CN114614691B - A model predictive control algorithm for optimal switching sequence of MMC - Google Patents
A model predictive control algorithm for optimal switching sequence of MMC Download PDFInfo
- Publication number
- CN114614691B CN114614691B CN202210218901.2A CN202210218901A CN114614691B CN 114614691 B CN114614691 B CN 114614691B CN 202210218901 A CN202210218901 A CN 202210218901A CN 114614691 B CN114614691 B CN 114614691B
- Authority
- CN
- China
- Prior art keywords
- voltage
- vector
- current
- bridge arm
- phase
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000013598 vector Substances 0.000 claims abstract description 134
- 238000000034 method Methods 0.000 claims abstract description 27
- 230000009471 action Effects 0.000 claims abstract description 24
- 238000012163 sequencing technique Methods 0.000 claims abstract description 10
- 238000012216 screening Methods 0.000 claims abstract description 6
- 230000016507 interphase Effects 0.000 claims abstract description 4
- 238000005070 sampling Methods 0.000 claims description 20
- 239000003990 capacitor Substances 0.000 claims description 10
- 238000013178 mathematical model Methods 0.000 claims description 7
- 230000002194 synthesizing effect Effects 0.000 claims description 5
- 230000003247 decreasing effect Effects 0.000 claims description 4
- 230000000694 effects Effects 0.000 claims description 4
- 230000015572 biosynthetic process Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 230000001629 suppression Effects 0.000 claims description 3
- 238000003786 synthesis reaction Methods 0.000 claims description 3
- 238000007599 discharging Methods 0.000 claims description 2
- 238000005457 optimization Methods 0.000 claims description 2
- 238000005096 rolling process Methods 0.000 claims description 2
- 230000001131 transforming effect Effects 0.000 claims description 2
- 238000009795 derivation Methods 0.000 claims 1
- 238000004364 calculation method Methods 0.000 abstract description 3
- 230000008901 benefit Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 238000011217 control strategy Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
- H02M7/00—Conversion of AC power input into DC power output; Conversion of DC power input into AC power output
- H02M7/42—Conversion of DC power input into AC power output without possibility of reversal
- H02M7/44—Conversion of DC power input into AC power output without possibility of reversal by static converters
- H02M7/48—Conversion of DC power input into AC power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode
- H02M7/53—Conversion of DC power input into AC power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal
- H02M7/537—Conversion of DC power input into AC power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only, e.g. single switched pulse inverters
- H02M7/5387—Conversion of DC power input into AC power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only, e.g. single switched pulse inverters in a bridge configuration
- H02M7/53871—Conversion of DC power input into AC power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only, e.g. single switched pulse inverters in a bridge configuration with automatic control of output voltage or current
- H02M7/53873—Conversion of DC power input into AC power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only, e.g. single switched pulse inverters in a bridge configuration with automatic control of output voltage or current with digital control
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
- H02M1/00—Details of apparatus for conversion
- H02M1/08—Circuits specially adapted for the generation of control voltages for semiconductor devices incorporated in static converters
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
- H02M1/00—Details of apparatus for conversion
- H02M1/12—Arrangements for reducing harmonics from AC input or output
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
- H02M7/00—Conversion of AC power input into DC power output; Conversion of DC power input into AC power output
- H02M7/42—Conversion of DC power input into AC power output without possibility of reversal
- H02M7/44—Conversion of DC power input into AC power output without possibility of reversal by static converters
- H02M7/48—Conversion of DC power input into AC power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode
- H02M7/483—Converters with outputs that each can have more than two voltages levels
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Inverter Devices (AREA)
Abstract
The invention provides an optimal switching sequence model prediction control algorithm of a modularized multi-level converter, which comprises the following steps of converting a current prediction model of a three-phase MMC into a voltage prediction model, converting a prediction voltage vector into a g-h coordinate system, selecting three nearest voltage base vectors, screening switching state combinations with minimum switching action times to obtain a symmetrical seven-segment switching sequence, solving an extremum value function to obtain optimal action time of each vector, enabling the sum of upper bridge arm voltage and lower bridge arm voltage to follow an expected value to inhibit interphase circulation, and completing switching control of the MMC converter by using a sequencing voltage equalizing algorithm. The method can be popularized to any level, and the complexity of the algorithm is unchanged. Compared with the traditional model prediction, the method realizes fixed switching frequency, eliminates weight factors, reduces harmonic content, reduces calculation load and improves output current tracking precision.
Description
Technical Field
The invention relates to the technical field of power electronic converter model predictive control, in particular to an optimal switching sequence model predictive control algorithm applied to a modularized multi-level converter.
