CN115758914A

CN115758914A - Method, device, equipment and medium for determining reference value of regenerative feedwater heating system

Info

Publication number: CN115758914A
Application number: CN202211584280.6A
Authority: CN
Inventors: 程明; 孙安娜; 张铎; 康磊; 王宏光; 钟嶒楒; 翟金星; 庄伟�; 马骏; 周作发; 许涛; 薛云海; 郭健; 张博; 叶晶; 王亚超; 杨志强
Original assignee: Tongliao Huolinhe Kengkou Power Generation Co ltd; Shanghai Power Equipment Research Institute Co Ltd
Current assignee: Tongliao Huolinhe Kengkou Power Generation Co ltd; Shanghai Power Equipment Research Institute Co Ltd
Priority date: 2022-12-09
Filing date: 2022-12-09
Publication date: 2023-03-07

Abstract

The invention discloses a method, a device, equipment and a medium for determining a reference value of a regenerative feedwater heating system. The method comprises the following steps: acquiring historical operating data and current operating data of a water supply regenerative system of a steam turbine set; constructing a benchmark library of the water supply temperature based on a k-means clustering algorithm according to historical operating data; determining a current reference value of the water supply temperature according to the current operation data and the benchmark library; and determining a current reference value of the end difference of the heater based on a particle swarm optimization algorithm and a wolf algorithm according to the current operation data. The scheme provided by the invention can quickly and accurately determine the reference value of the difference between the feed water temperature and the heater end without changing the structure of the feed water regenerative system, and performs rolling optimization on the reference value, so as to reduce the energy consumption of the system, improve the heat economy of the steam turbine set, and have important guiding significance for the exploitation of energy-saving potential and the optimization design of a thermodynamic system.

Description

Method, device, equipment and medium for determining reference value of regenerative feedwater heating system

Technical Field

The invention relates to the technical field of thermodynamic systems, in particular to a method, a device, equipment and a medium for determining a reference value of a regenerative feedwater heating system.

Background

The water supply backheating system of the steam turbine set is not only the basis of a steam turbine thermodynamic system, but also the core of a power plant thermodynamic system, and the key for effectively reducing the energy consumption of the power plant is to reasonably select the reference value of the thermodynamic parameters of the water supply backheating system to enable the reference value to reach the optimal matching.

At present, the determination of the reference value of the thermodynamic parameter mainly adopts a design value method, a thermodynamic test method, a variable working condition calculation method and the like, but the operation condition of each thermodynamic device can be slowly changed along with the extension of the continuous operation time of the unit by the above method, the accuracy of a thermodynamic calculation mathematical model is greatly influenced, and the obtained reference value and the actual operation state have larger deviation.

In addition, aiming at the problem of regenerative distribution of feedwater, the existing distribution method adopts an enthalpy drop distribution method, an average distribution and geometric progression method, an equivalent enthalpy drop method, a cyclic function distribution method and the like. However, these methods are complicated to deduce, and if the system preconditions are simplified, the application range of the methods is limited, so that the calculation results are not accurate enough, and although the methods have certain theoretical guidance meanings, the methods have great limitations and are rarely applied in the actual production field.

Disclosure of Invention

The invention provides a method, a device, equipment and a medium for determining a reference value of a regenerative feedwater system, which can quickly and accurately determine the reference values of feedwater temperature and heater end difference under the condition of not changing the structure of the regenerative feedwater system, and perform rolling optimization on the reference values so as to reduce the energy consumption of the system, improve the heat economy of a steam turbine set and have important guiding significance on the energy-saving potential excavation and the optimization design of a thermodynamic system.

According to an aspect of the present invention, there is provided a method for determining a reference value of a regenerative feedwater heating system, including:

acquiring historical operating data and current operating data of a water supply regenerative system of a steam turbine set;

constructing a benchmark library of the water supply temperature based on a k-means clustering algorithm according to historical operating data;

determining a current reference value of the water supply temperature according to the current operation data and the benchmark library;

and determining a current reference value of the end difference of the heater based on a particle swarm optimization algorithm and a wolf algorithm according to the current operation data.

Optionally, constructing a benchmarking library of the water supply temperature based on a k-means clustering algorithm according to historical operating data includes:

constructing a first objective function;

processing historical operating data based on a K-means clustering algorithm to obtain K groups of sample data, wherein one group of sample data corresponds to one clustering center, and K is a positive integer;

and performing optimization operation on the K groups of sample data by taking the first objective function as a reference to obtain a benchmark library.

