CN113676065B - Model prediction control method and system for Vienna rectifier for optimizing voltage vector - Google Patents
Model prediction control method and system for Vienna rectifier for optimizing voltage vector Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
- H02M7/00—Conversion of ac power input into dc power output; Conversion of dc power input into ac power output
- H02M7/02—Conversion of ac power input into dc power output without possibility of reversal
- H02M7/04—Conversion of ac power input into dc power output without possibility of reversal by static converters
- H02M7/12—Conversion of ac power input into dc power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode
- H02M7/21—Conversion of ac power input into dc power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal
- H02M7/217—Conversion of ac power input into dc power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only
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- B60L53/10—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles characterised by the energy transfer between the charging station and the vehicle
- B60L53/11—DC charging controlled by the charging station, e.g. mode 4
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
- H02M1/00—Details of apparatus for conversion
- H02M1/08—Circuits specially adapted for the generation of control voltages for semiconductor devices incorporated in static converters
- H02M1/088—Circuits specially adapted for the generation of control voltages for semiconductor devices incorporated in static converters for the simultaneous control of series or parallel connected semiconductor devices
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- H—ELECTRICITY
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- H02M1/00—Details of apparatus for conversion
- H02M1/12—Arrangements for reducing harmonics from ac input or output
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
- H02M1/00—Details of apparatus for conversion
- H02M1/32—Means for protecting converters other than automatic disconnection
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- 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
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- Y02T10/00—Road transport of goods or passengers
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- 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
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Abstract
The disclosure provides a model prediction method and a system for a Vienna rectifier for optimizing a voltage vector, wherein the model prediction method comprises the following steps: sampling the Vienna rectifier at the current moment to obtain three-phase input current and voltage of the Vienna rectifier at the current moment and power grid voltage; three-phase input current and voltage of the Vienna rectifier at the current moment and grid voltage are input into a mathematical model of the Vienna rectifier, and reference predicted input voltage at the next moment is obtained; analyzing the influence of all different types of candidate space voltage vectors on the midpoint balance based on a finite set model, and constructing a cost function without a weight factor to obtain the minimum value of the cost function; and (3) performing midpoint floating judgment, calculating the optimal acting time of the candidate vector according to the minimum value of the cost function, and performing duty ratio calculation so as to control the action of the rectifier switching tube.
Description
Technical Field
The disclosure relates to the technical field of switching converters, and in particular relates to a model prediction method and a system for a Vienna rectifier for optimizing a voltage vector.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In order to solve the problem that the traditional fuel-oil automobile consumes fossil energy, an electric automobile using electricity as clean energy enters the field of view of people, and a Vienna rectifier is used as an AC/DC converter and is also applied to an electric automobile charging pile.
In high-voltage high-power occasions, the diode clamping three-level rectifier is widely applied due to the advantages of high withstand voltage level, high output voltage level number, less alternating current harmonic waves, low voltage stress of a switching device and the like. However, compared with the traditional two-level rectifier, the three-level rectifier comprises more switching devices and is complex to regulate and control. Therefore, in order to combine the characteristics of three-level high-quality output waveforms with the advantages of fewer two-level switching devices, three-level vienna rectifiers have been developed. The Vienna rectifier has high efficiency and small switching number, and is widely applied to electric automobile charging systems and industrial application, thereby having great application prospect.
However, since the vienna rectifier has two diodes per phase, this necessitates the input current and voltage being in phase, otherwise serious zero crossing distortion occurs, and this distortion is coupled to the control of the dc side midpoint balance. Improper regulation increases input current harmonics and reduces system efficiency. Increasing the inductance reduces current harmonics, but increases the volume and increases the visual cost. In addition, larger switching losses reduce system lifetime and uncertain switching frequencies increase filter design difficulty. These problems affect the safe, stable, efficient and reliable operation of the vienna rectifier, greatly increasing the system losses.
With the development of microprocessor technology, model predictive control (model predictive control, MPC) is capable of achieving multi-target synchronous optimal control without the need for PWM modules or proportional-integral (PI) controllers. It is a strategy to predict and optimize control by building an online model of the controlled process, calculating the error between the desired object and the predicted object in a cost function, and imposing constraints in each sampling period. Based on the advantages of MPC, a multi-objective control of the vienna rectifier can be achieved. However, as the converter voltage level increases, the computational effort of the MPC grows exponentially. For example, for a three-phase rectifier, 8 (2 3 ) Each vector, 27 (3 in three levels 3 ) And a vector. Furthermore, in performing multi-objective MPC, weighting factors are typically introduced in the cost function. Not only does the weighting factor increase the computational burden of the MPC, but its process of selection and debugging is often a cumbersome task. In order to improve the calculation efficiency, the prior art proposes an MPC method based on simplified simulated annealing, which optimizes the weight factors online, instead of removing the weight factors. However, the remaining weight factors still increase the computational burden. Thus, MPC needs to overcomeOne of the problems is that the calculated amount is reduced, the weight factor is eliminated, and the response speed of the controller is improved while the multi-objective optimization control is realized.
