CN110729937A - Asynchronous motor parameter identification method based on improved particle swarm optimization - Google Patents
Asynchronous motor parameter identification method based on improved particle swarm optimization Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
- H02P21/0017—Model reference adaptation, e.g. MRAS or MRAC, useful for control or parameter estimation
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P2207/00—Indexing scheme relating to controlling arrangements characterised by the type of motor
- H02P2207/01—Asynchronous machines
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Abstract
The invention provides an asynchronous motor parameter identification method based on an improved particle swarm algorithm, which comprises the following steps: 1, acquiring the rotating speed, the rotor flux linkage and the stator current of an asynchronous motor; 2, acquiring a time constant and an excitation inductance of a motor rotor in real time through an improved particle swarm algorithm; the improved particle swarm algorithm specifically comprises the following steps: randomly generating an initial population with dimensions of a given range; updating the position information of the particles by tracking individual extrema of the individual particles and group extrema of the particle group; recalculating the fitness value of each particle, and then carrying out update assignment on the individual extreme value of the particle and the group extreme value of the particle group; and judging whether the iteration interruption times reach the set maximum iteration times or not, and stopping running if the iteration interruption times reach the set maximum iteration times, so as to realize the identification and tracking of the parameters of the asynchronous motor. The invention can stably, quickly and accurately identify and track the parameters of the asynchronous motor by utilizing the improved simplified particle swarm algorithm.
Description
Technical Field
The invention relates to the technical field of motor parameter identification, in particular to an asynchronous motor parameter identification method based on an improved particle swarm optimization.
Background
Since the operating characteristics of asynchronous motors are complex rational functions with respect to slip, the methods currently used for identifying the parameters of asynchronous motors are mainly the following: generalized Kalman filtering, least squares, Genetic Algorithms (GA), and the like.
After a lot of searches, some typical prior arts are found, for example, patent application No. 201710163793.2 discloses an asynchronous motor parameter identification method based on an improved particle swarm optimization algorithm, the patent obtains the measurement values of various working characteristics of an asynchronous motor through measurement, the improved particle swarm optimization algorithm is applied to realize the static parameter identification of the asynchronous motor, and the asynchronous motor still has higher identification accuracy under the condition of noise. For another example, the patent with application number 201410539036.7 discloses an asynchronous motor parameter tracking method based on an improved particle swarm algorithm, and the particle swarm algorithm has the capability of detecting the change of a target function and tracking the change of parameters in real time, and can identify the key state information of two asynchronous motors. For another example, patent with application number 20131048889.8 discloses a method for identifying parameters of a generator speed regulating system, which maps the parameters of the speed regulating system to be identified into 'particles' of a particle swarm algorithm, thereby improving the accuracy of the parameters and improving the simulation calculation results to enable the parameters to accurately reflect the characteristics of a power grid.
It can be seen that, the particle group is used to identify the asynchronous motor parameters, and many practical problems to be dealt with (such as improving the identification precision of the motor parameters) in practical application still do not provide a specific solution.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an asynchronous motor parameter identification method based on an improved particle swarm algorithm, which has the following specific technical scheme:
an asynchronous motor parameter identification method based on an improved particle swarm algorithm comprises the following steps:
step 1, acquiring the rotating speed, the rotor flux linkage and the stator current of an asynchronous motor;
step 2, acquiring a time constant and an excitation inductance of a motor rotor in real time through an improved particle swarm algorithm;
in step 2, the specific method for acquiring the time constant and the excitation inductance of the motor rotor in real time through the improved particle swarm optimization comprises the following steps:
2a, randomly generating NP initial populations x with the dimension D in a given [ xmax, xmin ] range;
2b, by tracking individual extrema p of individual particlesijPopulation extremum p of sum particle populationgjUpdating the position information of the particles;
2c, recalculating the fitness value of each particle, and performing update assignment on the individual extreme value of the particle and the group extreme value of the particle group again according to the calculation result;
and 2d, judging whether the iteration frequency reaches the set maximum iteration frequency, if so, stopping the operation, and realizing the identification and tracking of the parameters of the asynchronous motor, otherwise, repeating the steps 2b to 2 d.
Optionally, in step 2a, the generation equation of the initial population x is: x ═ rand (NP, D) × (x)max-xmin)+xmin。
Optionally, in step 2b, the position information update equation of the particle is: x is the number ofij(t+1)＝w*xij(t)+c1r1[pij(t)-xij(t)]+c2r2[pgj(t)-xij(t)]And
wherein, c1sAnd c2sAre respectively a learning factor c1And c2Initial setting value of c1fAnd c2fAre respectively a learning factor c1And c2Iter represents the current number of iterations, ItermaxRepresenting the maximum number of iterations of the algorithm.
