CN114371614A - Genetic algorithm-based pump station and pump set operation determination method and system - Google Patents
Genetic algorithm-based pump station and pump set operation determination method and system Download PDFInfo
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Abstract
The invention relates to a method and a system for determining the operation of a pump station and a pump set based on a genetic algorithm, wherein the method comprises the following steps: acquiring a characteristic curve of a water pump in a pump station pump set; determining an efficient operation interval of the water pump according to the characteristic curve; constructing an optimization control objective function according to the objective with optimal energy efficiency; determining the constraint condition of the objective function according to the characteristic curve and the efficient operation interval of the water pump; and carrying out optimization solution on the objective function according to a genetic algorithm and the constraint condition to obtain a pump station and pump set operation scheme. The invention can reasonably select the water pump combination and the operation frequency which accord with the current pump station operation condition, so that the water pump can operate efficiently, and the operation energy consumption of the water pump is reduced.
Description
Technical Field
The invention relates to the technical field of optimization control, in particular to a method and a system for determining the operation of a pump station and a pump set based on a genetic algorithm.
Background
The pump station is an important component of the urban water supply transmission and distribution system, and the improvement of the economic benefit of the water supply system depends on the operation condition of the pump station to a certain extent. At present, most pump stations in China still adopt constant frequency scheduling and experience scheduling, but the working point of the pump station is continuously changed along with the change of water consumption. Therefore, the operation mode of the pump station can be adjusted according to the actual working condition, so that the pump station realizes the optimized operation of the pump set in a high-benefit state, and the pump station is a focus of the optimization control, energy conservation and consumption reduction of the current pump station.
Disclosure of Invention
The invention aims to provide a method and a system for determining the operation of a pump station and a pump set based on a genetic algorithm, which can reasonably select the combination and the operation frequency of a water pump according with the current operation condition of the pump station, so that the water pump can operate efficiently, and the energy consumption for the operation of the water pump is reduced.
In order to achieve the purpose, the invention provides the following scheme:
a pump station and pump set operation determination method based on genetic algorithm comprises the following steps:
acquiring a characteristic curve of a water pump in a pump station pump set;
determining an efficient operation interval of the water pump according to the characteristic curve;
constructing an optimization control objective function according to the objective with optimal energy efficiency;
determining the constraint condition of the objective function according to the characteristic curve and the efficient operation interval of the water pump;
and carrying out optimization solution on the objective function according to a genetic algorithm and the constraint condition to obtain a pump station and pump set operation scheme.
Optionally, the characteristic curve is obtained as follows:
acquiring a delivery characteristic curve of the water pump;
acquiring historical operating data of the water pump;
and carrying out correction fitting on the factory characteristic curve according to the historical operating data to obtain the characteristic curve.
Optionally, the characteristic curve includes:
flow-head curve: h ═ a1Q2+b1Qs+c1s2；
Flow-power curve: a is N2Q2s+b2Qs2+c1s3；
Flow-efficiency curve:
wherein Q is flow, s is rotation speed ratio, H is lift, N is power, eta is efficiency, rho is density, g is gravity to mass ratio, and 9.8N/kg, a are taken1、b1、c1、a2、b2、c2All are characteristic curve coefficients.
Optionally, the objective function is:
wherein N is the number of pumps, ω is a decision variable, and N is power.
Optionally, the constraint condition includes: the lift of each water pump is equal; the total flow rate of the water plant scheduling is the sum of the flow rates of the single pumps; the flow of the single pump is within the efficient operation range of the water pump; the water pump is started and stopped frequently; avoiding starting the water pumps in the same water suction well at the same time; avoiding the simultaneous activation of water pumps on the same power distribution section; avoiding the activation of a faulty pump.
Optionally, the optimizing and solving the objective function according to the genetic algorithm and the constraint condition to obtain a pump set operation scheme of the pump station includes:
outputting a chromosome sequence with highest fitness in the chromosome population when the iteration times of the genetic algorithm reach a limited number;
and decoding the chromosome sequence to obtain a pump station and pump set operation scheme.
Optionally, the water pump is a power frequency pump or a variable frequency pump.
