-G _ei|, | XG _i2-G _ei| ..., | XG _i12-G _ei|);

C, ask given number of nozzle combination condition under, the total deviation of calculated flow rate and theoretical delivery: the operation frequency considering each loading point, is weighted process:

$Y=\underset{i=1}{\overset{i=6}{\mathrm{\&Sigma;}}}{W}_i*{(x{G}_i-G_i)}^2$

D, number of nozzle is combined into row iteration and calculates, to each group combination, the minimum total deviation of a calculated flow rate and theoretical delivery can be calculated.Find out all minimum total deviations, the number of nozzle combination of its correspondence is optimum number of nozzle combination.

Accompanying drawing explanation

Fig. 1 is structural representation (the 1-main steam of nozzle of steam turbine group; 2-modulating valve; Fig. 1 a is plan view, Fig. 1 b is the left view of Fig. 1 a), as the cut-out governing mode of Fig. 1, the static cascade of governing stage is separated into several air inlet segmental arc, each air inlet segmental arc is made up of the nozzle of some, different governing stage valves to each group of independent steam supply of nozzle, according to the needs of load by changing the aperture of governing stage valve and opening the throttle flow that number changes steam turbine.Pump for Medium and Small Power Generating Set generally has 4 ~ 7 modulating valve, and Large-scale machine set generally has 4 ~ 6 modulating valve;

Fig. 2 is the nozzle sets design interface sectional drawing of the software utilizing the inventive method to develop, and Fig. 3 is optimizing index plotted curve; Fig. 4 is the step velocity triangular relationship figure utilized in the detailed process according to thermodynamic computing formulae discovery parameters.

Embodiment

Embodiment one: the number of nozzle Optimization Design of each nozzle sets of the consideration steam turbine actual motion constraint described in present embodiment realizes in accordance with the following steps:

Step one, structure number of nozzle Optimized model:

Y＝W ₁(xgz ₁-Ge ₁) ²+W ₂(xgz ₂-Ge ₂) ²+......+W _l(xgz _l-Ge _l) ² (2-10)

W ₁, W ₂... W _lrepresent each loading point percentage working time;

Xgz ₁, xgz ₂... xgz _lrepresent the actual flow of each given loading point;

Ge ₁, Ge ₂... Ge _lrepresent the theoretical delivery of each given loading point;

The comprehensive departure degree Y of actual flow and theoretical delivery represents;

Step 2, calculate the actual flow of each given loading point:

The actual flow (haveing nothing to do with given loading point) of 1......l given loading point is calculated by the valve opening of each valve nozzle number and correspondence thereof:

${\mathrm{xgz}}_{\mathrm{kj}}=\frac{0.648A_{\mathrm{nk}}}{\sqrt{\frac{p_{0k}}{{\mathrm{\&rho;}}_{0k}}}}\frac{{\mathrm{\&beta;}}_{1k}}{\frac{p_2}{p_{0k}}}{p}_2=A_k{\mathrm{\&mu;}}_k{p}_2---(1-1)$

In formula: j is the aperture combining form of valve, β _1kfor the governing stage flow-rate ratio of a kth nozzle sets, A _nkfor each nozzle throat sectional area sum of a kth nozzle sets, p _0kfor the pressure (before the nozzle sets that namely modulating valve is corresponding) after a kth modulating valve valve, ρ _0kfor the density (before the nozzle sets that namely modulating valve is corresponding) after a kth modulating valve valve, p _1kfor the nozzle back pressure of a kth nozzle sets, p ₂for the pressure in steam chest after governing stage;

In Practical Calculation process, p _1kwith p ₂value not etc., nozzle sets flow equation is changed into:

${\mathrm{xgz}}_{\mathrm{kj}}^{\&prime;}=\frac{0.648A_{\mathrm{nk}}}{\sqrt{\frac{p_{0k}}{{\mathrm{\&rho;}}_{0k}}}}\frac{{\mathrm{\&beta;}}_{2k}{\mathrm{\&lambda;}}_k}{\frac{p_2}{p_{0k}}}{p}_2---(1-2),$

In formula: β _2kfor the flow-rate ratio of governing stage, λ _kit is the function of pressure ratio before and after governing stage;

P is calculated by dichotomy ₂: pressure p after steps A, a given nozzle _1k; Step B, enthalpy h according to thermodynamic computing formulae discovery delivery nozzle outlet port steam ₁, steam speed c ₁with the relative velocity w of movable vane inlet steam ₁, then the rate of discharge G of a nozzle steam is calculated by formula (1-2) _n; The pressure of step C, then a hypothesis movable vane outlet vapor, i.e. pressure p after governing stage ₂; Step D, drawn the enthalpy h of movable vane outlet port steam by thermodynamic computing formula ₂, outlet port steam density p ₂, outlet vapor relative velocity w ₂with absolute velocity c ₂, then the flow G of movable vane outlet vapor is gone out by formulae discovery _b; If step e G _b≠ G _n, be back to step C and continue to calculate, until obtain G _b=G _n, draw pressure p after governing stage ₂; G _b=ρ ₂w ₂a _b, in formula: ρ ₂---movable vane outlet actual density; A _b---movable vane outlet throat area;

According to pressure p after the governing stage that dichotomy calculates ₂be updated in formula (1-2) and calculate actual flow xgz;

Step 3, calculate the theoretical delivery of each given loading point:

Ge _h=Ge ξ _h, wherein Ge _hfor the theoretical delivery of loading point, Ge is the rated flow of steam turbine, ξ _hfor accounting for the percentage of rated power in loading point tubine operate power, h=1,2 ... l, l are the number of working load point;

The constraint conditio of step 4, structure number of nozzle Optimized model:

X ₁+X ₂+X ₃+X ₄＝X _z，X _z＝const (2-2)

X _min≤X _i≤X _max，i＝1，2，3，4；X _min＝const，X _max＝const(2-3)

