CN107609298A - A kind of Jiles Atherton model parameter identification methods and device - Google Patents

A kind of Jiles Atherton model parameter identification methods and device Download PDF

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CN107609298A
CN107609298A CN201710884764.5A CN201710884764A CN107609298A CN 107609298 A CN107609298 A CN 107609298A CN 201710884764 A CN201710884764 A CN 201710884764A CN 107609298 A CN107609298 A CN 107609298A
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parameter
magnetization
atherton
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CN107609298B (en
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林国营
宋强
张鼎衢
潘峰
孟庆亮
党三磊
肖厦颖
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Electric Power Research Institute of Guangdong Power Grid Co Ltd
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Abstract

The invention discloses a kind of Jiles Atherton model parameter identification methods and device, solves the complicated optimum problem for handling this multiple target of J A models, notable defect often be present in single intelligent optimization method, it is unfavorable for obtaining accurate globally optimal solution, it is caused while be difficult to extensive search, the ended questions for obtaining more accurate result are difficult in later stage searching process.Wherein method includes：Jiles Atherton transformer hysteresis curve models are established, obtain Jiles Atherton transformer hysteresis curve model formations；The span of predetermined Jiles Atherton transformer hysteresis curve model parameters is received, obtains object function corresponding to Jiles Atherton transformer hysteresis curve model formations；Variable step artificial fish-swarm algorithm is performed to the initial value and object function initial value of the model parameter in the span by object function and carries out object function optimizing acquisition globally optimal solution；Parallel Simulated Annealing Algorithm is performed to globally optimal solution and continues object function optimizing, obtains the value of model parameter corresponding to object function optimal value.

Description

Method and device for identifying parameters of Jiles-Atherton model
Technical Field
The invention relates to the technical field of electricians, in particular to a method and a device for identifying parameters of a Jiles-Atherton model.
Background
The electromagnetic type mutual inductor is mostly made of nonlinear ferromagnetic materials, and theoretical models for describing the hysteresis phenomenon include Lucas models, jiles-Atherton models (J-A models for short) and the like, wherein the J-A models are widely applied to hysteresis modeling and simulation of the ferromagnetic materials due to the fact that the J-A models have few parameters and are convenient to achieve, the models have clear physical significance and can truly describe the nonlinear relation of B-H, and a more accurate B-H hysteresis loop can be obtained by solving a J-A model equation.
Parameter identification is often used in situations where it is difficult to directly obtain parameters, i.e. fitting parameters are used to approximate theoretical parameters, and if the error is small enough, the fitting parameters are considered to be accurate enough to replace the theoretical parameters. Although the parameters of the J-A hysteresis model have practical significance, the parameters are difficult to directly obtain through actual measurement, so that accurate and reliable parameter identification is required.
At present, common algorithms applied to the field of parameter identification are mainly bionic-based intelligent optimization algorithms, such as genetic algorithms, differential evolution algorithms, neural networks, particle swarm algorithms and the like. The J-A model is composed of five nonlinear equation sets with practical significance, and has five parameters to be identified, such as Ms, a, alpha, c and k, and if the parameters are determined, a unique corresponding B-H curve can be obtained. For the parameter identification problem of the J-A model, the hysteresis loop of the transformer is taken as a known condition and is brought into five equations of the J-A model because the hysteresis loop can be obtained by measurement, and an optimal parameter combination is obtained by reverse derivation of an optimization algorithm, so that a fitting curve is closest to the original hysteresis loop. When a hysteresis loop model is researched, the more discrete points of a B-H curve are selected to participate in calculation, the more the model is accurate, because the change trend and the numerical value of each section of the curve of the hysteresis loop represent some characteristics of a ferromagnetic material, and the J-A model comprises the following steps: ms is saturation magnetization, a is a Langmuir function parameter; alpha is the average field parameter of the magnetic domain internal coupling; c is the reversible susceptibility; k is the loss factor. Therefore, the problem of parameter identification of the J-A model of the mutual inductor can be regarded as a nonlinear equation system with huge calculation amount, and a group intelligent optimization algorithm is often applied to solving the problem due to good calculation capability under a big data environment. However, for processing the multi-objective complex optimization problem of the J-A model, the single intelligent optimization method often has significant defects, and is not beneficial to obtaining an accurate global optimal solution, so that the problem that a more accurate result is difficult to obtain in a later optimization process while large-scale searching is difficult to be performed is solved.
Disclosure of Invention
The invention provides a method and a device for identifying parameters of a Jiles-Atherton model, which solve the problem of complex optimization of multiple targets of a processing J-A model, wherein a single intelligent optimization method often has obvious defects and is not beneficial to obtaining an accurate global optimal solution, so that the problem of ending that a more accurate result is difficult to obtain in a later optimization process while large-scale searching is difficult to obtain is solved.
The invention provides a method for identifying parameters of a Jiles-Atherton model, which comprises the following steps:
s1: obtaining a Jiles-Atherton transformer hysteresis loop model formula B = f ([ Ms, a, alpha, c, k)]H), where H is the magnetic field strength, B is the magnetic flux density, M) s For saturation magnetization, a is a Langmuir functionThe parameter, alpha is the average field parameter of magnetic domain internal coupling, c is the reversible magnetization coefficient, and k is the loss coefficient;
s2: receiving a predetermined saturation magnetization M s Obtaining a target function corresponding to a Jiles-Atherton mutual inductor hysteresis loop model formula by the value ranges of the Langmuim function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c and the loss coefficient k;
s3: by said target function, the saturation magnetization M in said range of values s Performing a step-variable artificial fish swarm algorithm on the Langmuir function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c, the initial value of the loss coefficient k and the initial value of the target function, and iteratively optimizing the target function to obtain a global optimal solution;
s4: executing a parallel simulated annealing algorithm on the global optimal solution to continuously carry out the optimization of the objective function and obtain the saturation magnetization M corresponding to the optimal value of the objective function s Values of the langevin function parameter a, the average field parameter a of the magnetic domain internal coupling, the reversible susceptibility c and the loss factor k.
Preferably, the step S1 specifically includes:
obtaining a model formula of a hysteresis loop of the Jiles-Atherton mutual inductor through a first formula of magnetization M, magnetic field intensity H and magnetic flux density B, a second formula obtained through an energy conservation principle, a predetermined third formula, a predetermined fourth formula, a predetermined fifth formula and a predetermined sixth formula,
the hysteresis loop model formula of the Jiles-Atherton transformer is B = f ([ Ms, a, alpha, c, k)]H), wherein H is the magnetic field strength; b is the magnetic flux density; m s Is the saturation magnetization; a is a Langey function parameter; alpha is the average field parameter of the magnetic domain internal coupling; c is the reversible susceptibility; k is the loss coefficient;
the first formula is B = mu 0 (H + M), wherein μ 0 ＝4π×10 -7 Is the vacuum magnetic permeability, and M is the magnetization;
the second formula is
Wherein H e For effective magnetic field strength, M an Without hysteresis magnetization, M irr Being an irreversible magnetization component in the magnetization M, M rev Is the reversible magnetization component in the magnetization M;
the third formula is H e ＝H+αM，
The fourth formula is
The fifth formula is M = M irr +M rev ，
The sixth formula is M rev ＝c(M an -M irr )。
Preferably, the step S2 specifically includes:
receiving a predetermined saturation magnetization M s Obtaining an objective function Fitness corresponding to a Jiles-Atherton mutual inductor hysteresis loop model formula through a seventh formula, an eighth formula and a ninth formula, wherein the seventh formula is as follows:
B calculating i ＝f([Ms，a，α，c，k]，H Actually measured i )，
The eighth formula is:
the ninth formula is:
wherein N is the number of groups, B Calculate i For calculated i-th group of magnetic flux densities, H Actually measured i For the measured i-th set of magnetic field strengths, B Actually measured i Measured i group magnetic flux density, B Measured in fact Is the measured magnetic flux density.
