CN111143988B - Modeling method and device for self-adaptive three-phase PWM converter - Google Patents
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Abstract
A modeling method and device for a self-adaptive three-phase PWM converter can solve the technical problem that the existing method is difficult to control the simulation speed to be not too long while ensuring the simulation precision of a three-phase PWM converter system. Determining a basic kinetic equation of a three-phase PWM converter system; establishing a state space average model of the three-phase PWM converter; taking the voltage amplitude of the system network terminal as the basis of a switching model; the simulation accuracy of the model is controlled by setting a ripple accuracy limit value. The method utilizes KBM average theory to calculate the average value of the system state variable x of the three-phase PWM converter; the simulation precision of the model is controlled by setting the ripple precision limit value sigma, and the ripple value epsilon phi 1 or epsilon phi 1+ε2φ2 of the system state variable is directly obtained in the system simulation process, so that convenience is brought to the analysis of the system ripple in the future.
Description
Technical Field
The invention relates to the technical field of three-phase PWM converters in the power industry, in particular to a modeling method and device of a self-adaptive three-phase PWM converter.
Background
Distributed power sources are increasingly studied at home and abroad in recent years, so that the permeability of the distributed power sources in the current power grid is also increased. Typically, when the distributed power source is connected to the power grid, the power needs to be converted through a converter, so that a large number of converters also intervene in the power grid system. A current transformer, in particular a three-phase PWM current transformer, is a strongly non-linear element whose strong non-linearity is mainly derived from the switching elements inside it. The on-off operation of the switching element is an intermittent operation, and may cause generation of harmonics. Therefore, a large number of converters are connected to the power grid to introduce harmonics into the power grid, so that fluctuation of the voltage amplitude and the frequency of the power grid is caused, and stable operation of the whole power grid is affected. Therefore, in order to study the influence of the access of the converter, particularly the three-phase PWM converter, on the operation of the power grid, efficient and accurate modeling simulation needs to be carried out on the converter.
The main methods currently used for modeling three-phase PWM converters are: KBM averaging, zero-order state space averaging, generalized state space averaging, piecewise state space averaging, etc. Because the electromagnetic transient simulation time scale of the three-phase PWM converter system is of microsecond level, the simulation speed is difficult to control while the simulation precision of the three-phase PWM converter system is ensured. The simulation speed of the common zero-order state space average model is high, but the precision is difficult to ensure; the generalized state space averaging method can improve the simulation accuracy by expanding the high-order fourier series of the state variables, but relatively consumes a large amount of simulation time. In general, simulation models for realizing the three-phase PWM converter under the electromagnetic transient condition are rarely available in the existing models.
Disclosure of Invention
The modeling method and device for the self-adaptive three-phase PWM converter can solve the technical problem that the existing method is difficult to control the simulation speed to be not too long while guaranteeing the simulation precision of the three-phase PWM converter system.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a modeling method for an adaptive three-phase PWM converter, comprising:
S100, determining a basic kinetic equation of a three-phase PWM converter system;
S200, based on a basic dynamics equation, establishing a state space average model of the three-phase PWM converter system;
And S300, controlling the precision of the three-phase PWM converter system state space average model simulation by setting a ripple precision limit value.
Furthermore, before the step S300, when the system running state is changed between a steady state and a transient state, the system network voltage amplitude is used as a basis for switching the system state space average model of the three-phase PWM converter.
Further, the step S100 is to determine a basic kinetic equation of the three-phase PWM converter system;
The method specifically comprises the following steps:
the basic kinetic equation of the three-phase PWM converter system is shown in formula (1):
In the formula (1), x is a state variable of the converter system; is the derivative of the state variable x; f (t, x) is a function of time t and a state variable x; x (0) is the state variable x initial value; epsilon is a small parameter describing the disturbance; a is the initial value of the state variable x.