X ₁, X ₂, X ₃, X ₄represent the nozzle number of four nozzle sets, X _zfor total nozzle number, const represents definite value;

Step 5, obtain given loading point based on Genetic Algorithms Theory under the combination of optimum number of nozzle corresponding when making actual flow and the comprehensive departure degree Y of theoretical delivery minimum, detailed process is as follows:

Step 5 (one), initial population set:

Adopting floating-point encoding, is [X between code area _min, X _max],

$\mathrm{Chrom}=\left[\begin{array}{c}{x}_{11},x_{12},x_{13}\\ x_{21},x_{22},x_{23}\\ ..........\\ x_{m1},x_{m2},x_{m3}\end{array}\right],$ x _ij∈R ₊，i＝1，2，...，m，j＝1，2，3.(2-4)

In formula, the individual amount of m representative coding;

For body one by one, its each chromosome x _i1, x _i2, x _i3be respectively the nozzle sets number of nozzle X that first three regulating valve is corresponding ₁, X ₂, X ₃coding, its corresponding relation is:

X _ij＝round(x _ij)，i＝1，2...m，j＝1，2，3(2-5)

Round represents round, calculates X ₁, X ₂, X ₃after, X ₄for:

X _i4＝X _z-(X _i1+X _i2+X _i3) (2-7)

Step 5 (two), structure fitness function ObjV: calculated by fitness, realize individual optimum choice, make the 4th nozzle sets number of nozzle in optimum results meet constraint conditio (2-3) formula simultaneously, the individuality (2-8) not meeting constraint conditio given up in iteration:

X _i4＞X _max ORX _i4＜X _min (2-8)

If fitness function is:

ObjV＝OBJ_func(Ge，Fa，X ₁，X ₂，X ₃，X ₄，others)

Above-mentioned fitness function does not have explicit mathematic(al) representation, and the mapping relations of above-mentioned fitness function are as follows:

Input:

1), the flow Ge of all given loading point ₁, Ge ₂... Ge _l, and each loading point percentage working time W ₁, W ₂... W _l;

2), valve opening (12 kind 0,1 combination): [0000; 0011; 0101; 1001; 0110; 1100; 1010; 1110; 1101; 1011; 0111; 1111];

3), valve nozzle group number of nozzle combination X ₁, X ₂, X ₃, X ₄, produced by the coding of body one by one;

4), the thermodynamic parameter of variable condition calculation:

Jet expansion steam flow rate c ₁, movable vane import relative velocity w ₁, jet expansion enthalpy h ₁, absolute velocity c ₂, movable vane outlet relative velocity w ₂, movable vane outlet enthalpy h ₂, wheel efficiency η _u;

Governing stage characteristic curve: μ-ε, λ-ε, Ω _m-ε and η _u-x _a;

The peripheral velocity of u---movable vane;

λ---coefficient,

Ω _m---the average degree of reaction of level;

ε---stage pressure ratio;

η---wheel efficiency;

X _a---speed ratio;

μ---coefficient,

Geometric parameter: each nozzle throat sectional area A _nk, movable vane discharge area A _b;

Sliding pressure operation law curve: power-main steam pressure;

Map:

1), by valve nozzle number of combinations X ₁, X ₂, X ₃, X ₄with the thermodynamic parameter (c of variable condition calculation ₁, w ₁, h ₁, c ₂, w ₂, h ₂, η _u), governing stage characteristic curve (μ-ε, λ-ε, Ω _m-ε and η-x _a.), geometric parameter (each nozzle throat sectional area A _nk, movable vane discharge area A _b), sliding pressure operation law curve (power-main steam pressure), add 12 kinds of valve openings, substitute into variable condition calculation function described in step one to four;

The process of step one to four represents with following formula,

[Xgz, Xgz1, Xgz2, Xgz3, Xgz4, Xnri, Fux, Fuy]=Changingstate (Ge ₁, Fa, X ₁, X ₂, X ₃, X ₄, others), obtain 12 flow value [Xgz ₁, Xgz ₂... .Xgz ₁₂];

2) flow value, calculating valve opening corresponding to each loading point and calculate; Check l loading point, then the calculated flow rate xgz of h the loading point of governing stage valve under standard-sized sheet or complete shut-down condition successively _hfor:

Xgz _h=Xgz _jmake | Xgz _j-Ge _h|=min (| Xgz ₁-Ge _h|, | Xgz ₂-Ge _h| ..., | Xgz ₁₂-Ge _h|) (2-9)

h＝1，2，...，l；j＝1，2，...，12

Obtain l flow xgz ₁, xgz ₂... xgz _l

Export:

Formula 2-10 characterizes the comprehensive effect of unit under different load point runs of this Nozzle combination, and Y is less, illustrates that this Nozzle combination is better;

Y＝W ₁(xgz ₁-Ge ₁) ²+W ₂(xgz ₂-Ge ₂) ²+......+W _l(xgz _l-Ge _l) ²(2-10)

Finally being expressed as of fitness function:

Work as X _min≤ X ₄≤ X _maxtime:

$\mathrm{ObjV}=\frac{1}{Y}---(2-11)$

Work as X ₄> X _maxoRX ₄< X _mintime:

$\mathrm{ObjV}=\frac{1}{Y}/e^{\mathrm{\&alpha;\&delta;}}---(2-12)$

Show not meet the reduced e of its fitness value of Nozzle combination of constraint 2-3 in formula 2-12 ^{α δ}doubly;

$\mathrm{\&delta;}=\left\{\begin{array}{cc}\frac{X_4-X_{\max }}{X_{\max }},\mathrm{if}& X_4>X_{\max }\\ \frac{X_{\min }-X_4}{X_{\min }},\mathrm{if}& X_4<X_{\min }\end{array}\right.---(2-13)$

α is coefficient of reduction;

Step 5 (three), complete step after, then carry out based on the selection of traditional genetic algorithm, intersection, mutation process; When genetic algebra reach end condition N for time, genetic process stops, export meet actual flow minimum with the comprehensive departure degree Y of theoretical delivery time corresponding optimum number of nozzle combination.