Preferably, the step-length-variable artificial fish school algorithm specifically includes:
a: initializing the number FISHNUM of artificial fish, the maximum iteration number MAXGEN, searching an early-stage threshold value Ymax1 and a fixed-STEP STEP, and initializing fish school positions { X1, X2, …, xn } and fish school targets { Y1, Y2, …, yn } in the value range;
b: updating the fish school positions in the fish school clustering and rear-end collision behaviors, and if a moving condition Y (X) > Yi (Xi) is met, enabling position coordinates Xi = Xi + rand STEP | X-Xi | of the current artificial fish, wherein rand is a random number smaller than 1 and corresponds to a fish school target value Yi of the artificial fish;
c: acquiring a maximum fish school target value BestY = max [ Yi ] and a corresponding fish school position BestX = X [ Y = max [ Yi ] ];
d: judging whether the maximum fish school target value BestY is smaller than a search early-stage threshold value Ymax1, if so, returning to the step b for iterative updating until the maximum fish school target value BestY is not smaller than the search early-stage threshold value Ymax1 or the iteration times are larger than the maximum iteration times MAXGEN, and if not, ending the shift to the parallel simulated annealing algorithm;
wherein the fish school position Xi is the saturation magnetization M s A parameter a of the langevin function, a parameter α of the mean field coupled inside the magnetic domain, a reversible susceptibility c and a set of values [ Ms, a, α, c, k of the loss coefficient k]And the fish school target Yi is a corresponding target function Fitness.
Preferably, the parallel simulated annealing algorithm specifically includes:
a: acquiring an initial temperature T0, a termination temperature Tend, a corresponding fixed iteration time chain length L, a cooling speed q and a maximum allowable error per _ erro at each temperature, and inputting the global optimal solution serving as an initial solution of the initial temperature T0 into a parallel simulated annealing model;
b: iteratively updating the parallel simulated annealing model, and meanwhile obtaining the current optimal solution besty, the corresponding bestx and the current error erro according to the Metropolis rule;
c: and judging whether the current error erro is larger than the maximum allowable error per _ erro, if so, enabling the initial temperature T0= q x T0, taking the current optimal solution BestY as the initial solution of the initial temperature T0, and returning to execute the step b, otherwise, enabling BestY = BestY and BestX = BestX, and obtaining the optimal value of the target function.
Preferably, the step S4 is followed by:
s5, judging whether the optimal value of the objective function is larger than a preset threshold value maxY2, if not, enabling the maximum iteration times MAXGEN = MAXGEN +1, returning to execute the step S3, if so, ending, and enabling the saturation magnetization M corresponding to the optimal value of the objective function s A parameter a of the langevin function, a parameter α of the mean field coupled inside the magnetic domain, a reversible susceptibility c and a set of values [ Ms, a, α, c, k of the loss coefficient k]The results are the results of the parameter identification of the Jiles-Atherton model.
The invention provides a Jiles-Atherton model parameter identification device, which comprises:
a first obtaining unit, configured to obtain a Jiles-Atherton transformer hysteresis loop model formula B = f ([ Ms, a, α, c, k)]H), wherein H is the magnetic field strength; b is the magnetic flux density; m s Is the saturation magnetization; a is a Langey function parameter; alpha is the average field parameter of the magnetic domain internal coupling; c is the reversible susceptibility; k is a loss coefficient;
a second acquisition unit for receiving a predetermined saturation magnetization M s Obtaining a target function corresponding to a Jiles-Atherton mutual inductor hysteresis loop model formula by the value ranges of the Langmuim function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c and the loss coefficient k;
a first optimizing unit for optimizing the saturation magnetization M in the value range by the objective function s Performing a step-length-variable artificial fish swarm algorithm to carry out the target by the initial values of the Langmuir function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c, the loss coefficient k and the target function initial valueOptimizing the function to obtain a global optimal solution;
a second optimizing unit for executing parallel simulated annealing algorithm to continuously optimize the objective function to obtain the saturation magnetization M corresponding to the optimal value of the objective function s Values of the langevin function parameter a, the average field parameter a of the magnetic domain internal coupling, the reversible susceptibility c and the loss factor k.
Preferably, the first obtaining unit specifically includes:
the first obtaining subunit is specifically configured to obtain a hysteresis loop model formula of the Jiles-Atherton transformer according to a first formula of the magnetization M, the magnetic field intensity H, and the magnetic flux density B, a second formula obtained according to the principle of conservation of energy, a predetermined third formula, a predetermined fourth formula, a predetermined fifth formula, and a predetermined sixth formula,
the model formula of the hysteresis loop of the Jeles-Atherton transformer is B = f ([ Ms, a, alpha, c, k)]H), wherein H is the magnetic field strength; b is the magnetic flux density; m s Is the saturation magnetization; a is a Langey function parameter; alpha is the average field parameter of the magnetic domain internal coupling; c is the reversible susceptibility; k is a loss coefficient;
the first formula is B = mu 0 (H + M), wherein μ 0 ＝4π×10 -7 Is the vacuum magnetic permeability, and M is the magnetization;
the second formula is
Wherein H e For effective magnetic field strength, M an Without hysteresis magnetization, M irr Being an irreversible magnetization component in the magnetization M, M rev Is the reversible magnetization component in the magnetization M;
the third formula is H e ＝H+αM，
The fourth formula is
The fifth formula is M = M irr +M rev ，
The sixth formula is M rev ＝c(M an -M irr )。
Preferably, the second acquiring unit specifically includes:
a first receiving subunit, in particular for receiving a predetermined saturation magnetization M s The value ranges of the Langmuir function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c and the loss coefficient k;
the second obtaining subunit is specifically configured to obtain an objective function Fitness corresponding to the resize loop model formula of the Jiles-Atherton transformer according to a seventh formula, an eighth formula, and a ninth formula, where the seventh formula is:
B calculate i ＝f([Ms，a，α，c，k]，H Actually measured i )，
The eighth formula is:
the ninth formula is:
wherein N is the number of groups, B Calculate i For calculated i-th group of magnetic flux densities, H Actually measured i For the measured i-th set of magnetic field strengths, B Actually measured i Measured i group magnetic flux density, B Measured in fact Is the measured magnetic flux density.
Preferably, the step-length-variable artificial fish school algorithm specifically includes:
a: initializing the number FISHNUM of artificial fish, the maximum iteration number MAXGEN, a search early stage threshold value Ymax1 and a fixed STEP size STEP, and initializing a fish school position { X1, X2, …, xn } and a fish school target { Y1, Y2, …, yn } in the value range;
b: updating the fish school position in the fish school clustering and rear-end collision behaviors, and if the movement condition Y (X) > Yi (Xi) is met, updating the position coordinates Xi = Xi + rand STEP | X-Xi | of the current artificial fish, wherein rand is a random number smaller than 1 and corresponds to the fish school target value Yi of the artificial fish;
c: obtaining a maximum shoal target value BestY = max [ Yi ] and a corresponding shoal position BestX = X [ Y = max [ Yi ];
d: judging whether the maximum fish school target value BestY is smaller than a search early-stage threshold value Ymax1, if so, returning to the step b for iterative updating until the maximum fish school target value BestY is not smaller than the search early-stage threshold value Ymax1 or the iteration times are larger than the maximum iteration times MAXGEN, and if not, ending the shift to the parallel simulated annealing algorithm;
wherein the fish school position Xi is the saturation magnetization M s A parameter a of the langevin function, a parameter α of the mean field coupled inside the magnetic domain, a reversible susceptibility c and a set of values [ Ms, a, α, c, k of the loss coefficient k]And the fish school target Yi is a corresponding target function Fitness.
Preferably, the parallel simulated annealing algorithm specifically includes:
a: acquiring an initial temperature T0, a termination temperature Tend, a corresponding fixed iteration time chain length L, a cooling speed q and a maximum allowable error per _ erro at each temperature, and inputting the global optimal solution serving as an initial solution of the initial temperature T0 into a parallel simulated annealing model;
b: iteratively updating the parallel simulated annealing model, and meanwhile obtaining the current optimal solution besty, the corresponding bestx and the current error erro according to the Metropolis rule;
c: and judging whether the current error erro is larger than the maximum allowable error per _ erro, if so, enabling the initial temperature T0= q x T0, taking the current optimal solution BestY as the initial solution of the initial temperature T0, and returning to execute the step b, otherwise, enabling BestY = BestY and BestX = BestX, and obtaining the optimal value of the target function.
Preferably, the device for identifying parameters of a Jiles-Atherton model provided by the invention further comprises:
an iteration optimizing unit, specifically configured to determine whether the optimal value of the objective function is greater than a preset late threshold Ymax2, if not, make the maximum iteration number MAXGEN = MAXGEN +1, and return to performing step S3, if so, end the process, and the saturation magnetization M corresponding to the optimal value of the objective function s A parameter a of the langevin function, a parameter α of the mean field coupled inside the magnetic domain, a reversible susceptibility c and a set of values [ Ms, a, α, c, k of the loss coefficient k]The results are the results of the parameter identification of the Jiles-Atherton model.