The S300 controls the precision of simulation of the three-phase PWM converter system state space average model by setting a ripple precision limit value;
specifically, the model precision is controlled by applying a KBM averaging method, and the basic equation is as follows:
in the formula (2), y (t) is an average value of the state variable x in a unit period; phi n (t, y) (n=1, 2.) is a function of time t and state variable average y (t); g n (y) (n=1, 2..) is a function of the state variable average y (t).
Further, the step S200 is to build a three-phase PWM converter system state space average model based on a basic dynamics equation;
The method specifically comprises the following steps:
For a three-phase PWM converter, the state equation is written as shown in the formula (3):
Where x (t) is a state variable with respect to time t, S n (t) (n=1, 2.) is a switching function with respect to time t, a n and b n (n=1, 2.) are corresponding coefficient matrices and coefficient vectors; a 0 and b 0 are a constant matrix and a constant vector;
the basic state equation of the three-phase PWM converter system is arranged to obtain the formula (4):
According to KBM averaging
Obtaining
Wherein:
Obtaining
Wherein:
Obtaining an expression of a system state variable x of the three-phase PWM converter:
x＝y+εφ1+ε2φ2 (10)
Solving a system state equation flow chart by using a KBM averaging method;
S n is a switching function of an nth switch of the three-phase PWM converter system.
Further, in the step S300, the system network voltage amplitude is used as the basis of the switching model;
The method specifically comprises the following steps:
S3011, setting an initial value;
s3012, obtaining a coefficient matrix of a state equation of the converter system;
s3013, judging whether the system network terminal voltage is a steady-state voltage, if so, jumping to S3014; if not, jumping to S3016;
S3014, taking the two switching periods as a simulation unit period, and jumping to S3018;
s3015, taking a switching period as a simulation unit period, and jumping to S3018;
s3016, judging whether the simulation initial time is the simulation initial time, if so, jumping to S3015; if not, jumping to S3017;
S3017, returning to the previous simulation time, and jumping to S3015;
S3018, obtaining a state equation of a converter system, and solving the state equation of the system;
S3019, judging whether the simulation is finished, if so, jumping to S30110; if not, updating the simulation time, and returning to S3013;
s30110, ending.
Further, the initial values in S3011 include the system network voltage amplitude and phase, the modulation wave amplitude, the phase and frequency, the carrier frequency, the converter resistance, the inductance and capacitance parameters, the state variable initial value and the differential initial value thereof.
Further, in S300, the controlling the model simulation accuracy by setting the ripple accuracy limit value includes:
Controlling the number of ripple terms developed when KBM is averaged by the state variable x through setting a ripple precision limit value sigma, and then controlling the simulation precision of the model;
S3021, setting a ripple limit value sigma of a state variable;
S3022, solving the integral according to the method in S200 Then phi 1 is calculated;
S3023, judging If so, jump to S3024; if not, jumping to S3025;
s3024, solving the integral according to the method in the step 2 Then, phi 2 is calculated, and the process jumps to S3026;
s3025, jumping to S3027 by letting x=y+ε phi 1;
S3026, jumping to S3027 by letting x=y+ε phi 1+ε2φ2;
S3027, ending.
On the other hand, the invention also discloses a modeling device of the self-adaptive three-phase PWM converter based on ripple precision control, which comprises the following units:
The modeling unit is used for establishing a three-phase PWM converter system state space average model based on the determined basic dynamics equation;
And the precision control unit is used for controlling the precision of the three-phase PWM converter system state space average model simulation by setting a ripple precision limit value.
Furthermore, when the system running state generates steady state and transient state transition, the precision control unit uses the system network terminal voltage amplitude as a basis for switching the three-phase PWM converter system state space average model.
In a third aspect, the present invention also discloses a storage medium having stored thereon a computer program which, when executed by a processor, implements the method described above.
