CN116470564A - Multi-reactive-power-source-coordinated automatic control system and method for reactive voltage of offshore wind farm - Google Patents
Multi-reactive-power-source-coordinated automatic control system and method for reactive voltage of offshore wind farm Download PDFInfo
- Publication number
- CN116470564A CN116470564A CN202211742919.9A CN202211742919A CN116470564A CN 116470564 A CN116470564 A CN 116470564A CN 202211742919 A CN202211742919 A CN 202211742919A CN 116470564 A CN116470564 A CN 116470564A
- Authority
- CN
- China
- Prior art keywords
- reactive
- optimization
- voltage
- power
- offshore wind
- 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
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000005457 optimization Methods 0.000 claims abstract description 84
- 238000012545 processing Methods 0.000 claims abstract description 25
- 239000002245 particle Substances 0.000 claims abstract description 20
- 238000012544 monitoring process Methods 0.000 claims abstract description 7
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000000694 effects Effects 0.000 claims description 5
- 238000005259 measurement Methods 0.000 claims description 4
- 238000011217 control strategy Methods 0.000 description 6
- 238000011160 research Methods 0.000 description 4
- 230000001105 regulatory effect Effects 0.000 description 3
- 238000013480 data collection Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- 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
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
-
- 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
-
- 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
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/50—Controlling the sharing of the out-of-phase component
-
- 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)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Control Of Electrical Variables (AREA)
Abstract
The invention discloses a multi-reactive-power-source-coordinated automatic control system and method for reactive voltage of an offshore wind farm, wherein the system comprises the following steps: based on actual application scenes, considering optimization targets of offshore wind power reactive voltage control, and adaptively adjusting weight coefficients of the optimization targets according to power grid working conditions, firstly, performing data processing on data transmitted by a data acquisition and monitoring control system by a data processing module, and then determining a total optimization objective function by an optimization objective function software module; and determining constraint conditions of reactive power optimization control of the offshore wind power by an optimization constraint condition module, constructing an optimization solving model of reactive power voltage control of the offshore wind power based on the determined optimization objective function and the determined optimization constraint conditions, and finally solving by a particle swarm optimization algorithm module to obtain an optimization coordination control scheme of multiple reactive power sources, and completing reactive power voltage control according to the control scheme. The method is used for solving the problem of multi-objective collaborative optimization control of reactive voltage of offshore wind power by combining various reactive power sources in the current wind power field.
Description
Technical Field
The invention relates to an automatic control system and method for reactive voltage of a multi-reactive-power-source cooperative offshore wind farm. Belongs to the technical field of large-scale wind farm grid-connected reactive voltage control in wind power generation technology.
Background
The offshore wind resource in China is far beyond land, the current development of the offshore wind resource becomes a research hot spot, the installed amount of the offshore wind power is increasingly improved, and the offshore wind power occupies a great proportion of the wind power. However, the randomness and fluctuation of the offshore wind power output make the safe and stable operation of the novel power system face a great challenge, so that the research on supporting the reactive voltage of the power grid by means of fans in the offshore wind power plant and a plurality of reactive compensation devices is necessary to restrain the voltage fluctuation of the grid connection point.
At present, many students have conducted intensive researches on offshore wind power reactive voltage control technologies, but the current researches only consider control targets such as minimum network loss and minimum voltage deviation, and important indexes such as voltage fluctuation degree and adjustment time of grid-connected points are not included in a control range. Meanwhile, the reactive compensation output range of the fan is not regulated and controlled, the fan only works in two states of no output or output according to reactive limits, and flexible regulation of the reactive output of the fan is lacking. The interval of the fans in the offshore wind farm is larger, even if wind energy received by adjacent fans is different, the corresponding reactive compensation limit capacity is also different, if all fans are regulated according to the same reactive compensation standard, the reactive voltage control can not reach an ideal effect, even damage is caused to the overload operation of the fans, and therefore the constraint of the reactive compensation limit capacity of each fan needs to be considered simultaneously.
