CN110262236B - Order-reducing variable selection method for order reduction of power electronic interface grid-connected system model - Google Patents

Order-reducing variable selection method for order reduction of power electronic interface grid-connected system model Download PDF

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CN110262236B
CN110262236B CN201910537918.2A CN201910537918A CN110262236B CN 110262236 B CN110262236 B CN 110262236B CN 201910537918 A CN201910537918 A CN 201910537918A CN 110262236 B CN110262236 B CN 110262236B
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王磊
龙文浩
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Hefei University of Technology
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Abstract

A method for selecting a reduced variable of a power electronic interface grid-connected system model reduced order can improve the adverse effect that the reduced order processing is carried out on different state variables aiming at different model reduced order errors in the current application; the method comprises the following steps of 1, establishing an electromagnetic transient model of a power electronic interface grid-connected system; 2. obtaining a characteristic value of the system at a steady-state operating point, and introducing a characteristic value sensitivity method to analyze the correlation between the characteristic value and each state variable of the system; 3. extracting singular perturbation parameters corresponding to each state variable of the system, and establishing a singular perturbation model of the system; 4. and according to a given model order reduction error range, obtaining a leading influence state variable of the leading characteristic value by applying different leading characteristic value selection modes, and performing order reduction processing on the rest state variables. The invention can select different state variables to carry out order reduction treatment according to the requirement of the order reduction error of the model, and fully considers the practicability and effectiveness of the order reduction variable selection method.

Description

Order-reducing variable selection method for order reduction of power electronic interface grid-connected system model
Technical Field
The invention relates to the technical field of new energy power generation systems, in particular to a reduced order variable selection method for reducing an order of a power electronic interface grid-connected system model.
Background
At present, a large number of new energy power supplies are connected to the power grid through power electronic interfaces, so that a power system is in a development trend of high power electronization, and a series of problems that the order of a nonlinear high-order model for describing the dynamic behavior of the power system is obviously increased, the time scale span is increased, the solving is difficult and the like are caused. Model reduction is one of the effective methods to solve the above problems.
In the current research results, methods for reducing the order of a power electronic interface grid-connected system model mainly include a balance method, a homodyne equivalence method and the like, but the methods cannot consider different time scales of system state variables and are not suitable for reducing the order of the power electronic interface grid-connected system model. In the field of new energy power generation systems, the singular perturbation method is widely applied to model order reduction, mainly due to the superiority of the method in processing a multi-time scale system.
However, the current application state does not consider reducing different state variables for different model reduction errors.
Disclosure of Invention
According to the order-reducing variable selection method for the order reduction of the power electronic interface grid-connected system model, the adverse effect on the order reduction of the power electronic interface grid-connected system model due to the fact that the order reduction processing is carried out on different state variables aiming at different model order-reducing errors is not considered in the current application situation, balance of calculation efficiency and order reduction precision in transient simulation of the power electronic interface grid-connected system is achieved, and the method can be suitable for efficient simulation of a large-scale new energy grid-connected system.
In order to achieve the purpose, the invention adopts the following technical scheme:
a reduced order variable selection method for reducing an order of a power electronic interface grid-connected system model comprises the following steps:
performing, by a computer device, the steps of:
s100, establishing an electromagnetic transient model of a power electronic interface grid-connected system;
s200, establishing a small signal model of the power electronic interface grid-connected system based on the electromagnetic transient model, solving a steady-state operation point of the system, solving a characteristic value of a state matrix of the small signal model at the steady-state operation point, and introducing a characteristic value sensitivity method to analyze the correlation between the characteristic value and each state variable of the system;
s300, extracting singular perturbation parameters corresponding to each state variable of the power electronic interface grid-connected system, and establishing a singular perturbation model of the system;
s400, based on the singular perturbation model, according to the range of the given model reduced error, different dominant characteristic value selection modes are applied to respectively obtain dominant influence state variables of the dominant characteristic values, and the fast dynamic reduced processing is conducted on the rest state variables until the model reduced error requirement is met.