Optionally, the water pump operates at the same frequency.
A genetic algorithm-based pump station and pump set operation determination system comprises:
the characteristic curve acquisition module is used for acquiring a characteristic curve of a water pump in a pump station pump set;
the efficient operation interval determining module is used for determining an efficient operation interval of the water pump according to the characteristic curve;
the target function construction module is used for constructing an optimized control target function according to the target with optimal energy efficiency;
the constraint condition determining module is used for determining the constraint condition of the objective function according to the characteristic curve and the efficient operation interval of the water pump;
and the optimization solving module is used for carrying out optimization solving on the objective function according to the genetic algorithm and the constraint condition to obtain a pump station and pump set operation scheme.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the method is based on a genetic algorithm, takes the lowest energy efficiency as a target function, and can work out the optimal pump set combination and the optimal operation frequency by combining different requirements in the actual production operation process as constraint conditions.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described 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 without inventive exercise.
FIG. 1 is a flow chart of a method for determining the operation of a pump station and a pump set based on a genetic algorithm;
FIG. 2 is a flow chart of a genetic algorithm solution model of the present invention;
FIG. 3 is a block diagram of a genetic algorithm-based pump station and pump set operation determination system.
Detailed Description
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.
The invention aims to provide a method and a system for determining the operation of a pump station and a pump set based on a genetic algorithm, which can reasonably select the combination and the operation frequency of a water pump according with the current operation condition of the pump station, so that the water pump can operate efficiently, and the energy consumption for the operation of the water pump is reduced.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the present invention provides a method for determining the operation of a pump station and a pump set based on a genetic algorithm, which comprises:
step 101: and acquiring a characteristic curve of a water pump in a pump set of the pump station.
During the operation of the water pump, the characteristic curve of the water pump generates certain deviation. Therefore, the delivery characteristic curve of the water pump and the historical operating data of the water pump need to be combined to perform curve correction fitting to obtain the characteristic curve of the water pump.
Wherein, water pump characteristic curve includes:
Q-H (flow-head) curve: h ═ a1Q2+b1Qs+c1s2；
Q-N (flow-Power) curve: a is N2Q2s+b2Qs2+c1s3；
Q- η (flow-efficiency) curve:
wherein Q is flow, s is rotation ratio, H is lift, N is power, and eta is efficiency; a is1、b1、c1、a2、b2、c2Is a characteristic curve coefficient. Specifically, the water pump is a power frequency pump or a variable frequency pump. The water pump operates at the same frequency.
Step 102: and determining the efficient operation interval of the water pump according to the characteristic curve.
Setting the minimum efficiency eta of water pump operation at the same timeminAnd the flow rate-efficiency curve equation is introduced into the flow rate-efficiency curve equation to obtain the running water pumpHigh efficiency flow interval [ Qmin,Qmax]。
Step 103: and constructing an objective function of optimization control according to the objective with optimal energy efficiency.
Counting the quantity of power frequency pumps and variable frequency pumps of a pump set of a pump station, and constructing an optimal control objective function J by taking the optimal energy efficiency as a target:
wherein m is the number of power frequency pumps, N is the number of variable frequency pumps, omega is a decision variable, N is power, Q is total water volume, and P is total pressure.
Step 104: and determining the constraint condition of the objective function according to the characteristic curve and the efficient operation interval of the water pump.
The constraint conditions include: the lift of each water pump is equal; the total flow rate of the water plant scheduling is the sum of the flow rates of the single pumps; the flow of the single pump is within the efficient operation range of the water pump; the water pump is started and stopped frequently; avoiding starting the water pumps in the same water suction well at the same time; avoiding the simultaneous activation of water pumps on the same power distribution section; avoiding the activation of a faulty pump.
Step 105: and carrying out optimization solution on the objective function according to a genetic algorithm and the constraint condition to obtain a pump station and pump set operation scheme.
Solving the objective function by using a genetic algorithm, wherein the solving process is shown in fig. 2 and specifically comprises the following steps:
3.1) encoding.
3.2) initializing.
3.3) calculating the individual fitness.
3.4) selection, crossover and mutation operations.
3.5) judging whether the iteration times of the algorithm reach the limited times, if so, outputting a chromosome sequence with the highest fitness in the chromosome population, decoding, and taking the decoded pump operation signal as an optimal pump set operation scheme, wherein the optimal pump set operation scheme comprises an operation signal and operation frequency; otherwise, on the basis of the new population, returning to the step 3.2 to continue the iteration.
Specifically, the step 3.1 adopts a binary coding method, and the expression type is as follows:
s·ω1ω2···ωn
wherein s represents the speed regulating ratio of the parallel water pumps running at the same frequency, omega1ω2···ωnRepresenting operating signals, ω, of n pumpsiRepresenting the operating state (omega) of the ith water pumpi1 denotes water pump operation, ωi0 means water pump is not running).
The individual genotypes are:
b1b2···bl·ω1ω2···ωn
the encoding precision is as follows:
step 3.2 initialization comprises: determining genetic parameters, including crossover probability PcProbability of mutation PmA population size pop; determining the iteration times of the algorithm; and randomly generating an initialization population, deleting the initialization population, and deleting the frequency which does not meet the conditions under the target lift, so that the initialization population is close to the optimal solution as much as possible, and the operation speed of the algorithm is increased.
The fitness function in step 3.3 is:
fitness＝-J(x)+J(max)
wherein max is the set of chromosome sequences in the current population for which the objective function is maximal.
And 3.4, carrying out selection operation among chromosome sequences, wherein:
the selection operation adopts a proportional selection method, namely the probability of each individual being selected is in direct proportion to the size of the fitness value thereof, and comprises the following steps:
1) calculating the probability of individual selection according to the individual fitness of each individual:
2) calculating cumulative probability of individual being selected
3) Comparing randomly generated one [0,1 ]]Random number r and cumulative probability q betweeniThe size of (2). If r is less than or equal to qiSelecting an individual i to enter a progeny population;
further, each individual chromosome is subjected to cross operation, and the cross operation adopts single-point cross operation, namely, a cross point is randomly arranged in an individual code string, and the cross probability P is used for the cross operationcAt which point the partial chromosomes of the two paired individuals are interchanged.
Furthermore, carrying out mutation operation on each individual chromosome, wherein the mutation operation adopts basic bit mutation, namely, carrying out mutation probability P on the individual chromosomemAnd performing mutation operation.
Based on the method, the invention also discloses a system for determining the operation of the pump station and the pump set based on the genetic algorithm, which is shown in figure 3 and comprises the following steps:
a characteristic curve obtaining module 201, configured to obtain a characteristic curve of a water pump in a pump set of a pump station;
the efficient operation interval determining module 202 is used for determining an efficient operation interval of the water pump according to the characteristic curve;
the objective function construction module 203 is used for constructing an objective function for optimization control according to the objective with optimal energy efficiency;
a constraint condition determining module 204, configured to determine a constraint condition of the objective function according to the characteristic curve and an efficient operation interval of the water pump;
and the optimization solving module 205 is configured to perform optimization solving on the objective function according to a genetic algorithm and the constraint condition to obtain a pump station and pump group operation scheme.
The technical effect obtained by adopting the technical scheme is as follows:
the method utilizes a genetic algorithm, takes the lowest energy efficiency as a target function, combines different requirements in the actual production operation process as constraint conditions, and obtains an optimal pump set operation scheme comprising operation signals and operation frequency after operation iteration such as coding, selection, crossing, variation and the like. The scheme can reasonably select the water pump combination and the operation frequency which accord with the operation condition of the current pump station, so that the water pump can operate efficiently, and the operation energy consumption of the water pump is reduced.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (9)
1. A pump station and pump set operation determination method based on genetic algorithm is characterized by comprising the following steps:
acquiring a characteristic curve of a water pump in a pump station pump set;
determining an efficient operation interval of the water pump according to the characteristic curve;
constructing an optimization control objective function according to the objective with optimal energy efficiency;
determining the constraint condition of the objective function according to the characteristic curve and the efficient operation interval of the water pump;
and carrying out optimization solution on the objective function according to a genetic algorithm and the constraint condition to obtain a pump station and pump set operation scheme.
2. The genetic algorithm-based pump station and pump group operation determination method according to claim 1, wherein the characteristic curve is obtained by:
acquiring a delivery characteristic curve of the water pump;
acquiring historical operating data of the water pump;
and carrying out correction fitting on the factory characteristic curve according to the historical operating data to obtain the characteristic curve.
3. The genetic algorithm-based pump station and pump group operation determination method according to claim 1, wherein the characteristic curve comprises:
flow-head curve: h ═ a1Q2+b1Qs+c1s2
Flow-power curve: a is N2Q2s+b2Qs2+c1s3
Flow-efficiency curve:
wherein Q is flow, s is rotation speed ratio, H is lift, N is power, eta is efficiency, rho is density, g is gravity to mass ratio, and 9.8N/kg, a are taken1、b1、c1、a2、b2、c2All are characteristic curve coefficients.
4. The genetic algorithm-based pump station and pump group operation determination method according to claim 1, wherein the objective function is:
wherein N is the number of pumps, ω is a decision variable, and N is power.
5. The genetic algorithm-based pump station and pump group operation determination method according to claim 1, wherein the constraint condition comprises: the lift of each water pump is equal; the total flow rate of the water plant scheduling is the sum of the flow rates of the single pumps; the flow of the single pump is within the efficient operation range of the water pump; the water pump is started and stopped frequently; avoiding starting the water pumps in the same water suction well at the same time; avoiding the simultaneous activation of water pumps on the same power distribution section; avoiding the activation of a faulty pump.
6. The method for determining the operation of the pump station and the pump set based on the genetic algorithm according to claim 1, wherein the optimization solution of the objective function according to the genetic algorithm and the constraint condition to obtain the operation scheme of the pump station and the pump set comprises the following steps:
outputting a chromosome sequence with highest fitness in the chromosome population when the iteration times of the genetic algorithm reach a limited number;
and decoding the chromosome sequence to obtain a pump station and pump set operation scheme.
7. The method for determining the operation of the pump station and the pump group based on the genetic algorithm according to claim 1, wherein the water pump is a power frequency pump or a variable frequency pump.
8. The method for determining the operation of the pump station and the pump set based on the genetic algorithm according to claim 1, wherein the water pumps operate at the same frequency.
9. A genetic algorithm-based pump station and pump set operation determination system is characterized by comprising:
the characteristic curve acquisition module is used for acquiring a characteristic curve of a water pump in a pump station pump set;
the efficient operation interval determining module is used for determining an efficient operation interval of the water pump according to the characteristic curve;
the target function construction module is used for constructing an optimized control target function according to the target with optimal energy efficiency;
the constraint condition determining module is used for determining the constraint condition of the objective function according to the characteristic curve and the efficient operation interval of the water pump;
and the optimization solving module is used for carrying out optimization solving on the objective function according to the genetic algorithm and the constraint condition to obtain a pump station and pump set operation scheme.
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| CN114909279A (en) * | 2022-06-16 | 2022-08-16 | 上海西派埃智能化系统有限公司 | Method and system for determining optimal pump set combination in water treatment pump set |
| CN115455812A (en) * | 2022-08-25 | 2022-12-09 | 临涣水务股份有限公司 | Water supply pump station optimization method and system |
| CN116596280A (en) * | 2023-07-17 | 2023-08-15 | 青岛国源中创电气自动化工程有限公司 | Cooperative scheduling method for water pump set of sewage treatment plant |
| CN118690662A (en) * | 2024-08-23 | 2024-09-24 | 西安交通大学 | Series compressor unit energy-saving operation optimizing method based on genetic algorithm |
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| CN114909279A (en) * | 2022-06-16 | 2022-08-16 | 上海西派埃智能化系统有限公司 | Method and system for determining optimal pump set combination in water treatment pump set |
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| CN118690662A (en) * | 2024-08-23 | 2024-09-24 | 西安交通大学 | Series compressor unit energy-saving operation optimizing method based on genetic algorithm |
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