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
- Publication number
- CN109149981A CN109149981A CN201810948515.2A CN201810948515A CN109149981A CN 109149981 A CN109149981 A CN 109149981A CN 201810948515 A CN201810948515 A CN 201810948515A CN 109149981 A CN109149981 A CN 109149981A
- Authority
- CN
- China
- Prior art keywords
- bridge arm
- voltage
- optimization
- mmc
- peak value
- 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.)
- Granted
Links
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
- H02M7/483—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 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 multilevel power electronic converter technical fields, more particularly, to a kind of base suitable for MMC
In the Multipurpose Optimal Method of genetic algorithm.
Background technique
MMC (Modular Multilevel Converter, modularization multi-level converter) is because having structure height mould
Block is easy to extend, the advantages such as harmonic wave of output voltage is low, is increasingly becoming the most promising inverter of HVDC transmission system and opens up
It flutters.High voltage direct current transmission project voltage class and capacity in recent years constantly increases, and improves fan-out capability and cost to inverter
More stringent requirements are proposed for control aspect.
The MMC-HVDC engineering to put into operation at present mainly uses half-bridge submodule HBSM (Half-Bridge SM) topological,
Fan-out capability is determined by bridge arm submodule number.Existing research is not being increased by the method for injecting common-mode voltage to bridge arm voltage
Add and realize equivalent ovennodulation in the case of MMC bridge arm submodule number purpose, improves inverter exchange side output.Submodule capacitor is to change
Flow the main manufacturing cost of the core energy-storage travelling wave tube and submodule of device in addition to switching tube.The design of capacitor is by inverter stable state
Voltage fluctuation of capacitor rate under operation determines, can be effectively reduced capacitor by the method for controlling bridge arm inner ring stream, injection circulation
Voltage fluctuation rate is to reduce the demand of capacitor's capacity.
However, injection common-mode voltage will affect voltage fluctuation of capacitor, bridge arm voltage influence can be changed by similarly injecting circulation
The effect of ovennodulation, the two inner couplings relationship is more complex, limits application of the optimization method in MMC to a certain extent.
Summary of the invention
Aiming at the above defects or improvement requirements of the prior art, the present invention provides a kind of suitable for MMC based on heredity
The Multipurpose Optimal Method of algorithm, thus solving the prior art in the presence of injection common-mode voltage will affect voltage fluctuation of capacitor, injection
Circulation, which can change bridge arm voltage, to be influenced the effect of ovennodulation and then limits the technical issues of optimization method is applied in MMC.
To achieve the above object, the present invention provides a kind of multiple-objection optimization sides based on genetic algorithm suitable for MMC
Method, comprising:
(1) electric parameter is obtained, comprising: rated power P, the DC side voltage rating U of MMCdc, the specified electricity of submodule capacitor
Press UcWith inverter modulation ratio m；
(2) according to electric parameter and the capacitance voltage maximum fluctuation rate ε of setting, electricity needed for submodule before optimizing is obtained
Hold capacitance；
(3) premised on optimization front and back does not improve switching device current class, presteady state is optimized according to electrical parameter calculation
Bridge arm current virtual value when operation, and then optimization front and back is obtained to the constraint condition of bridge arm current；
(4) it is based on constraint condition and electric parameter, with the voltage fluctuation of capacitor peak of bridge arm modulation voltage peak value and submodule
It is worth minimum target, obtains Pareto disaggregation using genetic algorithm；
(5) with bridge arm modulation voltage peak value is minimum or the minimum priority target of voltage fluctuation of capacitor peak value of submodule, from
Pareto solution, which is concentrated, determines common-mode voltage injection rate and circulation injection rate, the bridge arm modulation voltage after obtaining multiple-objection optimization；
(6) bridge arm modulation voltage is adjusted to the bridge arm modulation voltage after the multiple-objection optimization that step (5) obtains, is realized
The multiple-objection optimization of MMC.
Further, capacitor's capacity needed for submodule before optimizing are as follows:
Wherein, ImTo exchange side phase current magnitude, ω is ac output frequency,For power-factor angle.