The beneficial effects obtained by the invention comprise:
1. the learning capacity of the particles is effectively improved by introducing the asynchronous learning factor on the basis of simplifying the particle swarm optimization and dynamically changing the value of the learning factor by using the asynchronous change strategy of the learning factor;
2. and optimizing parameters of the asynchronous motor by using an improved simplified particle swarm algorithm, realizing intelligent optimization of the parameters and finally identifying the electrical parameters of the asynchronous motor.
3. The improved simplified particle swarm algorithm can effectively improve the convergence speed and the optimization precision of the algorithm, is applied to the field of asynchronous motor parameter identification, and can stably, quickly and accurately identify and track asynchronous motor parameters.
Drawings
The present invention will be further understood from the following description taken in conjunction with the accompanying drawings, the emphasis instead being placed upon illustrating the principles of the embodiments.
Fig. 1 is a schematic flow chart of an asynchronous motor parameter identification method based on an improved particle swarm optimization in one embodiment of the present invention;
FIG. 2 is a graph illustrating the convergence process of three different particle swarm algorithms under the Tablet test function in one embodiment of the present invention;
fig. 3 is a graph illustrating the convergence process of three different particle swarm algorithms under the schafer test function in one embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to embodiments thereof; it should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. Other systems, methods, and/or features of the present embodiments will become apparent to those skilled in the art upon review of the following detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the invention, and be protected by the accompanying claims. Additional features of the disclosed embodiments are described in, and will be apparent from, the detailed description that follows.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", etc. based on the orientation or positional relationship shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but it is not intended to indicate or imply that the device or component referred to must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and are not to be construed as limiting the present patent, and the specific meaning of the terms described above will be understood by those of ordinary skill in the art according to the specific circumstances.
The invention relates to an asynchronous motor parameter identification method based on an improved particle swarm algorithm, which is based on the following embodiments shown in figures 1-2:
the first embodiment is as follows:
particle Swarm Optimization (PSO) is a swarm intelligence optimization algorithm proposed by scientists Kennedy and Eberhart to simulate the foraging behavior of a bird swarm. The PSO algorithm treats a bird population as a population of particles, each of which has its own velocity and position information. The PSO algorithm randomly initializes the particle group, and then in each iteration process, the particles continuously track the individual extreme value and the group extreme value to update the speed and position information of the particles until the optimal solution of the particles is found. The PSO algorithm has a relatively simple structure, few parameters and relatively strong search capability, and is widely applied to the fields of numerical calculation, machine learning, pattern recognition and the like.
Although the PSO algorithm has strong searching capability, the PSO algorithm has a little defect. In the later iteration stage of the PSO algorithm, the convergence speed of the algorithm is easy to slow and the algorithm is easy to fall into a local optimal solution, so that the searching precision of the whole algorithm is influenced. In response to this situation, researchers have proposed a simplified particle swarm algorithm (SPSO) that does not include a velocity term by analyzing the structure of the PSO algorithm. The algorithm is derived through a strict mathematical formula, theoretically proves that the particle velocity term easily causes the particle divergence problem at the later stage of the algorithm, the convergence speed of the algorithm is improved by simplifying the velocity term of the particles, and the situation that the particle divergence causes the trapping of a local optimal solution at the later stage of the algorithm is avoided to a certain extent.
Although the simplified PSO algorithm simplifies the speed term of the PSO algorithm, the problems caused by particle divergence at the later stage of the algorithm are avoided to a certain extent. However, in the iteration process of the whole algorithm, the position updating formula of the particle is not changed, the learning factor of the particle is still a fixed value, in the iteration process of the whole algorithm, the learning capability of the particle is not changed, and in the iteration process of the algorithm, the situation that the particle is easy to fall into a local optimal solution still exists.
Since the operating characteristics of asynchronous motors are complex rational functions with respect to slip, the methods currently used for identifying the parameters of asynchronous motors are mainly the following: generalized Kalman filtering, least squares, Genetic Algorithms (GA), and the like. In the methods, the least square method, the genetic algorithm and other measurement results are not good in stability, the actual asynchronous motor parameter identification effect is poor, and further an asynchronous motor control system constructed on the basis of the parameters cannot achieve a good control effect and cannot obtain good steady-state and dynamic characteristics of the asynchronous motor.
The invention takes an asynchronous motor as an object, and identifies and tracks the time constant and the excitation inductance of the motor rotor on the basis of improving and simplifying the classic particle swarm algorithm so as to realize the identification of the parameters of the asynchronous motor.
