CN109149981A - A kind of Multipurpose Optimal Method based on genetic algorithm suitable for MMC - Google Patents
A kind of Multipurpose Optimal Method based on genetic algorithm suitable for MMC Download PDFInfo
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- 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
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Abstract
The invention discloses a kind of Multipurpose Optimal Methods based on genetic algorithm suitable for MMC, comprising: obtains electric parameter, according to electric parameter and the capacitance voltage maximum fluctuation rate ε of setting, obtains capacitor's capacity needed for submodule before optimizing；Premised on optimization front and back does not improve switching device current class, optimization front and back is obtained to the constraint condition of bridge arm current；Pareto disaggregation is obtained using genetic algorithm with the minimum target of the voltage fluctuation of capacitor peak value of bridge arm modulation voltage peak value and submodule based on constraint condition and electric parameter；With bridge arm modulation voltage peak value minimum or the minimum priority target of voltage fluctuation of capacitor peak value of submodule, is concentrated from Pareto solution and determine common-mode voltage injection rate and circulation injection rate, the bridge arm modulation voltage after obtaining multiple-objection optimization；Realize the multiple-objection optimization of MMC.The application obtains optimal common-mode voltage injection and circulation injection rate using genetic algorithm, so that MMC be made to obtain reasonably optimizing.
Description
Technical Field
The invention belongs to the technical field of multi-level power electronic converters, and particularly relates to a genetic algorithm-based multi-objective optimization method suitable for MMC.
Background
An MMC (Modular Multilevel Converter) gradually becomes the most promising Converter topology of a high-voltage dc transmission system due to its advantages of highly Modular structure, easy expansion, low output voltage harmonic, and the like. In recent years, the voltage grade and the capacity of high-voltage direct-current transmission engineering are continuously increased, and higher requirements on the aspects of improving the output capacity and controlling the cost of the converter are provided.
At present, Half-Bridge submodule HBSM (Half-Bridge SM) topology is mainly adopted in MMC-HVDC engineering which is put into operation, and the output capacity is determined by the number of Bridge arm submodules. The existing research realizes equivalent overmodulation by a method of injecting common-mode voltage into bridge arm voltage under the condition of not increasing the number of MMC bridge arm sub-modules, and improves the output of a converter at an alternating current side. The sub-module capacitor is a core energy storage element of the converter and also is the main manufacturing cost of the sub-module except for a switch tube. The design of the capacitor is determined by the fluctuation rate of the capacitor voltage under the steady-state operation of the converter, and the fluctuation rate of the capacitor voltage can be effectively reduced by controlling the circulating current in the bridge arm and injecting the circulating current, so that the requirement of the capacitance value of the capacitor is reduced.
However, the injection of common mode voltage affects the fluctuation of capacitance voltage, and the injection of circulating current also changes the influence of bridge arm voltage on the overmodulation effect, so that the internal coupling relationship between the two is complex, and the application of the optimization method in the MMC is limited to a certain extent.
Disclosure of Invention
Aiming at the defects or improvement requirements of the prior art, the invention provides a multi-objective optimization method based on a genetic algorithm and suitable for MMC, so that the technical problems that in the prior art, the injection of common-mode voltage influences the fluctuation of capacitance and voltage, the injection of circulating current changes the bridge arm voltage and influences the overmodulation effect, and the application of the optimization method in MMC is limited are solved.
In order to achieve the above object, the present invention provides a multi-objective optimization method based on genetic algorithm suitable for MMC, comprising:
(1) acquiring electrical parameters, including: rated power P of MMC, rated voltage U of direct current sidedcSubmodule capacitor rated voltage UcAnd a converter modulation ratio m;
(2) obtaining a capacitance value required by the optimized front sub-module according to the electrical parameters and the set maximum fluctuation rate epsilon of the capacitance voltage;
(3) calculating the effective value of the bridge arm current in steady-state operation before and after optimization according to the electrical parameters on the premise that the current grade of the switching device is not improved before and after optimization, and further obtaining constraint conditions on the bridge arm current before and after optimization;
(4) based on constraint conditions and electrical parameters, a pareto solution set is obtained by using a genetic algorithm with the aim that the peak value of the bridge arm modulation voltage and the peak value of the capacitance voltage fluctuation of the submodule are minimum;
(5) the method comprises the steps that the minimum peak value of bridge arm modulation voltage or the minimum fluctuation peak value of capacitance voltage of a submodule is taken as a priority target, common-mode voltage injection quantity and circulating current injection quantity are determined from a pareto solution set, and bridge arm modulation voltage after multi-objective optimization is obtained;
(6) and (5) regulating the bridge arm modulation voltage to the multi-target optimized bridge arm modulation voltage obtained in the step (5), so as to realize the multi-target optimization of the MMC.
