Frequency adaptive control method for inverter based on model predictive virtual synchronous generator

Abstract

A model predictive virtual synchronous generator inverter control method considering a frequency-movement direction includes: obtaining a current frequency absolute value based on fundamental frequency of an inverter and a steady-state absolute value based on the fundamental frequency of the inverter; determining a frequency-movement state by comparing the above two absolute values: in a state of deviating from the fundamental frequency or a state of regressing to the fundamental frequency; setting a corresponding prediction output horizon according to the frequency-movement state, and constructing a cost function considering the frequency-movement state; and calculating an optimal virtual power increment value based on the cost function, then calculating an optimal virtual power, inputting the optimal virtual power to a swing equation of a virtual synchronous generator, and performing frequency adjustment according to an output value.

Claims

1. A model predictive virtual synchronous generator inverter control method considering a frequency-movement direction, comprising obtaining a current frequency absolute value based on fundamental frequency of an inverter and a steady-state absolute value based on the fundamental frequency of the inverter; determining a frequency-movement state by comparing the above two absolute values: in a state of deviating from the fundamental frequency or a state of regressing to the fundamental frequency; setting a corresponding prediction output horizon according to the frequency movement state, and constructing a cost function considering the frequency-movement state; and calculating an optimal virtual power increment value based on the cost function, then calculating an optimal virtual power, inputting the optimal virtual power to a swing equation of a virtual synchronous generator, and performing frequency adjustment according to an output value based on the swing equation.

2. The model predictive virtual synchronous generator inverter control method considering a frequency-movement direction according to claim 1, wherein the obtaining a current frequency absolute value based on fundamental frequency of an inverter and a steady-state absolute value based on the fundamental frequency of the inverter comprises: detecting a system frequency of output of an inverter in a micro-grid island mode, calculating a system steady-state frequency from a current power of the inverter, and determining the current frequency absolute value based on the fundamental frequency and the steady-state absolute value based on the fundamental frequency.

3. The model predictive virtual synchronous generator inverter control method considering a frequency-movement direction according to claim 1, wherein when determining the frequency-movement state, if the current frequency absolute value based on the fundamental frequency is less than the steady-state absolute value based on the fundamental frequency, it is in a state of deviating from the fundamental frequency; and if the current frequency absolute value based on the fundamental frequency is greater than the steady-state absolute value based on the fundamental frequency, it is in a state of regressing to the fundamental frequency.

4. The model predictive virtual synchronous generator inverter control method considering a frequency-movement direction according to claim 1, wherein the cost function considering the frequency-movement state comprises a prediction horizon minimization target for a frequency-movement fluctuation value and also comprises a prediction horizon minimization target for a frequency regression fluctuation value; in a frequency-deviation state, priority is given to realizing the optimization of the prediction horizon output for the frequency-deviation fluctuation value through assignment; and in a frequency regression state, priority is given to realizing the optimization of the prediction horizon output for the frequency regression fluctuation value through assignment.

5. The model predictive virtual synchronous generator inverter control method considering a frequency-movement direction according to claim 1, wherein the optimal virtual power is calculated from the optimal virtual power increment value and the virtual power.

6. The model predictive virtual synchronous generator inverter control method considering a frequency-movement direction according to claim 1, wherein after calculating the optimal virtual power, a step of correcting the optimal virtual power is further comprised, whether the calculated optimal virtual power exceeds a virtual power selectable range is determined, and if so, the current frequency absolute value based on the fundamental frequency of the inverter and the steady-state absolute value based on the fundamental frequency of the inverter are re-obtained, and the optimal virtual power is calculated again; otherwise, the calculated optimal virtual power is directly input into the swing equation of the virtual synchronous generator, and the calculated virtual angular velocity is output to an integral link to achieve optimal virtual power frequency adjustment.

7. A model predictive virtual synchronous generator inverter control system considering a frequency-movement direction, comprising a frequency-movement state determination module configured to obtain a current frequency absolute value based on fundamental frequency of an inverter and a steadystate absolute value based on the fundamental frequency of the inverter; determine a frequency-movement state by comparing the above two absolute values: in a state of deviating from the fundamental frequency or a state of regressing to the fundamental frequency; a frequency-adjustment module configured to set a corresponding prediction output horizon according to the frequency-movement state, and construct a cost function considering the frequency-movement state; and calculate an optimal virtual power increment value based on the cost function, then calculate an optimal virtual power, input the optimal virtual power to a swing equation of a virtual synchronous generator, and perform frequency adjustment according to an output value based on the swing equation.

8. A computing device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the method according to claim 1.

9. A computer-readable storage medium having a computer program stored thereon, wherein the program when executed by a processor, performs the steps of the method according to claim 1.

10. A virtual synchronous generator inverter controlled by the method according to claim 1 to achieve different frequency adjustment targets.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) The accompanying drawings constituting a part of the present invention are used for providing a further understanding of the present invention. The exemplary examples of the present invention and descriptions thereof are used for explaining the present invention, and do not constitute an improper limitation of the present invention.

(2) FIG. 1 is a schematic control block diagram according to an embodiment of the present invention;

(3) FIG. 2 is a movement state determination diagram according to an embodiment of the present invention;

(4) FIG. 3 is a frequency waveform using a virtual synchronous generator method and a frequency waveform using the present invention in different load switching states; and

(5) FIG. 4 is a flow chart of virtual synchronous generator model predictive control according to an embodiment of the present invention.

DETAILED DESCRIPTION

(6) It should be noted that, the following detailed descriptions are all exemplary, and are intended to provide further descriptions for the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those usually understood by a person of ordinary skill in the art to which the present invention belongs.

(7) It should be noted that the terms used herein are merely used for describing specific implementations, and are not intended to limit exemplary implementations of the present invention.

(8) The embodiments of the present invention and the features of the embodiments may be combined with each other without conflict.

Embodiment I

(9) As described in the background, according to the existing virtual synchronous generator technology for inverters, it is difficult for the system frequency to regress to the fundamental frequency from which it once deviates under large inertia conditions. Under the condition of small inertia, the micro-grid system has weak anti-interference ability and poor system stability, which leads to poor control effect. In order to solve this problem, this embodiment provides a model predictive virtual synchronous generator inverter control method considering a frequency-movement direction.

(10) Specifically, as shown in FIG. 1 and FIG. 4, the model predictive virtual synchronous generator inverter control method considering a frequency-movement direction of this embodiment includes:

(11) Step 1: a system frequency .sub.m of output of an inverter in a micro-grid island mode is detected, a system steady-state frequency .sub.ref is calculated from the current output power of the inverter, and the frequency absolute value |.sub.m0(k)| based on the fundamental frequency is an absolute value of the difference between the system frequency .sub.m and the fundamental frequency .sub.0; and the steady-state frequency absolute value |.sub.ref0(k)| based on the fundamental frequency is an absolute value of the difference between the steady-state frequency .sub.ref and the fundamental frequency .sub.0.

(12) Specifically, the frequency absolute value |.sub.m0(k)| at the k.sup.th instant based on the fundamental frequency .sub.0 and the steady-state absolute value |.sub.ref0(k)| based on the fundamental frequency .sub.0 are determined, with the calculation formula thereof as follows:

(13) $\{\begin{array}{cc}.Math.{}_{m0}\left(k\right).Math.& =.Math.{}_m\left(k\right)-{}_0.Math.\\ .Math.{}_{\mathrm{ref}0}\left(k\right).Math.& =.Math.{}_{\mathrm{ref}}\left(k\right)-{}_0.Math.\end{array}$

(14) In this step, the system frequency .sub.m is an output frequency output by the inverter, and the steady-state frequency .sub.ref is derived from the curve of droop control P of the inverter.