In step 2, the detailed process according to thermodynamic computing formulae discovery parameters is:

1, jet expansion steam flow rate c ₁

C in formula _1s---the steam flow ideal velocity (m/s) of jet expansion;

---the stagnation isentropic enthalpy drop, ideal enthalpy drop (J/Kg) of steam in nozzle;

---the velocity coefficient of nozzle, get velocity coefficient

2, movable vane import relative velocity w ₁

Movable vane imports and exports velocity triangle can referring to Fig. 4,

$w_1=\sqrt{c_1^2+u^2-2{\mathrm{uc}}_1\cos {\mathrm{\&alpha;}}_1}---(3-12)$

${\mathrm{\&gamma;}}_1=\arcsin \frac{c_1\sin {\mathrm{\&alpha;}}_1}{w_1}---(3-13)$

U---movable vane peripheral velocity

γ ₁---relative velocity enters the angle of moving blades

3, jet expansion enthalpy h ₁

Because flow process is adiabatic, consuming with the kinetic transformation in loss is that heat heated again steam itself, so the actual enthalpy h of jet expansion steam flow ₁desirable enthalpy h will be greater than _1s, i.e. h ₁=h _1s+ Δ h _{n ξ}.

4, the relative velocity w of movable vane outlet ₂and absolute velocity c ₂

In order to the convenience of problem analysis, we introduce an imaginary speed c _a, corresponding kinetic energy equals the isentropic enthalpy drop of level, that is:

${\mathrm{\&Delta;h}}_s^{*}=\frac{{c_a}^2}{2}$ (3-24)

$w_{2s}=\sqrt{2\mathrm{\&Delta;}{h}_b+w_1^2}=\sqrt{2{\mathrm{\&Omega;}}_m{\mathrm{\&Delta;h}}_s^{*}+w_1^2}=\sqrt{2\mathrm{\&Delta;}{h}_b^{*}}---(3-16)$

Δ h in formula _b---movable vane isentropic enthalpy drop, ideal enthalpy drop;

W ₁---the effective relative velocity of movable vane import;

---level stagnation isentropic enthalpy drop, ideal enthalpy drop;

---movable vane is stagnant isentropic enthalpy drop, ideal enthalpy drop relatively only,

Ω _m---the average degree of reaction of level,

W _2s---the constant entropy speed of movable vane outlet

The actual relative velocity w of moving blades ₂can be expressed as: w ₂=ψ w _2s

$c_2=\sqrt{w_2^2+u^2-2u{w}_2\cos (180-{\mathrm{\&beta;}}_2)}---(3-14)$

${\mathrm{\&alpha;}}_2=\arcsin \frac{w_2\sin (180-{\mathrm{\&beta;}}_2)}{c_2\cos (180-{\mathrm{\&alpha;}}_2)}---(3-15)$

α ₂---flow outlet angle

5, movable vane outlet enthalpy h ₂

The outlet enthalpy of movable vane equals the difference of jet expansion enthalpy and enthalpy drop, that is:

h ₂＝h _2s-Δh ₂

1) movable vane entrance clashes into loss Δ h _β

${\mathrm{\&Delta;h}}_{\mathrm{\&beta;}}=\frac{{\left(w_1\sin \mathrm{\&theta;}\right)}^2}{2}---(3-11)$

The angle in θ---movable vane import relative velocity direction and movable vane inlet angle direction

2) moving blade loss Δ h _{b ξ}

Energy loss Δ h in movable vane _{b ξ}also can by representing, that is:

${\mathrm{\&Delta;h}}_{\mathrm{b\&xi;}}=\frac{1}{2}({w_{2s}}^2-{w_2}^2)=(1-{\mathrm{\&psi;}}^2)\mathrm{\&Delta;}{h}_b^{*}---(3-18)$

ψ---movable vane velocity coefficient

3) leaving loss Δ h _c2

${\mathrm{\&Delta;h}}_{c2}=\frac{{c_2}^2}{2}---(3-19)$

Enthalpy drop: Δ h ₂=Δ h _β+ Δ h _{b ξ}+ Δ h _c2

6, wheel efficiency η _u

Consider that the useful enthalpy drop of leaf height loss rear class is:

${\mathrm{\&Delta;h}}_u={\mathrm{\&Delta;h}}_s^{*}-{\mathrm{\&Delta;h}}_{\mathrm{n\&xi;}}-{\mathrm{\&Delta;h}}_{\mathrm{\&beta;}}-{\mathrm{\&Delta;h}}_{\mathrm{b\&xi;}}-{\mathrm{\&Delta;h}}_{c2}-{\mathrm{\&Delta;h}}_l---(3-23)$

Therefore wheel efficiency is: ${\mathrm{\&eta;}}_u=\frac{{\mathrm{\&Delta;h}}_u}{{\mathrm{\&Delta;h}}_s^{*}}---(3-21)$

Embodiment two: present embodiment calculates p in step 2 by dichotomy ₂process in, pressure p after described nozzle _1kcomputational process be: the sliding pressure operation law curve obtained in actual motion by steam turbine set: power-main steam pressure, then according to actual motion load, check in the pressure of main steam, then calculate p _1k.Other step is identical with embodiment one.

Elaborate as follows again for the inventive method:

1, genetic algorithm and improve and optimizate method

Genetic algorithm is based on natural selection and theory of heredity, by the efficient global optimization approach that survival of the fittest rule in biological evolution process combines with the random information exchanging mechanism of colony intrinsic stain body.Genetic algorithm has abandoned traditional way of search, the evolutionary process of simulation living nature, adopts the mode of artificial evolution to carry out random optimization search to object space.It solution will may regard body one by one in colony as in problem, and each coding is weaved into the form of symbol string, simulate the evolutionary process of Darwinian heredity selection and natural selection, the operation (heredity, intersection, variation) based on heredity is carried out repeatedly to colony.Target fitness function according to predeterminated target is evaluated each individuality, according to the evolutionary rule of the survival of the fittest, the survival of the fittest, constantly obtain optimum colony, search the optimum individual optimized in colony in overall parallel search mode, in the hope of the optimal solution satisfied condition simultaneously.

