CN115864878A - Control method of modular multilevel converter - Google Patents
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Abstract
The invention discloses a control method of a modular multilevel converter. The control method of the modular multilevel converter comprises the following steps: establishing an MMC electromagnetic transient model comprising a PI controller, a phase-shift carrier modulation controller and a modular multilevel converter; constructing a particle swarm optimization algorithm, and constructing a fitness function in the optimizing process based on a time error absolute value integral evaluation criterion and the output parameters of the modular multilevel converter; optimizing the adjusting coefficient of the PI controller by utilizing a particle swarm optimization algorithm based on the MMC electromagnetic transient model and the fitness function to obtain a target adjusting coefficient; and substituting the target regulation coefficient into the PI controller, and controlling the modular multilevel converter based on the MMC electromagnetic transient model. The embodiment of the invention is beneficial to quickly and reliably determining the adjustment coefficient of the PI controller in the MMC electromagnetic transient model, so that the modular multi-level converter is controlled to stably operate, and the operation stability of a direct-current transmission system is further ensured.
Description
Technical Field
The invention relates to the technical field of flexible direct current transmission, in particular to a control method of a modular multilevel converter.
Background
As a novel voltage source Converter, a Modular Multilevel Converter (MMC) has strong integrity and flexibility, and is a key device of a flexible direct current transmission system. The MMC can effectively improve the application voltage and power grade of the converter, can realize active and reactive decoupling independent control, and is beneficial to the application of the MMC in the fields of high-voltage direct-current transmission, static reactive compensation and the like.
As shown in fig. 1, the three-phase MMC is composed of three-phase bridge arms, each of which includes two Power units (Power units) PU, each of which includes a plurality of bridge arm sub-modules SM connected in series, and each of the bridge arm sub-modules SM includes two Power electronic switches and one capacitor C. In actual operation, MMC has numerous variables and passes through electricity in each bridge arm submodule SMCapacitor C for energy coupling and capacitor voltage v c The fluctuation randomness is large, the nonlinear characteristic is strong, and the voltage is difficult to measure. In the prior art, simulation control is usually carried out through the electromagnetic transient model for building the MMC, but a switch module in the MMC electromagnetic transient model can reach hundreds, so that the MMC has very strong nonlinear complexity, the performance requirement on a control system is very high, the adjusting coefficient setting difficulty of a PI controller in the MMC electromagnetic transient model is large, and the MMC is difficult to control in stable operation.
Disclosure of Invention
The invention provides a control method of a modular multilevel converter, which is beneficial to quickly and reliably determining the adjustment coefficient of a PI (proportional integral) controller in an MMC (modular multilevel converter) electromagnetic transient model, so that the modular multilevel converter is controlled to stably operate, and the operation stability of a direct-current power transmission system is further ensured.
The embodiment of the invention provides a control method of a modular multilevel converter, which comprises the following steps:
establishing an MMC electromagnetic transient model comprising a PI controller, a phase-shift carrier modulation controller and a modular multilevel converter;
constructing a particle swarm optimization algorithm, and constructing a fitness function in the optimization process based on a time error absolute value integral evaluation criterion and the output parameters of the modular multilevel converter;
optimizing the adjusting coefficient of the PI controller by utilizing the particle swarm optimization algorithm based on the MMC electromagnetic transient model and the fitness function to obtain a target adjusting coefficient;
substituting the target regulation coefficient into the PI controller, and controlling the modular multilevel converter based on the MMC electromagnetic transient model.
Optionally, the output parameters of the modular multilevel converter include: outputting current, direct current voltage and capacitance voltage of each bridge arm submodule in the modular multilevel converter;
the fitness function is:
wherein J is the fitness value, h 1 、h 2 、h 3 And h 4 For the set weight, e (t) is the system instantaneous error value, u (t) is the system output value, Δ u c (t) is the deviation value of the capacitance voltage of the bridge arm submodule,. DELTA.u dc And (t) is the deviation value of the direct current voltage.
Optionally, the optimizing the adjustment coefficient of the PI controller by using the particle swarm optimization algorithm based on the MMC electromagnetic transient model and the fitness function to obtain a target adjustment coefficient includes:
generating an initial particle swarm, initializing the position and the speed of each particle in the initial particle swarm, and recording the iteration frequency as 1; wherein the position of the particle is the value of an optimization variable, and the optimization variable is the adjustment coefficient;
substituting the positions of all particles in the initial particle swarm into the PI controller, controlling the modular multilevel converter based on the MMC electromagnetic transient model, and calculating the fitness value of all particles in the initial particle swarm according to the output parameters of the modular multilevel converter and the fitness function;
searching the individual optimal fitness value of each particle in the initial particle swarm according to the fitness value of each particle, obtaining the individual optimal position of each particle according to the individual optimal fitness value, searching the group optimal fitness value of the initial particle swarm, and obtaining the group optimal position according to the group optimal position;
updating the position and the speed of each particle according to the individual optimal position and the group optimal position to generate an updated particle swarm, and adding 1 to the iteration times;
substituting the positions of all particles in the updating particle swarm into the PI controller, controlling the modular multilevel converter based on the MMC electromagnetic transient model, and calculating the fitness value of all particles in the updating particle swarm according to the output parameters of the modular multilevel converter and the fitness function;
searching the individual optimal fitness value of each particle in the updated particle swarm according to the fitness value of each particle, obtaining the individual optimal position of each particle according to the individual optimal fitness value, searching the group optimal fitness value of the updated particle swarm, and obtaining the group optimal position according to the group optimal position;
judging whether an iteration termination condition is met;
if so, ending the optimization process, and taking the group optimal position obtained after the last updating as the target regulation coefficient;
if not, returning and executing the iteration steps of generating an updating particle swarm, calculating the fitness value of each particle in the updating particle swarm, and searching and updating the individual optimal position and the group optimal position in the particle swarm.
Optionally, the updating the position and the speed of each particle according to the individual optimal fitness value and the population optimal fitness value includes:
the velocity of the particles is updated according to the following equation:
where d is the number of iterations, v i Is the ith particle velocity, b i Is the ith particle position, w is the inertial weight of the velocity, c 1 And c 2 Is a learning factor, r 1 And r 2 Is in [0,1]Random number between, P ibest For the individual optimum position of the ith particle, G best The optimal position of the group is obtained;
the position of the particle is updated according to the following formula:
optionally, in the optimization process, as the number of iterations increases, the inertial weight of the velocity is decreased according to the following formula:
wherein，w max Is a preset maximum inertial weight, w min Is a preset minimum inertial weight, d max Is a preset maximum number of iterations.
Optionally, the iteration termination condition is: the iteration times reach the preset maximum iteration times, or the group optimal fitness value is smaller than the preset threshold value.
Optionally, the MMC electromagnetic transient model further includes a differential-mode and common-mode conversion module, configured to convert an output current of the modular multilevel converter into a common-mode current actual value and a differential-mode current actual value; the target adjusting coefficient comprises a target proportional adjusting coefficient and a target integral adjusting coefficient;
controlling the modular multilevel converter based on the MMC electromagnetic transient model, comprising:
the PI controller obtains a differential mode modulation coefficient according to a differential mode current reference value, the target proportion regulation coefficient, the target integral regulation coefficient and the differential mode current actual value;
the phase-shifting carrier modulation controller performs phase-shifting carrier modulation according to the differential mode modulation coefficient and the common mode modulation coefficient to obtain a trigger signal so as to control the modular multilevel converter to output stable direct-current voltage; and obtaining the common mode modulation coefficient according to the actual value of the common mode current.
Optionally, the output current of the modular multilevel converter is a three-phase current in a three-phase static coordinate system;
the differential mode and common mode conversion module is used for converting the output current of the modular multilevel converter into a common mode current actual value and a differential mode current actual value and comprises the following steps:
converting the three-phase current into differential mode current and common mode current under the three-phase static coordinate system;
converting the differential mode current under the three-phase static coordinate system into the differential mode current under the synchronous rotating coordinate system;
and taking the common-mode current in the three-phase static coordinate system as the actual value of the common-mode current, and taking the differential-mode current in the synchronous rotating coordinate system as the actual value of the differential-mode current.
Optionally, the differential mode current reference value is obtained according to the following formula:
wherein the content of the first and second substances,
for an AC-side active power reference value of a modular multilevel converter>
Is an AC side reactive power reference value u of the modular multilevel converter gd The d-axis component and the->
For the d-axis component of the differential mode current reference value in the synchronous rotating coordinate system, for>
Is the q-axis component of the differential mode current reference value in the synchronous rotating coordinate system.
Optionally, the common mode modulation coefficient is obtained according to the following formula:
wherein, the first and the second end of the pipe are connected with each other,
common-mode modulation coefficient R of j-th phase bridge arm of modular multilevel converter v Is a virtual resistance, is asserted>
For the actual value of the common-mode current of the j-th phase bridge arm of the modular multilevel converter, the actual value is greater than or equal to>
The reference value is the sum of the capacitance voltages of the bridge arm sub-modules of the upper bridge arm of the modular multilevel converter or the sum of the capacitance voltages of the bridge arm sub-modules of the lower bridge arm.
According to the control method of the modular multilevel converter, provided by the embodiment of the invention, a particle swarm optimization algorithm is introduced, and the optimal value of the regulating coefficient of the PI controller is globally searched through a heuristic algorithm, so that the set target regulating coefficient can be conveniently and rapidly obtained, and the output waveform quality of the modular multilevel converter is improved. And moreover, a fitness function is constructed based on a time error absolute value integral evaluation criterion, and the correlation quantity of the output parameters of the modular multilevel converter is introduced into the fitness function, so that the fitness function can better represent the output stability of the modular multilevel converter, the output fluctuation of the modular multilevel converter can be effectively reduced by optimizing according to the fitness function, and a better control effect is obtained. Therefore, compared with the prior art, the method and the device are beneficial to quickly and reliably determining the adjustment coefficient of the PI controller in the MMC electromagnetic transient model, so that the modular multi-level converter is controlled to stably operate, and the operation stability of the direct-current power transmission system is further ensured.
It should be understood that the statements in this section are not intended to identify key or critical features of the embodiments of the present invention, nor are they intended to limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a topology of a three-phase modular multilevel converter according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a method for controlling a modular multilevel converter according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an optimization process of a particle swarm optimization algorithm according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an MMC electromagnetic transient model combined with a PSO optimization algorithm according to an embodiment of the present invention;
fig. 5 is an active power step simulation result provided by the embodiment of the present invention;
fig. 6 is a reactive power step simulation result provided by the embodiment of the present invention;
fig. 7 is a simulation result of output voltage of a modular multilevel converter according to an embodiment of the present invention;
fig. 8 is an enlarged view of the capacitor voltage waveforms of the 1 st bridge arm submodule of the phase a upper bridge arm in the AREA1 of fig. 7.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions.
The embodiment of the invention provides a control method of a modular multilevel converter, which is suitable for the stable output requirement of a flexible direct current transmission system. To better explain the control method provided by the embodiment of the present invention, the structure of the Modular Multilevel Converter (MMC) is first described below. Illustratively, the multi-terminal flexible direct current transmission system topology comprises a plurality of converter stations, and each converter station comprises a modular multilevel converter for connecting an alternating current side power grid and a direct current side power grid and performing alternating current-direct current conversion. Fig. 1 is a schematic diagram of a topology structure of a three-phase modular multilevel converter according to an embodiment of the present invention. Referring to fig. 1, exemplarily, a three-phase MMC is taken as an example and includes three-phase bridge arms, each of which is composed of an upper power unit PU and a lower power unit PU, each of which is composed of N bridge arm sub-modules SM and a bridge arm reactor L having the same structure 0 Bridge arm reactors L connected in series 0 The current-limiting device is used for inhibiting the circulating current and buffering the bridge arm current in the short circuit. The three phases are respectively represented by subscripts j (j = a, b, c), subscript "p" represents the upper arm, and subscript "n" represents the lower arm. u. of jp And i jp Voltage and current u of the j-th phase upper bridge arm respectively jn And i jn Voltage and current u of the j-th lower bridge arm respectively dc For the DC bus voltage, each phase of the AC system passes through an AC reactor L ac Corresponding phase bridge arm u connected to MMC gj And i gj The j-th ac network side voltage and current respectively.
Each bridge arm submodule SM may have a half-bridge structure, and specifically may include a first power electronic switch S1, a second power electronic switch S2, a first diode D1, a second diode D2, and a capacitor C. The emitter of the first power electronic switch S1 is connected with the collector of the second power electronic switch S2, one end of the capacitor C is connected with the collector of the first power electronic switch S1, and the other end of the capacitor C is connected with the emitter of the second power electronic switch S2; the first diode D1 is reversely connected in parallel at two ends of the first power electronic switch S1; the second diode D2 is connected in anti-parallel to two ends of the second power electronic switch S2. Capacitance voltage meterIs v c The voltage across the second power electronic switch S2 is denoted as u sm 。
Fig. 2 is a schematic flowchart of a control method of a modular multilevel converter according to an embodiment of the present invention. Referring to fig. 2, the modular multilevel converter control method includes:
s110, establishing an MMC electromagnetic transient model comprising a PI controller, a phase-shift carrier modulation controller and a modular multilevel converter.
The MMC electromagnetic transient model can be built in PSCAD, for example, a 26-level PSCAD simulation model is adopted. When the system power changes, the active power and the reactive power of an alternating-current side power grid have a coupling relation, and the stable operation of the system is influenced, so that alternating-current side power decoupling control can be performed in an MMC electromagnetic transient model, a differential mode and common mode representation method can be specifically adopted to derive an MMC instantaneous value mathematical model, and a direct modulation method based on virtual resistance and direct-current bus voltage is adopted to avoid direct measurement of capacitance voltage in each bridge arm submodule SM. In an MMC electromagnetic transient model, quick adjustment can be realized based on a PI (proportional-integral) controller, and for bridge arms containing N bridge arm sub-modules SM, N groups of PWM signals can be obtained to drive the N bridge arm sub-modules SM by comparing N groups of isosceles triangular carriers with phases staggered by 2 PI/N angles in sequence with the same modulation wave based on a Phase-Shifted Carrier modulation (PSC-PWM) controller, so that higher equivalent switching frequency can be obtained at lower switching frequency, and the switching loss is reduced. The specific power decoupling control model is described in detail in the following embodiments.
And S120, constructing a particle swarm optimization algorithm, and constructing a fitness function in the optimizing process based on the time error absolute value integral evaluation criterion and the output parameters of the modular multilevel converter.
Wherein, particle Swarm Optimization (PSO) can be realized in MATLAB. The output parameters of the modular multilevel converter can comprise the direct current voltage u of the modular multilevel converter dc And the capacitance voltage v of each bridge arm submodule SM c And adjusting the time error absolute value integral based on the output parameters of the modular multilevel converterAnd (ITAE) evaluation criterion can enable the finally constructed fitness function to better represent the stability of system output.
S130, optimizing the adjusting coefficient of the PI controller by utilizing a particle swarm optimization algorithm based on the MMC electromagnetic transient model and the fitness function to obtain a target adjusting coefficient.
Wherein, the adjusting coefficient of the PI controller can comprise a proportional adjusting coefficient k P And/or integral adjustment coefficient k I . The optimization process of the particle swarm optimization algorithm can comprise the steps of initializing a particle swarm, calculating a particle fitness value, searching an individual optimal position and a group optimal position, updating the particle speed and position and the like. And when the iteration result meets the iteration termination condition, obtaining a target adjusting coefficient. The specific optimization process is described in detail in the examples below. In the embodiment, the particle swarm optimization algorithm is adopted to search the target adjustment coefficient, and compared with a manual trial and error method, the optimization efficiency can be improved, the target adjustment coefficient can be quickly and reliably searched, and the corresponding target adjustment coefficient can be dynamically adjusted according to different control targets of the system.
And S140, substituting the target regulation coefficient into the PI controller, and controlling the modular multilevel converter based on the MMC electromagnetic transient model.
According to the control method of the modular multilevel converter, provided by the embodiment of the invention, a particle swarm optimization algorithm is introduced, and the optimal value of the regulating coefficient of the PI controller is globally searched through a heuristic algorithm, so that the set target regulating coefficient can be conveniently and rapidly obtained, and the output waveform quality of the modular multilevel converter is improved. And moreover, a fitness function is constructed based on a time error absolute value integral evaluation criterion, and the correlation quantity of the output parameters of the modular multilevel converter is introduced into the fitness function, so that the output stability of the modular multilevel converter can be better represented by the fitness function, and accordingly, optimization can be effectively realized by compensating the disturbance term aiming at output fluctuation, and a better control effect can be obtained. Therefore, compared with the prior art, the method and the device are beneficial to quickly and reliably determining the adjustment coefficient of the PI controller in the MMC electromagnetic transient model, so that the modular multi-level converter is controlled to stably operate, and the operation stability of a direct-current transmission system is further ensured.
Next, a detailed description is given of an optimization process of the particle swarm optimization algorithm used in the embodiment of the present invention. First, a fitness function constructed according to the ITAE evaluation criterion in connection with the control target of the study object according to the embodiment of the present invention will be described. For example, the output parameters of the modular multilevel converter may include: and the output current, the direct current voltage, the capacitance voltage of each bridge arm submodule in the modular multilevel converter and the like. The fitness function is:
wherein J is the fitness value, h 1 、h 2 、h 3 And h 4 For the set weight, e (t) is the system instantaneous error value, u (t) is the system output value, Δ u c (t) is the deviation value of the capacitor voltage,. DELTA.u dc And (t) is the DC voltage deviation value. Exemplarily, h 1 Is set to be 1.2,h 2 Is set to be 1.1,h 3 Is set to be 1.3,h 4 Set to 1.5. The output current of the modular multilevel converter may specifically be: the three-phase current of the three-phase bridge arm is subjected to coordinate transformation between a three-phase stationary coordinate system and a synchronous rotating coordinate system to obtain d-axis and q-axis currents under the synchronous rotating coordinate system, and further can be d-axis and q-axis differential mode current components under the synchronous rotating coordinate system. Accordingly, the system instantaneous error value e (t) may represent an instantaneous error value of each output parameter of the modular multilevel converter. The system output value u (t) may represent the value of each output parameter of the modular multilevel converter. Delta u c (t) may include a capacitance voltage deviation value for each bridge arm sub-module SM. Adding Deltau to fitness function c (t) and. DELTA.u dc The part related to (t) is to emphasize the attention on the output voltage stability of the modular multilevel converter. Adjusting the proportion k corresponding to each particle P And integral adjustment coefficient k I After substituting the MMC electromagnetic transient model for simulation calculation, substituting the simulation result into the formula 1, and obtaining the fitness value of each particle.
Fig. 3 is a schematic diagram of an optimization process of a particle swarm optimization algorithm according to an embodiment of the present invention. Referring to fig. 3, the optimization process of the particle swarm optimization algorithm specifically includes the following steps:
s210, generating an initial particle group, and initializing the position and speed of each particle in the initial particle group, wherein d =1.
Wherein d represents the number of iterations, the position of the particle is the value of an optimization variable, the optimization variable is an adjustment coefficient, and the adjustment variable comprises a proportional adjustment coefficient k P And integral adjustment coefficient k I . Illustratively, the number of particles in the particle swarm can be selected according to actual requirements, and the position of each particle can be adjusted according to a proportional adjustment coefficient k P And integral adjustment coefficient k I The value range of (2) is randomly selected, the whole vector space is traversed as much as possible, and the speed of each particle can also be randomly set.
And S220, substituting the positions of all the particles in the initial particle swarm into a PI controller, controlling the modular multilevel converter based on the MMC electromagnetic transient model, and calculating the fitness value of all the particles in the initial particle swarm according to the output parameters and the fitness function of the modular multilevel converter.
S230, searching the individual optimal fitness value of each particle in the initial particle swarm according to the fitness value of each particle, obtaining the individual optimal position of each particle according to the individual optimal fitness value, searching the group optimal fitness value of the initial particle swarm, and obtaining the group optimal position according to the group optimal position.
Wherein, because the fitness function is set based on the ITAE evaluation criterion, the smaller the fitness value J of the particle, the better the system stability can be considered. Therefore, in the particle swarm, the individual optimal fitness value of the particle may be the minimum fitness value of the particle in the past iteration process, and the position corresponding to the fitness value is the individual optimal position of the particle. The optimal population fitness value is the minimum fitness value obtained by comparing all particles of the particle swarm in one iteration process, and the position corresponding to the fitness value is the optimal population position of the particle swarm; illustratively, when the group optimal fitness value obtained in the current iteration process is larger than the group optimal fitness value obtained in the previous iteration process, the group optimal fitness value obtained in the previous iteration process is used as the group optimal fitness value of the current iteration.
And S240, updating the position and the speed of each particle according to the individual optimal position and the group optimal position to generate an updated particle swarm, wherein d = d +1.
Illustratively, the velocity of the particles is updated according to the following formula:
where d is the number of iterations, v i Is the ith particle velocity, b i Is the ith particle position, w is the inertial weight of the velocity, c 1 And c 2 As a learning factor, c 1 And c 2 In each iteration can be [2,2.05]Random value within the range r 1 And r 2 Is in [0,1]Random number between, P ibest For the individual optimum position of the ith particle, G best And the optimal position of the population is obtained. The superscripts "d" and "d +1" indicate the corresponding number of iterations, e.g.,
the best position passed by the ith particle until the d iteration is the individual optimal position; />
For the d-th iteration, all particles pass through the best position, i.e. the population optimal position.
And, the position of the particle may be updated according to the following formula:
further, in the optimization process, in order to avoid the situation that the parameters are trapped in the local optimum and improve the global search optimum value, the embodiment also improves the inertia weight w, and under different iteration times, different inertia weights are adoptedw d . Specifically, as the number of iterations increases, the inertial weight of the velocity is decremented according to the following equation:
wherein, w max Is a preset maximum inertial weight, w min Is a preset minimum inertial weight, d max Is a preset maximum number of iterations. Exemplarily, w max And w min Can be selected according to actual requirements, such as setting w max ＝1.2，w min ＝0.9。
And S250, substituting the positions of all the particles in the updated particle swarm into a PI controller, controlling the modular multilevel converter based on the MMC electromagnetic transient model, and calculating the fitness value of each particle in the updated particle swarm according to the output parameters and the fitness function of the modular multilevel converter.
S260, searching and updating the individual optimal fitness value of each particle in the particle swarm according to the fitness value of each particle, obtaining the individual optimal position of each particle according to the individual optimal fitness value, searching and updating the group optimal fitness value of the particle swarm, and obtaining the group optimal position according to the group optimal position.
S270, judging whether an iteration termination condition is met; if yes, go to S280; if not, the process returns to step S240.
Wherein, the iteration termination condition may be: the iteration times reach the preset maximum iteration times, or the group optimal fitness value is smaller than a preset threshold value.
And S280, finishing the optimization process, and taking the optimal position of the group obtained after the last update as a target regulation coefficient.
In the MMC electromagnetic transient model provided by the embodiment of the invention, the PI controller has a proportional adjustment coefficient k P And integral adjustment coefficient k I Two values need to be set, in this embodiment, through S210 to S280, the two parameters are used as optimization particles to realize adaptive parameter setting for the PI controller, and illustratively, the optimization result is: k is a radical of P ＝300，k I ＝6000。
The above embodiments describe in detail the optimization process of the particle swarm optimization algorithm, and specifically describe the establishment of the MMC electromagnetic transient model below.
The modular multilevel converter is essentially a complex alternating current-direct current coupling system, and a differential mode and common mode component representation method is introduced to analyze the MMC system, so that conditions are provided for realizing active power decoupling control and reactive power decoupling control. Defining the common-mode component of variable x as
The difference mode component is->
Wherein the variable x may represent a voltage or current parameter, x jp Represents the variable corresponding to the upper bridge arm of the j-th phase, x jn And representing the variable corresponding to the j-th lower bridge arm.
As can be seen from FIG. 1, the differential and common mode component representations can be obtained according to kirchhoff's law (KVL, KCL)
In the formula (I), the compound is shown in the specification,
and &>
Differential mode and common mode components of the j-th phase leg voltage, respectively>
And &>
Respectively is the differential mode and common mode components of the j-th phase bridge arm current, and L is an alternating current reactor L ac The reactance value of (c).
As shown in fig. 1, in this embodiment, a PSC-PWM modulation method is adopted to control the bridge arm submodule SM to switch to obtain a bridge arm voltage, that is, to control the first power electronic switch S1 and the second power electronic switch S2 to turn on and off the complementary switches. Voltage u of j-th phase upper arm jp Equal to the modulation coefficient m of the jth upper bridge arm jp The sum v of the capacitor voltages of all bridge arm submodules SM in the j-th phase upper bridge arm jpc Product of (d), voltage u of the j-th lower arm jn Equal to the modulation coefficient m of the j-th lower bridge arm jn And the sum v of the capacitor voltages of all bridge arm submodules SM in the j-th phase lower bridge arm jnc The product of (a). Under the condition of assuming that the capacitor voltage of each bridge arm submodule SM is balanced and controlled, a differential mode and common mode representation method is adopted, and the dynamic characteristic of the capacitor voltage is as follows:
wherein C is the capacitance value of the bridge arm sub-modules SM, N is the number of the bridge arm sub-modules SM in the upper (or lower) bridge arm,
and &>
The difference mode and the common mode component of the sum of the capacitor voltages of all the bridge arm submodules SM of the jth phase bridge arm are selected and are selected>
And &>
Are the modulation coefficients of the j-th phase bridge arm respectivelyDifferential mode and common mode components, i.e., differential mode modulation factor and common mode modulation factor.
As can be seen from the expressions (7) and (8), the differential mode and common mode components of the capacitor voltage have strong nonlinearity, and it can be seen that
And &>
Can be selected from>
And &>
And (6) determining. Because bridge arm submodules SM is numerous and difficult to measure, and the direct current power grid lacks damping and is easy to generate resonance influence, a virtual resistor can be introduced to determine a modulation coefficient, and the modulation coefficient can be determined by the following formula:
wherein the content of the first and second substances,
reference value, R, of the sum of the upper (lower) bridge arm capacitance voltages v Is a virtual resistance, is asserted>
The control variable of the bridge arm voltage can be generated by a PI controller according to a differential mode current reference value and a differential mode current actual value. />
To sum up, the mathematical models of the instantaneous values of the MMC obtained by derivation by introducing the differential mode and common mode component representation method are shown in formulas (5) to (8), and the mathematical models show that the energy on the ac-dc side is coupled through the capacitance and voltage of the bridge arm sub-modules, so that the decoupling of the control variables on the ac-dc side can be realized.
Based on the analysis of the power flow under the synchronous rotating coordinate system (dq coordinate system), the expressions in the three-phase stationary coordinate system (abc coordinate system) can be converted into the expressions in the dq coordinate system through the Park transformation matrix in the equations (5) and (6), and the conversion results are respectively as follows:
in the formula, L eq ＝L 0 /2+L，
And &>
A d-axis component and a q-axis component, respectively, of the differential mode voltage of the MMC, based on the measured voltage level>
And &>
D-axis component and q-axis component, u, of the differential mode current of the MMC, respectively gd And u gq A d-axis component and a q-axis component of the ac grid side voltage, respectively, wherein the voltage and current each comprise a three-phase component.
The d axis is selected to be oriented by the space vector of the grid voltage, and u can be obtained gq =0, and then combining a differential mode and common mode component representation method of alternating current network side current to obtain active power P at the alternating current side g And reactive power Q g The expressions are respectively:
in the formula i gd And i gq The d-axis component and the q-axis component, respectively, of the ac grid-side current also include three-phase components.
From equations (11) and (12):
and &>
Not only is subjected to>
And &>
Is also influenced by the differential mode current cross-coupling term, i.e. with 2 ω L eq Correlation quantity, and grid voltage feed-forward term u gd And u gq Is not favorable for realizing the differential mode current control. However, it is clear from the formulae (13) and (14) obtained by subsequent analysis that the active power is determined by->
Determining that the reactive power is->
It is determined that power control can be simplified to differential mode current dq axis component control. However, since the bridge arm sub-module has many power electronic devices and definite nonlinear characteristics, and the setting process of the conventional PI controller is complex and it is difficult to obtain an optimal adjustment coefficient, the present embodiment provides an MMC electromagnetic transient model that combines with a PSO optimization algorithm, which can be specifically referred to in fig. 4. The optimization process of the PSO optimization algorithm has been described in detail in the above embodiments, and is not described herein again. The control process of the MMC electromagnetic transient model is described below with reference to fig. 4.
Referring to fig. 4, the MMC electromagnetic transient model further illustratively includes a differential-mode and common-mode conversion module for converting an output current of the modular multilevel converter MMC into an actual value of a common-mode current
And the differential-mode current actual value->
Specifically, the output current of the modular multilevel converter MMC is a three-phase current (denoted as i) in a three-phase stationary coordinate system a,b,c ). The differential mode and common mode conversion module firstly converts the three-phase current i according to a differential mode and common mode component representation method a,b,c Converting the current into differential mode current and common mode current under a three-phase static coordinate system; and then converting the differential mode current under the three-phase static coordinate system into the differential mode current under the synchronous rotating coordinate system. Wherein the common-mode current in the three-phase stationary coordinate system is taken as the actual value of the common-mode current>
Taking the differential mode current under the synchronous rotating coordinate system as the actual value of the differential mode current>
Then, controlling the modular multilevel converter based on the MMC electromagnetic transient model includes:
the PI controller is based on the reference value of the differential mode current
Target proportional adjustment factor, target integral adjustment factor and differential mode current actual value->
Control variable which obtains the bridge arm voltage>
Obtaining the differential mode modulation under the dq coordinate system according to the formula (10)The coefficient carries out coordinate conversion on the differential mode modulation coefficient under the dq coordinate system to obtain the differential mode modulation coefficient under the three-phase static coordinate system, namely the required differential mode modulation coefficient->
Phase-shifted carrier modulation controller (PSC-PWM controller) based on differential mode modulation factor
And a common mode modulation factor->
Phase-shifting carrier modulation is carried out to obtain a Trigger signal to control the modular multilevel converter MMC to output stable direct current voltage u dc . Wherein the common-mode modulation factor->
Based on the actual value of the common-mode current->
Virtual resistance R combined with input v And equation (9).
Illustratively, the differential mode current reference value may be obtained according to equations (13) and (14), specifically, let
Formula (13) is converted into->
Accordingly, can obtain>
Wherein it is present>
Is an active power reference value u of the alternating current side of the modularized multi-level converter gd AC for modular multilevel converter accessThe d-axis component of the network side voltage in the synchronous rotating coordinate system is analyzed and judged>
Is the d-axis component of the differential mode current reference value in the synchronous rotating coordinate system. Make/combine>
Formula (14) is converted into->
Accordingly, can obtain->
Wherein it is present>
For the AC side reactive power reference value of the modular multilevel converter, is->
Is the q-axis component of the differential mode current reference value in the synchronous rotating coordinate system.
On the basis of the foregoing embodiments, optionally, in the embodiment of the present invention, a steady-state point of the power station system is derived according to a small signal stability analysis of the power station under a condition including a virtual resistor.
In the absence of a virtual resistor, the steady state point of operation is:
i dc0 ＝P g0 /u dc0 (15)
v eqc0 ＝u dc0 (16)
it can be seen that the capacitance voltage of the bridge arm is determined by an external direct current voltage source, the direct current side current is obtained by power conservation, the balanced steady state point of the system conforms to the principle of energy conservation,
does not affect the balance point.
When the virtual resistance exists, the stable region range of the system can be deduced to be
And->
R v The value of (d) can be selected accordingly.
In order to verify the MMC power decoupling control strategy provided by the embodiment of the invention, the inventor constructs joint simulation based on MATLAB and PSCAD, and performs power decoupling control simulation analysis verification, wherein system simulation parameters are shown in Table 1.
TABLE 1
| Parameter(s) | Numerical value |
| Rated AC voltage/kV | 10.5 |
| Rated DC voltage/ |
20 |
| Bridge arm reactor L 0 /mH | 7 |
| Number of SM/number | 25 |
| SM capacitor C/ |
13 |
| Rated frequency f 0 / |
50 |
| Switching frequency f/Hz | 300 |
| Number of iterations d | 2000 |
| Learning factor c 1 、c 2 | Rand[2,2.05] |
The inventor respectively carries out simulation verification under the reactive power step condition and the active power step condition, and according to the simulation result, the control method provided by the embodiment of the invention has stronger robustness on power decoupling control, and the system increases damping to improve the operation stability of the MMC system.
Specifically, the active power step simulation result can be seen in fig. 5, and at 0.4s, the active power P jumps from 1MW to-1 MW; the active power can quickly respond to the jump instruction, small overshoot exists, the transient process is effectively improved, and the waveform quality of differential mode current/voltage, common mode current/voltage and MMC outlet voltage is good. In addition, at 0.7s, the inventor also controls the active power to jump from-1 MW to 1MW, and simulation results are not shown here, and the quality of each waveform is also better.
The reactive power step simulation result can be seen in fig. 6, and for some specific scene alternating current systems, an MMC is required to perform reactive compensation on the system. Fig. 6 shows a power decoupling control simulation result when reactive power is stepped under parameter control of a PSO algorithm setting MMC power decoupling controller, when 0.4s, reactive power Q is changed from 0.5MVar to-0.5 MVar, and when the reactive power Q is changed, active power P is kept stable. The waveform quality burrs of the differential mode current/voltage, the common mode current/voltage and the outlet voltage of the converter are less, the fluctuation is in a reasonable range, and the common mode voltage is always stabilized in a 10kV range. At 0.7s, the inventor also controls the reactive power to jump from-0.5 MVar to 0.5MVar, and simulation results are not shown here, and the quality of each waveform is also better.
In fig. 5 and 6, the results of the voltage and current waveforms include three-phase results, specifically, the dashed-dotted line represents the a-phase result, the solid line represents the B-phase result, and the long dashed line represents the C-phase result.
In addition, fig. 7 also provides a simulation result of the MMC output voltage under the conditions that the active power is 1MW and the reactive power is 0.5MVar, and specifically provides waveforms of the capacitance voltages of 25 bridge arm sub-modules in the a-phase upper bridge arm to representatively represent the capacitance voltages of the bridge arm sub-modules, and provides waveforms of the dc voltage output by the MMC. In order to clearly show the change of the capacitor voltage of the bridge arm sub-modules, fig. 8 also shows the waveform of the capacitor voltage of the 1 st bridge arm sub-module in the phase a upper bridge arm in the range of 0.9-1 s. As shown in fig. 7 and 8, by using the direct modulation method of the virtual resistor and the dc bus voltage, the capacitor voltage of each bridge arm submodule is always stabilized around 800V, the fluctuation of the capacitor voltage is lower than 1.5%, and the fluctuation of the dc voltage is lower than 1.5%.
In summary, the embodiment of the invention designs a new ITAE evaluation criterion, and brings capacitance voltage deviation and direct current voltage deviation of the bridge arm submodule into the evaluation criterion, thereby effectively realizing voltage-sharing control and direct current voltage control of the bridge arm submodule and practically improving the system fluctuation condition. Based on the analysis, the PSO algorithm-based control method for setting the parameters of the MMC power decoupling controller can realize the complete decoupling of active power and reactive power, obtain better test waveforms and provide practical significance for the stable operation of a system.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for controlling a Modular Multilevel Converter (MMC), comprising:
establishing an MMC electromagnetic transient model comprising a PI controller, a phase-shift carrier modulation controller and a modular multilevel converter;
constructing a particle swarm optimization algorithm, and constructing a fitness function in an optimization process based on a time error absolute value integral evaluation criterion and the output parameters of the modular multilevel converter;
optimizing the adjusting coefficient of the PI controller by utilizing the particle swarm optimization algorithm based on the MMC electromagnetic transient model and the fitness function to obtain a target adjusting coefficient;
substituting the target regulation coefficient into the PI controller, and controlling the modular multilevel converter based on the MMC electromagnetic transient model.
2. The modular multilevel converter control method according to claim 1, wherein the output parameters of the modular multilevel converter comprise: the direct current voltage and the capacitance voltage of each bridge arm submodule in the modular multilevel converter;
the fitness function is:
wherein J is the fitness value, h 1 、h 2 、h 3 And h 4 For the set weight, e (t) is the system instantaneous error value, u (t) is the system output value, Δ u c (t) is the deviation value of the capacitance voltage of the bridge arm submodule,. DELTA.u dc And (t) is the deviation value of the direct current voltage.
3. The method according to claim 1, wherein the optimizing the adjustment coefficient of the PI controller by using the particle swarm optimization algorithm based on the MMC electromagnetic transient model and the fitness function to obtain a target adjustment coefficient comprises:
generating an initial particle swarm, initializing the position and the speed of each particle in the initial particle swarm, and recording the iteration frequency as 1; wherein the position of the particle is the value of an optimization variable, and the optimization variable is the adjustment coefficient;
substituting the positions of all particles in the initial particle swarm into the PI controller, controlling the modular multilevel converter based on the MMC electromagnetic transient model, and calculating the fitness value of all particles in the initial particle swarm according to the output parameters of the modular multilevel converter and the fitness function;
searching the individual optimal fitness value of each particle in the initial particle swarm according to the fitness value of each particle, obtaining the individual optimal position of each particle according to the individual optimal fitness value, searching the group optimal fitness value of the initial particle swarm, and obtaining the group optimal position according to the group optimal position;
updating the position and the speed of each particle according to the individual optimal position and the group optimal position to generate an updated particle swarm, and adding 1 to the iteration times;
substituting the positions of all particles in the updating particle swarm into the PI controller, controlling the modular multilevel converter based on the MMC electromagnetic transient model, and calculating the fitness value of all particles in the updating particle swarm according to the output parameters of the modular multilevel converter and the fitness function;
searching the individual optimal fitness value of each particle in the updated particle swarm according to the fitness value of each particle, obtaining the individual optimal position of each particle according to the individual optimal fitness value, searching the group optimal fitness value of the updated particle swarm, and obtaining the group optimal position according to the group optimal position;
judging whether an iteration termination condition is met;
if so, ending the optimization process, and taking the optimal position of the group obtained after the last update as the target adjustment coefficient;
if not, returning and executing the iteration steps of generating an updating particle swarm, calculating the fitness value of each particle in the updating particle swarm, and searching and updating the individual optimal position and the group optimal position in the particle swarm.
4. The modular multilevel converter control method of claim 3, wherein the updating the position and the speed of each particle according to the individual optimal fitness value and the group optimal fitness value comprises:
the velocity of the particles is updated according to the following formula:
where d is the number of iterations, v i Is the ith particle velocity, b i Is the ith particle position, w is the inertial weight of the velocity, c 1 And c 2 Is a learning factor, r 1 And r 2 Is in [0,1]Random number between, P ibest For the individual optimum position of the ith particle, G best The optimal position of the group is obtained;
the position of the particle is updated according to the following formula:
5. the modular multilevel converter control method according to claim 4, wherein during the optimization process, as the number of iterations increases, the inertial weight of the speed is decreased according to the following formula:
wherein, w max To prepareSet maximum inertial weight, w min Is a preset minimum inertial weight, d max Is a preset maximum number of iterations.
6. The modular multilevel converter control method according to claim 3, wherein the iteration end condition is: the iteration times reach the preset maximum iteration times, or the group optimal fitness value is smaller than the preset threshold value.
7. The modular multilevel converter control method according to claim 1, further comprising a differential-mode-common-mode conversion module in the MMC electromagnetic transient model, for converting an output current of the modular multilevel converter into a common-mode current actual value and a differential-mode current actual value; the target adjusting coefficient comprises a target proportional adjusting coefficient and a target integral adjusting coefficient;
controlling the modular multilevel converter based on the MMC electromagnetic transient model, comprising:
the PI controller obtains a differential mode modulation coefficient according to a differential mode current reference value, the target proportion regulation coefficient, the target integral regulation coefficient and the differential mode current actual value;
the phase-shifting carrier modulation controller performs phase-shifting carrier modulation according to the differential mode modulation coefficient and the common mode modulation coefficient to obtain a trigger signal so as to control the modular multilevel converter to output stable direct-current voltage; and obtaining the common mode modulation coefficient according to the actual value of the common mode current.
8. The method as claimed in claim 7, wherein the output current of the modular multilevel converter is a three-phase current in a three-phase stationary coordinate system;
the differential mode and common mode conversion module converts the output current of the modular multilevel converter into a common mode current actual value and a differential mode current actual value, and comprises the following steps:
converting the three-phase current into differential mode current and common mode current under the three-phase static coordinate system;
converting the differential mode current under the three-phase static coordinate system into the differential mode current under the synchronous rotating coordinate system;
and taking the common-mode current in the three-phase static coordinate system as the actual common-mode current value, and taking the differential-mode current in the synchronous rotation coordinate system as the actual differential-mode current value.
9. The modular multilevel converter control method according to claim 7, wherein the differential mode current reference value is obtained according to the following formula:
wherein the content of the first and second substances,
for the active power reference value on the AC side of the modular multilevel converter, is->
Is an AC side reactive power reference value u of the modular multilevel converter gd The d-axis component and the->
For the d-axis component of the differential mode current reference value in the synchronous rotating coordinate system, for>
Is the q-axis component of the differential mode current reference value in the synchronous rotating coordinate system.
10. The modular multilevel converter control method according to claim 7, wherein the common mode modulation factor is obtained according to the following formula:
wherein the content of the first and second substances,
common mode modulation coefficient R of j phase bridge arm of modular multilevel converter v Is a virtual resistance, is asserted>
For the actual value of the common-mode current of the j-th phase bridge arm of the modular multilevel converter, the actual value is greater than or equal to>
The reference value is the sum of the capacitance voltages of the bridge arm sub-modules of the upper bridge arm of the modular multilevel converter or the sum of the capacitance voltages of the bridge arm sub-modules of the lower bridge arm. />
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