CN109149605B - VSG-based micro-grid transient state adaptive parameter control strategy - Google Patents

VSG-based micro-grid transient state adaptive parameter control strategy Download PDF

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CN109149605B
CN109149605B CN201811187447.9A CN201811187447A CN109149605B CN 109149605 B CN109149605 B CN 109149605B CN 201811187447 A CN201811187447 A CN 201811187447A CN 109149605 B CN109149605 B CN 109149605B
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殷桂梁
董浩
赵晓龙
陈建华
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Yanshan University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract

The invention discloses a VSG-based micro-grid transient state adaptive parameter control strategy, and belongs to the field of micro-grid frequency control. The invention comprises the following steps: constructing a micro-grid system dynamic model based on VSG; carrying out self-adaptive control on the virtual inertia torque and the virtual damping factor of the system model; carrying out stability evaluation on the system energy function; determining the examples and necessary characteristics, and performing simulation analysis on the examples by using matlab/simulink software. According to the method, the dynamic relation between the system inertia and the system frequency offset is established by analyzing the frequency oscillation process, and the virtual inertia factor is changed in real time according to the frequency change, so that the frequency oscillation is effectively inhibited, the system can better cope with transient disturbance, and the system frequency stability is provided.

Description

VSG-based micro-grid transient state adaptive parameter control strategy
Technical Field
The invention relates to the field of micro-grid frequency control, in particular to a transient adaptive parameter control strategy which constructs the relationship between system frequency and system virtual inertia and virtual damping and can solve the problem of frequency oscillation of a system caused by power fluctuation.
Background
Distributed Generation (DG) is mainly classified into renewable energy power Generation, non-renewable energy power Generation, and energy storage technology power Generation. The renewable energy power generation comprises small-sized hydroelectric power generation (10 kW-100 MW), wind power generation, photovoltaic power generation, geothermal power generation and the like, the non-renewable energy power generation comprises micro gas turbine power generation and fuel cell power generation, and the energy storage technology comprises super capacitor and flywheel energy storage technology.
With the increasing permeability of distributed power supplies in recent years, a great deal of green energy is integrated into the power grid, which undoubtedly alleviates the global energy crisis problem. However, due to the inherent intermittency, variability and uncertainty of renewable resources and the reduced inertia of the grid caused by the large access of distributed power sources, the system stability is reduced when the grid is subject to interference or sudden changes, and in turn, the large-scale DG is severely prevented from being connected to the grid. Therefore, in order to better introduce green energy into the microgrid, the Visscher K teaches a virtual synchronous machine concept at the "smart grids for Distribution" conference, which enables the inverter to have inertial characteristics. Subsequently, professor j.driesen and professor Chongchang successively put forward the concepts of "Virtual synchronous generators" and "synchronous generators". Although the three names are different, the control mechanism is approximately the same. The Virtual Synchronous Generator (VSG) control strategy has the advantages that on one hand, the VSG control strategy has the fast response and high controllability of a power electronic device, on the other hand, the performance of the VSG is simulated by introducing a rotor equation, the damping and Virtual inertia can be provided for a system, and the stability of the system is greatly improved. However, when the system is subjected to disturbance or sudden change, the transient capacity of the VSG-based generator set is much smaller than that of a real synchronous generator, which may cause the system to stop working due to rapid oscillation of the frequency, causing great loss to the system.
In order to solve the problem of power grid frequency oscillation, many scholars can utilize the control advantages of other controllers to improve the operation performance of the VSG by introducing the other controllers into the traditional VSG structure, such as the Kerdphol.T. professor simultaneously researching the uncertainty of renewable energy and the real-time load change, and through a novel H-based method∞To solve the problem of frequency disturbances caused by both of these. (see details in Hu Y, Wei W, Peng Y, et al. fuzzy virtual inertia control for virtual synchronization generator [ C)]Ieee,2016:8523 and 8527) but this Control strategy has difficulty achieving optimal configuration of both the performance of each controller and the VSG performance. Therefore, it is difficult to ensure that the system has both strong stability and excellent economy over a wide interference range. And a plurality of scholars analyze the VSG structure, establish a mathematical model capable of expressing the operation state of the microgrid and optimize the system. Y teaches analysis of the frequency stabilization effect of the VSG to improve system stability by adjusting the system parameters of the VSG, but this strategy may lack operability of the control strategy due to ignoring certain physical conditions, etc. (see detail in Hirase Y, Sugimoto K, Sakimoto K, et aln Using a Virtual Synchronous Generator[J].IEEE Journal of Emerging&Selected topocs in Power Electronics,2016, 4(4):1287-1298.) based on this existing literature, improvements in the inertia and damping factors of the system were made. In the VSG control technique, the virtual damping determines the steady-state characteristics of the angular frequency, while the dynamic characteristics can be optimized by the virtual inertia. By reasonably adjusting the two parameters, the VSG has better and flexible control performance. But the adaptive adjustment of the parameters which are rarely involved in the method makes the system unable to change the system structure parameters in real time according to the change of the system frequency, so that the frequency fluctuation of the system caused by the power fluctuation is difficult to suppress by using the existing method.
Disclosure of Invention
The invention aims to establish the relation between system frequency, system virtual inertia torque and virtual damping factors on the basis of a Virtual Synchronous Generator (VSG) control strategy and provide a transient adaptive parameter control strategy, so that the dynamic stability of a system is improved. Compared with the traditional control strategy, the self-adaptive change of the virtual inertia torque can be realized, and the introduced self-adaptive virtual damping factor control can well prevent the virtual inertia torque from being too large or too small, so that the phenomenon of 'super-large inertia' or 'negative inertia' is avoided.
In order to realize the purpose, the following technical scheme is adopted: a VSG-based microgrid transient adaptive parameter control strategy is characterized in that the method comprises the following steps:
step 1, connecting DG with stored energy into a power grid as a virtual synchronous motor through an inverter and an LC filter, and constructing a VSG-based micro-grid system dynamic model; the dynamic model of the microgrid system is a typical second-order model of a virtual synchronous generator based on virtual inertia torque and virtual damping factors;
step 2, establishing a corresponding relation between the virtual inertia torque and the damping factor of the dynamic model of the micro-grid system;
step 3, training the dynamic model of the micro-grid system to configure key parameters;
and 4, controlling the transient state adaptive parameters of the micro-grid based on the VSG by using the dynamic model of the micro-grid system determined based on the steps 2 and 3.
The further technical scheme is that the typical second-order model of the virtual synchronous generator mainly comprises an electromagnetic part and a mechanical motion part
Figure GDA0002635063770000041
Δ＝1-ref＝∫(ω-ωref)dt (2)
Figure GDA0002635063770000042
(4)
Δω＝ω-ωref
Wherein, PmIs mechanical power, PeIs the electromagnetic power, J is the virtual inertial torque, D is the virtual damping factor, omegarefIs a rated angular frequency, omega is the actual angular frequency of the power grid, delta omega is the angular frequency difference, VrefAndreffor the voltage amplitude and phase angle of DG under nominal conditions, E and1is the output voltage magnitude and phase angle of DG, is the phase angle of VSG, and Δ is the phase angle difference of VSG.
The further technical scheme is that the relationship between the virtual inertia torque and the damping factor in the step 2 is as follows:
J＝kJ(ω-ωref)2+J0 (5)
D＝kDJ (6)
in formula (5): j. the design is a square0And J is the steady-state inertia torque and the virtual inertia torque, k, of DGJTo adjust the coefficient, coefficient kJAnd d2ω/dt2Taking an abnormal number;
in formula (6): d is the virtual damping factor, k, of DGDThe damping adjustment coefficient.
The technical scheme is that a VSG-based micro-grid transient adaptive parameter control strategy comprises the following specific steps:
step 1, connecting DG with stored energy into a power grid as a virtual synchronous motor through an inverter and an LC filter, and constructing a VSG-based micro-grid system dynamic model; the dynamic model of the microgrid system is a typical second-order model of a virtual synchronous generator based on virtual inertia torque and virtual damping factors; it is mainly composed of two parts of electromagnetic part and mechanical movement, and is characterized by that
Figure GDA0002635063770000051
Δ＝1-ref＝∫(ω-ωref)dt (2)
Figure GDA0002635063770000052
Δω＝ω-ωref (4)
Wherein, PmIs mechanical power, PeIs the electromagnetic power, J is the virtual inertial torque, D is the virtual damping factor, omegarefIs a rated angular frequency, omega is the actual angular frequency of the power grid, delta omega is the angular frequency difference, VrefAndreffor the voltage amplitude and phase angle of DG under nominal conditions, E and1the amplitude and the phase angle of the output voltage of DG are the phase angle of VSG, and delta is the phase angle difference of VSG;
step 2, establishing a corresponding relation between the virtual inertia torque and the damping factor of the dynamic model of the micro-grid system:
J＝kJ(ω-ωref)2+J0 (5)
D＝kDJ (6)
in formula (5): j. the design is a square0And J is the steady-state inertia torque and the virtual inertia torque, k, of DGJFor adjusting coefficient of inertia torque, coefficient kJAnd d2ω/dt2Taking an abnormal number;
in formula (6): d is the virtual damping factor, k, of DGDIs a damping adjustment coefficient;
and 3, substituting the formulas (3) to (6) into the formula (1) to obtain a novel rotor dynamic swing equation:
Figure GDA0002635063770000053
the formula (7) is linearized and the formula (2) is substituted into it to obtain:
Figure GDA0002635063770000054
k in the formula (8)JThe value ignore is rewritten as:
Figure GDA0002635063770000055
by analyzing the expression (9), which is a typical second-order transfer function, the natural oscillation angular frequency and the damping coefficient can be obtained by the expressions (10) and (11)
Figure GDA0002635063770000061
Figure GDA0002635063770000062
Determining the steady-state inertia torque J according to the natural oscillation frequency and the damping coefficient range of the synchronous generator0And damping adjustment coefficient kDThe numerical range of (a);
adjusting coefficient k for inertia torqueJSince during the deceleration phase, its value is negative; so kJMust satisfy formula (12), i.e.
J0-|kJ|(ω-ωref)2＜0 (12)
So that the inertia torque adjustment coefficient kJThe numerical ranges of (A) are:
Figure GDA0002635063770000063
and 4, controlling the transient state adaptive parameters of the micro-grid based on the VSG by using the dynamic model of the micro-grid system determined based on the steps 2 and 3.
Compared with the prior art, the method has the following advantages:
1. on the basis of a Virtual Synchronous Generator (VSG) control strategy, the relation between system frequency and system virtual inertia torque and virtual damping is established, and a transient adaptive parameter control strategy is provided, so that the system can change system parameters in real time according to the frequency change condition, and the system structure is optimized, thereby improving the dynamic stability of the system.
2. Compared with the traditional control strategy, the self-adaptive change of the virtual inertia torque can be realized, and the introduced self-adaptive virtual damping factor control can well prevent the virtual inertia torque from being too large or too small, so that the phenomenon of 'super-large inertia' or 'negative inertia' is avoided. And the self-adaptive coefficient selection range is analyzed in detail to determine the selection range.
Drawings
Figure 1 is a block diagram of the adaptive VSG control of the method of the present invention.
FIG. 2 is a schematic diagram of the P-f and Q-v control strategies of the method of the present invention.
Figure 3 is a graph of VSG power oscillation and power angle change after system perturbation according to the method of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings:
the invention discloses a VSG-based micro-grid transient state adaptive parameter control strategy, which is characterized by comprising the following specific steps:
step 1, connecting DG with stored energy into a power grid as a virtual synchronous motor through an inverter and an LC filter, and constructing a VSG-based micro-grid system dynamic model; the dynamic model of the microgrid system is a typical second-order model of a virtual synchronous generator based on virtual inertia torque and virtual damping factors; it is mainly composed of two parts of electromagnetic part and mechanical movement, and is characterized by that
Figure GDA0002635063770000071
Δ＝1-ref＝∫(ω-ωref)dt (2)
Figure GDA0002635063770000072
Δω＝ω-ωref (4)
Wherein, PmIs mechanical power, PeIs the electromagnetic power, J is the virtual inertial torque, D is the virtual damping factor, omegarefIs a rated angular frequency, omega is the actual angular frequency of the power grid, delta omega is the angular frequency difference, VrefAndreffor the voltage amplitude and phase angle of DG under nominal conditions, E and1the amplitude and the phase angle of the output voltage of DG are the phase angle of VSG, and delta is the phase angle difference of VSG;
step 2, establishing a corresponding relation between the virtual inertia torque and the damping factor of the dynamic model of the micro-grid system:
J＝kJ(ω-ωref)2+J0 (5)
D＝kDJ (6)
in formula (5): j. the design is a square0And J is the steady-state inertia torque and the virtual inertia torque, k, of DGJFor adjusting coefficient of inertia torque, coefficient kJAnd d2ω/dt2Taking an abnormal number;
in formula (6): d is the virtual damping factor, k, of DGDIs a damping adjustment coefficient;
and 3, substituting the formulas (3) to (6) into the formula (1) to obtain a novel rotor dynamic swing equation:
Figure GDA0002635063770000081
the formula (7) is linearized and the formula (2) is substituted into it to obtain:
Figure GDA0002635063770000082
k in the formula (8)JThe value ignore is rewritten as:
Figure GDA0002635063770000083
by analyzing the expression (9), which is a typical second-order transfer function, the natural oscillation angular frequency and the damping coefficient can be obtained by the expressions (10) and (11)
Figure GDA0002635063770000084
Figure GDA0002635063770000085
Determining the steady-state inertia torque J according to the natural oscillation frequency and the damping coefficient range of the synchronous generator0And damping adjustment coefficient kDThe numerical range of (a);
adjusting coefficient k for inertia torqueJSince during the deceleration phase, its value is negative; so kJMust satisfy formula (12), i.e.
J0-|kJ|(ω-ωref)2＜0 (12)
So that the inertia torque adjustment coefficient kJThe numerical ranges of (A) are:
Figure GDA0002635063770000086
and 4, controlling the transient state adaptive parameters of the micro-grid based on the VSG by using the dynamic model of the micro-grid system determined based on the steps 2 and 3.
Examples
The technical scheme of the invention comprises the following specific steps:
(1) VSG control strategy and control model
(1-1) constructing a VSG-based traditional micro-grid system model
As shown in fig. 1, in a conventional VSG control system, renewable energy such as wind energy, solar energy, etc. may be connected to a power grid through a DG; the DG power module adopts droop control and is connected with the LC filter through the inverter; the filter is composed of an inductance element LfAnd a capacitor element CfThe composition, the influence of ripple in the output power can be reduced by reasonably adjusting the parameters;
constructing a micro-grid system dynamic model based on VSG; the dynamic model of the microgrid system is a typical second-order model of a virtual synchronous generator based on virtual inertia torque and virtual damping factors:
Figure GDA0002635063770000091
Δ＝1-ref＝∫(ω-ωref)dt (2)
Figure GDA0002635063770000092
Δω＝ω-ωref (4)
wherein, PmIs mechanical power, PeIs the electromagnetic power, J is the virtual inertial torque, D is the virtual damping factor, omegarefIs a rated angular frequency, omega is the actual angular frequency of the power grid, delta omega is the angular frequency difference, VrefAndreffor the voltage amplitude and phase angle of DG under nominal conditions, E and1is the output voltage magnitude and phase angle of DG, is the phase angle of VSG, and Δ is the phase angle difference of VSG.
In fig. 1, the DG active power and the reactive power are both controlled by droop, and the output power and the frequency thereof can be obtained by detecting the voltage and the current at the VSG output terminal and calculating;after the output power and the frequency are calculated by the VSG calculation module, the phase angle omega at each moment can be calculated by the integrator and fed to the PWM module and the VrefForming a modulation signal;
(1-2) inertia control and damping control of conventional VSG
In the VSG control strategy, inertia is introduced into the system, the virtual inertia torque J can be adjusted according to the actual running condition of the system, dynamic changes of power and frequency when the system is in fault are relieved, the limitation of physical conditions is avoided, and the disturbance resistance of the system can be improved undoubtedly
But at the same time, the VSG control system has the oscillation characteristics similar to a synchronous motor; when a system has a sudden accident or load shedding change, the dynamic balance of the system energy is broken, and the synchronous generator needs to stabilize the system frequency by increasing or reducing the rotor momentum; the power angle and power oscillation change of the system after disturbance is shown in figure 3, if the damping action of the system is not considered, the active power and frequency of the system can generate constant amplitude oscillation between 2 and 3 points in figure 3 (a) according to the equal area principle; the power difference between 2 and 3 in fig. 3 (a) can be reduced by adjusting the value of the virtual inertia torque J of the system to suppress the frequency oscillation
In order to improve the transient performance of the system, a virtual damping factor D is often introduced into the system, the dynamic characteristic of the angular frequency after system disturbance is improved, and the dynamic characteristic and the virtual inertia torque are matched with each other to inhibit power oscillation; as can be known from the VSG second-order model formula in the step 1-1, in the frequency oscillation process of the system, the power fluctuation of the system can be reduced by increasing or reducing the damping power, namely the system is changed from the original constant-amplitude oscillation between 2 and 3 to the amplitude-reduced oscillation between 2-3-5, so that the system can be more quickly stabilized; it is noted that, as can be seen from the second-order model, the system frequency is stable for the power grid with better quality, i.e., ω ═ ωrefThen the virtual damping factor can only improve the transient performance of the system; for a power grid with general quality, when the system is stable, the frequency may fluctuate slowly, and the virtual damping factor may also affect the steady-state performance of the system
In the traditional frequency oscillation process, the transient effect can be improved by configuring the virtual inertia torque J and the virtual damping factor D; however, the control strategy has obvious disadvantages, and because the virtual inertia torque and the virtual damping factor are both constant values, the rapidity and the stability of the system cannot be optimized, that is, when the virtual inertia torque J is selected to be too large, although the stability of the system can be improved, the response time of the system is slowed down, and the energy storage capacity of the system needs to be improved; when the values of the two are selected too small, the rapidity of the system is greatly improved, but the system is likely to be broken down after suffering from large interference; similarly, when the virtual damping factor D is selected to be too large, the system power overshoot is reduced, but the response time is slowed down, and the adjustment time is increased; when the virtual damping factor D is selected too small, the response time and the adjustment time are increased, but the system power overshoot is increased
(2) Self-adaptive control of VSG virtual inertia torque and virtual damping factor
(2-1) self-adaptive virtual inertia torque and virtual damping factor control
In order to overcome the defects in the traditional VSG power oscillation suppression strategy, the invention provides a transient adaptive parameter control strategy; as can be seen from the analysis of fig. 3, after the disturbance occurs, the VSG output power changes continuously between 2 and 3 from the equilibrium point 1, and finally reaches a stable state at a new equilibrium point under the influence of the virtual inertia torque and the damping factor. In a period of frequency oscillation, the virtual angular speed of the system is increased in the stage a, and the angular speed is greater than the rated angular speed of the power grid, so that the increase of the angular speed of the rotor is limited by the larger inertia of the rotor, and the stage is defined as a rotor angular speed acceleration stage; in the stage b, the virtual angular speed of the system is slowed down, and the angular speed is smaller than the rated angular speed of the power grid, so that the angular speed of the rotor is recovered to a stable value as soon as possible by using smaller rotor inertia, and the stage is defined as a rotor angular speed deceleration stage; in the same way, c is in the acceleration phase and d is in the deceleration phase
From the analysis of the oscillation process in mathematical terms, the acceleration of the angular velocity change is less than zero during the acceleration phases a and c, i.e. d2ω/dt2<0, rotational inertiaThe performance should increase with the increase of the system frequency deviation to suppress the frequency deviation from increasing; during the deceleration phases b and d, the acceleration of the angular velocity variation is greater than zero, i.e. d2ω/dt2>0, the rotational inertia should decrease as the system frequency deviation decreases, so that the frequency returns to a stable value as soon as possible; in summary, the relationship between the adaptive virtual inertia torque and the frequency deviation is as follows:
J＝kJ(ω-ωref)2+J0 (5)
in the formula: j. the design is a square0And J is the steady-state inertia torque and the virtual inertia torque, k, of DGJTo adjust the coefficients. It is noted that the coefficients k and d2ω/dt2Taking an opposite sign, i.e. d2ω/dt2Only positive or negative of k value is determined, and the specific value size is irrelevant to it
However, if the adaptive virtual inertia torque J control strategy is adopted alone, the value is too large or too small in the change process, so that the system rapidity and stability are affected, and therefore the virtual damping factor is changed along with the change of the virtual inertia torque J, and the relationship is as follows:
D＝kDJ (6)
in the formula: d is the actual virtual damping factor, k, of DGDIs a damping adjustment coefficient;
when the system changes the virtual inertia torque according to the change condition of the angular frequency in the oscillation process, the damping of the system is increased or reduced when the virtual inertia torque J is increased or reduced, so that the overshoot of the system can be reduced, and the stability of the system is enhanced; the response time of the system can be improved, the adjustment time can be shortened, and the rapidity of the system can be improved.
(2-2) configuration of self-adaptive virtual inertia torque control key parameters
The main parameters in the control strategy proposed herein are the steady-state inertia torque, the inertia torque adjustment system k and the damping factor D; the method comprises the following specific steps:
substituting the expressions (3) to (6) into the expression (1) can obtain a novel rotor dynamic swing equation as follows:
Figure GDA0002635063770000121
to determine steady-state inertia torque J0And damping adjustment coefficient kDA value obtained by linearizing equation (7) and substituting equation (2) therein:
Figure GDA0002635063770000131
since the time period taken for linearization is extremely small, k can be ignoredJThe value, so equation (8) can be rewritten as:
Figure GDA0002635063770000132
by analyzing the expression (9), which is a typical second-order transfer function, the natural oscillation angular frequency and the damping coefficient can be obtained by the expressions (10) and (11)
Figure GDA0002635063770000133
Figure GDA0002635063770000134
Therefore, when setting the VSG parameter, the steady-state inertia torque J can be determined according to the natural oscillation frequency and the damping coefficient range of the synchronous generator0And damping adjustment coefficient kDThe numerical range of (a);
adjusting coefficient k for inertia torqueJSince during the deceleration phase, its value is negative; so kJMust satisfy formula (12), i.e.
J0-|kJ|(ω-ωref)2＜0 (12)
So that the inertia torque adjustment coefficient kJThe numerical ranges of (A) are:
Figure GDA0002635063770000135
(3) stability assessment of system energy function
The time domain simulation method of the transient analysis of the power system has the defects of poor computing capability, undefined stability and the like, so that the Lyapunov direct method becomes an attention point for researching the transient stability at the present stage; the lyapunov function stability analysis criteria for the n-dimensional system are as follows:
for a dynamic system with a state quantity x, if it can be defined in a positive definite scalar function V (x) (i.e. V (x)), (i.e. V (x)), (ii)x＝0＝0,V(x)|x≠0> 0), and the time derivative of V (x)
Figure GDA0002635063770000136
Negative definite (i.e.
Figure GDA0002635063770000137
) The system finally tends to be stable after being disturbed;
for the analytical evaluation of the control strategy proposed herein for virtual inertia torque, the total transient energy of the system is determined by the kinetic energy EkAnd potential energy EpThe relation is as follows:
Figure GDA0002635063770000141
v is the total energy of the transient system after the system is disturbed,1and (a)1+ delta) is the start and end phase angles of the VSG at two optional times during oscillation of the system frequency, with the state variable x ═ x1,x2]＝[Δ,Δω]
For convenience of formula derivation, formula (2) is substituted into formula (7) to obtain:
Figure GDA0002635063770000142
from expression (15), one can see:
Figure GDA0002635063770000143
in the formula, a, b and c, the expression of the parameters is as follows:
Figure GDA0002635063770000144
substituting equation (16) into equation (14) yields a system lyapunov function as:
Figure GDA0002635063770000145
the expression (16) is re-described with the state variables as:
Figure GDA0002635063770000146
when the state variable x is known from expression (19)1The range is [ -pi, pi-2 [ -pi1]Then, the system lyapunov function v (x) is a positive number, and its derivative function is:
Figure GDA0002635063770000151
in the oscillation process of the system, when the angular speed of the system is in an acceleration stage,
Figure GDA0002635063770000152
negative values, corresponding to kJTaking a positive value, then
Figure GDA0002635063770000153
Its value is negative; when the angular velocity of the system is in the deceleration phase,
Figure GDA0002635063770000154
is a positive value, correspondinglykJTaking a negative value, then
Figure GDA0002635063770000155
When the value is negative, the following conditions are satisfied:
Figure GDA0002635063770000156
therefore, as can be seen from expressions (19) to (21), the system meets the Lyapunov function stability judgment standard, and can stably operate by reasonably adjusting each parameter;
(4) carrying out simulation analysis on the examples by matlab software;
(4-1) determining examples and essential features thereof
Based on the built model, the capacitance of a direct current side is selected to be 100mF, the voltage stability value of the direct current side is 1kV, the filter inductance is 5mH, the equivalent resistance is 0.1, the duty ratio change rate is 0.28kJ < -1 >, the integral time coefficient is 0.001s, the proportionality coefficient is 0.5333V/A, the stable capacity of a direct current link is 50kJ, the ultimate capacity of the direct current link is 60.5kJ, and the rated value of the voltage of a power grid is set to be 5mH
Figure GDA0002635063770000157
(4-2) simulation analysis is carried out on the sample by matlab/simulink software
Through the application of combining VSG and self-adaptive control, the fault period and the fault clearing system recovery stage under the action of disturbance variables are directly judged from simulation results, the fluctuation amplitude of the system frequency is smaller than that of the traditional VSG control strategy, the fluctuation time is shorter, and the system can be more stable;
the above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solution of the present invention by those skilled in the art should fall within the protection scope defined by the claims of the present invention without departing from the spirit of the present invention.

Claims (1)

1. A VSG-based microgrid transient adaptive parameter control strategy is characterized by comprising the following steps:
step 1, connecting DG with stored energy into a power grid as a virtual synchronous motor through an inverter and an LC filter, and constructing a VSG-based micro-grid system dynamic model; the dynamic model of the microgrid system is a typical second-order model of a virtual synchronous generator based on virtual inertia torque and virtual damping factors; it is mainly composed of two parts of electromagnetic part and mechanical movement, and is characterized by that
Figure FDA0002635063760000011
Δ＝1-ref＝∫(ω-ωref)dt (2)
Figure FDA0002635063760000012
Δω＝ω-ωref (4)
Wherein, PmIs mechanical power, PeIs the electromagnetic power, J is the virtual inertial torque, D is the virtual damping factor, omegarefIs a rated angular frequency, omega is the actual angular frequency of the power grid, delta omega is the angular frequency difference, VrefAndreffor the voltage amplitude and phase angle of DG under nominal conditions, E and1the amplitude and the phase angle of the output voltage of DG are the phase angle of VSG, and delta is the phase angle difference of VSG;
step 2, establishing a corresponding relation between the virtual inertia torque and the damping factor of the dynamic model of the micro-grid system:
J＝kJ(ω-ωref)2+J0 (5)
D＝kDJ (6)
in formula (5): j. the design is a square0And J is the steady-state inertia torque and the virtual inertia torque, k, of DGJFor adjustment of inertia torqueCoefficient, coefficient kJAnd d2ω/dt2Taking an abnormal number;
in formula (6): d is the virtual damping factor, k, of DGDIs a damping adjustment coefficient;
and 3, substituting the formulas (3) to (6) into the formula (1) to obtain a novel rotor dynamic swing equation:
Figure FDA0002635063760000021
the formula (7) is linearized and the formula (2) is substituted into it to obtain:
Figure FDA0002635063760000022
k in the formula (8)JThe value ignore is rewritten as:
Figure FDA0002635063760000023
by analyzing the expression (9), which is a typical second-order transfer function, the natural oscillation angular frequency and the damping coefficient can be obtained by the expressions (10) and (11)
Figure FDA0002635063760000024
Figure FDA0002635063760000025
Determining the steady-state inertia torque J according to the natural oscillation frequency and the damping coefficient range of the synchronous generator0And damping adjustment coefficient kDThe numerical range of (a);
adjusting coefficient k for inertia torqueJSince during the deceleration phase, its value is negative; so kJMust satisfy formula (12), i.e.
J0-|kJ|(ω-ωref)2＜0 (12)
So that the inertia torque adjustment coefficient kJThe numerical ranges of (A) are:
Figure FDA0002635063760000026
and 4, controlling the transient state adaptive parameters of the micro-grid based on the VSG by using the dynamic model of the micro-grid system determined based on the steps 2 and 3.
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