In order to meet the requirement of the wind power plant on active support of the grid voltage, one or more reactive compensation devices such as SVG, SVC, switching capacitor, synchronous camera and the like are installed in many wind power plants at present, and meanwhile, the reactive compensation capability of the wind turbine generator is fully developed. When the grid-connected point voltage is subjected to reactive power optimization control, the coordination of the output among the reactive power sources is usually performed according to a fixed strategy, the characteristics of each of the reactive power sources are not reasonably utilized, the waste of resources is easy to take into account, and meanwhile, the response speed, the precision and the adjustment time of reactive voltage adjustment are required to be improved.
Disclosure of Invention
The application provides an automatic control system and method for reactive voltage of offshore wind power with cooperation of multiple reactive sources, which are used for coordinating the multiple reactive sources and wind turbines to perform multi-objective optimal control on reactive voltage of an offshore wind farm, and solve the problems that coordination of the existing wind turbines with multiple reactive sources is insufficient and control objectives cannot meet requirements.
The invention discloses a multi-reactive-power-source-coordinated automatic control system for reactive voltage of an offshore wind farm, which comprises the following components: and a data processing module: processing the data transmitted by the data acquisition and monitoring control system, and calculating the voltage deviation of the point of connection for subsequent calculation;
and (3) an optimization objective function module: calculating a weight coefficient of an optimization objective function of the offshore wind power reactive power control based on the data processed by the data processing module, and determining a total optimization objective function;
and (3) optimizing constraint condition module: based on the data processed by the data processing module, determining constraint conditions of offshore wind power reactive power optimization control considering various reactive power sources;
particle swarm algorithm module: the particle swarm algorithm module is respectively connected with the optimization objective function module and the optimization constraint condition module; the particle swarm optimization algorithm module is based on an offshore wind power reactive voltage control optimization solving model and a particle swarm algorithm, and an optimization coordination control scheme of multiple reactive sources is obtained through solving.
The invention discloses a multi-reactive-source cooperative automatic control method for reactive voltage of an offshore wind farm, which is realized by the following steps:
the method comprises the steps that firstly, running state information and environment information of a fan are collected through a data collection and monitoring control system and related sensors, the data are transmitted to a data processing module, and the collected information comprises temperature, humidity, wind speed, active power P, reactive power Q, power factor delta, wake wind speed, voltage of each node and the like;
the data is processed through a data processing module, optimal power flow calculation is carried out based on the data, and a grid-connected point voltage reference value U is determined ref ；
And calculating the reactive compensation limit range of each fan through an optimization constraint condition module based on the processed data.
And step two, calculating weight coefficients of all optimization targets by an optimization objective function module according to the data processed by the data processing module, and finally determining the total optimization objective function.
The optimization target is set as the active network loss F 1 Offset F of voltage 2 Amount of voltage fluctuation F 3 And adjusting time F 4 Is a weight of (2); the control strategy of each target is as follows:
active loss: min F 1 ＝P loss
Voltage deviation amount: min F 2 ＝|U i -U ref | 2
Amount of voltage fluctuation:
adjusting time: min F 4 ＝t s
Reference voltage U based on grid connection point ref The weight coefficient of each optimization target is calculated by the optimization objective function module, and the specific calculation formula is as follows:
the weight coefficient of the voltage deviation is adaptively adjusted by using a logistic function:
wherein,,
in the above, P loss Is the active network loss, t s For the total adjustment time, U i Is obtained by a voltage detection device for the actual measurement value of the voltage of the grid-connected point, U ref As the reference value of the power grid voltage, deltaU max The maximum deviation range of the fan without off-grid operation is specified for the national standard.
