CN110311375B - Micro-grid transient stability control method containing multiple virtual synchronous machines - Google Patents

Micro-grid transient stability control method containing multiple virtual synchronous machines Download PDF

Info

Publication number
CN110311375B
CN110311375B CN201910687024.1A CN201910687024A CN110311375B CN 110311375 B CN110311375 B CN 110311375B CN 201910687024 A CN201910687024 A CN 201910687024A CN 110311375 B CN110311375 B CN 110311375B
Authority
CN
China
Prior art keywords
virtual
vsgi
microgrid
grid
power
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201910687024.1A
Other languages
Chinese (zh)
Other versions
CN110311375A (en
Inventor
马大中
李林娟
刘丽月
王睿
胡旌伟
孙秋野
林森
安恩慧
李宇阳
程科
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Northeastern University China
Original Assignee
Northeastern University China
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Northeastern University China filed Critical Northeastern University China
Priority to CN201910687024.1A priority Critical patent/CN110311375B/en
Publication of CN110311375A publication Critical patent/CN110311375A/en
Application granted granted Critical
Publication of CN110311375B publication Critical patent/CN110311375B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a micro-grid transient stability control method containing multiple virtual synchronous machines, and belongs to the technical field of micro-grid transient stability analysis and control. The method fully considers virtual inertia, virtual damping, a droop coefficient of a virtual speed regulator and a droop coefficient of a reactive control loop, constructs an energy function model of a microgrid with a plurality of virtual synchronous machines running in parallel, and analyzes and judges the stability of the system based on the energy function. And an adaptive control method based on an energy function is adopted to respectively carry out adaptive real-time adjustment on virtual inertia, virtual damping, a virtual speed regulator droop coefficient and a reactive control ring droop coefficient in the VSG, so that the system energy after system failure is reduced, the attraction domain of the system is enlarged, the process that the system energy reaches critical energy is slowed down, effective time is strived for failure removal, and the transient stability of the system is improved.