Optionally, before processing the historical operating data based on the k-means clustering algorithm, the method further includes:

and rejecting the operation data with unstable working conditions in the historical operation data according to the stability judging index, wherein the stability judging index at least comprises main steam pressure.

Optionally, determining a reference value of the feedwater temperature according to the current operating data and the benchmark library, including:

searching a benchmark working condition and a benchmark value which are matched with the current operation data from a benchmark library;

substituting the current operation data into a first objective function, and calculating an objective function value corresponding to the current operation data;

if the target function value is larger than the working condition of the benchmark, taking the benchmark value as the current reference value of the water supply temperature;

and if the target function value is less than or equal to the working condition of the benchmark, optimizing the benchmark library according to the current operation data, and taking the value of the water supply temperature in the current operation data as the current reference value of the water supply temperature.

Optionally, determining a reference value of the feedwater temperature according to the current operating data and the benchmarking library, including:

if the objective function value is less than or equal to the working condition of the benchmark, optimizing the benchmark library according to the current operation data;

determining a back calculation value according to a previous time reference value of the end difference of the heater;

if the back calculation value is inferior to the optimized benchmark value, taking the optimized benchmark value as the current reference value of the water supply temperature;

and if the back calculation value is superior to the optimized benchmark value, optimizing the benchmark library again according to the back calculation value, and taking the back calculation value as the current reference value of the water supply temperature.

Optionally, determining a current reference value of the heater end difference based on a particle swarm optimization algorithm and a gray wolf algorithm according to the current operating data includes:

constructing a second objective function and setting a constraint condition of the second objective function;

constructing a hybrid algorithm model based on a particle swarm optimization algorithm and a gray wolf algorithm;

and determining a current reference value of the end difference of the heater according to the current operation data, the second objective function, the constraint condition of the second objective function and the hybrid algorithm model.

According to another aspect of the present invention, there is provided a device for determining a reference value of a regenerative feedwater heating system, comprising: the system comprises a data acquisition module, a benchmarking library construction module and a determination module;

the system comprises a data acquisition module, a data acquisition module and a data processing module, wherein the data acquisition module is used for acquiring historical operating data and current operating data of a water supply regenerative system of the steam turbine set;

the marker post library construction module is used for constructing a marker post library of the water supply temperature based on a k-means clustering algorithm according to historical operating data;

the determining module is used for determining a current reference value of the water supply temperature according to the current operation data and the benchmark library; and determining a current reference value of the heater end difference based on the particle swarm optimization algorithm and the wolf algorithm according to the current operation data.

According to another aspect of the present invention, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the method for determining a reference value for a feedwater regenerative system of any of the embodiments of the present invention.

According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the method for determining a reference value of a regenerative feedwater heating system according to any of the embodiments of the present invention when the processor executes the method.

According to the technical scheme of the embodiment of the invention, a benchmark library of the water supply temperature is constructed based on a k-means clustering algorithm by utilizing massive historical operation data of a water supply regenerative system of a steam turbine set under different working conditions, the benchmark library reflects the optimal water supply temperature under different working conditions and different environmental temperatures in the historical operation data, and the benchmark library is modified and optimized by taking the minimum influence of the water supply temperature change in the water supply regenerative system on the coal consumption rate as a target; and on the basis, iterative calculation is carried out on the end difference of each heater based on a particle swarm optimization algorithm and a gray wolf algorithm to obtain a reference value of the end difference of each heater. And meanwhile, carrying out inverse calculation by using the reference value of the end difference of each heater to obtain the water supply temperature again, substituting the water supply temperature into the benchmark library for rolling optimization, and finally obtaining the combined optimization reference value of the water supply temperature and the end difference of each heater. The rolling optimization of the marker post library can acquire the optimal water supply temperature of each load working condition under different environments at any moment in real time on line to obtain the real-time optimal reference value of the water supply temperature of the turboset in the current running state; the particle swarm optimization algorithm is combined with the gray wolf algorithm, has the characteristics of simple calculation, high search efficiency, accurate optimization precision, high convergence speed and the like, effectively solves the limitations of complex calculation, poor universality and low precision of the traditional distribution method for regenerative water supply, and has important guiding significance for the exploitation of energy-saving potential and optimization design of a thermodynamic system.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a structural diagram of a regenerative feedwater heating system of a steam turbine set according to an embodiment of the present invention;

fig. 2 is a schematic flow chart of a method for determining a reference value of a regenerative feedwater heating system according to an embodiment of the present invention;

fig. 3 is a schematic flow chart of a method for determining a reference value of a regenerative feedwater heating system according to a second embodiment of the present invention;

fig. 4 is a schematic structural diagram of a reference value determining device of a regenerative feedwater heating system according to a third embodiment of the present invention;

fig. 5 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.

Detailed Description

In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described 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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example one

Fig. 1 is a structural diagram of a regenerative feedwater heating system of a steam turbine set according to an embodiment of the present invention. As shown in fig. 1, the regenerative feedwater heating system of a steam turbine includes: (1) the system comprises a boiler, (2) a high-pressure cylinder, (3) a medium-low pressure cylinder, (4) a condenser, (5) a condensate pump, (6) a low-pressure heater, (7) a deaerator, (8) a water feed pump and (9) a high-pressure heater. Wherein the total number of the low-pressure heaters and the high-pressure heaters is z, and z is an integer greater than or equal to 2. To giveThe hydronic system extracts a portion of the steam from the high/medium pressure cylinders (i.e., α in FIG. 1) ₁ ,α ₂ ,...,α _z ) Sent to a corresponding heater for heating the boiler feed water temperature.

Considering the thermal economy and the influence factors of the regenerative feedwater system of the steam turbine set, when the regenerative feedwater system is optimized, a thermal parameter having a large influence on the thermal efficiency of the thermal economy index set is usually selected as an optimization parameter. In the invention, the optimization parameters are selected from the feed water temperature and the heater end difference.

The feed water temperature can be measured and read by a sensor, and measuring points are generally arranged at positions with uniform speed and temperature distribution on the cross section of the feed water pipeline. The temperature of the feed water is adjusted by the heat exchange of the feed water regenerative system. On one hand, when the steam extraction amount is increased, the water supply temperature is increased, the steam flow discharged into the condenser is reduced due to the increase of the steam extraction amount, the cold source loss of the condenser is reduced, and the heat efficiency of the unit is further improved; on the other hand, after the feedwater temperature improves, the effective heat transfer difference in temperature between the exothermic process of boiler combustion when feedwater passes through boiler water wall, superheater, reheater reduces, and the irreversible loss that corresponds reduces, but the feedwater temperature improves and also can make the effective heat transfer difference in the feedwater backheat system improve, and the irreversible loss of the heater that corresponds increases, and simultaneously, the exhaust gas temperature also can increase along with the feedwater temperature improves, leads to boiler efficiency to reduce. Therefore, there must be an optimum feedwater temperature to maximize the thermal efficiency of the unit.

The end difference of the heaters reflects the quality of the heat exchange effect of each heater: the smaller the end difference, the higher the heater economy. The end difference of the heater is mainly divided into an upper end difference and a lower end difference; wherein, the upper end difference refers to the difference between the saturation temperature corresponding to the pressure of steam in the heater and the water temperature at the outlet of the heater; the lower end difference is the difference between the drain outlet temperature and the feed water inlet temperature. The end difference of the heater is increased, so that on one hand, the output of the heater is reduced, the steam extraction amount with lower energy level is reduced, and the steam exhaust amount of the steam turbine is increased; on the other hand, the load of the upper-stage heater is increased, the extraction steam with higher energy level is increased, and the work capacity of the steam turbine is reduced. In conclusion, the change of the end difference of the heater can cause the overall heat efficiency of the feedwater heat recovery system to change, and the operation economy is influenced. In addition, the occurrence of end difference can also cause the entropy increase of a system to be large, and the irreversibility of a heat exchange process is increased.

Fig. 2 is a schematic flow chart of a method for determining a reference value of a regenerative feedwater system according to an embodiment of the present invention, which is applicable to a case where a reference value of a feedwater temperature and a heater end difference of a regenerative feedwater system of a steam turbine set is determined, and the method may be performed by a device for determining a reference value of a regenerative feedwater system, where the device for determining a reference value of a regenerative feedwater system may be implemented in a form of hardware and/or software, and the device for determining a reference value of a regenerative feedwater system may be configured in an electronic device (e.g., a computer or a server). As shown in fig. 2, the method includes:

s110, obtaining historical operation data and current operation data of a feedwater regenerative system of the steam turbine set.

The historical operation data is the data of the regenerative feedwater heating system of the steam turbine set in the historical operation process. The current operation data is the data of the regenerative feedwater heating system of the steam turbine set at the current operation time. The quantity of the historical operating data is multiple, and it can be understood that the greater the quantity of the historical operating data is, the more accurate the constructed benchmarking library of the feedwater temperature is.

And S120, constructing a benchmark library of the water supply temperature based on a k-means clustering algorithm according to historical operating data.

Specifically, the method for "constructing the benchmarking library of the feedwater temperature based on the k-means clustering algorithm according to the historical operating data" in step S120 may include the following three steps:

step a1, constructing a first objective function.

The first objective function is used for representing the influence of feed water temperature change on the coal consumption rate in the feed water regenerative system.

Step a2, processing the historical operating data based on a K-means clustering algorithm to obtain K groups of sample data, wherein one group of sample data corresponds to one clustering center, and K is a positive integer.

Optionally, before the step a2 is executed, the historical operating data may be preprocessed, that is, the operating data with unstable working conditions in the historical operating data is removed according to the stability judgment index, so as to ensure the accuracy of the constructed benchmarking library of the feedwater temperature.

And a3, performing optimization operation on the K groups of sample data by taking the first objective function as a reference to obtain a benchmark library.

The marker post library reflects the optimal water supply temperature under different working conditions and different environmental temperatures in historical operation data.

And S130, determining a current reference value of the water supply temperature according to the current operation data and the benchmark library.

Specifically, the method of "determining the current reference value of the feedwater temperature according to the current operation data and the benchmarking library" in step S130 may adopt any one of the following two ways:

the method I comprises the following steps: searching a benchmark working condition and a benchmark value which are matched with the current operation data from a benchmark library; substituting the current operation data into a first objective function, and calculating an objective function value corresponding to the current operation data; if the target function value is larger than the working condition of the benchmark, taking the benchmark value as the current reference value of the water supply temperature; and if the target function value is less than or equal to the working condition of the benchmark, optimizing the benchmark library according to the current operation data, and taking the value of the water supply temperature in the current operation data as the current reference value of the water supply temperature.

The first mode can be used for correcting and optimizing the benchmark library by taking the minimum influence of the feed water temperature change in the feed water regenerative system on the coal consumption rate as a target, and the first mode has the advantages of simple scheme and low calculation force requirement on the system.

The second method comprises the following steps: searching a benchmark working condition and a benchmark value which are matched with the current operation data from a benchmark library; substituting the current operation data into a first objective function, and calculating an objective function value corresponding to the current operation data; if the objective function value is less than or equal to the working condition of the benchmark, optimizing the benchmark library according to the current operation data; determining a back calculation value according to a previous time reference value of the end difference of the heater; if the back-calculated value is inferior to the optimized benchmark value, taking the optimized benchmark value as the current reference value of the water supply temperature; if the back calculation value is superior to the optimized benchmark value, optimizing the benchmark library again according to the back calculation value, and taking the back calculation value as the current reference value of the water supply temperature; if the target function value is larger than the working condition of the benchmark, determining a back calculation value according to a previous moment reference value of the end difference of the heater; if the back calculation value is inferior to the benchmark value, the benchmark value is used as the current reference value of the water supply temperature; and if the back calculation value is superior to the benchmark value, optimizing the benchmark library according to the back calculation value, and taking the back calculation value as the current reference value of the water supply temperature.

Compared with the first mode, the second mode can also use the reference value of the end difference of each heater to perform inverse calculation to obtain the water supply temperature again, and the water supply temperature is substituted into the benchmark library to perform rolling optimization.

And S140, determining a current reference value of the end difference of the heater based on a particle swarm optimization algorithm and a gray wolf algorithm according to the current operation data.

Specifically, the method for determining the current reference value of the heater end difference based on the particle swarm optimization algorithm and the grayish wolf algorithm according to the current operation data in the step S140 may include the following three steps:

and b1, constructing a second objective function, and setting a constraint condition of the second objective function.

And b2, constructing a hybrid algorithm model based on the particle swarm optimization algorithm and the wolf algorithm.

And b3, determining a current reference value of the end difference of the heater according to the current operation data, the second objective function, the constraint condition of the second objective function and the hybrid algorithm model.

Therefore, the method can quickly and accurately determine the reference value of the difference between the feed water temperature and the heater end without changing the structure of the feed water regenerative system, and performs rolling optimization on the reference value to reduce the energy consumption of the system, improve the heat economy of the steam turbine unit, and has important guiding significance for the exploitation of energy-saving potential and the optimization design of a thermodynamic system.

Example two

Fig. 3 is a schematic flow chart of a method for determining a reference value of a regenerative feedwater heating system according to a second embodiment of the present invention. As shown in fig. 3, the method includes:

s201, obtaining historical operation data and current operation data of a feedwater regenerative system of the steam turbine set.

The historical operation data is data of a feedwater regenerative system of the steam turbine set in the historical operation process. The current operation data is the data of the regenerative feedwater heating system of the steam turbine set at the current operation time.

S202, constructing a first objective function.

Referring to fig. 1, according to the equivalent enthalpy drop theory, the feed water temperature reaches the highest at the outlet of the high-pressure heater No.1, the high-pressure heater No.1 has an end difference, and the corresponding 1-stage steam extraction share is increased, so that the working capacity of the new steam is reduced, and the circulating heat absorption capacity is reduced.

Specifically, end difference of high pressure Heater No.1

New steam equivalent enthalpy drop change

Cyclic variation of heat absorption

Coal rate change caused by feed water temperature change in feed water regenerative system

Wherein, t _1s Is the saturation temperature, t, of the high-pressure heater No.1 at the extraction pressure _1w The outlet water temperature of the high-pressure heater No. 1; eta ₁ The steam extraction efficiency of the high-pressure heater No. 1; q. q.s ₁ The heat release, Q, of 1kg of extracted steam in the high pressure heater No.1 _zr-1 Is the change in the heat absorption of the highest reheater; b is the fuel consumption and H is the equivalent enthalpy drop of the live steam.

Thus, the first objective function finally constructed is min Δ b = f (t) _1w )。

S203, removing the operation data with unstable working conditions in the historical operation data according to the stability judgment indexes, wherein the stability judgment indexes at least comprise main steam pressure.

Assuming that the stability judgment index is the main steam pressure, the historical operation data is considered as stable operation data only if the main steam pressure of the historical operation data is controlled within a certain fluctuation numerical range in a period of time; otherwise, the historical operating data is considered as unstable operating data.

S204, processing the historical operating data based on a K-means clustering algorithm to obtain K groups of sample data, wherein one group of sample data corresponds to one clustering center, and K is a positive integer.

S205, with the first objective function as a reference, performing optimization operation on the K groups of sample data to obtain a benchmark library.

The k-means clustering algorithm, namely a k-means clustering algorithm, is a clustering analysis algorithm for iterative solution. And processing the historical operating data by adopting a K-means clustering algorithm to obtain K groups of sample data, and then performing optimization operation on the K groups of sample data by taking the first objective function as a reference to obtain the optimal water supply temperature of the first objective function under different environmental temperatures and different working conditions, wherein the information jointly forms a benchmarking library of the water supply temperature.

S206, searching the benchmark working condition and the benchmark value matched with the current operation data from the benchmark library.

And S207, substituting the current operation data into the first objective function, and calculating an objective function value corresponding to the current operation data.

And S208, judging whether the objective function value is less than or equal to the working condition of the benchmark. If yes, go to S209; if not, go to step S214.

And S209, optimizing the benchmark library according to the current operation data.

When the objective function value is less than or equal to the working condition of the benchmark, the coal consumption rate of the current operation data is smaller than that of the benchmark working condition, so that the benchmark library can be optimized according to the current operation data.

And S210, determining a back calculation value according to the previous moment reference value of the end difference of the heater.

And S211, judging whether the back calculation value is superior to the optimized benchmark value. If yes, go to step S212; if not, S213 is executed.

And S212, optimizing the benchmark library again according to the back calculation value, and taking the back calculation value as the current reference value of the water supply temperature.

And S213, taking the optimized benchmark value as the current reference value of the water supply temperature.

And S214, determining a back calculation value according to the previous time reference value of the end difference of the heater.

When the objective function value is greater than the benchmark working condition, the coal consumption rate of the current operation data is greater than that of the benchmark working condition, so that the benchmark library is not required to be optimized temporarily according to the current operation data.

And S215, judging whether the inverse value is superior to the benchmark value. If yes, go to S216; if not, go to S217.

S216, optimizing the benchmark library according to the back calculation value, and taking the back calculation value as the current reference value of the water supply temperature.

And S217, taking the benchmark value as the current reference value of the water supply temperature.

S218, constructing a second objective function, and setting a constraint condition of the second objective function.

Under the condition that the design conditions such as main steam parameters, reheated steam temperature, optimal water supply temperature, steam exhaust dryness and steam extraction stage number are fixed, the end difference of each heater is taken as an optimization variable, and the unit circulating heat efficiency is taken as an optimization target, so that the following can be obtained:

cycle thermal efficiency of unit

Thermal efficiency after heater end difference change

Wherein N is the work load of the steam turbine, and q is the heat absorption capacity of the boiler;

outlet water of j-th stage heaterChange in enthalpy, T _j The change value P of the work doing amount of the steam turbine when the enthalpy of the outlet water of the j-th stage heater is changed by 1kj/kg due to the end difference change _j The change value of the boiler heat absorption when the outlet water enthalpy of the j-th stage heater is changed by 1kj/kg is caused by end difference change.

Thus, the second objective function that is finally constructed is

The constraint of the second objective function is p _c <p _j <p _o ；

Wherein p is _c Designed value for the exhaust pressure of a steam turbine, p _j For extraction pressure, p, of each stage _o And designing the initial pressure of the steam turbine.

S219, constructing a hybrid algorithm model based on the particle swarm optimization algorithm and the gray wolf algorithm.

The particle swarm optimization algorithm, i.e. the dynamic adaptive particle swarm optimization algorithm, can be expressed as:

wherein the content of the first and second substances,

refers to the jth dimension of the ith particle velocity for the tth iteration;

refers to the jth dimension position of the ith particle of the tth iteration; p is a radical of _best The historical optimal position of each particle is referred to; g _best The method refers to a global optimal position of a particle swarm; r1 and r2 are distributed in [0,1]A random number of (c); w is an inertia weight factor, a dynamic nonlinear adjustment method is adopted, and a proper function is selected, so that w is changed along with a fitness function value (namely a second objective function), wherein in the early stage, in order to avoid falling into local optimization, w is a large value, and in the later stage, in order to improve the search precision, w is a small value; c1 is local search acceleration factor, c2 is global search acceleration factor, and dynamic self-adaption is adoptedAnd a control mechanism, which considers the balance of global and local searching capabilities and respectively carries out nonlinear time-varying dynamic adjustment on c1 and c 2.

The gray wolf algorithm can be expressed as:

wherein r1 and r2 are distributed in [0,1]A random number of (c); t is the number of iterations; a is convergence factor, in [0,2]The range is linearly decreased along with the iteration times;

respectively corresponding to the j-th dimension historical optimal solution, the second optimal solution and the third optimal solution in the wolf population; d (alpha), D (beta) and D (delta) respectively correspond to the distances between the wolf individual in the population and the first three optimal solutions;

respectively corresponding to the moving directions of the wolf population to 3 elite individuals;

the final updated state after booting for the combination of 3 elite individuals.

In order to introduce the advantage of strong global search capability of the gray wolf algorithm surrounding search, a surrounding updating strategy of the gray wolf algorithm is added in a particle swarm optimization algorithm formula.

Specifically, a model from an operation variable to an optimization target in the particle swarm optimization algorithm is established, namely a second target function, the end difference of each heater is the operation variable, and the second target function is in the particle swarm optimization algorithm

Meanwhile, a model from the operation variable to the optimization target in the gray wolf algorithm, namely a second objective function, is established, and each heating is carried outThe difference between the ends of the device is an operation variable, and the second objective function is in the particle swarm optimization algorithm

Different from the particle swarm optimization algorithm, the gray wolf algorithm has three advancing directions alpha, beta and delta in each iteration. Therefore, in the particle swarm optimization algorithm

Respectively modified into three optimal solutions in the gray wolf algorithm

Keeping the model unchanged to obtain the mixed algorithm model.

The hybrid algorithm model can be expressed as:

and S220, determining a current reference value of the end difference of the heater according to the current operation data, the second objective function, the constraint condition of the second objective function and the hybrid algorithm model.

As can be seen from the above-mentioned hybrid algorithm model, each iteration of the calculation will result in

Closer to the global optimum. When a certain convergence is satisfiedAnd when the condition is met, stopping the second objective function value near the global optimal value, wherein the end difference is the optimal solution.

The embodiment of the invention provides a method for determining a reference value of a regenerative feedwater heating system, which comprises the following steps: acquiring historical operating data and current operating data of a water supply regenerative system of a steam turbine set; constructing a benchmark library of the water supply temperature based on a k-means clustering algorithm according to historical operation data; determining a current reference value of the water supply temperature according to the current operation data and the benchmark library; and determining a current reference value of the end difference of the heater based on a particle swarm optimization algorithm and a wolf algorithm according to the current operation data. The method comprises the steps that a mark post library of water supply temperature is constructed on the basis of a k-means clustering algorithm by utilizing mass historical operating data of a water supply regenerative system of a steam turbine set under different working conditions, the mark post library reflects the optimal water supply temperature under different working conditions and different environmental temperatures in the historical operating data, and the mark post library is corrected and optimized by taking the minimum influence of water supply temperature change in the water supply regenerative system on coal consumption rate as a target; and on the basis, iterative calculation is carried out on the end difference of each heater based on a particle swarm optimization algorithm and a gray wolf algorithm to obtain a reference value of the end difference of each heater. And meanwhile, carrying out inverse calculation by using the reference value of the end difference of each heater to obtain the water supply temperature again, substituting the water supply temperature into the benchmark library for rolling optimization, and finally obtaining the combined optimization reference value of the water supply temperature and the end difference of each heater. The rolling optimization of the marker post library can acquire the optimal water supply temperature of each load working condition under different environments at any moment in real time on line to obtain the real-time optimal reference value of the water supply temperature of the turboset in the current running state; the particle swarm optimization algorithm is combined with the Hui wolf algorithm, the method has the characteristics of simple calculation, high search efficiency, accurate optimization precision, high convergence speed and the like, effectively solves the limitations of complex calculation, poor universality and low precision of the traditional distribution method for the regenerative feedwater heating, and has important guiding significance for the energy-saving potential excavation and the optimization design of a thermodynamic system.

EXAMPLE III

Fig. 4 is a schematic structural diagram of a reference value determining device of a regenerative feedwater heating system according to a third embodiment of the present invention. As shown in fig. 4, the apparatus includes: a data acquisition module 401, a benchmarking library construction module 402 and a determination module 403.

The data acquisition module 401 is configured to acquire historical operating data and current operating data of a feedwater regenerative system of the steam turbine set;

a benchmark library construction module 402, configured to construct a benchmark library of the water supply temperature based on a k-means clustering algorithm according to historical operating data;

a determining module 403, configured to determine a current reference value of the feedwater temperature according to the current operating data and the benchmark library; and determining a current reference value of the heater end difference based on the particle swarm optimization algorithm and the wolf algorithm according to the current operation data.

Optionally, the benchmarking library constructing module 402 is specifically configured to construct a first objective function; processing historical operating data based on a K-means clustering algorithm to obtain K groups of sample data, wherein one group of sample data corresponds to one clustering center, and K is a positive integer; and performing optimization operation on the K groups of sample data by taking the first objective function as a reference to obtain a benchmark library.

Optionally, the data obtaining module 401 is further configured to, before the benchmark base building module 402 processes the historical operating data based on the k-means clustering algorithm, remove operating data with unstable working conditions in the historical operating data according to a stability judgment index, where the stability judgment index at least includes the main steam pressure.

Optionally, the determining module 403 is specifically configured to search a benchmark working condition and a benchmark value that are matched with the current operation data from the benchmark library; substituting the current operation data into a first objective function, and calculating an objective function value corresponding to the current operation data; if the target function value is larger than the working condition of the benchmark, taking the benchmark value as the current reference value of the water supply temperature; and if the target function value is less than or equal to the working condition of the benchmark, optimizing the benchmark library according to the current operation data, and taking the value of the water supply temperature in the current operation data as the current reference value of the water supply temperature.

Optionally, the determining module 403 is specifically configured to search a benchmark working condition and a benchmark value that are matched with the current operation data from a benchmark library; substituting the current operation data into a first objective function, and calculating an objective function value corresponding to the current operation data; if the objective function value is less than or equal to the working condition of the benchmark, optimizing the benchmark library according to the current operation data; determining a back calculation value according to a previous time reference value of the end difference of the heater; if the back-calculated value is inferior to the optimized benchmark value, taking the optimized benchmark value as the current reference value of the water supply temperature; and if the back calculation value is superior to the optimized benchmark value, optimizing the benchmark library again according to the back calculation value, and taking the back calculation value as the current reference value of the water supply temperature.

Optionally, the determining module 403 is further configured to determine a back calculation value according to a reference value of the end difference of the heater at the previous time if the objective function value is greater than the working condition of the benchmark; if the back calculation value is inferior to the benchmark value, the benchmark value is used as the current reference value of the water supply temperature; and if the back calculation value is superior to the benchmark value, optimizing the benchmark library according to the back calculation value, and taking the back calculation value as the current reference value of the water supply temperature.

Optionally, the determining module 403 is specifically configured to construct a second objective function, and set a constraint condition of the second objective function; constructing a hybrid algorithm model based on a particle swarm optimization algorithm and a gray wolf algorithm; and determining a current reference value of the end difference of the heater according to the current operation data, the second objective function, the constraint condition of the second objective function and the hybrid algorithm model.

The device for determining the reference value of the regenerative feedwater heating system, provided by the embodiment of the invention, can execute the method for determining the reference value of the regenerative feedwater heating system, provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

Example four

FIG. 5 illustrates a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to the bus 14.

A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as the determination of a reference value for a regenerative feedwater heating system.

In some embodiments, the method of determining a reference value for a regenerative feedwater heating system may be implemented as a computer program tangibly embodied in a computer readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the above described method of determining a reference value for a regenerative feedwater heating system may be performed. Alternatively, in other embodiments, the processor 11 may be configured by any other suitable means (e.g., by means of firmware) to perform the method of determining the reference value of the regenerative feedwater heating system.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.

The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining a reference value of a regenerative feedwater heating system is characterized by comprising the following steps:

constructing a benchmark library of the water supply temperature based on a k-means clustering algorithm according to the historical operating data;

and determining a current reference value of the end difference of the heater based on a particle swarm optimization algorithm and a gray wolf algorithm according to the current operation data.

2. The method of claim 1, wherein constructing a benchmarking library of feedwater temperatures based on a k-means clustering algorithm from the historical operating data comprises:

constructing a first objective function;

processing the historical operating data based on a K-means clustering algorithm to obtain K groups of sample data, wherein one group of sample data corresponds to one clustering center, and K is a positive integer;

and performing optimization operation on the K groups of sample data by taking the first objective function as a reference to obtain the benchmark library.

3. The method of claim 2, further comprising, prior to processing the historical operating data based on a k-means clustering algorithm:

and rejecting the operation data with unstable working conditions in the historical operation data according to a stability judging index, wherein the stability judging index at least comprises main steam pressure.

4. A method according to claim 2 or 3, wherein said determining a reference value for the feedwater temperature from said current operating data and said benchmarking library comprises:

searching a benchmark working condition and a benchmark value which are matched with the current operation data from the benchmark library;

substituting the current operation data into the first objective function, and calculating an objective function value corresponding to the current operation data;

if the objective function value is larger than the working condition of the benchmark, taking the benchmark value as the current reference value of the water supply temperature;

and if the objective function value is less than or equal to the working condition of the benchmark, optimizing the benchmark library according to the current operation data, and taking the value of the water supply temperature in the current operation data as the current reference value of the water supply temperature.

5. A method according to claim 2 or 3, wherein said determining a reference value for the feedwater temperature from said current operating data and said benchmarking library comprises:

if the objective function value is smaller than or equal to the working condition of the benchmark, optimizing the benchmark library according to the current operation data;

6. The method of claim 5, further comprising:

if the objective function value is larger than the working condition of the marker post, determining a back calculation value according to a reference value of the heater end difference at the last moment;

if the reverse calculation value is inferior to the benchmark value, taking the benchmark value as the current reference value of the water supply temperature;

and if the back calculation value is superior to the benchmark value, optimizing the benchmark library according to the back calculation value, and taking the back calculation value as the current reference value of the water supply temperature.

7. The method of claim 1, wherein determining a current baseline value for heater end-to-end variation based on a particle swarm optimization algorithm and a grayish wolf algorithm based on the current operating data comprises:

constructing a second objective function, and setting a constraint condition of the second objective function;

8. A device for determining a reference value of a regenerative feedwater heating system, comprising: the system comprises a data acquisition module, a benchmark library construction module and a determination module;

the data acquisition module is used for acquiring historical operating data and current operating data of a water supply regenerative system of the steam turbine set;

the benchmark library construction module is used for constructing a benchmark library of the water supply temperature based on a k-means clustering algorithm according to the historical operation data;

the determining module is used for determining a current reference value of the water supply temperature according to the current operation data and the benchmark library; and determining a current reference value of the end difference of the heater based on a particle swarm optimization algorithm and a gray wolf algorithm according to the current operation data.

9. An electronic device, characterized in that the electronic device comprises:

at least one processor; and

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of determining a reference value for a regenerative feedwater heating system of any of claims 1-7.

10. A computer readable storage medium storing computer instructions for causing a processor to perform the method of determining a reference value for a regenerative feedwater heating system according to any of claims 1 to 7.