In recent years, many scholars have conducted extensive research into the multi-target MPC control strategy for vienna rectifiers. The following are provided:
(1) An improved MPC is proposed to handle multiple control objectives. However, the above method only selects one space vector to track the reference voltage in one sampling time, increasing the current harmonics.
(2) A dual vector MPC method is proposed to improve the current quality. Unfortunately, due to the particularities of the vienna rectifier topology, severe current zero crossing distortion can occur when the voltage and current are not in the same direction. Thus, the conventional bi-vector MPC method cannot be directly transplanted into the vienna rectifier.
(3) A bi-vector MPC method for a vienna rectifier topology is presented that does not contain weighting factors. However, the reduction of current harmonics is greatly affected by the inductance size, and most importantly, the method cannot fix the switching frequency in each period, nor does it take into account reducing switching losses.
In summary, aiming at a three-level Vienna rectifier system in an electric automobile charging system, the problems of unfixed switching frequency, larger switching loss, large current harmonic wave, distortion of zero crossing points of current at an alternating current side and unbalanced voltage at a middle point of the direct current side, large calculation burden when the traditional MPC realizes multi-target control, and weight factors are contained in a cost function exist.
Disclosure of Invention
An object of the embodiments of the present specification is to provide a model prediction method and system of a vienna rectifier that optimizes a voltage vector, reduces an input current harmonic of a three-level vienna rectifier system, and outputs a model prediction method of a fixed switching frequency, and on the basis thereof, reduces switching loss in adjacent sampling times. The invention can effectively eliminate the zero crossing point distortion of the alternating current side, balance the midpoint voltage, reduce the input current harmonic wave, fix the switching frequency of each period, optimize the switching sequence of adjacent sampling periods, reduce the switching loss, realize the whole multi-objective control by a model prediction method without weight factor cost function, and solve the control coupling problem of the zero crossing point distortion of the alternating current side and the unbalanced midpoint voltage of the direct current side.
The first aspect of the present invention provides a model prediction method of a vienna rectifier for optimizing a voltage vector, which is implemented by the following technical scheme:
a model predictive method of a vienna rectifier for optimizing a voltage vector, comprising:
sampling the Vienna rectifier at the current moment to obtain three-phase input current and voltage of the Vienna rectifier at the current moment and power grid voltage;
three-phase input current and voltage of the Vienna rectifier at the current moment and grid voltage are input into a mathematical model of the Vienna rectifier, and reference predicted input voltage at the next moment is obtained;
analyzing the influence of all different types of candidate space voltage vectors on the midpoint balance based on a finite set model, and constructing a cost function without a weight factor to obtain the minimum value of the cost function;
and (3) performing midpoint floating judgment, calculating the optimal acting time of the candidate vector according to the minimum value of the cost function, and performing duty ratio calculation so as to control the action of the rectifier switching tube.
According to a further technical scheme, the construction of the mathematical model of the Vienna rectifier specifically comprises the following steps:
the three-phase input current of the Vienna rectifier and the power grid voltage at the current moment are subjected to Clark transformation to obtain the three-phase input current and the power grid voltage under an alpha beta-orthogonal coordinate system;
calculating three-phase reference input current of the Vienna rectifier at the current moment by adopting voltage outer loop control;
based on Lagrangian extrapolation theorem, obtaining the power grid voltage and the reference input current at the next moment;
and constructing a dynamic model of a voltage vector based on a discrete time domain to obtain the three-phase reference input voltage at the next moment.
According to a further technical scheme, the candidate space voltage vector is generated based on three-phase reference input current of the Vienna rectifier at the current moment, a space vector diagram of the Vienna rectifier is divided into large sectors according to positive and negative polarities of the three-phase input current, each large sector is divided into small sectors, and the candidate space voltage vector is obtained according to space vector classification of the Vienna rectifier.
According to a further technical scheme, the calculation of the minimum value of the cost function is specifically as follows: all vertexes of the polygon formed by the candidate space voltage vectors are in a cost function without weight factors, then the cost function results of each adjacent vertex are added, and the smallest of all calculation results is the area where the three-phase input current of the Vienna rectifier at the current moment is located.
Further technical scheme, midpoint floating judgment includes: and combining the small sectors into a new large sector, performing midpoint floating judgment, selecting a proper N-type or P-type sequence to balance the midpoint according to the fluctuation condition of the midpoint, and realizing the fixation of the switching frequency in each sampling period.
According to a further technical scheme, the combination of the cell sectors into a new large sector is carried out by merging and classifying according to the parity of the small sector.
According to a further technical scheme, the fixing of the switching frequency in each sampling period is performed by the proper N-type or P-type sequence, and the method comprises the following steps: only one of two phases in each group of sequences acts once in each period, and the other phase switch is kept unchanged, so that the switching frequency in each sampling period is fixed.