Further, the step S100 establishes an electromagnetic transient model of the power electronic interface grid-connected system; the method comprises the following steps:
based on a typical power electronic interface grid-connected PQ control system, the electromagnetic transient model is established as follows:
Figure GDA0003509364120000021
wherein, theta is a phase locking angle of the phase-locked loop, P and Q are respectively active power and reactive power output by the system, i1dAnd i1qD-axis component and q-axis component, u, of the filter inductor current on the AC side of the inverter2dAnd u2qRespectively d-axis component of the filter capacitor voltageAnd q-axis component, i2dAnd i2qD-and q-components, gamma, of the line output current, respectivelyd、γq、λqAnd λdIntegral variables, u, of the input signals of the power outer loop and the current inner loop, respectivelydAnd uqThe d-axis component and the q-axis component of the grid-connected point voltage, respectively.
Further, the electromagnetic transient model comprises a PLL control and output power model, a double-loop control model and an inverter model.
Further, in step S200, based on the electromagnetic transient model, a small signal model of the power electronic interface grid-connected system is established, a steady-state operating point of the system is obtained, a characteristic value of a state matrix of the small signal model at the steady-state operating point is solved, and a characteristic value sensitivity method is introduced to analyze correlations between the characteristic value and each state variable of the system; comprises the steps of (a) preparing a mixture of a plurality of raw materials,
s201, establishing a small signal model of the system:
Figure GDA0003509364120000031
s202, solving a steady-state operation point, and solving a state matrix A by using the steady-state operation pointSCharacteristic value λ of1～λ13And the eigenvalues are sorted in turn from small to large according to the absolute value of the real part;
s203, analyzing the characteristic value lambda by introducing a characteristic value sensitivity methodiAnd each state variable x of the systemkIn which p iski＝ukiυkiReferred to as participation factor, vkiAnd ukiAre respectively a state matrix ASK rows and i columns of elements, p, in the corresponding left and right eigenvector matrices V and UkiReflecting the mutual participation degree of the ith modality and the kth state variable, and carrying out normalization processing on the left and right feature vectors, namely:
Figure GDA0003509364120000032
further, in the step S300, singular perturbation parameters corresponding to each state variable of the power electronic interface grid-connected system are extracted, and a singular perturbation model of the system is established; comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the established power electronic interface grid-connected system singular perturbation model is as follows:
Figure GDA0003509364120000033
where x is the PLL state variable, y1Is a state variable of output power, y2Integral variable, y, of input signals for power outer loop and current inner loop3For filtering the state variable, y, of the inductor current on the AC side of the inverter4Is a state variable of the filter capacitor voltage, y5For the state variable of the line output current, epsilon1、ε2、ε3、ε4And ε5Respectively corresponding singular perturbation parameters.
Further, the S400 obtains dominant influence state variables of the dominant eigenvalues respectively by applying different dominant eigenvalue selection modes according to a given range of model order reduction errors based on the singular perturbation model, and ignores the fast dynamic order reduction processing on the remaining state variables until the requirement of the model order reduction errors is satisfied;
s401, taking state variables x of an electromagnetic transient model and a reduced order model during disturbance occurrencekThe relative difference of the fluctuation ranges as the state variable xkReduced order error ofkI.e. by
Figure GDA0003509364120000041
Wherein, OkpAnd OkbRespectively, state variable x during disturbance of detailed electromagnetic transient modelkPeak and valley of, RkpAnd RkbRespectively a reduced order model state variable x in the same periodkThe peak value and the valley value of the state variable, the reduced order error of all the state variables is integrated, and the sigma is taken0＝max{σ1,σ2,…σ13The model reduced order error is obtained;
s402, aiming at the state matrix ASAll the characteristic values are subjected to characteristic value sensitivity analysis to obtain various state variables related to the characteristic values, when p iskiThe state variable x can be considered to be more than or equal to 0.1kIs a characteristic value λiMainly affecting state variables;
s403, setting the original main guide characteristic value as lambda1～λtT < 13, given a model reduced order error of σ, removing the eigenvalue λ1～λtAll the main influence state variables of the system are subjected to order reduction treatment, and the order reduction error sigma of the model is calculated0If σ is0If not more than sigma, the order reduction model meets the requirement, and the order reduction process is finished, otherwise, S404 is carried out;
s404, if sigma0If the value is more than sigma, the range of the dominant characteristic value is further expanded; if λt+1And λt+2Taking the dominant eigenvalue as the conjugate eigenvalue, and taking the dominant eigenvalue as lambda1～λt+2If λt+1And λt+2Taking the dominant eigenvalue as lambda if the non-conjugate eigenvalue is1～λt+1Continue execution as per S403 if σ0And if the difference is larger than the sigma, the range of the dominant characteristic value is continuously and repeatedly expanded until the requirement of the given model reduced order error is met.