The general process of genetic algorithm is: arrange initial population (coding), calculate fitness, select, intersect, variation, produce new population, recalculate fitness, successively loop iteration, until iterations reaches initial set value, heredity terminates, and last population obtained is optimum population in generation, and the individuality in population is optimum individual.

2, number of nozzle coding, variable load operation parameter and other parameters are determined

2.1 consider that the number of nozzle of actual motion constraint is encoded

In genetic algorithm, amount to be optimized is generally input in optimization system as random coded, but because optimized amount is often as actual production, operating parameter, there is actual physical meaning, the restriction of extraneous all factors can be subject to, value has certain constraint, directly can not bring as coding individual in initial population, need to carry out suitable conversion.So be that each individuality must be the feasible solution of this optimization problem for the requirement of population setting, optimize like this and be just of practical significance.Illustrate:

Find function

$y=f(x_1,x_2,x_3,x_4)=x_1^2+x_2^2+x_3^2+x_4^2---2-1$

Minimum value, due to for function f, to its independent variable of any real number x ₁, x ₂, x ₃, x ₄again the feasible solution of function f, thus coding process in x ₁, x ₂, x ₃, x ₄without any constraint conditio within the scope of real number, the random coded of generation meets optimal conditions, can substitute into function and calculate, draw corresponding value.

In this example, the nozzle sets number of nozzle optimized not is that any value is all of practical significance.On the one hand, in Design of Steam Turbine manufacture process, / 4th segmental arcs of four corresponding governing stages of regulating valve difference, because the governing stage gross area is fixed, each jet size is also setting value, and nozzle is evenly distributed on circumferentially, this just requires that the number of nozzle in each segmental arc can not too much can not be very little.On the other hand, the nozzle of steam turbine is made up of first stage stator blades grid and first order movable vane, governing stage (steam turbine first stage) not only serves acting effect, also the guide functions to main steam is served, if therefore a certain segmental arc top nozzle number very little, does not have such effect, number is too many, then segmental arc is not held.

General when nozzle of steam turbine group designs, total number of nozzle is definite value, and each nozzle sets number of nozzle has minimum and maximum value.Constraint conditio is:

X ₁+X ₂+X ₃+X ₄＝X _z，X _z＝const 2-2

X _min≤X _i≤X _max，i＝1，2，3，4；X _min＝const，X _max＝const 2-3

Genetic algorithm, due to its randomness, is difficult to the constraint solving above-mentioned two equatioies, but can solve constraint conditio by suitable conversion;

Adopting floating-point encoding, is [X between code area _min, X _max]

$\mathrm{Chrom}=\left[\begin{array}{c}{x}_{11},x_{12},x_{13}\\ x_{21},x_{22},x_{23}\\ ..........\\ x_{m1},x_{m2},x_{m3}\end{array}\right],$ x _ij∈R ₊，i＝1，2，...，m，j＝1，2，3. 2-4

In formula, the individual amount of m representative coding, default before optimizing, m is larger, and the iterations that optimization system enters optimum stable solution is fewer, but the amount of calculation of each iteration is more consuming time more, generally, when system enters optimal solution stable region, allow iteration terminate as far as possible, therefore individual amount and iterations are debugged by test of many times to draw, the highest to ensure optimization efficiency.

For body one by one, its each chromosome x _i1, x _i2, x _i3, be respectively the nozzle sets number of nozzle X that first three regulating valve is corresponding ₁, X ₂, X ₃coding, its corresponding relation is:

X _ij＝round(x _ij)，i＝1，2...m，j＝1，2，3 2-5

Round represents round.Calculate X1, after X2, X3, X4 is:

X _i4＝X _z-(X _i1+X _i2+X _i3) 2-7

Being arranged within the scope of one of such initial population is random, and first constraint conditio (formula 2-2) also well meets.But for formula (2-3), initial population carries out not being well positioned to meet when encoding, that is, some insignificant individualities can be produced in cataloged procedure and make:

X _i4＞X _maxORX _i4＜X _min 2-8

These individualities are not actual meeting the demands, if but rejected these individualities artificially, then could destroy the diversity of population, run counter to the optimization principles of genetic algorithm, therefore need to take other modes to solve the constraint of formula 2-3, concrete grammar will at 2.2 introduction.

2.2 variable load operation parameters and other parameter

In optimizing process, number of nozzle is not only as amount to be optimized (output quantity), also be the input quantity of optimization system, from the first part, Governing Stage Characteristics of Steam Turbine curve can be obtained, the parameters such as the governing stage downstream pressure under each stock air-flow that four regulating valves are corresponding acts on simultaneously, each valve corresponding flow, internal efficiency, air-flow power by Control Stage of Steam Turbine variable condition calculation.Vital effect is served in the optimizing process of these thermodynamic parameters exported below, consider that this is governing stage number of nozzle optimization under variable load operation, the parameter needed before optimization is all parameters of variable load operation parameter and governing stage variable condition calculation.Before optimization, user inputs (parameter of variable condition calculation), given each loading point and percentage working time corresponding to each loading point, exports the governing stage downstream pressure under each loading point, each valve corresponding flow, internal efficiency, air-flow power etc.

In variable condition calculation before, main steam pressure is generally definite value, changes main steam flow by changing main stop valve and control valve opening.And in sliding pressure operation, main stop valve and controlling opening of valve constant, regulate the effect of main steam flow by the pressure changing main steam to reach.In actual motion, both carry out often simultaneously: the aperture of main stop valve and modulating valve changes along with the change of steam turbine power, and the pressure of main steam also changes along with power and changes, so just can reach the pressure and other parameters changing main steam under the condition of valve wide open or complete shut-down makes unit run in given loading point, reduces restriction loss.The pressure of the main steam at a certain loading point (power) place can be found by sliding pressure operation curve (power-main steam pressure).Sliding pressure curve is obtained by unit actual tests.

