CN113572199A - Smooth switching method of network-forming type current converter based on model prediction algorithm - Google Patents

Smooth switching method of network-forming type current converter based on model prediction algorithm Download PDF

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CN113572199A
CN113572199A CN202110917689.4A CN202110917689A CN113572199A CN 113572199 A CN113572199 A CN 113572199A CN 202110917689 A CN202110917689 A CN 202110917689A CN 113572199 A CN113572199 A CN 113572199A
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CN113572199B (en
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孟建辉
赵鹏飞
王毅
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North China Electric Power 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/041—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a variable is automatically adjusted to optimise the performance
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
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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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/388—Islanding, i.e. disconnection of local power supply from the network
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/40—Synchronising a generator for connection to a network or to another generator
    • H02J3/44—Synchronising a generator for connection to a network or to another generator with means for ensuring correct phase sequence
    • 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
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    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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    • H—ELECTRICITY
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    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation

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Abstract

The invention discloses a smooth switching method of a network-building type converter based on a model prediction algorithm, which comprises the following steps of 100, discretizing an active frequency model of a VSG (voltage-to-grid generator) system of a simulation synchronous generator to obtain a state space model; 200, describing the state space model in an incremental form, wherein the incremental form model predicts the state of the system in one or more future periods through the state space models at the current time and the last time; step 300, setting a target function for predicting smooth switching control; step 400, obtaining an optimal solution of the objective function under constraint conditions, wherein the optimal solution is a power increment required by the current moment compared with the previous moment and is added with the active power input value of the current moment to obtain the active power input value of the current moment; step 500, when the alternating current micro grid system is switched from an island operation mode to a grid-connected operation mode, the predictive smooth switching control is completed through the steps 100 to 400, and the obtained optimal input is input into the alternating current micro grid system.

Description

Smooth switching method of network-forming type current converter based on model prediction algorithm
Technical Field
The patent belongs to the technical field of control, and particularly relates to a smooth switching method of a network-forming type converter based on a model prediction algorithm.
Background
Distributed Generation (DG) is operated in the form of an intelligent alternating current micro-grid and becomes a new development direction of a future power grid, but most of distributed generation is connected into the power grid through a power electronic converter with rapid response, inertia and damping are lacked, inertia of the whole system can be reduced after the distributed generation is connected into a large power grid, and when disturbance occurs in the system, frequency is easy to fluctuate and even exceeds a safe operation range, so that stable operation and dynamic response of the whole power system are influenced. The virtual synchronous generator technology is that a converter control algorithm is designed, so that the converter simulates the frequency and voltage control characteristics of a Synchronous Generator (SG) from the external characteristics, and the dynamic regulation performance of a power grid is improved.
In addition, the alternating-current micro-grid can be connected to a large power grid for grid-connected operation and can be separated from the large power grid for off-grid operation according to actual requirements, but in the operation mode conversion process of the alternating-current micro-grid, the problem that the output power of a micro-power source is not matched with a load is easily caused in the alternating-current micro-grid, so that transient current or voltage impact is caused, and therefore smooth switching control needs to be carried out on the alternating-current micro-grid. In the traditional control, PI controllers are usually adopted to realize the presynchronization of frequency and phase angle, the control method is really classic, but the presynchronization of frequency and phase angle is mediated by two PI controllers, and the frequency and the phase angle are correlated with each other, so that large fluctuation is easy to occur in the actual control due to the lack of unified control, and the operation of a power system is damaged. In addition, the PID control has the disadvantages of slow response speed, low control accuracy for a nonlinear system, and the like.
Disclosure of Invention
The present invention is proposed based on the above requirements of the prior art, and the technical problem to be solved by the present invention is to provide a method for smoothly switching a network-forming converter based on a model prediction algorithm, so as to perform dynamic response quickly and efficiently.
In order to solve the above problem, the technical scheme provided by the patent comprises:
provides a model prediction algorithmThe smooth switching method of the grid-connected converter comprises the following steps: step 100, discretizing an active frequency model of a simulation synchronous generator VSG system to obtain a state space model of the active frequency of the discrete-time VSG system, wherein the state space model obtains a difference value between the actual angular frequency and the rated angular frequency of the alternating-current microgrid at the current moment through the difference value between the actual angular frequency and the rated angular frequency of the alternating-current microgrid at the previous moment, the mechanical power of a converter and the electromagnetic power output by the converter; step 200, describing the state space model in an incremental mode, wherein the incremental mode model obtains the difference value between the actual angular frequency and the rated angular frequency of the alternating current micro-grid at the later moment compared with the current moment through the difference value between the actual angular frequency and the rated angular frequency of the alternating current micro-grid at the current moment and the last moment, the mechanical power difference value of the converter and the electromagnetic power difference output by the converter, namely the required power increment, and predicting the state of the system in one or more periods in the future through the mode; step 300, setting a target function of prediction smooth switching control, wherein the prediction smooth switching control aims at controlling the frequency and the phase angle of an alternating current micro-grid to be equal to those of a large grid, performance indexes of the prediction smooth switching control comprise the phase angle and the angular speed, and the introduced angular acceleration variable quantity is subjected to adaptive adjustment through a weighting coefficient; step 400, obtaining an optimal solution of the objective function under constraint conditions, wherein the constraint conditions comprise a state space model in an increment form at the k + i moment, and the optimal solution set of the objective function is obtained as delta PM＝[ΔPm(k) ΔPm(k+1) ... ΔPm(k+p-1)]TIn which Δ Pm(k) Representing the difference, Δ P, between the mechanical power input to the converter at time k and at time k-1m(k +1) represents the difference between the mechanical power input to the converter at the time k +1 and at the time k, Δ Pm(k + p-1) represents a mechanical power difference value of the converter input at the k + p-1 moment and the k + p-2 moment, a first item in the optimal solution set is an optimal solution which is a power increment required by the current moment compared with the previous moment and is added with an active power input value of the current moment to obtain an active power input value of the current moment, and p is a prediction time domain; step 500, when the alternating current micro grid system is switched from an island operation mode to a grid-connected operation modeAnd then, completing the predictive smooth switching control through steps 100 to 400, and inputting the obtained optimal input into the alternating current micro-grid system.
Preferably, the model simulating the active frequency of the synchronous generator VSG can be expressed as:
Figure BDA0003206232450000031
wherein J is moment of inertia, DPTo be damping coefficient, Pm、PeRespectively mechanical power and electromagnetic power of the converter, and respectively active power set value and actual output value, wherein omega represents virtual angular velocity output by the converter, and omega represents virtual angular velocity output by the converter0Is the nominal angular velocity.
Preferably, the discretized active frequency model expression of the analog synchronous generator VSG system is as follows:
Figure BDA0003206232450000032
wherein ω' (k +1) represents the difference between the actual angular frequency and the rated angular frequency of the alternating-current microgrid at the moment of k + 1; omega' (k) represents the difference value between the actual angular frequency and the rated angular frequency of the alternating-current microgrid at the moment k; pm(k) Representing the mechanical power input into the converter at the moment k; pe(k) Representing the electromagnetic power output by the converter at the moment k; y isc(k) Representing the controlled output variable at time k, TsIs the sampling time of the system, and
Figure BDA0003206232450000033
Figure BDA0003206232450000034
Figure BDA0003206232450000035
preferably, the state space model is described in incremental form:
Figure BDA0003206232450000036
wherein, Δ ω' (k +1) represents the difference between the actual angular frequency of the ac microgrid at the time k +1 and the actual angular frequency of the ac microgrid at the time k; Δ ω' (k) represents the difference between the actual angular frequency of the AC microgrid at time k and at time k-1, Δ Pm(k) Representing the difference, Δ P, between the mechanical power input to the converter at time k and at time k-1e(k) Representing the difference in electromagnetic power output by the converter at time k and at time k-1, yc(k) Representing the controlled output variable at time k, yc(k-1) represents the controlled output variable at time k-1, CcRepresenting the output state coefficient matrix.