According to the technical scheme, when the model order reduction is carried out on the power electronic interface grid-connected system, the fast and slow dynamic characteristics of each state variable of the system can be accurately divided, the order reduction model of the power electronic interface grid-connected system can meet the error requirement, and the balance of the order reduction precision and the simulation efficiency is finally realized.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention introduces a characteristic value sensitivity method to analyze the correlation between the characteristic value and the state variable of the system, obtains the main influence state variable of each characteristic value, and provides a method for dynamically dividing the speed of different state variables according to the dominance of the characteristic value so as to establish different order reduction models.
2. The invention also analyzes the order reduction error of the model, because the requirement of the order reduction error of the model is not generally considered in the current situation of order reduction application, the balance of the order reduction precision and the simulation efficiency cannot be realized in practical application. Therefore, the invention changes the selection range of the leading characteristic value according to different model order reduction error requirements to obtain different leading influence state variables, and then carries out order reduction processing on the system to meet the given model order reduction error requirements.
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FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of an exemplary power electronics interface grid-tie control system;
FIG. 3 is a schematic flow diagram of the present invention;
FIG. 4 is a schematic diagram of the dual loop control system of the present invention;
fig. 5 is a model diagram of a three-phase bridge inverter according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention.
The order-reducing variable selection method for the order reduction of the power electronic interface grid-connected system model mainly considers the order reduction processing of the system under the condition that the requirement of the order reduction error of the model is met. And analyzing the correlation between the characteristic values and the state variables by using a characteristic value sensitivity method to obtain the main influence state variables of the characteristic values. And (3) continuously expanding the selection range of the leading characteristic value and obtaining the corresponding main influence state variable, further performing order reduction processing, and calculating the error of the order reduction model until the requirement of the given order reduction error of the model is met, so that the corresponding order reduction variable can be selected.
As shown in fig. 1 in detail, the following steps are performed by a computer device:
s100, establishing an electromagnetic transient model of a power electronic interface grid-connected system;
s200, establishing a small signal model of the power electronic interface grid-connected system based on the electromagnetic transient model, solving a steady-state operation point of the system, solving a characteristic value of a state matrix of the small signal model at the steady-state operation point, and introducing a characteristic value sensitivity method to analyze the correlation between the characteristic value and each state variable of the system;
s300, extracting singular perturbation parameters corresponding to each state variable of the power electronic interface grid-connected system, and establishing a singular perturbation model of the system;
s400, based on the singular perturbation model, according to the range of the given model reduced error, different dominant characteristic value selection modes are applied to respectively obtain dominant influence state variables of the dominant characteristic values, and the fast dynamic reduced processing is conducted on the rest state variables until the model reduced error requirement is met.
Can be interpreted as a schematic flow chart as shown in fig. 2; in particular, the present invention relates to a method for producing,
the method comprises the following steps:
step 1, establishing a detailed electromagnetic transient model of a power electronic interface grid-connected system, wherein the detailed electromagnetic transient model comprises a PLL (phase locked loop) control model, an output power model, a double-loop control model, an inverter model and the like;
step 2, establishing a small signal model of the power electronic interface grid-connected system based on the detailed electromagnetic transient model obtained in the step 1, solving a steady-state operation point of the system, solving a characteristic value of a state matrix of the small signal model at the steady-state operation point, and introducing a characteristic value sensitivity method to analyze the correlation between the characteristic value and each state variable of the system;
step 3, selecting corresponding singular perturbation parameters of each state variable of the system, and establishing a singular perturbation model of the power electronic interface grid-connected system;
and 4, based on the singular perturbation model of the power electronic interface grid-connected system obtained in the step 3, obtaining dominant influence state variables of the dominant characteristic values by applying different dominant characteristic value selection modes according to the required error range, and performing neglect fast dynamic order reduction processing on the remaining state variables until the requirement of model order reduction errors is met.
The following detailed description is made with reference to the accompanying drawings:
a typical power electronic interface grid-connected control system is taken as an example, and is shown in fig. 3.