Further, the capacitance values required for optimizing the front sub-module are as follows:
wherein, ImIs the amplitude of the phase current on the AC side, omega is the AC output frequency,is the power factor angle.
Further, the step (3) comprises:
on the premise of not improving the current grade of the switching device before and after optimization, calculating the effective value of the bridge arm current in steady-state operation before optimization according to the electrical parametersWherein,Idcfor a rated current on the DC side, ImOptimizing and injecting circulation current with double fundamental frequency for the amplitude of the AC side phase current, and optimizing the effective value of the current of the rear bridge armWherein, I2mCirculating current amplitude of double injected fundamental frequency, l'mAnd for the optimized alternating-current side phase current amplitude, the constraint conditions for the bridge arm currents before and after optimization comprise the bridge arm current effective value during steady-state operation before optimization and the bridge arm current effective value after optimization.
Further, the step (4) comprises:
(4-1) according to constraint conditions and electrical parameters, randomly establishing an initial solution set of common-mode voltage injection quantity and circulating current injection quantity by taking the minimum peak value of the bridge arm modulation voltage and the minimum peak value of the capacitance voltage fluctuation of the submodule as a target;
(4-2) calculating to obtain a plurality of bridge arm modulation voltage peak values and capacitance voltage fluctuation peak values of the submodules by using a plurality of initial solutions in the initial solution set, calculating an adaptive function between each bridge arm modulation voltage peak value and the capacitance voltage fluctuation peak value of the submodule as well as a target to obtain adaptive values of the plurality of initial solutions in the initial solution set, and selecting a pareto solution set from the initial solution set according to the adaptive values;
(4-3) performing cross operation and mutation operation on the initial solution set to generate a new solution set, replacing the initial solution set with the new solution set, and then executing the step (4-2);
(4-4) repeating the steps (4-2) - (4-3) until the maximum evolution algebra is reached to obtain a final pareto solution set.
Further, the bridge arm modulation voltage after multi-objective optimization comprises an upper bridge arm modulation voltage u and a lower bridge arm modulation voltage u after common-mode voltage injectionp、un：
Wherein, Um' for the optimized AC side output voltage amplitude, Um′x1Amplitude of sinusoidal component, U, of common mode voltage injectionm′x2The amplitude of the cosine component of the injection amount of the common mode voltage, omega is the alternating current output frequency, and t is the running time of the MMC.
Further, the amplitude of the output voltage at the ac side after optimization is:
Um′＝kUm
wherein, UmFor outputting voltage amplitude and equivalent modulation ratio on the AC sideupmIs the minimum bridge arm modulation voltage peak value obtained by the genetic algorithm.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
the bridge arm modulation voltage is introduced into common-mode voltage injection, so that the peak value of the bridge arm modulation voltage is reduced, and the capability of improving the output amplitude of the alternating-current side fundamental frequency is realized under the condition that the number of bridge arm submodules is not changed; the current of the bridge arm is introduced into the circulating current injection, so that the fluctuation peak value of the capacitor voltage can be effectively reduced. Due to the internal coupling of the two optimization methods, the invention introduces a multi-objective optimization genetic algorithm, gives consideration to the aims of improving the output capacity of the alternating current side and reducing the fluctuation rate of the capacitance voltage, and obtains the optimal common mode voltage injection amount and the circulating current injection amount, thereby leading the MMC to be reasonably optimized. Therefore, the technical problems that in the prior art, the injection of common-mode voltage influences the fluctuation of capacitance voltage, the injection of circulating current changes the bridge arm voltage and influences the overmodulation effect, and the application of an optimization method in MMC is limited are solved.
Drawings
FIG. 1 is a flow chart of a multi-objective optimization method based on genetic algorithm for MMC according to an embodiment of the present invention;
fig. 2 is a topology structure diagram of a three-phase modular multilevel converter according to an embodiment of the present invention;
fig. 3 is a schematic diagram of upper bridge arm modulation voltage waveforms before and after common-mode voltage injection according to an embodiment of the present invention;
fig. 4 is a schematic diagram of upper bridge arm current waveforms before and after injection of a circulating current according to an embodiment of the present invention;
FIG. 5 is a block flow diagram of a genetic algorithm for multi-objective optimization provided by an embodiment of the present invention;
FIG. 6 is a pareto frontier plot of the genetic algorithm results based on MATLAB multi-objective optimization provided in example 1 of the present invention;
FIG. 7 is a diagram of an upper bridge arm modulation voltage waveform based on MATLAB/Simulink simulation provided in embodiment 1 of the present invention;
fig. 8 is a voltage waveform diagram of a submodule capacitor based on MATLAB/Simulink simulation provided in embodiment 1 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, a multi-objective optimization method based on genetic algorithm for MMC includes:
(1) acquiring electrical parameters, including: rated power P of MMC, rated voltage U of direct current sidedcSubmodule capacitor rated voltage UcAnd current conversionA modulator-to-modulator ratio m; specifically, the quantity of the bridge arm sub-modules of the half-bridge type MMC is calculated according to the obtained electrical parameters without considering the redundancy of the sub-modules
(2) Calculating the phase current amplitude of the non-optimized front alternating current side according to the electrical parametersObtaining a capacitance value required by the optimized front sub-module according to the electrical parameters and the set maximum fluctuation rate epsilon of the capacitance voltage:
wherein, ImIs the amplitude of the phase current on the AC side, omega is the AC output frequency,is the power factor angle.
(3) Calculating the effective value of the bridge arm current in steady-state operation before and after optimization according to the electrical parameters on the premise that the current grade of the switching device is not improved before and after optimization, and further obtaining constraint conditions on the bridge arm current before and after optimization; specifically, neglecting the AC-DC side power balance of the loss converter, the DC side current isThe effective value of bridge arm current is completely restrained before optimization and in steady-state operationAnd optimally injecting circulating current with double fundamental frequency, wherein the injected upper and lower bridge arm currents are as follows:
wherein x is3Amplitude of sinusoidal component, x, for injecting circulating current of double fundamental frequency4Is the amplitude of the cosine component of the circulating current injected at twice the fundamental frequency.
Optimizing rear bridge arm current effective valueWherein I2mIs the injected circulating current amplitude, I'mAnd for the optimized alternating-current side phase current amplitude, the constraint conditions for the bridge arm currents before and after optimization comprise the bridge arm current effective value during steady-state operation before optimization and the bridge arm current effective value after optimization.
(4) Based on constraint conditions and electrical parameters, a pareto solution set is obtained by using a genetic algorithm with the aim that the peak value of the bridge arm modulation voltage and the peak value of the capacitance voltage fluctuation of the submodule are minimum;
(5) the method comprises the steps that the minimum peak value of bridge arm modulation voltage or the minimum fluctuation peak value of capacitance voltage of a submodule is taken as a priority target, common-mode voltage injection quantity and circulating current injection quantity are determined from a pareto solution set, and bridge arm modulation voltage after multi-objective optimization is obtained;
(6) and (4) regulating the bridge arm modulation voltage to the multi-target optimized bridge arm modulation voltage obtained in the step (5), so that the output of the MMC alternating current side is improved to the maximum extent under the constraint of bridge arm current, the fluctuation rate of the capacitance voltage is reduced to the maximum extent, and the multi-target optimization of the MMC is realized.
As shown in fig. 2, each phase of the three-phase MMC is composed of an upper bridge arm and a lower bridge arm which are identical, each bridge arm comprises N sub-modules, the sub-modules are connected in a cascade manner, the upper bridge arm and the lower bridge arm are respectively connected by connecting a bridge arm inductor in series, and a connection point is an ac side output point. The sub-module comprises two IGBTs, two anti-parallel diodes and a capacitor.
FIG. 3 is a schematic diagram of upper arm modulation voltage waveforms before and after common mode voltage injection, and FIG. 4 is a schematic diagram of upper arm current waveforms before and after circulating current injection; the peak value of the bridge arm modulation voltage is reduced after the common-mode voltage is injected, and the amplitude of the fundamental frequency output voltage on the alternating current side can be increased under the condition that the bridge arm submodule is not changed, so that equivalent overmodulation is realized.
FIG. 5 is a flow chart of a genetic algorithm of multi-objective optimization, the genetic algorithm has unique advantages in solving the problems of multivariable, multi-constraint, multi-peak (valley) value, nonlinearity and discreteness, and the multi-objective optimization method based on pareto sorting is suitable for multi-objective optimization considering the minimum bridge arm modulation voltage peak value and the minimum capacitance voltage fluctuation peak value simultaneously in the scheme. The genetic algorithm leads the next generation group to be closer to the optimal solution as a whole by carrying out a series of seed selection, crossing and variation on the first generation group according to the optimizing target. The invention introduces pareto sequencing in a selection operator to form a genetic algorithm of multi-objective optimization, which comprises the following steps:
(4-1) according to constraint conditions and electrical parameters, randomly establishing an initial solution set of common-mode voltage injection quantity and circulating current injection quantity by taking the minimum peak value of the bridge arm modulation voltage and the minimum peak value of the capacitance voltage fluctuation of the submodule as a target;
(4-2) calculating to obtain a plurality of bridge arm modulation voltage peak values and capacitance voltage fluctuation peak values of the submodules by using a plurality of initial solutions in the initial solution set, calculating an adaptive function between each bridge arm modulation voltage peak value and the capacitance voltage fluctuation peak value of the submodule as well as a target to obtain adaptive values of the plurality of initial solutions in the initial solution set, and selecting a pareto solution set from the initial solution set according to the adaptive values;
(4-3) performing cross operation and mutation operation on the initial solution set to generate a new solution set, replacing the initial solution set with the new solution set, and then executing the step (4-2);
(4-4) repeating the steps (4-2) - (4-3) until the maximum evolution algebra is reached to obtain a final pareto solution set.
Further, the bridge arm modulation voltage after multi-objective optimization comprises an upper bridge arm modulation voltage u and a lower bridge arm modulation voltage u after common-mode voltage injectionp、un：
Wherein, Um' for the optimized AC side output voltage amplitude, Um′x1Amplitude of sinusoidal component, U, of common mode voltage injectionm′x2The amplitude of the cosine component of the injection amount of the common mode voltage, omega is the alternating current output frequency, and t is the running time of the MMC.
Further, the amplitude of the output voltage at the ac side after optimization is:
Um′＝kUm
wherein, UmFor outputting voltage amplitude and equivalent modulation ratio on the AC sideupmIs the minimum bridge arm modulation voltage peak value obtained by the genetic algorithm.
Example 1
The embodiment is used for explaining that the fluctuation of the modulation voltage and the capacitance voltage of the bridge arm can be effectively reduced by injecting the common-mode voltage and the circulating current, the optimal injection quantity of the common-mode voltage and the circulating current can be quickly searched through a genetic algorithm, and the multi-objective optimization is realized. For clarity of illustration, the following analyses were performed:
taking the bridge arm as an example, the bridge arm switching function is:
the bridge arm current after the circulation current is injected is as follows:
considering the unity power factor, the capacitive current can be obtained from the product of the bridge arm switching function and the bridge arm current:
icp＝Sp·Irp
in combination with the above formula, the relation between the submodule capacitor voltage fluctuation and the common mode voltage and the circulation injection amount is as follows:
the main parameters of this example are shown in table 1:
TABLE 1
Optimizing pre-I according to the foregoing analysisrThe inductance value of the bridge arm is designed according to the condition that the injection quantity of the circulating current does not exceed 20% of the fundamental wave amplitude of the current of the bridge arm, and the injection quantity of the common-mode voltage does not exceed 20% of the alternating-current output voltage, so that the multi-objective optimization problem is as follows:
min Δucp(x1，x2，x3，x4)
min up(x1，x2，x3，x4)
a global optimization tool box is used in MATLAB to realize a multi-objective genetic algorithm, FIG. 6 is a genetic algorithm result graph based on MATLAB multi-objective optimization, a pareto optimal leading edge can be obtained from the algorithm result, an optimal solution set considering two optimization objectives is arranged on the leading edge, the embodiment selects a minimum bridge arm modulation voltage peak value as a priority objective, and a minimum bridge arm modulation voltage peak value u is selected as a priority objectivepmAt 373.2V, the capacitor voltage fluctuates to Δ ucpDetermining the injection quantity of the common mode voltage and the circulation injection quantity of the common mode voltage as 1.403VAnd obtaining the bridge arm modulation voltage and the circulating current set value of multi-objective optimization.
A simulation model is built in MATLAB/Simulink, common-mode voltage injection quantity and circulation injection quantity obtained by a multi-objective optimization genetic algorithm are added into the MMC before optimization, bridge arm modulation voltage of the MMC is shown in figure 7, and the peak value of the bridge arm modulation voltage is reduced after injection and accords with an algorithm result.
