CN112286060A - Heat supply network temperature control method based on genetic algorithm and fuzzy control technology - Google Patents
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Abstract
The invention provides a heat supply network temperature control method based on a genetic algorithm and a fuzzy control technology, which comprises the following steps: dividing variable subsets, selecting a membership function to realize variable fuzzification, fuzzy reasoning, determining initialization parameters, processing individual chromosomes and selecting chromosomes. The provided heat supply network temperature control method based on the genetic algorithm and the fuzzy control technology selects the fuzzy control model with high operation speed; aiming at the requirement of real-time control of the water temperature of the user pipeline, a fuzzy control algorithm with a higher operation speed is selected, the calculation requirement on hardware of controller equipment is reduced, and compared with a mathematical model method and a neural network algorithm, the fuzzy control method does not need a complex mathematical model and does not need a large amount of data for training the model, and is very suitable for being applied to temperature control of a heat supply network.
Description
Technical Field
The invention relates to the technical field of heat supply network temperature regulation, in particular to a heat supply network temperature control method based on a genetic algorithm and a fuzzy control technology.
Background
The heat supply network is a main component of a central heating system and is responsible for heat energy conveying tasks. The system form of the heat supply network depends on heat media, the mutual positions of heat sources (thermal power plants or regional boiler houses, etc.) and heat users, the types of heat users in heat supply areas, the size and the nature of heat loads, and the like. The principle to be followed in selecting the form of the heat network system is safe heating and economy.
In the prior art, in an urban hot water heating (warming) system, a plurality of user systems of buildings are connected with a hot water network, and a heating area is large. Therefore, when determining the form of the hot water heating system, special attention should be paid to the reliability of heating, the hot water temperature required by a user cannot be regulated in time, and it is difficult to ensure that the control effect of the system is always in a relatively ideal state.
Disclosure of Invention
The invention aims to provide a heat supply network temperature control method based on a genetic algorithm and a fuzzy control technology, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a heat supply network temperature control method based on a genetic algorithm and a fuzzy control technology comprises the following steps:
the method comprises the following steps: dividing variable subsets, and dividing the lengths of a plurality of user pipelines, the hot water flow rate in the user pipelines in a detection time period and the hot water temperature into a plurality of area subsets respectively;
step two: selecting a membership function to realize variable fuzzification, wherein the time of using a heat supply network pipeline by a user has larger randomness, and the triangular membership function is easy to realize, so that the triangular membership function is selected as the membership function of the length of the user pipeline, the hot water flow rate in the user pipeline in a detection time period and the hot water temperature, fuzzification is carried out on the variable according to the membership function, namely an accurate value is converted into a fuzzy value, for example: when the temperature of hot water in a user pipeline is 80-100 ℃, the highest membership value corresponding to 'higher' at 80-100 ℃ is found through a membership function, and the temperature of 80-100 ℃ is converted into 'higher' in a regional subset; the membership function divides different data segments into different fuzzy sets, for example, the membership function is relatively low at 10-20 ℃ and relatively high at 40-60 ℃, and only the value range of the data needs to be known for determining the membership function;
step three: fuzzy reasoning, namely specifying a fuzzy control rule, fuzzifying the acquired user pipeline length, the user pipeline hot water flow and the hot water temperature in the detection time period to obtain the user pipeline length, the user pipeline hot water flow and the hot water temperature fuzzy quantity in the detection time period, carrying out fuzzy reasoning according to the fuzzy control rule, outputting the fuzzy quantity of the hot water temperature to be regulated, and carrying out fuzzy resolving on the fuzzy quantity of the hot water temperature to be regulated to obtain t, namely obtaining a fuzzy control model;
step four: determining initialization parameters, namely population scale S, cross probability Pc, variation probability Pm and maximum evolution algebra Gm, and sequentially arranging vertex positions of three triangular membership functions, wherein the specific arrangement sequence is as follows: firstly, the length of a user pipeline is detected, then the hot water flow in the user pipeline in a time period is detected, then the hot water temperature in the user pipeline forms a vector, and binary coding (namely, a 10-system number is converted into a 2-system number consisting of 0 and 1) is carried out to be used as an individual chromosome in a population;
step five: individual chromosome processing, namely performing cross operation on individual chromosomes in the population, performing mutation operation on the individual chromosomes in the population, decoding the individual chromosomes in the population, and calculating the fitness of the individual chromosomes; binary decoding is carried out on each individual chromosome, the obtained vector is used as a triangular membership function parameter, a membership function is established according to the parameter, a fuzzy control model is established, hot water data in a user pipeline is collected through a temperature sensor and a flow sensor, the temperature of the required water temperature is controlled by using the established fuzzy control model, the control effect of the model is checked, the hot water flow and the hot water temperature in the user pipeline in a detection time period are input into the fuzzy control model, after the required water temperature is output by the fuzzy control model, the fitness of the individual chromosome is calculated, the smaller the error of the control water temperature is, the larger the adaptation function is, and the better the control effect is;
step six: selecting chromosomes, namely selecting individual chromosomes in the population according to the fitness of the individual chromosomes; selecting individual chromosomes according to the probability Pi by adopting a selection method of a roulette method; judging whether the iteration times reach the maximum evolution algebra, if so, ending, and outputting a fuzzy control model corresponding to the individual with the maximum fitness, namely the optimized fuzzy control model; otherwise, the individual chromosome poor operation is performed again.
Compared with the prior art, the invention has the beneficial effects that:
1. the heat supply network temperature control method based on the genetic algorithm and the fuzzy control technology selects the fuzzy control model with high operation speed; aiming at the requirement of real-time control of the water temperature of the user pipeline, a fuzzy control algorithm with a higher operation speed is selected, the calculation requirement on hardware of controller equipment is reduced, and compared with a mathematical model method and a neural network algorithm, the fuzzy control does not need a complex mathematical model and does not need a large amount of data for training the model, so that the fuzzy control method is very suitable for being applied to the temperature control of a heat supply network;
2. the heat supply network temperature control method based on the genetic algorithm and the fuzzy control technology uses the GA algorithm to optimize a water temperature fuzzy control model; the selection of membership function parameters of input and output parameters in fuzzy control is particularly critical, the conversion from a variable clear value to a fuzzy value is determined, and the membership function set by a general method is difficult to realize the conversion relation well, so that the membership function parameters of three variables of user pipeline length, hot water flow and hot water temperature in a detection time period in the fuzzification process are further improved by adopting the global optimization characteristic of a GA algorithm, and a more accurate water temperature prediction effect is obtained.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to 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.
Example one
The invention provides a technical scheme that: a heat supply network temperature control method based on a genetic algorithm and a fuzzy control technology comprises the following steps:
the method comprises the following steps: dividing variable subsets, and dividing the lengths of a plurality of user pipelines, the hot water flow rate in the user pipelines in a detection time period and the hot water temperature into a plurality of area subsets respectively;
step two: selecting a membership function to realize variable fuzzification, wherein the time of using a heat supply network pipeline by a user has larger randomness, and the triangular membership function is easy to realize, so that the triangular membership function is selected as the membership function of the length of the user pipeline, the hot water flow rate in the user pipeline in a detection time period and the hot water temperature, fuzzification is carried out on the variable according to the membership function, namely an accurate value is converted into a fuzzy value, for example: when the temperature of the hot water in the user pipeline is 80 ℃, the highest membership value corresponding to 'higher' at 80 ℃ is found through a membership function, and the temperature of 80 ℃ is converted into 'higher' in a regional subset; the membership function divides different data segments into different fuzzy sets, for example, 10 ℃ is low correspondingly, 40 ℃ is high correspondingly, and the value range of the data is only required to be known for determining the membership function;
step three: fuzzy reasoning, namely specifying a fuzzy control rule, fuzzifying the acquired user pipeline length, the user pipeline hot water flow and the hot water temperature in the detection time period to obtain the user pipeline length, the user pipeline hot water flow and the hot water temperature fuzzy quantity in the detection time period, carrying out fuzzy reasoning according to the fuzzy control rule, outputting the fuzzy quantity of the hot water temperature to be regulated, and carrying out fuzzy resolving on the fuzzy quantity of the hot water temperature to be regulated to obtain t, namely obtaining a fuzzy control model;
step four: determining initialization parameters, namely population scale S, cross probability Pc, variation probability Pm and maximum evolution algebra Gm, and sequentially arranging vertex positions of three triangular membership functions, wherein the specific arrangement sequence is as follows: firstly, the length of a user pipeline is detected, then the hot water flow in the user pipeline in a time period is detected, then the hot water temperature in the user pipeline forms a vector, and binary coding (namely, a 10-system number is converted into a 2-system number consisting of 0 and 1) is carried out to be used as an individual chromosome in a population;
step five: individual chromosome processing, namely performing cross operation on individual chromosomes in the population, performing mutation operation on the individual chromosomes in the population, decoding the individual chromosomes in the population, and calculating the fitness of the individual chromosomes; binary decoding is carried out on each individual chromosome, the obtained vector is used as a triangular membership function parameter, a membership function is established according to the parameter, a fuzzy control model is established, hot water data in a user pipeline is collected through a temperature sensor and a flow sensor, the temperature of the required water temperature is controlled by using the established fuzzy control model, the control effect of the model is checked, the hot water flow and the hot water temperature in the user pipeline in a detection time period are input into the fuzzy control model, after the required water temperature is output by the fuzzy control model, the fitness of the individual chromosome is calculated, the smaller the error of the control water temperature is, the larger the adaptation function is, and the better the control effect is;