Background
The modularized multi-level converter (Modular Multilevel Converter, MMC) has the advantages of high efficiency, low harmonic content, low switching frequency and the like, and is widely applied to a high-voltage direct-current transmission system, a high-voltage frequency converter and an active power filter. Due to the highly modularized structure, the MMC has good expansibility and can adapt to application occasions of various voltage and power levels.
The mathematical model of the MMC has the characteristics of multiple inputs, multiple outputs and nonlinearity, and a plurality of control targets need to be considered in the operation process, including output voltage or current control, circulation suppression, submodule capacitor voltage balance control and the like. The traditional control strategy mostly adopts closed-loop control, the control method is complex, the parameter setting of the proportional integral regulator is difficult, compared with the control method, the model predictive control (Model Predictive Control, MPC) is easy to process multi-system constraint, multivariable and nonlinear systems, and meanwhile, the control method has the advantage of high response speed and has a plurality of advantages in the aspect of MMC control. The traditional model prediction control has the problems of large calculated amount, more traversal optimizing times, difficult weight factor setting and the like, only one optimal switching state is selected in one control period, the required sampling frequency is high, the fixed switching frequency cannot be realized, the harmonic spectrum distribution is wide, and the design of an output filter is particularly difficult. Aiming at the problem, a learner puts forward a multi-section model prediction control strategy, which can improve the control precision of output current, has lower harmonic content and can realize fixed switching frequency, but still has higher operation burden. Therefore, further research into model predictive control algorithms applied to MMCs is highly necessary.
Disclosure of Invention
The invention aims to optimize the control target implementation mode on the basis of multi-stage model predictive control, improve the tracking precision of output current, reduce the harmonic content, and greatly reduce the operation burden of a controller while being applicable to multi-level series. The method is characterized in that the strategy converts a current prediction model of a three-phase MMC into a voltage prediction model, converts a predicted voltage vector into a g-h coordinate system, selects three nearest voltage base vectors, screens a switch state combination with minimum switch action times to obtain a symmetrical seven-segment switch sequence, obtains an extremum value function, obtains the optimal action time of each vector, suppresses inter-phase circulation by adjusting the number of submodules put into each bridge arm, and finally completes the switch control of the MMC converter by using a sequencing voltage equalizing algorithm, and the method specifically comprises the following steps:
step one, establishing an MMC converter output voltage vector discrete prediction model, and performing overmodulation processing on the predicted voltage vector;
Step two, converting the predicted voltage vector into a g-h coordinate system, selecting three nearest voltage base vectors, and screening a switch state combination which minimizes the number of switch actions to obtain a symmetrical seven-segment switch sequence;
Selecting a variance function of the predicted current and the reference current as a cost function, and obtaining the optimal acting time of each voltage base vector when the cost function takes the minimum value by utilizing a extremum solving method;
Calculating the expected value of the sum of the voltages of the upper bridge arm and the lower bridge arm of each phase at the moment k+1 by using a circulation discrete prediction model, regarding the bridge arms as a whole according to the discrete prediction model of the capacitance voltage of the sub-module, calculating the predicted value of the sum of the voltages of the upper bridge arm and the lower bridge arm of each phase, and eliminating the error of the expected value and the predicted value by adjusting the number of the sub-modules put into each bridge arm so as to inhibit circulation;
Combining the seven-segment switching sequence and the circulation suppression target to obtain an optimal switching sequence, and further balancing the capacitance voltage of the submodule by using a sequencing method and sending out control pulses;
The method comprises the steps of firstly, establishing an output voltage vector discrete prediction model, obtaining a time domain mathematical model of MMC converter output current according to kirchhoff's law and voltage-current constraint relation of capacitance and inductance, discretizing the time domain mathematical model by utilizing a forward Euler method to obtain a discrete domain mathematical model, obtaining a phase voltage discrete model according to the model, and synthesizing three-phase electric quantity to obtain the output voltage vector prediction model, wherein the sampling time T s is very small, the voltage vector at the moment k+1 in the formula can be approximated to the voltage vector at the moment k, and the current vector at the moment k+1 can be approximated to the reference current vector at the moment k through voltage sampling in the same way so as to control alternating-current side current;
In the first step, the predicted voltage vector is subjected to overmodulation, in order to prevent the predicted voltage vector from exceeding a modulation area, a geometric relation is used for calculating a module of a maximum vector allowed by the same angle with the predicted voltage, if the module of the predicted vector is larger than the module of the maximum vector, the predicted vector is reset to the maximum vector of the same angle, and if the module of the predicted vector is smaller than the module of the maximum vector, the module of the predicted vector is kept unchanged;
screening switch state combinations with minimum switch action times in the step two to obtain a symmetrical seven-segment switch sequence, defining a cost function because one basic vector possibly corresponds to a plurality of different switch state combinations, performing rolling optimization on redundant switch states of each voltage vector in the sequence, and selecting the switch state with minimum change relative to the previous switch state to obtain the symmetrical seven-segment switch sequence;
In the fifth step, the capacitor voltage of the submodule is balanced by using a sequencing method, and according to the real-time sequencing result of the capacitor voltage of all the submodules in the bridge arm, the submodule with lower capacitor voltage is put into when the bridge arm current is in a charging characteristic, and the submodule with higher capacitor voltage is put into when the bridge arm current is in a discharging characteristic.