Description

Micro-grid transient stability control method containing multiple virtual synchronous machines
Technical Field
The invention relates to the technical field of micro-grid transient stability analysis and control, in particular to a micro-grid transient stability control method with multiple virtual synchronous machines.
Background
In recent years, depletion of fossil energy and exacerbation of environmental crisis have promoted rapid development of distributed power generation technology. Micro-grids are becoming increasingly popular in modern power networks as systems of Distributed Generation (DG), energy storage devices, power electronic converters, loads, and so on. A virtual synchronous generator control strategy (VSG) introduces an electromechanical transient equation similar to that of a traditional synchronous generator SG into a control link of a power electronic converter, and a distributed power supply can simulate the external operation characteristics of the SG such as active frequency modulation and reactive power voltage regulation, and also has dynamic characteristics such as inertia characteristic and damping characteristic. When the micro-grid or the large-grid is disturbed or fails, the VSG can effectively adjust the frequency and the voltage and maintain the stability of the system. In addition, virtual inertia in an active control loop (APCM), a virtual damping coefficient and a droop coefficient in a VG and a droop coefficient in a reactive control loop (QPCM) can be flexibly adjusted according to actual needs to improve the stability of the system, and corresponding parameters of the traditional SG are fixed.
The existing transient stability analysis of the microgrid with the VSG is basically implemented according to two indexes of frequency change rate and frequency change after the system is disturbed, and the transient stability control is basically implemented by adjusting two parameters of virtual inertia or virtual damping to improve the frequency and active power output of the system. Although convenient and direct, the analysis method is single, the analysis of the fault conditions such as voltage dip, short circuit and the like is slightly insufficient, and the change mechanism and the reaction change essence of the system cannot be explained only by the frequency. For the VSG, in addition to the virtual inertia and damping coefficients, the droop coefficient in VG and that of QPCM may also affect its transient stability. In addition, the transient stability analysis based on frequency needs time domain simulation to verify the effect of the VSG on the transient stability of the microgrid, the stability margin of the system is difficult to judge, the disturbed degree of the system cannot be quantitatively measured, and when the microgrid comprises a plurality of VSGs which are connected in parallel, the frequency deviation among different VSGs also easily influences the accuracy of the analysis.
Disclosure of Invention
In view of the above-mentioned deficiencies of the prior art, the present invention provides a method for controlling transient stability of a micro-grid including multiple virtual synchronous machines.
In order to solve the above technical problems, the technical solution adopted by the present invention is a method for controlling transient stability of a microgrid having multiple virtual synchronizers, wherein a flow of the method is shown in fig. 1, and the method comprises the following steps:
step 1: constructing a power balance model of a grid-connected microgrid with a plurality of VSGs running in parallel, wherein the model comprises a VSG equivalent output power model, a connection line equivalent transmission power model and a power balance equation of the grid-connected microgrid;
step 1.1: constructing a VSG equivalent output power model, wherein the VSG equivalent output power model comprises a parameter virtual speed regulator VG, a virtual inertia and a virtual damping coefficient and comprises an active control module APCM and a reactive control module QPCM;
step 1.1.1: the active control module APCM is composed of VG and a virtual mechanical rotation module, and simulates the mechanical motion equations of a speed regulator and a rotor of the SG respectively, and the active control module APCM specifically comprises the following components:
Figure BDA0002146672940000021
wherein, P ei 、P refi An active power reference value set for the output active power of the ith VSG (VSGi, i =1,2.., N) in the microgrid and the controller respectively;
Figure BDA0002146672940000022
the virtual moment of inertia and the virtual damping coefficient are respectively VSGi and are used for simulating the rotor motion characteristic of SG; omega n 、ω i Rated angular velocity of VSGi and virtual rotor angular velocity of VSGi, ω ni ＝ω n -ω i Represents the difference between the two; delta ni The relative displacement angle of the VSGi virtual rotor is represented, and the phase angle difference between the output voltage and the rated voltage in the QPCM is also represented;
Figure BDA0002146672940000023
and
Figure BDA0002146672940000024
the virtual mechanical power and the virtual droop coefficient of the VG output in VSGi are represented.
Step 1.1.2: the reactive control module QPCM, which is for simulating the reactive-voltage regulation characteristics of the VSG, is specifically as follows:
Figure BDA0002146672940000025
wherein Q is ei ，Q refi The reactive power reference value is respectively set for the output reactive power of the VSGi and the controller; e i 、E n Respectively representing the voltage amplitude and the rated voltage amplitude of the VSGi output;
Figure BDA0002146672940000026
the QPCM droop coefficient.
Step 1.2: constructing an equivalent transmission power model of a contact line:
Figure BDA0002146672940000027
wherein,
Figure BDA0002146672940000028
respectively representing equivalent active and reactive transmission power of VSGi in the micro-grid connected with the grid;
Figure BDA0002146672940000029
representing the transmission line impedance and the inductive reactance, respectively;
step 1.3: considering the system load condition, establishing a power balance equation of the grid-connected microgrid:
Figure BDA0002146672940000031
wherein, P Li 、Q Li Respectively representing the active power and the reactive power of a load connected with the microgrid;
step 2: the first-time integration method is utilized to construct a Lyapunov energy function of a grid-connected microgrid with VG, virtual inertia and virtual damping coefficient considered and with multiple VSGs running in parallel, and the total energy V in the microgrid system is calculated TOT ；
Step 2.1: constructing a kinetic energy total energy function V considering weight relation KE ：
Step 2.1.1: setting weight factors for kinetic energy items
Figure BDA0002146672940000032
Wherein M is ki I =1,2,3, a threshold value for the kinetic energy function;
step 2.1.2: the method comprises the following steps of constructing kinetic energy caused by acceleration/deceleration of virtual rotors simulated by virtual inertia of all VSGs in the microgrid, and specifically comprising the following steps:
Figure BDA0002146672940000033
wherein M is k1 As a function of kinetic energy V KE1 A threshold value of (d);
step 2.1.3: the method comprises the following steps of constructing a system kinetic energy function consumed by a virtual damping coefficient and a droop coefficient of a virtual speed regulator, and specifically comprising the following steps:
Figure BDA0002146672940000034
wherein, t s In order to be the time when the failure occurs,
Figure BDA0002146672940000035
representing the angular frequency of rotation, M, at the point of stable equilibrium of the system after a fault k2 As a function of kinetic energy V KE2 A threshold value of (d);
