CN107633144A - Large-scale permanent-magnetic wind driven generator Parameters design based on electromagnetism Thermal-mechanical Coupling field - Google Patents
Large-scale permanent-magnetic wind driven generator Parameters design based on electromagnetism Thermal-mechanical Coupling field Download PDFInfo
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Abstract
The invention discloses a kind of large-scale permanent-magnetic wind driven generator Parameters design based on electromagnetism Thermal-mechanical Coupling field, implementation steps include：It is determined that it is designed the system design parameterses of large-scale permanent-magnetic wind driven generator；Electricity, magnetic, heat, the constraints of four aspects of power are considered, with the weighted average efficiency highest of multiple working conditions or the minimum object function of weighted average power loss, based on system design parameterses founding mathematical models；Optimal system design parameterses are obtained using simulated annealing iterative.The present invention is while electromagnetism Thermal-mechanical Coupling field is considered using the optimal method of multiple working condition weighted average efficiencies, on the premise of can having stronger bending resistance torsional property and preferable hot property ensureing generator, solve the problems, such as that conventional electric generators whole efficiency under multiple working conditions is low, make operational efficiency of the generator under the complex environments such as power network fluctuation, wind speed change higher.
Description
Technical Field
The invention relates to a parameter design and optimization technology of a wind driven generator, in particular to a parameter design method of a large permanent magnet wind driven generator based on an electromagnetic thermal coupling field.
Background
At present, a general design method of a permanent magnet generator is designed on the premise of constant voltage and constant speed and frequency operation by only combining a specific magnetic circuit design method of the permanent magnet motor under a rated state based on a traditional synchronous generator design method. The designed permanent magnet wind driven generator can not be well adapted to complex environments such as power grid fluctuation, wind speed change and the like, and the operation characteristics are easily influenced by a converter and a control strategy.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: the invention provides a parameter design method of a large permanent magnet wind driven generator based on an electromagnetic thermal coupling field, which is based on the traditional design method, considers the electromagnetic thermal coupling field and adopts a method with optimal weighted average efficiency of a plurality of working states, can solve the problem of low overall efficiency of the traditional generator under a plurality of working states on the premise of ensuring stronger bending resistance and torsion resistance and better thermal performance of the generator, and enables the generator to have higher operation efficiency under complex environments such as power grid fluctuation, wind speed change and the like.
In order to solve the technical problems, the invention adopts the technical scheme that:
a parameter design method for a large permanent magnet wind driven generator based on an electromagnetic thermal coupling field comprises the following implementation steps:
1) Determining system design parameters of a designed large permanent magnet wind driven generator;
2) Considering constraint conditions in the aspects of electricity, magnetism, heat and force, taking the weighted average efficiency highest or the weighted average power loss minimum of a plurality of working states as an objective function, and establishing a mathematical model based on system design parameters;
3) Designing a group of large permanent magnet wind driven generator system parameters based on a rated state as an initial working state value; and based on the initial working state value, performing multi-working state iterative solution on the mathematical model by adopting a simulated annealing algorithm, obtaining multiple groups of solution results after iterative convergence, and selecting one group of optimal results as optimal system design parameters for output.
Preferably, the system design parameters of the large permanent magnet wind power generator designed in the step 1) comprise the inner diameter D of the stator core i1 Length L of stator core t Outer diameter d of the rotating shaft and length L of the rotating shaft a N conductors per slot s Height h of stator slot s And groove width b s Width a of permanent magnet m And thickness of magnetization direction h m And the pole arc coefficient alpha p 。
Preferably, the constraint conditions in the aspects of electricity, magnetism, heat and force in the step 2) comprise a terminal voltage U and an air gap flux density B of the permanent magnet wind driven generator g Stator tooth magnetic density B t Stator yoke magnetic density B j Stator linear load A, thermal load AJ, average temperature rise delta tau, bending and twisting stress F borne by rotating shaft and slot filling rate S f The preset constraint condition as shown in (1);
in the formula (1), g 1 (x)～g 9 (x) The U is the terminal voltage of the permanent magnet wind driven generator and the U is the preset nine constraint conditions dc For the converter DC bus voltage, B g Is air gap flux density, B g0 Is a constraint value of air gap flux density, B t The stator tooth part is magnetic flux density B t0 Is a constraint value of the magnetic flux density of the stator teeth, B j Is the magnetic density of the stator yoke part B j0 Is the constraint value of the magnetic density of the stator yoke, A is the linear load of the stator, A 0 Is a constraint value of a linear load of the stator, AJ is a thermal load, AJ 0 Is the constraint value of thermal load, and is the average temperature rise 0 Is the constraint value of average temperature rise, F is the bending and twisting stress borne by the rotating shaft, F 0 For the constraint value, S, of the bending-twisting stress to which the shaft is subjected f Is the slot fill factor, S f0 Is a constraint value of the slot full rate.
Preferably, in the step 2), a mathematical model is established based on system design parameters as shown in the formula (2);
in the formula (2), X is a matrix formed by the system design parameters to be solved, D is the total number of the system design parameters to be solved, eta i (X) is the weighted average efficiency, p, of the ith operating state i (X) is the weighted average power loss of the ith working state, i is each working state, i is not less than 3 j (X) is the jth constraint.
Preferably, the detailed steps of step 3) include:
3.1 Based on the nominal state, an initial solution x is randomly generated for the design mathematical model by using a simulated annealing algorithm 0 And x is 0 As the current optimal solution, calculating an objective function based on the current optimal solution x 0 Objective function value ofSetting the initial temperature T of the simulated annealing algorithm 0 1.0, the value of the number k of temperature changes is initially set to 1 and a maximum number of iterations L is given k ；
3.2 ) randomly perturbing the current optimal solution, and generating a new solution x of the iteration based on a simulated annealing algorithm j k+1 Calculating a new solution x for the objective function based on the iteration j k+1 New value of objective function ofCalculating new objective function valuesAnd the objective function value of the last iterationThe difference between them as the increment of the objective function value
3.3 Determine the increment of the objective function valueIf the value is less than 0, accepting the newly generated optimal solution as the current optimal solution; otherwise, the probability P is calculated according to the Metropolis criterion, and a probability value of [0,1] is randomly generated]Uniformly distributing random numbers zeta in the interval, if the probability P is greater than the random number zeta, the newly generated optimal solution is the current optimal solution, and otherwise, the newly generated optimal solution is abandoned to be accepted as the current optimal solution;
3.4 Add 1 to the number k of temperature changes to determine whether the number k of temperature changes is equal to the maximum number of iterations L k If not equal to the maximum number of iterations L k If yes, skipping to execute the step 3.2); otherwise, skipping to execute the next step;
3.5 Judging whether a preset termination condition is met, wherein the preset termination condition means that N continuous new solutions are not accepted or the increment of the objective function value is less than a given value epsilon in a [0,1] interval, if the preset termination condition is not met, calculating the annealing temperature of the iteration, tempering every m received solutions, quenching, and skipping to execute the step 3.2); and if the preset termination condition is met, outputting the current solution as the optimal solution, and ending.
Preferably, step 3.2) generates a new solution x for this iteration j k+1 The functional expression of (a) is represented by the formula (3);
in the formula (3), x j k+1 For the new solution of the current iteration,for the new solution of the last iteration, y j J is more than or equal to 1 and is less than or equal to D for the disturbance of the jth parameter to be solved, D is the total number of the design parameters of the system to be solved, B j For the maximum value, A, that the jth parameter to be solved can take j Is the minimum value possible to be taken by the jth parameter to be solved, c is the temperature control coefficient, T k For the annealing temperature of this iteration, T 0 To simulate the initial temperature of the annealing algorithm, k is the number of temperature changes, u j Is a random value between 0 and 1.
Preferably, step 3.3) calculates the functional expression of the probability P as shown in formula (4) using Metropolis criterion;
in the formula (3), P is the calculated probability,in increments of the value of the objective function, T k The annealing temperature of this iteration.
Preferably, the functional expression of the annealing temperature of the iteration calculated in the step 3.5) is shown as the formula (5);
T k ＝T 0 exp(-ck 1/D )(5)
in the formula (4), T k For the annealing temperature of this iteration, T 0 In order to simulate the initial temperature of the annealing algorithm, c is a temperature control coefficient, k is the temperature change times, and D is the total number of the system design parameters to be solved.
Preferably, the step of tempering every m received solutions in step 3.5) comprises:
s1) calculating the gradient S of the jth variable in a matrix X formed by the system design parameters to be solved according to the formula (6) j ；
In formula (6), s j Gradient of j-th variable in matrix X formed by system design parameters to be solved, X opt As found so farOptimal solution, δ being the step size, e j Is a vector of dimension D and the jth component is 1, the remainder are 0;
s2) judging the gradient S of the jth variable in a matrix X formed by the system design parameters to be solved j Equal to a preset gradient threshold s max If true, the annealing temperature T is adjusted according to the equation (7) k Resetting the value of (a) and the value of the temperature change times k;
t 'in the formula (7)' k ' is the annealing temperature T k Reset value, s max Is a predetermined gradient threshold value, s j Gradient, T, of jth variable in matrix X formed for system design parameters to be solved k The annealing temperature before resetting; k' is a value of the number of temperature changes k reset, T 0 In order to simulate the initial temperature of the annealing algorithm, c is a temperature control coefficient, and D is the total number of the system design parameters to be solved.
Preferably, the functional expression of the quenching treatment in the step 3.5) is shown as the formula (8);
in formula (8), T k For the annealing temperature of this iteration, T 0 To simulate the initial temperature of the annealing algorithm, c is the temperature control coefficient, k is the number of temperature changes, q j For the quenching factor, D is the total number of system design parameters to be solved.
The parameter design method of the large permanent magnet wind driven generator based on the electromagnetic thermal coupling field has the following advantages: the invention relates to a parameter design method of a large permanent magnet wind driven generator based on an electromagnetic thermal coupling field, which determines system design parameters of the designed large permanent magnet wind driven generator; considering constraint conditions in the aspects of electricity, magnetism, heat and force, taking the weighted average efficiency or the weighted average power loss of a plurality of working states as an objective function, and establishing a mathematical model based on system design parameters; a group of large permanent magnet wind driven generator system parameters designed and obtained based on a rated state are initial working state values; and based on the initial working state value, performing multi-working state iterative solution on the mathematical model by adopting a simulated annealing algorithm, obtaining a plurality of groups of solution results after iterative convergence, and selecting a group of optimal results as optimal system design parameters for output. On the basis of the traditional design method, the method for optimizing the weighted average efficiency of a plurality of working states is adopted while considering the electromagnetic thermal coupling field, so that the problem of low overall efficiency of the traditional generator in a plurality of working states can be solved on the premise of ensuring that the generator has stronger bending resistance and torsion resistance and better thermal property, and the running efficiency of the generator in complex environments such as power grid fluctuation, wind speed change and the like is higher.
Drawings
FIG. 1 is a schematic diagram of a topological structure of a large permanent magnet wind generator system.
FIG. 2 is a schematic diagram of a basic flow of a method according to an embodiment of the present invention.
FIG. 3 is a detailed flowchart of step 3) according to an embodiment of the present invention.
Detailed Description
As shown in fig. 1, the large-scale permanent magnet wind power generator system includes a fan, a permanent magnet wind power generator PMSM, a converter, a filter and a transformer, an output shaft of the fan is connected with the permanent magnet wind power generator PMSM, and each phase of a power generation output end of the permanent magnet wind power generator PMSM is connected with a power grid through the converter, the filter and the transformer in sequence. The parameter design method of the large permanent magnet wind driven generator based on the electromagnetic thermal coupling field aims at designing and optimizing system design parameters for a PMSM (permanent magnet wind driven generator). As shown in fig. 2, the implementation steps of the parameter design method for the large permanent magnet wind turbine based on the electromagnetic thermal coupling field in this embodiment include:
1) Determining system design parameters of a designed large permanent magnet wind driven generator;
2) Considering constraint conditions in the aspects of electricity, magnetism, heat and force, taking the weighted average efficiency highest or the weighted average power loss minimum of a plurality of working states as an objective function, and establishing a mathematical model based on system design parameters;
3) Designing a group of large permanent magnet wind driven generator system parameters based on a rated state as an initial working state value; and based on the initial working state value, performing multi-working state iterative solution on the mathematical model by adopting a simulated annealing algorithm, obtaining multiple groups of solution results after iterative convergence, and selecting one group of optimal results as optimal system design parameters for output.
In this embodiment, the system design parameters of the large permanent magnet wind turbine designed in step 1) include the inner diameter D of the stator core i1 Length L of stator core t Outer diameter d of rotating shaft and length L of rotating shaft a N conductors per slot s Height h of stator slot s And slot width b s Width a of permanent magnet m And thickness of magnetization direction h m And the pole arc coefficient alpha p The total number of the system design parameters is 10, and the 10 system design parameters form a system design parameter matrix:
X＝[D i1 ,L t ,d,L a ,N s ,h s ,b s ,a m ,h m ,α p ] T
in this embodiment, the constraint conditions in the aspects of electricity, magnetism, heat and force in step 2) include a terminal voltage U and an air gap flux density B of the permanent magnet wind power generator g Stator tooth magnetic density B t Stator yoke magnetic density B j Stator linear load A, thermal load AJ, average temperature rise delta tau, bending and twisting stress F borne by rotating shaft and slot filling rate S f The preset constraint condition as shown in (1);
in the formula (1), g 1 (x)～g 9 (x) The U is the terminal voltage of the permanent magnet wind driven generator and the U is the preset nine constraint conditions dc For the converter DC bus voltage, B g Is air gap flux density, B g0 Is a constraint value of air gap flux density, B t The stator tooth part is magnetic flux density B t0 Is a constraint value of the magnetic flux density of the stator teeth, B j Is the magnetic density of the stator yoke part B j0 Is the constraint value of the magnetic density of the stator yoke, A is the linear load of the stator, A 0 Is a constraint value of a linear load of the stator, AJ is a thermal load, AJ 0 For the constraint value of the thermal load, Δ τ is the average temperature rise, Δ τ 0 Is the constraint value of average temperature rise, F is the bending and twisting stress borne by the rotating shaft, F 0 For the constraint value, S, of the bending-twisting stress to which the shaft is subjected f Is the slot fill factor, S f0 Is a constraint value of the slot full rate.
The calculation function expression of the terminal voltage U of the permanent magnet wind driven generator is shown as a formula (1-1);
in the formula (1-1), U is the end voltage of the permanent magnet wind driven generator, f is the frequency of the generator, and N s Number of conductors per slot, Q 1 Number of stator slots, B g Is an air gap flux density, alpha p Is the polar arc coefficient, tau is the polar distance, L t Is the length of the stator core, K dp Is the winding coefficient of the armature, K φ The form factor of the air gap magnetic flux is shown, a is the number of parallel branches, and m is the number of stator phases. The generator terminal voltage U is determined by the constraint relation between the generator terminal voltage and the direct current bus voltage under the control of the converterCan be obtained by reducing the thickness h of the magnetization direction m The value of the no-load electromotive force is reduced, so that the terminal voltage U of the permanent magnet wind driven generator is reduced.
Air gap flux density B g The formula (1) is shown as the formula (2);
in the formula (1-2), B g Is the air gap flux density, K m Is a permanent magnet pole face a m b m ，a m And b m Respectively the width and length (a) of the permanent magnet m <b m )，B r Is the remanence of permanent magnet, sigma is the leakage coefficient, delta is the length of air gap, and the thickness of magnetization direction is h m The permanent magnet end face coefficient of (1).
Magnetic density of stator tooth part B t The formula (1) is shown as the formula (3);
in the formula (1-3), B t Magnetic density of stator teeth, B δ Is air gap flux density, alpha p And (b) is an integer part b in the number of slots per phase per pole (q = b + c/d), wherein q is the number of slots per phase per pole, and c/d is fractional expression of a fractional part of q. Magnetic density of tooth part B t When the width of the stator tooth is larger than the allowable value, the width of the stator tooth needs to be determined again according to the width b of the stator slot s Relationship to tooth width t (b) s ＝t 1 -t)，t 1 For the generator stator pitch, the width b of the stator slot can be reduced s To optimize the magnetic density of the teeth.
Stator yoke magnetic density B j The formula (1) is shown as the formula (4);
in the formula (1-4), B j Magnetic density of stator yoke, B t B is an integer part b in the number of slots per pole per phase (q = b + c/d), t is the tooth width of the stator armature, and h is the tooth density j The generator stator is yoke high. To air gap magnetic density B g Stator tooth magnetic density B t And the statorPartial magnetic density B j By means of restriction, the magnetic circuit of the generator can be prevented from being saturated and heated due to overhigh magnetic density.
The calculation function expression of the stator linear load A is shown as the formula (1-5);
in the formula (1-5), A is the stator linear load, N s Number of conductors per slot, Q 1 Is the number of stator slots, I N A is the rated current of the generator, a is the number of parallel branches, D i1 Is the inner diameter of the stator core,
the calculation function expression of the heat load AJ is shown as the formula (1-6);
in the formula (1-6), AJ is a thermal load, N s Number of conductors per slot, Q 1 Number of stator slots, I N For generator rated current, j a Is the stator winding current density, a is the number of parallel branches, D i1 Is the stator core inner diameter. The thermal performance of the generator can be improved by constraining the stator line load a and the thermal load AJ.
The expression of the calculation function of the average temperature rise delta tau is shown as the formula (1-7);
in the formula (1-7), delta tau is the average temperature rise of the stator, and Delta tau Fe ,△τ Cu Average temperature rise, P, of armature winding copper and core, respectively Fe 、P Cut And P CuE The iron core loss, the effective part (namely the part in the slot) copper loss and the end part copper loss are respectively; a. The 0 Is the stator cylindrical cooling surface area, A CF The area of contact between the insulation and the iron core; l is t And L E As armature windingsX is the surface perimeter of the conductor together with the insulation; delta CF Is the total thickness of the insulation (including the air gap layer) between copper and iron CF To synthesize the thermal conductivity. Alpha and alpha E The stator cylindrical surface heat dissipation coefficient and the equivalent end heat dissipation coefficient can be determined according to similar motor temperature rise test results. Z is the number of grooves.
The calculation function expression of the bending and twisting stress F borne by the rotating shaft is shown as the formula (1-8);
in the formula (1-8), F represents the bending and twisting stress borne by the rotating shaft, M is the bending moment borne by the rotating shaft, T is the torque borne by the rotating shaft, W is the bending section coefficient of the rotating shaft, and F r Is the total radial force on the shaft, L a The length of the rotating shaft, P is the output power of the wind driven generator, and n is the output rotating speed of the wind driven generator. Wherein the total radial force F on the shaft r Comprises the self gravity G and the magnetic pull F of the rotor m Equal, magnetic pull force F m The functional expression of (a) is:
in the above formula, α p Is the polar arc coefficient, D i1 Is the inner diameter of the stator core, L t Is the active part of the armature winding, delta is the air gap length, B δ Is the magnetic density of air gap, mu 0 Is a vacuum permeability, e 0 The rotor is eccentric with respect to the stator.
The dangerous section of the bending-resistant section coefficient W of the rotating shaft is taken at the middle part of the rotating shaft, and the function expression is as follows:
in the above formula, d is the outer diameter of the rotating shaft.
When the bending and twisting stress F borne by the rotating shaft is calculated, the allowable stress is set to be F 0 ＝δ s N, where n is the safety factor, delta s And considering the instantaneous overload multiple K during yield checking calculation as the yield limit of the rotating shaft material.
Tank fullness rate S f The formula (1) is shown as (9);
in the formula (1-9), S f To the tank fullness, S L Area for each slot to accommodate an insulated conductor, S ef The effective area of the groove, the groove fullness S f I.e. the area S per slot that can accommodate an insulated conductor L Effective area S of groove ef It is provided with and (4) a ratio. In the optimization calculation process, the slot full rate is necessarily designed to be high, but the motor is difficult to manufacture due to the excessively high slot full rate, so that the slot full rate S needs to be calculated f And (5) carrying out constraint.
In the embodiment, the method of linear weighting is adopted to optimize the global efficiency of the large permanent magnet wind power generator with the aim of the highest weighted average efficiency (or the smallest weighted average power loss) in a plurality of working states. Thus, the problem is converted into that X = [ D ] i1 ,L t ,d,L a ,N s ,h s ,b s ,a m ,h m ,α p ] T And (3) leading the following components:
or
In the above formula, the first and second carbon atoms are,weighted average efficiency for each operating state;weighted average power loss for each operating state; i is in a working state, and i is more than or equal to 3; w is a group of i A weight factor for each operating state; eta i (X) efficiency for the ith operating state; p is a radical of i (X) is the power loss in the ith operating state. Therefore, in the step 2), a mathematical model is established based on the system design parameters as shown in the formula (2);
in the formula (2), X is a matrix formed by the system design parameters to be solved, D is the total number of the system design parameters to be solved, eta i (X) is the weighted average efficiency, p, of the ith operating state i (X) is the weighted average power loss of the ith working state, i is each working state, i is not less than 3 j And (X) is a jth constraint condition, and comprises constraints in four aspects of electricity, magnetism, heat and force.
The simulated annealing algorithm is a process of solving a target minimum value, the convergence rate is high, and the generated solution is a global optimal solution. Since the problem of overall efficiency optimization design of large permanent magnet wind driven generator is a maximum value problem, the large permanent magnet wind driven generator needs to be converted into a large permanent magnet wind driven generatorAnd (6) solving. As shown in fig. 3, the detailed steps of step 3) include:
3.1 Based on the nominal state, using simulated annealing algorithm to randomly generate an initial solution x for the design mathematical model 0 And x is 0 As the current optimal solution, calculating an objective function based on the current optimal solution x 0 Objective function value ofSetting the initial temperature T of the simulated annealing algorithm 0 1.0, the value of the number k of temperature changes is initially set to 1 and a maximum number of iterations L is given k ；
3.2 Random perturbation of the current optimal solution based on simulated annealingThe algorithm generates a new solution x for the iteration j k+1 Calculating a new solution x for the objective function based on the iteration j k+1 New value of objective function ofCalculating a new value of the objective functionAnd the objective function value of the last iterationThe difference between them as an increment of the objective function value
3.3 Determine the increment of the objective function valueIf the value is less than 0, accepting the newly generated optimal solution as the current optimal solution; otherwise, the probability P is calculated according to the Metropolis criterion, and a probability value of [0,1] is randomly generated]If the probability P is greater than the random number zeta, a newly generated optimal solution is a current optimal solution, otherwise, the newly generated optimal solution is abandoned to be accepted as the current optimal solution;
3.4 Add 1 to the number k of temperature changes to determine whether the number k of temperature changes is equal to the maximum number of iterations L k If not equal to the maximum number of iterations L k If yes, skipping to execute the step 3.2); otherwise, skipping to execute the next step;
3.5 Judging whether a preset termination condition is met, wherein the preset termination condition means that N continuous new solutions are not accepted or the increment of the objective function value is less than a given value epsilon in a [0,1] interval, if the preset termination condition is not met, calculating the annealing temperature of the iteration, tempering every m received solutions, quenching, and skipping to execute the step 3.2); and if the preset termination condition is met, outputting the current solution as the optimal solution, and ending.
In this embodiment, step 3.2) generates a new solution x for this iteration j k+1 The functional expression of (a) is represented by the formula (3);
in formula (3), x j k+1 For the new solution of the current iteration,for the new solution of the last iteration, y j J is more than or equal to 1 and is less than or equal to D for the disturbance of the jth parameter to be solved, D is the total number of the design parameters of the system to be solved, B j For the maximum value, A, that the jth parameter to be solved may take j Is the minimum value possible to be taken by the jth parameter to be solved, c is the temperature control coefficient, T k Annealing temperature, T, for this iteration 0 To simulate the initial temperature of the annealing algorithm, k is the number of temperature changes, u j Is a random value between 0 and 1.
In this embodiment, step 3.3) calculates the function expression of the probability P according to Metropolis criterion as shown in formula (4);
in the formula (3), P is the calculated probability,in increments of the value of the objective function, T k The annealing temperature of the iteration.
In this embodiment, the functional expression for calculating the annealing temperature of the current iteration in step 3.5) is shown in formula (5);
T k ＝T 0 exp(-ck 1/D )(5)
in formula (4), T k For the annealing temperature of this iteration, T 0 To simulate the initial temperature of the annealing algorithm, c is a temperature control systemAnd D, the total number of the system design parameters to be solved.
In this embodiment, the step of performing tempering treatment every m receiving solutions in step 3.5) includes:
s1) calculating the gradient S of the jth variable in a matrix X formed by the system design parameters to be solved according to the formula (6) j ；
In the formula (6), s j Gradient of j-th variable in matrix X formed by system design parameters to be solved, X opt For the optimal solution found so far, δ is the step size, e j Is a vector of dimension D and the jth component is 1, the remainder are 0;
s2) judging the gradient S of the jth variable in a matrix X formed by the system design parameters to be solved j Is equal to a preset gradient threshold value s max If true, the annealing temperature T is adjusted according to equation (7) k Resetting the value of (a) and the value of the temperature change times k;
in formula (7), T' k Is' annealing temperature T k Reset value, s max Is a preset gradient threshold value, s j Gradient, T, of the j-th variable in a matrix X of design parameters for the system to be solved k The annealing temperature before resetting; k' is a value of the number of temperature changes k reset, T 0 In order to simulate the initial temperature of the annealing algorithm, c is a temperature control coefficient, and D is the total number of the system design parameters to be solved.
In this embodiment, the functional expression of the quenching treatment in step 3.5) is shown in formula (8);
in the formula (8), T k For the annealing temperature of this iteration, T 0 To simulate the initial temperature of the annealing algorithm, c is the temperature control coefficient, k is the number of temperature changes, q j For the quenching factor, D is the total number of system design parameters to be solved.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to those skilled in the art without departing from the principles of the present invention should also be considered as within the scope of the present invention.
Claims (10)
1. A parameter design method of a large permanent magnet wind driven generator based on an electromagnetic thermal coupling field is characterized by comprising the following implementation steps:
1) Determining system design parameters of a designed large permanent magnet wind driven generator;
2) Considering constraint conditions in the aspects of electricity, magnetism, heat and force, taking the weighted average efficiency or the weighted average power loss of a plurality of working states as an objective function, and establishing a mathematical model based on system design parameters;
3) Designing a group of large permanent magnet wind driven generator system parameters based on a rated state as an initial working state value; and based on the initial working state value, performing multi-working state iterative solution on the mathematical model by adopting a simulated annealing algorithm, obtaining a plurality of groups of solution results after iterative convergence, and selecting a group of optimal results as optimal system design parameters for output.
2. The method for designing parameters of large permanent magnet wind power generator based on electromagnetic thermal coupling field according to claim 1, wherein the system design parameters of the large permanent magnet wind power generator designed in step 1) comprise the inner diameter D of the stator core i1 Length L of stator core t Outer diameter d of rotating shaft and length L of rotating shaft a Number of conductors per slot N s Height h of stator slot s And slot width b s Width a of permanent magnet m And thickness h in the direction of magnetization m And the polar arc coefficient alpha p 。
3. The method for designing parameters of large permanent magnet wind driven generator based on electromagnetic thermal coupling field according to claim 1, wherein the constraints of electricity, magnetism, heat and force in step 2) comprise the terminal voltage U of permanent magnet wind driven generator and the air gap flux density B g Stator tooth magnetic density B t Stator yoke magnetic density B j Stator linear load A, thermal load AJ, average temperature rise delta tau, bending and twisting stress F borne by rotating shaft and slot filling rate S f The preset constraint conditions as shown in (1);
in the formula (1), g 1 (x)～g 9 (x) The U is the terminal voltage of the permanent magnet wind driven generator and the U is the preset nine constraint conditions dc Is a DC bus voltage of a converter, B g Is air gap flux density, B g0 Is a constraint value of air gap flux density, B t The stator tooth part is magnetic flux density B t0 A constraint value of the magnetic density of the stator teeth, B j Is the magnetic density of the stator yoke part B j0 Is the constraint value of the magnetic density of the stator yoke part, A is the stator linear load, A 0 Is a constraint value of linear load of the stator, AJ is thermal load, AJ 0 For the constraint value of the thermal load, Δ τ is the average temperature rise, Δ τ 0 Is the constraint value of average temperature rise, F is the bending and twisting stress borne by the rotating shaft, F 0 For the constraint value, S, of the bending-twisting stress to which the shaft is subjected f Is the slot fill factor, S f0 Is a constraint value of the slot full rate.
4. The parameter design method of the large permanent magnet wind power generator based on the electromagnetic thermal coupling field according to claim 1, characterized in that in step 2), a mathematical model is established based on system design parameters as shown in formula (2);
in the formula (2), X is a matrix formed by the system design parameters to be solved, D is the total number of the system design parameters to be solved, eta i (X) weighted average efficiency, p, for the ith operating state i (X) is the weighted average power loss of the ith working state, i is each working state, i is not less than 3 j (X) is the jth constraint.
5. The parameter design method for the large permanent magnet wind generator based on the electromagnetic thermal coupling field according to claim 1, wherein the detailed steps of step 3) comprise:
3.1 Based on the nominal state, using simulated annealing algorithm to randomly generate an initial solution x for the design mathematical model 0 And x is combined 0 As the current optimal solution, calculating an objective function based on the current optimal solution x 0 Objective function value ofSetting the initial temperature T of the simulated annealing algorithm 0 1.0, the value of the number k of temperature changes is initially set to 1 and a maximum number of iterations L is given k ；
3.2 ) randomly perturbing the current optimal solution, and generating a new solution x of the iteration based on a simulated annealing algorithm j k+1 Calculating a new solution x for the objective function based on the iteration j k+1 New value of objective function ofCalculating a new value of the objective functionAnd the objective function value of the last iterationThe difference between them as the increment of the objective function value
3.3 Determine the increment of the objective function valueIf the value is less than 0, accepting the newly generated optimal solution as the current optimal solution; otherwise, calculate the probability P with Metropolis criterion, randomly generating a probability P in [0,1]If the probability P is greater than the random number zeta, a newly generated optimal solution is a current optimal solution, otherwise, the newly generated optimal solution is abandoned to be accepted as the current optimal solution;
3.4 Add 1 to the number k of temperature changes to determine whether the number k of temperature changes is equal to the maximum number of iterations L k If not equal to the maximum number of iterations L k If yes, skipping to execute the step 3.2); otherwise, skipping to execute the next step;
3.5 Judging whether a preset termination condition is met, wherein the preset termination condition means that N continuous new solutions are not accepted or the increment of the objective function value is less than a given value epsilon in a [0,1] interval, if the preset termination condition is not met, calculating the annealing temperature of the iteration, tempering every m received solutions, quenching, and skipping to execute the step 3.2); and if the preset termination condition is met, outputting the current solution as the optimal solution, and ending.
6. The parameter design method for the large permanent magnet wind driven generator based on the electromagnetic thermal coupling field according to claim 5, characterized in that step 3.2) generates a new solution x of the iteration j k+1 The functional expression of (a) is represented by the formula (3);
in formula (3), x j k+1 For the new solution of the current iteration,new solution for last iteration, y j J is more than or equal to 1 and less than or equal to D for the disturbance of the jth parameter to be solved, D is the total number of design parameters of the system to be solved, B j For the maximum value, A, that the jth parameter to be solved may take j Is the minimum value possible to be taken by the jth parameter to be solved, c is the temperature control coefficient, T k Annealing temperature, T, for this iteration 0 To simulate the initial temperature of the annealing algorithm, k is the number of temperature changes, u j Is a random value between 0 and 1.
7. The parameter design method for the large permanent magnet wind turbine based on the electromagnetic thermal coupling field as claimed in claim 5, wherein step 3.3) is to calculate the functional expression of the probability P according to Metropolis criterion as shown in formula (4);
in the formula (3), P is the calculated probability,in increments of the value of the objective function, T k The annealing temperature of this iteration.
8. The parameter design method of the large permanent magnet wind driven generator based on the electromagnetic thermal coupling field according to claim 5, characterized in that the functional expression of the annealing temperature of the iteration calculated in step 3.5) is as shown in formula (5);
T k ＝T 0 exp(-ck 1/D ) (5)
in the formula (4), T k For the annealing temperature of this iteration, T 0 In order to simulate the initial temperature of the annealing algorithm, c is the temperature control coefficient, k is the temperature change times, and D is the system to be solvedThe total number of design parameters.
9. The parameter design method for the large permanent magnet wind power generator based on the electromagnetic thermal coupling field according to claim 5, wherein the step of tempering every m receiving solutions in the step 3.5) comprises:
s1) calculating the gradient S of the jth variable in a matrix X formed by the system design parameters to be solved according to the formula (6) j ；
In formula (6), s j Gradient of j-th variable in matrix X formed by system design parameters to be solved, X opt For the optimal solution found so far, δ is the step size, e j Is a vector of dimension D and the jth component is 1, the remainder are 0;
s2) judging the gradient S of the jth variable in a matrix X formed by the system design parameters to be solved j Equal to a preset gradient threshold s max If true, the annealing temperature T is adjusted according to equation (7) k Resetting the value of (a) and the value of the temperature change times k;
in formula (7), T' k′ Is an annealing temperature T k Reset value, s max Is a preset gradient threshold value, s j Gradient, T, of the j-th variable in a matrix X of design parameters for the system to be solved k The annealing temperature before resetting; k' is a value of the number of temperature changes k reset, T 0 In order to simulate the initial temperature of the annealing algorithm, c is a temperature control coefficient, and D is the total number of the system design parameters to be solved.
10. The parameter design method of the large permanent magnet wind driven generator based on the electromagnetic thermal coupling field according to claim 5, characterized in that the functional expression of the quenching treatment in the step 3.5) is as shown in formula (8);
in the formula (8), T k For the annealing temperature of this iteration, T 0 To simulate the initial temperature of the annealing algorithm, c is the temperature control coefficient, k is the number of temperature changes, q j For the quenching factor, D is the total number of system design parameters to be solved.
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| CN108223632A (en) * | 2018-01-29 | 2018-06-29 | 西安科技大学 | The detection method of temperature change in disk based on coal mining brake |
| CN109657325A (en) * | 2018-12-13 | 2019-04-19 | 西安航天动力研究所 | A kind of straight-through electromagnetic valve electromagnetic coil parameter determination method |
| CN110210086A (en) * | 2019-05-20 | 2019-09-06 | 上海电机系统节能工程技术研究中心有限公司 | A kind of motor electromagnetic design method based on probability analysis |
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| CN106295004A (en) * | 2016-08-12 | 2017-01-04 | 江苏大学 | A kind of Optimal Design Study of Permanent Magnet Motor method considering that disturbance type design variable is interval |
| CN106528948A (en) * | 2016-10-19 | 2017-03-22 | 江苏航天动力机电有限公司 | Teaching and learning algorithm-based calculation method for permanent magnet synchronous motor design |
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| CN108223632A (en) * | 2018-01-29 | 2018-06-29 | 西安科技大学 | The detection method of temperature change in disk based on coal mining brake |
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| CN110210086A (en) * | 2019-05-20 | 2019-09-06 | 上海电机系统节能工程技术研究中心有限公司 | A kind of motor electromagnetic design method based on probability analysis |
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