Further, the MMC optimal switch sequence model prediction control algorithm is characterized by converting a reference output current into an expected output voltage, and specifically comprises the following steps:
the discrete prediction model of the MMC inverter alternating current phase j current is as follows:
Wherein i j(k+1),upj (k+1) and u nj (k+1) are j-phase current, j-phase upper bridge arm voltage and lower bridge arm voltage which are measured in an alternating current manner at the moment of k+1 respectively, L and R are reactance and resistance of an alternating current side respectively, L o and R o are bridge arm reactance and resistance respectively, and T s is sampling time;
The following relationship can be obtained from kirchhoff's voltage law:
wherein u j (k+1) is the j-phase output voltage of MMC at the moment k+1;
The method comprises the steps of combining the above model with a phase current discrete model, and synthesizing three-phase electric quantity to obtain the three-phase electric quantity:
Wherein i (k+1), u (k+1) and i (k) are related three-phase electric quantity synthesis vectors;
In order to realize tracking of output current, a current vector at the moment k+1 is set as a reference current vector i * (k+1), and the sampling time T s is very small and can be approximately i * (k), and the voltage vector prediction model can be obtained by transforming the above formula:
where u p (k+1) is the desired output voltage vector.
Further, the MMC optimal switching sequence model predictive control algorithm is characterized in that a predicted voltage vector is converted into a g-h coordinate system, and three nearest voltage base vectors are selected, wherein the three nearest voltage base vectors are specifically as follows:
for any predicted voltage vector, four voltage base vector coordinates are obtained by rounding up and down the coordinates of the predicted voltage vector, three nearest voltage base vectors can be selected through logic judgment, and the selected seven-segment switching sequence consists of switching states corresponding to the three base vectors;
the method ensures the MPC algorithm control effect and simultaneously greatly reduces the operation amount, and the complexity of the operation is unchanged when the method is used for any level.
Further, the MMC optimal switch sequence model prediction control algorithm is characterized in that a variance function of a predicted current and a reference current is selected as a cost function, and the action time of three nearest voltage base vectors when the cost function takes the minimum value is obtained by utilizing an extremum solving method, wherein the action time is as follows:
Since the sampling time T s is very small, when predicting the current after each voltage vector acts, the initial current is approximated by the sampling current at k time, as shown in the following formula:
Wherein, the value range of m is 1 to 3;i α,m、iβ,m which is the current predicted value after the action of the selected mth voltage vector, t mi is the action time of the mth voltage vector, and t 1i+t2i+t3i＝Ts;uα,m、uβ,m、iα(k)、iβ (k) is the component of the related electric quantity on the alpha axis and the beta axis;
the cost function is established by considering the current error under the action of each voltage base vector as follows:
Wherein i * α、i* β is the component of i * (k) on the alpha and beta axes;
and obtaining an extremum for the value function, and obtaining the optimal acting time of the three voltage base vectors by using a partial derivative formula.
Compared with the prior art, the invention has the advantages of realizing fixed switching frequency, concentrating harmonic waves near the switching frequency, reducing the design difficulty of an output filter, eliminating weight factors, improving the tracking precision of output current, reducing the harmonic wave content, reducing the calculation load, being popularized in any level and keeping the complexity of an algorithm unchanged.
Drawings
FIG. 1 is a topology of a modular multilevel inverter;
FIG. 2 is a five-level vector sector diagram in one embodiment;
FIG. 3 is a five-level space vector diagram in one embodiment;
Fig. 4 is a carrier comparison chart.
Detailed Description
In order to clarify the basic principle, technical solution and performance advantages of the present invention, an embodiment of the present invention will be further described with reference to the accompanying drawings. It should be noted that the invention can be implemented in numerous different manners, which are covered by the claims.