Aiming at the parameters of the asynchronous motor, the invention provides an asynchronous motor parameter identification method based on an improved particle swarm optimization, which comprises the following steps:
step 1, acquiring the rotating speed, the rotor flux linkage and the stator current of an asynchronous motor;
step 2, acquiring a time constant and an excitation inductance of a motor rotor in real time through an improved particle swarm algorithm;
in step 2, the specific method for acquiring the time constant and the excitation inductance of the motor rotor in real time through the improved particle swarm optimization comprises the following steps:
2a, randomly generating NP initial populations x with the dimension D in a given [ xmax, xmin ] range;
2b, by tracking individual extrema p of individual particlesijPopulation extremum p of sum particle populationgjUpdating the position information of the particles;
2c, recalculating the fitness value of each particle, and performing update assignment on the individual extreme value of the particle and the group extreme value of the particle group again according to the calculation result;
and 2d, judging whether the iteration frequency reaches the set maximum iteration frequency, if so, stopping the operation, and realizing the identification and tracking of the parameters of the asynchronous motor, otherwise, repeating the steps 2b to 2 d.
Example two:
an asynchronous motor parameter identification method based on an improved particle swarm algorithm comprises the following steps:
step 1, acquiring the rotating speed, the rotor flux linkage and the stator current of an asynchronous motor;
step 2, acquiring a time constant and an excitation inductance of a motor rotor in real time through an improved particle swarm algorithm;
in step 2, the specific method for acquiring the time constant and the excitation inductance of the motor rotor in real time through the improved particle swarm optimization comprises the following steps:
2a, randomly generating NP initial populations x with the dimension D in a given [ xmax, xmin ] range;
2b, by tracking individual extrema p of individual particlesijPopulation extremum p of sum particle populationgjUpdating the position information of the particles;
2c, recalculating the fitness value of each particle, and performing update assignment on the individual extreme value of the particle and the group extreme value of the particle group again according to the calculation result;
and 2d, judging whether the iteration frequency reaches the set maximum iteration frequency, if so, stopping the operation, and realizing the identification and tracking of the parameters of the asynchronous motor, otherwise, repeating the steps 2b to 2 d.
In step 2b, the position information update equation of the particle is: x is the number ofij(t+1)＝w*xij(t)+c1r1[pij(t)-xij(t)]+c2r2[pgj(t)-xij(t)]Andwherein, c1sAnd c2sAre respectively a learning factor c1And c2Initial setting value of c1fAnd c2fRespectively studyHabit factor c1And c2Iter represents the current number of iterations, ItermaxRepresenting the maximum number of iterations of the algorithm.
By introducing the asynchronous learning factor on the basis of simplifying the particle swarm algorithm and dynamically changing the value of the learning factor by using the asynchronous change strategy of the learning factor, the learning capability of the particles is effectively improved.
The following table is a comparison graph of results of Benchmark test functions of different algorithms, and it can be known from fig. 2 and fig. 3 that, compared with the conventional PSO algorithm and the simplified PSO algorithm, the improved particle swarm algorithm described in step 2 of this embodiment can effectively improve the convergence speed and the optimization accuracy of the algorithm.
And optimizing parameters of the asynchronous motor by using an improved simplified particle swarm algorithm, realizing intelligent optimization of the parameters and finally identifying the electrical parameters of the asynchronous motor. The improved simplified particle swarm algorithm can effectively improve the convergence speed and the optimization precision of the algorithm, is applied to the field of asynchronous motor parameter identification, and can stably, quickly and accurately identify and track asynchronous motor parameters.
Example three:
an asynchronous motor parameter identification method based on an improved particle swarm algorithm comprises the following steps:
step 1, acquiring the rotating speed, the rotor flux linkage and the stator current of an asynchronous motor;
step 2, acquiring a time constant and an excitation inductance of a motor rotor in real time through an improved particle swarm algorithm;
in step 2, the specific method for acquiring the time constant and the excitation inductance of the motor rotor in real time through the improved particle swarm optimization comprises the following steps:
2a, randomly generating NP initial populations x with the dimension D in a given [ xmax, xmin ] range;
2b, by tracking individual extrema p of individual particlesijPopulation extremum p of sum particle populationgjUpdating the position information of the particles;
2c, recalculating the fitness value of each particle, and performing update assignment on the individual extreme value of the particle and the group extreme value of the particle group again according to the calculation result;
and 2d, judging whether the iteration frequency reaches the set maximum iteration frequency, if so, stopping the operation, and realizing the identification and tracking of the parameters of the asynchronous motor, otherwise, repeating the steps 2b to 2 d.