According to the technical scheme, the modeling method and the modeling device of the self-adaptive three-phase PWM converter have the following beneficial effects:
1. The invention provides a concept of switching between two models with different simulation periods based on the amplitude of the network terminal voltage. When the system runs in a steady state, two switching periods are used as a simulation unit period, so that the simulation efficiency is improved; when the system operates in a transient state, a switching period is used as a simulation unit period, so that the simulation precision of the system in the transient state is ensured.
2. The method utilizes KBM average theory to calculate the average value of the system state variable x of the three-phase PWM converter; the simulation precision of the model is controlled by setting a ripple precision limit value sigma, and the ripple value epsilon phi 1 or epsilon phi 1+ε2φ2 of the system state variable is directly obtained in the system simulation process. And provides convenience for later analysis of system ripple.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a basic simulation flow chart of the three-phase PWM converter system of the present invention;
FIG. 3 is a block diagram of a three-phase PWM converter;
FIG. 4 is a system simulation accuracy control diagram of the present invention;
Fig. 5 is a flow chart of the system state equation solving method by KBM averaging method according to the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention.
As shown in fig. 1, the modeling method of the adaptive three-phase PWM converter according to the present embodiment includes:
S100, determining a basic kinetic equation of a three-phase PWM converter system;
S200, based on a basic dynamics equation, establishing a state space average model of the three-phase PWM converter system;
And S300, controlling the precision of the three-phase PWM converter system state space average model simulation by setting a ripple precision limit value.
The specific step S300 further includes taking the system network voltage amplitude as a basis for switching the three-phase PWM converter system state space average model when the system running state is in steady state and transient state transition.
The above steps are specifically described with reference to fig. 2 to 5:
s100, determining a basic kinetic equation of a three-phase PWM converter system; the method specifically comprises the following steps:
the basic kinetic equation of the three-phase PWM converter system is shown in formula (1):
In the formula (1), x is a state variable of the converter system; is the derivative of the state variable x; f (t, x) is a function of time t and a state variable x; x (0) is the state variable x initial value; epsilon is a small parameter describing the disturbance; a is the initial value of the state variable x.
In step S300, a KBM averaging method is used to control the model accuracy, and the basic equation is as follows:
In the formula (2), y (t) is an average value of the state variable x in a unit period; phi n (t, y) (n=1, 2.) is a function of time t and state variable average y (t); g n (y) (n=1, 2..) is a function of the state variable average y (t); epsilon is a small parameter describing the disturbance.
S200, based on a basic dynamics equation, establishing a three-phase PWM converter system state space average model; the method specifically comprises the following steps:
For a three-phase PWM converter, the state equation is written as shown in the formula (3):
Where x (t) is a state variable with respect to time t, S n (t) (n=1, 2.) is a switching function with respect to time t, a n and b n (n=1, 2.) are corresponding coefficient matrices and coefficient vectors; a 0 and b 0 are a constant matrix and a constant vector;
the basic state equation of the three-phase PWM converter system is arranged to obtain the formula (4):
According to KBM averaging
Obtaining
Wherein:
Obtaining
Wherein:
Obtaining an expression of a system state variable x of the three-phase PWM converter:
x＝y+εφ1+ε2φ2 (10)
And solving a system state equation flow chart by using a KBM averaging method, wherein S n is a switching function of an nth switch of the three-phase PWM converter system.
When the running state of the system is in steady state and transient state transition, taking the voltage amplitude of the system network terminal as a basis for switching the system state space average model of the three-phase PWM converter; the method specifically comprises the following steps:
S3011, setting an initial value;
s3012, obtaining a coefficient matrix of a state equation of the converter system;
s3013, judging whether the system network terminal voltage is a steady-state voltage, if so, jumping to S3014; if not, jumping to S3016;
S3014, taking the two switching periods as a simulation unit period, and jumping to S3018;
s3015, taking a switching period as a simulation unit period, and jumping to S3018;
s3016, judging whether the simulation initial time is the simulation initial time, if so, jumping to S3015; if not, jumping to S3017;
S3017, returning to the previous simulation time, and jumping to S3015;
S3018, obtaining a state equation of a converter system, and solving the state equation of the system;
S3019, judging whether the simulation is finished, if so, jumping to S30110; if not, updating the simulation time, and returning to S3013;
s30110, ending.