The detailed electromagnetic transient model built is then:
Figure GDA0003509364120000061
in FIG. 3, PrefAnd QrefReference values for active and reactive power, respectively, of the system output, P and Q, respectively, active and reactive power, i1For filtering the inductor current, i, on the AC side of the inverter1dAnd i1qD-axis component and q-axis component, i, of the filter inductor current on the AC side of the inverter1drefAnd i1qrefFor current-loop input signal u1drefAnd u1qrefFor the current inner loop output signal u1Is the AC side voltage of the inverter, u2And i2Respectively, the voltage of the filter capacitor and the output current of the line, u is the voltage of the grid point, udAnd uqThe d-axis component and the q-axis component of the grid-connected point voltage are respectively, and theta is a phase locking angle of the phase-locked loop. And gamma isd、γq、λqAnd λdIntegral variables of the input signals of the power outer loop and the current inner loop are defined as follows:
Figure GDA0003509364120000071
specifically, the step 1 comprises the following steps:
1.1 PLL control and output Power model
The state equation of the system phase-locked loop is as follows:
Figure GDA0003509364120000072
kp-PLLθ is the phase angle of the phase-locked loop, the state variable of the phase-locked loop.
The output power state equation is:
Figure GDA0003509364120000073
ωcfor the cut-off frequency of the low-pass filter, P and Q are respectively the active power and the reactive power of the system output, u2dAnd u2qFor the component of the filter capacitor voltage in the dq axis, i2dAnd i2qThe component of the line output current in the dq axis.
1.2 Dual-ring control model
Fig. 4 shows a power outer loop and a current inner loop control system:
the power outer loop adopts Proportional Integral (PI) regulation, and the output quantity i1drefAnd i1qrefThe difference between the measured values of P and Q and the given reference value is obtained by a PI link and used as the input signal of the current inner loop:
Figure GDA0003509364120000081
the current inner loop adopts a filter inductance current feedback and PI regulation mode, and the output voltage reference value is as follows:
Figure GDA0003509364120000082
to facilitate the problem analysis, the integrated state variables of the power outer loop and current inner loop input signals may be defined as follows:
Figure GDA0003509364120000083
1.3 three-phase bridge inverter model
Fig. 5 shows a typical structure of a three-phase bridge type high-frequency PWM inverter circuit, and the dynamic influence of the switching part can be ignored at a higher switching frequency. And (3) converting the components in the abc coordinate system to the components in the dq coordinate system, and respectively writing a model expression in the dq coordinate system, wherein the specific model of the inverter is as follows:
filter inductor current i1The expression under dq axis is:
Figure GDA0003509364120000084
filter capacitor voltage u2The dq axis component of (1) is:
Figure GDA0003509364120000085
line output current i2The dq axis component of (1) is:
Figure GDA0003509364120000091
and (3) to (10) are combined to establish a detailed electromagnetic transient model of the power electronic interface grid-connected system.
The step 2 comprises the following steps:
2.1 obtaining the steady-state operating point, combining the equations (3) to (4) and (7) to (10), and obtaining 0 for the left differential terms, the steady-state operating point of the system can be obtained:
(θ0,P0,Q0,γd0,γq0,λd0,λq0,i1d0,i1q0,u2d0,u2q0,i2d0,i2q0)；
2.2 Small Signal model of the system is established as follows:
Figure GDA0003509364120000092
based on the steady state operating point of the system obtained at 2.1, the state matrix A can be obtainedSAll eigenvalues λ of1～λ13The eigenvalue is the eigenvalue of the system, and the eigenvalues are sequentially sorted according to the absolute value of the real part from small to large;
2.3 analysis of the eigenvalue lambda by introducing eigenvalue sensitivity methodsiAnd each state variable xkIn which p iski＝ukiυkiReferred to as participation factor, vkiAnd ukiAre respectively a state matrix ASK rows and i columns of elements, p, in the corresponding left and right eigenvector matrices V and UkiReflecting the mutual participation degree of the ith modality and the kth state variable, and carrying out normalization processing on the left and right feature vectors, namely:
Figure GDA0003509364120000093
wherein p iskiAnd the k state variable is considered to be the main influence state variable of the i mode, and the influence of the rest state variables is negligible.
The step 3 comprises the following steps:
the established singular perturbation model of the power electronic interface grid-connected system is as follows:
Figure GDA0003509364120000101
where x is the PLL state variable, y1Is a state variable of output power, y2Integral variable, y, for the power outer loop and current inner loop inputs3For filtering the state variable, y, of the inductor current on the AC side of the inverter4Is a state variable of the filter capacitor voltage, y5For the state variable of the line output current, epsilon1、ε2、ε3、ε4And ε5Respectively corresponding singular perturbation parameters.
The step 4 comprises the following steps:
4.1 taking the state variables x of the detailed electromagnetic transient model and the reduced order model during the occurrence of the disturbancekThe relative difference of the fluctuation ranges as the state variable xkReduced order error ofkI.e. by
Figure GDA0003509364120000102
Wherein, OkpAnd OkbRespectively, state variable x during disturbance of detailed electromagnetic transient modelkPeak and valley of, RkpAnd RkbRespectively a reduced order model state variable x in the same periodkThe peak value and the valley value of the state variable, the reduced order error of all the state variables is integrated, and the sigma is taken0＝max{σ1,σ2,…σ13The model reduced order error is obtained;
4.2 for the State matrix ASAll the characteristic values are subjected to characteristic value sensitivity analysis to obtain various state variables related to the characteristic values, when p iskiThe state variable x can be considered to be more than or equal to 0.1kIs a characteristic value λiThe influence of the other state variables is ignored;
4.3 according to a strict definition of the dominant eigenvalues: the characteristic value closest to the virtual axis in the left half plane of the complex plane, and the rest characteristic values are positioned on the left half plane outside the real part of the dominant characteristic value by 5 times, and the original dominant characteristic value of the system is obtained as lambda1～λt(t < 13) to obtain a characteristic value lambda1～λtAll the leading influence state variables are subjected to order reduction processing on all the remaining state variables, and order reduction errors sigma of all the state variables are calculated1～σ13Taking the maximum value as the model order reduction error sigma0Let the given model reduced order error be σ, if σ0If not more than sigma, the order reduction model meets the requirement, and the order reduction process is finished, otherwise, the order reduction process is carried out for 4.4;
4.4 when σ0If σ is exceeded, the range of dominant eigenvalues needs to be further expanded. If λt+1And λt+2For conjugate eigenvalues, the dominant eigenvalue is expanded to λ1～λt+2If λt+1And λt+2The non-conjugate eigenvalue, the dominant eigenvalue is expanded to λ1～λt+1And continuing to execute according to 4.3 until the reduced order error requirement of the model is met.
The embodiment of the invention introduces the characteristic value sensitivity method to deeply consider the correlation between the characteristic value and the state variable of the system, so that different dominant characteristic values and dominant influence state variables are selected according to different model order reduction error requirements, thereby carrying out order reduction processing on the system and obtaining a relatively better order reduction model. The method has certain reference significance for model reduction of the power electronic interface grid-connected system.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (2)

1. A reduced order variable selection method for reducing order of a power electronic interface grid-connected system model is characterized by comprising the following steps: performing, by a computer device, the steps of:
s100, establishing an electromagnetic transient model of a power electronic interface grid-connected system;
s200, establishing a small signal model of the power electronic interface grid-connected system based on the electromagnetic transient model, solving a steady-state operation point of the system, solving a characteristic value of a state matrix of the small signal model at the steady-state operation point, and introducing a characteristic value sensitivity method to analyze the correlation between the characteristic value and each state variable of the system;
s300, extracting singular perturbation parameters corresponding to each state variable of the power electronic interface grid-connected system, and establishing a singular perturbation model of the system;
s400, based on the singular perturbation model, according to a given range of model order reduction errors, different dominant characteristic value selection modes are applied to respectively obtain dominant influence state variables of the dominant characteristic values, and fast dynamic order reduction processing is omitted on the remaining state variables until the requirements of the model order reduction errors are met;
the step S100 is to establish an electromagnetic transient model of the power electronic interface grid-connected system; the method comprises the following steps:
based on a typical power electronic interface grid-connected PQ control system, the electromagnetic transient model is established as follows:
Figure FDA0003509364110000011