Given loading point 1:

[Xgz ₁，Xgz1 ₁，Xgz2 ₁，Xgz3 ₁，Xgz4 ₁，Xnri ₁，Fux ₁，Fuy ₁]＝Changingstate(Ge ₁，P ₁，Fa，X ₁，X ₂，X ₃，X ₄，others)

Given loading point 2:

[Xgz ₂，Xgz1 ₂，Xgz2 ₂，Xgz3 ₂，Xgz4 ₂，Xnri ₂，Fux ₂，Fuy ₂]＝Changingstate(Ge ₂，P ₂，Fa，X ₁，X ₂，X ₃，X ₄，others)

Given loading point 1:

[Xgz _l，Xgz1 _l，Xgz2 _l，Xgz3 _l，Xgz4 _l，Xnri _l，Fux _l，Fuy _l]＝Changingstate(Ge _l，P _l，Fa，X ₁，X ₂，X ₃，X ₄，others)

For each loading point, the symbol implication of its input value is respectively: the actual flow of loading point, main steam pressure (when inputting main steam pressure, sliding pressure curve is made look-up-table function, the loading point different according to unit, draws corresponding main steam pressure), valve opening, valve 1 corresponding nozzle group number of nozzle, valve 2 corresponding nozzle group number, valve 3 corresponding nozzle group number, valve 4 corresponding nozzle group number, other parameters.Its value symbol implication exported is followed successively by from left to right: flow, the rear flow of valve 1, the rear flow of valve 2, the rear flow of valve 3, the rear flow of valve 4, internal efficiency ratio, horizontal gas flow power, vertically air-flow power after governing stage.

The power of steam turbine under running at full capacity is rated power, and its main steam flow is rated flow.Power is directly proportional to flow, as long as the steam turbine power therefore under given rated flow and different load point accounts for the percentage of rated power, then can draw the main steam flow of steam turbine under this loading point (input quantity Section 1).Fa is the aperture of valve, and X1, X2, X3, X4 are the number of nozzle that steam turbine 4 valve is corresponding, and in optimization, each valve nozzle number is produced by coding automatically by system, and valve opening is also produced by optimization system.For same unit, other parameters remain unchanged substantially, and user can these parameters of sets itself as required.

3, the determination of the fitness function based on Control Stage of Steam Turbine variable condition calculation of actual motion constraint is considered

In genetic algorithm, fitness function is used to the standard distinguishing individual in population quality, is unique foundation of carrying out natural selection.Fitness function characterizes a virtual physical environment, and the individuality in population is multiplied in virtual environment.The individuality wherein conformed will be retained, and maladjusted individuality will be eliminated.Fitness has quantized individual adaptedness in virtual environment, calculates by being substituted in fitness function by individuality.

In this example, the target of optimization makes under the combination of optimum nozzle sets number of nozzle, and the restriction loss of governing stage is minimum.Restriction loss is not exclusively opened due to steam turbine regulating valve in intake process and is caused, and under the condition of regulating valve standard-sized sheet or complete shut-down, restriction loss is minimum.If there is the combination of certain valve nozzle group number of nozzle, make each valve aperture be 1 or 0 time energy run (the flow Xgz namely calculated at given loading point place _iequal it to the flow Ge of constant load _i), then illustrate that this Nozzle combination is optimum combination.In fact, so perfect Nozzle combination does not exist, the flow calculated and must have certain difference to flow corresponding to constant load.If but difference is less, then also can illustrates that this Nozzle combination can reach and preferably combine.In unit actual motion, the difference making up above-mentioned flow a little be opened or be closed to regulating valve can, and the restriction loss of generation is less.

Consider variable load operation, a kind of Nozzle combination can will run preferably in multiple loading point, the restriction loss produced is less, and valve opening is tending towards 1 or 0 as far as possible, then need to propose a kind of overall target to weigh the effect of this number of nozzle under these loading point are run.

By above-mentioned analysis, the structure of fitness function can determine substantially.This fitness function is as input by multiple governing stage relevant parameters such as codings, flow after governing stage is calculated in variable condition calculation by each loading point that constantly iterates, to flow and given flow-rate ratio be calculated comparatively by corresponding rule again, obtain the Nozzle combination that effect under comprehensive each loading point is best.The valve opening be combined under the condition of four valve wide opens or complete shut-down has plant combination, due in actual moving process, there is not the situation of only opening a valve, therefore be left the combination of valve opening in 12.

If fitness function is:

ObjV＝OBJ_func(Ge，Fa，X ₁，X ₂，X ₃，X ₄，others)

This function does not have explicit mathematic(al) representation, elaborates the mapping relations of this function below:

Input:

1) the flow Ge of all given loading point ₁, Ge ₂... Ge _l, and each loading point percentage working time W ₁, W ₂... W _l

2) valve opening (12 kind 0,1 combination): [0000; 0011; 0101; 1001; 0110; 1100; 1010; 1110; 1101; 1011; 0111; 1111];

3) combination of valve nozzle group number of nozzle X1, X2, X3, X4, is produced by the coding of body one by one

4) by the thermodynamic parameter (c of variable condition calculation ₁, w ₁, h ₁, c ₂, w ₂, h ₂, η _u), governing stage characteristic curve (μ-ε, λ-ε, Ω _m-ε and η-x _a.), geometric parameter (each nozzle throat sectional area A _nk, movable vane discharge area A _b), sliding pressure operation law curve (power-main steam pressure) etc.

Map:

1) by valve nozzle number of combinations X1, X2, X3, X4 and other parameter, add 12 kinds of valve openings, substitutes into variable condition calculation function

[Xgz，Xgz1，Xgz2，Xgz3，Xgz4，Xnri，Fux，Fuy]＝Changingstate(Ge ₁，Fa，X ₁，X ₂，X ₃，X ₄，others)

Obtain 12 flow value [Xgz ₁, Xgz ₂... .Xgz ₁₂]

2) flow value calculating valve opening corresponding to each loading point and calculate.Check 1 loading point, then the calculated flow rate xgz of i-th loading point of governing stage valve under standard-sized sheet or complete shut-down condition successively _ifor: xgz _i=Xgz _jmake | Xgz _j-Ge _i|=min (| Xgz ₁-Ge _i|, | Xgz ₂-Ge _i| ..., | Xgz ₁₂-Ge _i|) 2-9i=1,2 ..., l; J=1,2 ..., 12

Obtain 1 flow xg ₁, xgz ₂... xgz _l

Export:

Each unit is under actual motion condition, and frequency working time of each load is different, in number of nozzle Optimized model, probability working time is weighted process.Formula 2-10 characterizes the comprehensive effect of unit under different load point runs of this Nozzle combination, and Y is less, illustrates that this Nozzle combination is better.

Y＝W ₁(xgz ₁-Ge ₁) ²+W ₂(xgz ₂-Ge ₂) ²+......+W _l(xgz _l-Ge _l) ² 2-10

W ₁, W ₂... W _lrepresent each loading point percentage working time;

In genetic algorithm, fitness function is larger, is genetic to follow-on probability larger, and the value non-negative of fitness function, Y value is less is in this example that we more wish to obtain.So Y is not the final expression of fitness function.On the other hand, 2.1.1 the constraint of Chinese style 2-3 is not solved in coding, if be optimized according to said process, the Nozzle combination more not meeting constraint conditio 2-3 to be also updated in calculating and may to be genetic in the next generation along with calculating larger fitness, causes optimization mistake.Therefore we need when fitness function defines, and adopt suitable conversion to be eliminated gradually in iteration by nonsensical individuality, to retain qualified individuality.

Finally being expressed as of fitness function:

Work as X _min≤ X ₄≤ X _maxtime:

$\mathrm{ObjV}=\frac{1}{Y}---2-11$

Work as X ₄> X _maxoRX ₄< X _mintime:

$\mathrm{ObjV}=\frac{1}{Y}/e^{\mathrm{\&alpha;\&delta;}}---2-12$

Show not meet the reduced e of its fitness value of Nozzle combination of constraint 2-3 in formula 2-12 ^{α δ}doubly.

$\mathrm{\&delta;}=\left\{\begin{array}{cc}\frac{X_4-X_{\max }}{X_{\max }},\mathrm{if}& X_4>X_{\max }\\ \frac{X_{\min }-X_4}{X_{\min }},\mathrm{if}& X_4<X_{\min }\end{array}\right.---2-13$

Above formula illustrates, X4 gets over off-design value, and fitness value does not have the minification of off-design value larger relatively.α is coefficient of reduction, sets before optimization.We reduce 1000 times by design fitness value when X4 off-design is worth 100% when testing.Now α=6.9078.Like this, the individuality of reduced fitness value will be eliminated gradually in genetic iteration process, reach the object realizing constraint conditio 2-3.

Claims (2)

1. consider a number of nozzle Optimization Design for each nozzle sets that steam turbine actual motion retrains, it is characterized in that: described method realizes in accordance with the following steps:

Step one, structure number of nozzle Optimized model:

Y＝W ₁(xgz ₁-Ge ₁) ²+W ₂(xgz ₂-Ge ₂) ²+......+W _l(xgz _l-Ge _l) ² (2-10)

W ₁, W ₂w _lrepresent each loading point percentage working time;

Xgz ₁, xgz ₂xgz _lrepresent the actual flow of each given loading point;

Ge ₁, Ge ₂ge _lrepresent the theoretical delivery of each given loading point;

The comprehensive departure degree Y of actual flow and theoretical delivery represents;

Step 2, calculate the actual flow of each given loading point:

1 is calculated by the valve opening of each valve nozzle number and correspondence thereof ... the actual flow of l given loading point:

${\mathrm{xgz}}_{\mathrm{kj}}=\frac{0.648A_{\mathrm{nk}}}{\sqrt{\frac{p_{0k}}{{\mathrm{\&rho;}}_{0k}}}}\frac{{\mathrm{\&beta;}}_{1k}}{\frac{p_2}{p_{0k}}}{p}_2=A_k{\mathrm{\&mu;}}_k{p}_2---(1-1)$

In formula: j is the aperture combining form of valve, β _1kfor the governing stage flow-rate ratio of a kth nozzle sets, A _nkfor each nozzle throat sectional area sum of a kth nozzle sets, p _0kfor the pressure after a kth modulating valve valve, ρ _0kfor the density after a kth modulating valve valve, p _1kfor the nozzle back pressure of a kth nozzle sets, p ₂for the pressure in steam chest after governing stage;

In Practical Calculation process, p _1kwith p ₂value not etc., nozzle sets flow equation is changed into:

${\mathrm{xgz}}_{\mathrm{kj}}^{\&prime;}=\frac{0.648A_{\mathrm{nk}}}{\sqrt{\frac{p_{0k}}{p_{0k}}}}\frac{{\mathrm{\&beta;}}_{2k}{\mathrm{\&lambda;}}_k}{\frac{p_2}{p_{0k}}}{p}_2---(1-2),$

In formula: β _2kfor the flow-rate ratio of governing stage, λ _kit is the function of pressure ratio before and after governing stage;

P is calculated by dichotomy ₂: pressure p after steps A, a given nozzle _1k; Step B, enthalpy h according to thermodynamic computing formulae discovery delivery nozzle outlet port steam ₁, steam speed c ₁with the relative velocity w of movable vane inlet steam ₁, then the rate of discharge G of a nozzle steam is calculated by formula (1-2) _n, the detailed process according to thermodynamic computing formulae discovery parameters is:

Jet expansion steam flow rate c ₁:

C in formula _1s---the steam flow ideal velocity (m/s) of jet expansion;

---the stagnation isentropic enthalpy drop, ideal enthalpy drop (J/Kg) of steam in nozzle;

---the velocity coefficient of nozzle, get velocity coefficient 2, movable vane import relative velocity w ₁

Movable vane imports and exports speed,

$w_1=\sqrt{c_1^2+u^2-2u{c}_1\cos {\mathrm{\&alpha;}}_1}---(3-12)$

${\mathrm{\&gamma;}}_1=\arcsin \frac{c_1\sin {\mathrm{\&alpha;}}_1}{w_1}---(3-13)$

U---movable vane peripheral velocity

γ ₁---relative velocity enters the angle of moving blades

Jet expansion enthalpy h ₁:

Because flow process is adiabatic, consuming with the kinetic transformation in loss is that heat heated again steam itself, so the actual enthalpy h of jet expansion steam flow ₁desirable enthalpy h will be greater than _1s, i.e. h ₁=h _1s+ △ h _{n ξ};

The pressure of step C, then a hypothesis movable vane outlet vapor, i.e. pressure p after governing stage ₂; Step D, drawn the enthalpy h of movable vane outlet port steam by thermodynamic computing formula ₂, outlet port steam density p ₂, outlet vapor relative velocity w ₂with absolute velocity c ₂, then the flow G of movable vane outlet vapor is gone out by formulae discovery _b; If step e G _b≠ G _n, be back to step C and continue to calculate, until obtain G _b=G _n, draw pressure p after governing stage ₂; G _b=ρ ₂w ₂a _b, in formula: ρ ₂---movable vane outlet actual density; A _b---movable vane outlet throat area;

The relative velocity w of movable vane outlet ₂and absolute velocity c ₂:

In order to the convenience of problem analysis, we introduce an imaginary speed c _a, corresponding kinetic energy equals the isentropic enthalpy drop of level, that is:

$\mathrm{\&Delta;}{h}_s^{*}=\frac{{c_a}^2}{2}---(3-24)$

$w_{2s}=\sqrt{2\mathrm{\&Delta;}{h}_b+w_1^2}=\sqrt{2{\mathrm{\&Omega;}}_m\mathrm{\&Delta;}{h}_s^{*}+w_1^2}=\sqrt{2\mathrm{\&Delta;}{h}_b^{*}}---(3-16)$

△ h in formula _b---movable vane isentropic enthalpy drop, ideal enthalpy drop;

W ₁---the effective relative velocity of movable vane import;

---level stagnation isentropic enthalpy drop, ideal enthalpy drop;

---movable vane is stagnant isentropic enthalpy drop, ideal enthalpy drop relatively only,

Ω _m---the average degree of reaction of level,

W _2s---the constant entropy speed of movable vane outlet

The actual relative velocity w of moving blades ₂be expressed as: w ₂=ψ w _2s

$c_2=\sqrt{w_2^2+u^2-2u{w}_2\cos (180-{\mathrm{\&beta;}}_2)}---(3-14)$

${\mathrm{\&alpha;}}_2=\arcsin \frac{w_2\sin (180-{\mathrm{\&beta;}}_2)}{c_2\cos (180-{\mathrm{\&alpha;}}_2)}---(3-15)$

α ₂---flow outlet angle

Movable vane outlet enthalpy h ₂:

The outlet enthalpy of movable vane equals the difference of jet expansion enthalpy and enthalpy drop, that is:

h ₂＝h _2s-△h ₂

1) movable vane entrance clashes into loss △ h _β

$\mathrm{\&Delta;}{h}_{\mathrm{\&beta;}}=\frac{{\left(w_1\sin \mathrm{\&theta;}\right)}^2}{2}---(3-11)$

The angle in θ---movable vane import relative velocity direction and movable vane inlet angle direction

2) moving blade loss △ h _{b ξ}

Energy loss △ h in movable vane _{b ξ}be expressed as:

${\mathrm{\&Delta;h}}_{\mathrm{b\&xi;}}=\frac{1}{2}({w_{2s}}^2-{w_2}^2)=(1-{\mathrm{\&psi;}}^2)\mathrm{\&Delta;}{h}_b^{*}---(3-18)$

ψ---movable vane velocity coefficient

3) leaving loss △ h _c2

$\mathrm{\&Delta;}{h}_{c2}=\frac{{c_2}^2}{2}---(3-19)$

Enthalpy drop: △ h ₂=△ h _β+ △ h _{b ξ}+ △ h _c2

According to pressure p after the governing stage that dichotomy calculates ₂be updated in formula (1-2) and calculate actual flow xgz;

Step 3, calculate the theoretical delivery of each given loading point:

Ge _h=Ge ξ _h, wherein Ge _hfor the theoretical delivery of loading point, Ge is the rated flow of steam turbine, ξ _hfor accounting for the percentage of rated power in loading point tubine operate power, h=1,2 ... l, l are the number of working load point;

The constraint conditio of step 4, structure number of nozzle Optimized model:

X ₁+X ₂+X ₃+X ₄＝X _z,X _z＝const (2-2)

X _min≤X _i≤X _max,i＝1,2,3,4；X _min＝const,X _max＝const (2-3)

X ₁, X ₂, X ₃, X ₄represent the nozzle number of four nozzle sets, X _zfor total nozzle number, const represents definite value;

Step 5, obtain given loading point based on Genetic Algorithms Theory under the combination of optimum number of nozzle corresponding when making actual flow and the comprehensive departure degree Y of theoretical delivery minimum, detailed process is as follows:

Step 5 (one), initial population set:

Adopting floating-point encoding, is [X between code area _min, X _max],

$\mathrm{Chrom}=\left[\begin{array}{c}{x}_{11},x_{12},x_{13}\\ x_{21},x_{22},x_{23}\\ .................\\ x_{m1},x_{m2,}{x}_{m3}\end{array}\right],x_{\mathrm{ij}}\&Element;R_{+},i=\mathrm{1,2},...,m,j=\mathrm{1,2,3}.---(2-4)$