(15) Step 2: in order to determine the system frequency-movement state, an absolute value |.sub.m0(k)| of the current frequency value .sub.m based on the fundamental frequency is compared with an absolute value |.sub.ref0(k)| of the steady-state frequency value .sub.ref based on the fundamental frequency, with the determination formula thereof as follows:

(16) $\{\begin{array}{cc}.Math.{}_{m0}\left(k\right).Math.-.Math.{}_{\mathrm{ref}0}\left(k\right).Math.<0& \mathrm{deviating}\mathrm{from}\mathrm{the}\mathrm{fundamental}\mathrm{frequency}\\ .Math.{}_{m0}\left(k\right).Math.-.Math.{}_{\mathrm{ref}0}\left(k\right).Math.>0& \mathrm{regressing}\mathrm{to}\mathrm{the}\mathrm{fundamental}\mathrm{frequency}\end{array}$

(17) In order to realize the frequency control considering the frequency-movement state, it is necessary to determine the current system frequency state. when |.sub.m0(k)||.sub.ref0(k)|<0, it is determined that the system frequency .sub.m is in a state of deviating from the fundamental frequency; and when |.sub.m0(k)||.sub.ref0(k)|>0, it is determined that the system frequency .sub.m is in a state of regressing to the fundamental frequency.

(18) FIG. 2 show waveforms of the system frequency and the absolute value of the frequency based on the fundamental frequency, and it can be seen from the simulation results that in different load switching states, the system frequency is in a deviation state, and the absolute value of the frequency is less than the absolute value of the expected frequency; and when the system frequency is in a regression state, the absolute value of the frequency is greater than the absolute value of the expected frequency.

(19) Step 3: a corresponding prediction output horizon is set according to the frequency-movement state, and a cost function considering the frequency-movement state is constructed, including a prediction horizon minimization target for a frequency-movement fluctuation value and a prediction horizon minimization target for a frequency regression fluctuation value.

(20) When the system frequency om is in a state of deviating from the fundamental frequency, a prediction horizon output W.sub.pd (j+1|j) of the frequency-movement fluctuation value is constructed, i.e., the value of j+1 instant is predicted from the j instant (j=k, k+1 . . . ), and an expected value R.sub.d(k+1)=[0 0 . . . 0].sup.T in the deviation state (an expected value at k+1 instant) is set; and when the system frequency .sub.m is in a state of regressing to the fundamental frequency, the prediction horizon output W.sub.pb(j+1|j) of the frequency regression fluctuation value is constructed, and an expected value R.sub.b(k+1)=[0 0 . . . 0].sup.T in the regression state is set.

(21) Further, by optimizing the virtual power increment value in an incremental model, the system frequency .sub.m can be adjusted effectively in different states.

(22) In order to adapt to different optimization objectives in a frequency-deviation state and a frequency regression state, the cost function F.sub.p considering the frequency-movement state is constructed.

(23) The derivation process is described in detail below:

(24) The incremental model is derived from a discrete model of a swing equation of the virtual synchronous generator technology, and its calculation formula is as follows:

(25) $\begin{array}{c}{}_m\left(k+1\right)={}_m\left(k+1\right)-{}_m\left(k\right)\\ =A_e{}_m\left(k\right)+B_u{P}_m\left(k\right)+B_d{P}_{\mathrm{out}}\left(k\right)\end{array}$ where

(26) $A_e=1-\frac{T_{s}D}{J},B_u=\frac{T_s}{J},B_d=-\frac{T_s}{J},T_s$
is a control period, J is a moment of inertia, P.sub.m is virtual power, P.sub.out is output power and D is a damping coefficient. 1). When in the state of the system frequency deviating from the fundamental frequency, the frequency-movement fluctuation value .sub.d is defined to be
.sub.d(j)=.sub.m(j).sub.m(k)(j=k,k+1, . . . ,p) a prediction horizon is defined asp steps, assuming that the system reaches a steady state in step m. Then, the prediction horizon output W.sub.pd(j+1|j) for the frequency-movement fluctuation value .sub.d is
W.sub.pd(j+1|j)=S.sub.x.sub.d(j)+I.sub.d(j)+S.sub.dP.sub.out(j)+S.sub.uP(j) where

(27) $I={\left[\begin{array}{cccc}1& 1& .Math.& 1\end{array}\right]}_{1p}^T,P\left(k\right)={\left[P_m\left(k\right)P_m\left(k+1\right).Math.P_m\left(k+m-1\right)\right]}_{1m}^T,$

(28) $S_x={\left[\begin{array}{cccc}{A}_e& \underset{i=1}{\overset{2}{.Math.}}{A}_e^i& .Math.& \underset{i=1}{\overset{p}{.Math.}}{A}_e^i\end{array}\right]}_{1p}^T,$$S_d={\left[\begin{array}{cccc}{B}_d& \underset{i=1}{\overset{2}{.Math.}}{A}_e^{i-1}{B}_d& .Math.& \underset{i=1}{\overset{p}{.Math.}}{A}_e^{i-1}{B}_d\end{array}\right]}_{1p}^T,$$S_u={\left[\begin{array}{cccc}{B}_u& 0& .Math.& 0\\ \underset{i=1}{\overset{2}{.Math.}}{A}_e^{i-1}{B}_u& B_u& .Math.& 0\\ .Math.& .Math.& .Math.& 0\\ \underset{i=1}{\overset{p}{.Math.}}{A}_e^{i-1}{B}_u& \underset{i=1}{\overset{p-1}{.Math.}}{A}_e^{i-1}{B}_u& .Math.& \underset{i=1}{\overset{p-m+1}{.Math.}}{A}_e^{i-1}{B}_u\end{array}\right]}_{pm}$ 2). When the frequency regresses to the fundamental frequency from the deviation value, the frequency regression fluctuation value .sub.b is defined to be
.sub.b(j)=.sub.m(j).sub.refj=k,k+1, . . . ,p

(29) Where .sub.ref is a steady-state frequency expected value.

(30) Further, the prediction horizon output W.sub.pb(j+1|j) for the frequency regression fluctuation value .sub.b is
W.sub.pb(j+1|j)=S.sub.x.sub.b(j)+I.sub.b(j)+S.sub.dP.sub.out(j)+S.sub.uP(j)

(31) Therefore, when the frequency deviates from the fundamental frequency, in order to achieve slow frequency movement, the prediction horizon output W.sub.pd(j+1|j) of the frequency-deviation fluctuation value .sub.d should be 0, i.e., R.sub.d(k+1)=[0 0 . . . 0].sup.T; and when the frequency regresses from the deviation value to the fundamental frequency, in order to achieve frequency-accelerated regression, the prediction horizon output W.sub.pb(j+1|j) of the frequency regression fluctuation value .sub.b is 0, i.e., R.sub.b(k+1)=[0 0 . . . 0].sup.T.

(32) Further, the prediction horizon output W.sub.pd(j+1|j) for the frequency-deviation fluctuation value .sub.d and the prediction horizon output W.sub.pb(j+1|j) for the frequency regression fluctuation value w.sub.b are integrated, and the cost function considering the frequency-movement state is constructed as follows:
F.sub.p=min{.sub.dW.sub.pd(k+1|k)R.sub.d(k+1).sup.2+.sub.bW.sub.pd(k+1|k)R.sub.b(k+1).sup.2+.sub.pP(k).sup.2} where .sub.d is a deviation state weight factor matrix, .sub.b is a regression state weight factor matrix, and is a power fluctuation penalty factor matrix.

(33) Step 4; in order to achieve the frequency optimization control targets under different movement states, the assignments of matrixes .sub.d, .sub.b and are different according to different frequency-movement states.

(34) When in the state of the system frequency deviating from the fundamental frequency, a weighting factor and a penalty factor are assigned to optimize the frequency change rate to be the minimum, and an optimal virtual power increment value under the frequency-movement state is calculated to slow down the deviation of the system frequency .sub.m and effectively reduce the frequency change rate.

(35) Specifically, in the frequency-deviation state, priority is given to realizing the optimization of the prediction horizon output W.sub.pd(j+1|j) for the frequency-deviation fluctuation value .sub.d, and its assignment is as follows: frequency-deviation state:

(36) $\{\begin{array}{cc}{}_d& =\mathrm{diag}\left({}_d,{}_d,.Math.,{}_d\right)\\ {}_b& =\mathrm{diag}\left(0,0,.Math.,0\right)\\ {}_p& =\mathrm{diag}\left({}_1,{}_1,.Math.,{}_1\right)\end{array}$

(37) When in the state of the frequency regressing to the fundamental frequency state, the weight factor and the penalty factor are assigned to optimize the difference between a steady-state value and a frequency to be the minimum, and the optimal virtual power increment value is calculated.

(38) Specifically, when in the state of the system frequency regressing to the fundamental frequency state, the weighting factor and the penalty factor are assigned to calculate the optimal virtual power increment value in the frequency regression state to accelerate the system frequency .sub.m to regress to the fundamental frequency, and effectively and rapidly increase the frequency change rate, priority is given to realizing the dominance of optimization of the prediction horizon output W.sub.pd(j+1|j) for the frequency regression fluctuation value .sub.b, and the assignment thereof is as follows:

(39) $\mathrm{frequency}\mathrm{regression}\mathrm{state}:\{\begin{array}{cc}{}_d& =\mathrm{diag}\left(0,0,.Math.,0\right)\\ {}_b& =\mathrm{diag}\left({}_b,{}_b,.Math.,{}_b\right)\\ {}_p& =\mathrm{diag}\left({}_2,{}_2,.Math.,{}_2\right)\end{array}$

(40) According to the extreme value theory, the optimal virtual power increment P*(k) under different frequency-movement states is calculated.

(41) Step 5: the optimal virtual power is calculated according to the optimal virtual power increment value and the virtual power, and the expression thereof is
P*(k)=P*(k)+P(k)

(42) In order to ensure the reliability of the system, it needs to determine whether the optimal virtual power P*(k) exceeds an adjustable range thereof, and when correcting the correctness of the virtual power, whether the optimal virtual power exceeds a selectable range of the virtual power, and if so, step 1 is performed; otherwise, the calculated virtual power is input into a swing equation of a virtual synchronous generator, i.e.,

(43) $J\frac{d\left({}_m-{}_0\right)}{\mathrm{dt}}=P_m-P_{\mathrm{out}}-D\left({}_m-{}_0\right)$ the calculated virtual angular velocity .sub.m is input to an integral link to achieve optimal virtual power frequency adjustment.

(44) According to the present invention, the system frequency is ultimately adjusted by injecting the optimal virtual power into the virtual synchronous power generation swing equation considering the frequency-movement state.

(45) It should be noted that, the technical solutions of the present invention include virtual synchronous generator increment model establishment, system frequency-movement state determination, cost function considering the frequency-movement state, optimization of virtual power increment, and virtual power adjustment of frequency. The present invention determines a system frequency-movement state by using an absolute value of a frequency-movement based on the fundamental frequency, and further sets a frequency optimization target in different frequency-movement states.

(46) The present invention considers the frequency-movement direction, and is based on a model predictive method of the virtual synchronous generator, realizing realizes the adjustment of the system frequency by optimizing the virtual power in different movement states. When the frequency deviates from the fundamental frequency, the frequency changes slowly to suppress the frequency-deviation; and when the frequency regresses to the fundamental frequency, the frequency change is accelerated to cause the frequency to quickly regress to the fundamental frequency. In addition, the characteristics of real-time optimization of model prediction improve the control effect. The present invention can solve the effect on the frequency caused micro-grid load switching, and the implementation method is simple and reliable. Therefore, it is of great significance for the application of the inverter.

(47) The present invention is a real-time optimization process to achieve the optimal regulation of the system frequency in a frequency-deviation state and a frequency regression state to improve the performance of the inverter.

(48) In addition, the present invention is not limited to specific micro-grid practical requirements, is not limited to inverter topologies, and is applicable to micro-grid single-phase bus and three-phase bus forms. In addition, the present invention is not limited to a DC side power supply form of the inverter, and is applicable to different situations such as low voltage, medium voltage, and high voltage, and has strong scalability and practicality.

(49) Simulation Case

(50) FIG. 3 shows a frequency waveform controlled by a fixed inertia virtual synchronous generator and a frequency waveform controlled by the method of the present invention, and it can be seen from the simulation results that the frequency-movement speed of the method of the present invention is less than the frequency-movement speed of the virtual synchronous generator when the frequency deviates from the fundamental frequency in different load switching states; and when the frequency regresses to the fundamental frequency, the frequency-movement speed of the method of the present invention is greater than the frequency-movement speed of the virtual synchronous generator.

(51) It can be seen from the above simulation results that the model predictive virtual synchronous generator inverter control method considering a frequency-movement direction provided by the present invention can effectively improve the dynamic characteristic capability of the inverter to regulate frequency.

(52) In another embodiment, a virtual synchronous generator inverter is further included, which is controlled by using the above-mentioned method to achieve different frequency adjustment targets, i.e.: when the frequency deviates from the fundamental frequency, the frequency change rate is effectively reduced to achieve slow frequency-movement; and when the frequency regresses to the fundamental frequency from the non-fundamental frequency value, the frequency change rate is increased effectively and quickly, so that the frequency regresses quickly.

Embodiment II

(53) An objective of this embodiment is to provide a computing device including a memory, a processor and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the steps of the method described above are implemented.

Embodiment III

(54) An objective of this embodiment is to provide a computer-readable storage medium.

(55) The computer-readable storage medium has a computer program stored thereon, and the program when executed by a processor, performs the steps of the above method.

Embodiment IV

(56) An objective of this embodiment is to provide a model predictive virtual synchronous generator inverter control system considering a frequency-movement direction, which includes: a frequency-movement state determination module configured to obtain a current frequency absolute value based on fundamental frequency of an inverter and a steady-state absolute value based on the fundamental frequency of the inverter; determining a frequency-movement state by comparing the above two absolute values: in a state of deviating from the fundamental frequency or a state of regressing to the fundamental frequency; a frequency-adjustment module configured to set a corresponding prediction output horizon according to the frequency-movement state, and construct a cost function considering the frequency-movement state; and calculate an optimal virtual power increment value based on the cost function, then calculate an optimal virtual power, input the optimal virtual power to a swing equation of a virtual synchronous generator, and perform frequency adjustment according to an output value.

(57) The steps involved in the devices of Embodiments II, III and IV above correspond to those of method Embodiment I, and for specific implementations, reference can be made to the relevant descriptions of Embodiment I. The term computer-readable storage medium shall mean a single medium or multiple media including one or more sets of instructions; It should also be understood to include any medium capable of storing, encoding, or carrying a set of instructions for execution by the processor and causing the processor to perform any of the methods of the present invention.

(58) It will be appreciated by a person skilled in the art that the various modules or steps of the present invention described above may be implemented in a general-purpose computing device. Optionally, they may be implemented in program code executable by the computing device, such that they may be stored in a storage device and executed by the computing device, or they may be fabricated as individual integrated circuit modules separately, or multiple modules or steps thereof may be fabricated as a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.

(59) The specific implementations of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the protection scope of the present invention. A person skilled in the art should understand that various modifications or deformations may be made without creative efforts based on the technical solutions of the present invention, and such modifications or deformations shall fall within the protection scope of the present invention.