CN112953329A - Copper consumption minimum control system and method for non-salient pole type hybrid excitation motor - Google Patents

Copper consumption minimum control system and method for non-salient pole type hybrid excitation motor Download PDF

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CN112953329A
CN112953329A CN202110244792.7A CN202110244792A CN112953329A CN 112953329 A CN112953329 A CN 112953329A CN 202110244792 A CN202110244792 A CN 202110244792A CN 112953329 A CN112953329 A CN 112953329A
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value
torque
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CN112953329B (en
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杜怿
康柯柯
肖凤
朱孝勇
全力
张超
徐磊
张丽
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Jiangsu University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P21/0017—Model reference adaptation, e.g. MRAS or MRAC, useful for control or parameter estimation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H02P21/16—Estimation of constants, e.g. the rotor time constant
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H02P21/20—Estimation of torque
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
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    • Y02T10/64—Electric machine technologies in electromobility

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Abstract

The invention discloses a control system and a control method for minimizing copper consumption of a non-salient pole type hybrid excitation motor in the field of motor controlegGiven value of torque TegAs the input of the copper consumption minimum current distribution module, the q-axis current given value i is output after being processedqgWith given value of exciting current ifg(ii) a The torque observation module designed by adopting the active disturbance rejection control idea does not depend on a specific motor parameter model, can estimate the required electromagnetic torque only according to the error between the given rotating speed and the feedback rotating speed, takes the minimum copper consumption as the target, takes the given torque as the equality constraint condition, and adopts the sequential quadratic programming algorithm to distribute the problemsAnd an optimal solution is solved, the problem that the numerical value is unstable in the iteration process possibly occurs in the traditional Newton-Lagrange extreme value solving problem is avoided, and the purpose of distributing the minimum copper consumption current of the motor is realized.

Description

Copper consumption minimum control system and method for non-salient pole type hybrid excitation motor
Technical Field
The invention belongs to the field of motor control, and particularly relates to a control system and a control method for minimizing copper consumption of a non-salient pole type hybrid excitation motor, which are suitable for electric automobiles.
Background
The non-salient pole type hybrid excitation motor is a novel motor with high torque density and wide speed regulation range, and has wide application prospect in the field of electric automobiles. Because a set of excitation winding is introduced on the basis of the traditional permanent magnet synchronous motor, the non-salient pole type hybrid excitation motor has the problem that armature current and excitation current work cooperatively. In order to solve the problem, a hybrid excitation synchronous motor torque estimation method is proposed in chinese patent publication No. CN111697890A, and a given dq axis current and an excitation current are obtained by a table lookup method according to a given target torque, but the table lookup method does not relate to how to determine the given target torque, and the table lookup method depends on a large amount of simulation data. The document with the chinese patent publication No. CN105281632A proposes a current coordination control method for a hybrid excitation motor, which obtains multiple sets of data by calculating output mechanical power and mechanical rotation speed, where each set of data includes three variables, namely armature current, internal power factor angle, and excitation current, then calculates electromagnetic loss of each set of data, and selects one set with the smallest loss. Chinese patent publication No. CN103401506A proposes a method for controlling direct torque of a non-salient pole type hybrid excitation motor for an electric vehicle, which calculates a given torque according to a given rotation speed and a feedback rotation speed, but does not relate to how to calculate the given torque, because how to obtain the given torque is a crucial step as an equality constraint condition of current distribution, and the proposed control strategy does not consider the problem of copper consumption of the motor. Although the problem of motor loss is considered, the method for predicting and controlling the constant power loss model of the non-salient pole type hybrid excitation motor provided in the document with the Chinese patent publication number of CN108418485A has the defects of large calculated amount, complex calculation and unstable numerical value in the iterative solution process due to the adoption of the mathematical method for solving the extreme value by Newton-Lagrange.
Disclosure of Invention
The invention aims to provide a system and a method for controlling the minimum copper consumption of a non-salient pole type hybrid excitation motor in order to solve the problems that the given torque in the control strategy of the non-salient pole type hybrid excitation motor is unknown, the copper consumption and current distribution of the motor are not considered, and the algorithm is complex.
In order to achieve the purpose, the technical scheme of the minimum control system for the copper consumption of the non-salient pole type hybrid excitation motor is as follows: three-phase current i of non-salient pole type hybrid excitation motora、ib、icInputting the current feedback value into a first coordinate transformation module, and transforming the current feedback value into a dq-axis current feedback value i through coordinate transformationdf、iqfSpeed n of feedback line of non-salient pole type mixed excitation motorfInput into a speed conversion module to be converted into a feedback rotating speed wfFeedback of the rotational speed wfAs a first input to the torque observer module, a given rotational speed wgAs a second input of the torque monitoring module, the torque monitoring module outputs a given torque value TegGiven value of torque TegAs the input of the copper consumption minimum current distribution module, the q-axis current given value i is output after being processedqgWith given value of exciting current ifg(ii) a The given value of q-axis current iqgAnd q-axis current feedback value iqfThe q-axis current difference obtained by difference is input into a first PI regulating module, and q-axis given voltage u is obtained through PI regulationqg(ii) a The d-axis current given value iqgAnd d-axis current feedback value idfD-axis current difference obtained by difference is input into a second PI regulating module and is processed by PI regulating to obtain d-axis given voltage udg(ii) a The d-axis given voltage udgAnd q-axis given voltage uqgThe output end of the second coordinate transformation module is sequentially connected with the SVPWM modulation module, the armature circuit power transformation module and the non-salient pole type hybrid excitation motor in series; output exciting current feedback value i of non-salient pole type hybrid excitation motorffFeedback value of exciting current iffWith said given value of exciting current ifgThe difference value of the exciting current obtained by difference is input into a third PI regulating module, and the given value u of the exciting voltage is obtained by PI regulationfgGiven value u of excitation voltagefgThe PWM modulation module generates PWM wave as the input of the excitation circuit power conversion module, and the excitation circuit power conversion module outputs excitation voltage ufA non-salient pole type hybrid excitation motor is provided.
Furthermore, the torque observation module consists of a linear control rate module, a state observer module and a limiting module, and the given rotating speed wgWith feedback speed wfIs estimated value ZT1Difference of rotation speed eTThe total system disturbance estimated value Z output by the state observer module is used as the first input of the linear control rate moduleT2As a second input to the linear control rate module, the linear control rate module outputs an initial estimate of the given torque Teg0(ii) a The feedback rotating speed wfAnd given initial torque estimate Teg0As two inputs to the state observer module, the state observer module output wfIs estimated value ZT1And the total disturbance estimate Z of the systemT2(ii) a The amplitude limiting module is used for setting the initial estimated value T of the given torqueeg0Amplitude limiting to obtain the given torque value Teg。
Furthermore, the copper consumption minimum current distribution module consists of a copper consumption calculation module, a torque calculation module, a subproblem conversion module and a subproblem iteration module, wherein the input of the copper consumption calculation module is the nth iteration value i of the q-axis component of the armature current output by the subproblem iteration moduleqnAnd the nth iteration value i of the exciting currentfnThe output is the copper loss P of the nth iterationcu(in) The nth iteration value i of the q-axis component of the armature currentqnAnd the nth iteration value i of the exciting currentfnAs two inputs to a torque calculation module, said given torque value TegAs a third input to the torque calculation module, the torque calculation module outputs the torque T of the nth iterationeg(in) (ii) a The copper loss Pcu(in) And torque Teg(in) As two inputs to the subproblem transformation module, the subproblem transformation module transforms the sequence quadratic programming subproblem Q3And input into a sub-problem iteration module which performs sub-problem Q3Solving and outputting the optimal solution as (i)qg,ifg)。
Furthermore, the subproblem conversion module is formed by connecting an equality constraint optimization problem conversion module, a Newton-Lagrange extreme value problem conversion module and a quadratic programming problem conversion module in series in sequence, and the equality constraint problem module uses copper consumption Pcu(in) As an objective function, with a torque Teg(in) As an input to the equality constraint condition, when the torque T is satisfiedeg(in) Under the condition of calculating copper consumption Pcu(in) The problem of minima is converted into an optimization problem Q that takes into account pure equality constraints1Optimization problem Q1Inputting the data into a Newton-Lagrange extremum problem conversion module, and enabling the Newton-Lagrange extremum problem conversion module to optimize a problem Q1Conversion to newton-lagrange extremum problem Q2And inputting the data into a quadratic programming problem conversion module which solves the extreme value problem Q of Newton-Lagrange2Convert to said sequence quadratic programming subproblem Q3
The technical scheme of the control method of the hidden pole type hybrid excitation motor copper consumption minimum control system comprises the following steps:
step 1: the state observer module is designed to expand a state observer into
Figure BDA0002963697860000031
e is ZT1And wfAn error of (2); beta is aT1、βT2Is the gain coefficient of the state observer, b is the compensation coefficient;
step 2: the discretized extended state observer is
Figure BDA0002963697860000032
h is the sampling period, wf(k) Is the feedback speed at time k, ZT1(k) Is an estimate of the feedback speed at time k, ZT1(k +1) is the feedback speed estimate at time k +1, ZT2(k) Is the total disturbance estimate of the system at time k, e (k) is time k, ZT1(k) And wf(k) Error of (1), Teg0(k) Is an initial estimate of the torque given at time k, ZT2(k +1) is the total disturbance estimate at time k +1, ZT1(k +1) as the feedback rotation speed wfState estimator ZT1Output, ZT2(k +1) as the total disturbance estimate Z of the systemT2Outputting;
and step 3: the amplitude limiting module initially estimates the value T for a given torqueeg0Comparing and judging when the electromagnetic torque estimated value Teg0In the motor load range [ T ]min，Tmax]Internal time, Teg＝Teg0(ii) a When T iseg0≥Tmax，Teg＝Tmax(ii) a When T iseg0≤Tmin，Teg＝Tmin，TmaxIs the limit value of the carrying capacity T when the motor rotates forwardsminIs the limit value of the carrying capacity when the motor rotates reversely.
The technical scheme of the control method of the hidden pole type hybrid excitation motor copper consumption minimum control system comprises the following steps:
step 1): equality constraint problem model copper consumption Pcu(in) As an objective function, with a torque Teg(in) As an equality constraint, an optimization problem Q is obtained1Comprises the following steps:
Figure BDA0002963697860000033
step 2): Newton-Lagrange extreme value problem conversion module simplified optimization problem Q1To obtain the bestProblem Q1Lagrange function L (i)n,un)＝Pcu(in)-unHeq(in)，unIs the nth iteration Lagrangian multiplier, Heq(in) Is a torque-dependent constraint function;
step 3): solving the copper loss Pcu(in) Gradient of (2)
Figure BDA0002963697860000041
And second derivative
Figure BDA0002963697860000042
And a constraint function Heq(in) Gradient of (2)
Figure BDA0002963697860000043
And second derivative
Figure BDA0002963697860000044
Thereby obtaining the Lagrangian function L (i)n,un) The first and second derivatives of the first and second,
step 4): given iteration point Zn＝(in,un) Solving the problem of extreme value Q of Newton-Lagrange2Is composed of
Figure BDA0002963697860000045
The iteration points satisfy: zn+1(in+1,un+1)＝Zn(in,un)+Pn(dn,vn)，dnRepresents the current inMicro increment of vnRepresenting lagrange multipliers unIs measured in increments of (a).
The invention has the beneficial effects that:
1) the torque observation module designed by adopting the active disturbance rejection control idea can estimate the required electromagnetic torque only according to the error between the given rotating speed and the feedback rotating speed without depending on a specific motor parameter model.
2) The method takes the minimum copper loss as a target, takes the given torque as an equality constraint condition, adopts a sequential quadratic programming algorithm to calculate the optimal solution of the q-axis current and the exciting current, and effectively realizes the purpose of the minimum copper loss current distribution of the motor.
3) The method adopts the sequential quadratic programming algorithm to solve the current distribution problem, effectively avoids the situation that the numerical value is unstable in the iteration process in the traditional Newton-Lagrange extremum solving problem, and reduces the iteration times and the calculated amount due to higher convergence speed in the iteration process compared with the Newton-Lagrange extremum solving algorithm.
3) The PI regulator of the rotating speed ring is replaced by the torque observation module, so that the given electromagnetic torque can be estimated, and the total disturbance Z of the system can be estimated by the state observerT2Therefore, the anti-interference capability of the system is improved, and the robustness of the system is enhanced.
4) The torque observation method based on the active disturbance rejection control technology and the current distribution method based on the sequence quadratic programming are simultaneously applied to the non-salient pole type hybrid excitation motor control strategy, and the defects of fuzzy torque observation and unstable current distribution iteration process in the traditional non-salient pole type hybrid excitation motor control scheme are overcome.
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In order that the invention may be more fully understood, reference is now made to the following detailed description of the invention taken in conjunction with the accompanying drawings in which:
fig. 1 is a structural block diagram of a minimum copper consumption control system of a non-salient pole type hybrid excitation motor according to the invention;
FIG. 2 is a block diagram of the torque observation module 4 in FIG. 1;
fig. 3 is a block diagram of the minimum copper current distribution module 10 in fig. 1;
fig. 4 is a block diagram of the sub-problem conversion module 7 in fig. 3;
FIG. 5 is a flow chart of an algorithm implementation of the subproblem iteration module 8 of FIG. 3;
in the figure: 1. a linear control rate module; 2. a state observer module; 3. an amplitude limiting module; 4. a torque observation module; 5. a copper consumption calculation module; 6. a torque calculation module; 7. a sub-problem conversion module; 8. a sub-problem iteration module; 9. a sequence quadratic programming module; 10. a copper consumption minimum current distribution module; 11. an equality constraint optimization problem conversion module; 12. a Newton-Lagrange extreme value problem conversion module; 13. a quadratic programming problem conversion module; 14. an armature circuit power conversion module; 15. an excitation circuit power conversion module; a PWM modulation module; 17. a speed conversion module; 18. a first PI regulation module; 19. a second PI regulation module; 20. a first coordinate transformation module; 21. a non-salient pole type hybrid excitation motor; 22. a second coordinate transformation module; an SVPWM modulation module; 24. and the third PI regulation module.
Detailed Description
Referring to fig. 1, the minimum copper loss control system for a non-salient pole type hybrid excitation motor of the present invention is connected to a non-salient pole type hybrid excitation motor 21, and includes a PI regulation module, a coordinate transformation module, a speed conversion module 17, an SVPWM modulation module 23, an armature circuit power transformation module 14, and an excitation circuit power transformation module 15.
Feedback line speed n of non-salient pole type hybrid excitation motor 21fInput into a speed conversion module 17, and converted into a feedback rotating speed w by the speed conversion module 17fFeedback of the rotational speed wfAs a first input to the torque observer module 4, a given rotational speed wgThe output end of the torque observation module 4 is connected with a copper consumption minimum current distribution module 10 as a second input of the torque observation module 4, and the torque observation module 4 outputs a given torque value Teg. Given torque value TegUsed as the input of the copper consumption minimum current distribution module 10, and processed to output the given value i of the q-axis currentqgWith given value of exciting current ifg。
Three-phase current i for detecting non-salient pole type hybrid excitation motor 21a、ib、icAnd apply three-phase current ia、ib、icInput into a first coordinate transformation module 20, and transformed into dq-axis current feedback value i through coordinate transformationdf、iqf。
Setting q-axis current to be a given value iqgAnd q-axis current feedback value iqfThe difference is made and the resulting q-axis current difference is input to the first PI regulation module 18. Obtaining a given voltage u of a q axis through PI regulationqg。
D-axis current is set to a given value iqgAnd d-axis current feedback value idfThe difference is made, the obtained d-axis current difference value is input into a second PI regulation module 19, and d-axis given voltage u is obtained through PI regulationdg. Because the motor is a non-salient pole motor, the influence of d-axis current is ignored, so i is adoptedqgControl strategy of 0.
The output ends of the first PI regulation module 18 and the second PI regulation module 19 are connected with the input end of the second coordinate transformation module 22, and dq-axis given voltage udg、uqgInputting into a second coordinate transformation module 22, transforming into α β axis voltage uα、uβ. The output end of the second coordinate transformation module 22 is sequentially connected in series with the SVPWM modulation module 23, the armature circuit power transformation module 14 and the non-salient pole type hybrid excitation motor 21. Alpha beta axis voltage uα、uβThe input SVPWM modulation module 23, the SVPWM modulation module 23 outputs SVPWM wave as the input signal of the armature circuit power conversion module 14, the armature circuit power conversion module 14 outputs three-phase armature voltage ua、ub、ucThe non-salient pole type hybrid excitation motor 21 is driven.
Non-salient pole type hybrid excitation motor 21 outputs excitation current feedback value iffGiven value i of exciting current output by minimum current distribution module 10fgWith the excitation current feedback value iffMaking difference, inputting the obtained exciting current difference value into a third PI regulation module 24, and obtaining an exciting voltage given value u through PI regulationfg. The output end of the third PI regulating module 24 is sequentially connected with the PWM modulating module 16, the exciting circuit power conversion module 15 and the non-salient pole type hybrid excitation motor, and the given value u of the exciting voltagefgThe PWM wave is inputted to a PWM modulation module 16, the generated PWM wave is inputted to an exciting circuit power conversion module 15, and the exciting circuit power conversion module 15 outputs an exciting voltage ufThe magnetic field of the non-salient pole type hybrid excitation motor 21 is modulated.
Referring to fig. 2, based on the active disturbance rejection control technology, the torque observation module 4 is composed of a linear control rate module 1, a state observer module 2, and a limiting module 3. Given rotational speed wgAnd state observer module2 feedback speed wfIs estimated value ZT1Making difference to obtain rotation speed difference eT＝wg-ZT1As a first input to the linear control rate module 1, the estimate Z of the total disturbance of the system output by the state observer module 2T2As a second input to the linear control rate module 1, the linear control rate module 1 provides an initial estimate T of torqueeg0Outputting as a compensation amount:
Teg0＝kpeT+zT2 (1)
wherein k ispIs a proportional gain coefficient, obtained by the state observer module 2.
Feedback speed wfAs a first input to the state observer module 2, an initial estimate of torque T is giveneg0As a second input to the state observer module 2, the state observer module 2 outputs wfIs estimated value ZT1And the total disturbance estimate Z of the systemT2. The control process of the state observer module 2 is as follows:
for the convenience of system design, the mechanical motion equation (2) of the non-salient pole type hybrid excitation motor 1 is rewritten into a first-order system expression (3):
Figure BDA0002963697860000061
Figure BDA0002963697860000062
in formulae (2) to (3), TLIs the load torque; j is the moment of inertia; b is the coefficient of friction.
For the first order system shown in equation (3), -B ωfthe/J is regarded as internal disturbance; -TLtreating/J as external disturbance; - (B omega)f+TL) the/J is regarded as the total disturbance; can expand to a new state x2＝-(Bωf+TL) J; the original system formula (3) is changed into:
Figure BDA0002963697860000063
in the formula, x2Is a new state, considered as total disturbance; y is the output of the system expressed by equation (4) and is equal to the feedback rotation speed wf。
For the system represented by equation (4), the following extended state observer is designed:
Figure BDA0002963697860000071
in which e is ZT1And wfAn error of (2); beta is aT1、βT2Is the gain coefficient of the state observer, related to the observer system bandwidth; and b is a compensation coefficient and is related to the controlled object model.
To facilitate the program implementation of the algorithm, equation (5) needs to be discretized, and the corresponding discrete form is:
Figure BDA0002963697860000072
in the formulas (5) to (6), h is a sampling period; after discretization, wf(k) The rotating speed is fed back at the moment k; zT1(k) Is an estimated value of the feedback rotating speed at the k moment; zT1(k +1) is a feedback rotation speed estimation value at the k +1 moment; zT2(k) Is the total disturbance estimated value of the system at the moment k; e (k) is time kT1(k) And wf(k) An error of (2); t iseg0(k) Is an initial estimation value of the given torque at the moment k; zT2(k +1) is the total disturbance estimate at time k + 1; final ZT1(k +1) as the feedback rotation speed wfState estimator ZT1Output, ZT2(k +1) as the total disturbance estimate Z of the systemT2And (6) outputting.
Clipping module 3 sets an initial estimate T for a given torqueeg0Carrying out amplitude limiting treatment to obtain a final torque given value Teg. Initial estimate T for preventing given torqueeg0Out of the range of motor loadmin，Tmax]It needs to be subjected to clipping processing. It is composed ofIn, TmaxIndicating a limit value of the load capacity, T, of the motor in forward rotationminIndicating the load capacity limit when the motor is rotating in reverse. Giving an initial estimate T of torqueeg0Inputting the torque into the amplitude limiting module 3, and outputting the final torque set value T by the amplitude limiting module 3eg：
Figure BDA0002963697860000073
Initial estimation value T of given torqueeg0After the comparison and judgment of the amplitude limiting module 3, the final torque set value T is outputeg. When electromagnetic torque estimated value Teg0In the motor load range, Teg＝Teg0(ii) a When T iseg0≥Tmax，Teg＝Tmax(ii) a When T iseg0≤Tmin，Teg＝Tmin. Thus, the torque module 4 can effectively be based on a given rotational speed wgWith feedback speed wfGiven value of output torque Teg。
Referring to fig. 3, the copper consumption minimum current distribution module 10 is composed of a copper consumption calculation module 5, a torque calculation module 6 and a sequence quadratic programming module 9, and the sequence quadratic programming module 9 is composed of a sub-problem conversion module 7 and a sub-problem iteration module 8 which are connected in series. Wherein, the copper consumption calculation module 5 inputs the nth iteration value i of the q-axis component of the armature current fed back by the output of the subproblem iteration module 8qnAnd the nth iteration value i of the exciting currentfnThe output is the copper loss P of the nth iterationcu(in)：
Figure BDA0002963697860000074
In the formula, RsIs the armature winding resistance; rfIs the excitation winding resistance; i.e. in＝(iqn,ifn) Wherein iqnIs the nth iteration value of the q-axis component of the armature current; i.e. ifnIs the nth iteration value of the exciting current; pcu(in) Is the copper loss for the nth iteration.
Copper loss Pcu(in) As an objective function into the sub-problem transformation module 7 as a first input to the sub-problem transformation module 7.
Meanwhile, the nth iteration value i of the q-axis component of the armature currentqnAnd the nth iteration value i of the exciting currentfnAs a first and a second input of the torque calculation module 6, a given torque value TegAs a third input of the torque calculation module 6, the torque T of the nth iteration is calculatedeg(in) Is that;
Figure BDA0002963697860000081
in the formula, P is the number of poles of the motor rotor;
Figure BDA0002963697860000082
indicating the permanent magnet flux linkage of the motor, LfIs the field winding inductance value.
Torque Teg(in) Is input into the sub-problem transformation module 7 as a second input to the sub-problem transformation module 7.
The subproblem conversion module 7 receives the copper consumption Pcu(in) And torque Teg(in) Converting the copper consumption minimum current distribution problem into a sequence quadratic programming subproblem Q3Sequence quadratic programming subproblem Q3The input is input into a subproblem iteration module 8, and the subproblem iteration module 8 processes the subproblem Q3Iterative solution is carried out until the optimal solution (i) which meets the condition of minimum copper consumption is foundqn,ifn) And assigns it as the final given current value to (i)qg,ifg)，iqgAs a given value of q-axis current, ifgAs excitation current setpoint, optimal solution (i)qn,ifn) Output by the copper consumption minimum current distribution module 10.
Referring to fig. 4, the sub-problem transformation module 7 is formed by sequentially connecting an equality constraint optimization problem transformation module 11, a newton-lagrangian extreme value problem transformation module 12, and a quadratic programming problem transformation module 13 in series. Wherein the equation constraint problem module11 is the copper consumption Pcu(in) As an objective function, with a torque Teg(in) As a constraint of equation, the torque T will be satisfiedeg(in) Under the condition of calculating copper consumption Pcu(in) The problem of minima is converted into an optimization problem Q that takes into account pure equality constraints1Optimization problem Q1The expression is as follows:
Figure BDA0002963697860000083
in the formula, min represents the minimum value, and st represents the limit; expression (10) shows the satisfaction of Teg(in)-TegCopper loss P of 0 constraintcu(in) Minimum value of (1), i.e. finding the copper consumption Pcu(in) Current sharing at minimum. Therefore, i output by the minimum copper consumption current distribution module 10 in the inventionqgAnd ifgNot only can satisfy TegThe optimal solution for minimizing the copper consumption of the motor is required.
Because the constraint condition T is satisfiedeg(in)-TegCurrent (i) of 0qn,ifn) There are many combinations, so the invention uses the sequence quadratic programming algorithm to solve a group (i)qg,ifg) So that the current distribution satisfies both T in the formula (10)eg(in)-TegWhen the torque requirement is 0, the formula (9) can be used
Figure BDA0002963697860000084
The copper consumption is minimum, so that the minimum control of the copper consumption of the non-salient pole type hybrid excitation motor 1 is realized.
Optimization problem Q1Inputting the data into a Newton-Lagrange extremum problem transformation module 12, the Newton-Lagrange extremum problem transformation module 12 will optimize the problem Q1Conversion to newton-lagrange extremum problem Q2. Newton-lagrange extremum problem Q2Inputting the input into a quadratic programming problem conversion module 13, the quadratic programming problem conversion module 13 solving the extreme value problem Q of Newton-Lagrange2Conversion to sequential quadratic programming subproblem Q3。Q2To Q3The specific implementation method comprises the following steps:
step 1: newton-lagrange extreme problem transformation module 12 simplifies problem Q11.5RSNotation C1、RfNotation C2、
Figure BDA0002963697860000091
Notation C3、1.5PLfNotation C4Equation (10) is briefly described as follows:
Figure BDA0002963697860000092
step 2: lagrangian function L (i) for writing problem (11)qn,ifnU), the function is as follows:
Figure BDA0002963697860000093
in the formula unIs the nth iteration lagrange multiplier; to facilitate problem analysis, equation (12) can be simplified as:
L(in,un)＝Pcu(in)-unHeq(in) (13)
wherein:
Heq(in)＝C3iqn+C4ifniqn-Teg (14)
in the formula, Heq(in) Representing a constraint function related to torque.
And step 3: consuming copper by Pcu(in) As an objective function, solving the gradient of the objective function is:
Figure BDA0002963697860000094
in the formula (I), the compound is shown in the specification,
Figure BDA0002963697860000095
represents Pcu(in) A gradient of (a); target function Pcu(i) Are respectively corresponding to the gradient of iqAnd ifThe derivation is carried out, and the derivation is carried out,
Figure BDA0002963697860000096
representing the target function Pcu(iqn) To iqnThe first-order derivation is carried out,
Figure BDA0002963697860000097
representing the target function Pcu(iqn) To ifnA first derivative is performed.
Solving an objective function Pcu(in) Hesse array of (c):
Figure BDA0002963697860000098
in the formula (I), the compound is shown in the specification,
Figure BDA0002963697860000099
represents Pcu(in) The second derivative of (a) is,
Figure BDA00029636978600000910
represents Pcu(in) To iqnThe second derivation is carried out to obtain the second derivative,
Figure BDA00029636978600000911
represents Pcu(in) First to iqnDerivative and then pair ifnThe derivation is carried out by the derivation,
Figure BDA00029636978600000912
represents Pcu(in) To ifnThe second derivation is carried out to obtain the second derivative,
Figure BDA00029636978600000913
represents Pcu(in) First to ifnDerivative and then pair iqnAnd (6) derivation.
Solving a constraint function Heq(in) Gradient (2):
Figure BDA00029636978600000914
in the formula (I), the compound is shown in the specification,
Figure BDA00029636978600000915
representing the first derivative of the constraint function, again for i separatelyqnAnd ifnThe derivation is carried out, and the derivation is carried out,
Figure BDA00029636978600000916
representing a constraint function HeqTo iqnThe derivation is carried out, and the derivation is carried out,
Figure BDA0002963697860000101
representing a constraint function HeqTo ifnAnd (6) carrying out derivation.
Solving a constraint function Heq(inHesse matrix of (1):
Figure BDA0002963697860000102
in the formula (I), the compound is shown in the specification,
Figure BDA0002963697860000103
represents Heq(in) The second derivative of (a) is,
Figure BDA0002963697860000104
represents Heq(in) To iqnThe second derivation is carried out to obtain the second derivative,
Figure BDA0002963697860000105
represents Heq(in) First to iqnDerivative and then pair ifnThe derivation is carried out by the derivation,
Figure BDA0002963697860000106
represents Heq(in) To ifnThe second derivation is carried out to obtain the second derivative,
Figure BDA0002963697860000107
represents Heq(in) First to ifnDerivative and then pair iqnAnd (6) derivation.
Solving the Lagrangian function L (i)nU) Jacobi matrix:
Figure BDA0002963697860000108
in the formula (I), the compound is shown in the specification,
Figure BDA0002963697860000109
representing the Lagrangian function L (i, u)n) The first derivative of (a) needs to be respectively applied to iqn、ifn、unThree variables are derived. Wherein
Figure BDA00029636978600001010
Represents the Larsian function L (i)n,un) For current inMaking a derivative including L (i)nV) pairs of iqnConducting derivation, L (i)n,un) To ifnAnd (6) carrying out derivation.
Figure BDA00029636978600001011
Represents the Langi function L (i)n,un) The lagrange multiplier u is derived.
Solving the Lagrangian function L (i, u)n) Hesse matrix of (1):
Figure BDA00029636978600001012
wherein:
Figure BDA00029636978600001013
in the formula (I), the compound is shown in the specification,
Figure BDA00029636978600001014
representing the Lagrangian function L (i)n,un) The second derivative of (a); w (i)n,un) Representing the Lagrangian function L (i)n,un) For current inIncluding the objective function Pcu(in) Second derivative of (2)
Figure BDA00029636978600001015
And second derivative of the constraint function
Figure BDA00029636978600001016
To represent
Figure BDA00029636978600001017
Is transferred to
And step 3: combining equations (19) and (20), and giving an iteration point Zn＝(in,un) Solving the problem of extreme value Q of Newton-Lagrange2The following were used:
Figure BDA00029636978600001018
and the iteration points satisfy:
Zn+1(in+1,un+1)＝Zn(in,un)+Pn(dn,vn) (23)
in the formula (d)nRepresents the current inMicro increment of vnRepresenting lagrange multipliers unIs measured in increments of (a).
Problem of extreme value Q due to Newton-Lagrange2The values are not stable during the iteration process, so Q needs to be further adjusted2Conversion to strict convex quadratic programming problem Q3The implementation process of the quadratic programming problem conversion module 13 is as follows:
step 1: using positive definite matrix Bn(in,un) In place of Zn(in,un) Wherein the parameter τ>0 and less than a set value, Bn(in,un) Satisfies the following conditions:
Figure BDA0002963697860000111
step 2: by bringing formula (24) into formula (22):
Figure BDA0002963697860000112
in the formula, tnSatisfies the following conditions:
Figure BDA0002963697860000113
an essential condition for the establishment of the formula (26) is dnSatisfies the following conditions:
Figure BDA0002963697860000114
the equation described by equation (27) is the quadratic programming problem Q3Wherein q isn(dn) Is about the current increment dnAs a function of (c). The sub-problem Q3 is to find d satisfying the torque constraintkIs measured. If d iskIs small enough and smaller than the set value, meets the precision requirement, and then (i) corresponding to the precision requirement is obtainedqn,ifn) Is an optimal solution that satisfies both the torque Teg(in) The requirement can also ensure that the copper consumption of the motor is Pcu(in) And if not, re-iterating according to the flow shown in fig. 4 until an optimal solution is found.
Quadratic programming problem Q3The input is input into a subproblem iteration module 8, and the subproblem iteration module 8 represents a quadratic programming problem Q according to formula (27)3The calculation is performed, as shown in fig. 5, with the following specific steps:
step 1: given an initialValue i0、u0τ, ρ, γ, ε wherein i0＝(iq0,if0) For the initial set value of the current, and the other parameters are parameters used in the subsequent iteration process, wherein 0<ρ<1、0<γ<1、0<ε<<1、0<τ<<1。
Step 2: calculating P (i)n,un) If P (i)n,un)<And e, stopping calculation, and turning to the step 6, otherwise, turning to the step 3.
Wherein the penalty function P (i)n,un) Is composed of
Figure BDA0002963697860000115
And step 3: solving a two-sequence quadratic programming sub-problem Q3To obtain dn,dnSubstitution of formula (25) to tnOrder:
Figure BDA0002963697860000116
and 4, step 4: determine i descending search direction, let mnIs the smallest non-negative integer m that holds the following inequality:
P(in+ρmdn,un+pmvn)≤(1-γρm)P(in,un) (30)
and 5: put alphan＝ρmnLet in+1＝in+αndn，un+1＝un+αnνnAnd (5) making n equal to n +1, and turning to the step 1.
Step 6: output current (i)qn,ifn) The current is a current combination mode which satisfies the minimum copper loss, iqnGiven value i as armature current q-axis componentqgOutput, will ifnAs given component i of the excitation currentfgAnd (6) outputting.
Referring to fig. 1, when the non-salient pole type hybrid excitation motor 1 performs the minimum copper consumption control, the motor is reversedVelocity n of the feed linefW is obtained by the speed conversion module 17fThe speed conversion module meets the following requirements:
Figure BDA0002963697860000121
feedback rotating speed w of non-salient pole type hybrid excitation motor 1fWith a given rotational speed w of the motorgAs input of the torque observation module 4, the final torque set value T is outputeg。TegInputting a copper consumption minimum current distribution module 10 and outputting a q-axis current given value iqgWith given value of exciting current ifg. Three-phase current i output by motora、ib、icOutputs a dq-axis current feedback value i through a first coordinate transformation module 20df、iqfWhile simultaneously outputting an exciting current feedback value iff。iqgAnd iqfIs input into the first PI regulation block 18 and outputs a q-axis given voltage uqg。idgAnd idfIs input into a second PI regulation module 19, outputs a d-axis given voltage udg。uqgAnd udgInputting the second coordinate transformation module 22 and outputting the alpha beta axis voltage uα、uβ；uα、uβThe input SVPWM modulation module 23 outputs SVPWM wave as the input signal of the armature circuit power conversion module 14, and the armature circuit power conversion module 14 outputs three-phase armature voltage ua、ub、ucThe non-salient pole type hybrid excitation motor 21 is driven. i.e. ifgAnd iffPasses through the third PI regulation module 24 and outputs the given value u of the exciting currentfg。ufgInputting the PWM modulation module 16 to generate PWM wave as the exciting circuit power conversion module 15, the exciting circuit power conversion module 15 outputting the exciting voltage ufThe magnetic field of the non-salient pole type hybrid excitation motor 21 is modulated.

Claims (10)

1. A control system for minimizing copper consumption of a hidden-pole hybrid excitation motor, a three-phase current i of the hidden-pole hybrid excitation motora、ib、icInput the firstIn a coordinate conversion module (20), the current feedback value i is coordinate-converted into dq-axis current feedback value idf、iqfSpeed n of feedback line of non-salient pole type mixed excitation motorfIs input into a speed conversion module (17) and is converted into a feedback rotating speed wfFeedback of the rotational speed wfAs a first input of the torque monitoring module (4), a given rotational speed wgAs a second input to the torque monitoring module (4), characterized by: the torque observation module (4) outputs a given torque value TegGiven value of torque TegUsed as the input of a copper consumption minimum current distribution module (10) and processed to output a q-axis current given value iqgWith given value of exciting current ifg(ii) a The given value of q-axis current iqgAnd q-axis current feedback value iqfThe q-axis current difference obtained by the difference is input into a first PI regulation module (18) and q-axis given voltage u is obtained by PI regulationqg(ii) a The d-axis current given value iqgAnd d-axis current feedback value idfThe d-axis current difference obtained by the difference is input into a second PI regulation module (19) and is regulated by a PI to obtain a d-axis given voltage udg(ii) a The d-axis given voltage udgAnd q-axis given voltage uqgThe output end of the second coordinate transformation module (22) is sequentially connected with an SVPWM modulation module (23), an armature circuit power transformation module (14) and a non-salient pole type hybrid excitation motor in series; output exciting current feedback value i of non-salient pole type hybrid excitation motorffFeedback value of exciting current iffWith said given value of exciting current ifgThe difference value of the exciting current obtained by difference is input into a third PI regulating module (24) and is regulated by PI to obtain the given value u of the exciting voltagefgGiven value u of excitation voltagefgThe PWM modulation module (16) generates PWM wave as the input of the excitation circuit power conversion module (15), and the excitation circuit power conversion module (15) outputs the excitation voltage ufA non-salient pole type hybrid excitation motor is provided.
2. The system for controlling the minimum copper loss of the non-salient pole type hybrid excitation motor according to claim 1, is characterized in that: the torque observation module (4) is controlled by a linear control rate moduleThe device comprises a block (1), a state observer module (2) and a limiting module (3), wherein the given rotating speed wgWith feedback speed wfIs estimated value ZT1Difference of rotation speed eTThe estimated value Z of the total system disturbance output by the state observer module (2) is used as a first input of the linear control rate module (1)T2As a second input to the linear control rate module (1), the linear control rate module 1 outputs an initial estimate T of the given torqueeg0(ii) a The feedback rotating speed wfAnd given initial torque estimate Teg0As two inputs to the state observer module (2), the state observer module (2) outputs wfIs estimated value ZT1And the total disturbance estimate Z of the systemT2(ii) a Said limiter module (3) being adapted to determine an initial estimate T of said given torqueeg0Amplitude limiting to obtain the given torque value Teg。
3. The system for controlling the minimum copper loss of the non-salient pole type hybrid excitation motor according to claim 2, is characterized in that: the copper consumption minimum current distribution module (10) consists of a copper consumption calculation module (5), a torque calculation module (6), a sub-problem conversion module (7) and a sub-problem iteration module (8), wherein the input of the copper consumption calculation module (5) is an nth iteration value i of an armature current q-axis component output by the sub-problem iteration module (8)qnAnd the nth iteration value i of the exciting currentfnThe output is the copper loss P of the nth iterationcu(in) The nth iteration value i of the q-axis component of the armature currentqnAnd the nth iteration value i of the exciting currentfnAs two inputs of a torque calculation module (6), the given torque value TegAs a third input of the torque calculation module (6), the torque calculation module (6) outputs the torque T of the nth iterationeg(in) (ii) a The copper loss Pcu(in) And torque Teg(in) As two inputs to the subproblem transformation module (7), the subproblem transformation module (7) transforms the sequence quadratic programming subproblem Q3And input into a sub-problem iteration module (8), and the sub-problem iteration module (8) performs sub-problem Q3Solving and outputting the optimal solution as (i)qg,ifg)。
4. The system for controlling the minimum copper loss of the non-salient pole type hybrid excitation motor according to claim 3, is characterized in that: the subproblem conversion module (7) is formed by sequentially connecting an equality constraint optimization problem conversion module (11), a Newton-Lagrangian extreme value solving problem conversion module (12) and a quadratic programming problem conversion module (13) in series, and the equality constraint problem conversion module (11) uses copper consumption Pcu(in) As an objective function, with a torque Teg(in) As an input to the equality constraint condition, when the torque T is satisfiedeg(in) Under the condition of calculating copper consumption Pcu(in) The problem of minima is converted into an optimization problem Q that takes into account pure equality constraints1Optimization problem Q1Inputting the data into a Newton-Lagrange extremum problem conversion module (12), and enabling the Newton-Lagrange extremum problem conversion module (12) to optimize a problem Q1Conversion to newton-lagrange extremum problem Q2And input into a quadratic programming problem conversion module (13), and the quadratic programming problem conversion module (13) solves the extreme value problem Q of Newton-Lagrange2Convert to said sequence quadratic programming subproblem Q3。
5. The system for controlling the minimum copper loss of the non-salient pole type hybrid excitation motor according to claim 2, is characterized in that: the initial estimation value T of the given torqueeg0＝kpeT+zT2，kpIs a proportional gain factor.
6. The system for controlling the minimum copper loss of the non-salient pole type hybrid excitation motor according to claim 3, is characterized in that: said copper loss
Figure FDA0002963697850000021
The torque of the nth iteration
Figure FDA0002963697850000022
RsIs the resistance of the armature winding, RfIs an exciting windingGroup resistance value in＝(iqn,ifn) And P is the number of poles of the motor rotor,
Figure FDA0002963697850000023
indicating the permanent magnet flux linkage of the motor, LfIs the field winding inductance value.
7. A control method of the copper consumption minimum control system of the non-salient pole type hybrid excitation motor according to claim 2, which is characterized by comprising the following steps:
step 1: the state observer module (2) is designed as an extended state observer
Figure FDA0002963697850000024
e is ZT1And wfAn error of (2); beta is aT1、βT2Is the gain coefficient of the state observer, b is the compensation coefficient;
step 2: the discretized extended state observer is
Figure FDA0002963697850000025
h is the sampling period, wf(k) Is the feedback speed at time k, ZT1(k) Is an estimate of the feedback speed at time k, ZT1(k +1) is the feedback speed estimate at time k +1, ZT2(k) Is the total disturbance estimate of the system at time k, e (k) is time k, ZT1(k) And wf(k) Error of (1), Teg0(k) Is an initial estimate of the torque given at time k, ZT2(k +1) is the total disturbance estimate at time k +1, ZT1(k +1) as the feedback rotation speed wfState estimator ZT1Output, ZT2(k +1) as the total disturbance estimate Z of the systemT2Outputting;
and step 3: the amplitude limiting module (3) performs initial estimation on the given torqueeg0Comparing and judging when the electromagnetic torque estimated value Teg0In the motor load range [ T ]min，Tmax]Internal time, Teg＝Teg0(ii) a When T iseg0≥Tmax，Teg＝Tmax(ii) a When T iseg0≤Tmin，Teg＝Tmin，TmaxIs the limit value of the carrying capacity T when the motor rotates forwardsminIs the limit value of the carrying capacity when the motor rotates reversely.
8. The control method of the copper consumption minimum control system of the non-salient pole type hybrid excitation motor according to claim 4, which is characterized by comprising the following steps:
step 1): the equation constraint problem module (11) uses the copper consumption Pcu(in) As an objective function, with a torque Teg(in) As an equality constraint, an optimization problem Q is obtained1Comprises the following steps:
Figure FDA0002963697850000031
step 2): Newton-Lagrange extremum problem transformation module (12) simplifies optimization problem Q1To obtain an optimization problem Q1Lagrange function L (i)n,un)＝Pcu(in)-unHeq(in)，unIs the nth iteration Lagrangian multiplier, Heq(in) Is a torque-dependent constraint function;
step 3): solving the copper loss Pcu(in) Gradient of (2)
Figure FDA0002963697850000032
And second derivative
Figure FDA0002963697850000033
And a constraint function Heq(in) Gradient of (2)
Figure FDA0002963697850000034
And second derivative
Figure FDA0002963697850000035
Thereby obtaining the Lagrangian function L (i)n,un) The first and second derivatives of the first and second,
step 4): given iteration point Zn＝(in,un) Solving the problem of extreme value Q of Newton-Lagrange2Is composed of
Figure FDA0002963697850000036
The iteration points satisfy: zn+1(in+1,un+1)＝Zn(in,un)+Pn(dn,vn)，dnRepresents the current inMicro increment of vnRepresenting lagrange multipliers unIs measured in increments of (a).
9. The control method according to claim 8, wherein: in step 4), solving the problem Q of extreme value by Newton-Lagrange2Conversion to sequential quadratic programming subproblem Q3Using positive definite matrix first
Figure FDA0002963697850000037
In place of Zn(in,un) Then according to formula
Figure FDA0002963697850000038
Deriving a sequence quadratic programming subproblem Q3The method comprises the following steps:
Figure FDA0002963697850000039
τ>0，
Figure FDA00029636978500000310
qn(dn) Is about the current increment dnA function of (a); if d iskIf the current is less than the set value, the corresponding current (i) is at the momentqn,ifn) Is the optimal solution.
10. The control method according to claim 9, wherein: sub-problem iteration module (8) plans sub-problem Q for sequence quadratic3The treatment method comprises the following specific steps:
step (1): given an initial value, the system will,i0、u0τ, ρ, γ, ε, where i0＝(iq0,if0) For the initial set value of the current, and the other parameters are parameters used in the subsequent iteration process, wherein 0<ρ<1、0<γ<1、0<ε<<1、0<τ<<1。
Step (2): calculating a penalty function
Figure FDA0002963697850000041
If P (i)n,un)<E, stopping calculation, turning to the step (6), and otherwise, turning to the step (3);
and (3): solving a sequential quadratic programming subproblem Q3To obtain dnAnd tnLet us order
Figure FDA0002963697850000042
And (4): determine i descending search direction, let mnIs such that the inequality P (i)n+ρmdn,un+pmvn)≤(1-γρm)P(in,un) Minimum non-negative integer m of true:
and (5): device for placing
Figure FDA0002963697850000043
Let in+1＝in+αndn，un+1＝un+αnνnTurning to the step (1) when n is equal to n + 1;
and (6): output current (i)qn,ifn)。
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