step six: selecting chromosomes, namely selecting individual chromosomes in the population according to the fitness of the individual chromosomes; selecting individual chromosomes according to the probability Pi by adopting a selection method of a roulette method; judging whether the iteration times reach the maximum evolution algebra, if so, ending, and outputting a fuzzy control model corresponding to the individual with the maximum fitness, namely the optimized fuzzy control model; otherwise, the individual chromosome poor operation is performed again.
Example two
The invention provides a technical scheme that: a heat supply network temperature control method based on a genetic algorithm and a fuzzy control technology comprises the following steps:
the method comprises the following steps: dividing variable subsets, and dividing the lengths of a plurality of user pipelines, the hot water flow rate in the user pipelines in a detection time period and the hot water temperature into a plurality of area subsets respectively;
step two: selecting a membership function to realize variable fuzzification, wherein the time of using a heat supply network pipeline by a user has larger randomness, and the triangular membership function is easy to realize, so that the triangular membership function is selected as the membership function of the length of the user pipeline, the hot water flow rate in the user pipeline in a detection time period and the hot water temperature, fuzzification is carried out on the variable according to the membership function, namely an accurate value is converted into a fuzzy value, for example: when the temperature of hot water in a user pipeline is 100 ℃, the highest membership value corresponding to 'higher' at 100 ℃ is found through a membership function, and then the temperature of 100 ℃ is converted into 'higher' in a regional subset; the membership function divides different data segments into different fuzzy sets, for example, 20 ℃ is low and 60 ℃ is high, and the value range of the data is only required to be known for determining the membership function;
step three: fuzzy reasoning, namely specifying a fuzzy control rule, fuzzifying the acquired user pipeline length, the user pipeline hot water flow and the hot water temperature in the detection time period to obtain the user pipeline length, the user pipeline hot water flow and the hot water temperature fuzzy quantity in the detection time period, carrying out fuzzy reasoning according to the fuzzy control rule, outputting the fuzzy quantity of the hot water temperature to be regulated, and carrying out fuzzy resolving on the fuzzy quantity of the hot water temperature to be regulated to obtain t, namely obtaining a fuzzy control model;
step four: determining initialization parameters, namely population scale S, cross probability Pc, variation probability Pm and maximum evolution algebra Gm, and sequentially arranging vertex positions of three triangular membership functions, wherein the specific arrangement sequence is as follows: firstly, the length of a user pipeline is detected, then the hot water flow in the user pipeline in a time period is detected, then the hot water temperature in the user pipeline forms a vector, and binary coding (namely, a 10-system number is converted into a 2-system number consisting of 0 and 1) is carried out to be used as an individual chromosome in a population;
step five: individual chromosome processing, namely performing cross operation on individual chromosomes in the population, performing mutation operation on the individual chromosomes in the population, decoding the individual chromosomes in the population, and calculating the fitness of the individual chromosomes; binary decoding is carried out on each individual chromosome, the obtained vector is used as a triangular membership function parameter, a membership function is established according to the parameter, a fuzzy control model is established, hot water data in a user pipeline is collected through a temperature sensor and a flow sensor, the temperature of the required water temperature is controlled by using the established fuzzy control model, the control effect of the model is checked, the hot water flow and the hot water temperature in the user pipeline in a detection time period are input into the fuzzy control model, after the required water temperature is output by the fuzzy control model, the fitness of the individual chromosome is calculated, the smaller the error of the control water temperature is, the larger the adaptation function is, and the better the control effect is;
step six: selecting chromosomes, namely selecting individual chromosomes in the population according to the fitness of the individual chromosomes; selecting individual chromosomes according to the probability Pi by adopting a selection method of a roulette method; judging whether the iteration times reach the maximum evolution algebra, if so, ending, and outputting a fuzzy control model corresponding to the individual with the maximum fitness, namely the optimized fuzzy control model; otherwise, the individual chromosome poor operation is performed again.
EXAMPLE III
The invention provides a technical scheme that: a heat supply network temperature control method based on a genetic algorithm and a fuzzy control technology comprises the following steps:
the method comprises the following steps: dividing variable subsets, and dividing the lengths of a plurality of user pipelines, the hot water flow rate in the user pipelines in a detection time period and the hot water temperature into a plurality of area subsets respectively;
step two: selecting a membership function to realize variable fuzzification, wherein the time of using a heat supply network pipeline by a user has larger randomness, and the triangular membership function is easy to realize, so that the triangular membership function is selected as the membership function of the length of the user pipeline, the hot water flow rate in the user pipeline in a detection time period and the hot water temperature, fuzzification is carried out on the variable according to the membership function, namely an accurate value is converted into a fuzzy value, for example: when the temperature of hot water in a user pipeline is 90 ℃, the highest membership value corresponding to 'higher' at 90 ℃ is found through a membership function, and the temperature of 90 ℃ is converted into 'higher' in a regional subset; the membership function divides different data segments into different fuzzy sets, for example, 15 ℃ is low correspondingly, 50 ℃ is high correspondingly, and the value range of the data is only required to be known for determining the membership function;
step three: fuzzy reasoning, namely specifying a fuzzy control rule, fuzzifying the acquired user pipeline length, the user pipeline hot water flow and the hot water temperature in the detection time period to obtain the user pipeline length, the user pipeline hot water flow and the hot water temperature fuzzy quantity in the detection time period, carrying out fuzzy reasoning according to the fuzzy control rule, outputting the fuzzy quantity of the hot water temperature to be regulated, and carrying out fuzzy resolving on the fuzzy quantity of the hot water temperature to be regulated to obtain t, namely obtaining a fuzzy control model;
step four: determining initialization parameters, namely population scale S, cross probability Pc, variation probability Pm and maximum evolution algebra Gm, and sequentially arranging vertex positions of three triangular membership functions, wherein the specific arrangement sequence is as follows: firstly, the length of a user pipeline is detected, then the hot water flow in the user pipeline in a time period is detected, then the hot water temperature in the user pipeline forms a vector, and binary coding (namely, a 10-system number is converted into a 2-system number consisting of 0 and 1) is carried out to be used as an individual chromosome in a population;
step five: individual chromosome processing, namely performing cross operation on individual chromosomes in the population, performing mutation operation on the individual chromosomes in the population, decoding the individual chromosomes in the population, and calculating the fitness of the individual chromosomes; binary decoding is carried out on each individual chromosome, the obtained vector is used as a triangular membership function parameter, a membership function is established according to the parameter, a fuzzy control model is established, hot water data in a user pipeline is collected through a temperature sensor and a flow sensor, the temperature of the required water temperature is controlled by using the established fuzzy control model, the control effect of the model is checked, the hot water flow and the hot water temperature in the user pipeline in a detection time period are input into the fuzzy control model, after the required water temperature is output by the fuzzy control model, the fitness of the individual chromosome is calculated, the smaller the error of the control water temperature is, the larger the adaptation function is, and the better the control effect is;
step six: selecting chromosomes, namely selecting individual chromosomes in the population according to the fitness of the individual chromosomes; selecting individual chromosomes according to the probability Pi by adopting a selection method of a roulette method; judging whether the iteration times reach the maximum evolution algebra, if so, ending, and outputting a fuzzy control model corresponding to the individual with the maximum fitness, namely the optimized fuzzy control model; otherwise, the individual chromosome poor operation is performed again.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (1)
1. A heat supply network temperature control method based on a genetic algorithm and a fuzzy control technology is characterized by comprising the following steps:
the method comprises the following steps: dividing variable subsets, and dividing the lengths of a plurality of user pipelines, the hot water flow rate in the user pipelines in a detection time period and the hot water temperature into a plurality of area subsets respectively;
step two: selecting a membership function to realize variable fuzzification, wherein the time of using a heat supply network pipeline by a user has larger randomness, and the triangular membership function is easy to realize, so that the triangular membership function is selected as the membership function of the length of the user pipeline, the hot water flow rate in the user pipeline in a detection time period and the hot water temperature, fuzzification is carried out on the variable according to the membership function, namely an accurate value is converted into a fuzzy value, for example: when the temperature of hot water in a user pipeline is 80-100 ℃, the highest membership value corresponding to 'higher' at 80-100 ℃ is found through a membership function, and the temperature of 80-100 ℃ is converted into 'higher' in a regional subset; the membership function divides different data segments into different fuzzy sets, for example, the membership function is relatively low at 10-20 ℃ and relatively high at 40-60 ℃, and only the value range of the data needs to be known for determining the membership function;
step three: fuzzy reasoning, namely specifying a fuzzy control rule, fuzzifying the acquired user pipeline length, the user pipeline hot water flow and the hot water temperature in the detection time period to obtain the user pipeline length, the user pipeline hot water flow and the hot water temperature fuzzy quantity in the detection time period, carrying out fuzzy reasoning according to the fuzzy control rule, outputting the fuzzy quantity of the hot water temperature to be regulated, and carrying out fuzzy resolving on the fuzzy quantity of the hot water temperature to be regulated to obtain t, namely obtaining a fuzzy control model;
step four: determining initialization parameters, namely population scale S, cross probability Pc, variation probability Pm and maximum evolution algebra Gm, and sequentially arranging vertex positions of three triangular membership functions, wherein the specific arrangement sequence is as follows: firstly, the length of a user pipeline is detected, then the hot water flow in the user pipeline in a time period is detected, then the hot water temperature in the user pipeline forms a vector, and binary coding (namely, a 10-system number is converted into a 2-system number consisting of 0 and 1) is carried out to be used as an individual chromosome in a population;
step five: individual chromosome processing, namely performing cross operation on individual chromosomes in the population, performing mutation operation on the individual chromosomes in the population, decoding the individual chromosomes in the population, and calculating the fitness of the individual chromosomes; binary decoding is carried out on each individual chromosome, the obtained vector is used as a triangular membership function parameter, a membership function is established according to the parameter, a fuzzy control model is established, hot water data in a user pipeline is collected through a temperature sensor and a flow sensor, the temperature of the required water temperature is controlled by using the established fuzzy control model, the control effect of the model is checked, the hot water flow and the hot water temperature in the user pipeline in a detection time period are input into the fuzzy control model, after the required water temperature is output by the fuzzy control model, the fitness of the individual chromosome is calculated, the smaller the error of the control water temperature is, the larger the adaptation function is, and the better the control effect is;
step six: selecting chromosomes, namely selecting individual chromosomes in the population according to the fitness of the individual chromosomes; selecting individual chromosomes according to the probability Pi by adopting a selection method of a roulette method; judging whether the iteration times reach the maximum evolution algebra, if so, ending, and outputting a fuzzy control model corresponding to the individual with the maximum fitness, namely the optimized fuzzy control model; otherwise, the individual chromosome poor operation is performed again.
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| CN114517929A (en) * | 2022-02-14 | 2022-05-20 | 青岛未来能源环境技术有限公司 | Solar heating system remote control method based on wireless network bridge |
| CN117742426A (en) * | 2024-02-20 | 2024-03-22 | 北京金博众科技有限公司 | Intelligent control method and system for constant-temperature and constant-pressure water supply unit |
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