CN110783962A - Control method of virtual synchronous generator grid-connected inverter - Google Patents
Control method of virtual synchronous generator grid-connected inverter Download PDFInfo
- Publication number
- CN110783962A CN110783962A CN201910309434.2A CN201910309434A CN110783962A CN 110783962 A CN110783962 A CN 110783962A CN 201910309434 A CN201910309434 A CN 201910309434A CN 110783962 A CN110783962 A CN 110783962A
- Authority
- CN
- China
- Prior art keywords
- longicorn
- control
- grid
- synchronous generator
- virtual synchronous
- 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.)
- Pending
Links
- 230000001360 synchronised effect Effects 0.000 title claims abstract description 34
- 238000000034 method Methods 0.000 title claims abstract description 22
- 241001481710 Cerambycidae Species 0.000 claims abstract description 52
- 238000005457 optimization Methods 0.000 claims abstract description 17
- 238000010845 search algorithm Methods 0.000 claims abstract description 12
- 238000004364 calculation method Methods 0.000 claims description 8
- 239000003990 capacitor Substances 0.000 claims description 6
- 230000001939 inductive effect Effects 0.000 claims description 6
- 230000000694 effects Effects 0.000 abstract description 3
- 230000007246 mechanism Effects 0.000 abstract description 2
- 238000004422 calculation algorithm Methods 0.000 description 12
- 238000005516 engineering process Methods 0.000 description 4
- 238000010248 power generation Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000013461 design Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000002068 genetic effect Effects 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 241000254173 Coleoptera Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000013016 damping Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000012938 design process Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/40—Synchronising a generator for connection to a network or to another generator
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Control Of Eletrric Generators (AREA)
Abstract
The invention relates to a control method of a virtual synchronous generator grid-connected inverter, which comprises the following steps: step 1: establishing a control model of the virtual synchronous generator; step 2: optimizing related control parameters in the control model by using a longicorn stigma search algorithm to obtain optimal control parameters; and step 3: and inputting the optimal control parameters into a control model to perform stable operation control after grid connection. Compared with the prior art, the method adopts the longicorn beard search algorithm to search the best parameter combination of the control effect of the controlled object. In order to better achieve two external characteristics of active power-frequency and reactive power-voltage of a power grid, the grid-connected inverter adopts bottom layer and outer layer control, the outer layer control is a power ring simulating the external characteristics of a generator, and two mechanisms of frequency droop and voltage droop are simulated, so that the final optimization effect is good, and the realization is simple.
Description
Technical Field
The invention relates to the technical field of distributed wind power generation systems, in particular to a control method for a virtual synchronous generator grid-connected inverter, which adopts an algorithm of Beetle Antenna Search (BAS) Search for searching for optimal control parameters of virtual synchronous generators.
Background
At present, the Virtual Synchronous generator (Virtual Synchronous generator vsg) technology aims to control a grid-connected inverter to enable an inverter power supply to simulate the operating characteristics of a Synchronous generator, so that a distributed power supply based on the grid-connected inverter also has a body model, active frequency modulation, reactive voltage regulation, good damping characteristics and the like as the Synchronous generator, and shows excellent control performance. With the development of micro-grids for wind energy, light energy and the like, the VSG technology is also attracting more and more attention.
Common control algorithms of the virtual synchronous power generation grid-connected inverter include hysteresis control, double-loop control, dead-beat control and the like, a control optimization target is given along with the development of an intelligent algorithm, appropriate controller parameters are found by means of the intelligent algorithm, optimal control parameters are sought by an intelligent control method, the trial and error link in the parameter design process can be reduced, the coupling among control loops is not required to be considered, and the commonly adopted control algorithms include a particle swarm algorithm, a genetic algorithm, a differential evolution algorithm and the like.
The effect of the hysteresis control depends on the selection of the width of the hysteresis loop, is limited by the technical level of a switching device, and is not beneficial to the design of a filter, so the dynamic performance of the system is limited; the dead beat control is based on a system control model and parameters for prediction, and when the model parameters are not matched with actual parameters, transient overshoot is easily generated. The intelligent control algorithm such as genetic algorithm, particle swarm algorithm and the like has complex codes and large computation amount, needs to know the specific form of the function, needs gradient information and is not easy to realize.