According to the technical scheme, the invention has the following advantages:
the invention provides a method and a device for identifying the parameters of a Jiles-Atherton model, wherein the method for identifying the parameters of the Jiles-Atherton model comprises the following steps: s1: obtaining a model formula B = f ([ Ms, a, alpha, c, k) of a hysteresis loop of the Jeles-Atherton transformer]H), where H is the magnetic field strength, B is the magnetic flux density, M) s Is saturation magnetization, a is a langevin function parameter, alpha is an average field parameter coupled inside a magnetic domain, c is a reversible magnetization coefficient, and k is a loss coefficient; s2: receiving a predetermined saturation magnetization M s Obtaining a target function corresponding to a Jiles-Atherton mutual inductor hysteresis loop model formula by the value ranges of the Langmuim function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c and the loss coefficient k; s3: by said objective function, the saturation magnetization M in said range of values s Performing a step-variable artificial fish swarm algorithm on the Langmuir function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c, the initial value of the loss coefficient k and the initial value of the target function, and iteratively optimizing the target function to obtain a global optimal solution; s4: executing a parallel simulated annealing algorithm on the global optimal solution to continuously carry out the optimization of the objective function and obtain the saturation magnetization M corresponding to the optimal value of the objective function s Values of the langevin function parameter a, the average field parameter a of the magnetic domain internal coupling, the reversible susceptibility c and the loss factor k.
According to the invention, through the established Jiles-Atherton mutual inductor hysteresis loop model and the corresponding objective function, optimization is carried out on the objective function through the variable-step-size artificial fish swarm algorithm in the early stage to quickly approach to a global optimal solution, the global optimal solution can be determined in about 3-4 steps, then local search is carried out near the global optimal solution of the variable-step-size artificial fish swarm algorithm by using a parallel simulated annealing algorithm, more accurate global optimal solution and a corresponding parameter identification result are obtained, timeliness and accuracy of J-A model parameter identification can be effectively improved, the problem of complex optimization of processing the multiple targets of the J-A model is solved, a single intelligent optimization method often has obvious defects, the accurate global optimal solution is not easy to obtain, and the problem that the more accurate result is difficult to obtain in the large-range search is caused while the later-stage optimization process is difficult to obtain is solved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art 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 for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
FIG. 1 is a schematic flow chart diagram of an embodiment of a method for identifying parameters of a Jiles-Atherton model according to the embodiment of the present invention;
FIG. 2 is a schematic flow chart diagram illustrating another embodiment of a method for parameter identification of a Jiles-Atherton model according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an embodiment of a Jiles-Atherton model parameter identification device provided in an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of another embodiment of a Jiles-Atherton model parameter identification device provided in the embodiment of the present invention;
FIG. 5 is a comparison graph of B-H curve identification results provided in the embodiment of the present invention;
FIG. 6 is a comparison graph of an optimized value evolutionary curve provided in an embodiment of the present invention;
FIG. 7 is a schematic flow chart of an embodiment of a variable step size artificial fish school algorithm provided in an embodiment of the present invention;
FIG. 8 is a schematic flow diagram of one embodiment of a parallel simulated annealing algorithm provided in embodiments of the present invention.
Detailed Description
The embodiment of the invention provides a method and a device for identifying parameters of a Jiles-Atherton model, which solve the problem of complex optimization of multiple targets for processing a J-A model, wherein a single intelligent optimization method often has obvious defects and is not beneficial to obtaining an accurate global optimal solution, so that the problem of ending that a more accurate result is difficult to obtain in a later optimization process while large-scale search is difficult to obtain is solved.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below 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.
Referring to fig. 1, an embodiment of a method for identifying a Jiles-Atherton model parameter according to the embodiment of the present invention includes:
101. obtaining a model formula B = f ([ Ms, a, alpha, c, k) of a hysteresis loop of the Jeles-Atherton transformer]H), where H is the magnetic field strength, B is the magnetic flux density, M) s Is saturation magnetization, a is a langevin function parameter, alpha is an average field parameter coupled inside a magnetic domain, c is a reversible magnetization coefficient, and k is a loss coefficient;
and establishing a Jiles-Atherton transformer hysteresis loop model, wherein B = f ([ Ms, a, alpha, c, k ], H). Where H is the magnetic field strength and B is the magnetic flux density. Ms, a, alpha, c, k and H are five parameters of the Jeles-Atherton hysteresis loop model, wherein Ms represents saturation magnetization, a is a Langmian function parameter, alpha is an average field parameter coupled in a magnetic domain, c is a reversible magnetization coefficient, and k is a loss coefficient. If N sets of actually measured B, H values are known, the values of the model parameters Ms, a, α, c, k with solution can be identified by solving the model function B = f (Ms, a, α, c, k, H).
102. Receiving a predetermined saturation magnetization M s Obtaining a target function corresponding to a Jiles-Atherton mutual inductor hysteresis loop model formula by the value ranges of the Langmuim function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c and the loss coefficient k;
the method comprises the steps of giving a value range of parameters of a hysteresis loop model of the Jiles-Atherton transformer, carrying out value taking and substituting calculation on five parameters including Ms, a, alpha, c and k in the range, and establishing a target function Fitness corresponding to the hysteresis loop model of the Jiles-Atherton transformer through N groups of actually measured B, H values.
103. By said objective function, the saturation magnetization M in said range of values s Performing a step-variable artificial fish swarm algorithm on the Langmuir function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c, the initial value of the loss coefficient k and the initial value of the target function, and iteratively optimizing the target function to obtain a global optimal solution;
receiving the saturation magnetization M s Parameter a of Langewaten function, average field parameter alpha coupled in magnetic domain, reversible magnetization coefficient c, initial value of loss coefficient k and initial value of target function Fitness, passing through target function and saturation magnetization M s The value ranges of 5 parameters of Langmuir function parameter a, average field parameter alpha coupled in magnetic domain, reversible magnetization coefficient c and loss coefficient k are from saturation magnetization M s Initial values of 5 parameters including Langewan function parameter a, average field parameter alpha coupled in a magnetic domain, reversible magnetization coefficient c and loss coefficient k and initial values of a target function Fitness are started, and value combinations [ Ms, a, alpha, c, k ] of the 5 parameters are represented by a fish school position Xi]The fish school target Yi is the corresponding Fitness, and the step changing is executedA long artificial fish swarm algorithm is adopted, and target function optimization is iteratively carried out to obtain a global optimal solution;
104. executing a parallel simulated annealing algorithm on the global optimal solution to continuously carry out the optimization of the objective function and obtain the saturation magnetization M corresponding to the optimal value of the objective function s Values of the langevin function parameter a, the average field parameter a of the magnetic domain internal coupling, the reversible susceptibility c and the loss factor k.
Firstly, carrying out early iterative computation by using a variable-step-length artificial fish swarm algorithm to narrow a search domain to a smaller range near an optimal solution; and setting the global optimal value of the fish swarm algorithm as an initial value of the simulated annealing algorithm, continuing to perform target function optimization through the parallel simulated annealing algorithm, acquiring values of saturation magnetization Ms, langmuir function parameter a, average field parameter alpha coupled inside a magnetic domain, reversible magnetization coefficient c and loss coefficient k corresponding to the optimal value of the target function, and outputting a parameter identification result.
In the embodiment, through the established Jiles-Atherton mutual inductor hysteresis loop model and the corresponding objective function, optimization is performed on the objective function through the variable-step-size artificial fish swarm algorithm in the early stage to quickly approach to the global optimal solution, the global optimal solution can be determined in about 3 to 4 steps, then local search is performed near the global optimal solution of the variable-step-size artificial fish swarm algorithm by using the parallel simulated annealing algorithm, the more accurate global optimal solution and the corresponding parameter identification result are obtained, timeliness and accuracy of J-A model parameter identification can be effectively improved, the problem of complex optimization of processing the multiple targets of the J-A model is solved, a single intelligent optimization method often has obvious defects, the accurate global optimal solution is not favorably obtained, and the problem that the more accurate result is difficult to obtain in the large-range search is caused while the more accurate result is difficult to obtain in the later-stage optimization process is solved.
The above describes a method for identifying a Jiles-Atherton model parameter in detail, and the following describes a process of the method for identifying the Jiles-Atherton model parameter in detail, and another embodiment of the method for identifying the Jiles-Atherton model parameter provided in the embodiment of the present invention includes:
201、acquiring a Jiles-Atherton mutual inductor magnetic hysteresis loop model formula through a preset first formula of magnetization intensity M, magnetic field intensity H and magnetic flux density B, a preset second formula obtained through an energy conservation principle, a preset third formula, a preset fourth formula, a preset fifth formula and a preset sixth formula, wherein the Jiles-Atherton mutual inductor magnetic hysteresis loop model formula is B = f ([ Ms, a, alpha, c, k)]H), where H is the magnetic field strength, B is the magnetic flux density, M) s Is saturation magnetization, a is a langevin function parameter, alpha is an average field parameter coupled inside a magnetic domain, c is a reversible magnetization coefficient, and k is a loss coefficient;
the preset first formula is B = μ 0 (H + M), wherein μ 0 ＝4π×10 -7 Is the vacuum magnetic permeability, and M is the magnetization;
preset the second formula as
Wherein H e For effective magnetic field strength, M an Without hysteresis magnetization, M irr Being irreversible magnetization components in the magnetization M, M rev Is the reversible magnetization component in the magnetization M;
preset the third formula as H e ＝H+αM，
The preset fourth formula is
The preset fifth formula is M = M irr +M rev ；
Preset the sixth formula as M rev ＝c(M an -M irr )。
A hysteresis loop model of the Jiles-Atherton transformer is established as follows:
the relationship among the magnetization M, the magnetic field strength H and the magnetic flux density B is as the formula (1), namely, a preset first formula:
B＝μ 0 (H+M) (1)
wherein mu 0 ＝4π×10 -7 The magnetic permeability is vacuum magnetic permeability.
Obtaining an energy equation, namely a preset second formula according to the energy conservation principle:
wherein H e Representing the effective magnetic field strength, and α is the average field parameter characterizing the coupling inside the domain. M an For hysteresis-free magnetization, i.e. the magnetization of an ideal material, M irr Representing the irreversible magnetization component in M, M rev Representing the reversible magnetization component in M. The preset third formula, the preset fourth formula, the preset fifth formula and the preset sixth formula are respectively H e 、M an 、M irr And M rev The expression of (c) is as follows:
H e ＝H+αM (3)
M＝M irr +M rev (5)
M rev ＝c(M an -M irr ) (6)
and then obtained from formula (5) and formula (6):
M irr ＝(M-cM an )/(1-c) (7)
substituting the formulas (3) to (7) into the energy conservation formula (2) can obtain:
where δ is a sign function, δ = -1 when dH/dt > 0, δ =1, dH/dt < 0.
Through the formulas (1), (2), (3), (4), (5), (6), (7) and (8), jiles-A can be deducedthe hysteresis loop model formula of the thermal transformer is B = f ([ Ms, a, alpha, c, k)]，H)。M s A, alpha, c and k are 5 unknown parameters to be solved in the J-A model, and all have definite physical meanings: m s Representing the saturation magnetization, a is a parameter of a langevin function, alpha is an average field parameter coupled inside a magnetic domain, c is a reversible magnetization coefficient, and k is a loss coefficient. Thus, for each ferromagnetic material, the parameters are determined and the magnetization characteristics of the material can be accurately reflected.
202. Receiving a predetermined saturation magnetization M s Obtaining an objective function Fitness corresponding to a hysteresis loop model formula of the Jiles-Atherton mutual inductor through a preset seventh formula, a preset eighth formula and a preset ninth formula according to the value ranges of the Lanwangtian function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c and the loss coefficient k, wherein the preset seventh formula is as follows:
B calculate i ＝f([Ms，a，α，c，k]，H Actually measured i )，
The preset eighth formula is:
the preset ninth formula is:
wherein N is the number of groups, B Calculate i For calculated i-th group of magnetic flux densities, H Actually measured i For the measured i-th set of magnetic field strengths, B Actually measured i Measured i group magnetic flux density, B Measured in fact Is the measured magnetic flux density.
The value range of the parameter of the hysteresis loop model of the Jiles-Atherton mutual inductor is given, and the value range and the theoretical value of the parameter of the J-A model are as shown in the following table 1:
TABLE 1
Obtaining an objective function Fitness corresponding to the Sieles-Atherton transformer hysteresis loop model formula through a preset seventh formula, a preset eighth formula and a preset ninth formula, wherein the preset seventh formula is as follows:
B calculating i ＝f([Ms，a，α，c，k]，H Actually measured i ) In which the saturation magnetization M s The langevin function parameter a, the average field parameter α coupled inside the magnetic domain, the reversible magnetization coefficient c and the loss coefficient k are randomly valued in the value range in table 1;
the preset eighth formula is:
the preset ninth formula is:
wherein N is the number of groups, B Calculate i For calculated i-th group of magnetic flux densities, H Actually measured i For the measured i-th set of magnetic field strengths, B Actually measured i Measured i group magnetic flux density, B Measured in fact For the measured magnetic flux density, if N sets of measured B, H values are known, the values of the model parameters Ms, a, α, c, k to be found can be identified by solving the model function B = f (Ms, a, α, c, k, H). When the objective function is maximum, the parameter identification level is highest.
203. By means of an objective function, the saturation magnetization M in the value range is compensated s Performing a step-variable artificial fish swarm algorithm on the initial values of the Langmuir function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c, the loss coefficient k and the initial value of the target function to optimize the target function to obtain a global optimal solution;
the step-length-variable artificial fish school algorithm specifically comprises the following steps:
a: initializing the number FISHNUM of artificial fish, the maximum iteration number MAXGEN, a search early stage threshold value Ymax1 and a fixed STEP size STEP, and initializing a fish school position { X1, X2, …, xn } and a fish school target { Y1, Y2, …, yn } in the value range;
b: updating the fish school position in the fish school clustering and rear-end collision behaviors, and if the movement condition Y (X) > Yi (Xi) is met, updating the position coordinates Xi = Xi + rand STEP | X-Xi | of the current artificial fish, wherein rand is a random number smaller than 1 and corresponds to the fish school target value Yi of the artificial fish;
c: acquiring a maximum fish school target value BestY = max [ Yi ] and a corresponding fish school position BestX = X [ Y = max [ Yi ] ];
d: judging whether the maximum fish school target value BestY is smaller than a search early-stage threshold value Ymax1, if so, returning to the step b for iterative updating until the maximum fish school target value BestY is not smaller than the search early-stage threshold value Ymax1 or the iteration times are larger than the maximum iteration times MAXGEN, and if not, ending the shift to the parallel simulated annealing algorithm;
wherein the fish school position Xi is the saturation magnetization M s A set of values [ Ms, a, c, k ] for a Langmuim function parameter a, a mean field parameter α coupled inside the magnetic domain, a reversible magnetization coefficient c and a loss coefficient k]And the fish school target Yi is a corresponding target function Fitness.
The saturation magnetization M in the value range in Table 1 is set by the objective function Fitness s Performing fast iterative optimization of a variable-step artificial fish swarm algorithm to a near range of a global optimal solution by a Langmuir function parameter a, an average field parameter alpha coupled in a magnetic domain, a reversible magnetization coefficient c, an initial value of a loss coefficient k and an initial value of a target function, and performing target function optimization to obtain the global optimal solution; as shown in fig. 7, the step-size-variable artificial fish school algorithm comprises the following steps:
1) Initializing the number FISHNUM of artificial fish, the maximum iteration number MAXGEN, a search early stage threshold value Ymax1 and a fixed STEP size STEP, initializing a fish school position { X1, X2, …, xn } and a fish school target { Y1, Y2, …, yn } in the value range, wherein the fish school position Xi represents a group of parameter value combinations [ Ms, a, alpha, c, k ], the given range is the value range of 5 parameters in the table 1, and the fish school target Yi is the corresponding Fitness;
2) Simulating clustering behavior and rear-end collision behavior for each artificial fish, comparing, selecting behavior capable of obtaining a larger target value to perform actual operation, if a movement condition Y (X) > Yi (Xi) is met, selecting behavior capable of enabling the artificial fish to free farther from clustering and rear-end collision behavior to perform position updating, and updating the position Xi of the artificial fish and the corresponding target value Yi of the artificial fish, wherein rand is a random number smaller than 1 and corresponds to the fish swarm target value Yi of the artificial fish;
3) Obtaining a maximum objective function value BestY = max [ Yi ] in a fish school and a corresponding fish school position BestX = X [ Y = max [ Yi ] ], obtaining a group of J-A model parameter values which can enable the objective function to be as large as possible, and updating the optimizing bulletin board according to the result;
4) If BestY of the bulletin board is smaller than a search early-stage threshold Ymax1, enabling the iteration times gen = gen +1, and continuing to execute the iteration processes of 2) -3) until BestY is larger than Ymax1 or the iteration times is larger than the maximum iteration times MAXGEN, and then entering a parallel simulated annealing algorithm; and if the BestY of the bulletin board is not less than the early-stage search threshold Ymax1 at the moment, ending the process and entering a parallel simulated annealing algorithm.