In formula, the individual amount of m representative coding;

For body one by one, its each chromosome x _i1, x _i2, x _i3, be respectively the nozzle sets number of nozzle X that first three regulating valve is corresponding ₁, X ₂, X ₃coding, its corresponding relation is:

X _ij＝round(x _ij),i＝1,2...m,j＝1,2,3 (2-5)

Round represents round, calculates X ₁, X ₂, X ₃after, X ₄for:

X _i4＝X _z-(X _i1+X _i2+X _i3) (2-7)

Step 5 (two), structure fitness function ObjV: calculated by fitness, realize individual optimum choice, make the 4th nozzle sets number of nozzle in optimum results meet constraint conditio (2-3) formula simultaneously,

The individuality (2-8) not meeting constraint conditio is given up in iteration:

X _i4>X _max OR X _i4<X _min (2-8)

If fitness function is:

ObjV＝OBJ_func(Ge,Fa,X ₁,X ₂,X ₃,X ₄,others)

Above-mentioned fitness function does not have explicit mathematic(al) representation, and the mapping relations of above-mentioned fitness function are as follows:

Input:

1), the flow Ge of all given loading point ₁, Ge ₂ge _l, and each loading point percentage working time W ₁, W ₂w _l;

2), 12 kinds of valve openings: [0 000; 0011; 0101; 1001; 0110; 1100; 1010; 1110; 1101; 1011; 0111; 111 1];

3), valve nozzle group number of nozzle combination X ₁, X ₂, X ₃, X ₄, produced by the coding of body one by one;

4), the thermodynamic parameter of variable condition calculation:

Jet expansion steam flow rate c ₁, movable vane import relative velocity w ₁, jet expansion enthalpy h ₁, absolute velocity c ₂, movable vane outlet relative velocity w ₂, movable vane outlet enthalpy h ₂, wheel efficiency η _u;

Governing stage characteristic curve: μ-ε, λ-ε, Ω _m-ε and η _u-x _a;

The peripheral velocity of u---movable vane;

λ---coefficient, $\mathrm{\&lambda;}=\frac{{\mathrm{\&beta;}}_1}{{\mathrm{\&beta;}}_2};$

Ω _m---the average degree of reaction of level;

ε---stage pressure ratio;

η---wheel efficiency;

X _a---speed ratio;

μ---coefficient, $\mathrm{\&mu;}=\frac{{\mathrm{\&beta;}}_2\mathrm{\&lambda;}}{\mathrm{\&epsiv;}};$

Fa---valve opening;

Geometric parameter: each nozzle throat sectional area A _nk, movable vane discharge area A _b;

Sliding pressure operation law curve: power-main steam pressure;

Map:

1), by valve nozzle number of combinations X ₁, X ₂, X ₃, X ₄be c with the thermodynamic parameter of variable condition calculation ₁, w ₁, h ₁, c ₂, w ₂, h ₂, η _u, governing stage characteristic curve is μ-ε, λ-ε, Ω _m-ε and η-x _a, geometric parameter is each nozzle throat sectional area A _nk, movable vane discharge area A _b, sliding pressure operation law curve, adds 12 kinds of valve openings, substitutes into the variable condition calculation function described in step one to four;

The process of step one to four represents with following formula,

[Xgz, Xgz1, Xgz2, Xgz3, Xgz4, Xnri, Fux, Fuy]=Changingstate (Ge ₁, Fa, X ₁, X ₂, X ₃, X ₄, others), obtain 12 flow value [Xgz ₁, Xgz ₂.Xgz ₁₂], what Xnri, Fux, Fuy represented respectively is internal efficiency ratio, horizontal gas flow power, vertically air-flow power;

2) flow value, calculating valve opening corresponding to each loading point and calculate; Check l loading point, then the calculated flow rate xgz of h the loading point of governing stage valve under standard-sized sheet or complete shut-down condition successively _hfor:

Xgz _h=Xgz _jmake | Xgz _j-Ge _h|=min (| Xgz ₁-Ge _h|, | Xgz ₂-Ge _h| ..., | Xgz ₁₂-Ge _h|) (2-9)

h＝1,2,...,l；j＝1,2,...,12

Obtain l flow xgz ₁, xgz ₂... xgz _l

Export:

Formula 2-10 characterizes the comprehensive effect of unit under different load point runs of this Nozzle combination, and Y is less, illustrates that this Nozzle combination is better;

Y＝W ₁(xgz ₁-Ge ₁) ²+W ₂(xgz ₂-Ge ₂) ²+......+W _l(xgz _l-Ge _l) ² (2-10)

Finally being expressed as of fitness function:

Work as X _min≤ X ₄≤ X _maxtime:

$\mathrm{ObjV}=\frac{1}{Y}---(2-11)$

Work as X ₄>X _maxoR X ₄<X _mintime:

$\mathrm{ObjV}=\frac{1}{Y}/e^{\mathrm{\&alpha;\&delta;}}---(2-12)$

Show not meet the reduced e of its fitness value of Nozzle combination of constraint 2-3 in formula 2-12 ^{α δ}doubly;

α is coefficient of reduction;

Step 5 (three), complete step after, then carry out based on the selection of traditional genetic algorithm, intersection, mutation process; When genetic algebra reach end condition N for time, genetic process stops, export meet actual flow minimum with the comprehensive departure degree Y of theoretical delivery time corresponding optimum number of nozzle combination.

2. the number of nozzle Optimization Design of each nozzle sets of consideration steam turbine actual motion according to claim 1 constraint, is characterized in that: calculate p in step 2 by dichotomy ₂process in, pressure p after described nozzle _1kcomputational process be: the sliding pressure operation law curve obtained in actual motion by steam turbine set: power-main steam pressure, then according to actual motion load, check in the pressure of main steam, then calculate p _1k.