Further, step (3) includes:
Premised on optimization front and back does not improve switching device current class, presteady state operation is optimized according to electrical parameter calculation
When bridge arm current virtual valueWherein, IdcFor DC side rated current, ImIt is mutually electric for exchange side
Flow amplitude, the circulation of optimization two times of fundamental frequencies of injection, bridge arm current effective value after optimization
Wherein, I2mFor the circulation amplitude of two times of fundamental frequencies of injection, I 'mFor the exchange side phase current magnitude after optimization, the optimization front and back
Constraint condition to bridge arm current includes that bridge arm current virtual value when optimizing presteady state operation and bridge arm current after optimization are effective
Value.
Further, step (4) includes:
(4-1) according to constraint condition and electric parameter, with the voltage fluctuation of capacitor of bridge arm modulation voltage peak value and submodule
The minimum target of peak value, establishes the initial disaggregation of common-mode voltage injection rate and circulation injection rate at random；
(4-2) concentrates multiple initial solutions that the electricity of multiple bridge arm modulation voltage peak values and submodule is calculated using initial solution
Hold voltage fluctuation peak value, calculates between each bridge arm modulation voltage peak value and the voltage fluctuation of capacitor peak value and target of submodule
Fitness function obtains the adaptive value that initial solution concentrates multiple initial solutions, is concentrated according to adaptive value from initial solution and chooses Pareto solution
Collection；
(4-3) carries out crossing operation and mutation operator to initial disaggregation, generates new disaggregation, is replaced just using new disaggregation
Then beginning disaggregation executes step (4-2)；
(4-4) repeats step (4-2)-(4-3) until reaching maximum evolutionary generation, obtains final Pareto disaggregation.
Further, the bridge arm modulation voltage after multiple-objection optimization includes the upper and lower bridge arm modulation after injecting common-mode voltage
Voltage up、un:
Wherein, Um' for optimization after exchange side output voltage amplitude, Um′x1For the sine component width of common-mode voltage injection rate
Value, Um′x2For the cosinusoidal component amplitude of common-mode voltage injection rate, ω is ac output frequency, and t is the runing time of MMC.
Further, the exchange side output voltage amplitude after optimization are as follows:
Um'=kUm
Wherein, UmTo exchange side output voltage amplitude, equivalent modulation ratioupmIt is the minimum that genetic algorithm obtains
Bridge arm modulation voltage peak value.
In general, through the invention it is contemplated above technical scheme is compared with the prior art, can obtain down and show
Beneficial effect:
Bridge arm modulation voltage introduce common-mode voltage injection reduces bridge arm modulation voltage peak value, bridge arm submodule number not
Possess the ability for improving exchange side group frequency output amplitude in the case where change；Bridge arm current, which introduces circulation injection, can be effectively reduced capacitor
Voltage fluctuation peak value.Since there are inner couplings for two kinds of optimization methods, present invention introduces the genetic algorithms of multiple-objection optimization, take into account
It improves exchange side fan-out capability and reduces the target of voltage fluctuation of capacitor rate, obtain optimal common-mode voltage injection and circulation injection
Amount, so that MMC be made to obtain reasonably optimizing.Thus solve the prior art exist injection common-mode voltage will affect voltage fluctuation of capacitor,
Injection circulation, which can change the effect of bridge arm voltage influence ovennodulation and then limit the technology that optimization method is applied in MMC, asks
Topic.
