CN110543101A - method and device for optimizing fan fuzzy controller based on genetic algorithm - Google Patents

method and device for optimizing fan fuzzy controller based on genetic algorithm Download PDF

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Publication number
CN110543101A
CN110543101A CN201910953574.3A CN201910953574A CN110543101A CN 110543101 A CN110543101 A CN 110543101A CN 201910953574 A CN201910953574 A CN 201910953574A CN 110543101 A CN110543101 A CN 110543101A
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fuzzy controller
individual
optimal
optimal target
genetic algorithm
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郑世光
许志宇
潘正祥
乔羽
单杰
曹铸
刘清峰
卜冠南
田源
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Fujian University of Technology
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    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03DWIND MOTORS
    • F03D7/00Controlling wind motors 
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/0275Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using fuzzy logic only
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/12Computing arrangements based on biological models using genetic models
    • G06N3/126Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05BINDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00Control
    • F05B2270/30Control parameters, e.g. input parameters
    • F05B2270/328Blade pitch angle
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00Energy generation through renewable energy sources
    • Y02E10/70Wind energy
    • Y02E10/72Wind turbines with rotation axis in wind direction

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Abstract

the invention discloses a method for optimizing a fan fuzzy controller based on a genetic algorithm. Combining a quantization factor and a scale factor in a fuzzy controller according to a preset rule to obtain individual information; obtaining the fitness value of each individual information through simulink simulation; and selecting, copying, crossing and mutating the population of the individual information according to the fitness value to obtain an optimal target individual, and obtaining the optimal parameter of the fuzzy controller according to the optimal target individual. The invention also discloses a device for optimizing the fan fuzzy controller based on the genetic algorithm. The invention greatly improves the performance of the controller by combining the fuzzy control and the genetic algorithm, solves the problem of parameter optimization of the fuzzy controller by adding the genetic algorithm, greatly improves the dynamic performance and the steady-state performance of the optimized fuzzy controller compared with the unoptimized fuzzy controller, and can effectively avoid the loss caused by frequent starting of the blades and reduce the fluctuation of the output power of the fan.