The loop current injection amount is given in the loop current suppressor, the fluctuation of the capacitor voltage is suppressed by changing the waveform of the bridge arm current, the fluctuation of the capacitor voltage is shown in figure 8, the fluctuation of the capacitor voltage is seen to fluctuate near the rated voltage, the fluctuation mainly comprises a fundamental frequency component and a frequency doubling component, the maximum fluctuation is reduced after injection, and the maximum fluctuation basically accords with the algorithm result.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (6)
1. A multi-objective optimization method based on genetic algorithm and suitable for MMC is characterized by comprising the following steps:
(1) acquiring electrical parameters, including: rated power P of MMC, rated voltage U of direct current sidedcSubmodule capacitor rated voltage UcAnd a converter modulation ratio m;
(2) obtaining a capacitance value required by the optimized front sub-module according to the electrical parameters and the set maximum fluctuation rate epsilon of the capacitance voltage;
(3) calculating the effective value of the bridge arm current in steady-state operation before and after optimization according to the electrical parameters on the premise that the current grade of the switching device is not improved before and after optimization, and further obtaining constraint conditions on the bridge arm current before and after optimization;
(4) based on constraint conditions and electrical parameters, a pareto solution set is obtained by using a genetic algorithm with the aim that the peak value of the bridge arm modulation voltage and the peak value of the capacitance voltage fluctuation of the submodule are minimum;
(5) the method comprises the steps that the minimum peak value of bridge arm modulation voltage or the minimum fluctuation peak value of capacitance voltage of a submodule is taken as a priority target, common-mode voltage injection quantity and circulating current injection quantity are determined from a pareto solution set, and bridge arm modulation voltage after multi-objective optimization is obtained;
(6) and (5) regulating the bridge arm modulation voltage to the multi-target optimized bridge arm modulation voltage obtained in the step (5), so as to realize the multi-target optimization of the MMC.
2. The multi-objective genetic algorithm-based optimization method for the MMC of claim 1, wherein the capacitance values required by the sub-module before the optimization are as follows:
wherein, ImIs the amplitude of the phase current on the AC side, omega is the AC output frequency,is the power factor angle.
3. The multi-objective genetic algorithm-based optimization method for MMC according to claim 1 or 2, characterized in that, said step (3) comprises:
on the premise of not improving the current grade of the switching device before and after optimization, calculating the effective value of the bridge arm current in steady-state operation before optimization according to the electrical parametersWherein, IdcFor a rated current on the DC side, ImOptimizing and injecting circulation current with double fundamental frequency for the amplitude of the AC side phase current, and optimizing the effective value of the current of the rear bridge armWherein, I2mCirculating current amplitude of double injected fundamental frequency, l'mAnd for the optimized alternating-current side phase current amplitude, the constraint conditions for the bridge arm currents before and after optimization comprise the bridge arm current effective value during steady-state operation before optimization and the bridge arm current effective value after optimization.
4. The multi-objective genetic algorithm-based optimization method for MMC according to claim 1 or 2, characterized in that, said step (4) comprises:
(4-1) according to constraint conditions and electrical parameters, randomly establishing an initial solution set of common-mode voltage injection quantity and circulating current injection quantity by taking the minimum peak value of the bridge arm modulation voltage and the minimum peak value of the capacitance voltage fluctuation of the submodule as a target;
(4-2) calculating to obtain a plurality of bridge arm modulation voltage peak values and capacitance voltage fluctuation peak values of the submodules by using a plurality of initial solutions in the initial solution set, calculating an adaptive function between each bridge arm modulation voltage peak value and the capacitance voltage fluctuation peak value of the submodule as well as a target to obtain adaptive values of the plurality of initial solutions in the initial solution set, and selecting a pareto solution set from the initial solution set according to the adaptive values;
(4-3) performing cross operation and mutation operation on the initial solution set to generate a new solution set, replacing the initial solution set with the new solution set, and then executing the step (4-2);
(4-4) repeating the steps (4-2) - (4-3) until the maximum evolution algebra is reached to obtain a final pareto solution set.
5. The multi-objective optimization method based on genetic algorithm for MMC according to claim 1 or 2, characterized in that, the multi-objective optimized bridge arm modulation voltage includes upper and lower bridge arm modulation voltage u after injecting common mode voltagep、un：
Wherein, Um' for the optimized AC side output voltage amplitude, Um′x1Amplitude of sinusoidal component, U, of common mode voltage injectionm′x2The amplitude of the cosine component of the injection amount of the common mode voltage, omega is the alternating current output frequency, and t is the running time of the MMC.
6. The multi-objective genetic algorithm-based optimization method for MMC according to claim 5, wherein the optimized AC side output voltage amplitude is:
Um′＝kUm
wherein, UmFor outputting voltage amplitude and equivalent modulation ratio on the AC sideupmIs the minimum bridge arm modulation voltage peak value obtained by the genetic algorithm.
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| CN117350223A (en) * | 2023-09-13 | 2024-01-05 | 广东电网有限责任公司电力科学研究院 | Multi-objective optimization parameter design method for modularized multi-level converter based on SiC MOSFET |
| CN117350223B (en) * | 2023-09-13 | 2024-08-27 | 广东电网有限责任公司电力科学研究院 | Multi-objective optimization parameter design method for modularized multi-level converter based on SiC MOSFET |
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