Detailed description of the invention
Fig. 1 is a kind of Multipurpose Optimal Method based on genetic algorithm suitable for MMC provided in an embodiment of the present invention
Flow chart；
Fig. 2 is the topology diagram of three-phase modular multilevel inverter provided in an embodiment of the present invention；
Fig. 3 is the upper bridge arm modulation voltage waveform diagram of common-mode voltage injection provided in an embodiment of the present invention front and back；
Fig. 4 is the upper bridge arm current waveform diagram of circulation injection provided in an embodiment of the present invention front and back；
Fig. 5 is the flow diagram of the genetic algorithm of multiple-objection optimization provided in an embodiment of the present invention；
Fig. 6 is the genetic algorithm result Pareto forward position based on MATLAB multiple-objection optimization that the embodiment of the present invention 1 provides
Figure；
Fig. 7 is the upper bridge arm modulation voltage waveform based on MATLAB/Simulink emulation that the embodiment of the present invention 1 provides
Figure；
Fig. 8 is the submodule capacitor voltage waveform based on MATLAB/Simulink emulation that the embodiment of the present invention 1 provides
Figure.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.As long as in addition, technical characteristic involved in the various embodiments of the present invention described below
Not constituting a conflict with each other can be combined with each other.
As shown in Figure 1, a kind of Multipurpose Optimal Method based on genetic algorithm suitable for MMC, comprising:
(1) electric parameter is obtained, comprising: rated power P, the DC side voltage rating U of MMCdc, the specified electricity of submodule capacitor
Press UcWith inverter modulation ratio m；Specifically, submodule redundancy is not considered, by the electrical parameter calculation semi-bridge type MMC bridge arm obtained
Submodule quantity
(2) side phase current magnitude is exchanged before being not optimised according to electrical parameter calculationAccording to electrical
Parameter and the capacitance voltage maximum fluctuation rate ε of setting obtain capacitor's capacity needed for submodule before optimizing:
Wherein, ImTo exchange side phase current magnitude, ω is ac output frequency,For power-factor angle.
(3) premised on optimization front and back does not improve switching device current class, presteady state is optimized according to electrical parameter calculation
Bridge arm current virtual value when operation, and then optimization front and back is obtained to the constraint condition of bridge arm current；Specifically, ignore loss to change
Device alternating current-direct current side power-balance is flowed, DC side electric current isCirculation is totally constrained before optimizing, bridge when steady-state operation
Arm current effective valueThe circulation of optimization two times of fundamental frequencies of injection, the upper and lower bridge arm electricity after injection
Stream are as follows:
Wherein, x3For the sine component amplitude of the circulation of two times of fundamental frequencies of injection, x4For the cosine of the circulation of two times of fundamental frequencies of injection
Ingredient amplitude.
Bridge arm current effective value after optimizationWherein I2mFor the ring of injection
Flow amplitude, I 'mIt include optimization to the constraint condition of bridge arm current before and after the optimization for the exchange side phase current magnitude after optimization
Bridge arm current effective value after bridge arm current virtual value and optimization when presteady state is run.
(4) it is based on constraint condition and electric parameter, with the voltage fluctuation of capacitor peak of bridge arm modulation voltage peak value and submodule
It is worth minimum target, obtains Pareto disaggregation using genetic algorithm；
(5) with bridge arm modulation voltage peak value is minimum or the minimum priority target of voltage fluctuation of capacitor peak value of submodule, from
Pareto solution, which is concentrated, determines common-mode voltage injection rate and circulation injection rate, the bridge arm modulation voltage after obtaining multiple-objection optimization；
(6) bridge arm modulation voltage is adjusted to the bridge arm modulation voltage after the multiple-objection optimization that step (5) obtains, makes MMC
The output of exchange side farthest improves under bridge arm current constraint, and voltage fluctuation of capacitor rate farthest reduces, and realizes
The multiple-objection optimization of MMC.
As shown in Fig. 2, the every phase of three-phase MMC is made of upper and lower two identical bridge arms, each bridge arm includes N number of son
Module, submodule connect in cascaded fashion, and upper and lower bridge arm respectively passes through one bridge arm inductance of concatenation and is connected, and tie point is that exchange side is defeated
Point out.The submodule includes two IGBT, two antiparallel diodes and a capacitor.
Fig. 3 is the upper bridge arm modulation voltage waveform diagram of common-mode voltage injection front and back, and Fig. 4 is the upper of circulation injection front and back
Bridge arm current waveform diagram；As can be seen that bridge arm modulation voltage peak value after injecting common-mode voltage reduces, bridge arm is being kept
In the case that module is constant, the amplitude of the side group that can increase exchanges frequency output voltage realizes equivalent ovennodulation.
Fig. 5 is the flow diagram of the genetic algorithm of multiple-objection optimization, and genetic algorithm is solving multivariable, multiple constraint, multimodal
There is unique advantage when the problem of (paddy) value, non-linear, discreteness, the Multipurpose Optimal Method based on Pareto sequence is applicable in
Consider the multiple-objection optimization of minimum bridge arm modulation voltage peak value and minimum capacity voltage fluctuation peak value simultaneously in this programme.Heredity
Algorithm makes next-generation group from entirety and carrying out a series of seed selection, intersection according to Optimization goal to generation group, making a variation
Upper closer optimal solution.The present invention introduces Pareto sequence in selection operator, forms the genetic algorithm of multiple-objection optimization, wraps
It includes:
(4-1) according to constraint condition and electric parameter, with the voltage fluctuation of capacitor of bridge arm modulation voltage peak value and submodule
The minimum target of peak value, establishes the initial disaggregation of common-mode voltage injection rate and circulation injection rate at random；
(4-2) concentrates multiple initial solutions that the electricity of multiple bridge arm modulation voltage peak values and submodule is calculated using initial solution
Hold voltage fluctuation peak value, calculates between each bridge arm modulation voltage peak value and the voltage fluctuation of capacitor peak value and target of submodule
Fitness function obtains the adaptive value that initial solution concentrates multiple initial solutions, is concentrated according to adaptive value from initial solution and chooses Pareto solution
Collection；
(4-3) carries out crossing operation and mutation operator to initial disaggregation, generates new disaggregation, is replaced just using new disaggregation
Then beginning disaggregation executes step (4-2)；
(4-4) repeats step (4-2)-(4-3) until reaching maximum evolutionary generation, obtains final Pareto disaggregation.
Further, the bridge arm modulation voltage after multiple-objection optimization includes the upper and lower bridge arm modulation after injecting common-mode voltage
Voltage up、un:
Wherein, Um' for optimization after exchange side output voltage amplitude, Um′x1For the sine component width of common-mode voltage injection rate
Value, Um′x2For the cosinusoidal component amplitude of common-mode voltage injection rate, ω is ac output frequency, and t is the runing time of MMC.
Further, the exchange side output voltage amplitude after optimization are as follows:
Um'=kUm
Wherein, UmTo exchange side output voltage amplitude, equivalent modulation ratioupmIt is the minimum that genetic algorithm obtains
Bridge arm modulation voltage peak value.
Embodiment 1
Bridge arm modulation voltage and capacitance voltage can be effectively reduced by injection common-mode voltage and circulation to illustrate in this example
Fluctuation can quickly seek the optimal injection rate of the two by genetic algorithm, realize multiple-objection optimization.For clearer explanation, into
The following analysis of row:
For the above bridge arm, bridge arm switch function are as follows:
Bridge arm current after injecting circulation are as follows:
Consider that unity power factor, capacitance current can be obtained by the product of bridge arm switch function and bridge arm current:
icp=Sp·Irp
In conjunction with above formula, submodule capacitor voltage fluctuation is as follows with the relationship of common-mode voltage and circulation injection rate:
This example major parameter is as shown in table 1:
Table 1
According to Such analysis, I before optimizingr=6.93A, bridge arm inductance value are no more than bridge arm current base according to circulation injection rate
20% design of wave amplitude, common-mode voltage injection is by 20% design no more than ac output voltage, then the multiple-objection optimization is asked
It is entitled:
min Δucp(x1, x2, x3, x4)
min up(x1, x2, x3, x4)
Realize that multi-objective genetic algorithm, Fig. 6 are based on MATLAB multiple target using global optimization tool box in MATLAB
The genetic algorithm result figure of optimization, can obtain Pareto optimality forward position by arithmetic result, be to consider two optimization mesh on the forward position
Target optimal solution set, this example is selected using minimum bridge arm modulation voltage peak value as priority target, minimum bridge arm modulation voltage peak value upm
=373.2V, voltage fluctuation of capacitor are down to Δ ucp=1.403V determines common-mode voltage injection rate and circulation injection rateObtain the bridge arm modulation voltage and circulation given value of multiple-objection optimization.
Simulation model is built in MATLAB/Simulink, the common-mode voltage that the genetic algorithm of multiple-objection optimization is obtained
Injection rate and circulation injection rate are added in the MMC before optimization, and bridge arm modulation voltage is as shown in Figure 7, it is seen that bridge arm modulation voltage
Peak value reduces after the implantation, meets arithmetic result.
Circulation injection rate is given in loop current suppression device, the waveform by changing bridge arm current generates voltage fluctuation of capacitor
Inhibit, voltage fluctuation of capacitor is as shown in Figure 8, it is seen that capacitance voltage fluctuates near voltage rating, and fluctuation mainly includes fundamental frequency
Component and two harmonics, maximum fluctuation reduce after the implantation, substantially conform to arithmetic result.
As it will be easily appreciated by one skilled in the art that the foregoing is merely illustrative of the preferred embodiments of the present invention, not to
The limitation present invention, any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should all include
Within protection scope of the present invention.
Claims (6)
1. a kind of Multipurpose Optimal Method based on genetic algorithm suitable for MMC characterized by comprising
(1) electric parameter is obtained, comprising: rated power P, the DC side voltage rating U of MMCdc, submodule capacitor voltage rating Uc
With inverter modulation ratio m；
(2) it according to electric parameter and the capacitance voltage maximum fluctuation rate ε of setting, obtains capacitor needed for submodule before optimizing and holds
Value；
(3) premised on optimization front and back does not improve switching device current class, presteady state operation is optimized according to electrical parameter calculation
When bridge arm current virtual value, and then obtain optimization front and back to the constraint condition of bridge arm current；
(4) it is based on constraint condition and electric parameter, it is equal with the voltage fluctuation of capacitor peak value of bridge arm modulation voltage peak value and submodule
Minimum target obtains Pareto disaggregation using genetic algorithm；
(5) tired from pa with bridge arm modulation voltage peak value minimum or the minimum priority target of voltage fluctuation of capacitor peak value of submodule
Support solution, which is concentrated, determines common-mode voltage injection rate and circulation injection rate, the bridge arm modulation voltage after obtaining multiple-objection optimization；
(6) bridge arm modulation voltage is adjusted to the bridge arm modulation voltage after the multiple-objection optimization that step (5) obtains, realizes MMC's
Multiple-objection optimization.
2. a kind of Multipurpose Optimal Method based on genetic algorithm suitable for MMC as described in claim 1, feature exist
In capacitor's capacity needed for submodule before the optimization are as follows:
Wherein, ImTo exchange side phase current magnitude, ω is ac output frequency,For power-factor angle.
3. a kind of Multipurpose Optimal Method based on genetic algorithm suitable for MMC as claimed in claim 1 or 2, feature
It is, the step (3) includes:
Premised on optimization front and back does not improve switching device current class, optimized when presteady state is run according to electrical parameter calculation
Bridge arm current virtual valueWherein, IdcFor DC side rated current, ImTo exchange side phase current
Amplitude, the circulation of optimization two times of fundamental frequencies of injection, bridge arm current effective value after optimizationWherein, I2mFor the circulation amplitude of two times of fundamental frequencies of injection, I 'mAfter optimization
Exchange side phase current magnitude, include bridge arm when optimizing presteady state operation to the constraint condition of bridge arm current before and after the optimization
Bridge arm current effective value after current effective value and optimization.
4. a kind of Multipurpose Optimal Method based on genetic algorithm suitable for MMC as claimed in claim 1 or 2, feature
It is, the step (4) includes:
(4-1) according to constraint condition and electric parameter, with the voltage fluctuation of capacitor peak value of bridge arm modulation voltage peak value and submodule
Minimum target establishes the initial disaggregation of common-mode voltage injection rate and circulation injection rate at random；
(4-2) is electric using the capacitor that initial solution concentrates multiple initial solutions that multiple bridge arm modulation voltage peak values and submodule is calculated
Pressure fluctuation peak value, calculates the adaptation between each bridge arm modulation voltage peak value and the voltage fluctuation of capacitor peak value and target of submodule
Function obtains the adaptive value that initial solution concentrates multiple initial solutions, is concentrated according to adaptive value from initial solution and chooses Pareto disaggregation；
(4-3) carries out crossing operation and mutation operator to initial disaggregation, generates new disaggregation, replaces initial solution using new disaggregation
Then collection executes step (4-2)；
(4-4) repeats step (4-2)-(4-3) until reaching maximum evolutionary generation, obtains final Pareto disaggregation.
5. a kind of Multipurpose Optimal Method based on genetic algorithm suitable for MMC as claimed in claim 1 or 2, feature
It is, the bridge arm modulation voltage after the multiple-objection optimization includes the upper and lower bridge arm modulation voltage u injected after common-mode voltagep、un:
Wherein, Um' for optimization after exchange side output voltage amplitude, Um′x1For the sine component amplitude of common-mode voltage injection rate,
Um′x2For the cosinusoidal component amplitude of common-mode voltage injection rate, ω is ac output frequency, and t is the runing time of MMC.
6. a kind of Multipurpose Optimal Method based on genetic algorithm suitable for MMC as claimed in claim 5, feature exist
In exchange side output voltage amplitude after the optimization are as follows:
Um'=kUm
Wherein, UmTo exchange side output voltage amplitude, equivalent modulation ratioupmIt is the minimum bridge arm tune that genetic algorithm obtains
Voltage peak processed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810948515.2A CN109149981B (en) | 2018-08-20 | 2018-08-20 | A kind of Multipurpose Optimal Method based on genetic algorithm suitable for MMC |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810948515.2A CN109149981B (en) | 2018-08-20 | 2018-08-20 | A kind of Multipurpose Optimal Method based on genetic algorithm suitable for MMC |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109149981A true CN109149981A (en) | 2019-01-04 |
| CN109149981B CN109149981B (en) | 2019-10-25 |
Family
ID=64790499
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810948515.2A Active CN109149981B (en) | 2018-08-20 | 2018-08-20 | A kind of Multipurpose Optimal Method based on genetic algorithm suitable for MMC |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109149981B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021179710A1 (en) * | 2020-03-11 | 2021-09-16 | 合肥科威尔电源系统股份有限公司 | Method and device for selecting dc capacitors of modular multilevel converter |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104484517A (en) * | 2014-12-03 | 2015-04-01 | 许继电气股份有限公司 | Method for optimizing parameters of bridge arm reactors of MMC (modular multi-level converters) |
| CN105720599A (en) * | 2016-03-31 | 2016-06-29 | 华中科技大学 | Acquisition method for power running range of modular multilevel converter |
| CN105868490A (en) * | 2016-04-12 | 2016-08-17 | 温州大学 | Multi-target selected harmonics suppression pulse width modulation method of modular multilevel converter |
| CN106911257A (en) * | 2017-02-28 | 2017-06-30 | 湖南大学 | MMC converter valves control frequency optimization method in a kind of flexible direct current power transmission system |
| CN104638963B (en) * | 2013-11-14 | 2017-08-18 | Abb公司 | The method and apparatus minimized for the circulation or common-mode voltage that make inverter |
| CN107994573A (en) * | 2017-12-07 | 2018-05-04 | 国网山东省电力公司电力科学研究院 | A kind of Multi-end flexible direct current transmission system multi-objective optimization design of power method |
-
2018
- 2018-08-20 CN CN201810948515.2A patent/CN109149981B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104638963B (en) * | 2013-11-14 | 2017-08-18 | Abb公司 | The method and apparatus minimized for the circulation or common-mode voltage that make inverter |
| CN104484517A (en) * | 2014-12-03 | 2015-04-01 | 许继电气股份有限公司 | Method for optimizing parameters of bridge arm reactors of MMC (modular multi-level converters) |
| CN105720599A (en) * | 2016-03-31 | 2016-06-29 | 华中科技大学 | Acquisition method for power running range of modular multilevel converter |
| CN105868490A (en) * | 2016-04-12 | 2016-08-17 | 温州大学 | Multi-target selected harmonics suppression pulse width modulation method of modular multilevel converter |
| CN106911257A (en) * | 2017-02-28 | 2017-06-30 | 湖南大学 | MMC converter valves control frequency optimization method in a kind of flexible direct current power transmission system |
| CN107994573A (en) * | 2017-12-07 | 2018-05-04 | 国网山东省电力公司电力科学研究院 | A kind of Multi-end flexible direct current transmission system multi-objective optimization design of power method |
Non-Patent Citations (1)
| Title |
|---|
| 郭艳华，高跃: ""模块化多电平逆变器共模电压研究"", 《电力传动》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021179710A1 (en) * | 2020-03-11 | 2021-09-16 | 合肥科威尔电源系统股份有限公司 | Method and device for selecting dc capacitors of modular multilevel converter |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109149981B (en) | 2019-10-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Beerten et al. | Modeling and control of multi-terminal VSC HVDC systems | |
| CN108649780A (en) | A kind of LCL filter parameter optimization method considering light current inverter stability off the net | |
| CN104810857B (en) | Single-phase grid-connected photovoltaic power generation system output power smooth control device and control method | |
| Li et al. | A modular multilevel converter type solid state transformer with internal model control method | |
| Bashir et al. | An improved voltage balancing algorithm for grid connected MMC for medium voltage energy conversion | |
| CN109586269A (en) | Consider the direct-current grid virtual inertia control method and system of parameter self-optimization | |
| CN105656022B (en) | A kind of distribution light storage DC power-supply system non-linear differential smooth control method | |
| Chen et al. | Control loop design of a two-stage bidirectional AC/DC converter for renewable energy systems | |
| WO2024016749A1 (en) | Flexible dc-to-dc converter capable of multi-port dc power flow control and control method thereof | |
| CN105634305B (en) | A kind of closed loop control method of quantitative control IGBT average frequency of switching suitable for high level modularization multi-level converter | |
| CN110943634A (en) | Energy type router and soft charging control method and system thereof | |
| Eriksson | Current sharing in multiterminal DC grids—The analytical approach | |
| CN108306334A (en) | Idle work optimization strategy inside wind power plant based on particle swarm optimization algorithm | |
| CN107659194A (en) | A kind of optimal control collection model predictive control method of Modular multilevel converter | |
| CN109861226A (en) | A kind of LCL filter design method of complex optimum harmonic stability and damping loss | |
| CN109149981B (en) | A kind of Multipurpose Optimal Method based on genetic algorithm suitable for MMC | |
| CN106026737B (en) | A kind of three-level current transformer compound circulation inhibition method in parallel | |
| CN105141159B (en) | A kind of three-phase modular multilevel inverter parallel system and its control method | |
| Hao et al. | A control strategy for voltage source inverter adapted to multi—Mode operation in microgrid | |
| Marei et al. | A novel control scheme for STATCOM using space vector modulation based hysteresis current controller | |
| Azizi et al. | Stability analysis of a DC microgrid with constant power loads using small-signal equivalent circuit | |
| Liao et al. | Enhanced Voltage Control of Bipolar DC Distribution System Based on Modulus Decomposition | |
| CN107565839A (en) | A kind of design and control method for reducing bridge-type MMC submodule electric capacity | |
| Zhang et al. | Optimized LCR output filter of APF based on genetic algorithm | |
| Demirdelen et al. | Pso-pi based dc link voltage control technique for shunt hybrid active power filter |
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 |