Description

method and device for optimizing fan fuzzy controller based on genetic algorithm
Technical Field
the invention relates to the field of fan parameter optimization, in particular to a method and a device for optimizing a fan fuzzy controller based on a genetic algorithm.
background
wind energy is taken as clean energy, has the characteristics of large reserve and wide distribution, can effectively relieve the current increasingly serious energy crisis problem and environmental problem, and is widely welcomed. Wind energy, which can absorb wind energy and convert the wind energy into electric energy, has the disadvantages of low energy density and strong fluctuation, so that research on wind power equipment with high efficiency and high reliability is a long-term goal of researchers. The variable pitch control technology is a key technology of a large-scale wind turbine set and is of great importance to the conversion efficiency and stable operation of the whole wind power system. The traditional PID control is difficult to achieve a good control effect, in recent years, intelligent control technologies such as domestic fuzzy control and neural networks are started, and a plurality of scholars apply the intelligent control technologies to pitch control and achieve a good effect. Sonsipu et al proposed replacing the PID controller with a fuzzy controller and verified the advantages of fuzzy control with simulation software. However, the performance of the unoptimized fuzzy controller is not satisfactory enough, and there is also a large room for improvement.
The tuning of the quantization and scaling factors in a fuzzy controller is critical to the performance of the controller. The tuning of the parameters of fuzzy controllers designed by many scholars is typically adjusted by a formula method in combination with experimental data and experience. The method has the problems of complicated setting process, long time consumption and the like, and the setting value is not necessarily the optimal solution, so that the output response of the controller cannot meet the requirement.
disclosure of Invention
the invention aims to provide a method and a device for optimizing a fan fuzzy controller based on a genetic algorithm, which can calculate the optimal parameters of a fan fuzzy processor.
according to a first aspect of the invention, there is provided a method of optimizing a fan fuzzy controller based on a genetic algorithm, comprising:
combining the quantization factor and the scale factor in the fuzzy controller according to a preset rule to obtain individual information;
obtaining the fitness value of each individual information through simulink simulation;
and selecting, copying, crossing and mutating the population of the individual information according to the fitness value to obtain an optimal target individual, and obtaining the optimal parameter of the fuzzy controller according to the optimal target individual.
Further, "obtaining the optimal target individual" also includes:
and judging whether the genetic algebra of the population is equal to a preset algebra, if so, acquiring the optimal target individual, otherwise, returning to the second step, and acquiring the fitness value of each individual information through simulink simulation again.
Further, "obtaining the optimal parameter of the fuzzy controller according to the optimal target individual" specifically includes:
acquiring individual information of an optimal target individual;
determining a quantization factor and a scale factor of an optimal target individual according to individual information;
and taking the quantization factor and the scale factor as the optimal parameters of the fuzzy controller.
According to a second aspect of the present invention, there is provided an apparatus for optimizing a fuzzy controller of a wind turbine based on a genetic algorithm, comprising:
a detection module: an acquisition module: combining the quantization factor and the scale factor in the fuzzy controller according to a preset rule to obtain individual information;
a calculation module: obtaining the fitness value of each individual information through simulink simulation;
a processing module: and selecting, copying, crossing and mutating the population of the individual information according to the fitness value to obtain an optimal target individual, and obtaining the optimal parameter of the fuzzy controller according to the optimal target individual.
further, "obtaining the optimal target individual" also includes:
a judging module: and judging whether the genetic algebra of the population is equal to a preset algebra, if so, acquiring the optimal target individual, otherwise, returning to the second step, and acquiring the fitness value of each individual information through simulink simulation again.
Further, "obtaining the optimal parameter of the fuzzy controller according to the optimal target individual" specifically includes:
acquiring individual information of an optimal target individual;
Determining a quantization factor and a scale factor of an optimal target individual according to individual information;
And taking the quantization factor and the scale factor as the optimal parameters of the fuzzy controller.
the invention has the beneficial effects that: the combination of the fuzzy control and the genetic algorithm greatly improves the performance of the controller, the parameter optimization problem of the fuzzy controller is solved by adding the genetic algorithm, the dynamic performance and the steady-state performance of the optimized fuzzy controller are greatly improved compared with those of the unoptimized fuzzy controller, the loss caused by frequent starting of the blades can be effectively avoided, and the fluctuation of the output power of the fan is reduced.
Drawings
FIG. 1 is a flow diagram of a method for optimizing a fan fuzzy controller based on a genetic algorithm according to an embodiment of the present invention;
FIG. 2 is a block diagram of an apparatus for optimizing a fan fuzzy controller based on a genetic algorithm according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
FIG. 1 shows a flow of a method for optimizing a fuzzy controller of a wind turbine based on a genetic algorithm, according to an embodiment of the present invention, including:
and S11, combining the quantization factor and the scale factor in the fuzzy controller according to a preset rule to obtain individual information.
The execution subject of the method may be a processor.