The topology of the modularized multi-level inverter is shown in fig. 1, and the modularized multi-level inverter can be divided into three layers, namely a phase unit, a bridge arm unit and a submodule unit, wherein the submodule can be of any structure such as a full bridge, a half bridge and the like, is connected with a direct current capacitor in parallel and then is connected to a main circuit, the direct current side of the MMC can be connected with a direct current bus of a power grid to output constant direct current voltage U dc, the alternating current side can be connected with an alternating current power supply or a three-phase load, and three-phase sinusoidal alternating current is output.
Taking a five-level MMC inverter as an example, the implementation of the control method comprises the following steps:
And 1, establishing a discrete prediction model of an output voltage vector of the MMC inverter. The discrete prediction model of the MMC inverter alternating current measured j-phase current is as follows:
Wherein i j(k+1),upj (k+1) and u nj (k+1) are j-phase current, j-phase upper bridge arm voltage and lower bridge arm voltage which are measured in an alternating current manner at the moment of k+1 respectively, L and R are reactance and resistance of an alternating current side respectively, L o and R o are bridge arm reactance and resistance respectively, and T s is sampling time;
The following relationship can be obtained from kirchhoff's voltage law:
wherein u j (k+1) is the j-phase output voltage of MMC at the moment k+1;
The method comprises the steps of combining the above model with a phase current discrete model, and synthesizing three-phase electric quantity to obtain the three-phase electric quantity:
Wherein i (k+1), u (k+1) and i (k) are related three-phase electric quantity synthesis vectors;
in order to realize tracking of output current, a current vector at the moment k+1 is set as a reference current vector i * (k+1), and the sampling time T s is very small and can be approximately i * (k), and the above formula is transformed to obtain a voltage vector prediction model:
where u p (k+1) is the desired output voltage vector.
And 2, performing overmodulation processing on the predicted voltage vector to prevent the predicted voltage vector from exceeding a modulation area. One sector in the five-level space vector diagram is shown in FIG. 2, and the maximum voltage vector allowed by the same angle with u p is expressed asIf the modulus of u p If it is large, u p is replaced byIf smaller than, the value remains unchanged.The calculation mode of (2) is as follows:
wherein: is the vector angle of the reference voltage.
And 3, converting the output basic vector of the inverter into a g-h coordinate system, and converting the coordinates of all basic vectors into integers. The five-level space vector in the g-h coordinate system is shown in fig. 3, wherein the g axis coincides with the alpha axis in the rectangular coordinate system, and the h axis forms an angle of 60 degrees with the g axis.
Let u p be u α、uβ in α - β coordinate system, and the coordinate after transformation to g-h coordinate system be u g、uh, and the transformation formula of the two coordinate systems can be obtained by geometric relationship:
let u p be u a、ub、uc in the a-b-c coordinate system, and the transformation formula of the three-phase coordinate system and the g-h coordinate system can be obtained by Clark formula and the above formula:
For normalization, the coordinates of all base vectors are made to integers.
And 4, selecting three required nearest voltage base vectors. Because the coordinates of the basic vectors are all integers, four basic voltage vectors nearest to the reference vector can be obtained by rounding up and down the u p coordinates, and the four basic voltage vectors are shown in the following formula:
Wherein the upper and lower underlines represent the upper and lower rounding of the variables, respectively, and u ul、ulu、uuu、ull represents the four base vectors nearest to u p, respectively.
From the geometric relationship, it is known that u ul and u lu are always the nearest base vectors to the reference vector, denoted as u 1、u2, and that the third nearest vector is determined from the sign of u g+uh-(uulg+uulh), if positive, u uu is the desired third base vector, whereas if negative, u ll is the desired third base vector, denoted as u 3.
And 5, screening the switch state combination with the minimum switch action times to obtain a symmetrical seven-segment switch sequence. For a five-level MMC converter, in the g-h coordinate system, all switch states corresponding to any one voltage base vector can be determined by the following formula:
And is also provided with
Wherein, the value range of q is 1 to 4;u optg and u opth are the projection of the selected voltage base vector u opt on g and h coordinate axes respectively.
It can be seen that the same voltage base vector may correspond to a plurality of switch states, and it is important to select an appropriate switch state combination. In order to reduce the number of switching operations, assuming that the last switching state is (S aold,Sbold,Scold) and the next switching state is (S a,Sb,Sc), a switching state change value Y is defined as:
Y=|Sa-Saold|+|Sb-Sbold|+|Sc-Scold|
First, determining a first section of switch state, comparing the switch state corresponding to u 1 with the last switch state of the last sampling period, and selecting the switch state with the minimum Y value. The second section of switch state corresponds to u 2, the third section of switch state corresponds to u 3, the fourth section of switch state is identical to the basic voltage vector corresponding to the first section of switch state, the second section, the third section and the fourth section all calculate Y values according to the selected previous section of switch state, and the switch state with the minimum Y value is determined to be the required switch state.