step 2.1.4: the kinetic energy consumed in performing one adjustment of frequency and power is constructed as follows:
Figure BDA0002146672940000036
wherein, χ = d ω/dt, M k3 As a function of kinetic energy V KE3 A threshold value of (d);
step 2.1.5: constructing a kinetic energy total energy function V according to the steps 2.1.1-2.14 KE ：
V KE ＝(V KE1 +V KE2 +V KE3 ) (8)
Step 2.2: constructing a potential energy function V considering the weight relation PE ：
Step 2.2.1: setting weight factors of different potential energy items
Figure BDA0002146672940000041
Wherein M is pi Threshold for potential energy function, i =1,2,3,4;
step 2.2.2: the method comprises the following steps of constructing potential energy functions caused by active reference power and active loads of all VSGs in the micro-grid system, and specifically comprising the following steps:
Figure BDA0002146672940000042
wherein M is p1 As a function of potential energy V PE1 A threshold value of (d);
step 2.2.3: constructing a potential energy function caused by reactive reference power and reactive load of the VSG, which comprises the following steps:
Figure BDA0002146672940000043
wherein,
Figure BDA0002146672940000044
amplitude of the system voltage after a fault, M p2 As a function of potential energy V PE2 A threshold value of (d);
step 2.2.4: constructing a potential energy function represented by active power transmitted on a connecting line between the VSG and a power grid, wherein the potential energy function is as follows:
Figure BDA0002146672940000045
wherein,
Figure BDA0002146672940000046
Figure BDA0002146672940000047
is the phase angle of the system voltage after the fault, M p3 As a function of potential energy V PE3 A threshold value of (d);
step 2.2.5: constructing a potential energy function represented by reactive power transmitted on a connecting line between the VSG and the power grid, wherein the potential energy function is as follows:
Figure BDA0002146672940000048
wherein, M p4 As a function of potential energy V PE4 A threshold value of (d);
step 2.2.6: according to the steps 2.2.1-2.2.5, a potential energy total energy function is constructed, which is as follows:
V PE ＝V PE1 +V PE2 +V PE3 +V PE4 (13)
step 2.3: calculating total energy function V of grid-connected microgrid TOT ：
V TOT ＝V PE +V KE 。 (14)
And step 3: setting a threshold value of an energy function according to the field requirement, and judging the running state of the system;
Figure BDA0002146672940000051
wherein M is AVE Threshold value of energy function, V, for stable operation of system TOT Is the total energy in the microgrid system.
Step 4, if the system runs stably, executing the step 2 to continue calculating;
and 5: if the system fails, calculating the critical energy V of the system by adopting a dominant unstable balance point method cr ；
Step 5.1: calculating the time from the system fault track to the fault clearing time and the exit point, specifically as follows:
Figure BDA0002146672940000052
and step 5.2: taking the exit point as an initial value, and calculating the minimum gradient point of the gradient system after the fault by adopting a Gear method, wherein the method specifically comprises the following steps:
Figure BDA0002146672940000053
wherein,
Figure BDA0002146672940000054
is the minimum gradient point;
step 5.3: at the point of minimum gradient
Figure BDA0002146672940000055
As an initial value, solving a power balance equation of the system to obtain a stable balance point and a dominant unstable balance point of the system, which are specifically as follows:
Figure BDA0002146672940000056
step 5.4: calculating critical energy of the microgrid system according to state quantity at dominant unstable balance point of the system
Figure BDA0002146672940000061
Wherein,
Figure BDA0002146672940000062
respectively setting a stable balance point and an unstable balance point of VSGi in the microgrid after the system fault;
Figure BDA0002146672940000063
respectively representing the virtual inertia, the virtual damping coefficient, the VG droop coefficient and the QPCM droop coefficient of VSGi during the stable operation of the microgrid;
Figure BDA0002146672940000064
representing the angular frequency of rotation, the phase angle and the amplitude of the voltage, χ = d ω/dt, t, respectively, at the point of stable equilibrium of the system after a fault s Is the time of occurrence of the failure.
Step 6: starting a self-adaptive control method of VSG virtual inertia, a virtual damping coefficient, a VG droop coefficient and a QPCM droop coefficient based on an energy function;
step 6.1: the VSG virtual inertia self-adaptive control method based on the energy function comprises the following steps:
Figure BDA0002146672940000065
wherein,
Figure BDA0002146672940000066
respectively the virtual inertia and the maximum value of the virtual inertia, M, of VSGi when the micro-grid operates stably AVE Is a threshold value as a function of the total energy.
Step 6.2: the self-adaptive control method of the VSG virtual damping coefficient based on the energy function comprises the following steps:
Figure BDA0002146672940000067
wherein,
Figure BDA0002146672940000068
and respectively representing the virtual damping coefficient and the maximum value of the virtual damping coefficient of VSGi when the micro-grid operates stably.
Step 6.3: designing an adaptive control method of a VG droop coefficient based on an energy function:
Figure BDA0002146672940000071
wherein,
Figure BDA0002146672940000072
and respectively representing the VG droop coefficient and the VG droop coefficient minimum value of VSGi in the stable operation of the microgrid.
Step 6.4: the self-adaptive control method for designing the QPCM droop coefficient based on the energy function comprises the following steps:
Figure BDA0002146672940000073
in the formula,
Figure BDA0002146672940000074
and respectively representing the maximum value of the QPCM droop coefficient and the QPCM droop coefficient of the VSGi during the stable operation of the micro-grid.
And 7: judging whether the fault is cleared;
and 8: step 2 is executed to calculate the total energy V in the microgrid system at the moment of fault clearance cl ；
And step 9: total energy V in microgrid system at fault clearing time cl And critical energy V cr And comparing, analyzing and judging the stability of the microgrid.
Figure BDA0002146672940000075
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in:
1. in the invention, the virtual inertia, the virtual damping, the droop coefficient of the virtual speed regulator and the droop coefficient of the reactive control loop are considered, and the energy function model of the microgrid with a plurality of virtual synchronous machines running in parallel is constructed, so that the influence of disturbance on the microgrid system can be analyzed, and the change of system energy after a fault occurs can be reflected.
2. According to the invention, the stability of the system is analyzed and judged based on the energy function, when the system fails, the change of the system energy is far greater than the change of the frequency, so that the detection and calculation are convenient, the sensitivity to the failure is high, the timely and effective control can be favorably carried out at the initial stage of the failure, the damage of the failure to the system is reduced, and the transient stability of the system is improved.
3. According to the invention, a self-adaptive control method of 4 VSG parameters based on an energy function is adopted for the first time in a microgrid with a plurality of virtual synchronous machines running in parallel, the self-adaptive control method of the 4 parameters is respectively designed according to the influence of virtual inertia, virtual damping, a virtual speed regulator droop coefficient and a reactive control ring droop coefficient in the VSG on system energy, the system energy after system failure is reduced through real-time self-adaptive adjustment of the VSG parameters, the attraction domain of the system is increased, the process that the system energy reaches critical energy is slowed down, effective time is strived for fault removal, and the transient stability of the system is improved.
4. The transient stability of the micro-grid with a plurality of virtual synchronous machines running in parallel is analyzed by using the transient energy after the fault, so that the transient stability of the system can be judged, and the stability margin of the system can be quantitatively measured.
Drawings
FIG. 1 is a flowchart of a method for controlling transient stability of a micro-grid including multiple virtual synchronizers according to the present invention;
FIG. 2 is a system diagram and a control structure diagram of a virtual synchronous machine according to an embodiment of the present invention;
FIG. 3 is an equivalent circuit diagram of an embodiment of the present invention;
FIG. 4 is a diagram of energy variation for a microgrid system according to an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating adaptive control changes of a virtual inertia, a virtual damping coefficient, a VG droop coefficient, and a QPCM droop coefficient of a VSG according to an energy function in an embodiment of the present invention;
fig. 6 is a schematic diagram of actual values of the virtual inertia, the virtual damping coefficient, the VG droop coefficient, and the QPCM droop coefficient of the VSG according to the embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
In this embodiment, a grid-connected microgrid including three virtual synchronous machines operating in parallel is used for analysis, and a system structure diagram and a control block diagram of a VSG of the embodiment are shown in fig. 2. Three VSGs are operated in parallel to supply power to a load and are connected with a power grid through a communication line, an equivalent circuit diagram is shown in figure 3, the three VSGs have the same rated capacity, the active rated power is 6kW, the reactive rated power is 1kVA, the rated voltage is 380V, the rated angular speed is 314rad/s, and the VSGs and the power grid jointly supply power to the load (20kW 4 kVA).
Step 1: constructing a power balance model of a grid-connected microgrid with three VSGs running in parallel, wherein the model comprises a VSG equivalent output power model, a connection line equivalent transmission power model and a power balance equation of the grid-connected microgrid;
step 1.1: constructing a VSG equivalent output power model, wherein the VSG equivalent output power model comprises a parameter virtual speed regulator VG, a virtual inertia and a virtual damping coefficient and comprises an active control module APCM and a reactive control module QPCM;
step 1.1.1: the active control module APCM consists of two parts, namely a VG and a virtual mechanical rotation module, and respectively simulates a SG speed regulator and a mechanical motion equation of a rotor, wherein the active control module APCM specifically comprises the following components:
Figure BDA0002146672940000091
wherein, P ei 、P refi An active power reference value set for the output active power of the ith VSG (VSGi, i =1,2,3) and the controller in the micro-grid respectively;
Figure BDA0002146672940000092
the virtual inertia moment and the virtual damping coefficient are respectively the VSGi and are used for simulating the movement characteristic of the rotor of the SG; omega n 、ω i Rated angular velocity of VSGi and virtual rotor angular velocity of VSGi, ω ni ＝ω n -ω i Represents the difference between the two; delta ni The relative displacement angle of the VSGi virtual rotor is represented, and the phase angle difference between the output voltage and the rated voltage in the QPCM is also represented;
Figure BDA0002146672940000093
and
Figure BDA0002146672940000094
the virtual mechanical power and the virtual droop coefficient of the VG output in VSGi are represented. In this embodiment, three VSGs have the same rated capacity and have an active power reference value P refi Are all 6kW and the rated angular velocity omega n ＝314rad/s；
Step 1.1.2: the reactive control module QPCM, which is for simulating the reactive-voltage regulation characteristics of the VSG, is specifically as follows:
Figure BDA0002146672940000095
wherein Q is ei ，Q refi The reactive power reference value is respectively set for the output reactive power of the VSGi and the controller; e i 、E n Respectively representing the voltage amplitude and the rated voltage amplitude output by VSGi;
Figure BDA0002146672940000096
the QPCM droop coefficient. Reactive power reference value Q of three VSGs in this embodiment refi All of which are 1kVA, rated voltage amplitude E of the microgrid n ＝380V；
Step 1.2: constructing an equivalent transmission power model of a contact line:
Figure BDA0002146672940000097
wherein,
Figure BDA0002146672940000098
respectively representing equivalent active and reactive transmission power of VSGi in the micro-grid connected with the grid;
Figure BDA0002146672940000099
representing the transmission line impedance and the inductive reactance, respectively; impedance and inductive reactance of transmission line in this embodiment
Figure BDA00021466729400000910
0.64 omega/km and 0.10 omega/km respectively, amplitude E of rated voltage of the microgrid n =380V, phase angle δ n ＝0°；
Step 1.3: considering the system load condition, establishing a power balance equation of the grid-connected microgrid:
Figure BDA0002146672940000101
wherein, P Li 、Q Li Respectively representing the active power and the reactive power of a load connected with the microgrid; active power P of load connected to microgrid in this embodiment Li Reactive power Q Li 20kW and 4kVA respectively;
step 2: the first-time integration method is utilized to construct a Lyapunov energy function of a grid-connected microgrid with VG, virtual inertia and virtual damping coefficient considered and with multiple VSGs running in parallel, and the total energy V in the microgrid system is calculated TOT ；
Step 2.1: constructing a kinetic energy total energy function V considering weight relation KE ：
Step 2.1.1: setting weight factors for kinetic energy items
Figure BDA0002146672940000102
Wherein M is ki Threshold for kinetic energy function, i =1,2,3;
step 2.1.2: the method comprises the following steps of constructing kinetic energy caused by virtual rotor acceleration/deceleration simulated by virtual inertia of all VSGs in the microgrid, and specifically comprising the following steps:
Figure BDA0002146672940000103
wherein, M k1 ＝140；
Step 2.1.3: the method comprises the following steps of constructing a system kinetic energy function consumed by a virtual damping coefficient and a droop coefficient of a virtual speed regulator, and specifically comprising the following steps:
Figure BDA0002146672940000104
wherein, t s ＝6s，M k2 ＝170，
Figure BDA0002146672940000105
Representing the rotational angular frequency at the stable equilibrium point of the system after the fault;
step 2.1.4: the kinetic energy consumed in performing one adjustment of frequency and power is constructed as follows:
Figure BDA0002146672940000106
wherein, χ = d ω/dt, M k3 ＝500；
Step 2.1.5: constructing a kinetic energy total energy function V according to the steps 2.1.1-2.14 KE ：
V KE ＝(V KE1 +V KE2 +V KE3 ) (8)
Step 2.2: constructing a potential energy function V considering the weight relation PE ：
Step 2.2.1: setting weight factors of different potential energy items
Figure BDA0002146672940000111
Wherein M is pi Threshold for potential energy function, i =1,2,3,4;
step 2.2.2: the method comprises the following steps of constructing potential energy functions caused by active reference power and active loads of all VSGs in the micro-grid system, and specifically comprising the following steps:
Figure BDA0002146672940000112
wherein M is P1 ＝1.8×10 3 ；
Step 2.2.3: constructing a potential energy function caused by reactive reference power and reactive load of the VSG, which comprises the following steps:
Figure BDA0002146672940000113
wherein,
Figure BDA0002146672940000114
amplitude of the system voltage after a fault, M P2 ＝5×10 3 ；
Step 2.2.4: constructing a potential energy function represented by active power transmitted on a connecting line between the VSG and a power grid, wherein the potential energy function is as follows:
Figure BDA0002146672940000115
wherein,
Figure BDA0002146672940000116
Figure BDA0002146672940000117
is the phase angle of the system voltage after the fault, M P3 ＝1.12×10 3 ；
Step 2.2.5: constructing a potential energy function represented by reactive power transmitted on a connecting line between the VSG and the power grid, wherein the potential energy function is as follows:
Figure BDA0002146672940000118
wherein M is P4 ＝7.33×10 3 ；
Step 2.2.6: according to the steps 2.2.1-2.2.5, a potential energy total energy function is constructed, which is as follows:
V PE ＝V PE1 +V PE2 +V PE3 +V PE4 (13)
step 2.3: calculating total energy function V of grid-connected microgrid TOT ：
V TOT ＝V PE +V KE 。 (14)
In the embodiment, when a plurality of VSGs running in parallel are designed to stably run for 6s, a short-circuit fault occurs at a grid-connected position, and the fault is removed at 6.1 s. When the micro-grid operates stably, the total energy of the micro-grid is 1kJ, and after a fault occurs, the change of the total energy is shown in fig. 4.
And step 3: setting a threshold value of an energy function according to the field requirement, and judging the running state of the system;
Figure BDA0002146672940000121
wherein M is AVE Threshold value of energy function, V, for stable operation of system TOT Is the total energy in the microgrid system.
In order to make the system more sensitive to faults in this embodiment, a threshold M for the total energy function is set AVE ＝V TOT =1kJ, in the period from 5.8s to 6s in fig. 4, the total energy of the system calculated is less than or equal to 1kJ, and the system is judged to be in a stable state;
step 4, if the system runs stably, executing the step 2 to continue calculating;
when 6s, calculating the total energy V of the system TOT When the voltage is more than 1kJ, the system can be judged to have faults according to the method.
And 5: if the system fails, calculating the critical energy V of the system by adopting a dominant unstable balance point method cr ；
Step 5.1: calculating the time from the system fault track to the fault clearing time and the exit point, specifically as follows:
Figure BDA0002146672940000122
step 5.2: taking the exit point as an initial value, and calculating the minimum gradient point of the gradient system after the fault by adopting a Gear method, wherein the method specifically comprises the following steps:
Figure BDA0002146672940000123
wherein,
Figure BDA0002146672940000124
is the minimum gradient point;
step 5.3: at the point of minimum gradient
Figure BDA0002146672940000125
As an initial value, solving a power balance equation of the system to obtain a stable balance point and a dominant unstable balance point of the system, which are specifically as follows:
Figure BDA0002146672940000131
step 5.4: calculating critical energy V of the microgrid system according to state quantity at dominant unstable balance point of the system cr ：
Figure BDA0002146672940000132
Wherein,
Figure BDA0002146672940000133
respectively setting a stable balance point and an unstable balance point of VSGi in the microgrid after the system fault;
Figure BDA0002146672940000134
respectively representing the virtual inertia, the virtual damping coefficient, the VG droop coefficient and the QPCM droop coefficient of VSGi during the stable operation of the microgrid;
Figure BDA0002146672940000135
representing the angular frequency of rotation, the phase angle and the amplitude of the voltage, χ = d ω/dt, t, respectively, at the point of stable equilibrium of the system after a fault s Is the time of occurrence of the fault.
Time t when fault occurs in the present embodiment s When =6s, the critical energy V of the system is calculated cr ＝3.25kJ。
Step 6: starting a self-adaptive control method of VSG virtual inertia, a virtual damping coefficient, a VG droop coefficient and a QPCM droop coefficient based on an energy function;
step 6.1: the VSG virtual inertia self-adaptive control method based on the energy function comprises the following steps:
Figure BDA0002146672940000136
wherein the maximum value of virtual inertia of VSGi when the micro-grid is stably operated
Figure BDA0002146672940000137
Threshold value M of energy function AVE ＝1kJ；
Step 6.2: the self-adaptive control method of the VSG virtual damping coefficient based on the energy function comprises the following steps:
Figure BDA0002146672940000141
wherein, when the micro-grid operates stably, the maximum value of the virtual damping coefficient of VSGi
Figure BDA0002146672940000142
Step 6.3: designing an adaptive control method of a VG droop coefficient based on an energy function:
Figure BDA0002146672940000143
wherein, VG droop coefficient minimum value of VSGi during stable operation of micro-grid
Figure BDA0002146672940000144
Step 6.4: the self-adaptive control method for designing the QPCM droop coefficient based on the energy function comprises the following steps:
Figure BDA0002146672940000145
wherein, when the micro-grid is in stable operation, the maximum value of QPCM droop coefficient of VSGi
Figure BDA0002146672940000146
In this embodiment, the adaptive control changes of the virtual inertia, the virtual damping coefficient, the VG droop coefficient, and the QPCM droop coefficient of the VSG according to the energy function are shown in fig. 5, and specific values thereof are shown in fig. 6.
And 7: fault clearance at 6.1 s;
and step 8: step 2 is executed to calculate the total energy V in the microgrid system at the moment of fault clearance cl ；
And step 9: total energy V in microgrid system at fault clearing time cl And a critical energy V cr And comparing, analyzing and judging the stability of the microgrid.
Figure BDA0002146672940000147
In the embodiment, due to the application of the self-adaptive control method, the total energy V in the micro-grid system after the fault is cleared cl Below critical energy V cr The system stability can be judged according to the method.
As shown in fig. 4, if there is no adaptive control of the virtual inertia, the virtual damping coefficient, the VG droop coefficient, and the QPCM droop coefficient, the energy of the system will exceed the system critical energy V at about 6.08s cr The method can judge the system instability, at the moment, if the fault is not removed in time, huge damage is certainly brought to the microgrid system, and after the fault is removed in 6.1s, the total energy of the system is reduced to the critical energy V in 6.12s cr In this case, the system can be judged to be stable. After the adaptive control of the virtual inertia, the virtual damping coefficient, the VG droop coefficient and the QPCM droop coefficient is added, the total energy of the system during the fault period is relatively small, the growth trend is slower than that of the system without the adaptive control, effective time is strived for fault removal,when the fault is removed within 6.1s, the total energy V of the system TOT Does not exceed the critical energy V cr The system has no instability state, and good transient stability is maintained.

Claims (9)

1. A micro-grid transient stability control method containing multiple virtual synchronous machines is characterized by comprising the following steps:
step 1: constructing a power balance model of a grid-connected microgrid with a plurality of VSGs running in parallel, wherein the model comprises a VSG equivalent output power model, a connection line equivalent transmission power model and a power balance equation of the grid-connected microgrid;
step 2: utilizing a first integration method to construct a Lyapunov energy function V of a grid-connected microgrid with VG, virtual inertia and virtual damping coefficients considered and multiple VSGs running in parallel TOT Calculating the total energy in the microgrid system;
and step 3: setting a threshold value of an energy function according to the field requirement, and judging the running state of the system;
step 4, if the system runs stably, executing the step 2 to continue calculating;
and 5: if the system fails, calculating the critical energy V of the system by adopting a dominant unstable balance point method cr ；
Step 6: starting a VSG virtual inertia, a virtual damping coefficient, a VG droop coefficient and a QPCM droop coefficient self-adaptive control method based on an energy function;
and 7: judging whether the fault is cleared;
and step 8: step 2 is executed to calculate the total energy V in the microgrid system at the moment of fault clearance cl ；
And step 9: total energy V in microgrid system at fault clearing time cl And critical energy V cr And comparing, analyzing and judging the stability of the microgrid.
2. The method according to claim 1, wherein the method comprises the steps of: the VSG equivalent output power model in the step 1 comprises a parameter virtual speed regulator VG, a virtual inertia and a virtual damping coefficient and comprises an active control module APCM and a reactive control module QPCM;
the active control module APCM consists of two parts, namely a VG and a virtual mechanical rotation module, and respectively simulates a SG speed regulator and a mechanical motion equation of a rotor, wherein the active control module APCM specifically comprises the following components:
Figure FDA0002146672930000011
wherein, P ei 、P refi An active power reference value set for the output active power of the ith VSG (VSGi, i =1,2.., N) in the microgrid and the controller respectively;
Figure FDA0002146672930000012
the virtual moment of inertia and the virtual damping coefficient are respectively VSGi and are used for simulating the rotor motion characteristic of SG; omega n 、ω i Rated angular velocity of VSGi and virtual rotor angular velocity of VSGi, ω ni ＝ω n -ω i Represents the difference between the two; delta. For the preparation of a coating ni The relative displacement angle of the VSGi virtual rotor is represented, and the phase angle difference between the output voltage and the rated voltage in the QPCM is also represented;
Figure FDA0002146672930000013
and
Figure FDA0002146672930000014
representing the virtual mechanical power and the virtual droop coefficient of the VG output in the VSGi;
the reactive power control module QPCM simulates the reactive-voltage regulation characteristics of the VSG, as follows:
Figure FDA0002146672930000021
wherein Q is ei ，Q refi The reactive power reference value is respectively set for the output reactive power of the VSGi and the controller; e i 、E n Respectively representing the voltage amplitude and the rated voltage amplitude of the VSGi output;
Figure FDA0002146672930000022
the QPCM droop coefficient.
3. The method according to claim 1, wherein the method comprises the steps of: in the step 1, an equivalent transmission power model of the contact line is constructed, and a calculation formula is as follows:
Figure FDA0002146672930000023
wherein,
Figure FDA0002146672930000024
respectively representing equivalent active and reactive transmission power of VSGi in the micro-grid connected with the grid;
Figure FDA0002146672930000025
representing the transmission line impedance and the inductive reactance, respectively; delta ni Representing the phase angle difference between the rated voltage of the power grid and the output voltage of the VSG; e i 、E n Representing the voltage magnitude of the VSGi output and the nominal voltage magnitude, respectively.
4. The method according to claim 1, wherein the method comprises: in the step 1, the power balance equation of the grid-connected microgrid considers the system load condition, and the following equation is constructed:
Figure FDA0002146672930000026
wherein, P refi 、Q refi The active power reference value and the reactive power reference value are respectively set for the controller; omega n 、ω i A nominal angular velocity of VSGi and a virtual rotor angular velocity of VSGi, respectively; e i 、E n Respectively representing the voltage amplitude and the rated voltage amplitude output by VSGi;
Figure FDA0002146672930000027
respectively representing the impedance and the inductive reactance of the communication line;
Figure FDA0002146672930000028
virtual moment of inertia and virtual damping coefficient of VSGi respectively; delta. For the preparation of a coating ni Representing the phase angle difference between the rated voltage of the power grid and the VSGi output voltage; p Li 、Q Li Respectively representing the active power and the reactive power of a load connected with the microgrid;
Figure FDA0002146672930000029
a virtual droop coefficient representing the VG output in VSGi;
Figure FDA00021466729300000210
the QPCM droop coefficient.
5. The method according to claim 1, wherein the method comprises: in the step 2, a first integration method is utilized to construct a Lyapunov energy function V of a grid-connected microgrid with a plurality of VSGs running in parallel and considering VG, virtual inertia and virtual damping coefficient TOT The process of (2) is as follows:
step 2.1: constructing a kinetic energy function V taking into account a weight relationship KE ；
Step 2.1.1: setting weight factors for kinetic energy items
Figure FDA0002146672930000031
Wherein M is ki Threshold for kinetic energy function, i =1,2,3;
step 2.1.2: the method comprises the following steps of constructing kinetic energy caused by acceleration/deceleration of virtual rotors simulated by virtual inertia of all VSGs in the microgrid, and specifically comprising the following steps:
Figure FDA0002146672930000032
wherein,
Figure FDA0002146672930000033
virtual moment of inertia, ω, for VSGi n Rated angular velocity, ω, of VSGi ni Is the difference between the rated angular velocity of VSGi and the virtual rotor angular velocity of VSGi;
step 2.1.3: the method comprises the following steps of constructing a system kinetic energy function consumed by a virtual damping coefficient and a droop coefficient of a virtual speed regulator, wherein the system kinetic energy function comprises the following specific steps:
Figure FDA0002146672930000034
wherein, t s In order to be the time when the failure occurs,
Figure FDA0002146672930000035
indicating the rotational angular frequency at the stable equilibrium point of the system after a fault,
Figure FDA0002146672930000036
for the virtual damping coefficient of VSGi,
Figure FDA0002146672930000037
a virtual droop coefficient representing the VG output in VSGi;
step 2.1.4: the kinetic energy consumed in performing one adjustment of frequency and power is constructed as follows:
Figure FDA0002146672930000038
wherein χ = d ω/dt;
step 2.1.5: constructing a kinetic energy total energy function V according to the steps 2.1.1-2.14 KE ：
V KE ＝(V KE1 +V KE2 +V KE3 ) (8)
Step 2.2: constructing a potential energy function V considering the weight relation PE ；
Step 2.2.1: setting weight factors of different potential energy items
Figure FDA0002146672930000039
Wherein M is pi Threshold for potential energy function, i =1,2,3,4;
step 2.2.2: the method comprises the following steps of constructing potential energy functions caused by active reference power and active loads of all VSGs in the micro-grid system, and specifically comprising the following steps:
Figure FDA0002146672930000041
wherein, delta ni Representing the phase angle difference, P, between the rated voltage of the grid and the VSGi output voltage refi Active power reference value, P, set for the controller Li Active power for a load connected to the microgrid;
step 2.2.3: constructing a potential energy function caused by reactive reference power and reactive load of the VSG, which comprises the following steps:
Figure FDA0002146672930000042
wherein,
Figure FDA0002146672930000043
amplitude of the system voltage after a fault, Q refi Reactive power reference value, Q, set for the controller Li Reactive power of loads connected to the microgrid, E ni The difference value of the rated voltage of the power grid and the rated voltage of the ith VSG in the micro-grid is obtained;
step 2.2.4: constructing a potential energy function represented by active power transmitted on a connecting line between the VSG and a power grid, wherein the potential energy function is as follows:
Figure FDA0002146672930000044
wherein,
Figure FDA0002146672930000045
Figure FDA0002146672930000046
is the phase angle of the system voltage after the fault,
Figure FDA0002146672930000047
respectively representing the impedance and inductive reactance of the interconnection line, E i The voltage amplitude output for VSGi;
step 2.2.5: constructing a potential energy function represented by reactive power transmitted on a connecting line between the VSG and the power grid, wherein the potential energy function is as follows:
Figure FDA0002146672930000048
wherein,
Figure FDA0002146672930000049
for QPCM sag factor, E n A rated voltage amplitude of VSGi;
step 2.2.6: according to the steps 2.2.1-2.2.5, a potential energy and total energy function is constructed, which is as follows:
V PE ＝V PE1 +V PE2 +V PE3 +V PE4 (13)
step 2.3: calculating total energy function V of grid-connected microgrid TOT ：
V TOT ＝V PE +V KE 。 (14)
6. The method according to claim 1, wherein the method for determining the operating state of the system in step 3 is as follows:
Figure FDA0002146672930000051
wherein M is AVE Threshold value of energy function, V, for stable operation of system TOT The total energy in the microgrid system calculated for step 2 in claim 1.
7. The method according to claim 1, wherein the critical energy V of the system is calculated by using a dominant unstable equilibrium point method in the step 5 cr The process of (2) is as follows:
step 5.1: calculating the time from the system fault track to the fault clearing time and the exit point, specifically as follows:
Figure FDA0002146672930000052
wherein,
Figure FDA0002146672930000053
virtual moment of inertia and virtual damping coefficient, P, of VSGi, respectively refi Active power reference value, omega, set for the controller n 、ω i Rated angular velocity of VSGi and virtual rotor angular velocity of VSGi, ω ni ＝ω n -ω i Represents the difference between the two, P ei For the output active power, delta, of the ith VSG in the microgrid i Is the phase angle of the rated voltage of the ith station VSG in the microgrid,
Figure FDA0002146672930000054
a virtual droop coefficient representing the VG output in VSGi;
and step 5.2: taking the exit point as an initial value, and calculating the minimum gradient point of the gradient system after the fault by adopting a Gear method, wherein the method specifically comprises the following steps:
Figure FDA0002146672930000055
wherein,
Figure FDA0002146672930000056
is the minimum gradient point;
step 5.3: at the point of minimum gradient
Figure FDA0002146672930000057
As an initial value, solving a power balance equation of the system to obtain a stable balance point and a dominant unstable balance point of the system, which are specifically as follows:
Figure FDA0002146672930000061
wherein, delta ni Representing the phase angle difference between the rated voltage of the grid and the VSGi output voltage,
Figure FDA0002146672930000062
respectively representing the impedance and inductive reactance, P, of the interconnection line Li Active power of loads connected to the microgrid, E i 、E n Respectively representing the voltage amplitude and the rated voltage amplitude of the VSGi output;
step 5.4: calculating critical energy V of the microgrid system according to state quantity at dominant unstable balance point of the system cr ：
Figure FDA0002146672930000063
Wherein,
Figure FDA0002146672930000064
respectively setting a stable balance point and an unstable balance point of VSGi in the microgrid after the system fault;
Figure FDA0002146672930000065
respectively representing the virtual inertia, the virtual damping coefficient, the VG droop coefficient and the QPCM droop coefficient of VSGi during the stable operation of the microgrid;
Figure FDA0002146672930000066
representing the angular frequency of rotation, the phase angle and the amplitude of the voltage, χ = d ω/dt, t, respectively, at the point of stable equilibrium of the system after a fault s Is the time of occurrence of the fault.
8. The method according to claim 1, wherein the adaptive control method for the transient stability of the microgrid with multiple virtual synchronous machines is based on the energy function in the step 6, and comprises the following steps:
step 6.1: the VSG virtual inertia self-adaptive control method based on the energy function comprises the following steps:
Figure FDA0002146672930000071
wherein,
Figure FDA0002146672930000072
respectively the virtual inertia and the maximum value of the virtual inertia, M, of VSGi when the micro-grid operates stably AVE A threshold value that is a function of total energy;
step 6.2: the self-adaptive control method of the VSG virtual damping coefficient based on the energy function comprises the following steps:
Figure FDA0002146672930000073
wherein,
Figure FDA0002146672930000074
respectively representing the virtual damping coefficient and the maximum value of the virtual damping coefficient of VSGi when the micro-grid is in stable operation;
step 6.3: designing an adaptive control method of a VG droop coefficient based on an energy function:
Figure FDA0002146672930000075
wherein,
Figure FDA0002146672930000076
respectively representing the VG droop coefficient and the VG droop coefficient minimum value of VSGi during the stable operation of the microgrid;
step 6.4: the self-adaptive control method for designing the QPCM droop coefficient based on the energy function is as follows:
Figure FDA0002146672930000077
in the formula,
Figure FDA0002146672930000078
and respectively representing the maximum value of the QPCM droop coefficient and the QPCM droop coefficient of the VSGi during the stable operation of the micro-grid.
9. The method according to claim 1, wherein the step 9 is to remove the total energy V in the microgrid at the fault clearing time cl And critical energy V cr Comparing, analyzing and judging the stability of the microgrid as follows:
Figure FDA0002146672930000081
CN201910687024.1A 2019-07-29 2019-07-29 Micro-grid transient stability control method containing multiple virtual synchronous machines Active CN110311375B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910687024.1A CN110311375B (en) 2019-07-29 2019-07-29 Micro-grid transient stability control method containing multiple virtual synchronous machines

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910687024.1A CN110311375B (en) 2019-07-29 2019-07-29 Micro-grid transient stability control method containing multiple virtual synchronous machines

Publications (2)

Publication Number Publication Date
CN110311375A CN110311375A (en) 2019-10-08
CN110311375B true CN110311375B (en) 2022-11-29

Family

ID=68082423

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910687024.1A Active CN110311375B (en) 2019-07-29 2019-07-29 Micro-grid transient stability control method containing multiple virtual synchronous machines

Country Status (1)

Country Link
CN (1) CN110311375B (en)

Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111245014B (en) * 2020-02-27 2022-03-29 上海电力大学 Virtual inertia-based power system control method
CN111600293B (en) * 2020-07-01 2021-09-10 华北电力大学（保定） Transient electric quantity-based method for evaluating stability margin of direct current power distribution network
CN112186767B (en) * 2020-10-21 2022-05-13 国网福建省电力有限公司 Optimization control method for frequency stability of island microgrid containing high-proportion renewable energy
CN113552430B (en) * 2021-06-22 2023-06-20 南方电网科学研究院有限责任公司 Method and device for judging transient stability of converter based on critical index
CN113437761B (en) * 2021-07-20 2022-04-15 湖南大学 Island microgrid transient stability improving method considering mode switching
CN113792481B (en) * 2021-09-01 2022-05-31 湖南大学 VSG multi-machine system transient stability evaluation method based on artificial neural network
CN114268100B (en) * 2022-03-01 2022-07-12 国网江西省电力有限公司电力科学研究院 Method and device for analyzing transient stability performance of alternating current-direct current hybrid power system
CN116454910B (en) * 2023-01-17 2024-03-01 国网江苏省电力有限公司 Virtual synchronous machine inertia and primary frequency modulation cooperative self-adaptive control method and system
CN116231720B (en) * 2023-03-28 2023-10-27 山东大学 Method and system for improving transient stability of new energy through flexible direct current grid-connected system
CN116914757B (en) * 2023-07-20 2024-05-07 国网上海市电力公司 Method and device for determining maximum estimated attraction domain of flexible interconnection system of power distribution area

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2950809A1 (en) * 2013-11-12 2015-05-21 State Grid Corporation Of China Automatic droop control method for microgrid inverters based on small-signal stability analysis
CN109149605A (en) * 2018-10-12 2019-01-04 燕山大学 A kind of micro-capacitance sensor transient state adaptive parameter control strategy based on VSG

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10177574B2 (en) * 2016-09-28 2019-01-08 Nec Corporation Dynamic frequency control scheme for microgrids using energy storage

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2950809A1 (en) * 2013-11-12 2015-05-21 State Grid Corporation Of China Automatic droop control method for microgrid inverters based on small-signal stability analysis
CN109149605A (en) * 2018-10-12 2019-01-04 燕山大学 A kind of micro-capacitance sensor transient state adaptive parameter control strategy based on VSG

Also Published As

Publication number Publication date
CN110311375A (en) 2019-10-08

Similar Documents

Publication Publication Date Title
CN110311375B (en) Micro-grid transient stability control method containing multiple virtual synchronous machines
CN109149605B (en) VSG-based micro-grid transient state adaptive parameter control strategy
Pizano-Martianez et al. Global transient stability-constrained optimal power flow using an OMIB reference trajectory
CN102035203B (en) Fast calculation method of tie-line transient state stable limiting transmission power
CN109347097B (en) Doubly-fed wind power system subsynchronous oscillation suppression strategy based on improved particle swarm optimization algorithm
CN106849106A (en) DC distribution net system voltage flexible control method
CN111313435B (en) Photovoltaic power station multi-machine system low-frequency oscillation suppression strategy based on VSG technology
CN110277797A (en) Photovoltaic virtual synchronous coordinated controller of excitation and valve method based on common DC bus
CN111064232B (en) Virtual synchronous generator-based microgrid system inverter secondary frequency control method
He et al. An adaptive VSG control strategy of battery energy storage system for power system frequency stability enhancement
CN105870973A (en) Capacity allocation method of energy storage system to meet frequency modulation demand of high-wind-power-permeability system
CN109861285B (en) Multi-VSG micro-grid frequency recovery control method with time delay switch characteristic
CN109390959B (en) Storage battery energy storage control method based on virtual synchronous machine technology
CN109787274A (en) A kind of virtual synchronous control method and rotor-side frequency converter controller
CN105337290A (en) Reactive adjustment method applicable to low-frequency oscillation aid decision of electric system
CN107579519B (en) A kind of AC-DC hybrid power grid robust optimal control method considering multimode switching
Bao et al. Cooperative control strategy of multiple VSGs in microgrid based on adjacent information
CN110417033B (en) Extra-high voltage direct current transmission system cooperative frequency modulation control method and system
CN109975610B (en) Micro-grid dynamic frequency detection and adjustment method
CN106505582A (en) A kind of dynamic reactive power voltage cooperative control method based on neural network forecast mode
CN113991644B (en) Gas turbine black start improvement method considering energy storage auxiliary system
CN114825478A (en) Online grouping method for direct-drive fans
CN114204860A (en) Three-phase inverter control method and system based on virtual synchronous power generation technology
Lei et al. Inertia characteristics analysis of DFIG-based wind turbines with virtual synchronous control in different operation areas
CN115333121A (en) Cooperative control strategy for improving frequency stability of high-proportion new energy power system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant