CN114389307A - Cascaded microgrid control method for self-adaptive virtual synchronous generator - Google Patents
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- H—ELECTRICITY
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Abstract
The invention discloses a control method of a self-adaptive virtual synchronous generator cascade microgrid, which adopts a fuzzy controller to control the virtual synchronous generator cascade microgrid; input of the fuzzy controller is omegasD ω/dt, output Δ J and Δ D; omegasIs the angular frequency deviation; d ω/dt is the rate of change of angular frequency; Δ J is the amount of change in virtual inertia; Δ D is the amount of change in the damping coefficient; using quantization factors k1 and k2 to respectively correspond to omegasD ω/dt is normalized; the fuzzy controller obtains the delta J and the delta D through a given fuzzy rule and defuzzification reasoning. According to the change rate of the angular frequency of the synchronous generator and the change rule of the angular frequency change quantity, the invention subdivides and improves the oscillation period, redesigns the fuzzy rule table, and adjusts the virtual inertia and the damping coefficient to improve the control of the VSGAnd (5) effect.
Description
Technical Field
The invention relates to a cascade microgrid control method of a self-adaptive virtual synchronous generator.
Background
In order to solve the energy problem and relieve the environmental pollution, all countries in the world actively search for a method for improving the energy utilization efficiency and actively develop new energy. The new energy sources have the problems of scattered distribution, small capacity, intermittence, fluctuation and the like in the process of being merged into the traditional centralized power grid. To solve these problems, the american society for reliability and technology solutions (CERTS) proposed the concept of microgrid in 2002.
The microgrid refers to a regional power grid formed by combining a local load, a renewable energy source and an energy storage device, and can be divided into a cascade microgrid and a parallel microgrid according to the connection mode of a microgrid micro-source inverter. The cascaded microgrid is characterized in that all inverters are connected in series to provide power support for the microgrid, a step-up transformer is not needed, and low-voltage microgroups with different power outputs can be used as a whole to provide a higher alternating-current bus voltage level. Meanwhile, the micro-grid connected through the cascade inverter not only has many advantages of a multi-level inverter, but also has unique advantages. The cascade inverter does not need to balance capacitor voltage, the multilevel of the diode clamp inverter is obtained by dividing the voltage by a plurality of capacitors, and the stability of the capacitor voltage needs to be ensured during working. In the cascade inverter, each isolated direct current power supply is completely decoupled in charging and discharging, and special balance control is not needed as long as each direct current power supply has enough capacity. Meanwhile, the cascade inverter is easy to modularize and expand structurally, has the phase voltage redundancy characteristic, is convenient to realize the soft switching technology and the like, and arouses wide interest of scholars at home and abroad.
The existing mode adopts droop control, the control algorithm is complex, and the effect is not ideal.
Therefore, it is necessary to design an adaptive virtual synchronous generator cascade microgrid control method.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a control method of a cascade microgrid of a self-adaptive virtual synchronous generator.
The technical solution of the invention is as follows:
a self-adaptive virtual synchronous generator cascade microgrid control method is characterized in that a fuzzy controller is adopted to control a virtual synchronous generator cascade microgrid;
input of the fuzzy controller is omegasD ω/dt, output Δ J and Δ D;
ωsis the angular frequency deviation; d ω/dt is the rate of change of angular frequency; Δ J is the amount of change in virtual inertia; Δ D is the amount of change in the damping coefficient;
using quantization factors k1 and k2 to respectively correspond to omegasD ω/dt is normalized; the fuzzy controller obtains the delta J and the delta D through a given fuzzy rule and defuzzification reasoning.
In the fuzzy controller, a combination of triangular membership function and trapezoidal membership function is adopted, omegasThe basic domains of discourse to d ω/dt are [ -1,1 [ ]]And Δ J is set to [ -0.6,0.6 [)]And Δ D is set to the range of [ -6,6 [)]And quantizing factors of the input signal and the output signal and then mapping the quantized factors to corresponding intervals, wherein the corresponding fuzzy variables are in a set range of { NL, NS, NM, ZO, PS, PM and PL }, and the fuzzy variables NL, NS, NM, ZO, PS, PM and PL correspond to negative large, negative medium, negative small, zero, positive small, positive medium and positive large respectively.