In the symmetrical seven-segment switching sequence, the states of the fifth segment, the third segment, the sixth segment, the second segment, the seventh segment and the first switch are consistent.
And 6, selecting a variance function of the predicted current and the reference current as a cost function, and obtaining the optimal acting time of three nearest voltage base vectors when the cost function takes the minimum value by utilizing a extremum solving method.
Since the sampling time T s is very small, when predicting the current after each vector is applied, the initial current is approximated by the sampling current at time k, as shown in the following equation:
Wherein, the value range of m is 1 to 3;i α,m、iβ,m which is the current predicted value after the action of the selected mth voltage vector, t mi is the action time of the mth voltage vector, and t 1i+t2i+t3i＝Ts;uα,m、uβ,m、iα(k)、iβ (k) is the component of the related electric quantity on the alpha axis and the beta axis;
the cost function is established by considering the current error under the action of each voltage base vector as follows:
Wherein i * α、i* β is the component of i * (k) on the alpha and beta axes;
obtaining an extremum for the cost function, and obtaining the optimal acting time of the vector by using a partial derivative formula:
solving the above equation can obtain the optimal acting time t 1i、t2i and t 3i of the three voltage base vectors.
And 7, obtaining expected values of bridge arm voltages of each phase at the moment k+1 by using a circulation prediction model. The MMC inverter j-phase circulation discrete prediction model is shown as follows:
wherein U dc is direct-current side voltage, i dj (k+1) is j-phase internal circulation of MMC at k+1 moment;
As can be seen from the above, the generation of the circulating current is mainly due to the fact that the sum of the DC bus voltage and the voltages of the upper bridge arm and the lower bridge arm of the phase unit is not equal, when the difference between them is greater than zero, the circulation tends to increase, and when the difference is less than zero, the circulation decreases;
To eliminate the ac component in the circulating current, the following relationship needs to be satisfied:
wherein I dc is DC side current;
the expected value of the sum of the voltages of the upper bridge arm and the lower bridge arm of each phase at the moment k+1 can be obtained:
And 8, regarding the bridge arm as a whole to calculate the bridge arm voltage predicted value of each phase according to the discrete prediction model of the sub-module capacitance voltage. Because the voltage equalizing algorithm is adopted, the capacitance voltages of all the submodules in the same bridge arm are approximately equal, the bridge arm can be regarded as a whole to carry out voltage prediction, and therefore the average value of the capacitance voltages of all the submodules in the bridge arm at the moment k+1 is obtained, and the following formula is shown:
Wherein u rj,ave (k+1) represents the average value of the capacitance voltage of the j-phase bridge arm unit r (r=p, n) at the time of k+1, i rj (k) represents the bridge arm current of the j-phase bridge arm unit r, S rji represents the number of conduction sub-modules of the j-phase r bridge arm when the i-th switch state acts in the sequence, and t i represents the acting time of the i-th switch state in the sequence;
the predicted value u rj (k+1) of the arm voltage of the j-phase r at the time k+1 is:
urj(k+1)＝Srj7urj,ave(k+1)
S rj7 represents the number of conducting sub-modules of the j-phase r bridge arm when the state of the seventh section of switch in the sequence is in action.
And 9, properly increasing and decreasing the number of submodules put into each bridge arm so as to inhibit circulation. The number delta n rj,eq of the submodules to be increased or decreased in each bridge arm in one sampling period can be obtained by the following formula:
in the above formula, the upper bridge arm and the lower bridge arm of each phase respectively bear half of the errors of the expected value and the predicted value of the bridge arm voltage, and the method can effectively inhibit the circulation under the premise of not influencing the alternating current output.
Delta n rj,eq is typically a non-integer and its fractional part is compared to the carrier wave, as shown in figure 4, to achieve an equivalent control effect. In the figure, cmp is the fractional part of Δn rj,eq, and the number of submodules Δn rj to be inserted into each bridge arm is:
And 10, balancing the capacitance voltage of the submodule by using a sequencing method and sending out control pulses. When the bridge arm current is positive, the submodule with lower voltage is input preferentially, and conversely, when the bridge arm current is negative, the submodule with higher voltage is input.
The foregoing is a specific embodiment of the present invention, but the scope of the present invention is not limited thereto. It will be apparent to those skilled in the art that changes and modifications may be made to the above-described embodiments without departing substantially from the technical spirit and principles of the invention described herein, and such changes and modifications should also be considered as being within the scope of the invention.
Claims (4)
1. The MMC optimal switching sequence model prediction control algorithm is characterized in that a current prediction model of a three-phase MMC is converted into a voltage prediction model, a prediction voltage vector is converted into a g-h coordinate system, three nearest voltage base vectors are selected, a switching state combination with minimum switching action times is screened, a symmetrical seven-segment switching sequence is obtained, an extremum is obtained for a valence function, optimal acting time of each vector is obtained, the sum of voltages of an upper bridge arm and a lower bridge arm is enabled to follow an expected value to inhibit interphase circulation, and finally switching control of an MMC converter is completed by using a sequencing voltage equalizing algorithm, and the algorithm specifically comprises the following steps:
step one, establishing an MMC converter output voltage vector discrete prediction model, and performing overmodulation processing on the predicted voltage vector;
Step two, converting the predicted voltage vector into a g-h coordinate system, selecting three nearest voltage base vectors, and screening a switch state combination which minimizes the number of switch actions to obtain a symmetrical seven-segment switch sequence;
selecting a variance function of the predicted current and the reference current as a cost function, and performing bias derivation on the cost function to obtain the optimal action time of each voltage base vector;
Calculating the expected value of the sum of the voltages of the upper bridge arm and the lower bridge arm of each phase at the moment k+1 by using a circulation discrete prediction model, regarding the bridge arms as a whole according to the discrete prediction model of the capacitance voltage of the sub-module, calculating the predicted value of the sum of the voltages of the upper bridge arm and the lower bridge arm of each phase, and eliminating the error between the expected value and the predicted value of the voltage of the bridge arm by adjusting the number of the sub-modules input by each bridge arm so as to inhibit circulation;
Combining the seven-segment switching sequence and the circulation suppression target to obtain an optimal switching sequence, and further balancing the capacitance voltage of the submodule by using a sequencing method and sending out control pulses;
The method comprises the steps of firstly, establishing an output voltage vector discrete prediction model, obtaining a time domain mathematical model of MMC converter output current according to kirchhoff's law and voltage-current constraint relation of capacitance and inductance, discretizing the time domain mathematical model by utilizing a forward Euler method to obtain a discrete domain mathematical model, obtaining a phase voltage discrete model according to the model, and synthesizing three-phase electric quantity to obtain the output voltage vector prediction model, wherein the sampling time T s is very small, the voltage vector at the moment k+1 in the formula can be approximated to the voltage vector at the moment k, and the current vector at the moment k+1 can be approximated to the reference current vector at the moment k through voltage sampling in the same way so as to control alternating-current side current;
In the first step, the predicted voltage vector is subjected to overmodulation, in order to prevent the predicted voltage vector from exceeding a modulation area, a geometric relation is used for calculating a module of a maximum vector allowed by the same angle with the predicted voltage, if the module of the predicted vector is larger than the module of the maximum vector, the predicted vector is reset to the maximum vector of the same angle, and if the module of the predicted vector is smaller than the module of the maximum vector, the module of the predicted vector is kept unchanged;
screening switch state combinations with minimum switch action times in the step two to obtain a symmetrical seven-segment switch sequence, defining a cost function because one basic vector possibly corresponds to a plurality of different switch state combinations, performing rolling optimization on redundant switch states of each voltage vector in the sequence, and selecting the switch state with minimum change relative to the previous switch state to obtain the symmetrical seven-segment switch sequence;
In the fifth step, the capacitor voltage of the submodule is balanced by using a sequencing method, and according to the real-time sequencing result of the capacitor voltage of all the submodules in the bridge arm, the submodule with lower capacitor voltage is put into when the bridge arm current is in a charging characteristic, and the submodule with higher capacitor voltage is put into when the bridge arm current is in a discharging characteristic;
the MMC optimal switching sequence model prediction control algorithm is characterized by converting reference output current into expected output voltage, and specifically comprises the following steps:
the discrete prediction model of the MMC inverter alternating current phase j current is as follows:
Wherein i j(k+1),upj (k+1) and u nj (k+1) are j-phase current, j-phase upper bridge arm voltage and lower bridge arm voltage which are measured in an alternating current manner at the moment of k+1 respectively, L and R are reactance and resistance of an alternating current side respectively, L o and R o are bridge arm reactance and resistance respectively, and T s is sampling time;
The following relationship can be obtained from kirchhoff's voltage law:
wherein u j (k+1) is the j-phase output voltage of MMC at the moment k+1;
The method comprises the steps of combining the above model with a phase current discrete model, and synthesizing three-phase electric quantity to obtain the three-phase electric quantity:
Wherein i (k+1), u (k+1) and i (k) are related three-phase electric quantity synthesis vectors;
In order to realize tracking of output current, a current vector at the moment k+1 is set as a reference current vector i * (k+1), and the sampling time T s is very small and can be approximately i * (k), and the voltage vector prediction model can be obtained by transforming the above formula:
where u p (k+1) is the desired output voltage vector.