in the embodiment of the present specification, the preset rule may be a genetic algorithm. Genetic Algorithm (Genetic Algorithm) is a kind of randomized search method which is evolved by the evolution law (survival of the fittest and selection of the dominant Genetic mechanism) of the biology world. It was first proposed by professor j. holland in the united states in 1975, and its main feature is that the operation is directly performed on the structural object, and there is no derivation and restriction of function continuity; the method has the advantages of inherent hidden parallelism and better global optimization capability; by adopting a probabilistic optimization method, the optimized search space can be automatically acquired and guided, the search direction can be adaptively adjusted, and a determined rule is not needed. In fuzzy control, the quantity in the fundamental domain is an accurate quantity, and in order to perform the fuzzification process, the input variable must be converted from the fundamental domain to the corresponding fuzzy set domain, so that the quantization factors Ke and Kec and the scale factor Ku are introduced. For example, a physical quantity whose domain is X = [ -X, X ], and this domain is converted into an integer N = [ -N, -N + L, -L,0, L, N-1, N ]. then the quantization factor is k = N/X. The processor initializes the population and uses a genetic algorithm to combine the three parameters, namely the quantization factors Ke and Kec and the scale factor Ku, as each individual in the genetic algorithm in Matlab.
And S12, obtaining the fitness value of each individual information through simulink simulation.
in this embodiment, the processor may combine the fan models in the simulink simulation, calculate a fitness value of each individual according to the fitness function, and return the fitness value to Matlab. Fitness, which refers to the relative ability of an individual of a known genotype to transfer its genes to its progeny gene bank under certain environmental conditions, is a measure of the survival and reproductive chances of the individual.
S13, selecting, copying, crossing and mutating the population of the individual information according to the fitness value to obtain the optimal target individual, and obtaining the optimal parameter of the fuzzy controller according to the optimal target individual.
In the embodiment of the specification, selection, crossing and mutation operations are carried out on the population, the selection, crossing and mutation operations are three basic genetic operators of a genetic algorithm, the individuals with the superiority are selected from the population, and the selection operation of eliminating the inferior individuals is called as selection. The purpose of selection is to inherit the optimized individuals (or solutions) directly to the next generation or to generate new individuals by pairwise crossing and then to inherit to the next generation. Crossover refers to the operation of replacing and recombining partial structures of two parent individuals to generate a new individual. Through crossover, the search capability of genetic algorithms is dramatically improved. The purpose of mutation is two, one is to make the genetic algorithm have local random search capability. Secondly, the genetic algorithm can maintain the diversity of the population so as to prevent the premature convergence phenomenon. The convergence probability should take a larger value at this time. After the operation of the process, the processor can obtain the optimal target individual from the group, namely the individual which is led into the fan model, has no curve fluctuation and overshoot and reaches the steady-state value at the fastest speed, and obtains the optimal parameter of the fuzzy controller according to the optimal target individual.
As a preferred embodiment, "obtaining the optimal target individual" further includes:
And judging whether the genetic algebra of the population is equal to a preset algebra, if so, acquiring the optimal target individual, otherwise, returning to the second step, and acquiring the fitness value of each individual information through simulink simulation again.
In the embodiment of the present specification, after each generation of genetic calculation is performed, the processor may determine a genetic algebra of the population, that is, how many times the population has been inherited, and when the genetic algebra reaches a preset algebra, for example, 100 generations, it is considered that the genetic algebra has enough obtained the optimal individual at this time, that is, a termination condition is satisfied, the optimal target individual therein is directly obtained, otherwise, the second step is returned, and the evolution is continued until the target individual with the optimal fitness value in the population is found, so as to obtain the optimal parameter of the fuzzy controller.
As a preferred embodiment, "obtaining the optimal parameter of the fuzzy controller according to the optimal target individual" specifically includes:
acquiring individual information of an optimal target individual;
Determining a quantization factor and a scale factor of an optimal target individual according to individual information;
And taking the quantization factor and the scale factor as the optimal parameters of the fuzzy controller.
In this embodiment of the present specification, after obtaining the individual information of the optimal target individual, the processor may obtain, from a preset database, a numerical value of a quantization factor and a scaling factor corresponding to the individual information in the first step of combining according to the individual information, and optimize the fuzzy controller by using the quantization factor and the scaling factor as optimal parameters of the fuzzy controller, that is, complete optimization of the fuzzy controller.
FIG. 2 shows a structure of an apparatus for optimizing a fuzzy controller of a wind turbine based on a genetic algorithm according to an embodiment of the present invention, including:
the acquisition module 21: combining the quantization factor and the scale factor in the fuzzy controller according to a preset rule to obtain individual information;
The calculation module 22: obtaining the fitness value of each individual information through simulink simulation;
the processing module 23: and selecting, copying, crossing and mutating the population of the individual information according to the fitness value to obtain an optimal target individual, and obtaining the optimal parameter of the fuzzy controller according to the optimal target individual.
As a preferred embodiment, "obtaining the optimal target individual" further includes:
A judging module: and judging whether the genetic algebra of the population is equal to a preset algebra, if so, acquiring the optimal target individual, otherwise, returning to the second step, and acquiring the fitness value of each individual information through simulink simulation again.
as a preferred embodiment, "obtaining the optimal parameter of the fuzzy controller according to the optimal target individual" specifically includes:
acquiring individual information of an optimal target individual;
Determining a quantization factor and a scale factor of an optimal target individual according to individual information;
And taking the quantization factor and the scale factor as the optimal parameters of the fuzzy controller.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.
those of ordinary skill in the art will understand that: the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same, although the present invention is described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it is possible to modify the solutions described in the above embodiments or to substitute some or all of the technical features of the embodiments, without departing from the scope of the present invention as defined in the claims.

Claims (6)

1. A method for optimizing a fuzzy controller of a fan based on a genetic algorithm is characterized by comprising the following steps:
Combining the quantization factor and the scale factor in the fuzzy controller according to a preset rule to obtain individual information;
Obtaining a fitness value of each individual information through simulink simulation;
And selecting, copying, crossing and varying the population of the individual information according to the fitness value to obtain an optimal target individual, and obtaining the optimal parameter of the fuzzy controller according to the optimal target individual.
2. The method for optimizing the fan fuzzy controller based on the genetic algorithm as claimed in claim 1, wherein before "obtaining the optimal target individual" further comprises:
And judging whether the genetic algebra of the population is equal to a preset algebra, if so, acquiring the optimal target individual, otherwise, returning to the second step, and acquiring the fitness value of each individual information through simulink simulation again.
3. The method for optimizing the fan fuzzy controller based on the genetic algorithm as claimed in claim 1, wherein "and obtaining the optimal parameter of the fuzzy controller according to the optimal target individual" specifically comprises:
acquiring individual information of the optimal target individual;
Determining a quantization factor and a scale factor of the optimal target individual according to the individual information;
And taking the quantization factor and the scale factor as optimal parameters of the fuzzy controller.
4. An apparatus for optimizing a fuzzy controller of a wind turbine based on a genetic algorithm, comprising:
An acquisition module: combining the quantization factor and the scale factor in the fuzzy controller according to a preset rule to obtain individual information;
A calculation module: obtaining a fitness value of each individual information through simulink simulation;
A processing module: and selecting, copying, crossing and varying the population of the individual information according to the fitness value to obtain an optimal target individual, and obtaining the optimal parameter of the fuzzy controller according to the optimal target individual.
5. the apparatus for optimizing a fan fuzzy controller based on genetic algorithm as claimed in claim 4, wherein before "obtaining the optimal target individual" further comprises:
A judging module: and judging whether the genetic algebra of the population is equal to a preset algebra, if so, acquiring the optimal target individual, otherwise, returning to the second step, and acquiring the fitness value of each individual information through simulink simulation again.
6. The device for optimizing the fan fuzzy controller based on the genetic algorithm as claimed in claim 4, wherein "and obtaining the optimal parameter of the fuzzy controller according to the optimal target individual" specifically comprises:
Acquiring individual information of the optimal target individual;
determining a quantization factor and a scale factor of the optimal target individual according to the individual information;
and taking the quantization factor and the scale factor as optimal parameters of the fuzzy controller.
CN201910953574.3A 2019-10-09 2019-10-09 method and device for optimizing fan fuzzy controller based on genetic algorithm Pending CN110543101A (en)

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Citations (4)

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Publication number Priority date Publication date Assignee Title
CN102720634A (en) * 2012-07-09 2012-10-10 兰州交通大学 Variable universe fuzzy electric pitch control method for optimizing parameters
CN102968055A (en) * 2012-12-07 2013-03-13 上海电机学院 Fuzzy PID (Proportion Integration Differentiation) controller based on genetic algorithm and control method thereof
CN104155877A (en) * 2014-08-19 2014-11-19 江苏科技大学 Brushless DC motor fuzzy control system based on genetic algorithm and control method thereof
CN109947124A (en) * 2019-04-25 2019-06-28 南京航空航天大学 Improve particle swarm algorithm Optimization of Fuzzy PID unmanned helicopter attitude control method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102720634A (en) * 2012-07-09 2012-10-10 兰州交通大学 Variable universe fuzzy electric pitch control method for optimizing parameters
CN102968055A (en) * 2012-12-07 2013-03-13 上海电机学院 Fuzzy PID (Proportion Integration Differentiation) controller based on genetic algorithm and control method thereof
CN104155877A (en) * 2014-08-19 2014-11-19 江苏科技大学 Brushless DC motor fuzzy control system based on genetic algorithm and control method thereof
CN109947124A (en) * 2019-04-25 2019-06-28 南京航空航天大学 Improve particle swarm algorithm Optimization of Fuzzy PID unmanned helicopter attitude control method

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