Preferably, the states in p prediction time domains in the future of the prediction system can be described as follows:
Yp(k+1|k)＝SxΔω′(k)+SuΔPM(k)+SdΔPe(k)+γω′(k)
wherein, Yp(k +1| k) are system state output variables that predict p steps in the future,
Sx＝[A A2+A ... Ap+Ap-1+...+A]T
Figure BDA0003206232450000041
ΔPM＝[ΔPm(k) ΔPm(k+1) ... ΔPm(k+p-1)]T
Sd＝[Bd ABd+Bd ... Ap-1Bd+Ap-2Bd+...+Bd]T
γ＝[1 1 ... 1]T。
preferably, the objective function includes two parts of phase angle and angular velocity, and is represented as:
Figure BDA0003206232450000042
wherein, gamma isyiIs an angular frequency weight coefficient, θgAt a time k + iPhase angle, omega, of large power gridsg' (k + i) is the large grid angular frequency at time k + i, [ theta ] (k + i | k) is the VSG phase angle at time k + i, [ omega ] (k + i | k) is the VSG angular frequency at time k + i,
Figure BDA0003206232450000043
wherein gamma is0As initial value of the weighting coefficient, kΓThe weighting adjustment coefficients.
Preferably, the optimization problem of model predictive control is expressed as follows by using constraints including an incremental state space model and the like:
Figure BDA0003206232450000044
wherein J (ω '(k)) is an objective function, ω'min(k + i) is the minimum value of VSG angular frequency at time k + i, ω'max(k + i) is the maximum value of VSG angular frequency at the time of k + i, and is taken as delta Pm(k) Δ P of the first termm(k) As an optimal input power increment.
Preferably, the optimal input of the current system is the power increment required by the current time compared with the previous time, and then added to the active power input value of the previous time, that is: pm(k)＝Pm(k-1)+ΔPm(k)。
Preferably, the obtained optimal input of the current system is input into an alternating current micro-grid system, and the alternating current micro-grid system comprises a simulation synchronous generator VSG unit, an active load and a synchronous generator set SG of the distributed generation DG.
Compared with the prior art, the invention has the advantages of high dynamic response speed, strong control purpose, high precision, flexible nonlinear constraint condition processing and easy satisfaction of the requirement of high-performance processing.
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In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the embodiments of the present specification, and other drawings can be obtained by those skilled in the art according to the drawings.
FIG. 1 is a flow chart of steps of a network-forming type converter smooth switching method based on a model prediction algorithm;
FIG. 2 is a diagram of an AC microgrid system in accordance with an embodiment of the present invention;
fig. 3 is a block diagram of active-frequency control of model predictive control in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
For the purpose of facilitating understanding of the embodiments of the present application, the following description will be made in terms of specific embodiments with reference to the accompanying drawings, which are not intended to limit the embodiments of the present application.
Example 1
The embodiment provides a smooth switching method of a network-forming type converter based on a model prediction algorithm.
The method is a method for uniformly controlling frequency presynchronization and phase angle presynchronization by utilizing a model prediction algorithm aiming at an alternating current micro-grid system consisting of VSG units and active loads and a large grid system consisting of a generator set when the alternating current micro-grid system is converted from an island mode to a grid-connected mode, and refer to fig. 1-3.
Step 100, discretizing an active frequency model of the analog synchronous generator VSG system to obtain a state space model of the active frequency of the discrete-time VSG system, wherein the state space model obtains a difference value between the actual angular frequency and the rated angular frequency of the alternating-current microgrid at the current moment through the difference value between the actual angular frequency and the rated angular frequency of the alternating-current microgrid at the last moment, the mechanical power of the converter and the electromagnetic power output by the converter.
The active frequency rotor equation of the analog synchronous generator VSG is as follows:
Figure BDA0003206232450000061
j is rotational inertia, and provides inertial support when the system frequency oscillates; dPSimulating the damping oscillation capacity of the synchronous generator for a damping coefficient; pm、PeThe power is respectively the mechanical power and the electromagnetic power of the converter, and is also respectively the given value and the actual output value of the active power; ω represents the virtual angular velocity of the converter output, ω0Is the nominal angular velocity.
Inertia of the synchronous generator SG is simulated through the rotational inertia J, although frequency fluctuation of a system after disturbance can be relieved to a certain extent, the fundamental reason is power unbalance, and therefore power shortage needs to be rapidly and accurately complemented by utilizing model prediction control to achieve a control target.
Discretizing the active frequency rotor equation to obtain a state space model of the active frequency of the discrete time system, wherein the state space model is expressed as follows:
Figure BDA0003206232450000062
wherein ω' (k +1) represents the difference between the actual angular frequency and the rated angular frequency of the alternating-current microgrid at the moment of k + 1; omega' (k) represents the difference value between the actual angular frequency and the rated angular frequency of the alternating-current microgrid at the moment k; pm(k) Representing the mechanical power input into the converter at the moment k; pe(k) Representing the electromagnetic power output by the converter at the moment k; y isc(k) Representing the controlled output variable at time k.
And is
Figure BDA0003206232450000063
Figure BDA0003206232450000071
Figure BDA0003206232450000072
Step 200, describing the state space model in an incremental mode, wherein the incremental mode obtains the difference value between the actual angular frequency and the rated angular frequency of the alternating current micro-grid at the later moment compared with the current moment through the difference value between the actual angular frequency and the rated angular frequency of the alternating current micro-grid at the current moment and the last moment, the mechanical power difference value of the converter and the electromagnetic power difference output by the converter, namely the required power increment, and predicting the state of the system in one or more periods in the future through the mode.
To introduce integration to reduce or eliminate static errors, the state space model is changed to an incremental form:
Figure BDA0003206232450000073
wherein, Δ ω' (k +1) represents the difference between the actual angular frequency of the ac microgrid at the time k +1 and the actual angular frequency of the ac microgrid at the time k; delta omega' (k) represents the difference value of the actual angular frequency of the alternating-current microgrid at the moment k and the moment k-1 and represents the increment of the state variable; delta Pm(k) Representing the mechanical power difference value of the input converter at the k moment and the k-1 moment, and representing the increment of the control input variable; delta Pe(k) Representing the difference value of the electromagnetic power output by the converter at the moment k and the moment k-1, and representing the measurable external interference variable increment; y isc(k) Representing a controlled output variable at the moment k; y isc(k-1) represents the controlled output variable at time k-1, CcRepresenting the output state coefficient matrix.
And predicting the state of the system in p periods in the future by using the incremental state space model. The p is a prediction time domain.
In this embodiment, if p is 3, the output equation for the future 3-step prediction of the system is:
Y3(k+1|k)＝SxΔω′(k)+SuΔPM(k)+SdΔPe(k)+γω′(k)
wherein, Y3(k +1| k) is the system state output variable that predicts 3 steps into the future.
Sx＝[A A2+A A3+A2+A]T
Figure BDA0003206232450000074
ΔPM＝[ΔPm(k) ΔPm(k+1) ΔPm(k+2)]T
Sd＝[Bd ABd+Bd A2Bd+ABd+Bd]T
γ＝[1 1 1]T
And step 300, setting an objective function of the predictive smooth switching control, wherein the predictive smooth switching control aims to control the frequency and the phase angle of the alternating current micro-grid to be equal to those of the large grid, and performance indexes of the predictive smooth switching control comprise the phase angle and the angular speed.
The target of the prediction smooth switching control is to control the frequency and the phase angle of the alternating current micro-grid to be equal to those of the large grid so as to achieve the target of pre-synchronization control.
In the presynchronization process, when the phase angle difference is 0, the frequency difference is also 0, so the main performance index in the objective function is the phase angle difference.
At a very short period TsThe relationship between voltage phase angle and angular frequency can be expressed as:
Figure BDA0003206232450000081
θ (k +1) represents the voltage phase angle at the time k +1, θ (k) represents the voltage phase angle at the time k, ω (k +1) represents the angular frequency at the time k +1, ω0Is the nominal angular velocity.
From the above relational expression of the voltage phase angle and the angular frequency, if only the phase angle difference is considered to be reduced to 0, the alternating-current microgrid phase angle theta can rapidly approach the large power grid angular frequency thetagHowever, at the same time, the angular frequency ω may also fluctuate greatly, even exceeding the safe operation range, which is not favorable for the stable operation of the system, and therefore, the angular frequency difference needs to be added as a performance index.
The objective function derived from the above analysis is:
Figure BDA0003206232450000082
in the formulayiThe angular frequency weight coefficient is used for representing the importance degree of each target item in the performance index. It can be seen that the main purpose of the objective function is to pass the large grid angular frequency ω at times k +1 to k +3g' sum phase angle θgThe optimal angular frequency dynamic response is obtained by comprehensively considering the back-stepping calculation, so that the purposes of phase angle pre-synchronization and frequency pre-synchronization can be achieved, the system frequency can be maintained in a normal operation range in the process, the optimal input power of the VSG at the current k moment is obtained by back-stepping, and the optimal control effect is achieved by inputting the optimal input power into the system. Thetag(k + i) is the large grid phase angle at time k + i, ωg'is the large grid angular frequency at time k + i, theta (k + i | k) is the VSG phase angle at time k + i, and omega' (k + i | k) is the VSG angular frequency at time k + i.
The objective function comprises two parts of a phase angle theta and an angular speed omega, and not only is the phase difference ensured to be fast and accurately reduced to 0, but also the angular speed is prevented from changing too much to influence the stability of the system frequency in the presynchronization process.
When the weighting coefficient is smaller, the phase difference is a main control target, the phase difference is reduced rapidly, but meanwhile, the angular speed is equivalent to the frequency, and larger-range fluctuation is generated, so that the normal and stable operation of the system is influenced; when the weighting coefficient is large, the angular speed is equivalent to the frequency as a main control target, the frequency fluctuation range is small at the moment, the system is stable, the time for reducing the phase difference to 0 is greatly prolonged, and the pre-synchronization time is long, so that the quick recovery of the grid-connected state of the alternating-current micro-grid is not facilitated.
Therefore, the adaptive adjustment can be performed by introducing the angular velocity variation into the weighting coefficient, and the specific equation is as follows:
Figure BDA0003206232450000091
Γ0as initial value of the weighting coefficient, kΓThe weighting adjustment coefficients. When the angular velocity difference is large, the weighting coefficient is large, which is beneficial to the stability of frequency, and when the angular velocity difference is small, the weighting coefficient is small, which is beneficial to the rapid implementation of phase pre-synchronization.
According to the invention, the phase angle difference and the frequency difference between the micro-grid and the large-grid are taken as performance indexes of a target function, and the weights of the two parts are timely adjusted through variable weighting coefficients, so that the stable operation and the good dynamic response characteristic of the micro-grid are ensured while the presynchronous control is realized.
Step 400, obtaining an optimal solution of the objective function under constraint conditions, wherein the constraint conditions comprise a state space model in an increment form at the k + i moment, and the optimal solution set of the objective function is obtained as delta PM＝[ΔPm(k)ΔPm(k+1)ΔPm(k+2)]TAnd adding the first item in the optimal solution set as the optimal solution to the active power input value at the current moment by comparing the power increment required at the current moment with the power increment required at the previous moment to obtain the active power input value at the current moment.
Using constraints including an incremental form state space model, and the like, an optimization problem of model predictive control is expressed as:
Figure BDA0003206232450000101
wherein J (ω '(k)) is an objective function, ω'min(k + i) is the minimum value of VSG angular frequency at time k + i，ω′max(k + i) is the maximum value of the VSG angular frequency at the time k + i,
solving the optimal solution of the model predictive control MPC problem with the constraint, and taking the delta Pm(k) Δ P of the first termm(k) As an input power increment.
Obtaining the optimal input of the current system:
Pm(k)＝Pm(k-1)+ΔPm(k)。
step 500, when the alternating current micro grid system is switched from an island operation mode to a grid-connected operation mode, the predictive smooth switching control is completed through the steps 100 to 400, and the obtained optimal input is input into the alternating current micro grid system.