Further, the initial values in S3011 include the system network voltage amplitude and phase, the modulation wave amplitude, the phase and frequency, the carrier frequency, the converter resistance, the inductance and capacitance parameters, the state variable initial value and the differential initial value thereof.
The step 300 of controlling the accuracy of simulation of the three-phase PWM converter system state space average model by setting a ripple accuracy limit value comprises the following steps:
Controlling the number of ripple terms developed when KBM is averaged by the state variable x through setting a ripple precision limit value sigma, and then controlling the simulation precision of the model;
S3021, setting a ripple limit value sigma of a state variable;
S3022, solving the integral according to the method in S200 Then phi 1 is calculated;
S3023, judging If so, jump to S3024; if not, jumping to S3025;
s3024, solving the integral according to the method in the step 2 Then, phi 2 is calculated, and the process jumps to S3026;
s3025, jumping to S3027 by letting x=y+ε phi 1;
S3026, jumping to S3027 by letting x=y+ε phi 1+ε2φ2;
S3027, ending.
The following is a specific simulation example of the present embodiment:
step 1: referring to the three-phase PWM converter structure diagram of fig. 3, a three-phase PWM converter basic state variable expression is determined, as shown in the formula (3):
Where x (t) is a state variable with respect to time t, S n (t) (n=1, 2.) is a switching function with respect to time t, a n and b n (n=1, 2.) are corresponding coefficient matrices and coefficient vectors; a 0 and b 0 are constant matrices and constant vectors.
Step 2: setting an initial value of a three-phase PWM converter system, wherein the initial value comprises the amplitude value and the phase of the voltage of the three-phase PWM converter system; modulating wave amplitude, phase and frequency, carrier frequency, converter resistance, inductance and capacitance parameters, state variable initial values and differential initial values thereof;
Step 3: setting a state variable precision limit value sigma of the converter system;
step 4: obtaining a coefficient matrix of a state equation of the converter system;
Step5: judging whether the system network terminal voltage is a steady-state voltage or not, if so, jumping to the step 6; if not, jumping to the step 8;
step 6: taking the two switching periods as a simulation unit period T, and jumping to the step 10;
step 7: taking a switching period as a simulation unit period T', and jumping to the step 10;
Step 8: judging whether the simulation initial time is the simulation initial time, if so, jumping to the step 7; if not, jumping to the step 9;
Step 9: returning to the previous simulation moment, and jumping to the step 7;
Step 10: obtaining according to (5) by KBM averaging theory
Wherein:
Step 11: for a pair of Integrating;
step 12: judging If true, jumping to step 15; if not, jumping to the step 13;
step 13: obtaining according to (8) by KBM averaging theory
Wherein:
step 14: for a pair of Integrating and jumping to the step 16;
Step 15: obtain x=y+ε phi 1;
step 16: obtain x=y+ε phi 1+ε2φ2;
Step 17: judging whether the simulation is finished, if so, jumping to the step 18; if not, jumping to the step 5;
Step 18: and outputting, and ending simulation.
In summary, the embodiment of the invention provides a concept of switching between two models with different simulation periods based on the network voltage amplitude. When the system runs in a steady state, two switching periods are used as a simulation unit period, so that the simulation efficiency is improved; when the system operates in a transient state, a switching period is used as a simulation unit period, so that the simulation precision of the system in the transient state is ensured. Meanwhile, the embodiment of the invention utilizes KBM average theory to calculate the average value of the system state variable x of the three-phase PWM converter; the simulation precision of the model is controlled by setting the ripple precision limit value sigma, and the ripple value epsilon phi 1 or epsilon phi 1+ε2φ2 of the system state variable is directly obtained in the system simulation process, so that convenience is brought to the analysis of the system ripple in the future.
Meanwhile, the embodiment of the invention also discloses a modeling device of the self-adaptive three-phase PWM converter based on ripple precision control, which comprises the following units:
The modeling unit is used for establishing a three-phase PWM converter system state space average model based on the determined basic dynamics equation;
And the precision control unit is used for controlling the precision of the three-phase PWM converter system state space average model simulation by setting a ripple precision limit value.
Furthermore, when the system running state generates steady state and transient state transition, the precision control unit uses the system network terminal voltage amplitude as a basis for switching the three-phase PWM converter system state space average model.
It may be understood that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and explanation, examples and beneficial effects of the related content may refer to corresponding parts in the above method.
The embodiment of the application also provides an electronic device, which comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus,
A memory for storing a computer program;
The processor is used for realizing the modeling method of the self-adaptive three-phase PWM converter when executing the program stored in the memory, and the method comprises the following steps:
S100, determining a basic kinetic equation of a three-phase PWM converter system;
S200, based on a basic dynamics equation, establishing a state space average model of the three-phase PWM converter system;
And S300, controlling the precision of the three-phase PWM converter system state space average model simulation by setting a ripple precision limit value.
Furthermore, before the step S300, when the system running state is changed between a steady state and a transient state, the system network voltage amplitude is used as a basis for switching the system state space average model of the three-phase PWM converter.
The communication bus mentioned by the above electronic device may be a peripheral component interconnect standard (english: PERIPHERAL COMPONENT INTERCONNECT, abbreviated as PCI) bus or an extended industry standard architecture (english: extended Industry Standard Architecture, abbreviated as EISA) bus, etc. The communication bus may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
The communication interface is used for communication between the electronic device and other devices.
The Memory may include random access Memory (RAM, english: random Access Memory) or nonvolatile Memory (NVM, english: non-Volatile Memory), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.
The processor may be a general-purpose processor, including a central processing unit (english: central Processing Unit, abbreviated as CPU), a network processor (english: network Processor, abbreviated as NP), etc.; it may also be a digital signal processor (English: DIGITAL SIGNAL Processing: DSP), an Application specific integrated Circuit (English: application SPECIFIC INTEGRATED Circuit: ASIC), a Field Programmable gate array (English: field-Programmable GATE ARRAY; FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components.
In yet another embodiment of the present application, a computer readable storage medium is provided, in which a computer program is stored, which when executed by a processor, implements the steps of the modeling method of any of the adaptive three-phase PWM converters described above.
In yet another embodiment of the present application, a computer program product containing instructions that, when run on a computer, cause the computer to perform the modeling method of the adaptive three-phase PWM converter of any of the above embodiments is also provided.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk Solid STATE DISK (SSD)), etc.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. The modeling method of the self-adaptive three-phase PWM converter is characterized by comprising the following steps of:
S100, determining a basic kinetic equation of a three-phase PWM converter system;
S200, based on a basic dynamics equation, establishing a state space average model of the three-phase PWM converter system;
S300, controlling the precision of simulation of the three-phase PWM converter system state space average model by setting a ripple precision limit value;
the determining the basic kinetic equation of the three-phase PWM converter system in S100 specifically includes:
A basic kinetic equation for a three-phase PWM converter system, as shown in equation (1):
(1)
in the formula (1), the components are as follows, Is a state variable of the converter system; /(I)Is a state variable/>Is a derivative of (2); /(I)Is related to time/>And state variables/>Is a function of (2); /(I)Is a state variable/>An initial value; /(I)Is a small parameter describing disturbance; /(I)Is a state variable/>An initial value;
the step S200 is based on a basic dynamics equation, and the step of establishing a state space average model of the three-phase PWM converter system specifically comprises the following steps:
For a three-phase PWM converter, the state equation is written as shown in the formula (3):
(3)
Wherein, For/>State variable of/>Is a state variable/>With respect to time/>Is used for the purpose of determining the derivative of (c),For/>Is a switching function of/>And/>Corresponding coefficient matrixes and coefficient vectors; /(I)And/>Is a constant matrix and a constant vector;
the basic state equation of the three-phase PWM converter system is arranged to obtain the formula (4):
(4)
According to KBM averaging
Obtaining：
(5)
Wherein:
(6)
(7)
Obtaining ：
(8)
Wherein:
(9)
Obtaining system state variables of three-phase PWM converter Is represented by the expression:
(10)
Solving a system state equation flow chart by using a KBM averaging method;
Sn is the third phase PWM converter system A switching function of the switches;
The step 300 of controlling the accuracy of simulation of the three-phase PWM converter system state space average model by setting a ripple accuracy limit value comprises the following steps:
by setting ripple accuracy limits To control state variables/>The number of ripple terms developed when KBM is averaged is controlled to control the simulation precision of the model;
s3021 setting a ripple limit value of the state variable ；
S3022, solving the integral according to the method in S200Then find/>；
S3023, judgingIf so, jump to S3024; if not, jumping to S3025;
s3024, solving the integral according to the method in the step 2 Then find/>Jump to S3026;
S3025 order Jump to S3027;
s3026 order Jump to S3027;
S3027, ending.
2. The modeling method of an adaptive three-phase PWM converter according to claim 1, wherein: and before the step S300, when the running state of the system is in steady state and transient state transition, the system network terminal voltage amplitude is used as a basis for switching the system state space average model of the three-phase PWM converter.
3. The modeling method of an adaptive three-phase PWM converter according to claim 1, wherein: the S300 controls the precision of simulation of the three-phase PWM converter system state space average model by setting a ripple precision limit value;
The model accuracy is controlled by applying KBM averaging, and the basic equation is as follows:
(2)
In the formula (2), y (t) is a state variable Average value in unit period; /(I)Is related to time/>And a state variable average y (t); /(I)Is a function of the state variable average y (t).
4. The modeling method of an adaptive three-phase PWM converter according to claim 2, wherein: the system network terminal voltage amplitude is used as a basis for switching the system state space average model of the three-phase PWM converter;
The method specifically comprises the following steps:
S3011, setting an initial value;
s3012, obtaining a coefficient matrix of a state equation of the converter system;
s3013, judging whether the system network terminal voltage is a steady-state voltage, if so, jumping to S3014; if not, jumping to S3016;
S3014, taking the two switching periods as a simulation unit period, and jumping to S3018;
s3015, taking a switching period as a simulation unit period, and jumping to S3018;
s3016, judging whether the simulation initial time is the simulation initial time, if so, jumping to S3015; if not, jumping to S3017;
S3017, returning to the previous simulation time, and jumping to S3015;
S3018, obtaining a state equation of a converter system, and solving the state equation of the system;
S3019, judging whether the simulation is finished, if so, jumping to S30110; if not, updating the simulation time, and returning to S3013;
s30110, ending.
5. The modeling method of an adaptive three-phase PWM converter according to claim 4, wherein: the initial values in S3011 include the system network voltage amplitude and phase, the modulation wave amplitude, the phase and frequency, the carrier frequency, the converter resistance, the inductance and capacitance parameters, the state variable initial values and the differential initial values thereof.
6. A modeling apparatus implementing the modeling method of an adaptive three-phase PWM converter according to claim 1, characterized in that: comprising the following units:
The modeling unit is used for establishing a three-phase PWM converter system state space average model based on the determined basic dynamics equation;
And the precision control unit is used for controlling the precision of the three-phase PWM converter system state space average model simulation by setting a ripple precision limit value.
7. The modeling device for the adaptive three-phase PWM converter based on ripple precision control of claim 6, wherein:
and when the system running state is in steady state and transient state transition, the precision control unit uses the system network terminal voltage amplitude as a basis for switching the three-phase PWM converter system state space average model.
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