CN114156935B - Multi-parameter stability domain analysis method for sagging control inverter grid-connected system - Google Patents
Multi-parameter stability domain analysis method for sagging control inverter grid-connected system Download PDFInfo
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Abstract
The invention belongs to the technical field of intelligent power grids, and particularly relates to a multi-parameter stability domain analysis method of a droop control inverter grid-connected system, which comprises the following steps of: s1, establishing a small signal model of a droop control inverter; s2, deducing output impedance of the droop control grid-connected inverter; s3, equivalent grid-connected inverter systems are formed into cascade systems, and a rate matrix of the cascade systems is determined; s4, determining two parameters affecting the stability of the system, establishing a parameter space, searching critical stable points in the parameter space, obtaining a critical stable point set and a boundary of a stable domain, and S5, carrying out regression on a boundary curve of the stable domain by using a kernel ridge regression algorithm.
Description
Technical Field
The invention belongs to the technical field of intelligent power grids, and particularly relates to a multi-parameter stability domain analysis method of a droop control inverter grid-connected system.
Background
The inverter using droop control is an important power electronic interface element in ac microgrids, and although each droop control inverter may be designed individually as a stable module, there may be some interactions when interconnecting with other inverters and loads, resulting in stability problems, reducing the reliability of the overall microgrid. The stability of the micro-grid is closely related to the circuit parameters and the control parameters of the inverter, a great deal of research on modeling and stability analysis is carried out on the inverter in the prior art, and the small signal stability analysis on the sagging control inverter mostly adopts a small signal modeling method based on a state space model at present, but the method has inherent limitations when being applied to a parallel system: the order of the model grows with increasing numbers of DG in parallel, which presents challenges for the feasibility of modeling and computation.
The analysis method based on the impedance model can reduce the calculated amount, is not limited by the number of parallel converters, and is more suitable for stability analysis of a three-phase alternating current system. Therefore, modeling the impedance of the droop control inverter and analyzing the effect of the parameters on stability has important implications for efficient operation and design optimization of the microgrid.
According to the stability analysis method based on the impedance model, the micro-grid system is equivalent to a source module and a load module which are connected in series, and stability is analyzed through the relation between the impedance models of the source subsystem and the load subsystem. Because the method considers each part in the micro-grid system to be mutually independent subsystems, the structure and the parameters of one subsystem cannot influence the impedance models of other subsystems when being changed, so that the method can greatly simplify the stability analysis process for a complex system. The impedance modeling method is different, and the stability analysis method is also different.
For a positive-negative sequence decoupling system, the system can be regarded as a single-input single-output system, and a Nyquist criterion can be applied in stability analysis. However, when the grid-connected inverter system considers the influence of the factors such as the phase-locked loop, the asymmetry of the controller, the power calculation unit and the like, positive and negative sequence impedance coupling exists, the system cannot be regarded as a single-input single-output (Single Input Single output, SISO) system, and the Nyquist criterion is not used any more. An axial output impedance model is established for the three-phase grid-connected inverter, and at the moment, the grid-connected cascade system belongs to a typical two-input two-output system, and stability judgment needs to be carried out by using GNC. Based on this method, wen B et al analyze the interaction when the three-phase voltage source inverters are connected in parallel, and analyze the dynamic performance of the phase-locked loop, study the system stability based on the impedance model of the parallel system; liu Jinjun et al, in addition to the impedance matrix and admittance matrix of a conventional cascade system, add an additional term to the system's slew rate matrix that is related to the inverter port characteristics to cover the fundamental frequency interactions between the parallel droop control inverters, and employ GNC to perform stability analysis on the system. But such methods only qualitatively describe whether the system is stable.
The stability analysis method mainly analyzes the stability of the system when a single parameter is changed, and can not analyze the stability when two parameters are changed simultaneously.
Disclosure of Invention
The invention overcomes the defects existing in the prior art and provides a multi-parameter stable domain analysis method for a sagging control inverter grid-connected system based on an impedance model.
In order to solve the technical problems, the invention adopts the following technical scheme:
a multi-parameter stable domain analysis method for a droop control inverter grid-connected system comprises the following steps:
s1, establishing a small signal model of a droop control inverter;
s2, deducing output impedance Z of the droop control grid-connected inverter based on the small signal model in the step S1 o ；
S3, equivalent grid-connected inverter systems are formed into cascade systems, and a rate matrix of the cascade systems is determined;
s4, determining two parameters affecting the stability of the system, namely lambda 1 And lambda (lambda) 2 Establishing a parameter space { lambda } 1 ，λ 2 Judging the stability of the cascade system of a group of parameters according to the stability criterion of the matrix infinity norm shown in the formula (1):
||Z g (jω)|| ∞ ·||Y o (jω)|| ∞ ＜1，ω∈(-∞，+∞) (1)
wherein Z is g Is a grid impedance matrix, Y o Outputting an admittance matrix for the inverter if the dot isThe combination (1) is a critical stable point, and the critical stable point is searched in the parameter space to obtain a critical stable point set { Λ } b,j Boundaries of the } and stability domains
S5, based on critical stable point set { Λ ] b,j Boundaries of the } and stability domainsAnd (3) regression is carried out on the boundary curve of the stable domain by using a Kernel Ridge Regression (KRR) algorithm in the artificial intelligence field.
Further, in the step S1, establishing the small signal model of the droop control inverter includes: modeling an inverter main circuit, droop control loop modeling and voltage and current control double loop modeling;
modeling an inverter main circuit:
modeling the inverter main circuit to filter the voltage u of the capacitor according to KVL and KCL laws o Current i flowing through inductance L L As state variables, the column writes the state equation:
the inverter model under the dq coordinate system is obtained through coordinate transformation:
subjecting the formula (4) and the formula (5) to per unit treatment and Laplace transformObtaining the formula (6) and the formula (7), wherein omega b Is the angular frequency reference value, is used for per unit processing,
droop control loop modeling:
the droop control equation in step S1 is shown in formula (8):
wherein: omega nom Rated angular frequency for the inverter; u (u) od-nom Rated output voltage for the inverter; p, Q the inverter outputs active and reactive power; k (k) 1 、k 2 Is the active and reactive sag coefficient; omega * ,Angular frequency reference and voltage amplitude reference, respectively;
the instantaneous output power of the inverter can be obtained by a first-order low-pass filter, and the average output power of the inverter is shown as a formula (9):
where f(s) is a first order low pass filter,ω c cut-off angular frequency for the filter;
voltage-current control double-loop modeling:
the voltage and current loops all adopt PI regulators, and the transfer function of the regulator adopts G um 、G im Represented by the formula(10) The following is shown:
k pu 、k pi the proportional coefficients of the voltage loop and the current loop controller are respectively; k (k) iu 、k ii Integrating coefficients of the voltage ring and the current ring controller respectively; thereby, a voltage controller expression (11) and a current controller expression (12) are obtained:
further, small-signal modeling is performed on the formulas (4) to (12) to obtain formulas (13) to (18), wherein G represents a steady-state operating point of the corresponding variable, delta represents a disturbance component of the corresponding variable,
G LL Δi L +G ωL Δω＝Δu i -Δu o (13)
G CC Δu o +G ωC Δω＝Δi L -Δi o (14)
Δω＝G ωu Δu o +G ωi Δi o (15)
in the method, in the process of the invention,
G ωu ＝[-k 1 f(s)I od -k 1 f(s)I oq ]G ωi ＝[-k 1 f(s)U od -k 1 f(s)U oq ] (22)
Δu o ＝[Δu od Δu oq ] T ，Δi o ＝[Δi od Δi oq ] T (24)
G de1 ＝[-Cu oq Cu od ] T G de2 ＝[-Li Lq Li Ld ] T (27)
in the step S2 of the process,
deducing according to the small signal model in the step S1
(T 1 +T 3 G ωu -T 4 G uu )Δu o ＝(T 4 G ui -T 2 -T 3 G ωi )Δi o (28)
In the formula (28), the amino acid sequence of the compound,
T 1 ＝G LL G CC -G PWM G i G 1 +I-G PWM G 2 G CC (29)
T 2 ＝G LL -G PWM G 2 (30)
T 3 ＝G LL G ωC +G ωL -G PWM (G i G de1 +G de2 )-G PWM G 2 G ωC (31)
T 4 ＝G PWM G i G u (32)
and then, the following steps are obtained:
further, in step S3, the recovery matrix is:
wherein, the grid impedance matrix is defined as:
further, in the step S4, the step of determining the set of critical stable points is as follows:
a1 is provided with two parameters affecting the stability of the system, namely lambda 1 And lambda (lambda) 2 Establishing a two-dimensional parameter space { lambda } 1 ，λ 2 -selecting a point Λ in the parameter space 0 And ensures that the system can operate stably at this point, which can be represented as Λ in the parameter space 0 (λ 1,0 ，λ 2,0 )；
A2, set Λ 0 (λ 1,0 ，λ 2,0 ) As an initial point, θ 0 In order to search the initial direction of the critical stable point, delta theta is the rotation angle, delta lambda is the searching step length from the ith point to the i+1 point;
a3, let j=0;
a4, let i=0;
a5, solving for lambda i+1 (λ 1,i+1 ，λ 2,i+1 ) The formula is as follows:
λ 1,i+1 ＝λ 1,i +Δλ·cosθ j
λ 2,i+1 ＝λ 2,i +Δλ·sinθ j
a6, obtaining M m The formula is as follows:
M m ＝max{||z g (jω)|| ∞ ·||Y o (jω)|| ∞ }
wherein Z is g Is a grid impedance matrix, Y o Outputting an admittance matrix for the inverter;
a7, judging M m If not less than 1.01, carrying out step A8, otherwise, enabling i=i+1, and returning to step A5;
a8, point-to-point Λ i Sum point lambda i+1 Obtaining a critical stability point Λ by using a dichotomy b,j ；
A9, find θ j+1 The formula is: θ j+1 ＝θ j +△θ；
A10, judging theta j+1 If equal to 2 pi, step A11 is carried out, otherwise j=j+1 is carried out, and step A4 is returned;
a11, obtaining a critical stable point set { Λ } b,j And then get the boundary of the stable domain
Compared with the prior art, the invention has the following beneficial effects.
The traditional stability analysis method only can qualitatively analyze the stability of the system when a single parameter is changed, and can not analyze the stability when two parameters are changed simultaneously.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a block diagram of an ac grid system to which the droop control inverter of the present invention is connected.
Fig. 2 is an equivalent circuit diagram of the grid-connected inverter system equivalent to the cascade system in step S3 of the present invention.
FIG. 3 is a flowchart illustrating the determination of the set of critical stable points in step S4 of the present invention.
Fig. 4 is a schematic diagram of the principle of searching for the critical stable point in step S4 of the present invention.
FIG. 5 is a schematic view of a stable region according to the present invention.
Detailed Description
The invention is further illustrated below with reference to specific examples.
A multi-parameter stable domain analysis method for a droop control inverter grid-connected system comprises the following steps:
s1, establishing a small signal model of a droop control inverter;
s2, deducing output impedance Z of the droop control grid-connected inverter based on the small signal model in the step S1 o ；
S3, equivalent grid-connected inverter systems are formed into cascade systems, and a rate matrix of the cascade systems is determined;
s4, determining two parameters affecting the stability of the system, namely lambda 1 And lambda (lambda) 2 Establishing a parameter space { lambda } 1 ，λ 2 Judging the stability of the cascade system of a group of parameters according to the stability criterion of the matrix infinity norm shown in the formula (1):
||Z g (jω)||∞·||Y o (jω)||∞＜1，ωE(-∞，+∞) (1)
wherein Z is g For the power grid impedance matrix, yo is the inverter output admittance matrix, if the point accords with the formula (1), the point is a critical stable point, the critical stable point is searched in the parameter space, and a critical stable point set { Λ } is obtained b,j Boundaries of the } and stability domains
S5, based on critical stable point set { Λ ] b,j Boundaries of the } and stability domainsAnd (3) carrying out regression on the boundary curve of the stable domain by using a Kernel Ridge Regression (KRR) algorithm.
In the step S1, establishing a small signal model of the droop control inverter includes: modeling an inverter main circuit, droop control loop modeling and voltage and current control double loop modeling;
modeling an inverter main circuit:
the inverter main circuit shown in fig. 1 is modeled according to KVL and KCL laws to filter the voltage u of the capacitor o Current i flowing through inductance L L As state variables, the column writes the state equation:
the inverter model under the dq coordinate system is obtained through coordinate transformation:
subjecting formula (4) and formula (5) to per unit treatment and Laplacian transformation to obtain formula (6) and formula (7), wherein ω b Is the angular frequency reference value, is used for per unit processing,
droop control loop modeling:
the droop control equation in step S1 is shown in formula (8):
wherein: omega nom Rated angular frequency for the inverter; u (u) od-nom Rated output voltage for the inverter; p, Q the inverter outputs active and reactive power; k (k) 1 、k 2 Is the active and reactive sag coefficient; omega * ,Angular frequency reference and voltage amplitude reference, respectively;
the instantaneous output power of the inverter can be obtained by a first-order low-pass filter, and the average output power of the inverter is shown as a formula (9):
where f(s) is a first order low pass filter,ω c cut-off angular frequency for the filter;
voltage-current control double-loop modeling:
PI regulation is adopted for voltage and current loopsJoint device, transfer function of regulator G um 、G im The expression is as shown in formula (10):
k pu 、k pi the proportional coefficients of the voltage loop and the current loop controller are respectively; k (k) iu 、k ii Integrating coefficients of the voltage ring and the current ring controller respectively; thereby, a voltage controller expression (11) and a current controller expression (12) are obtained:
small-signal modeling is performed on the formulas (4) to (12) to obtain formulas (13) to (18), wherein G represents a steady-state operating point of the corresponding variable, delta represents a disturbance component of the corresponding variable,
G LL Δi L +G ωL Δω＝Δu i -Δu o (13)
G CC Δu o +G ωC Δω＝Δi L -Δi o (14)
Δω＝G ωu Δu o +G ωi Δi o (15)
in the method, in the process of the invention,
Δi L ＝[Δi Ld Δi Lq ] T ，Δu i ＝[Δu id Δu iq ] T (21)
G ωu ＝[-k 1 f(s)I od -k 1 f(s)I oq ]G ωi ＝[-k 1 f(s)U od -k 1 f(s)U oq ] (22)
Δu o ＝[Δu od Δu oq ] T ，Δi o ＝[Δi od Δi oq ] T (24)
G de1 ＝[-Cu oq Cu od ] T G de2 ＝[-Li Lq Li Ld ] T (27)
in the step S2 of the process,
deducing according to the small signal model in the step S1
(T 1 +T 3 G ωu -T 4 G uu )Δu o ＝(T 4 G ui -T 2 -T 3 G ωi )Δi o (28)
In the formula (28), the amino acid sequence of the compound,
T 1 ＝G LL G CC -G PWM G i G 1 +I-G PWM G 2 G CC (29)
T 2 ＝G LL -G PWM G 2 (30)
T 3 ＝G LL G ωC +G ωL -G PWM (G i G de1 +G de2 )-G PWM G 2 G ωC (31)
T 4 ＝G PWM G i G u (32)
and then, the following steps are obtained:
in step S3, the three-phase inverter is connected to the ac power grid system, and according to the related theory, the grid-connected inverter system can be regarded as a cascade system as shown in fig. 2, and the inverter works stably under the ideal power grid, so that the admittance matrix Y is output through the inverter o And grid impedance matrix Z g The product of (2) can analyze the stability of the ac cascade system, the product of which is called the rate-return matrix, expressed by equation (34):
wherein, the grid impedance matrix is defined as:
as shown in fig. 3, in the step S4, the step of determining the set of critical stable points is as follows:
a1 is provided with two parameters affecting the stability of the system, namely lambda 1 And lambda (lambda) 2 Establishing a two-dimensional parameter space { lambda } 1 ，λ 2 -selecting a point Λ in the parameter space 0 And ensures that the system can operate stably at this point, which can be represented as Λ in the parameter space 0 (λ 1,0 ，λ 2,0 )；
A2, set Λ 0 (λ 1,0 ，λ 2,0 ) As an initial point, θ 0 In order to search the initial direction of the critical stable point, delta theta is the rotation angle, delta lambda is the searching step length from the ith point to the i+1 point;
a3, let j=0;
a4, let i=0;
a5, solving for lambda i+1 (λ 1,i+1 ，λ 2,i+1 ) The formula is as follows:
λ 1,i+1 ＝λ 1,i +Δλ·cosθ j
λ 2,i+1 ＝λ 2,i +Δλ·sinθ j
a6, obtaining M m The formula is as follows:
M m ＝max{||Z g (jω)|| ∞ ·||Y o (jω)|| ∞ }
wherein Z is g Is a grid impedance matrix, Y o Outputting an admittance matrix for the inverter;
a7, judging M m If not less than 1.01, carrying out step A8, otherwise, enabling i=i+1, and returning to step A5;
a8, point-to-point Λ i Sum point lambda i+1 Obtaining a critical stability point Λ by using a dichotomy b,j ；
A9, find θ j+1 The formula is: θ j+1 ＝θ j +△θ；
A10, judging theta j+1 If equal to 2 pi, step A11 is carried out, otherwise j=j+1 is carried out, and step A4 is returned;
a11, obtaining a critical stable point set { Λ } b,j And then obtain the stable domainBoundary ofStep S5, using the critical stable points obtained in step S4 as a data set, performing regression prediction on the stable domain boundary curve by using a KRR algorithm, wherein for a new sample x ', a predicted value y' can be expressed as a formula (38),
wherein α= (k+λi) -1 Y, where K is an N matrix, K ij ＝k(x i ，x j ) And k (·, ·) is a kernel function, so that regression prediction can be performed on the stable region boundary curve by using a limited critical stable point through a kernel ridge regression algorithm.
The above embodiments are merely illustrative of the principles of the present invention and its effects, and are not intended to limit the invention. Modifications and improvements to the above-described embodiments may be made by those skilled in the art without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications and changes which have been accomplished by those skilled in the art without departing from the spirit and technical spirit of the present invention should be covered by the appended claims.
Claims (4)
1. The multi-parameter stability domain analysis method for the droop control inverter grid-connected system is characterized by comprising the following steps of:
s1, establishing a small signal model of a droop control inverter;
s2, deducing output impedance Z of the droop control grid-connected inverter based on the small signal model in the step S1 o ；
S3, equivalent grid-connected inverter systems are formed into cascade systems, and a rate matrix of the cascade systems is determined;
s4, determining two parameters affecting the stability of the system, namely lambda 1 And lambda (lambda) 2 Establishing a parameter space { lambda } 1 ，λ 2 Matrix infinity norm according to equation (1)Judging the stability of a cascade system of a group of parameters, searching critical stable points in a parameter space, and obtaining a critical stable point set { Λ } b,j Boundaries of the } and stability domains
||Z g (jω)|| ∞ ·||Y o (ωj)|| ∞ ＜1，ω∈(-∞，+∞) (1)
Wherein Z is g Is a grid impedance matrix, Y o Outputting an admittance matrix for the inverter;
s5, based on critical stable point set { Λ ] b,j Boundaries of the } and stability domainsRegression is carried out on the boundary curve of the stable domain by using a kernel ridge regression algorithm;
in the step S4, the step of determining the set of critical stable points is as follows:
a1 is provided with two parameters affecting the stability of the system, namely lambda 1 And lambda (lambda) 2 Establishing a two-dimensional parameter space { lambda } 1 ，λ 2 -selecting a point Λ in the parameter space 0 And ensures that the system can operate stably at this point, which can be represented as Λ in the parameter space 0 (λ 1,0 ，λ 2,0 )；
A2, set Λ 0 (λ 1,0 ，λ 2,0 ) As an initial point, θ 0 In order to search the initial direction of the critical stable point, delta theta is the rotation angle, delta lambda is the searching step length from the ith point to the i+1 point;
a3, let j=0;
a4, let i=0;
a5, solving for lambda i+1 (λ 1,i+1 ，λ 2,i+1 ) The formula is as follows:
λ 1,i+1 ＝λ 1,i +Δλ·cosθ j
λ 2,i+1 ＝λ 2,i +Δλ·sinθ j
a6, calculatingM m The formula is as follows:
M m ＝max{||Z g (jω)|| ∞ ·||Y o (jω)|| ∞ }
wherein Z is g Is a grid impedance matrix, Y o Outputting an admittance matrix for the inverter;
a7, judging M m If not less than 1.01, carrying out step A8, otherwise, enabling i=i+1, and returning to step A5;
a8, point-to-point Λ i Sum point lambda i+1 Obtaining a critical stability point Λ by using a dichotomy b,j ；
A9, find θ j+1 The formula is: θ j+1 ＝θ j +△θ；
A10, judging theta j+1 If equal to 2 pi, step A11 is carried out, otherwise j=j+1 is carried out, and step A4 is returned;
a11, obtaining a critical stable point set { Λ } b,j And then get the boundary of the stable domain
2. The multi-parameter stability domain analysis method of a droop control inverter grid-tie system according to claim 1, wherein in step S1, establishing a small signal model of the droop control inverter comprises: modeling an inverter main circuit, droop control loop modeling and voltage and current control double loop modeling;
modeling an inverter main circuit:
with voltage u of filter capacitor o Current i flowing through inductance L L As state variables, the column writes the state equation:
the inverter model under the dq coordinate system is obtained through coordinate transformation:
subjecting formula (4) and formula (5) to per unit treatment and Laplacian transformation to obtain formula (6) and formula (7), wherein ω b Is the angular frequency reference value, is used for per unit processing,
droop control loop modeling:
the droop control equation in step S1 is shown in equation (8)
Wherein: omega nom Rated angular frequency for the inverter; u (u) od-nom Rated output voltage for the inverter; p, Q the inverter outputs active and reactive power; k (k) 1 、k 2 Is the active and reactive sag coefficient; omega * ,Angular frequency reference and voltage amplitude reference;
the instantaneous output power of the inverter can be obtained by a first-order low-pass filter, and the average output power of the inverter is shown as a formula (9):
where f(s) is a first order low pass filter,ω c cut-off angular frequency for the filter;
voltage-current control double-loop modeling:
the voltage and current loops all adopt PI regulators, and the transfer function of the regulator adopts G um 、G im The expression is as shown in formula (10):
k pu 、k pi the ratio coefficient of the current loop controller to the voltage loop is set; k (k) iu 、k ii Integrating coefficients for the voltage loop and the current loop controller; thereby, a voltage controller expression (11) and a current controller expression (12) are obtained:
3. the multi-parameter stability domain analysis method of a droop control inverter grid-connected system according to claim 2, wherein small signal modeling is performed on formulas (4) to (12) to obtain formulas (13) to (18), wherein G represents a steady-state operating point of a corresponding variable, and Δ represents a disturbance component of the corresponding variable,
G LL Δi L +G ωL Δω＝Δu i -Δu o (13)
G CC Δu o +G ωC Δω＝Δi L -Δi o (14)
Δω＝G ωu Δu o +G ωi Δi o (15)
in the method, in the process of the invention,
Δi L ＝[Δi Ld Δi Lq ] T ，Δu i ＝[Δu id Δu iq ] T (21)
G ωu ＝[-k 1 f(s)I od -k 1 f(s)I oq ]G ωi ＝[-k 1 f(s)U od -k 1 f(s)U oq ] (22)
Δu o ＝[Δu od Δu oq ] T ，Δi o ＝[Δi od Δi oq ] T (24)
G de1 ＝[-Cu oq Cu od ] T G de2 ＝[-Li Lq Li Ld ] T (27)
in step S2, deducing from the small signal model in step S1
(T 1 +T 3 G ωu -T 4 G uu )Δu o ＝(T 4 G ui -T 2 -T 3 G ωi )Δi o (28)
In the formula (28), the amino acid sequence of the compound,
T 1 ＝G LL G CC -G PWM G i G 1 +I-G PWM G 2 G CC (29)
T 2 ＝G LL -G PWM G 2 (30)
T 3 ＝G LL G ωC +G ωL -G PWM (G i G de1 +G de2 )-G PWM G 2 G ωC (31)
T 4 ＝G PWM G i G u (32)
and then, the following steps are obtained:
4. the multi-parameter stability domain analysis method of a droop control inverter grid-tie system according to claim 1, wherein in step S3, the recovery matrix is:
wherein, the grid impedance matrix is defined as:
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015165191A1 (en) * | 2014-04-30 | 2015-11-05 | 湖南大学 | Steady state control method for three-phase double-mode inverter |
| CN110011364A (en) * | 2019-04-29 | 2019-07-12 | 华北电力大学（保定） | A kind of active power load fluctuation that reduces is to the control method of system stabilizing influence |
| CN110165709A (en) * | 2019-07-01 | 2019-08-23 | 湖南大学 | Consider the virtual synchronous machine grid-connected inverting system stability method for improving of sampling time delay |
| CN111355241A (en) * | 2019-12-03 | 2020-06-30 | 上海交通大学 | Alternating current-direct current hybrid micro-grid droop coefficient selection method based on tidal current feasible region |
| CN113131521A (en) * | 2021-04-07 | 2021-07-16 | 国家电网有限公司 | Virtual synchronous machine multi-machine parallel stable control and inertia matching method thereof |
-
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015165191A1 (en) * | 2014-04-30 | 2015-11-05 | 湖南大学 | Steady state control method for three-phase double-mode inverter |
| CN110011364A (en) * | 2019-04-29 | 2019-07-12 | 华北电力大学（保定） | A kind of active power load fluctuation that reduces is to the control method of system stabilizing influence |
| CN110165709A (en) * | 2019-07-01 | 2019-08-23 | 湖南大学 | Consider the virtual synchronous machine grid-connected inverting system stability method for improving of sampling time delay |
| CN111355241A (en) * | 2019-12-03 | 2020-06-30 | 上海交通大学 | Alternating current-direct current hybrid micro-grid droop coefficient selection method based on tidal current feasible region |
| CN113131521A (en) * | 2021-04-07 | 2021-07-16 | 国家电网有限公司 | Virtual synchronous machine multi-machine parallel stable control and inertia matching method thereof |
Non-Patent Citations (1)
| Title |
|---|
| 基于下垂策略的风光储低压微电网优化控制;赵兴勇;张惠生;荀之;王凯武;黄华;;自动化技术与应用(第08期);全文 * |
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