CN114725948B - Wind power plant distributed sub-gradient voltage control method based on data driving sensitivity - Google Patents
Wind power plant distributed sub-gradient voltage control method based on data driving sensitivity Download PDFInfo
- Publication number
- CN114725948B CN114725948B CN202111674316.5A CN202111674316A CN114725948B CN 114725948 B CN114725948 B CN 114725948B CN 202111674316 A CN202111674316 A CN 202111674316A CN 114725948 B CN114725948 B CN 114725948B
- Authority
- CN
- China
- Prior art keywords
- node
- fan
- voltage
- input variable
- dimension
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 31
- 230000035945 sensitivity Effects 0.000 title claims abstract description 26
- 239000011159 matrix material Substances 0.000 claims abstract description 16
- 238000005259 measurement Methods 0.000 claims abstract description 15
- 238000012549 training Methods 0.000 claims abstract description 11
- 230000009466 transformation Effects 0.000 claims abstract description 6
- 230000000630 rising effect Effects 0.000 claims abstract description 5
- 239000013598 vector Substances 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 8
- 230000000694 effects Effects 0.000 abstract description 5
- 238000004891 communication Methods 0.000 description 5
- 238000012423 maintenance Methods 0.000 description 3
- 238000010248 power generation Methods 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- 230000004044 response Effects 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 230000001174 ascending effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000001105 regulatory 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
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- 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
- H02J3/381—Dispersed generators
-
- 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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- 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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/30—Reactive power compensation
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Power Engineering (AREA)
- Theoretical Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Control Of Eletrric Generators (AREA)
Abstract
The invention discloses a wind power plant distributed sub-gradient voltage control method based on data driving sensitivity, wherein a wind power plant centralized controller constructs a training sample set according to historical measurement data of each node, and an input variable sample set after dimension rising is obtained through dimension rising function transformation; calculating the sensitivity between the data driving linearization power flow matrix and the node voltage and the reactive power based on the output variable sample set and the input variable sample set after dimension rising, and issuing the sensitivity to each fan controller; measuring the voltage of the local node by each fan controller, and exchanging the measuring result with the adjacent node to calculate the local objective function gradient and the reactive power set value; and calculating the set value of the circulating reactive power of each fan controller and sending the set value to the fan for adjustment. Compared with the prior art, the method and the device fully utilize the historical measurement data to correct the iteration direction of the distributed voltage control, avoid the influence of inaccuracy of the model on the convergence and the control effect of the distributed iteration, and are suitable for rapid voltage control of the wind power plant.
Description
Technical Field
The invention belongs to the technical field of operation and control of power systems, and particularly relates to a distributed secondary gradient voltage control method for a wind power plant.
Background
With the development of economy and society, the problem of environmental pollution caused by energy supply is increasingly prominent, and innovation and transformation upgrading of modern energy systems have become necessary. Wind power is used as one of typical new energy power generation modes, and grid-connected permeability of the wind power is continuously improved. However, wind farm power output often exhibits significant fluctuations due to the uncertainties that wind power generation naturally has. Because the novel fan adopts the power electronic converter to realize grid-connected power generation, the dynamic characteristics of the novel fan change faster and more complicated, and therefore, the novel fan brings great challenges to the safe operation of a wind power plant. The injection of fan power causes problems of voltage lifting and network loss increase at the tail end of a network in a wind power plant, and the problem of off-grid accidents of the fan caused by voltage out-of-limit can be caused.
Because the grid connection is realized in a power electronic mode, the reactive power regulation capability of the fan grid-connected converter can play a key role in voltage control, and the safe and efficient operation of the wind power plant is ensured. Therefore, there is a great deal of attention to ways to improve the voltage distribution by regulating the reactive output of the converter. Under the centralized voltage control strategy, the wind farm centralized controller communicates with all the fan converters and issues control instructions, so that the global optimal voltage control can be realized. However, centralized control requires complex communication to collect and process a large amount of data, so control delay is long and single point of failure problems may be encountered.
Distributed control, which is widely used in power systems, can significantly increase the response speed of voltage control due to a reduction in the centralized computation burden. However, since the voltage control problem is closely related to the wind farm power flow distribution, the distributed control does not need centralized model maintenance and solution, but still needs each fan to acquire the impedance parameters of the adjacent branches, so as to calculate the power adjustment direction in each iteration step. Once the internal model of the wind power plant is incomplete or the impedance parameter has larger error, adverse effect on the distributed voltage control effect is likely to be generated. In a wind power plant with incomplete model, an equivalent tide model is established by utilizing a historical measurement data sample in a data driving mode, so that a more accurate iteration direction is provided for distributed secondary gradient control, and the method is a feasible alternative idea. According to the Koopman space transformation theory, historical operation data is used as a training sample, and a high-dimensional linearization equation between node voltage and fan reactive power can be obtained in a data driving mode. Therefore, it is highly desirable to combine a data driving method with distributed sub-gradient voltage control to achieve a highly reliable distributed voltage control objective for a wind farm that is independent of accurate model parameters.
Disclosure of Invention
In order to solve the problem that wind power plant voltage control depends on accurate model parameters, the invention provides a wind power plant distributed secondary gradient voltage control method based on data driving sensitivity, a wind power plant high-precision global tide model is obtained based on dimension-lifting linearization processing, and reactive power set values of a fan controller of each node are obtained through an iterative process of distributed secondary gradient voltage control and are issued to a fan for execution.
The invention is realized by adopting the following technical scheme:
step 1.1, according to historical measurement data of each node in a wind power plant, a training sample set required by data driving calculation is established, wherein the training sample set totally comprises data of S time sections and is divided into an input variable sample set X and an output variable sample set Y, and the definition is as follows:
X＝[x 1 x 2 … x S ] (1)
Y＝[y 1 y 2 … y S ] (2)
wherein ,xS and yS Respectively representing the input variable and the output variable of the S-th time section, wherein the input variable x S Consisting of active and reactive power injected by each node, i.e. [ p q ]] T P and q respectively represent column vectors formed by active power and reactive power injected by each node, and output variable y S Is composed of voltage amplitude and phase angle of each node, i.e. [ V theta ]] T V and theta respectively represent column vectors formed by the voltage amplitude and phase angle of each node;
step 1.2, carrying out dimension-lifting transformation on the input variable x of each training sample to obtain a dimension-lifted input variable sample x lift The expression is as follows:
wherein, psi (x) represents an up-scaling operation function;
the dimension-increasing function ψ of the i-th dimension i (x) The definition is as follows:
wherein ,xi Represents the ith element of the input variable x, K represents the total number of dimensions of the input variable x, c ij Represents the i-th dimension up-scaling function ψ i (x) Is the j-th dimensional basis vector element of (a);
according to the input variable x i Obtaining an input variable x after the S-th time section sample rises in dimension lift,S Input variable sample set X after dimension rising lift The expression is as follows:
X lift ＝[x lift,1 x lift,2 … x lift,S ] (5)
step 1.3, based on the output variable sample set Y and the input variable sample set X after dimension increase lift The data-driven linearization power flow matrix M is calculated, and the formula is as follows:
wherein ,
the Moore-Penrose inverse of the matrix;
step 1.4, calculating the sensitivity X between the voltage of the node a and the reactive power of the node b according to the data driving linearization power flow matrix M ab The calculation formula is as follows:
wherein ,Mab Voltage V representing corresponding node a in matrix M a Reactive power q with node b b Element M of (2) a,(K+i) Voltage V representing corresponding node a in matrix M a And the ith up-scaling function psi i (x) Is an element of (2);
step 1.5, the central controller of the wind farm transmits the calculated sensitivity value to the fan controllers of all the nodes;
step 2.1, starting distributed iteration control, and enabling an iteration step k=0;
step 2.2, each fan controller measures the voltage of the node where the fan is located, and exchanges the voltage measurement results with the adjacent node controllers, for example, for the fan controller of the node a, measures the current voltage amplitude V of the node a a (k) Send out the resultA fan controller sent to the adjacent node b and acquiring the current voltage amplitude V of the node b b (k)；
Step 2.3, the fan controller of each node calculates a local objective function gradient, e.g. for the fan controller of node a, the local objective function gradient g of its kth iteration a (k) The calculation formula of (2) is as follows:
wherein ,μb The ideal voltage value representing point b is usually 1, N a Represents a set of nodes that are topologically adjacent to node a;
step 2.4, calculating a reactive power set value of a next iteration step of the fan by the fan controller of each node, for example, for the fan controller of the ith node, a reactive power calculation formula of the k+1 iteration step is as follows:
q i (k+1)＝q i (k)-ε·g i (k) (9)
wherein ,qi (k) Representing the reactive power of the kth iteration of the fan of the ith node, wherein epsilon represents the iteration step length;
step 2.5, the fan controller of each node transmits the reactive power set value to the fan for execution;
step 2.6, let k=k+1, and return to step 2.2.
Compared with the prior art, the invention can achieve the following beneficial technical effects:
1) Because a data-driven distributed voltage control framework is established, a high-precision global tide model of the wind power plant is obtained based on an ascending dimension linearization method, and the iteration direction of distributed secondary gradient voltage control is corrected in an off-line mode, the influence of inaccuracy of the model on distributed iteration convergence and control effect is avoided, the dependence of the distributed voltage control on parameters is solved, and the method is suitable for practical application in the wind power plant with incomplete model parameters;
2) The sensitivity calculation of data driving is only needed to be executed once under the condition that the topology is not changed, other links only need to carry out data interaction through the fan controllers of all nodes and the controllers adjacent to the communication topology, and finally the overall optimal solution is converged through iterative control, so that the operation cost and the communication cost are reduced, the reliability is obviously improved, the response speed of distributed control is higher, and the method is suitable for rapid dynamic voltage regulation control;
3) Only the fan controller of each node is required to measure reactive power and voltage amplitude information, the centralized solution of the voltage optimization model is not required to be carried out depending on the wind field controller, and the centralized data acquisition and the complicated model maintenance are not required to be carried out on line, so that the method can be directly expanded and modified on the basis of the original fan controller, has low construction, operation and maintenance costs, and is suitable for large-scale engineering application.
4) Only each fan controller is required to be communicated with the similar fan controllers, so that the communication cost is low, the speed is high, the method is suitable for rapid voltage control, the cost is low, and the method is suitable for large-scale application.
Drawings
FIG. 1 is a flowchart of a distributed sub-gradient voltage control method for a wind farm based on data driving sensitivity.
Detailed Description
The technical scheme of the invention is described in detail below with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a flowchart of a distributed sub-gradient voltage control method of a wind farm based on data driving sensitivity. The method specifically comprises the following steps:
step 1.1, according to historical measurement data of each node in the wind power plant, a training sample set required by data driving calculation is established. The training sample set comprises data of S time sections (the value of S is usually between 2000 and 3000) and is divided into an input variable sample set X and an output variable sample set Y, and the definition is as follows:
X＝[x 1 x 2 … x S ] (1)
Y＝[y 1 y 2 … y S ] (2)
wherein ,xS and yS Respectively representing the input variable and the output variable of the S-th time section, wherein the input variable x S Consisting of active and reactive power injected by each node, i.e. [ p q ]] T P and q respectively represent column vectors formed by active power and reactive power injected by each node, and output variable y S Is composed of voltage amplitude and phase angle of each node, i.e. [ V theta ]] T V and theta respectively represent column vectors formed by the voltage amplitude and phase angle of each node;
step 1.2, carrying out dimension-lifting transformation on the input variable x of each training sample to obtain a dimension-lifted input variable sample x lift The expression is as follows:
wherein, psi (x) represents an up-scaling operation function;
in the invention, the dimension of the upgoing operation function psi (x) is recorded as n, and the value is usually between 2000 and 5000;
the dimension-increasing function ψ of the i-th dimension i (x) The definition is as follows:
wherein ,xi Represents the ith element of the input variable x (i.e., the ith dimension in the input variable x), K represents the total number of dimensions of the input variable x, c ij Represents the i-th dimension up-scaling function ψ i (x) The j-th dimension of the base vector element (j takes a value between 1 and K) which takes a value of a random number more than 0 and less than 1;
according to the input variable x i Obtaining an input variable x after the S-th time section sample rises in dimension lift,S Input variable sample after dimension increaseCollection X lift The expression is as follows:
X lift ＝[x lift,1 x lift,2 … x lift,S ] (5)
step 1.3, based on the output variable sample set Y and the input variable sample set X after dimension increase lift The data-driven linearization power flow matrix M is calculated, and the formula is as follows:
wherein ,
the Moore-Penrose inverse of the matrix;
step 1.4, calculating the sensitivity X between the voltage of the node a and the reactive power of the node b according to the data driving linearization power flow matrix M ab The calculation formula is as follows:
wherein ,Mab Voltage V representing corresponding node a in matrix M a Reactive power q with node b b Element M of (2) a,(K+i) Voltage V representing corresponding node a in matrix M a And the ith up-scaling function psi i (x) Is an element of (2);
step 1.5, the central controller of the wind farm transmits the calculated sensitivity value to fan controllers of all nodes, such as X ab A fan controller which is issued to the node b;
step 2.1, starting distributed iteration control, and enabling an iteration step k=0;
step 2.2, each fan controller measures the voltage of the node where the fan is located, and exchanges the voltage measurement results with the adjacent node controllers, for example, for the fan controller of the node a, measures the current voltage amplitude V of the node a a (k) Sending the result to the fan controller of the adjacent node b, and acquiring the current voltage amplitude V of the node b b (k)；
Step 2.3, the fan controller of each node calculates a local objective function gradient, e.g. for the fan controller of node a, the local objective function gradient g of its kth iteration a (k) The calculation formula of (2) is as follows:
wherein ,μb The ideal voltage value representing point b is usually 1, N a Represents a set of nodes topologically adjacent to node a and having b e N a and a∈Nb ；
Step 2.4, calculating a reactive power set value of a next iteration step of the fan by the fan controller of each node, for example, for the fan controller of the ith node, a reactive power calculation formula of the k+1 iteration step is as follows:
q i (k+1)＝q i (k)-ε·g i (k) (9)
wherein ,qi (k) The reactive power of the kth iteration of the fan representing the ith node, epsilon represents the iteration step length, and the value range of epsilon is usually 0.01-0.1.
And 2.5, the fan controller of each node transmits the reactive power set value to the fan for execution.
Step 2.6, let k=k+1, and return to step 2.2.
In the iterative control process, the wind farm centralized controller is not required to be relied on, and each fan controller is only required to be communicated with the similar fan controllers, so that the method is low in communication cost, high in speed, suitable for rapid voltage control, low in cost and suitable for large-scale application.
Claims (2)
1. The wind farm distributed sub-gradient voltage control method based on the data driving sensitivity is characterized by comprising the following steps of:
step 1, a central controller of a wind power plant calculates a sensitivity value according to historical measurement data of each node through a data driving method, and the calculated sensitivity value is sent to a fan controller of each node, wherein the specific process of the step is as follows:
step 1.1, according to historical measurement data of each node in a wind power plant, a training sample set required by data driving calculation is established, wherein the training sample set totally comprises data of S time sections and is divided into an input variable sample set X and an output variable sample set Y, and the definition is as follows:
X＝[x 1 x 2 …x S ](1)
Y＝[y 1 y 2 …y S ](2)
wherein ,xS and yS Respectively representing the input variable and the output variable of the S-th time section, wherein the input variable x S Consisting of active and reactive power injected by each node, i.e. [ pq ]] T P and q respectively represent column vectors formed by active power and reactive power injected by each node, and output variable y S Is composed of voltage amplitude and phase angle of each node, i.e. [ V theta ]] T V and theta respectively represent column vectors formed by the voltage amplitude and phase angle of each node;
step 1.2, carrying out dimension-lifting transformation on the input variable x of each training sample to obtain a dimension-lifted input variable sample x lift The expression is as follows:
wherein, psi (x) represents an up-scaling operation function;
the dimension-increasing function ψ of the i-th dimension i (x) The definition is as follows:
wherein ,xi Represents the ith element of the input variable x, K represents the total number of dimensions of the input variable x, c ij Represents the i-th dimension up-scaling function ψ i (x) Is the j-th dimensional basis vector element of (a);
according to the input variable x i Obtaining an input variable x after the S-th time section sample rises in dimension lift,S Input variable sample set X after dimension rising lift The expression is as follows:
X lift ＝[x lift,1 x lift,2 …x lift,S ] (5)
step 1.3, based on the output variable sample set Y and the input variable sample set X after dimension increase lift The data-driven linearization power flow matrix M is calculated, and the formula is as follows:
wherein ,
the Moore-Penrose inverse of the matrix;
step 1.4, calculating the sensitivity X between the voltage of the node a and the reactive power of the node b according to the data driving linearization power flow matrix M ab The calculation formula is as follows:
wherein ,Mab Voltage V representing corresponding node a in matrix M a Reactive power q with node b b Element M of (2) a,(K+i) Voltage V representing corresponding node a in matrix M a And the ith up-scaling function psi i (x) Is an element of (2);
step 1.5, the central controller of the wind farm transmits the calculated sensitivity value to the fan controllers of all the nodes;
step 2, the fan controllers of all the nodes interact with the fan controllers of the adjacent nodes through the sensitivity issued by the wind power plant centralized controller and the voltage measurement data of the nodes where the fans are located, and the voltage control is realized in a distributed iteration mode, and the specific process of the step is as follows:
step 2.1, starting distributed iteration control, and enabling an iteration step k=0;
step 2.2, each fan controller measures the voltage of the node where the fan is located, and exchanges the voltage measurement result with the adjacent node controller, and for the fan controller of the node a, the current voltage amplitude V of the node a is measured a (k) Sending the result to the fan controller of the adjacent node b, and acquiring the current voltage amplitude V of the node b b (k)；
Step 2.3, calculating a local objective function gradient by the fan controller of each node, and for the fan controller of the node a, calculating a local objective function gradient g of the kth iteration a (k) The calculation formula of (2) is as follows:
wherein ,μb The ideal voltage value of the representative point b is 1, N a Represents a set of nodes that are topologically adjacent to node a;
step 2.4, calculating a reactive power set value of a next iteration step of the fan by the fan controller of each node, wherein for the fan controller of the ith node, a reactive power calculation formula of the k+1 iteration step is as follows:
q i (k+1)＝q i (k)-ε·g i (k)(9)
wherein ,qi (k) Representing the reactive power of the kth iteration of the fan of the ith node, wherein epsilon represents the iteration step length;
step 2.5, the fan controller of each node transmits the reactive power set value to the fan for execution;
step 2.6, let k=k+1, and return to step 2.2.
2. A method of distributed sub-gradient voltage control of a wind farm based on data driven sensitivity according to claim 1, wherein step 2 finally converges to a globally optimal solution by iterative control.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111674316.5A CN114725948B (en) | 2021-12-31 | 2021-12-31 | Wind power plant distributed sub-gradient voltage control method based on data driving sensitivity |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111674316.5A CN114725948B (en) | 2021-12-31 | 2021-12-31 | Wind power plant distributed sub-gradient voltage control method based on data driving sensitivity |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114725948A CN114725948A (en) | 2022-07-08 |
| CN114725948B true CN114725948B (en) | 2023-06-20 |
Family
ID=82235804
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111674316.5A Active CN114725948B (en) | 2021-12-31 | 2021-12-31 | Wind power plant distributed sub-gradient voltage control method based on data driving sensitivity |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114725948B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115360765A (en) * | 2022-10-19 | 2022-11-18 | 苏州谦德益科技研发有限公司 | New forms of energy electricity generation intelligence control system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110556878A (en) * | 2019-09-05 | 2019-12-10 | 山东大学 | Distributed voltage control optimization method and system applied to wind power plant |
| CN113852091A (en) * | 2021-09-28 | 2021-12-28 | 国网冀北电力有限公司电力科学研究院 | New energy grid-connected reactive voltage adjusting method based on MPC |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107294106B (en) * | 2017-08-11 | 2020-04-24 | 清华大学 | Distributed photovoltaic cluster dynamic voltage regulation control method based on distributed communication |
| CN110601252B (en) * | 2019-06-18 | 2022-12-16 | 武汉大学 | MPC-based feeder-level rapid voltage control method for distribution-type photovoltaic power distribution network |
| CN110957731B (en) * | 2019-11-04 | 2023-05-02 | 天津大学 | Distributed power supply on-site cluster voltage control method based on model predictive control |
-
2021
- 2021-12-31 CN CN202111674316.5A patent/CN114725948B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110556878A (en) * | 2019-09-05 | 2019-12-10 | 山东大学 | Distributed voltage control optimization method and system applied to wind power plant |
| CN113852091A (en) * | 2021-09-28 | 2021-12-28 | 国网冀北电力有限公司电力科学研究院 | New energy grid-connected reactive voltage adjusting method based on MPC |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114725948A (en) | 2022-07-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hong et al. | Development of intelligent MPPT (maximum power point tracking) control for a grid-connected hybrid power generation system | |
| CN106786677B (en) | A kind of interconnected electric power system distributed dynamic matrix frequency control method | |
| Tang et al. | Configuration of marine photovoltaic system and its MPPT using model predictive control | |
| Ramesh et al. | Intelligent adaptive LFC via power flow management of integrated standalone micro-grid system | |
| CN114725948B (en) | Wind power plant distributed sub-gradient voltage control method based on data driving sensitivity | |
| Hosseini et al. | Design and construction of photovoltaic simulator based on dual-diode model | |
| CN106786606B (en) | A kind of calculation method of the Probabilistic Load based on a variety of stochastic variables | |
| WO2022121446A1 (en) | Control system, reactive voltage control method and device, medium, and calculation device | |
| CN104500336A (en) | Constant power generalized predictive control method for wind power generator set based on Hammerstein-Wiener model | |
| CN114583710B (en) | Wind farm reactive voltage optimization control method based on data driving modeling | |
| CN111725840A (en) | Parameter identification method for direct-drive wind generating set controller | |
| Hosseini et al. | Partial-or full-power production in WECS: A survey of control and structural strategies | |
| CN105573123A (en) | Thermal power generating unit mechanical furnace coordination control method based on improved T-S fuzzy prediction modeling | |
| CN102709955B (en) | Multi-section-based power flow control method | |
| CN115807734A (en) | Offshore wind farm level cooperative control strategy based on wake flow tracking | |
| CN112688307A (en) | Alternating current-direct current hybrid microgrid controller and control method | |
| CN104578091A (en) | Non-delay optimal reactive power coordinated control system and method for multisource-containing power grid | |
| Jai Andaloussi et al. | Novel nonlinear control and optimization strategies for hybrid renewable energy conversion system | |
| CN102931819B (en) | Power electronic converter control method based on transient electromagnetic energy balance | |
| CN104638654A (en) | STATCOM (static synchronous compensator) control method for voltage adjusting of wind farm and network nodes | |
| CN111446732A (en) | Maximum power estimation method for photovoltaic power generation system | |
| CN113300379A (en) | Electric power system reactive voltage control method and system based on deep learning | |
| CN114899887A (en) | Method and system for analyzing grid-connected node frequency of each generator in wind-water combined system | |
| Hai et al. | A novel intelligent method to increase accuracy of hybrid photovoltaic-wind system-based MPPT and pitch angle controller | |
| CN113138620A (en) | Photovoltaic maximum power point tracking method and system based on hybrid BSO-FLC |
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 |