Fuzzy rules of Table 4D
Fuzzy rule of Table 5J
The fuzzy rule is shown in table 4 and table 5, and in case of input determination, the output fuzzy control amount is determined using the MAX-MIN method.
In the virtual synchronous generator cascade micro-grid, a cascade inverter is adopted to supply power to the grid; generating a PWM (pulse-width modulation) signal by adopting VSG (voltage-current double-loop control) to drive a cascade inverter; the generation of PWM modulation by voltage-current dual-loop control is well known in the art.
The fuzzy controller is used for adjusting the virtual inertia J and the damping coefficient D in real time, further adjusting the phase theta of the virtual potential in real time and adjusting the virtual potential E in the VSGmThe VSG refers to a virtual synchronous generator.
Has the advantages that:
in the cascade microgrid, when a load is disturbed, the frequency and the power fluctuate, and the problem of uneven power distribution is caused. A Virtual Synchronous Generator (VSG) technique may be applied in this case, and when power is unbalanced, the VSG may suppress fluctuations in its frequency and power by adjusting virtual inertia and a damping coefficient, and suppress system oscillation to some extent. Compared with droop control, the added rotor motion equation simulates the rotation inertia and damping characteristics of the synchronous generator, and the virtual inertia J and the damping coefficient D of the added rotor motion equation can effectively improve the frequency stability of the system. Compared with a synchronous generator, the actual rotational inertia of the synchronous generator is a constant value, and the virtual inertia J in the VSG is a virtual quantity, so that the virtual inertia J is not constrained by conditions, is flexible in value and can be changed according to actual conditions. Therefore, how to flexibly adjust the virtual inertia and the damping coefficient is the key of research.
Aiming at the problems of power equalization and frequency stability in a cascade microgrid, the invention discloses a fuzzy algorithm-based control method for a cascade microgrid of a self-adaptive virtual synchronous generator. When the VSG control system operates in an off-grid mode, load disturbance can cause system frequency fluctuation and generate steady state deviation. And finally, building a model on a MATALAB/Simulink simulation platform, and verifying the effectiveness of the strategy.
Drawings
FIG. 1 is a diagram of a cascaded inverter topology;
FIG. 2 is a power loop control block diagram;
FIG. 3 is a conventional power angle characteristic curve;
FIG. 4 is an angular frequency oscillation curve;
FIG. 5 is an angular frequency oscillation curve; FIG. 5 is a graph of (a) and (b) showing 8 regions and a graph of (5) and (b);
FIG. 6 is a schematic diagram of fuzzy controller input and output quantities;
FIG. 7 is a graph of angular frequency deviation and rate of change of angular frequency control;
FIG. 8 membership functions; wherein FIG. 8(a) is a damping coefficient membership function, FIG. 8(b) is a virtual inertia membership function, and FIG. 8(c) is an angular frequency change rate membership function;
FIG. 9 is a VSG active power curve;
FIG. 10 is a VSG frequency variation curve; fig. 10(a) is a frequency variation curve obtained by adjusting VSG in a conventional manner of changing virtual inertia and damping coefficient; fig. 10(b) is a frequency variation curve obtained by adjusting the virtual inertia and the damping coefficient according to the present invention.
Detailed Description
The invention will be described in further detail below with reference to the following figures and specific examples:
example (b):
1. cascade inverter system structure
1.1 cascaded inverter control strategy
The system control structure proposed by the present invention is shown in FIG. 1, wherein u0And i0Respectively, a load side output voltage and a load side output current; p and Q are u0And i0Obtaining active power and reactive power through power calculation; prefAnd QrefIs a reference value of active power and reactive power; emAnd θ is the output potential magnitude and phase angle of VSG; omegasIs the angular frequency deviation; d ω/dt is the rate of change of angular frequency; dP/dt is active workA rate of change of rate; Δ J is the amount of change in virtual inertia; Δ D is the amount of change in damping coefficient, θLRepresenting the phase angle at the load side, lZLl represents the amplitude of the load side.
Let the output active power of the ith inverter in the figure be PiWith reactive power QiThe phase angle of the inverter output of the ith module is UiAmplitude of θiThen the current i at the load end of the power gridLComprises the following steps:
then the active power P output by the inverter can be obtainediAnd reactive QiRespectively as follows:
according to the equations (1) and (2), the active power P and the reactive power Q output by the cascade inverter have relations with the output voltage and the frequency, and the reactive power can be adjusted by changing the voltage, and the active power can be adjusted by changing the frequency.
The control structure of the invention is a model for simulating a synchronous generator, wherein the virtual rotor motion equation of the VSG is shown as formula (3).
In the formula: t ism、TeAnd TdThe simulated mechanical torque, the electromagnetic torque and the damping torque of the VSG are respectively; j and D are virtual inertia and damping coefficient; pmAnd PeMechanical power input for the VSG and electromagnetic power output; theta is the power angle of the inverter power supply voltage; omega0Is the nominal angular frequency of the VSG.
Secondly, the process of regulating excitation of the synchronous generator is simulated to regulate the virtual potential E in the VSGm。
Em＝E0, (4)
In the formula: e0For the no-load potential of the VSG, the VSG control mainly refers to active loop control, as shown in fig. 2, where θ is the phase of the VSG, which can be obtained by integration of the frequency.
The VSG control-based inverter mainly generates PWM modulation signals through voltage and current double-loop control, and fuzzy control is mainly used for adjusting virtual inertia J and a damping coefficient D in real time in an active loop so as to adjust the phase theta of virtual potential in real time. Meanwhile, the VSG characteristic root can be calculated by using an optimal second-order system for analysis:
the two closed-loop characteristic roots are located on the left half plane, and the virtual inertia and the damping coefficient are adjusted within a certain range on the basis, so that the stability of the system can be ensured.
1.2 existing VSG control limitations
By adopting the control strategy, the frequency stability can be effectively adjusted by changing the virtual coefficient and the damping variable, most documents divide the oscillation period into four intervals according to the angular frequency deviation and the angular frequency change rate, as shown in the following table 1, the changes of the virtual coefficients J and D in different intervals are summarized, and the interval t is the interval t1～t2The time angular frequency deviation and the angular frequency change rate are both positive; in the interval t2～t3Then, the frequency deviation is positive and the angular frequency deviation is negative; in the interval t3～t4The angular frequency deviation and the angular frequency change rate are both negative; in the interval t4～t5The frequency deviation is negative and the angular frequency deviation is positive.
TABLE 1 conventional Interval partition Table
The power-angle curve and angular frequency oscillation curve of the VSG are shown in FIG. 3 and FIG. 4, when the power of the system is from P1To P3And the change of the power and the angular frequency of the system from a to c to e is also damping oscillation, and the change rate and the change trend of the power and the angular frequency of each interval are different, so that the virtual inertia and the virtual coefficient are different.
Although overshoot can be restrained to a certain degree through the control mode, the deviation is still large, on the basis, the oscillation period is subdivided and improved, the fuzzy rule table is redesigned, and the virtual inertia and the damping coefficient are adjusted, so that the control effect of the VSG is improved. Fuzzy controller
2.1 fuzzy variable analysis
By analyzing the model of the VSG, one can obtain:
wherein in equation (8), if D ω/dt is constant, increasing the damping coefficient D can make the angular frequency deviate by ωsDecrease, in (8), if TdConstant, then D ω/dt can be reduced by increasing the virtual inertia J, while adjusting the angular frequency deviation and the rate of change of angular frequency by changing J and D to better maintain frequency stability.
In order to minimize overshoot when the frequency is far away during oscillation and minimize time for frequency recovery, the curve shown in fig. 5b is obtained, the present invention makes the following improvements: the differential of power is introduced on the basis of angular frequency deviation and angular frequency change rate, the differential of power can reflect the change trend of angular frequency, the interval can be further subdivided into 8 intervals as shown in fig. 5a on the basis, inertia is increased when the frequency is far away through accurately reflecting the change of active power and angular frequency, overshoot is enabled to be as small as possible, and meanwhile, a small inertia is applied when the frequency is recovered, and the recovery speed is accelerated.
The angular frequency oscillation curve is divided into 8 intervals as shown in the following table 2
TABLE 2 control laws of J and D
(
It is shown that the reinforcement,
indicating weakening, the number of stars indicating the degree of weakness)
Table 2 summarizes the variation of the respective quantities in the respective intervals and the variation in the intervals J and D. In the interval 1 and the interval 2, in the frequency separation stage, the angular frequency deviation and the angular frequency change rate are positive, but the power change rate is negative, and the angular frequency change rate gradually decreases. Therefore, in the interval t1～t2The angular frequency change rate is large, the frequency deviation is small, the damping coefficient D takes the leading action at the moment, the virtual inertia is slightly increased, the damping coefficient is greatly increased to inhibit the angular frequency deviation, and the interval t is2～t3The angular frequency change rate is gradually reduced, the angular frequency deviation is increased, and the virtual inertia is increased to inhibit the angular frequency change rate, so that the overshoot is smaller when the frequency is far away; in the interval t3～t4Time and interval t4～t5In order to increase the frequency recovery speed, the frequency recovery stage is a frequency recovery stage in which the angular frequency deviation is positive, the angular frequency change rate is negative, and the power change rate is still negative. Therefore, in the interval t3～t4The frequency change rate is small, the frequency deviation is large, and the same interval t is needed2～t3Increasing the virtual inertia and damping coefficient to suppress the angular frequency change rate; in the interval t4～t5Frequency ofDeviation and frequency change rate and interval t1～t2Similarly, the values of J and D should continue to increase. By analogy, the changes of the virtual inertia and the damping coefficient of the following four intervals can be obtained.
By dividing the range of different angular frequencies in the power-angle characteristic curve and adopting a fuzzy VSG self-adaptive control strategy to control the cascaded micro-grid, the frequency overshoot can be effectively reduced when the frequency changes on the premise of keeping the power sharing.
3.3 fuzzy controller design
The invention provides a self-adaptive VSG control strategy based on a fuzzy algorithm on the basis that the range is divided for different conditions of angular frequency in a power angle characteristic curve, so that the virtual inertia and the damping coefficient are adjusted in real time according to the change condition of the angular frequency, the frequency overshoot is reduced, and the recovery speed is increased.
The fuzzy control rule is shown in the following figure 6, wherein the design of the fuzzy controller mainly comprises 3 parts of fuzzification, fuzzy logic reasoning and defuzzification.
Wherein k is1、k2Is the quantization factor, k1＝5,k2＝1/10。
Angular frequency deviation omegasAnd the rate of change of angular frequency d ω/dt are shown in FIG. 7 below:
the angular frequency deviation omega can be extracted by a fuzzy controllersAnd the rate of change of angular frequency d ω/dt, and let:
where Δ ω is the amount of frequency change at the moment Δ t is infinitesimal.
The exact input value is changed to a blurred value by blurring while normalizing the input signal using the quantization factors k1, k 2. The fuzzy controller obtains the virtual inertia delta J and the damping coefficient delta D through a given fuzzy rule and defuzzification reasoning.
The real-time virtual parameters are obtained by equation (11):
in the formula J0、D0Initial values for J and D, respectively.
In the fuzzy controller, the invention considers that a triangular membership function is combined with a trapezoidal membership function, the basic discourse domains of angular frequency difference and angular frequency change rate are both [ -1,1], the virtual inertia change coefficient is set to [ -0.6,0.6], the setting range of the damping coefficient is [ -6,6], the input signal and the output signal are mapped to a corresponding interval after quantization factors, and the set range of the corresponding fuzzy variables is { NL, NS, NM, ZO, PS, PM, PL }. The associated membership functions are shown in FIG. 8 below.
The fuzzy rules set by the invention are shown in the following tables 4 and 5, and under the condition of input determination, the MAX-MIN method is used for determining the output fuzzy control quantity.
Fuzzy rules of Table 4D
Fuzzy rule of Table 5J
2. Simulation verification
3.1 simulation parameters
In order to verify that the proposed adaptive VSG control based on the fuzzy algorithm has good dynamic response capability, the simulation structure shown in FIG. 1 is built on a Matlab/Simulink platform for testing, the simulation structure mainly comprises two cascade modules, wherein main simulation parameters are shown in a chart 6 below
TABLE 6 Main parameters and values
The simulation time length is set to be 10s, when the simulation is started, a disturbance deviation is added between the two cascading modules, and the VSG input active power output is set to be 8 kw. When t is 4s, the inductive load is suddenly changed.
3.2 simulation results
As shown in fig. 9, P1 is an active power change curve of the cascade module 1, P2 is an active power change curve of the cascade module 2, after the system is started, the VSG output active power is rapidly increased to about 9800w, when the adaptive virtual inertia and damping coefficient control strategy based on the fuzzy algorithm used in the present invention is adopted, the active power is stable in the transient process, the cascade inverter is stable in active power change, and power equalization is rapidly realized, and a disturbance is added when t is 4 s.
Fig. 10 is a frequency oscillation curve of the VSG, f1 is a frequency variation curve of the cascade module 1, f2 is a frequency variation curve of the cascade module 2, where (a) is a frequency variation curve obtained by adjusting the VSG in a conventional manner of changing the virtual inertia and damping coefficient, and (b) is a frequency variation curve obtained by adjusting the virtual inertia and damping coefficient according to the present invention. Compared with the traditional frequency change curve, the method provided by the invention has smaller frequency overshoot. In other simulation processes, the characteristic of faster recovery in the transient recovery process is also shown.
The simulation result shows that when the control strategy provided by the invention is adopted, the system can adaptively adjust the virtual inertia and the damping coefficient of the VSG according to the disturbance, so that the dynamic response characteristic and the system operation stability are obviously improved.
3. Conclusion
The invention provides a self-adaptive VSG cascade inverter control method based on a fuzzy algorithm in a cascade microgrid, which is characterized in that a VSG control method is deduced through a rotor motion equation of a simulated synchronous generator, control factors influencing virtual inertia and a damping coefficient are deduced according to a deduced control formula, and the influence of the virtual inertia and the damping coefficient on the angular frequency is verified by simulation. By analyzing the power-angle characteristic curve of angular frequency, taking angular frequency deviation, angular frequency change rate and frequency variation as references, dividing the angular frequency deviation, the angular frequency change rate and the frequency variation into a plurality of intervals, setting the control trends of corresponding virtual inertia and damping coefficients, and controlling the change of the two coefficients in real time by using a fuzzy controller, the simulation result shows that the dynamic response capability of the system is effectively improved.
Claims (4)
1. A self-adaptive virtual synchronous generator cascade microgrid control method is characterized in that a fuzzy controller is adopted to control a virtual synchronous generator cascade microgrid;
input of the fuzzy controller is omegasD ω/dt, output Δ J and Δ D;
ωsis the angular frequency deviation; d ω/dt is the rate of change of angular frequency; Δ J is the amount of change in virtual inertia; Δ D is the amount of change in the damping coefficient;
using quantization factors k1 and k2 to respectively correspond to omegasD ω/dt is normalized; the fuzzy controller obtains the delta J and the delta D through a given fuzzy rule and defuzzification reasoning.
2. The method as claimed in claim 1, wherein the fuzzy controller uses a combination of triangular and trapezoidal membership functions, ω, to control the virtual synchronous generator cascade microgridsThe basic domains of discourse to d ω/dt are [ -1,1 [ ]]And Δ J is set to [ -0.6,0.6 [)]And Δ D is set to the range of [ -6,6 [)]And after the input signal and the output signal are quantized, mapping the quantized factors to corresponding intervals, wherein the corresponding fuzzy variable set range is { NL, NS, NM, ZO, PS, PM, PL }.
3. The adaptive virtual synchronous generator cascaded microgrid control method of claim 2,
fuzzy rules of Table 4D
Fuzzy rule of Table 5J
The fuzzy rule is shown in table 4 and table 5, and in case of input determination, the output fuzzy control amount is determined using the MAX-MIN method.
4. The method for controlling the cascade microgrid of the self-adaptive virtual synchronous generator according to any one of claims 1 to 3, characterized in that in the cascade microgrid of the virtual synchronous generator, a cascade inverter is adopted to supply power to a power grid; generating a PWM (pulse-width modulation) signal by adopting VSG (voltage-current double-loop control) to drive a cascade inverter;
the fuzzy controller is used for adjusting the virtual inertia J and the damping coefficient D in real time, further adjusting the phase theta of the virtual potential in real time and adjusting the virtual potential E in the VSGmThe VSG refers to a virtual synchronous generator.
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| CN115663873A (en) * | 2022-05-11 | 2023-01-31 | 上海电力大学 | Improved VSG and series compensation capacitor subsynchronous oscillation suppression method |
| CN115663873B (en) * | 2022-05-11 | 2024-04-09 | 上海电力大学 | Improved VSG and series compensation capacitor subsynchronous oscillation suppression method |
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