In step 2b, the position information update equation of the particle is: x is the number ofij(t+1)＝w*xij(t)+c1r1[pij(t)-xij(t)]+c2r2[pgj(t)-xij(t)]And
wherein, c1sAnd c2sAre respectively a learning factor c1And c2Initial setting value of c1fAnd c2fAre respectively a learning factor c1And c2Iter represents the current number of iterations, ItermaxRepresenting the maximum number of iterations of the algorithm.
In step 2a, the generation equation of the initial population x is: x ═ rand (NP, D) × (x)max-xmin)+xmin。
The improved particle swarm algorithm can be used for identifying the parameters of the asynchronous motor and solving the problems of road network path optimization, PID parameter adjustment in the field of industrial control and network information tracking.
In summary, the asynchronous motor parameter identification method based on the improved particle swarm optimization disclosed by the invention has the following beneficial technical effects:
1. the learning capacity of the particles is effectively improved by introducing the asynchronous learning factor on the basis of simplifying the particle swarm optimization and dynamically changing the value of the learning factor by using the asynchronous change strategy of the learning factor;
2. and optimizing parameters of the asynchronous motor by using an improved simplified particle swarm algorithm, realizing intelligent optimization of the parameters and finally identifying the electrical parameters of the asynchronous motor.
3. The improved simplified particle swarm algorithm can effectively improve the convergence speed and the optimization precision of the algorithm, is applied to the field of asynchronous motor parameter identification, and can stably, quickly and accurately identify and track asynchronous motor parameters.
Although the invention has been described above with reference to various embodiments, it should be understood that many changes and modifications may be made without departing from the scope of the invention. That is, the methods, systems, and devices discussed above are examples, and various configurations may omit, replace, or add various processes or components as appropriate. For example, in alternative configurations, the methods may be performed in an order different than that described and/or various components may be added, omitted, and/or combined. Moreover, features described with respect to certain configurations may be combined in various other configurations, as different aspects and elements of the configurations may be combined in a similar manner. Further, elements therein may be updated as technology evolves, i.e., many of the elements are examples and do not limit the scope of the disclosure or claims.
Specific details are given in the description to provide a thorough understanding of the exemplary configurations including implementations. However, configurations may be practiced without these specific details, such as well-known circuits, processes, algorithms, structures, and techniques, which have been shown without unnecessary detail in order to avoid obscuring the configurations. This description provides example configurations only, and does not limit the scope, applicability, or configuration of the claims. Rather, the foregoing description of the configurations will provide those skilled in the art with an enabling description for implementing the described techniques. Various changes may be made in the function and arrangement of elements without departing from the spirit or scope of the disclosure.
It is intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that it is the following claims, including all equivalents, that are intended to define the spirit and scope of this invention. The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (3)
1. An asynchronous motor parameter identification method based on an improved particle swarm algorithm comprises the following steps:
step 1, acquiring the rotating speed, the rotor flux linkage and the stator current of an asynchronous motor;
step 2, acquiring a time constant and an excitation inductance of a motor rotor in real time through an improved particle swarm algorithm;
in step 2, the specific method for acquiring the time constant and the excitation inductance of the motor rotor in real time through the improved particle swarm optimization comprises the following steps:
2a, randomly generating NP initial populations x with the dimension D in a given [ xmax, xmin ] range;
2b, updating the position information of the particles by tracking individual extremum of the individual particles and group extremum of the particle group;
2c, recalculating the fitness value of each particle, and performing update assignment on the individual extreme value of the particle and the group extreme value of the particle group again according to the calculation result;
and 2d, judging whether the iteration frequency reaches the set maximum iteration frequency, if so, stopping the operation, and realizing the identification and tracking of the parameters of the asynchronous motor, otherwise, repeating the steps 2b to 2 d.
2. The method for identifying parameters of an asynchronous motor based on the improved particle swarm optimization as claimed in claim 1, wherein in step 2a, the generation equation of the initial population x is: x ═ rand (NP, D) × (x)max-xmin)+xmin。
3. A process as claimed in claim 2In step 2b, the position information updating equation of the particles is as follows: x is the number ofij(t+1)＝w*xij(t)+c1r1[pij(t)-xij(t)]+c2r2[pgj(t)-xij(t)]And
wherein, c1sAnd c2sAre respectively a learning factor c1And c2Initial setting value of c1fAnd c2fAre respectively a learning factor c1And c2Iter represents the current number of iterations, ItermaxRepresenting the maximum number of iterations of the algorithm.
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