204. Receiving the global optimal solution as a primary solution, executing a parallel simulated annealing algorithm on the primary solution to continuously perform the optimization of the objective function, obtaining the optimal value of the objective function, and obtaining the saturation magnetization M corresponding to the optimal value of the objective function s Values of a langevin function parameter a, a mean field parameter α coupled inside a magnetic domain, a reversible magnetization coefficient c and a loss coefficient k;
the parallel simulated annealing algorithm specifically comprises the following steps:
a: acquiring an initial temperature T0, a termination temperature Tend, a corresponding fixed iteration time chain length L, a cooling speed q and a maximum allowable error per _ erro at each temperature, and inputting the global optimal solution serving as an initial solution of the initial temperature T0 into a parallel simulated annealing model;
b: iteratively updating the parallel simulated annealing model, and meanwhile obtaining the current optimal solution besty, the corresponding bestx and the current error erro according to the Metropolis rule;
the Metropolis criterion is the key point for the simulated annealing algorithm to converge on the optimal solution of the objective function. Suppose the objective function of the problem to be optimized is f (x) and the argument is x. When the temperature is T, if the current solution is x 1 Is different from x 1 A new solution of is x 2 Target difference df = f (x) 1 )-f(x 2 ) Then Metropolis criterion is
If df <0, accepting the new solution with probability 1; otherwise, the new solution is accepted with probability exp (-df/T).
c: and judging whether the current error erro is larger than the maximum allowable error per _ erro, if so, enabling the initial temperature T0= q x T0, and taking the current optimal solution BestY as the initial solution of the initial temperature T0, and returning to execute the step b, otherwise, enabling BestY = BestY and BestX = BestX, and obtaining the optimal value of the target function.
205. Judging whether the optimal value of the objective function is larger than a preset threshold value maxY2, if not, enabling the maximum iteration times MAXGEN = MAXGEN +1, returning to execute the step S3, if so, ending, and enabling the saturation magnetization M corresponding to the optimal value of the objective function s A parameter a of the langevin function, a parameter α of the mean field coupled inside the magnetic domain, a reversible susceptibility c and a set of values [ Ms, a, α, c, k of the loss coefficient k]The results are the results of the parameter identification of the Jiles-Atherton model.
Firstly, carrying out early iterative computation by using a variable-step-length artificial fish swarm algorithm to narrow a search domain to a smaller range near an optimal solution; and setting the value BestX of the last bulletin board of the fish swarm algorithm as an initial value S1 of the parallel simulated annealing algorithm, setting the chain length L as a fixed iteration number corresponding to each temperature, randomly disturbing the S1 in each iteration to obtain a new solution S2, selecting whether to perform solution updating according to the Metropolis rule, extracting the maximum value of the target function after L iterations are finished, and updating the local search small bulletin boards Besty and Bestx. If the error erro is smaller than the set minimum error per _ erro at the current temperature T, stopping searching, updating the bulletin boards BestY and BestX according to the current small bulletin board value, otherwise, performing temperature attenuation, and continuing optimization calculation until an optimal solution of local searching, namely a global optimal solution of the improved algorithm, is obtained, and outputting a parameter identification result.
The method for identifying the parameters of the Jiles-Atherton model provided by the embodiment is used for identifying 5 key parameters of the J-A model. The method comprises the steps of utilizing a variable-step-size artificial fish swarm algorithm to approach a global optimal solution in the early stage of searching, and utilizing a simulated annealing algorithm to perform further local searching in the later stage so as to obtain a more accurate global optimal solution and a parameter identification result corresponding to the global optimal solution; the method mainly comprises the following steps: (1) establishing a Jiles-Atherton transformer hysteresis loop model; (2) Giving the value ranges of the model parameters Ms, a, alpha, c and k to be identified, and randomly taking values in the range to be introduced into the mathematical model in each iteration; (3) Establishing an optimized objective function Fitness, wherein the maximum Fitness value is used as the optimization; (4) Executing a variable-step artificial fish swarm algorithm to carry out iterative optimization until the maximum BestY of the objective function is greater than the early-stage searching threshold Ymax1, or stopping the calculation of the artificial fish swarm when the iteration times exceed the maximum iteration times MAXGEN; (5) Executing a simulated annealing algorithm, and continuously optimizing the target function in the local area obtained by the artificial fish swarm algorithm; (6) And ending iteration until BestY is greater than Yend (the maximum search value expected by optimization), and obtaining a parameter value combination corresponding to the optimal value of the objective function, namely a parameter identification result. The method provided by the embodiment introduces the parallel simulated annealing algorithm on the basis of the variable-step-size artificial fish swarm algorithm, and due to the introduction of the variable-step-size factor into the artificial fish swarm algorithm, the moving randomness of the artificial fish can be controlled, so that the artificial fish selects a proper step size to approach to the threshold according to the distance from the threshold, and the iteration speed of the artificial fish swarm algorithm in the early stage of searching is accelerated; after the threshold is reached, the simulated annealing algorithm of parallel processing is used to further search locally: because the current local optimal solution of each sub-thread is not only influenced by the solution calculated at the current annealing temperature, but also influenced by the last local optimal solution transmitted, the parallel simulated annealing algorithm of the master-slave mode is adopted, the parallel performance of the algorithm is expanded, the timeliness of the algorithm is improved, the restriction on the later-stage convergence speed of the original algorithm in searching is effectively solved, and the efficiency and the accuracy of the J-A model parameter identification are improved.
In the embodiment, the optimal parameter combination of the J-A model is found through an improved artificial fish school algorithm combined with a simulated annealing algorithm, wherein the artificial fish school algorithm is a random search optimization algorithm based on simulated fish school behaviors, a search domain can be quickly locked in a range near a global optimal solution, but the algorithm has high blindness, so that later-stage optimization is difficult to obtain a more accurate result, and the optimization speed is reduced. The simulated annealing algorithm based on the Metabolis criterion has relatively high probability of accepting the poor solution when the initial searching temperature is high, and is not easy to fall into the local optimal solution; the improved artificial fish school algorithm provided by the embodiment utilizes the artificial fish school algorithm to quickly approach the global optimal solution in the early stage of optimization, and then uses the simulated annealing algorithm to perform local search near the optimal solution of the fish school algorithm, so that the problems of low efficiency of the fish school algorithm and difficulty in large-scale search of the annealing algorithm are solved, and the timeliness and the accuracy of J-A model parameter identification can be effectively improved.
In this embodiment, the artificial fish school algorithm is improved: the variable-step-length artificial fish swarm algorithm has high convergence quality in the initial search stage, but is often low in convergence in the later stage, so that the required precision cannot be achieved, especially for the problem of complex optimization, when the search space is in an area with low change, the speed of convergence to the global optimal solution is reduced, the search performance in the later operation stage is degraded, the search result is difficult to further optimize, the operation speed is greatly reduced, and the search quality and efficiency are seriously influenced; in the embodiment, the improved artificial fish school algorithm combined with the simulated annealing algorithm can quickly converge at a high temperature and perform local search at a low temperature, which is specifically represented as follows: in each generation of optimization, the artificial fish carries out global optimization through various behaviors of the artificial fish, then carries out simulated annealing operation by selecting the state Xi with the highest target value in the current generation, implements local optimization, and updates the value of the bulletin board by using the more optimal target value obtained by a parallel simulated annealing algorithm, thereby further accurately carrying out parameter identification. Because the artificial fish school method simulates the behaviors of foraging, rear-end collision, herd clustering, randomness and the like of the fish school, the food concentration is taken as a search target on the optimization algorithm, and optimization is carried out in a search domain in a certain range; compared with intelligent optimization algorithms such as a particle swarm algorithm and the like, the artificial fish swarm algorithm only uses the function value of the target problem and has certain self-adaption capability to a search space; the method has the characteristics of insensitivity to initial value and parameter selection, strong robustness, simplicity, easiness in realization, rapidness and flexibility in convergence and the like. Due to the application of the simulated annealing algorithm, the method has stronger robustness, implicit parallelism and wide adaptability, can process different types of optimization design variables, does not need other auxiliary signals, and has no requirements on a target function and a constraint function; on the other hand, the parallel simulated annealing algorithm is based on the Metabolis criterion, and when the initial searching temperature is high, the probability of accepting the poor solution is relatively high, and the local optimal solution is not easy to fall into; at lower temperatures, however, the probability of accepting the difference becomes lower, making the search more accurate. Due to the fact that the advantages of the artificial fish school algorithm and the parallel simulated annealing algorithm are combined, the artificial fish school algorithm is used for being close to the global optimal solution quickly in the early stage of optimization, then the parallel simulated annealing algorithm is used for conducting local searching near the optimal solution of the fish school algorithm, the problems that the fish school algorithm is low in efficiency and the annealing algorithm is difficult to search in a large range are solved, and timeliness and accuracy of J-A model parameter identification can be effectively improved.
An application example of the method for identifying parameters of a Jiles-Atherton model provided by this embodiment is as follows: the experimental data used the measured B-H curve, consisting of 1000 groups of B-H values. General value ranges of five parameters of the J-A model and parameter theoretical values corresponding to the actually measured B-H curve are shown in a table 1.
And substituting the H into the formula (1), the formula (4) and the formula (6), and calculating a corresponding B value to enable the target function Fitness to reach a global optimal solution, and obtaining values of five parameters a, c, k, alpha and Ms corresponding to the optimal solution, namely the identification result.
The objective function Fitness is shown as formula (10), and the larger the objective function value obtained by the optimization algorithm is, the highest parameter identification level is.
In the application example, the particle swarm algorithm, the artificial fish swarm algorithm and the improved artificial fish swarm algorithm introduced with the simulated annealing algorithm are respectively used for carrying out parameter identification on the J-A model. The fish swarm algorithm and the improved algorithm thereof set the fish swarm number to be 20, each artificial fish represents a random value combination of a group of parameters, and the maximum iteration number is 20. The parameter identification result and the calculation time ratio are shown in table 2.
TABLE 2
The improved fish swarm algorithm and the fitting situation of the relative theoretical curve of the fish swarm algorithm are compared with, for example, FIG. 5, and the curve fitting effect of the two methods is found to be good and almost no visual error exists.
As can be seen from table 2, the improved mixed-population intelligent algorithm has the highest identification precision, the single artificial fish population algorithm has the second highest precision, the particle swarm algorithm has the lowest precision, and the time-consuming sequencing of the algorithms is just opposite, so that the improved fish population algorithm can identify a more accurate global optimal solution in the shortest time, the timeliness and the precision of parameter identification are superior to those of other two methods, and particularly, compared with the artificial fish population algorithm, the relative error of the optimal solution of the improved fish population algorithm is reduced by 0.2 percentage point and is only half of the relative error of the fish population algorithm.
The convergence speed ratio of the artificial fish swarm algorithm and the improved algorithm thereof is shown in FIG. 6. As is apparent from fig. 6, the target value optimization speed of the fish swarm algorithm is high in the first 5 iterations, but the target value stays around 1.48 from the 6 th iteration and does not increase continuously until the maximum number of iterations is reached, and the overall convergence speed is relatively slow. When the improved mixed group intelligent algorithm is adopted, the early-stage threshold value is quickly searched through 4 times of iteration objective functions, the constraint of the fish swarm algorithm is skipped after the 6 th iteration, the later-stage search is carried out by using a parallel simulated annealing algorithm, the parallel strategy enables the later stage to reach a higher convergence speed and quickly converge to the 10 th iteration, and then the target value is stabilized at 1.475 until the convergence is finished, so that the improved mixed group intelligent algorithm has better search quality. Parameter identification simulation experiments carried out by using the improved mixed group algorithm for multiple times can find that the optimization iteration curves are basically consistent, the problem of unstable convergence speed is avoided, the early-stage threshold can be reached after 4.8 iterations on average, and the global optimal solution can be reached after 11.5 iterations. Therefore, the maximum iteration times can be set within 15 times, the parameter identification result can be obtained within 300s by improving the mixed group intelligent algorithm, and the method is high in accuracy and high in searching speed.
The application example verifies that the improved mixed group algorithm can obtain a more accurate global optimal solution under the condition of less convergence times. Compared with a single artificial fish school algorithm, the accuracy can be improved by nearly one time under the condition of short consumed time, the restriction on the convergence speed of the original algorithm in the later searching stage is effectively solved, and the efficiency and the accuracy of the parameter identification of the J-A model of the mutual inductor are improved. The algorithm can effectively construct a more accurate and reliable transformer model, is used in a simulation experiment for transformer characteristic research, and has a higher practical application value.
Referring to fig. 3, an embodiment of a Jiles-Atherton model parameter identification apparatus according to the present invention includes:
a first obtaining unit 301, configured to obtain a Jiles-Atherton transformer hysteresis loop model formula B = f ([ Ms, a, α, c, k)]H), where H is the magnetic field strength, B is the magnetic flux density, M) s Is saturation magnetization, a is a langevin function parameter, alpha is an average field parameter coupled inside a magnetic domain, c is a reversible magnetization coefficient, and k is a loss coefficient;
a second acquisition unit 302 for receiving a predetermined saturation magnetization M s The value ranges of the Langmuir function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c and the loss coefficient k,acquiring a target function corresponding to a model formula of a hysteresis loop of the Jeles-Atherton transformer;
a first optimizing unit 303 for comparing the saturation magnetization M in a value range by an objective function s Performing a step-variable artificial fish swarm algorithm on the Langmuir function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c, the initial value of the loss coefficient k and the initial value of the target function, and iteratively optimizing the target function to obtain a global optimal solution;
a second optimizing unit 304, configured to perform parallel simulated annealing algorithm on the global optimal solution to continue optimizing the objective function, and obtain a saturation magnetization M corresponding to the optimal value of the objective function s Values of the langevin function parameter a, the average field parameter a of the magnetic domain internal coupling, the reversible magnetization coefficient c and the loss coefficient k.
In the above, the units of a Jiles-Atherton model parameter identification apparatus are described in detail, and in the following, additional units of a Jiles-Atherton model parameter identification apparatus are described in detail, referring to fig. 4, another embodiment of a Jiles-Atherton model parameter identification apparatus provided in an embodiment of the present invention includes:
a first obtaining unit 401, configured to obtain a smiles-Atherton transformer hysteresis loop model formula B = f ([ Ms, a, α, c, k)]H), where H is the magnetic field strength, B is the magnetic flux density, M) s Is saturation magnetization, a is a langevin function parameter, alpha is an average field parameter coupled inside a magnetic domain, c is a reversible magnetization coefficient, and k is a loss coefficient;
the first obtaining unit 401 specifically includes:
the first obtaining sub-unit 4011 is specifically configured to obtain a magnetic hysteresis loop model formula of the Jiles-Atherton transformer according to a preset first formula of the magnetization M, the magnetic field strength H, and the magnetic flux density B, a preset second formula obtained according to the energy conservation principle, a preset third formula, a preset fourth formula, a preset fifth formula, and a preset sixth formula,
the hysteresis loop model formula of the Jiles-Atherton transformer is B = f ([ Ms, a, alpha, c, k)]H), where H is the magnetic field strength, B)Is magnetic flux density, M s In order to saturate magnetization, a is a langevin function parameter, alpha is an average field parameter coupled inside a magnetic domain, c is a reversible magnetization coefficient, and k is a loss coefficient;
the preset first formula is B = μ 0 (H + M), wherein μ 0 ＝4π×10 -7 Is the vacuum magnetic permeability, and M is the magnetization;
preset the second formula as
Wherein H e For effective magnetic field strength, M an Without hysteresis magnetization, M irr Being an irreversible magnetization component in the magnetization M, M rev Is the reversible magnetization component in the magnetization M;
preset the third formula as H e ＝H+αM，
The preset fourth formula is
The preset fifth formula is M = M irr +M rev ，
Preset the sixth formula as M rev ＝c(M an -M irr )。
A second acquisition unit 402 for receiving a predetermined saturation magnetization M s Obtaining a target function corresponding to a Jiles-Atherton mutual inductor hysteresis loop model formula by the value ranges of the Langmuim function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c and the loss coefficient k;
the second obtaining unit 402 specifically includes:
a first receiving subunit 4021, specifically configured to receive a predetermined saturation magnetization M s The value ranges of the Langmuir function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c and the loss coefficient k;
the second obtaining subunit 4022 is specifically configured to obtain an objective function Fitness corresponding to the hysteresis loop model formula of the Jiles-Atherton transformer through a preset seventh formula, a preset eighth formula, and a preset ninth formula, where the preset seventh formula is:
B calculating i ＝f([Ms，a，α，c，k]，H Actually measured i )，
The preset eighth formula is:
the preset ninth formula is:
wherein N is the number of groups, B Calculate i For calculated i-th group of magnetic flux densities, H Actually measured i For the measured i-th set of magnetic field strengths, B Actually measured i Measured i group magnetic flux density, B Measured actually Is the measured magnetic flux density.
A first optimizing unit 403 for comparing the saturation magnetization M in a range of values by an objective function s Performing a step-variable artificial fish swarm algorithm on the Langmuir function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c, the initial value of the loss coefficient k and the initial value of the target function, and iteratively optimizing the target function to obtain a global optimal solution;
the step-length-variable artificial fish school algorithm specifically comprises the following steps:
a: initializing the number FISHNUM of artificial fish, the maximum iteration number MAXGEN, a search early stage threshold value Ymax1 and a fixed STEP size STEP, and initializing a fish school position { X1, X2, …, xn } and a fish school target { Y1, Y2, …, yn } in the value range;
b: updating the fish school position in the fish school clustering and rear-end collision behaviors, and if the movement condition Y (X) > Yi (Xi) is met, updating the position coordinates Xi = Xi + rand STEP | X-Xi | of the current artificial fish, wherein rand is a random number smaller than 1 and corresponds to the fish school target value Yi of the artificial fish;
c: acquiring a maximum fish school target value BestY = max [ Yi ] and a corresponding fish school position BestX = X [ Y = max [ Yi ] ];
d: judging whether the maximum fish school objective value BestY is smaller than a search early-stage threshold value Ymax1, if yes, returning to the step b for iterative updating until the maximum fish school objective value BestY is not smaller than the search early-stage threshold value Ymax1 or the iteration times are larger than the maximum iteration times MAXGEN, and if not, ending the process of transferring to a parallel simulated annealing algorithm;
wherein the fish school position Xi is the saturation magnetization M s A parameter a of the langevin function, a parameter α of the mean field coupled inside the magnetic domain, a reversible susceptibility c and a set of values [ Ms, a, α, c, k of the loss coefficient k]And the fish school target Yi is a corresponding target function Fitness.
A second optimizing unit 404, configured to perform parallel simulated annealing algorithm on the global optimal solution to continue optimizing the objective function, and obtain a saturation magnetization M corresponding to the optimal value of the objective function s Values of a langevin function parameter a, a mean field parameter α coupled inside a magnetic domain, a reversible magnetization coefficient c and a loss coefficient k;
the parallel simulated annealing algorithm specifically comprises the following steps:
a: acquiring an initial temperature T0, a termination temperature Tend, a corresponding fixed iteration time chain length L, a cooling speed q and a maximum allowable error per _ erro at each temperature, and inputting a global optimal solution serving as an initial solution of the initial temperature T0 into a parallel simulated annealing model;
b: iteratively updating the parallel simulated annealing model, and meanwhile obtaining the current optimal solution besty, the corresponding bestx and the current error erro according to the Metropolis rule;
c: and judging whether the current error erro is larger than the maximum allowable error per _ erro, if so, enabling the initial temperature T0= q × T0, taking the current optimal solution BestY as the initial solution of the initial temperature T0, and returning to execute the step b, otherwise, enabling BestY = BestY and BestX = BestX, and obtaining the optimal value of the objective function.
A second optimizing unit 404 for performing an optimization on the global optimal solutionContinuously optimizing the objective function by the line simulated annealing algorithm to obtain the saturation magnetization M corresponding to the optimal value of the objective function s Values of the langevin function parameter a, the average field parameter a of the magnetic domain internal coupling, the reversible susceptibility c and the loss factor k.
The iteration optimizing unit 405 is specifically configured to determine whether the optimal value of the objective function is greater than a preset late threshold Ymax2, if not, make the maximum iteration number MAXGEN = MAXGEN +1, and return to the step S3, if so, end the process, and the saturation magnetization M corresponding to the optimal value of the objective function is obtained s A parameter a of the langevin function, a parameter α of the mean field coupled inside the magnetic domain, a reversible susceptibility c and a set of values [ Ms, a, α, c, k of the loss coefficient k]The results are the results of the parameter identification of the Jiles-Atherton model.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is only a logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (12)

1. A method for identifying parameters of a Jiles-Atherton model is characterized by comprising the following steps:
s1: obtaining a model formula B = f ([ Ms, a, alpha, c, k) of a hysteresis loop of the Jeles-Atherton transformer]H), where H is the magnetic field strength, B is the magnetic flux density, M) s Is saturation magnetization, a is a langevin function parameter, alpha is an average field parameter coupled inside a magnetic domain, c is a reversible magnetization coefficient, and k is a loss coefficient;
s2: receiving a predetermined saturation magnetization M s Obtaining a target function corresponding to a Jiles-Atherton mutual inductor hysteresis loop model formula by the value ranges of the Langmuim function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c and the loss coefficient k;
s3: by said target function, the saturation magnetization M in said range of values s Performing a step-variable artificial fish swarm algorithm on the Langmuir function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c, the initial value of the loss coefficient k and the initial value of the target function, and iteratively optimizing the target function to obtain a global optimal solution;
s4: executing a parallel simulated annealing algorithm on the global optimal solution to continuously carry out the optimization of the objective function and obtain the saturation magnetization M corresponding to the optimal value of the objective function s Values of the langevin function parameter a, the average field parameter a of the magnetic domain internal coupling, the reversible susceptibility c and the loss factor k.
2. The method of Jiles-Atherton model parameter identification according to claim 1, wherein the step S1 specifically comprises:
acquiring a Jiles-Atherton mutual inductor hysteresis loop model formula through a preset first formula of magnetization M, magnetic field intensity H and magnetic flux density B, a preset second formula obtained through an energy conservation principle, a preset third formula, a preset fourth formula, a preset fifth formula and a preset sixth formula;
the model formula of the hysteresis loop of the Jeles-Atherton transformer is B = f ([ Ms, a, alpha, c, k)]H), wherein H is the magnetic field strength; b is the magnetic flux density; m s Is the saturation magnetization; a is a Langey function parameter; alpha is the average field parameter of the magnetic domain internal coupling; c is the reversible susceptibility; k is a loss coefficient;
the preset first formula is B = μ 0 (H + M), wherein μ 0 ＝4π×10 -7 Is the vacuum magnetic permeability, and M is the magnetization;
the preset second formula is
Wherein H e For effective magnetic field strength, M an Without hysteresis magnetization, M irr Being an irreversible magnetization component in the magnetization M, M rev Is the reversible magnetization component in the magnetization M;
the preset third formula is H e ＝H+αM，
The preset fourth formula is
The preset fifth formula is M = M irr +M rev ，
The preset sixth formula is M rev ＝c(M an -M irr )。
3. The method of Jiles-Atherton model parameter identification according to claim 2, wherein the step S2 specifically comprises:
receiving a predetermined saturation magnetization M s Obtaining an objective function Fitness corresponding to a Sieles-Atherton transformer hysteresis loop model formula through a preset seventh formula, a preset eighth formula and a ninth formula according to the value ranges of the Landaun function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c and the loss coefficient k, wherein the preset seventh formula is as follows:
B calculate i ＝f([Ms，a，α，c，k]，H Actually measured i )，
The preset eighth formula is:
the preset ninth formula is:
wherein N is the number of groups, B Calculate i For calculated i-th group of magnetic flux densities, H Actually measured i For the measured i-th set of magnetic field strengths, B Actually measured i Measured i group magnetic flux density, B Measured in fact Is the measured magnetic flux density.
4. The method of Jiles-Atherton model parameter identification according to claim 3, wherein the step-size-variable artificial fish-swarm algorithm specifically comprises:
a: initializing the number FISHNUM of artificial fish, the maximum iteration number MAXGEN, a search early stage threshold value Ymax1 and a fixed STEP size STEP, and initializing a fish school position { X1, X2, …, xn } and a fish school target { Y1, Y2, …, yn } in the value range;
b: updating the fish school position in the fish school clustering and rear-end collision behaviors, and if the movement condition Y (X) > Yi (Xi) is met, updating the position coordinates Xi = Xi + rand STEP | X-Xi | of the current artificial fish, wherein rand is a random number smaller than 1 and corresponds to the fish school target value Yi of the artificial fish;
c: acquiring a maximum fish school target value BestY = max [ Yi ] and a corresponding fish school position BestX = X [ Y = max [ Yi ] ];
d: judging whether the maximum fish school target value BestY is smaller than a search early-stage threshold value Ymax1, if so, returning to the step b for iterative updating until the maximum fish school target value BestY is not smaller than the search early-stage threshold value Ymax1 or the iteration times are larger than the maximum iteration times MAXGEN, and if not, ending the shift to the parallel simulated annealing algorithm;
wherein the fish school position Xi is the saturation magnetization M s A set of values [ Ms, a, c, k ] for a Langmuim function parameter a, a mean field parameter α coupled inside the magnetic domain, a reversible magnetization coefficient c and a loss coefficient k]And the fish school target Yi is a corresponding target function Fitness.
5. The method of Jiles-Atherton model parameter identification, as set forth in claim 4, wherein the parallel simulated annealing algorithm specifically comprises:
a: acquiring an initial temperature T0, a termination temperature Tend, a corresponding fixed iteration time chain length L, a cooling speed q and a maximum allowable error per _ erro at each temperature, and inputting the global optimal solution serving as an initial solution of the initial temperature T0 into a parallel simulated annealing model;
b: iteratively updating the parallel simulated annealing model, and meanwhile obtaining the current optimal solution besty, the corresponding bestx and the current error erro according to the Metropolis rule;
c: and judging whether the current error erro is larger than the maximum allowable error per _ erro or not, if so, enabling the initial temperature T0= q × T0, taking the current optimal solution BestY as the initial solution of the initial temperature T0, and returning to execute the step b, otherwise, enabling BestY = BestY and BestX = BestX, and obtaining the optimal value of the target function.
6. The method of Jiles-Atherton model parameter identification according to claim 5, further comprising after step S4:
s5, judging whether the optimal value of the objective function is larger than a preset late threshold Ymax2, if not, enabling the maximum iteration times MAXGEN = MAXGEN +1, returning to the step S3, if so, ending, and enabling the saturation magnetization M corresponding to the optimal value of the objective function s A parameter a of the langevin function, a parameter α of the mean field coupled inside the magnetic domain, a reversible susceptibility c and a set of values [ Ms, a, α, c, k of the loss coefficient k]The results are the results of the parameter identification of the Jiles-Atherton model.
7. A device for identifying parameters of a Jiles-Atherton model is characterized by comprising:
a first obtaining unit, configured to obtain a Jiles-Atherton transformer hysteresis loop model formula B = f ([ Ms, a, α, c, k)]H), where H is the magnetic field strength, B is the magnetic flux density, M) s For saturation magnetization, a is a Langmuir function parameterAlpha is the average field parameter of magnetic domain internal coupling, c is the reversible magnetization coefficient, and k is the loss coefficient;
a second acquisition unit for receiving a predetermined saturation magnetization M s Obtaining a target function corresponding to a Jiles-Atherton mutual inductor hysteresis loop model formula by the value ranges of the Langmuim function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c and the loss coefficient k;
a first optimizing unit for optimizing the saturation magnetization M in the value range by the objective function s Performing a step-variable artificial fish swarm algorithm on the Langmuir function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c, the initial value of the loss coefficient k and the initial value of the target function, and iteratively optimizing the target function to obtain a global optimal solution;
a second optimizing unit for executing parallel simulated annealing algorithm to continuously optimize the objective function to obtain the saturation magnetization M corresponding to the optimal value of the objective function s Values of the langevin function parameter a, the average field parameter a of the magnetic domain internal coupling, the reversible magnetization coefficient c and the loss coefficient k.
8. The device for identifying Jiles-Atherton model parameters of claim 7, wherein the first obtaining unit specifically comprises:
the first obtaining subunit is specifically configured to obtain a Jiles-Atherton transformer hysteresis loop model formula through a preset first formula of the magnetization M, the magnetic field strength H and the magnetic flux density B, a preset second formula obtained through the energy conservation principle, a preset third formula, a preset fourth formula, a preset fifth formula and a preset sixth formula;
the model formula of the hysteresis loop of the Jeles-Atherton transformer is B = f ([ Ms, a, alpha, c, k)]H), wherein H is the magnetic field strength; b is the magnetic flux density; m is a group of s Is the saturation magnetization; a is a Langey function parameter; alpha is the average field parameter of the magnetic domain internal coupling; c is the reversible susceptibility; k is the loss coefficient;
the preset first formula is B = μ 0 (H + M), wherein μ 0 ＝4π×10 -7 Is the vacuum magnetic permeability, and M is the magnetization;
the preset second formula is
Wherein H e For effective magnetic field strength, M an Without hysteresis magnetization, M irr Being an irreversible magnetization component in the magnetization M, M rev Is the reversible magnetization component in the magnetization M;
the preset third formula is H e ＝H+αM，
The preset fourth formula is
The preset fifth formula is M = M irr +M rev ，
The preset sixth formula is M rev ＝c(M an -M irr )。
9. The Jiles-Atherton model parameter identification device of claim 8, wherein the second obtaining unit specifically comprises:
a first receiving subunit, in particular for receiving a predetermined saturation magnetization M s The value ranges of the Langmuir function parameter a, the average field parameter alpha coupled in the magnetic domain, the reversible magnetization coefficient c and the loss coefficient k;
the second obtaining subunit is specifically configured to obtain an objective function Fitness corresponding to the hysteresis loop model formula of the Jiles-Atherton transformer through a preset seventh formula, a preset eighth formula, and a preset ninth formula, where the preset seventh formula is:
B calculate i ＝f([Ms，a，α，c，k]，H Actually measured i )，
The preset eighth formula is:
the preset ninth formula is as follows:
wherein N is the number of groups, B Calculate i For calculated i-th group of magnetic flux densities, H Actually measured i For the measured i-th set of magnetic field strengths, B Actually measured i Measured i group magnetic flux density, B Measured actually Is the measured magnetic flux density.
10. The device of claim 9, wherein the artificial fish school algorithm specifically comprises:
a: initializing the number FISHNUM of artificial fish, the maximum iteration number MAXGEN, a search early stage threshold value Ymax1 and a fixed STEP size STEP, and initializing a fish school position { X1, X2, …, xn } and a fish school target { Y1, Y2, …, yn } in the value range;
b: updating the fish school position in the fish school clustering and rear-end collision behaviors, and if the movement condition Y (X) > Yi (Xi) is met, updating the position coordinates Xi = Xi + rand STEP | X-Xi | of the current artificial fish, wherein rand is a random number smaller than 1 and corresponds to the fish school target value Yi of the artificial fish;
c: acquiring a maximum fish school target value BestY = max [ Yi ] and a corresponding fish school position BestX = X [ Y = max [ Yi ] ];
d: judging whether the maximum fish school target value BestY is smaller than a search early-stage threshold value Ymax1, if so, returning to the step b for iterative updating until the maximum fish school target value BestY is not smaller than the search early-stage threshold value Ymax1 or the iteration times are larger than the maximum iteration times MAXGEN, and if not, ending the shift to the parallel simulated annealing algorithm;
wherein the fish school position Xi is the saturation magnetization M s Langmuir function parameter a, average field parameter of magnetic domain internal couplingA set of values [ Ms, a, α, c, k ] for α, the reversible magnetization coefficient c and the loss coefficient k]And the fish school target Yi is a corresponding target function Fitness.
11. The device for Jiles-Atherton model parameter identification according to claim 10, wherein the parallel simulated annealing algorithm specifically comprises:
a: acquiring an initial temperature T0, a termination temperature Tend, a corresponding fixed iteration time chain length L, a cooling speed q and a maximum allowable error per _ erro at each temperature, and inputting the global optimal solution serving as an initial solution of the initial temperature T0 into a parallel simulated annealing model;
b: iteratively updating the parallel simulated annealing model, and meanwhile obtaining the current optimal solution besty, the corresponding bestx and the current error erro according to the Metropolis rule;
c: and judging whether the current error erro is larger than the maximum allowable error per _ erro, if so, enabling the initial temperature T0= q x T0, taking the current optimal solution BestY as the initial solution of the initial temperature T0, and returning to execute the step b, otherwise, enabling BestY = BestY and BestX = BestX, and obtaining the optimal value of the target function.
12. The Jiles-Atherton model parameter identification device of claim 11, further comprising:
an iteration optimizing unit, specifically configured to determine whether the optimal value of the objective function is greater than a preset late threshold Ymax2, if not, make the maximum iteration number MAXGEN = MAXGEN +1, and return to execute step S3, if so, end the process, and the saturation magnetization M corresponding to the optimal value of the objective function s A parameter a of the langevin function, a parameter α of the mean field coupled inside the magnetic domain, a reversible susceptibility c and a set of values [ Ms, a, α, c, k of the loss coefficient k]The results are the results of the parameter identification of the Jiles-Atherton model.
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