Disclosure of Invention
The invention aims to solve the problems that the parameter control of a virtual synchronous motor inverter can not be rapidly and accurately carried out, the result can not be estimated better and the optimal solution can not be obtained in the prior optimization technology, and provides a control method of a virtual synchronous generator grid-connected inverter.
The purpose of the invention can be realized by the following technical scheme:
a control method of a virtual synchronous generator grid-connected inverter comprises the following steps: step 1: establishing the deficiency
A control model of the pseudo-synchronous generator;
step 2: optimizing related control parameters in the control model by using a longicorn stigma search algorithm to obtain optimal control parameters;
and step 3: and inputting the optimal control parameters into a control model to perform stable operation control after grid connection.
Further, the optimization objective function of the control model in step 1 is:
in the formula of U
zolAnd U
zonThe fundamental wave amplitude and each harmonic amplitude of the output voltage of the capacitor are respectively represented by a theoretical value, i
d2And i
q2Actual values of d-axis and q-axis of grid-connected side inductive current, e
dAnd e
qErrors of a d axis and a q axis of the grid-connected side inductive current are respectively shown, a and b are constant coefficients and are both 0.5, and F represents an optimization objective function.
Further, the optimization interval of the relevant control parameter in step 2 includes [0.1, 3], [1, 100], [1000, 15000] and [500, 600 ].
Further, the longicorn whisker search algorithm in the step 2 comprises the following steps:
step 21: giving relevant control parameter dimensions, iteration times and iteration weights in a control model, and initializing an initial position, a global optimal value and an optimal function value;
step 22: generating a random vector as an antenna direction;
step 23: calculating the positions of the two antennae;
step 24: obtaining objective function values corresponding to the left antenna and the right antenna and comparing the objective function values, wherein if the left antenna is smaller than the right antenna, the longicorn walks leftwards, otherwise, the longicorn walks rightwards;
step 25: updating the next position of the longicorn;
step 26: comparing the objective function value corresponding to the new position of the longicorn with the optimal function value and updating the optimal function value;
step 27: calculating the next step length of the longicorn;
step 28: and (5) repeating the steps 23 to 27 until the optimal solution is reached or the iteration times are met and finally outputting the optimal solution.
Further, the positions of the two antennas in step 23 include the position of the left antenna and the position of the right antenna, and the corresponding calculation formula is as follows:
Xl＝Xt+dt*dir/2
Xr＝Xt-dt*dir/2
in the formula, Xt represents the position of the longicorn at the time t, dt represents the distance between two whiskers of the longicorn at the time t, dir represents the antenna direction of the longicorn, Xl represents the position of the left antenna of the longicorn, and Xr represents the position of the right antenna of the longicorn.
Further, the calculation formula of the next position of the skynet in the step 25 is as follows:
Xt＝X(t-1)-step*dir*sign[F(Xl)-F(Xr)]
in the formula, X (t-1) represents the position of the celestial cow at the time of t-1, step represents the search step length of the celestial cow, sign represents a sign function, the function value is-1 when the independent variable is less than 0, the function value is 0 when the independent variable is equal to 0, the function value is 1 when the independent variable is greater than 0, and F (Xl) and F (Xr) are respectively target function values corresponding to the left antenna position and the right antenna position of the celestial cow.
Further, the calculation formula of the next step length of the skynet in the step 27 is as follows:
stept＝step(t-1)*w
in the formula, step represents the search step length of the longicorn at the time t, step (t-1) represents the search step length of the longicorn at the time t-1, and w represents the iteration weight, and the value of the iteration weight is less than 1.
Compared with the prior art, the invention has the following advantages:
(1) the virtual synchronous generator technology aims to control a grid-connected inverter to enable an inverter-side power supply to simulate the operating characteristics of the synchronous generator. The converter is used as a key component in the wind turbine generator, and accurate control parameters can keep the voltage and the frequency of a power grid stable.
(2) The method adopts the longicorn stigma search algorithm to obtain a group of optimal parameter combinations, ensures the robustness and effectiveness of wind power generation grid connection, has great significance for improving the operation stability of a distributed wind power generation system, and has simple integral calculation formula, so the optimization time is short.
Drawings
FIG. 1 is a system control block diagram of a virtual synchronous generator of the present invention;
FIG. 2 is a block diagram of system control for optimization using Seirus antenna search in the present invention;
fig. 3 is a flowchart of a method for searching and optimizing long horns in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
Examples
The invention relates to a control method of a grid-connected inverter of a virtual synchronous generator, which comprises the steps of firstly combining a mathematical model of a grid-connected side inverter of a distributed virtual synchronous generator and a control method thereof under the Matlab/Simulink environmentAnd (3) establishing an integral control simulation model of the virtual synchronous generator, wherein a topological graph is shown in figure 1, the upper half part is a three-phase grid-connected inverter, and the grid-connected point voltage is considered as the filter capacitor voltage. The output voltage of a capacitor in an abc phase and the current of a network side inductor are converted into a synchronous rotating coordinate system through park transformation, active power and reactive power are obtained through measurement and calculation, then P and Q are input into a body algorithm of a virtual synchronous generator, the external characteristic of the synchronous generator is simulated through an outer layer control system, and an internal potential instruction value is output. The upper frame in the figure is an active power control loop, the lower frame in the figure is a reactive power control loop, an outer layer system can correspondingly adjust output reactive power or active power after the amplitude or phase of the grid voltage is changed, a droop control mechanism is realized, a bottom layer controller is used as the basis for the outer layer control system to realize the regulation and control of capacitor voltage, the invention mainly focuses on the relation between the design of relevant parameters of power droop control during stable operation period of more than 1s after grid connection and the system stability in the primary frequency modulation and voltage regulation process after grid connection, for example, as shown in figure 2, a control block diagram of a virtual synchronous motor system adopting a Tianniu search algorithm is taken, and J is taken
pIs [0.1, 3]]，J
qIs [1, 100]]，K
mIs [1000, 150000 ]]，K
nHas an optimization interval of [500, 6000]。
In order to reduce the interference of initial error on performance index value and enhance the interference of nearby response, the dynamic response speed of the system is reflected by using ITAE (absolute value of deviation time integral), and the steady-state waveform quality of the system is evaluated by THD (total harmonic distortion), so that the weighted value of ITAE and THD is taken as the optimization objective function of control performance, and the expression is as follows:
in the formula of U
zolAnd U
zonThe fundamental wave amplitude and each harmonic amplitude of the output voltage of the capacitor are respectively represented by a theoretical value, i
d2And i
q2Actual values of d-axis and q-axis of grid-connected side inductive current, e
dAnd e
qErrors of a d axis and a q axis of the grid-connected side inductive current are respectively shown, a and b are constant coefficients and are both 0.5, and F represents an optimization objective function.
The parameter optimization method of the virtual synchronous motor inverter control system adopting the longicorn beard search algorithm comprises the following steps:
step 1: and giving the fitness function as weighted values of ITAE and THD as a control performance optimization target. Setting a constraint condition, giving a power droop control related parameter dimension D and an iteration number Max of the whole longicorn stigma search algorithm, giving an iteration weight w, wherein the value of the w is less than 1, randomly initializing a position X of the longicorn stigma search algorithm, and initializing a global optimal value X
bstOptimum function value F
bst。
Step 2: a random vector dir is generated as the direction of the antenna.
And step 3: the positions of the two antennas are calculated according to the formula:
Xl＝Xt+dt*dir/2
Xr＝Xt-dt*dir/2
in the formula, Xt represents the position of the longicorn at the time t, dt represents the distance between two whiskers of the longicorn at the time t, dir represents the antenna direction of the longicorn, Xl represents the position of the left antenna of the longicorn, and Xr represents the position of the right antenna of the longicorn.
And 4, step 4: and calculating objective function values corresponding to the left antenna and the right antenna and comparing the values, wherein if F (Xl) is less than F (Xr), the longicorn is walked to the left, otherwise, the longicorn is walked to the right.
And 5: updating the next position expression of the longicorn as follows:
Xt＝X(t-1)-step*dir*sign[F(Xl)-F(Xr)]
in the formula, X (t-1) represents the position of the longicorn at the t-1 moment, step represents the search step length of the longicorn, sign represents a symbolic function, the function value is-1 when the independent variable is less than 0, the function value is 0 when the independent variable is equal to 0, the function value is 1 when the independent variable is greater than 0, F (Xl) and F (Xr) are respectively target function values corresponding to the left antennal and the right antennal positions of the longicorn, and the target function values are specifically represented as the food intensity of the left and right whiskers.
The longicorn is continuously close to the direction with the strongest smell, namely the direction with the smallest objective function value, until the iteration times are reached to obtain the position of the longicorn at the moment, namely the optimized result.
Step 6: and comparing the objective function value corresponding to the new position of the longicorn with the optimal function value and updating the optimal function value.
And 7: calculating the next step of the longicorn:
stept＝step(t-1)*w
in the formula, step represents the search step length of the longicorn at the time t, step (t-1) represents the search step length of the longicorn at the time t-1, and w represents the iteration weight, and the value of the iteration weight is less than 1.
And 8: and (5) repeating the steps 3, 4, 5, 6 and 7 until the optimal solution is reached or the iteration times are met, and outputting the optimal solution. The optimization process is shown in fig. 3.
The virtual synchronous motor inverter control based on the longicorn whisker search algorithm is simulated in a Matlab/Simulink environment to obtain a group of optimal control parameter solutions, and the obtained optimal control parameter solutions are input into an originally established control model, so that the virtual synchronous generator obtains effective and stable operation control.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. A control method of a virtual synchronous generator grid-connected inverter is characterized by comprising the following steps:
step 1: establishing a control model of the virtual synchronous generator;
step 2: optimizing related control parameters in the control model by using a longicorn stigma search algorithm to obtain optimal control parameters;
and step 3: and inputting the optimal control parameters into a control model to perform stable operation control after grid connection.
2. The method for controlling the virtual synchronous generator grid-connected inverter according to claim 1, wherein the optimization objective function of the control model in the step 1 is as follows:
in the formula of U
zolAnd U
zonThe fundamental wave amplitude and each harmonic amplitude of the output voltage of the capacitor are respectively represented by a theoretical value, i
d2And i
q2Actual values of d-axis and q-axis of grid-connected side inductive current, e
dAnd e
qErrors of a d axis and a q axis of the grid-connected side inductive current are respectively shown, a and b are constant coefficients and are both 0.5, and F represents an optimization objective function.
3. The method for controlling the virtual synchronous generator grid-connected inverter according to claim 2, wherein the optimization interval of the related control parameters in the step 2 comprises [0.1, 3], [1, 100], [1000, 15000] and [500, 600 ].
4. The method for controlling the virtual synchronous generator grid-connected inverter according to claim 3, wherein the longicorn whisker search algorithm in the step 2 comprises the following steps:
step 21: giving relevant control parameter dimensions, iteration times and iteration weights in a control model, and initializing an initial position, a global optimal value and an optimal function value;
step 22: generating a random vector as an antenna direction;
step 23: calculating the positions of the two antennae;
step 24: obtaining objective function values corresponding to the left antenna and the right antenna and comparing the objective function values, wherein if the left antenna is smaller than the right antenna, the longicorn walks leftwards, otherwise, the longicorn walks rightwards;
step 25: updating the next position of the longicorn;
step 26: comparing the objective function value corresponding to the new position of the longicorn with the optimal function value and updating the optimal function value;
step 27: calculating the next step length of the longicorn;
step 28: and (5) repeating the steps 23 to 27 until the optimal solution is reached or the iteration times are met and finally outputting the optimal solution.
5. The method according to claim 4, wherein the positions of the two antennae in the step 23 include a left antenna position and a right antenna position, and a corresponding calculation formula is as follows:
Xl＝Xt+dt*dir/2
Xr＝Xt-dt*dir/2
in the formula, Xt represents the position of the longicorn at the time t, dt represents the distance between two whiskers of the longicorn at the time t, dir represents the antenna direction of the longicorn, Xl represents the position of the left antenna of the longicorn, and Xr represents the position of the right antenna of the longicorn.
6. The method for controlling the virtual synchronous generator grid-connected inverter according to claim 4, wherein the calculation formula of the next position of the Tianniu in the step 25 is as follows:
Xt＝X(t-1)-step*dir*sign[F(Xl)-F(Xr)]
in the formula, X (t-1) represents the position of the celestial cow at the time of t-1, step represents the search step length of the celestial cow, sign represents a sign function, the function value is-1 when the independent variable is less than 0, the function value is 0 when the independent variable is equal to 0, the function value is 1 when the independent variable is greater than 0, and F (Xl) and F (Xr) are respectively target function values corresponding to the left antenna position and the right antenna position of the celestial cow.
7. The method for controlling the virtual synchronous generator grid-connected inverter according to claim 4, wherein the calculation formula of the next step of the Tianniu in the step 27 is as follows:
stept＝step(t-1)*w
in the formula, step represents the search step length of the longicorn at the time t, step (t-1) represents the search step length of the longicorn at the time t-1, and w represents the iteration weight, and the value of the iteration weight is less than 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910309434.2A CN110783962A (en) | 2019-04-17 | 2019-04-17 | Control method of virtual synchronous generator grid-connected inverter |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910309434.2A CN110783962A (en) | 2019-04-17 | 2019-04-17 | Control method of virtual synchronous generator grid-connected inverter |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110783962A true CN110783962A (en) | 2020-02-11 |
Family
ID=69382964
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910309434.2A Pending CN110783962A (en) | 2019-04-17 | 2019-04-17 | Control method of virtual synchronous generator grid-connected inverter |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110783962A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112163376A (en) * | 2020-10-09 | 2021-01-01 | 江南大学 | Extreme random tree furnace temperature prediction control method based on longicorn stigma search |
| CN112564125A (en) * | 2020-12-14 | 2021-03-26 | 辽宁电能发展股份有限公司 | Power distribution network dynamic reactive power optimization method based on variable-step-size longicorn beard algorithm |
| CN113505873A (en) * | 2021-05-31 | 2021-10-15 | 南昌大学 | Method for setting control parameters of double parallel inverters based on sailfish algorithm |
| CN113534679A (en) * | 2021-07-06 | 2021-10-22 | 上海新氦类脑智能科技有限公司 | System monitoring model generation method, processor chip and industrial system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120259477A1 (en) * | 2011-04-05 | 2012-10-11 | King Fahd University Of Petroleum And Minerals | Particle swarm optimization system and method for microgrids |
| CN107069831A (en) * | 2017-03-13 | 2017-08-18 | 湖南大学 | A kind of droop control method |
| CN108563611A (en) * | 2018-03-27 | 2018-09-21 | 天津大学 | Cognition radar waveform optimization method based on longicorn palpus searching algorithm |
| CN109390969A (en) * | 2018-10-11 | 2019-02-26 | 国网江苏省电力有限公司泰州供电分公司 | A kind of microgrid virtual synchronous machine parameter optimization method based on parallel annealing algorithm |
-
2019
- 2019-04-17 CN CN201910309434.2A patent/CN110783962A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120259477A1 (en) * | 2011-04-05 | 2012-10-11 | King Fahd University Of Petroleum And Minerals | Particle swarm optimization system and method for microgrids |
| CN107069831A (en) * | 2017-03-13 | 2017-08-18 | 湖南大学 | A kind of droop control method |
| CN108563611A (en) * | 2018-03-27 | 2018-09-21 | 天津大学 | Cognition radar waveform optimization method based on longicorn palpus searching algorithm |
| CN109390969A (en) * | 2018-10-11 | 2019-02-26 | 国网江苏省电力有限公司泰州供电分公司 | A kind of microgrid virtual synchronous machine parameter optimization method based on parallel annealing algorithm |
Non-Patent Citations (2)
| Title |
|---|
| 朱宗耀: "计及电动汽车充电负荷的微电网能量优化调度研究" * |
| 黄杏: "基于虚拟同步发电机的并网逆变器控制算法的改进研究" * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112163376A (en) * | 2020-10-09 | 2021-01-01 | 江南大学 | Extreme random tree furnace temperature prediction control method based on longicorn stigma search |
| CN112163376B (en) * | 2020-10-09 | 2024-03-12 | 江南大学 | Extreme random tree furnace temperature prediction control method based on longhorn beetle whisker search |
| CN112564125A (en) * | 2020-12-14 | 2021-03-26 | 辽宁电能发展股份有限公司 | Power distribution network dynamic reactive power optimization method based on variable-step-size longicorn beard algorithm |
| CN113505873A (en) * | 2021-05-31 | 2021-10-15 | 南昌大学 | Method for setting control parameters of double parallel inverters based on sailfish algorithm |
| CN113534679A (en) * | 2021-07-06 | 2021-10-22 | 上海新氦类脑智能科技有限公司 | System monitoring model generation method, processor chip and industrial system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110783962A (en) | Control method of virtual synchronous generator grid-connected inverter | |
| Letting et al. | Optimization of a fuzzy logic controller for PV grid inverter control using S-function based PSO | |
| Liserre et al. | Genetic algorithm-based design of the active damping for an LCL-filter three-phase active rectifier | |
| WO2024021206A1 (en) | Method and system for energy storage system control based on grid-forming converter, storage medium, and device | |
| Ponce et al. | Doubly fed induction generator (DFIG) wind turbine controlled by artificial organic networks | |
| Hu et al. | Multi‐objective model predictive control of doubly‐fed induction generators for wind energy conversion | |
| Hamid et al. | Optimal MPPT and BES control for grid-tied DFIG-based wind energy conversion system | |
| Laina et al. | Intelligent control of a DFIG wind turbine using a PSO evolutionary algorithm | |
| CN111654052A (en) | Flexible direct current converter modeling device and method based on dynamic phasor method | |
| Benzouaoui et al. | Parallel model predictive direct power control of DFIG for wind energy conversion | |
| Pakkiraiah et al. | Enhanced performance of an asynchronous motor drive with a new modified adaptive Neuro-Fuzzy inference System-Based MPPT controller in interfacing with dSPACE DS-1104 | |
| Luo et al. | Small signal model of modular multilevel matrix converter for fractional frequency transmission system | |
| Gonzales-Zurita et al. | PSO Tuning of a Second-Order Sliding-Mode Controller for Adjusting Active Standard Power Levels for Smart Inverter Applications | |
| CN117318553B (en) | Low-wind-speed permanent magnet direct-driven wind turbine control method based on TD3 and Vienna rectifier | |
| Zeng et al. | Multiagent Soft Actor-Critic Aided Active Disturbance Rejection Control of DC Solid-State Transformer | |
| Bekakra et al. | DFIG sliding mode control driven by wind turbine with using a SVM inverter for improve the quality of energy injected into the electrical grid | |
| Qazi et al. | Performance evaluation of PI and PI-PSO in improving power quality of an autonomous microgrid | |
| Abad et al. | Predictive direct power control of the doubly fed induction machine with reduced power ripple at low constant switching frequency | |
| Hafez et al. | Power quality issues of grid connected wind energy system focus on DFIG and various control techniques of active harmonic filter: A review | |
| Karad et al. | Enhanced control of doubly fed induction generator based wind turbine system using soft computing assisted fractional order controller | |
| Alzain et al. | Optimization of sliding mode control based on BAT-algorithm for the DFIG-WT | |
| Faria et al. | Current control optimization for grid-tied inverters using cuckoo search algorithm | |
| Chen et al. | Analysis of Grid-Connected Stability of VSG-Controlled PV Plant Integrated with Energy Storage System and Optimization of Control Parameters | |
| Wong et al. | Direct control algorithm for doubly fed induction generators in weak grids | |
| Barakati et al. | Controller design for a wind turbine system including a matrix converter |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200211 |