2. The MMC optimal switching sequence model predictive control algorithm of claim 1, wherein the predictive voltage vector is converted into a g-h coordinate system, and three nearest voltage base vectors are selected, and the three nearest voltage base vectors are specifically as follows:
for any predicted voltage vector, four voltage base vector coordinates are obtained by rounding up and down the coordinates of the predicted voltage vector, three nearest voltage base vectors can be selected through logic judgment, and the selected seven-segment switching sequence consists of switching states corresponding to the three base vectors;
the method for converting the predicted voltage vector into the g-h coordinate system and selecting the three nearest voltage base vectors ensures the MPC algorithm control effect, greatly reduces the operation amount and is used for any level without changing the complexity of operation.
3. The MMC optimal switching sequence model predictive control algorithm of claim 1, wherein a variance function of a predicted current and a reference current is selected as a cost function, and the action time of three nearest voltage base vectors when the cost function takes a minimum value is obtained by utilizing an extremum solving method, and the method is specifically as follows:
Since the sampling time T s is very small, when predicting the current after each voltage vector acts, the initial current is approximated by the sampling current at k time, as shown in the following formula:
Wherein, the value range of m is 1 to 3;i α,m、iβ,m which is the current predicted value after the action of the selected mth voltage vector, t mi is the action time of the mth voltage vector, and t 1i+t2i+t3i＝Ts;uα,m、uβ,m、iα(k)、iβ (k) is the component of the related electric quantity on the alpha axis and the beta axis;
the cost function is established by considering the current error under the action of each voltage base vector as follows:
Wherein i * α、i* β is the component of i * (k) on the alpha and beta axes;
and obtaining an extremum for the value function, and obtaining the optimal acting time of the three voltage base vectors by using a partial derivative formula.
4. The MMC optimal switching sequence model predictive control algorithm of claim 1, wherein the number of submodules put into each bridge arm is appropriately increased and decreased to inhibit inter-phase circulation, and the algorithm is specifically as follows:
the MMC inverter j-phase circulation discrete prediction model is as follows:
wherein U dc is direct-current side voltage, i dj (k+1) is j-phase internal circulation of MMC at k+1 moment;
To eliminate the ac component in the circulating current, the following relationship needs to be satisfied:
wherein I dc is DC side current;
the expected value of the sum of the voltages of the upper bridge arm and the lower bridge arm of each phase at the moment k+1 can be obtained:
after the optimal switching sequence is selected, the bridge arm is regarded as a whole to carry out voltage prediction, and the average value of the sub-module capacitance voltages in each bridge arm at the moment k+1 is obtained, wherein the average value is shown in the following formula:
Wherein u rj,ave (k+1) represents the average value of the capacitance voltage of the j-phase bridge arm unit r (r=p, N) at the time of k+1, i rj (k) represents the bridge arm current of the j-phase bridge arm unit r, S rji represents the number of conduction sub-modules of the j-phase r bridge arm when the ith switch state in the sequence acts, t i represents the acting time of the ith switch state in the sequence, and N represents the number of bridge arm sub-modules;
the predicted value u rj (k+1) of the sum of the upper and lower arm voltages of j phases r at time k+1 is:
urj(k+1)＝Srj7urj,ave(k+1)
The number delta n rj,eq of the submodules which are required to be increased or decreased in each bridge arm in one sampling period is as follows:
Δn rj,eq is usually a non-integer, and in order to obtain an equivalent control effect, let the fractional part of Δn rj,eq be cmp, and t rj＝cmpTs, the number of submodules Δn rj to be inserted into each bridge arm is:
Because the upper bridge arm and the lower bridge arm of each phase bear half of the errors of the expected value and the predicted value of the bridge arm voltage respectively, the method can effectively inhibit the circulation current on the premise of not influencing the alternating current output.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210218901.2A CN114614691B (en) | 2022-03-07 | 2022-03-07 | A model predictive control algorithm for optimal switching sequence of MMC |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210218901.2A CN114614691B (en) | 2022-03-07 | 2022-03-07 | A model predictive control algorithm for optimal switching sequence of MMC |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114614691A CN114614691A (en) | 2022-06-10 |
| CN114614691B true CN114614691B (en) | 2025-04-04 |
Family
ID=81860840
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210218901.2A Active CN114614691B (en) | 2022-03-07 | 2022-03-07 | A model predictive control algorithm for optimal switching sequence of MMC |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114614691B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115765508A (en) * | 2022-11-25 | 2023-03-07 | 天津大学 | Equivalent Space Vector Model Predictive Control Method for Modular Multilevel Converter |
| CN116208017B (en) * | 2022-12-29 | 2024-04-05 | 苏州大学 | Inverter predictive control method based on optimal switching sequence model |
| CN116937999B (en) * | 2023-09-18 | 2024-02-13 | 深圳市德兰明海新能源股份有限公司 | Converter bridge arm circuit, converter device and precharge control method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107276107A (en) * | 2017-06-15 | 2017-10-20 | 上海电力学院 | Low frequency model forecast Control Algorithm based on mixed type module multilevel converter |
| CN110867887A (en) * | 2019-10-16 | 2020-03-06 | 南京理工大学 | Optimal sequence model predictive control device and method for NPC three-level inverter |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2978122A1 (en) * | 2014-07-22 | 2016-01-27 | ABB Technology AG | Model predictive control of a modular multilevel converter |
| CN110071655B (en) * | 2019-05-21 | 2020-06-09 | 南昌工程学院 | A Simplified Multilevel Converter Space Vector Modulation Method |
| CN112260563B (en) * | 2020-10-30 | 2022-03-25 | 南京师范大学 | Improved model prediction control method for 2N +1 type single-phase MMC |
-
2022
- 2022-03-07 CN CN202210218901.2A patent/CN114614691B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107276107A (en) * | 2017-06-15 | 2017-10-20 | 上海电力学院 | Low frequency model forecast Control Algorithm based on mixed type module multilevel converter |
| CN110867887A (en) * | 2019-10-16 | 2020-03-06 | 南京理工大学 | Optimal sequence model predictive control device and method for NPC three-level inverter |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114614691A (en) | 2022-06-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114614691B (en) | A model predictive control algorithm for optimal switching sequence of MMC | |
| CN114614690B (en) | Model predictive control algorithm for optimal switching sequence of MMC based on two-dimensional control area | |
| Kouro et al. | Predictive control based selective harmonic elimination with low switching frequency for multilevel converters | |
| CN112003491B (en) | Control method and system of modular parallel three-phase three-level inverter | |
| CN109565248B (en) | Two-stage control of converter system with floating unit | |
| CN104811069B (en) | A kind of forecast Control Algorithm of modular multilevel inverter | |
| CN109038673A (en) | The model prediction optimal control method of photovoltaic generating system | |
| CN106712556A (en) | Three-level H-bridge five-level inverter direct-current side capacitor voltage balancing method | |
| CN116827148B (en) | Optimized modulation method, device, terminal and medium for Vienna rectifier | |
| CN113809944A (en) | Grid-connected current tracking and circulating current prediction control method for offshore wind power flexible-direct current converter | |
| CN114785093A (en) | MMC model prediction control method of direct-drive permanent magnet wind power system | |
| CN117526675A (en) | Model prediction voltage control method for T-shaped three-phase three-level inverter | |
| Jacob et al. | A review paper on the elimination of harmonics in multilevel inverters using bioinspired algorithms | |
| CN116566179A (en) | A model predictive control method for a three-phase three-level T-type grid-connected inverter | |
| CN110323953A (en) | The Virtual Space vector modulation method and system of neutral point clamp multi-level converter | |
| CN117578899B (en) | Dynamic optimization and virtual voltage vector sliding mode prediction control method and system | |
| CN117792137B (en) | Model Predictive Control Method for Five-Level HERIC Active Clamped Inverter | |
| CN116722762B (en) | A control method for flying capacitor grid-connected inverter based on model predictive control | |
| CN112510749A (en) | Droop control method with network self-adaption capability in micro-grid island mode | |
| CN116208017B (en) | Inverter predictive control method based on optimal switching sequence model | |
| Betz et al. | Outline of the control design for a cascaded h-bridge statcom | |
| Sharma et al. | Indirect model predictive control of grid connected modular multilevel converter | |
| CN115528936A (en) | MPCC-DSVM-based neutral point potential balancing method for three-level T-type inverter | |
| CN114865632A (en) | Filter-based sequential model prediction control method and device | |
| Sharma et al. | Neural network-based three-phase NPC rectifier for DC bus capacitor voltage balancing under perturbed mains supply conditions |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |