CN112052570A - Economy backpressure optimization method of wet cooling unit of thermal power plant based on wolf algorithm - Google Patents

Economy backpressure optimization method of wet cooling unit of thermal power plant based on wolf algorithm Download PDF

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CN112052570A
CN112052570A CN202010856684.0A CN202010856684A CN112052570A CN 112052570 A CN112052570 A CN 112052570A CN 202010856684 A CN202010856684 A CN 202010856684A CN 112052570 A CN112052570 A CN 112052570A
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李俞迪
林志赟
韩志敏
王博
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Hangzhou Dianzi University
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Abstract

The invention discloses a heat-engine plant wet cooling unit economic backpressure optimization method based on a wolf algorithm. The method is based on a back pressure model of the wet cooling unit, the running working condition of the unit, the circulating water flow and the circulating water temperature at the inlet of the condenser are used as input variables, the back pressure of the unit is used as an output variable, a gray wolf algorithm which is modified by a position updating strategy and a population evolution mechanism is adopted, and the optimal back pressure can be found more quickly and accurately by random and quick traversal of the input variables. The method solves the problem that the economic backpressure of the thermal power plant is difficult to determine due to the change of equipment parameters and operation conditions, effectively shortens the time required for determining the economic backpressure of the unit, and improves the optimization precision of the economic backpressure.

Description

Economy backpressure optimization method of wet cooling unit of thermal power plant based on wolf algorithm
Technical Field
The invention belongs to the field of economic optimization of cold end systems of thermal power plants, and particularly relates to an economic backpressure optimization method of a wet cooling unit of a thermal power plant based on a wolf algorithm.
Background
The economic optimization of the thermal power plant is mainly embodied in improving the utilization rate of fuel in the power generation process, wherein the utilization rate of the fuel coal in a boiler, the effective heat utilization rate of heat generated after the fuel coal is combusted in the pipeline transmission process, the effective working power of hot steam in a steam turbine and the like are included. The cold end system is located the steam turbine end of thermal power unit, and the economic nature optimization of cold end system mainly improves the effective power reflection of doing of hot steam in the steam turbine through reducing low pressure jar terminal exhaust pressure. The data show that the standard coal consumption rate of power generation increases by 0.13g/kWh when the steam inlet pressure of a steam turbine is reduced by 0.1MPa, and the standard coal consumption rate of power generation increases by 5g/kWh when the steam exhaust pressure of the steam turbine is only increased by 0.1kPa, taking a 300MW steam turbine unit as an example. The method takes the reduction of the exhaust back pressure of 0.1kPa by a 300MW turboset as an optimization target, and can save the power generation cost by about 650 ten thousand yuan per year according to the calculation that the average cost of one ton of fire coal is 500 yuan, so that the method for optimizing the economy of a cold end system of a wet cooling unit of a thermal power plant is researched, the optimal economic back pressure value of the unit operation is determined, and the method plays an important role in improving the overall economic benefit of the power plant.
At present, a power micro-increasing curve observation method is generally adopted in an economic backpressure optimization method of a wet cooling unit of a thermal power plant. According to the method, the relation curves of the back pressure of the unit, the circulating water flow, the power consumption of a circulating water pump plant, the circulating water flow and the power consumption of the unit are drawn by collecting the operation data of the unit and taking the circulating water flow as an abscissa, and the power consumption of the unit and the power consumption of the circulating water pump plant are subtracted under a certain circulating water flow to obtain the relation curve of the net output of the unit and the circulating water flow. And recording the abscissa value corresponding to the maximum difference of the ordinate in the relation curve of the unit net output and the circulating water flow as the economic back pressure. The method mainly has the following defects: the parameters only consider the generating power of the unit and the flow of the circulating water, and do not consider the economic cost required by adjusting the temperature of the circulating water; data required by coordinate construction and curve analysis are based on historical operating data, many parameter values cannot be set in the actual operating process of the unit, data are few, and persuasion is lacked; the economic backpressure value determination process needs manual analysis, and is low in efficiency and poor in precision.
Disclosure of Invention
In order to overcome the defects of a traditional economic backpressure determining mode, the invention provides a method for optimizing the economic backpressure of a wet cooling unit of a thermal power plant based on a wolf algorithm, aiming at comprehensively considering three main variables influencing the backpressure, namely the unit operation condition, the circulating water flow and the condenser inlet circulating water temperature, and constructing a target function comprehensively considering the economic benefit brought by the increase of the power generation power of a steam turbine, the economic cost required by the regulation of the circulating water flow and the economic cost required by the regulation of the circulating water temperature; by improving a position updating strategy, a convergence strategy and a termination criterion of an original wolf optimization algorithm, a population evolution mechanism is added, the economic backpressure value and the corresponding parameter configuration are determined quickly, accurately and globally, and effective reference is provided for setting a backpressure design value of a thermal power plant.
The invention provides a heat-engine plant wet cooling unit economic backpressure optimizing method based on a wolf algorithm, which comprises the following concrete implementation steps:
step 1: collecting operation data of a power plant through a DCS distributed control system and storing the operation data in a database;
step 2: constructing a mathematical model for describing a cold end system of the wet cooling unit, and giving out constraint conditions by taking unit safety as a basis;
and step 3: analyzing the variable working condition characteristics of the wet cooling unit, and determining main variables influencing the net output of the steam turbine, wherein the main variables comprise the unit operation working condition, the circulating water flow and the condenser inlet circulating water temperature;
and 4, step 4: constructing a target function taking the highest net economic benefit of the power generation of the cold end system of the unit as an optimization target based on the main variables influencing the net output of the steam turbine in the step 3;
and 5: setting the size of a wolf population by taking the operating condition of a set of units, the circulating water flow and the circulating water temperature at the inlet of a condenser as a position coordinate of the wolf, and initializing the position of the wolf population;
step 6: taking the target function constructed in the step 4 as an adaptive value function, and calculating adaptive values of all wolfs in the initial iteration;
and 7: selecting three gray wolves with the minimum adaptation value as the elite wolves of the current iteration, and using the three elite wolves as guide wolves to update the positions of the gray wolves in the wolves; the method specifically comprises the following steps: firstly, updating the positions of all other gray wolves except the elite wolf based on a weighted average value method, and then updating the positions of the three elite wolfs based on a reverse learning strategy;
and 8: taking the wolf cluster with the updated position in the step 7 as a new wolf cluster, calculating the adaptive values of all gray wolfs in the wolf cluster, and reselecting the elite wolf;
and step 9: based on the principle of high-out and low-out, carrying out population evolution updating on the new wolf cluster in the step 8 to obtain a new wolf cluster, calculating the adaptive values of all wolfs in the wolf cluster, reselecting the elite wolf, and iterating by + 1;
step 10: iteration termination criterion, if not, jumping to the step 7 to continue circulation, if yes, entering the next step;
step 11: and after iteration is finished, the gray wolf with the minimum adaptive value in the finally confirmed elite wolfs is called as the optimal head wolf, the adaptive value of the optimal head wolf is taken as the optimal solution, and meanwhile, the position information corresponding to the wolf is obtained and is used as a guide parameter of an input variable in the optimization problem for configuration.
Further, when the mathematical model of the cold-end system is established in the step 2, the unit safety is used as a basis for establishing a constraint condition, and the constraint condition mainly comprises:
constraint of back pressure of the steam turbine: p is a radical ofcl,ξ≤pc,ξ≤pcu,ξ
And (3) restricting the power of the circulating water pump: n is a radical ofw,ξ≤5％NT,ξ
And (3) restricting the frequency of a motor of the circulating water pump: f is not less than 20Hzw,ξ≤50Hz
And (3) constraint of circulating water temperature: t is tw-ξ≤tw1,ξ≤tw+ξ,tw1,ξ≥twl
In the formula, xi represents the current operation condition of the unit, namely the percentage of the current load of the unit relative to the full load; p is a radical ofc,ξRepresenting the unit backpressure value of the unit under the xi working condition; p is a radical ofcu,ξThe warning back pressure value of the unit under the xi working condition is represented and is a back pressure upper bound protection value which is manually set; p is a radical ofcl,ξThe anti-freezing protection backpressure value under the xi working condition is represented and is a backpressure lower bound protection value which is manually set; n is a radical ofw,ξRepresenting the power consumption of the circulating water pump under xi working condition; n is a radical ofT,ξRepresenting the unit load under the xi working condition; f. ofw,ξRepresenting the working frequency of the circulating water pump under the xi working condition; t is tw1,ξThe circulating water temperature of the condenser inlet under xi working condition is expressed, and the default is 20 ℃; t is twIndicating the circulating water temperature in the current environment;ξthe temperature adjusting range under the xi working condition is shown and is determined by the temperature adjusting capacity of a temperature adjusting module configured by the unit; t is twlAnd the temperature of the circulating water at the inlet of the anti-freezing protection condenser in winter is shown.
Further, when the variable working condition characteristics of the unit are analyzed in the step 3, the backpressure of the steam turbine is approximate to the saturated steam pressure of the condenser, and the variable working condition characteristics of the backpressure of the steam turbine are the variable working condition characteristics of the saturated steam pressure of the condenser; in the main variable influencing the net output of the steam turbine in the step 3, the unit operating condition xi can be the unit generating power NT,ξAnd (4) equivalent replacement.
Further, the objective function which takes the highest net economic benefit of cold end system power generation as the optimization objective and is constructed in the step 4 is mainly composed of economic benefit brought by increase of power generation of the steam turbine, economic cost required by circulating water flow regulation and economic cost required by circulating water temperature regulation, and the objective function is expressed as follows:
max J(ξ,Dw,tw1)
wherein:
Figure BDA0002646608060000031
in the formula, the J (×) function represents the net economic benefit of the generator set; f. of0The function represents the net output of the steam turbine set and is the difference between the power generated by the steam turbine and the power consumed by a circulating water pump for adjusting the flow rate of circulating water; g0The function represents the economic cost of the temperature regulation of the circulating water temperature; f. of1The function represents the heat load of the steam turbine exhaust; the control variable xi represents the current operation condition of the unit, DwIndicating the circulating water flow rate, tw1Representing the temperature of circulating water at the inlet of the condenser; f0Representing a profit per degree of electricity generated; f1The cost required for the temperature of circulating water at the inlet of the condenser to deviate from the ambient water temperature by 1 ℃ after temperature adjustment is shown; t is twIndicating the circulating water temperature in the current environment; p is a radical ofcRepresenting the back pressure of the condenser;
Figure BDA0002646608060000032
is a constant; k1＝cpw，cpwThe specific constant pressure heat capacity of the circulating water is shown;
Figure BDA0002646608060000033
is a constant, where K represents the total heat transfer coefficient of the condenser, AcThe cooling area of the condenser is shown.
Further, the grey wolf population position initialization in step 5 adopts a random allocation rule, and a specific initialization method can be described by the following mathematical expression:
Figure BDA0002646608060000034
in the formula (I), the compound is shown in the specification,
Figure BDA0002646608060000035
represents the position vector of any one gray wolf in the wolf group,
Figure BDA0002646608060000036
the operation condition of the unit, the temperature of circulating water at the inlet of the condenser and the flow of the circulating water in the position vector are shownThe value of the boundary is measured,
Figure BDA0002646608060000037
the upper bound values of the unit operation condition, the condenser inlet circulating water temperature and the circulating water flow in the position vector are shown,
Figure BDA0002646608060000038
representing a random vector with elements all 0 to 1.
Further, the adaptive value function in the step 6 is the net economic gain function J (×) in the step 4, and the adaptive value of each sirius can be obtained by inputting the sirius position vector (unit operating condition, condenser inlet circulating water temperature and circulating water flow) in the step 5; wherein, the adaptive value expression of the wolf is as follows:
Figure BDA0002646608060000041
in the formula (I), the compound is shown in the specification,
Figure BDA0002646608060000042
representing the position vector of the ith grey wolf at the t iteration, f (. + -.) representing the fitness function, fiIndicating the fitness value of the ith gray wolf.
Further, the three wolfs with the minimum adaptation value in the wolf group in the step 7 are elite wolfs, which play a role of leading other wolfs to update the positions, and the three elite wolfs are respectively marked as X by sorting the adaptation values from small to largeα、Xβ、XThe other gray wolves in the wolves are marked as Xω. The location update of the gray wolf in said step 7 can be interpreted as the process of searching for prey, surrounding prey, which can be described by the following formula:
Figure BDA0002646608060000043
Figure BDA0002646608060000044
Figure BDA0002646608060000045
Figure BDA0002646608060000046
in the formula (I), the compound is shown in the specification,
Figure BDA0002646608060000047
representing a distance vector between the wolf individual and the prey at the t iteration; | here denotes taking positive values for each element in the vector;
Figure BDA0002646608060000048
a position vector representing a prey at the tth iteration;
Figure BDA0002646608060000049
representing the location vector of the individual wolf at the tth iteration;
Figure BDA00026466080600000410
is a random vector;
Figure BDA00026466080600000411
representing a random convergence vector; a represents a convergence factor, and the search behavior and the surrounding behavior of the gray wolf can be adjusted;
Figure BDA00026466080600000412
representing a random vector with elements all 0 to 1.
In the step 7, the convergence factor adopts a cosine strategy, and the expression is as follows:
Figure BDA00026466080600000413
where t represents the current iteration; t is tMaxThe maximum number of iterations is indicated.
X in said step 7ωThe wolf adopts a weighted average value method to update the position, and the specific position updating process can be described by the following mathematical expression:
Figure BDA00026466080600000414
Figure BDA00026466080600000415
Figure BDA00026466080600000416
Figure BDA00026466080600000417
Figure BDA00026466080600000418
Figure BDA00026466080600000419
Figure BDA0002646608060000051
Figure BDA0002646608060000052
in the formula (I), the compound is shown in the specification,
Figure BDA0002646608060000053
respectively representing relative distance vectors of the omega wolf respectively related to the alpha wolf, the beta wolf and the wolf at the t iteration; | here denotes taking positive values for each element in the vector;
Figure BDA0002646608060000054
vectors representing the omega wolf to be moved towards alpha wolf, beta wolf, wolf respectively at the t-th iteration; f. ofα、fβ、fRespectively representing adaptive values of alpha, beta and wolf;
Figure BDA0002646608060000055
representing the position vector of the omega wolf iterated at the t +1 th after the position is updated;
Figure BDA0002646608060000056
representing a random vector;
Figure BDA0002646608060000057
representing a random convergence vector;
in the step 7, a reverse learning strategy is adopted for updating the positions of the three wolfs of elite league, and the expression is as follows:
Figure BDA0002646608060000058
Figure BDA0002646608060000059
Figure BDA00026466080600000510
in the formula (I), the compound is shown in the specification,
Figure BDA00026466080600000511
represents the upper bound of the positions of alpha, beta, three wolfs of elite at the t-th iteration
Figure BDA00026466080600000512
And lower bound
Figure BDA00026466080600000513
Within a restricted range with respect to
Figure BDA00026466080600000514
The symmetrical position of (a). Computing
Figure BDA00026466080600000515
If the adaptive value is smaller than the adaptive value at the original position, the adaptive values are updated respectively
Figure BDA00026466080600000516
Further, the wolf cluster in step 8 includes the gray wolfs after the position update, and considering that the adaptation value of the original ω wolf after the position update may be smaller than the adaptation values of the original three gray wolfs, the adaptation values are all calculated for all the gray wolfs in the wolf cluster, and the elite wolfs α, β and other gray wolfs ω are selected again.
Further, the grey wolf population evolution mentioned in step 9 is based on the principle of dominant or recessive, and the ω grey wolf with poor adaptation value (accounting for 50% of the highest adaptation value of the ω wolf obtained in step 8) in the wolf population obtained in step 8 is responsible for exploring the prey in the global range, and the other ω grey wolfs with good adaptation value are responsible for approaching the elite wolf and cooperatively approach the target prey in the current range.
The exploration process expression is as follows:
Figure BDA00026466080600000517
in the formula (I), the compound is shown in the specification,
Figure BDA00026466080600000518
indicating the updated position of the omega wolf with poor adaptation value; r represents a random vector whose elements are all 0 to 1;
the approximation process expression is as follows:
Figure BDA00026466080600000519
Figure BDA00026466080600000520
in the formula (I), the compound is shown in the specification,
Figure BDA0002646608060000061
indicating the updated position of the omega wolf with a better adaptation value;
Figure BDA0002646608060000062
representing the position of a random elite wolf in the three elite wolfs; r represents a random vector whose elements are all 0 to 1;
and 9, calculating the adaptive values of all the gray wolves again and selecting new elite wolves alpha and beta and other gray wolves omega.
Further, the iteration termination criterion mentioned in step 10 includes termination of maximum iteration number and termination of convergence accuracy meeting requirements. The maximum iteration number is terminated when t is t ═ tMaxWhen the position of the population individual is not changed, the position is taken
Figure BDA0002646608060000063
For the global optimal position, namely the optimal parameter configuration in the economic backpressure optimization problem, the optimal position is taken
Figure BDA0002646608060000064
The optimal parameter configuration and the most economic backpressure value obtained by the above are the optimal solution; the convergence precision is terminated when the adaptive value of the alpha wolf of at least n iterations continues
Figure BDA0002646608060000065
And if the variation range does not exceed the preset value sigma, determining the final adaptive value and the corresponding position as the optimal solution.
Compared with the prior art, the technical scheme adopted by the invention has the following beneficial effects:
1. three variables of the unit operation condition, the circulating water flow and the condenser inlet circulating water temperature are selected as main factors influencing the backpressure, an objective function comprehensively considering the economic benefit brought by the increase of the turbine power, the economic cost required by the circulating water flow regulation and the economic cost required by the circulating water temperature regulation is established, and the rigor and the comprehensiveness of the optimization problem research are improved; meanwhile, values of the three variables can be selected randomly in the constraint range, the limitation that data of the traditional method is constrained by actual historical operation data of the unit is solved, the persuasion of the data is effectively improved, and the globality of the optimal solution is also improved.
2. The method improves the position updating strategy of the original gray wolf optimization algorithm, increases a population evolution mechanism, improves algorithm termination criteria, improves a convergence strategy, and improves population diversity, global searchability and rapid convergence of the original gray wolf algorithm, so that the optimal solution of the target function is closer to the global optimal solution, and the optimization performance of the original gray wolf algorithm is effectively improved.
3. By adopting the improved grey wolf optimization algorithm, the population diversity, the global searchability and the rapid convergence of the original grey wolf algorithm are improved, so that the optimal solution of the target function is closer to the global optimal solution, and the optimization performance of the original grey wolf algorithm is effectively improved.
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Fig. 1 is a flow chart of the invention for optimizing economic backpressure by using an improved grey wolf algorithm for a wet cooling unit of a thermal power plant.
Detailed Description
The technical solutions of the present invention will be described in detail below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
According to the economic backpressure optimizing method for the wet cooling unit of the thermal power plant based on the Grey wolf algorithm, three variables of the unit operation condition, the circulating water flow and the condenser inlet circulating water temperature are taken as main factors influencing backpressure, a target function comprehensively considering economic benefits brought by the increase of the power generation power of a steam turbine, economic cost required by circulating water flow regulation and economic cost required by circulating water temperature regulation is constructed, and the rigor and the comprehensiveness of the optimization problem research are improved; the method improves the original gray wolf optimization algorithm, improves the population diversity, the global searching property and the rapid convergence property in the gray wolf algorithm, and effectively improves the optimization performance of the original gray wolf algorithm.
As shown in fig. 1, the method of the present invention comprises the following steps:
step 1: and collecting the operation data of the power plant through the DCS distributed control system, and storing the operation data in a MySQL database.
Step 2: constructing a mathematical model for describing a cold end system of the wet cooling unit, and giving out constraint conditions by taking unit safety as a basis;
specifically, the constraints mainly include:
constraint of back pressure of the steam turbine: p is a radical ofcl,ξ≤pc,ξ≤pcu,ξ
And (3) restricting the power of the circulating water pump: n is a radical ofw,ξ≤5％NT,ξ
And (3) restricting the frequency of a motor of the circulating water pump: f is not less than 20Hzw,ξ≤50Hz
And (3) constraint of circulating water temperature: t is tw-ξ≤tw1,ξ≤tw+ξ,tw1,ξ≥twl
In the formula, xi represents the current operation condition of the unit, namely the percentage of the current load of the unit relative to the full load; p is a radical ofc,ξRepresenting the unit backpressure value of the unit under the xi working condition; p is a radical ofcu,ξThe warning back pressure value of the unit under the xi working condition is represented and is a back pressure upper bound protection value which is manually set; p is a radical ofcl,ξThe anti-freezing protection backpressure value under the xi working condition is represented and is a backpressure lower bound protection value which is manually set; n is a radical ofw,ξRepresenting the power consumption of the circulating water pump under xi working condition; n is a radical ofT,ξRepresenting the unit load under the xi working condition; f. ofw,ξRepresenting the working frequency of the circulating water pump under the xi working condition; t is tw1,ξThe circulating water temperature of the condenser inlet under xi working condition is expressed, and the default is 20 ℃; t is twIndicating the circulating water in the current environmentWarming;ξthe temperature adjusting range under the xi working condition is shown and is determined by the temperature adjusting capacity of a temperature adjusting module configured by the unit; t is twlAnd the temperature of the circulating water at the inlet of the anti-freezing protection condenser in winter is shown.
And step 3: analyzing the variable working condition characteristics of the wet cooling unit, and determining main variables influencing the net output of the steam turbine, wherein the main variables comprise the unit operation working condition, the circulating water flow and the condenser inlet circulating water temperature;
specifically, when the variable working condition characteristic of the unit is analyzed, the backpressure of the steam turbine is approximate to the saturated steam pressure of the condenser, and the variable working condition characteristic of the backpressure of the steam turbine is the variable working condition characteristic of the saturated steam pressure of the condenser; the unit running working condition xi can use the unit generating power NT,ξAnd (4) equivalent replacement.
And 4, step 4: constructing a target function taking the highest net economic benefit of the power generation of the cold end system of the unit as an optimization target based on the main variables influencing the net output of the steam turbine in the step 3;
specifically, an objective function which takes the highest net economic benefit of power generation of a cold end system as an optimization target is constructed, wherein the net economic benefit mainly comprises economic benefit brought by increase of power generation of a steam turbine, economic cost required by circulating water flow regulation and economic cost required by circulating water temperature regulation, and an objective function expression is as follows:
maxJ(ξ,Dw,tw1)
wherein:
Figure BDA0002646608060000081
in the formula, the J (×) function represents the net economic benefit of the generator set; f. of0The function represents the net output of the steam turbine set and is the difference between the power generated by the steam turbine and the power consumed by a circulating water pump for adjusting the flow rate of circulating water; g0The function represents the economic cost of the temperature regulation of the circulating water temperature; f. of1The function represents the heat load of the steam turbine exhaust; the control variable xi represents the current operation condition of the unit, DwIndicating the circulating water flow rate, tw1Indicating condenser inlet circulating water temperature；F0Representing a profit per degree of electricity generated; f1The cost required for the temperature of circulating water at the inlet of the condenser to deviate from the ambient water temperature by 1 ℃ after temperature adjustment is shown; t is twIndicating the circulating water temperature in the current environment; p is a radical ofcRepresenting the back pressure of the condenser;
Figure BDA0002646608060000082
is a constant; k1＝cpw，cpwThe specific constant pressure heat capacity of the circulating water is shown;
Figure BDA0002646608060000083
is a constant, where K represents the total heat transfer coefficient of the condenser, AcThe cooling area of the condenser is shown.
And 5: setting the size of a wolf population by taking the operating condition of a set of units, the circulating water flow and the circulating water temperature at the inlet of a condenser as a position coordinate of the wolf, and initializing the position of the wolf population;
specifically, the grey wolf population position initialization may adopt a random allocation rule, and a specific initialization method may be described by the following mathematical expression:
Figure BDA0002646608060000084
in the formula (I), the compound is shown in the specification,
Figure BDA0002646608060000085
represents the position vector of any one gray wolf in the wolf group,
Figure BDA0002646608060000086
the lower bound values of the unit operation condition, the condenser inlet circulating water temperature and the circulating water flow in the position vector are shown,
Figure BDA0002646608060000087
the upper bound values of the unit operation condition, the condenser inlet circulating water temperature and the circulating water flow in the position vector are shown,
Figure BDA0002646608060000088
representing a random vector with elements all 0 to 1.
Step 6: taking the target function constructed in the step 4 as an adaptive value function, and calculating adaptive values of all wolfs in the initial iteration;
specifically, the adaptive value function is the net economic gain function J (×) described in step 4, and the adaptive value of each sirius can be obtained by inputting the sirius position vector (unit operating condition, condenser inlet circulating water temperature, circulating water flow) described in step 5; wherein, the adaptive value expression of the wolf is as follows:
Figure BDA0002646608060000089
in the formula (I), the compound is shown in the specification,
Figure BDA00026466080600000810
representing the position vector of the ith grey wolf at the t iteration, f (. + -.) representing the fitness function, fiIndicating the fitness value of the ith gray wolf.
And 7: selecting three gray wolves with the minimum adaptation value as the elite wolves of the current iteration, and using the three elite wolves as guide wolves to update the positions of the gray wolves in the wolves; the method specifically comprises the following steps: firstly, updating the positions of all other gray wolves except the elite wolf based on a weighted average value method, and then updating the positions of the three elite wolfs based on a reverse learning strategy;
more specifically, the three wolfs with the minimum adaptation value in the gray wolf group are elite wolfs, which play a role in leading other gray wolfs to update the positions, and the three elite wolfs are respectively marked as X by sorting the adaptation values from small to largeα、Xβ、XThe other gray wolves in the wolves are marked as Xω。
The location update of the wolf can be interpreted as the process of searching for a prey, surrounding a prey, which can be described by the following formula:
Figure BDA0002646608060000091
Figure BDA0002646608060000092
Figure BDA0002646608060000093
Figure BDA0002646608060000094
in the formula (I), the compound is shown in the specification,
Figure BDA0002646608060000095
representing a distance vector between the wolf individual and the prey at the t iteration; | here denotes taking positive values for each element in the vector;
Figure BDA0002646608060000096
a position vector representing a prey at the tth iteration;
Figure BDA0002646608060000097
representing the location vector of the individual wolf at the tth iteration;
Figure BDA0002646608060000098
is a random vector;
Figure BDA0002646608060000099
representing a random convergence vector; a represents a convergence factor, and the search behavior and the surrounding behavior of the gray wolf can be adjusted;
Figure BDA00026466080600000910
representing a random vector with elements all 0 to 1.
The convergence factor adopts a cosine strategy, and the expression is as follows:
Figure BDA00026466080600000911
where t represents the current iteration; t is tMaxThe maximum number of iterations is indicated.
XωThe wolf adopts a weighted average value method to update the position, and the specific position updating process can be described by the following mathematical expression:
Figure BDA00026466080600000912
Figure BDA00026466080600000913
Figure BDA00026466080600000914
Figure BDA00026466080600000915
Figure BDA00026466080600000916
Figure BDA00026466080600000917
Figure BDA00026466080600000918
Figure BDA00026466080600000919
in the formula (I), the compound is shown in the specification,
Figure BDA00026466080600000920
respectively indicate omega wolfs at the t-th iterationRelative distance vectors for alpha wolves, beta wolves, wolves; | here denotes taking positive values for each element in the vector;
Figure BDA00026466080600000921
vectors representing the omega wolf to be moved towards alpha wolf, beta wolf, wolf respectively at the t-th iteration; f. ofα、fβ、fRespectively representing adaptive values of alpha, beta and wolf;
Figure BDA00026466080600000922
representing the position vector of t wolf iteration at the t +1 th position after position updating;
Figure BDA00026466080600000923
representing a random vector;
Figure BDA00026466080600000924
representing a random convergence vector.
The position updating of the three wolfs of elite adopts a reverse learning strategy, and the expression is as follows:
Figure BDA0002646608060000101
Figure BDA0002646608060000102
Figure BDA0002646608060000103
in the formula (I), the compound is shown in the specification,
Figure BDA0002646608060000104
represents the upper bound of the positions of alpha, beta, three wolfs of elite at the t-th iteration
Figure BDA0002646608060000105
And lower bound
Figure BDA0002646608060000106
Within a restricted range with respect to
Figure BDA0002646608060000107
The symmetrical position of (a). Computing
Figure BDA0002646608060000108
If the adaptive value is smaller than the adaptive value at the original position, the adaptive values are updated respectively
Figure BDA0002646608060000109
And 8: taking the wolf cluster with the updated position in the step 7 as a new wolf cluster, calculating the adaptive values of all gray wolfs in the wolf cluster, and reselecting the elite wolf; specifically, the method comprises the following steps: the wolf cluster includes the gray wolfs after the position update, and considering that the adaptation value of the original omega wolf after the position update is possibly smaller than the adaptation values of the original three gray wolfs, the adaptation values are all calculated for all the gray wolfs in the wolf cluster, and the elite wolf alpha, beta and other gray wolf omega are selected again.
And step 9: based on the principle of high-out and low-out, carrying out population evolution updating on the new wolf cluster in the step 8 to obtain a new wolf cluster, calculating the adaptive values of all wolfs in the wolf cluster, reselecting the elite wolf, and iterating by + 1;
specifically, the omega gray wolf with the poor adaptation value in the wolf group obtained in the step 8 (accounting for the highest 50% of the adaptation value in the omega wolf obtained in the step 8) is responsible for exploring the prey in the global range, and the other omega gray wolfs with the good adaptation value are responsible for being close to the elite wolf and cooperatively approaching the target prey in the current region.
The exploration process expression is as follows:
Figure BDA00026466080600001010
in the formula (I), the compound is shown in the specification,
Figure BDA00026466080600001011
indicating the updated position of the omega wolf with poor adaptation value; r represents a random vector whose elements are all 0 to 1.
The approximation process expression is as follows:
Figure BDA00026466080600001012
Figure BDA00026466080600001013
in the formula (I), the compound is shown in the specification,
Figure BDA00026466080600001014
indicating the updated position of the omega wolf with a better adaptation value;
Figure BDA00026466080600001015
representing the position of a random elite wolf in the three elite wolfs; r represents a random vector whose elements are all 0 to 1.
And (3) calculating the adaptive values of all the gray wolves again through a new wolf group obtained after population evolution, and selecting new elite wolves alpha and beta and other gray wolves omega.
Step 10: iteration termination criterion, if not, jumping to the step 7 to continue circulation, if yes, entering the next step;
specifically, the iteration termination criterion includes termination of maximum iteration times and termination of convergence accuracy meeting requirements. The maximum iteration number is terminated when t is t ═ tMaxWhen the position of the population individual is not changed, the position is taken
Figure BDA0002646608060000111
For the global optimal position, namely the optimal parameter configuration in the economic backpressure optimization problem, the optimal position is taken
Figure BDA0002646608060000112
And (3) obtaining a global optimal adaptive value, namely the most economic backpressure value in the economic backpressure optimization problem, wherein the obtained optimal parameter configuration and the most economic backpressure value are the optimal solution. The termination of convergence accuracy is when the convergence accuracy reaches the requirement for at least n times (in this embodiment, n is taken as10) Iterative adaptation of alpha wolf
Figure BDA0002646608060000113
The variation range does not exceed σ (in this embodiment, σ is equal to 3%), and the final adaptation value and the corresponding position are determined as the optimal solution.
Step 11: and after iteration is finished, the gray wolf with the minimum adaptive value in the finally confirmed elite wolfs is called as the optimal head wolf, the adaptive value of the optimal head wolf is taken as the optimal solution, and meanwhile, the position information corresponding to the wolf is obtained and is used as a guide parameter of an input variable in the optimization problem for configuration.
The optimal solution is actually a global suboptimal solution, and under the comprehensive consideration of time cost, calculation cost and precision cost, the solution obtained when iteration is carried out to the set maximum iteration number cannot be guaranteed to be the global optimal solution. Similarly, a solution obtained by setting the change percentage of the adaptive value in n iterations not to exceed σ cannot be guaranteed to be a globally optimal solution. However, in the invention, the final solution obtained by the two termination criteria is approximate to the global optimal solution required by the cold end system of the wet cooling unit of the thermal power plant.
The optimal solution mentioned in step 11 is not only the optimal adaptive value, but also the input parameter configuration corresponding to the optimal adaptive value can be obtained, and the parameters meet the adjustable range.
The foregoing is only a preferred embodiment of the present invention, and although the present invention has been disclosed in the preferred embodiments, it is not intended to limit the present invention. Those skilled in the art can make numerous possible variations and modifications to the present teachings, or modify equivalent embodiments to equivalent variations, without departing from the scope of the present teachings, using the methods and techniques disclosed above. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention are still within the scope of the protection of the technical solution of the present invention, unless the contents of the technical solution of the present invention are departed.

Claims (10)

1. A thermal power plant wet cooling unit economic backpressure optimizing method based on a wolf algorithm is characterized by comprising the following steps:
step 1: collecting operation data of a power plant through a DCS distributed control system and storing the operation data in a database;
step 2: constructing a mathematical model for describing a cold end system of the wet cooling unit, and giving out constraint conditions by taking unit safety as a basis;
and step 3: analyzing the variable working condition characteristics of the wet cooling unit, and determining main variables influencing the net output of the steam turbine, wherein the main variables comprise the unit operation working condition, the circulating water flow and the condenser inlet circulating water temperature;
and 4, step 4: constructing a target function taking the highest net economic benefit of the power generation of the cold end system of the unit as an optimization target based on the main variables influencing the net output of the steam turbine in the step 3;
and 5: setting the size of a wolf population by taking the operating condition of a set of units, the circulating water flow and the circulating water temperature at the inlet of a condenser as a position coordinate of the wolf, and initializing the position of the wolf population;
step 6: taking the target function constructed in the step 4 as an adaptive value function, and calculating adaptive values of all wolfs in the initial iteration;
and 7: selecting three gray wolves with the minimum adaptation value as the elite wolves of the current iteration, and using the three elite wolves as guide wolves to update the positions of the gray wolves in the wolves; the method specifically comprises the following steps: firstly, updating the positions of all other gray wolves except the elite wolf based on a weighted average value method, and then updating the positions of the three elite wolfs based on a reverse learning strategy;
and 8: taking the wolf cluster with the updated position in the step 7 as a new wolf cluster, calculating the adaptive values of all gray wolfs in the wolf cluster, and reselecting the elite wolf;
and step 9: based on the principle of high-out and low-out, carrying out population evolution updating on the new wolf cluster in the step 8 to obtain a new wolf cluster, calculating the adaptive values of all wolfs in the wolf cluster, reselecting the elite wolf, and iterating by + 1;
step 10: iteration termination criterion, if not, jumping to the step 7 to continue circulation, if yes, entering the next step;
step 11: and after iteration is finished, the gray wolf with the minimum adaptive value in the finally confirmed elite wolfs is called as the optimal head wolf, the adaptive value of the optimal head wolf is taken as the optimal solution, and meanwhile, the position information corresponding to the wolf is obtained and is used as a guide parameter of an input variable in the optimization problem for configuration.
2. The method for optimizing the economic backpressure of the wet cooling unit of the thermal power plant based on the wolf's head algorithm as claimed in claim 1, wherein: when a mathematical model of the cold-end system is established in the step 2, the unit safety is used as a basis for establishing a constraint condition, and the constraint condition mainly comprises:
constraint of back pressure of the steam turbine: p is a radical ofcl，ξ≤pc，ξ≤pcu，ξ
And (3) restricting the power of the circulating water pump: n is a radical ofw，ξ≤5％NT，ξ
And (3) restricting the frequency of a motor of the circulating water pump: f is not less than 20Hzw，ξ≤50Hz
And (3) constraint of circulating water temperature: t is tw-ξ≤tw1，ξ≤tw+ξ，tw1，ξ≥twl
In the formula, xi represents the current operation condition of the unit, namely the percentage of the current load of the unit relative to the full load; p is a radical ofc，ξRepresenting the unit backpressure value of the unit under the xi working condition; p is a radical ofcu，ξRepresenting the alarm back pressure value of the unit under the xi working condition; p is a radical ofcl，ξRepresenting the anti-freezing protection back pressure value under xi working condition; n is a radical ofw，ξRepresenting the power consumption of the circulating water pump under xi working condition; n is a radical ofT，ξRepresenting the unit load under the xi working condition; f. ofw，ξRepresenting the working frequency of the circulating water pump under the xi working condition; t is tw1，ξRepresenting the temperature of the circulating water at the inlet of the condenser under xi working condition; t is twIndicating the circulating water temperature in the current environment;ξthe temperature adjusting range under the xi working condition is shown and is determined by the temperature adjusting capacity of a temperature adjusting module configured by the unit; t is twlAnd the temperature of the circulating water at the inlet of the anti-freezing protection condenser in winter is shown.
3. The graying based algorithm of claim 1The economic backpressure optimizing method for the wet cooling unit of the thermal power plant is characterized by comprising the following steps of: when the variable working condition characteristics of the unit are analyzed in the step 3, the backpressure of the steam turbine is approximate to the saturated steam pressure of the condenser, and the analysis of the variable working condition characteristics of the backpressure of the steam turbine is the analysis of the variable working condition characteristics of the saturated steam pressure of the condenser; in the main variable influencing the net output of the steam turbine in the step 3, the unit operating condition xi can be the unit generating power NT，ξAnd (4) equivalent replacement.
4. The method for optimizing the economic backpressure of the wet cooling unit of the thermal power plant based on the wolf's head algorithm as claimed in claim 1, wherein: the objective function which takes the highest net economic benefit of the cold end system power generation as the optimization target and is constructed in the step 4 is mainly composed of the economic benefit brought by the increase of the power generation power of the steam turbine, the economic cost required by the circulating water flow regulation and the economic cost required by the circulating water temperature regulation, and the expression of the objective function is as follows:
max J(ξ，Dw，tw1)
wherein:
Figure FDA0002646608050000021
g0(tw1)＝F1·(tw1-tw)
in the formula, the J (×) function represents the net economic benefit of the generator set; f. of0The function represents the net output of the steam turbine set and is the difference between the power generated by the steam turbine and the power consumed by a circulating water pump for adjusting the flow rate of circulating water; g0The function represents the economic cost of the temperature regulation of the circulating water temperature; f. of1The function represents the heat load of the steam turbine exhaust; the control variable xi represents the current operation condition of the unit, Dw represents the circulating water flow, tw1Representing the temperature of circulating water at the inlet of the condenser; f0Representing a profit per degree of electricity generated; f1The cost required for the temperature of circulating water at the inlet of the condenser to deviate from the ambient water temperature by 1 ℃ after temperature adjustment is shown; tw represents the circulating water temperature in the current environment; p is a radical ofcRepresenting the back pressure of the condenser;
Figure FDA0002646608050000022
is a constant; k1＝cpw，cpwThe specific constant pressure heat capacity of the circulating water is shown;
Figure FDA0002646608050000023
is a constant, where K represents the total heat transfer coefficient of the condenser, AcThe cooling area of the condenser is shown.
5. The method for optimizing the economic backpressure of the wet cooling unit of the thermal power plant based on the wolf's head algorithm as claimed in claim 1, wherein: in the step 5, the initialization of the grey wolf population position adopts a random allocation rule, and a specific initialization method can be described by the following mathematical expression:
Figure FDA0002646608050000024
in the formula (I), the compound is shown in the specification,
Figure FDA0002646608050000031
represents the position vector of any one gray wolf in the wolf group,
Figure FDA0002646608050000032
the lower bound values of the unit operation condition, the condenser inlet circulating water temperature and the circulating water flow in the position vector are shown,
Figure FDA0002646608050000033
the upper bound values of the unit operation condition, the condenser inlet circulating water temperature and the circulating water flow in the position vector are shown,
Figure FDA0002646608050000034
representing a random vector with elements all 0 to 1.
6. The economic backpressure optimization method for the wet cooling unit of the thermal power plant based on the wolf's head algorithm as claimed in claim 4, wherein: the adaptive value function in the step 6 is the net economic gain function J (×) in the step 4, and the adaptive value of each wolf can be obtained by inputting the wolf position vector in the step 5; wherein, the adaptive value expression of the wolf is as follows:
Figure FDA0002646608050000035
in the formula (I), the compound is shown in the specification,
Figure FDA0002646608050000036
representing the position vector of the ith grey wolf at the t iteration, f (. + -.) representing the fitness function, fiIndicating the fitness value of the ith gray wolf.
7. The method for optimizing the economic backpressure of the wet cooling unit of the thermal power plant based on the wolf's head algorithm as claimed in claim 1, wherein: in the step 7, the three wolfs with the minimum adaptation value in the wolf group are elite wolfs, which play a role in leading other wolfs to update the positions, and the three elite wolfs are respectively marked as X by the sequence of the adaptation values from small to largeα、Xβ、XThe other gray wolves in the wolves are marked as Xω(ii) a The location update of the gray wolf in said step 7 can be interpreted as the process of searching for prey, surrounding prey, which can be described by the following formula:
Figure FDA0002646608050000037
Figure FDA0002646608050000038
Figure FDA0002646608050000039
Figure FDA00026466080500000310
in the formula (I), the compound is shown in the specification,
Figure FDA00026466080500000311
representing a distance vector between the wolf individual and the prey at the t iteration; | here denotes taking positive values for each element in the vector;
Figure FDA00026466080500000312
a position vector representing a prey at the tth iteration;
Figure FDA00026466080500000313
representing the location vector of the individual wolf at the tth iteration;
Figure FDA00026466080500000314
is a random vector;
Figure FDA00026466080500000315
representing a random convergence vector; a represents a convergence factor, and the search behavior and the surrounding behavior of the gray wolf can be adjusted;
Figure FDA00026466080500000316
a random vector representing elements all 0 to 1;
in the step 7, the convergence factor adopts a cosine strategy, and the expression is as follows:
Figure FDA00026466080500000317
where t represents the current iteration; t is tMaxRepresenting the maximum number of iterations;
said step (c) isX in 7ωThe wolf adopts a weighted average value method to update the position, and the specific position updating process can be described by the following mathematical expression:
Figure FDA00026466080500000318
Figure FDA00026466080500000319
Figure FDA0002646608050000041
Figure FDA0002646608050000042
Figure FDA0002646608050000043
Figure FDA0002646608050000044
Figure FDA0002646608050000045
Figure FDA0002646608050000046
in the formula (I), the compound is shown in the specification,
Figure FDA0002646608050000047
respectively representing relative distance vectors of the omega wolf respectively related to the alpha wolf, the beta wolf and the wolf at the t iteration; | XHere, | denotes taking a positive value for each element in the vector;
Figure FDA0002646608050000048
vectors representing the omega wolf to be moved towards alpha wolf, beta wolf, wolf respectively at the t-th iteration; f. ofα、fβ、fRespectively representing adaptive values of alpha, beta and wolf;
Figure FDA0002646608050000049
representing the position vector of the omega wolf iterated at the t +1 th after the position is updated;
Figure FDA00026466080500000410
representing a random vector;
Figure FDA00026466080500000411
representing a random convergence vector;
in the step 7, a reverse learning strategy is adopted for updating the positions of the three wolfs of elite league, and the expression is as follows:
Figure FDA00026466080500000412
Figure FDA00026466080500000413
Figure FDA00026466080500000414
in the formula (I), the compound is shown in the specification,
Figure FDA00026466080500000415
represents the upper bound of the positions of alpha, beta, three wolfs of elite at the t-th iteration
Figure FDA00026466080500000416
And lower bound
Figure FDA00026466080500000417
Within a restricted range with respect to
Figure FDA00026466080500000418
The symmetrical position of (a). Computing
Figure FDA00026466080500000419
If the adaptive value is smaller than the adaptive value at the original position, the adaptive values are updated respectively
Figure FDA00026466080500000420
8. The method for optimizing the economic backpressure of the wet cooling unit of the thermal power plant based on the wolf's head algorithm as claimed in claim 1, wherein: the wolf cluster in step 8 includes the gray wolfs after the position update, and considering that the adaptation value of the original ω wolf after the position update may be smaller than the adaptation values of the original three gray wolfs, the adaptation values are all calculated for all the gray wolfs in the wolf cluster, and the elite wolfs α, β and other gray wolfs ω are selected again.
9. The method for optimizing the economic backpressure of the wet cooling unit of the thermal power plant based on the wolf's head algorithm as claimed in claim 1, wherein: the grey wolf population evolution mentioned in the step 9 is based on the principle of high or low rejection, the omega grey wolf with poor adaptation value in the wolf population obtained in the step 8 is responsible for exploring the prey in the global range, and other omega grey wolfs with good adaptation value are responsible for being close to the elite wolf and cooperatively approach the target prey in the current region;
the exploration process expression is as follows:
Figure FDA00026466080500000421
in the formula (I), the compound is shown in the specification,
Figure FDA0002646608050000051
indicating the updated position of the omega wolf with poor adaptation value; r represents a random vector whose elements are all 0 to 1;
the approximation process expression is as follows:
Figure FDA0002646608050000052
Figure FDA0002646608050000053
in the formula (I), the compound is shown in the specification,
Figure FDA0002646608050000054
indicating the updated position of the omega wolf with a better adaptation value;
Figure FDA0002646608050000055
representing the position of a random elite wolf in the three elite wolfs; r represents a random vector whose elements are all 0 to 1;
and 9, calculating the adaptive values of all the gray wolves again and selecting new elite wolves alpha and beta and other gray wolves omega.
10. The method for optimizing the economic backpressure of the wet cooling unit of the thermal power plant based on the wolf's head algorithm as claimed in claim 1, wherein: the iteration termination criterion mentioned in the step 10 comprises termination of maximum iteration times and termination of convergence accuracy meeting requirements; the maximum iteration number is terminated when t is t ═ tMaxWhen the position of the population individual is not changed, the position is taken
Figure FDA0002646608050000056
For the global optimal position, namely the optimal parameter configuration in the economic backpressure optimization problem, the optimal position is taken
Figure FDA0002646608050000057
The optimal parameter configuration and the most economic backpressure value obtained by the above are the optimal solution; the convergence precision is terminated when the adaptive value of the alpha wolf of at least n iterations continues
Figure FDA0002646608050000058
And if the variation range does not exceed the preset value sigma, determining the final adaptive value and the corresponding position as the optimal solution.
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