The alternating-current microgrid system in the embodiment comprises a simulation synchronous generator VSG unit of a distributed generation DG, an active load and a synchronous generator set SG.
Under the initial operation state, the VSG unit is in an island operation mode and operates with load independently, and secondary frequency modulation control is adopted at the moment to ensure the frequency stability of the alternating current micro-grid; the synchronous generator set simulates the operation of a large power grid and is disconnected with an alternating current micro-power grid.
When the alternating-current microgrid is switched from an island operation mode to a grid-connected operation mode, the VSG unit is switched from the secondary frequency modulation control 0 to the model predictive control MPC, namely, the steps 100 to 400 are carried out to adjust the input power instruction of the VSG unit, pre-synchronization operation is carried out, and after the pre-synchronization is completed, grid-connected switching-on is carried out to complete the grid-connected operation.
The control of the MPC algorithm directly affects the pre-synchronization effect and the frequency dynamic characteristics of the power grid during the pre-synchronization process.
The above-mentioned embodiments, objects, technical solutions and advantages of the present application are described in further detail, it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present application, and are not intended to limit the scope of the present application, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present application should be included in the scope of the present application.

Claims (9)

1. A smooth switching method of a network-forming type converter based on a model prediction algorithm is characterized by comprising the following steps:
step 100, discretizing an active frequency model of a simulation synchronous generator VSG system to obtain a state space model of the active frequency of the discrete-time VSG system, wherein the state space model obtains a difference value between the actual angular frequency and the rated angular frequency of the alternating-current microgrid at the current moment through the difference value between the actual angular frequency and the rated angular frequency of the alternating-current microgrid at the previous moment, the mechanical power of a converter and the electromagnetic power output by the converter;
step 200, describing the state space model in an incremental mode, wherein the incremental mode model obtains the difference value between the actual angular frequency and the rated angular frequency of the alternating current micro-grid at the later moment compared with the current moment through the difference value between the actual angular frequency and the rated angular frequency of the alternating current micro-grid at the current moment and the last moment, the mechanical power difference value of the converter and the electromagnetic power difference output by the converter, namely the required power increment, and predicting the state of the system in one or more periods in the future through the mode;
step 300, setting a target function of prediction smooth switching control, wherein the prediction smooth switching control aims at controlling the frequency and the phase angle of an alternating current micro-grid to be equal to those of a large grid, performance indexes of the prediction smooth switching control comprise the phase angle and the angular speed, and the introduced angular acceleration variable quantity is subjected to adaptive adjustment through a weighting coefficient;
step 400, obtaining an optimal solution of the objective function under constraint conditions, wherein the constraint conditions comprise a state space model in an increment form at the k + i moment, and the optimal solution set of the objective function is obtained as delta PM＝[ΔPm(k)ΔPm(k+1)...ΔPm(k+p-1)]TIn which Δ Pm(k) Representing the difference, Δ P, between the mechanical power input to the converter at time k and at time k-1m(k +1) represents the difference between the mechanical power input to the converter at the time k +1 and at the time k, Δ Pm(k + p-1) represents the mechanical power difference value of the input converter at the k + p-1 moment and the k + p-2 moment, the first item in the optimal solution set is the optimal solution which is the current momentComparing the power increment required at the previous moment, adding the power increment to the active power input value at the current moment to obtain the active power input value at the current moment, wherein p is a prediction time domain;
step 500, when the alternating current micro grid system is switched from an island operation mode to a grid-connected operation mode, the predictive smooth switching control is completed through the steps 100 to 400, and the obtained optimal input is input into the alternating current micro grid system.
2. The method for smoothly switching the network-forming type converter based on the model prediction algorithm is characterized in that the model for simulating the active frequency of the VSG can be expressed as follows:
Figure FDA0003206232440000021
wherein J is moment of inertia, DPTo be damping coefficient, Pm、PeRespectively mechanical power and electromagnetic power of the converter, and respectively active power set value and actual output value, wherein omega represents virtual angular velocity output by the converter, and omega represents virtual angular velocity output by the converter0Is the nominal angular velocity.
3. The method for smoothly switching the network-forming type converter based on the model prediction algorithm as claimed in claim 2, wherein the discretized active frequency model expression of the analog synchronous generator VSG system is as follows:
Figure FDA0003206232440000022
wherein ω' (k +1) represents the difference between the actual angular frequency and the rated angular frequency of the alternating-current microgrid at the moment of k + 1; omega' (k) represents the difference value between the actual angular frequency and the rated angular frequency of the alternating-current microgrid at the moment k; pm(k) Representing the mechanical power input into the converter at the moment k; pe(k) Representing the electromagnetic power output by the converter at the moment k; y isc(k) Representing the controlled output variable at time k, TsIs the sampling time of the system, and
Figure FDA0003206232440000023
Figure FDA0003206232440000024
Figure FDA0003206232440000025
4. the method for smoothly switching the network-forming type converter based on the model prediction algorithm according to claim 3, wherein the state space model is described in an incremental form:
Figure FDA0003206232440000026
wherein, Δ ω' (k +1) represents the difference between the actual angular frequency of the ac microgrid at the time k +1 and the actual angular frequency of the ac microgrid at the time k; Δ ω' (k) represents the difference between the actual angular frequency of the AC microgrid at time k and at time k-1, Δ Pm(k) Representing the difference, Δ P, between the mechanical power input to the converter at time k and at time k-1e(k) Representing the difference in electromagnetic power output by the converter at time k and at time k-1, yc(k) Representing the controlled output variable at time k, yc(k-1) represents the controlled output variable at time k-1, CcRepresenting the output state coefficient matrix.
5. The method for smoothly switching the network-structured converter based on the model prediction algorithm according to claim 4, wherein the states in p future prediction time domains of the prediction system can be described as follows:
Yp(k+1|k)＝SxΔω′(k)+SuΔPM(k)+SdΔPe(k)+γω′(k)
wherein, Yp(k+1|k)To predict the system state output variables for p steps in the future,
Sx＝[A A2+A...Ap+Ap-1+...+A]T
Figure FDA0003206232440000031
ΔPM＝[ΔPm(k) ΔPm(k+1)...ΔPm(k+p-1)]T
Sd＝[Bd ABd+Bd...Ap-1Bd+Ap-2Bd+...+Bd]T
γ＝[1 1...1]T。
6. the method for smoothly switching the grid-connected inverter based on the model prediction algorithm according to claim 3, wherein the objective function comprises two parts of a phase angle and an angular speed, and is represented as:
Figure FDA0003206232440000032
wherein, gamma isyiIs an angular frequency weight coefficient, θg(k + i) is the large grid phase angle at time k + i, ωg' (k + i) is the large grid angular frequency at time k + i, [ theta ] (k + i | k) is the VSG phase angle at time k + i, [ omega ] (k + i | k) is the VSG angular frequency at time k + i,
Figure FDA0003206232440000033
wherein gamma is0As initial value of the weighting coefficient, kΓThe weighting adjustment coefficients.
7. The method for smoothly switching the network-constructed converter based on the model prediction algorithm according to claim 6, wherein the optimization problem of model prediction control is expressed as follows by using constraint conditions including an incremental state space model and the like:
Figure FDA0003206232440000041
wherein J (ω '(k)) is an objective function, ω'min(k + i) is the minimum value of VSG angular frequency at time k + i, ω'max(k + i) is the maximum value of VSG angular frequency at the time of k + i, and is taken as delta Pm(k) Δ P of the first termm(k) As an optimal input power increment.
8. The method according to claim 7, wherein the optimal input of the current system is a power increment required by the current time compared with the previous time, and the power increment is added to the active power input value of the previous time, that is:
Pm(k)＝Pm(k-1)+ΔPm(k)。
9. the method for smoothly switching the grid-structured converter based on the model prediction algorithm as claimed in claim 8, wherein the obtained optimal input of the current system is input into an alternating current micro-grid system, and the alternating current micro-grid system comprises a simulation synchronous generator VSG unit, an active load and a synchronous generator set SG of a distributed generation DG.
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