The second aspect of the present invention provides a model prediction system for a vienna rectifier for optimizing a voltage vector, which is implemented by the following technical scheme:
the model prediction system of the Vienna rectifier for optimizing the voltage vector comprises:
a data acquisition module configured to: sampling the Vienna rectifier at the current moment to obtain three-phase input current and voltage of the Vienna rectifier at the current moment and power grid voltage;
a reference input voltage prediction module configured to: three-phase input current and voltage of the Vienna rectifier at the current moment and grid voltage are input into a mathematical model of the Vienna rectifier, and reference predicted input voltage at the next moment is obtained;
a cost function calculation module configured to: analyzing the influence of all different types of candidate space voltage vectors on the midpoint balance based on a finite set model, and constructing a cost function without a weight factor to obtain the minimum value of the cost function;
a switching tube control module configured to: and (3) performing midpoint floating judgment, calculating the optimal acting time of the candidate vector according to the minimum value of the cost function, and performing duty ratio calculation so as to control the action of the rectifier switching tube.
A third aspect of the invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the steps of the method for model prediction of a vienna rectifier for any optimised voltage vector as described above.
A fourth aspect of the invention provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the model predictive method of a wiener rectifier of any of the optimized voltage vectors described above.
Compared with the prior art, the beneficial effects of the present disclosure are:
(1) The three-level Vienna rectifier system is adopted, compared with a traditional two-level rectifier, the three-level Vienna rectifier system has the characteristics of more output levels and high current quality, and has the remarkable advantages of fewer switching devices compared with the traditional three-level rectifier, so that the complexity of regulation and control is reduced, and the cost of the system is saved.
(2) The method is realized by adopting a model predictive control algorithm, and the cost function is only used for realizing current tracking, does not contain weight factors, and greatly reduces the calculation load of the controller. Meanwhile, the model prediction is used as a current inner loop of the whole system, a PI controller is not contained, and adjustment of PI parameters is omitted.
(3) The three-level Vienna rectifier space vector diagram is divided into small areas in two steps, wherein the first step is divided into six large sectors according to input current, and the second step is further divided into 6 small areas according to limited set model prediction. The number of candidate vectors in each period is reduced from 19 to 8, so that the calculation load is obviously reduced, the problems of alternating-current side current zero crossing distortion and direct-current side midpoint voltage balance are solved, and decoupling of alternating-current side current zero crossing distortion and direct-current side midpoint current unbalance control is realized.
(4) The application provides a method for solving a vector duty cycle based on a cost function calculation result. The cost function calculation result in the method is derived from the finite set model prediction of the divided sectors, so that the burden of a controller is not increased by extra calculation, and the optimal duty ratio can be obtained by simple four-rule operation.
(5) The small sectors divided in the second step are recombined to form different areas with positive polarity and negative polarity for the middle points, and the sequences with the positive polarity and the negative polarity in each area can not only keep the balance of the middle point voltage, but also restore larger voltage fluctuation when the middle point voltage is unbalanced.
(6) The application provides a method for solving a vector duty cycle based on a cost function calculation result. In the method, various vectors participate in tracking voltage, so that input current harmonic waves can be effectively reduced, and the electric energy quality is improved.
(7) According to the method, the sequence combination is optimized during adjacent sampling periods, the switching loss can be remarkably reduced, meanwhile, the current quality is not influenced, and in each sampling period, the switching frequency can be fixed by a five-segment sequence formed by combining a plurality of candidate vectors, so that the design of a filter is facilitated.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate and explain the exemplary embodiments of the disclosure and together with the description serve to explain the disclosure, and do not constitute an undue limitation on the disclosure.
FIG. 1 is a flow chart of a method for model prediction of a Vienna rectifier for optimizing voltage vectors in accordance with the present disclosure;
FIG. 2 is a schematic diagram of a three-level Vienna rectifier system in accordance with one embodiment of the present invention;
FIG. 3 is a schematic diagram of dividing sectors based on input current according to one disclosed embodiment of the invention;
FIG. 4 (a) is a graph showing the effect of a large space vector of a three-level Vienna rectifier on the DC side midpoint voltage according to an embodiment of the present invention;
FIG. 4 (b) is a graph showing the effect of three-level Vienna rectifier space vectors on DC side midpoint voltage in accordance with one embodiment of the present invention;
FIG. 4 (c) is a graph showing the effect of a small P-type vector of a three-level Vienna rectifier on the DC side midpoint voltage according to an embodiment of the present invention;
FIG. 4 (d) is a graph showing the effect of the N-type small vector of the three-level Vienna rectifier on the DC side midpoint voltage according to one embodiment of the present invention;
FIG. 5 (a) is a large sector partition diagram of a space vector diagram of a Vienna rectifier in accordance with an embodiment of the present invention;
FIG. 5 (b) is a small-sector partition diagram of a space vector diagram of a Vienna rectifier according to an embodiment of the present invention;
fig. 6 (a) illustrates a first sector N-type region in accordance with one embodiment of the present invention;
FIG. 6 (b) shows a first sector P-type region according to one embodiment of the present invention;
fig. 6 (c) illustrates a second sector N-type region in accordance with one embodiment of the present invention;
FIG. 6 (d) shows a second sector P-type region in accordance with one embodiment of the present invention;
FIG. 7 (a) shows a first sector P according to an embodiment of the present invention Ⅰ A sequence diagram of fixed switching frequency;
FIG. 7 (b) is a first sector P disclosed in one embodiment of the invention Ⅱ A sequence diagram of fixed switching frequency;
FIG. 7 (c) is a first sector N of a first disclosure of an embodiment of the invention Ⅰ A sequence diagram of fixed switching frequency;
FIG. 7 (d) shows a first sector N according to one embodiment of the present invention Ⅱ A sequence diagram of fixed switching frequency;
FIG. 7 (e) is a first sector N of a first disclosure of an embodiment of the present invention Ⅲ A sequence diagram of fixed switching frequency;
FIG. 7 (f) shows a first sector N according to one embodiment of the present invention Ⅳ A sequence diagram of fixed switching frequency;
FIG. 7 (g) is a first sector N of a first disclosure of an embodiment of the invention Ⅴ A sequence diagram of fixed switching frequency;
FIG. 7 (h) is a first sector N of a first disclosure of an embodiment of the invention Ⅵ A sequence diagram of fixed switching frequency;
FIG. 8 (a) shows a first sector P according to an embodiment of the present invention Ⅲ -N Ⅲ Reducing a sequence diagram of switching losses in adjacent sampling periods;
FIG. 8 (b) is a first sector P disclosed in one embodiment of the invention Ⅲ -N Ⅴ Reducing a sequence diagram of switching losses in adjacent sampling periods;
FIG. 8 (c) is a first sector P disclosed in one embodiment of the invention Ⅳ -N Ⅳ Reducing a sequence diagram of switching losses in adjacent sampling periods;
FIG. 8 (d) shows a first sector P according to one embodiment of the present invention Ⅳ -N Ⅵ Reducing a sequence diagram of switching losses in adjacent sampling periods;
FIG. 9 (a) is a simulation diagram of a 65 omega load using a fixed switching frequency in accordance with an embodiment of the present invention;
FIG. 9 (b) is a simulation diagram of a fixed switching frequency and reduced switching loss load of 65Ω in accordance with an embodiment of the present invention;
FIG. 9 (c) is a simulation diagram of a load of 100deg.C using a fixed switching frequency in accordance with an embodiment of the present invention;
fig. 9 (d) is a simulation diagram of a switching loss load of 100 Ω using a fixed switching frequency as disclosed in the first embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments in accordance with the present disclosure. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Example 1
The embodiment discloses a model prediction method of a Vienna rectifier for optimizing a voltage vector, as introduced by the background technology, the model prediction control algorithm for optimizing the voltage vector has the advantages that the fixed switching frequency of the Vienna topology faces, the switching loss is reduced, and meanwhile, the problems of alternating-current side current zero crossing distortion and direct-current side midpoint voltage unbalance and the control coupling problem of alternating-current side current zero crossing distortion and direct-current side midpoint voltage unbalance exist.
As shown in fig. 1, the present application provides a model prediction method of a vienna rectifier for optimizing a voltage vector, the method comprising the steps of:
step S1, sampling the Vienna rectifier at the current moment to obtain three-phase input current i of the Vienna rectifier at the current moment k x (k) Sum voltage u xo (k) Grid voltage e x (k)；
As shown in fig. 2, the input voltage of the vienna rectifier satisfies:
in the formula e x For the grid voltage, i x And u xo For three phases of input current and voltage, x=a, b, c, R and L are filter inductance and equivalent series resistance, respectively.
S2, constructing a mathematical model of the Vienna rectifier to obtain reference predicted input voltage at the next moment;
step S2-1, three-phase input current i of Vienna rectifier at current moment k x (k) Grid voltage e x (k) Performing Clark transformation to obtain three-phase input current i under alpha beta-orthogonal coordinate system y (k) Grid voltage e y (k) Where y=α, β.
Step S2-2 input i y (k)、e y (k) Real-time sampling of the DC side voltage V dc And a demanded DC side voltage V * dc Calculating three-phase reference input current i of Vienna rectifier at kth moment by adopting voltage outer loop control * y (k) Where y=α, β.
Step S2-3, obtaining the power grid voltage e at the (k+1) th moment based on Lagrange extrapolation theorem y (k+1) and reference input current i * y (k+1) expressed as:
wherein e y (k) Representing the current time k three-phase network voltage e y (k-1) represents the three-phase network voltage, e, at the previous instant k-1 y (k-2) represents the three-phase network voltages at the first two instants k-1; i.e * y (k) Representing the current moment k three-phase reference input current i * y (k-1) represents the three-phase reference input current, i, at the previous time k-1 * y (k-2) represents the three-phase reference input currents at the first two instants k-1.
Step S2-4: assuming NP voltage equalization, constructing a dynamic model of a voltage vector based on a discrete time domain under an alpha beta-orthogonal coordinate system to obtain a (k+1) th moment three-phase reference input voltage u * y (k+1)；
The calculation method comprises the following steps:
wherein i is * y (k+1) is the (k+1) th moment three-phase reference input current, e y (k+1) is the (k+1) th moment grid voltage, u * y (k+1) (y=α, β) is the (k+1) -th time three-phase reference input voltage, T s Is the sampling period, M is a constant value defined as:
M＝RT s +L (4)；
s3, analyzing the influence of all different types of candidate vectors on the midpoint balance based on a finite set model, constructing a cost function without a weight factor lambda, wherein the minimum value is the three-phase input current i of the Vienna rectifier at the current moment k x (k) The area where it is located.
Step S3-1: dividing a space vector diagram of the Vienna rectifier into large sectors according to positive and negative polarities of the three-phase input current based on the three-phase reference input current of the Vienna rectifier at the current moment, dividing each large sector into small sectors, and classifying the space vector diagram of the Vienna rectifier according to the space vector of the Vienna rectifier to obtain candidate space voltage vectors; the classification of the space vector diagram of the vienna rectifier is shown in table 1:
TABLE 1 space vector classification for Vienna rectifiers
As shown in fig. 3, the space vector diagram of the vienna rectifier can be divided into 6 large sectors according to the positive and negative polarities of the three-phase input current, for example, assume i x (k) Located in a sector II of the set of sectors, at this time three input currents is the sign of (2) is +++)]. Wherein two redundant small voltage vectors NON]And [ OON ]]Serious current distortion occurs and cannot be used as a candidate vector. That is, only 8 space voltage vectors (V 0 [OOO]、V 1 [POO]、 V 3 [PPO]、V 4 [OON]、V 5 [OPO]、V 14 [PPN]、V 19 [PON]And V 20 [OPN]) Synthesis of V ref . The number of candidate vectors will be reduced from 19 to 7 (including two redundant small vectors), which also reduces the computational burden by more than half.
Step S3-2: constructing a cost function containing weight factors;
defining the capacitance currents of the upper direct current link and the lower direct current link as follows:
wherein V is P And V N The voltages of the upper and lower capacitors, i p And i n Is the current through the upper and lower DC link capacitors, C P And C N The capacitance values of the upper and lower capacitors, respectively. Generally, they are identical and are all set to C.
Discretizing the formula (5) to obtain a discrete time model of the formula (5):
wherein i is n (k+1) and i p (k+1) is the predicted current passing through the upper and lower DC link capacitors at the (k+1) th moment; v (V) N (k+1) and V P (k+1) is the predicted upper and lower capacitor voltages, respectively.
The further resulting cost function comprising current tracking and midpoint balancing is specifically expressed as:
where λ is a weight factor.
Step S3-3: analyzing the influence of all different types of candidate vectors on the midpoint balance, and constructing a cost function without a weight factor lambda;
taking sector 1 as an example, the large vector [ PNN ] in FIG. 4 (a)]There is no effect on the NP voltage because none of the three phase voltages are connected to the neutral point. Whereas fig. 4 (b) shows the medium vector [ PON]The three phases are respectively connected to P, O, N states, so that the charge and discharge of the direct-current link capacitor are uncertain, and the NP voltage is unbalanced. Since B, C is connected with neutral point, P is connected, and P type small vector [ POO ]]The upper capacitor CP may be discharged,to the lower capacitor C N Charging is performed as shown in fig. 4 (c). N-type small vector [ ONN ]]The lower capacitance C in FIG. 4 (d) can be made N Discharging and making the upper capacitor C P And (5) charging. Thus, the midpoint voltage can be balanced by a small vector of either the P-type or N-type, which have opposite effects on the neutral point.
Therefore, the elimination of the weight factor lambda in the cost function (7) is realized, the balance of neutral points is realized, and the calculated amount and the complicated weight factor test work are greatly reduced. Thus, the new cost function contains only one term to predict the tracking current, and the new cost function without weighting factors is:
g 2 (k)＝|u * α (k+1)-u α (k+1)| 2 +|u * β (k+1)-u β (k+1)| 2 (8)
by the scheme, control decoupling of alternating-current side current zero crossing point distortion and direct-current side midpoint voltage unbalance is realized, influences of all different types of vectors on midpoint balance are analyzed, and decoupling with current distortion is realized. A cost function is constructed, which now contains only the effect of achieving the tracking current, the weighting factors having been removed at the time of decoupling.
Step S3-3: calculating the cost function of all vertexes of the polygon formed by the candidate space voltage vectors without the weight factor lambda, and adding the cost function results of each adjacent vertex, wherein the minimum of all calculation results is the three-phase input current i of the Vienna rectifier at the current moment k x (k) The area where it is located.
Taking the region I shown in FIG. 5 (a) as an example, the polygon is a hexagon, L 1 、M 1 、M 2 、S 1 、S 2 、Z 1 、 O 1 The cost function calculation results of one large, two medium, two small, one zero and one center voltage vector are respectively represented. The formula for determining six small areas is:
region (i) =min { p } i },i∈{1,2,3,4,5,6}
If S 1 And M 2 P of (2) 3 The sum is p i Minimum value of (i=1, 2 … … 6), then V ref In the region shown in fig. 5 (b).
Step S4: and (3) performing midpoint floating judgment, calculating the optimal acting time of the candidate vector according to the minimum value of the cost function, and performing duty ratio calculation so as to control the action of the rectifier switching tube.
S4-1, combining the small sectors X in the step S3-1 into a new large sector Y as shown in fig. 5 (a) and 5 (b), performing midpoint floating judgment, and selecting a proper N-type or P-type sequence to balance the midpoint according to the fluctuation condition of the midpoint;
wherein, the process of combining the small sectors X into a new large sector Y comprises the following steps:
in order to balance the NP voltage, as long as each region contains both N-type and P-type switching sequences, large sectors need to be classified according to parity due to the different characteristics of the odd and even sectors.
Taking the analysis of sector 1 and sector 2 in fig. 6 (a) -6 (d) as an example. In sector 1, as shown in FIG. 6 (a), due to the P-type vector [ PPO ]]And [ POP ]]Is removed leaving only one P-type vector [ POO ] in the entire sector]. To ensure that sequences in 6 cells tend to be P-type, each sequence must contain [ POO ]]While not being able to contain any N-type small vectors. Thus, N is Ⅲ And N Ⅴ Is combined into one region, denoted as P in FIG. 6 (b) Ⅲ . Also, P Ⅳ Can also be made of N Ⅲ And N Ⅴ And (5) merging. At the same time, P Ⅲ And P Ⅳ The P-type sequences in (a) can be respectively expressed as [ OOO ]]-[POO]-[PON]-[POO]-[OOO]And [ OOO ]]-[POO]-[PNO]-[POO]-[OOO]. For P Ⅰ And P Ⅱ The P-type sequences can be designed as [ PNN]-[PON]-[POO]-[PON]-[PNN]And [ PNN]-[PNO]-[POO]-[PNO]-[PNN]. However, the N-type small vector is not deleted in sector 1. Thus, without merging P-type sectors as described above, sector 1 is still divided into 6 negative areas (N in fig. 6 (a) Ⅰ ,N Ⅱ ,N Ⅲ ,N Ⅳ ,N Ⅴ And N Ⅵ ). In contrast to the sector P-type sequence,each N-type sequence contains only at least one N-type small vector, rendering the entire sequence negative.
Thus, sector 1 may also be N-type. According to the above division, the sector 1 contains both N-type and P-type switching sequences, which are determined by the magnitude of the NP voltage fluctuation. Furthermore, as can be seen from fig. 7 (a) -7 (h), the switching sequence is a five-segment sequence. In each sampling period, one of the three phases is always clamped and kept unchanged in state, and the switches of the other two phases only act once, so that the switching frequency is fixed while midpoint balance is realized.
For even large sectors, the situation is opposite to that of odd large sectors. Since in the even large sector, taking the second sector as an example, the N-type small vector is not eliminated, P can be calculated Ⅲ And P Ⅴ Merging into N Ⅲ Will P Ⅳ And P Ⅵ Merging into N Ⅳ As shown in fig. 6 (c) and (d). The same principle is consistent with the conclusion of the odd sectors in S4-1, the sector 2 can contain N-type and P-type sequences at the same time, and in each sampling period, one of three phases is always clamped and kept unchanged in state, and the switches of other two phases only act once, so that the switching frequency is fixed while the midpoint balance is realized.
S4-2, fixing the switching frequency in each sampling period according to the balanced midpoint N-type or P-type sequence;
any sequence is a five-segment sequence formed by three vectors, as shown in fig. 7 (a) -7 (h), only one of two phases in each group of sequences acts once in each period, and the other phase of switch is kept unchanged, so that the fixation of the switching frequency in each sampling period is realized;
taking into account the switching losses in adjacent sampling periods, the novel sequence is shown in fig. 7 (a) -7 (h). The group of sequences can be used for obviously reducing the harmonic amplitude at the switching frequency and the switching loss;
at N Ⅲ And N Ⅴ In which the switching sequences are [ PON ] shown in FIGS. 7 (e) and (g), respectively]-[OON]-[ONN]-[OON]-[OOO]And [ OOO ]]-[OON]-[OOO]. At N Ⅲ In a switching sequenceThe start and end are mid-vectors [ PON ]]And at N Ⅴ In zero vector [ OOO ]]. In addition, N Ⅲ And N Ⅴ The sum of the areas of (2) is P Ⅲ Is not limited in terms of the range of (a). Thus, suppose that a P-type sequence is required and V ref Located at N Ⅲ The P-type sequence is designed as [ PON ]]-[POO]-[OOO]-[POO]-[PON]As shown in fig. 8 (a); when V is ref Located at N Ⅴ When the required P-type sequence is designed as [ OOO ]]-[POO]-[PON]-[POO]-[OOO]As shown in fig. 8 (b). Thus, if an N-type or P-type sequence is required to balance the midpoint voltage, there is no switching action between adjacent sampling periods. Also according to V ref Located at N Ⅳ Or N Ⅵ Within the region, the required P-type sequences are also designed as [ PNO ] respectively according to the method]-[POO]-[OOO]-[POO]-[PNO]And [ OOO ]]-[POO]-[PNO]-[POO]-[OOO]As shown in fig. 8 (c) and 8 (d). At the same time, at N Ⅳ And N Ⅵ In the region of (2), the N-type sequence is designed as [ PNO ]]-[ONO]-[ONN]-[ONO]-[PNO]And [ OOO ]]-[ONO]-[ONN]-[ONO]-[OOO]. It follows that the switching losses between adjacent sampling periods can be reduced by the specific sequence described above.
P for sector 1 Ⅰ And N Ⅰ In other words, the P and N sequences shown in FIGS. 7 (a) and (c) are [ PNN]-[PON]-[POO]-[PON]-[PNN]And [ PON ]]-[PNN]-[ONN]-[PNN]-[PON]. It can be seen that the above sequence avoids a direct switching between the vienna rectifier states P and N when the sequence needs to switch at the next sampling period. Also, the first sector P Ⅱ And N Ⅱ Also following the above rule, as shown in fig. 7 (b) and (d), all switching sequences of the second sector are summarized in the following table.
S4-3, calculating the optimal action time of three vectors by using a cost function calculated value obtained by predictive control of a finite set model, and redistributing the optimal action time to five-segment PWM wave generation so as to control the action of a rectifier switching tube;
conventional SVPWM employsThe three closest voltage vectors, V synthesized by volt-second balancing ref . The constraint formula can be expressed as:
wherein dc 1 、dc 2 、dc 3 Three candidate vectors Vc respectively 1 、Vc 2 、Vc 3 Is a duty cycle of (c).
As can be seen from equation (9), the cost function employed herein calculates V in each large sector ref Distance to 7 fixed vectors. This means that the larger the calculation of the cost function, the more the fixed vector is from V ref The farther. Thus, the corresponding duty cycle is relatively small. In general, the duty cycle is inversely proportional to the result of the calculation of the cost function. Suppose that V is determined ref In the area, the duty cycle is defined as:
wherein K is a scale factor and K is not less than 0, g cx For three candidate vectors Vc 1 、Vc 2 、Vc 3 And (3) calculating a corresponding cost function (9). Substituting (10) into (11), and calculating to obtain a scaling factor K as follows:
since the result of the cost function is non-negative, it is ensured that the scaling factor K is greater than or equal to 0. Thus, the calculated duty cycle is also non-negative and can be expressed as:
based on the duty cycles of the three candidate vectors, a five-segment sequence can be generated for driving the vienna rectifier.
In the embodiment, the voltage peak value of the alternating current side is 150V, the required voltage of the direct current side is 400V, the equivalent load of the direct current side is 65 omega/100 omega, the filtering inductance is 5mH, and the sampling period is 100 mu s. Fig. 9 is a simulation result diagram of a method employing a fixed switching frequency, so that the current frequency is mainly concentrated on the switching frequency, that is, integer multiples of 10kHz, such as 20kHz and 30kHz, which is advantageous for the design of the filter. Since (c) and (d) in fig. 9 optimize the sequence switching order between adjacent sampling periods, the harmonic amplitude is significantly smaller than (a) and (c) in fig. 9 at a frequency of 10 kHz. For example, when the load size is 65Ω, the amplitude is reduced from 0.7 to 0.6; when the load size is 100deg.C, the amplitude is reduced from 1.3 to 0.9.
In addition, the THD difference between the two methods is small, and the THD of the sequence switching sequence between the optimized adjacent sampling periods is slightly higher than that of the non-optimized method, because the total switching frequency is reduced, but the THD is still less than 5% under the condition of different loads of 65Ω/100deg.OMEGA, and the input current waveform quality is high. The model prediction method provided by the embodiment is proved to be capable of fixing the switching frequency, reducing the switching loss, improving the electric energy quality and improving the safety and reliability of the rectifier.
Example two
The embodiment of the specification provides a transformer oil temperature prediction system based on a variable weight combination model, which is realized by the following technical scheme:
comprising the following steps:
a data acquisition module configured to: sampling the Vienna rectifier at the current moment to obtain three-phase input current and voltage of the Vienna rectifier at the current moment and power grid voltage;
a vienna rectifier building block configured to: constructing a mathematical model of the Vienna rectifier to obtain reference predicted input voltage at the next moment;
a cost function calculation module configured to: analyzing the influence of all different types of candidate space voltage vectors on the midpoint balance based on a finite set model, and constructing a cost function without a weight factor to obtain the minimum value of the cost function;
a switching tube control module configured to: and (3) performing midpoint floating judgment, calculating the optimal acting time of the candidate vector according to the minimum value of the cost function, and performing duty ratio calculation so as to control the action of the rectifier switching tube.
The implementation of the specific modules in this embodiment example can be found in the related art content in embodiment example one, and will not be described in detail here.
Example III
The present description provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the model predictive method of the vienna rectifier for optimizing voltage vectors in embodiment one when the program is executed.
Example IV
The present specification provides a computer-readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor implements the steps of the model prediction method of the vienna rectifier for optimizing a voltage vector in embodiment one.
It is appreciated that in the description herein, reference to the terms "one embodiment," "another embodiment," "other embodiments," or "first through nth embodiments," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, or material characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing description of the preferred embodiments of the present disclosure is provided only and not intended to limit the disclosure so that various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (7)
1. The model prediction method of the Vienna rectifier for optimizing the voltage vector is characterized by comprising the following steps of:
acquiring three-phase input current and voltage of a Vienna rectifier at the current moment and power grid voltage;
three-phase input current and voltage of the Vienna rectifier at the current moment and grid voltage are input into a mathematical model of the Vienna rectifier, and reference predicted input voltage at the next moment is obtained;
analyzing the influence of all different types of candidate space voltage vectors on the midpoint balance based on a finite set model, and constructing a cost function without a weight factor to obtain the minimum value of the cost function;
performing midpoint floating judgment, calculating the optimal acting time of the candidate vector according to the minimum value of the cost function, and performing duty ratio calculation so as to control the action of a rectifier switching tube;
the generating of the candidate space voltage vector comprises the following steps: dividing a space vector diagram of the Vienna rectifier into large sectors according to positive and negative polarities of the three-phase input current based on the three-phase reference input current of the Vienna rectifier at the current moment, dividing each large sector into small sectors, and classifying the space vector diagram of the Vienna rectifier according to the space vector of the Vienna rectifier to obtain candidate space voltage vectors;
the midpoint float determination includes: combining the small sectors into a new large sector, performing midpoint floating judgment, selecting an N-type or P-type sequence for balancing the midpoint according to the fluctuation condition of the midpoint to balance the midpoint, and fixing the switching frequency in each sampling period;
the combination of the cells into a new large sector is a combination classification based on the parity of the small sectors.
2. The model prediction method of the vienna rectifier for optimizing a voltage vector according to claim 1, wherein constructing the mathematical model of the vienna rectifier specifically comprises:
the three-phase input current of the Vienna rectifier and the power grid voltage at the current moment are subjected to Clark transformation to obtain the three-phase input current and the power grid voltage under an alpha beta-orthogonal coordinate system;
calculating three-phase reference input current of the Vienna rectifier at the current moment by adopting voltage outer loop control;
based on Lagrangian extrapolation theorem, obtaining the power grid voltage and the reference input current at the next moment;
and constructing a dynamic model of a voltage vector based on a discrete time domain to obtain the three-phase reference input voltage at the next moment.
3. The model prediction method of the vienna rectifier for optimizing voltage vectors according to claim 1, wherein the calculation of the minimum value of the cost function is specifically: all vertexes of the polygon formed by the candidate space voltage vectors are in a cost function without weight factors, then the cost function results of each adjacent vertex are added, and the smallest of all calculation results is the area where the three-phase input current of the Vienna rectifier at the current moment is located.
4. The model prediction method of the vienna rectifier for optimizing voltage vector according to claim 1, wherein the N-type or P-type sequence of balanced midpoints, performing fixation of switching frequency in each sampling period, comprises: only one of two phases in each group of sequences acts once in each period, and the other phase switch is kept unchanged, so that the switching frequency in each sampling period is fixed.
5. A model predictive system for a vienna rectifier for optimizing a voltage vector, comprising:
a data acquisition module configured to: acquiring three-phase input current and voltage of a Vienna rectifier at the current moment and power grid voltage;
a reference input voltage prediction module configured to: three-phase input current and voltage of the Vienna rectifier at the current moment and grid voltage are input into a mathematical model of the Vienna rectifier, and reference predicted input voltage at the next moment is obtained;
a cost function calculation module configured to: analyzing the influence of all different types of candidate space voltage vectors on the midpoint balance based on a finite set model, and constructing a cost function without a weight factor to obtain the minimum value of the cost function;
a switching tube control module configured to: performing midpoint floating judgment, calculating the optimal acting time of the candidate vector according to the minimum value of the cost function, and performing duty ratio calculation so as to control the action of a rectifier switching tube;
the generating of the candidate space voltage vector comprises the following steps: dividing a space vector diagram of the Vienna rectifier into large sectors according to positive and negative polarities of the three-phase input current based on the three-phase reference input current of the Vienna rectifier at the current moment, dividing each large sector into small sectors, and classifying the space vector diagram of the Vienna rectifier according to the space vector of the Vienna rectifier to obtain candidate space voltage vectors;
the midpoint float determination includes: combining the small sectors into a new large sector, performing midpoint floating judgment, selecting an N-type or P-type sequence for balancing the midpoint according to the fluctuation condition of the midpoint to balance the midpoint, and fixing the switching frequency in each sampling period;
the combination of the cells into a new large sector is a combination classification based on the parity of the small sectors.
6. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the model prediction method of the vienna rectifier for optimizing a voltage vector according to any one of claims 1-4 when the program is executed by the processor.
7. A computer readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor realizes the steps of the model prediction method of the wiener rectifier of the optimized voltage vector of any one of claims 1-4.
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