wherein, theta is a phase locking angle of the phase-locked loop, P and Q are respectively active power and reactive power output by the system, i1dAnd i1qD-axis component and q-axis component, u, of the filter inductor current on the AC side of the inverter2dAnd u2qD-axis component and q-axis component, i, of the filter capacitor voltage, respectively2dAnd i2qD-and q-components, gamma, of the line output current, respectivelyd、γq、λqAnd λdIntegral variables, u, of the input signals of the power outer loop and the current inner loop, respectivelydAnd uqRespectively a d-axis component and a q-axis component of the grid-connected point voltage;
the step S200 is to establish a small signal model of the power electronic interface grid-connected system based on the electromagnetic transient model, to obtain a steady-state operation point of the system, to solve a characteristic value of a state matrix of the small signal model at the steady-state operation point, and to introduce a characteristic value sensitivity method to analyze the correlation between the characteristic value and each state variable of the system; comprises the steps of (a) preparing a mixture of a plurality of raw materials,
s201, establishing a small signal model of the system:
Figure FDA0003509364110000021
s202, solving steady state operation points, and solving a state matrix A by using the steady state operation pointsSCharacteristic value λ of1～λ13And the eigenvalues are sorted in turn from small to large according to the absolute value of the real part;
s203, analyzing the characteristic value lambda by introducing a characteristic value sensitivity methodiAnd each state variable x of the systemkIn which p iski＝ukiυkiReferred to as participation factor, vkiAnd ukiAre respectively the stateMatrix ASK rows and i columns of elements, p, in the corresponding left and right eigenvector matrices V and UkiReflecting the mutual participation degree of the ith modality and the kth state variable, and carrying out normalization processing on the left and right feature vectors, namely:
Figure FDA0003509364110000022
step S300, extracting singular perturbation parameters corresponding to each state variable of the power electronic interface grid-connected system, and establishing a singular perturbation model of the system; comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the established power electronic interface grid-connected system singular perturbation model is as follows:
Figure FDA0003509364110000023
where x is the PLL state variable, y1Is a state variable of output power, y2As integral variable, y, of the input signals of the power outer loop and the current inner loop3For filtering the state variable, y, of the inductor current on the AC side of the inverter4Is a state variable of the filter capacitor voltage, y5For the state variable of the line output current, epsilon1、ε2、ε3、ε4And epsilon5Respectively corresponding singular perturbation parameters;
the S400 is based on the singular perturbation model, different dominant characteristic value selection modes are applied according to the range of the given model reduced error, dominant influence state variables of the dominant characteristic values are respectively obtained, and the fast dynamic reduced processing is carried out on the rest state variables until the requirement of the model reduced error is met;
s401, taking state variables x of an electromagnetic transient model and a reduced order model during disturbance occurrencekThe relative difference of the fluctuation ranges as the state variable xkReduced order error ofkI.e. by
Figure FDA0003509364110000031
Wherein, OkpAnd OkbRespectively, state variable x during disturbance of detailed electromagnetic transient modelkPeak and valley of, RkpAnd RkbRespectively a reduced order model state variable x in the same periodkThe peak value and the valley value of the state variable, the reduced order error of all the state variables is integrated, and the sigma is taken0＝max{σ1,σ2,…σ13The model reduced order error is obtained;
s402, aiming at the state matrix ASAll the characteristic values are subjected to characteristic value sensitivity analysis to obtain various state variables related to the characteristic values, when p iskiThe state variable x can be considered to be more than or equal to 0.1kIs a characteristic value λiMainly affecting state variables;
s403, setting the original main lead characteristic value as lambda1～λtT < 13, given a model reduced order error of σ, removing the eigenvalue λ1～λtAll the main influence state variables of the system are subjected to order reduction treatment, and the order reduction error sigma of the model is calculated0If σ is0If not more than sigma, the order reduction model meets the requirement, and the order reduction process is finished, otherwise, S404 is carried out;
s404, if sigma0If the value is more than sigma, the range of the dominant characteristic value is further expanded; if λt+1And λt+2Taking the dominant eigenvalue as lambda if the conjugate eigenvalue is1～λt+2If λt+1And λt+2Taking the dominant eigenvalue as lambda if the non-conjugate eigenvalue is1～λt+1Continue execution as per S403 if σ0And if the difference is larger than the sigma, the range of the dominant characteristic value is continuously and repeatedly expanded until the requirement of the given model reduced order error is met.
2. The method for selecting the reduced variable of the power electronic interface grid-connected system model reduced order according to claim 1, characterized by comprising the following steps: the electromagnetic transient model comprises a PLL control and output power model, a double-loop control model and an inverter model.
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