Weight coefficient alpha of voltage fluctuation quantity 3 The method comprises the following steps:
active net loss P loss Weight coefficient alpha of (2) 1 The method comprises the following steps:
α 1 ＝(1-α 2 -α 3 )×0.7
adjusting time t s Weight coefficient alpha of (2) 4 The method comprises the following steps:
α 4 ＝(1-α 2 -α 3 )×0.3
the overall optimization objective function is:
min F＝(α 1 F 1 +α 2 F 2 +α 3 F 3 +α 4 F 4 )
thirdly, calculating and determining constraint conditions of an optimization model by an optimization constraint condition module according to the data processed by the data processing module, wherein the constraint conditions comprise power grid power flow constraint, each control variable constraint, safety constraint conditions and adjustment time constraint and the method specifically comprises the following steps:
the power grid tide constraint is as follows:
the control variable constraints are respectively:
1) OLTC tap adjustment range constraints: n (N) min ≤N≤N max
2) Considering the interval between fans and wake effects, the running state of each fan is greatly different, so that the reactive compensation capacity constraint of each fan is determined:
wherein i is the ith fan.
3) Capacity constraint of each reactive source:
Q s_min ≤Q s ≤Q s_max
the safety constraint conditions are the voltage constraint of each node:
V imin ≤V i ≤V imax
the adjustment time constraint is:
0＜t s ＜t max
wherein t is max For a prescribed maximum adjustment time.
And fourthly, solving the optimization model by a particle swarm optimization algorithm module to obtain an optimal solution set, selecting a proper optimal solution as a control strategy of each reactive power source according to an actual application scene, and carrying out reactive voltage support on the power grid.
The beneficial effects are that: besides the traditional optimization targets with minimum active network loss and minimum voltage deviation, the optimization targets with minimum voltage fluctuation, minimum adjustment time and the like which are helpful for improving the reactive voltage control effect of the offshore wind farm are considered; meanwhile, the weight coefficient of the optimization objective function is adaptively adjusted, and the importance degree of each sub-optimization objective is adaptively adjusted according to the voltage deviation of the grid-connected point so as to make an optimal control strategy most suitable for the current power grid working condition; the constraint conditions are considered to newly increase the reactive compensation range of each fan, the reactive compensation capacity of each fan is more accurate when being called, the fan overload and even the fan damage are avoided, and meanwhile, the adjusting time is adopted as one of the constraint conditions, so that the influence on the safe and stable operation of the power system due to the fact that the adjusting time exceeds the regulated time is avoided.
Drawings
FIG. 1 is a schematic flow chart of the method for automatically controlling reactive voltage of an offshore wind farm by cooperation of multiple reactive power sources.
FIG. 2 is a block diagram of the multi-reactive-source coordinated offshore wind farm reactive voltage automatic control system of the present invention.
FIG. 3 is a flow chart of a particle swarm optimization algorithm.
Detailed Description
Embodiments of the present invention are further described below with reference to the accompanying drawings.
As shown in fig. 1-3, the invention discloses a multi-reactive-power-source-coordinated automatic control system and method for reactive voltage of an offshore wind farm, which are realized by the following steps:
the first step, the running state information and the environment information of the fan are collected through a data collection and monitoring control system and related sensors, the data are transmitted to a data processing module in an offshore wind power reactive voltage automatic control system, and the collected information comprises temperature, humidity, wind speed, active power P, reactive power Q, power factor delta, wake wind speed, voltage of each node and the like.
The offshore wind power reactive voltage automatic control system is shown in fig. 2, and comprises four modules, namely a data processing module, an optimization objective function module, an optimization constraint condition module and a particle swarm algorithm module, wherein the data processing module is respectively connected with the optimization objective function module and the optimization constraint condition module; the optimization objective function module and the optimization constraint condition module are respectively connected with the particle swarm algorithm module; the function and the work flow of each module are as follows:
the data processing module processes the data input by the data acquisition and monitoring control system, performs optimal power flow calculation based on the data, and determines a grid-connected point voltage reference value U ref Calculating the voltage deviation by combining the voltage measurement value of the grid-connected point;
and calculating the reactive compensation limit range of each fan through an optimization constraint condition module based on the processed data.
For a direct-drive fan, the reactive limit is:
wherein S is max The maximum value of the apparent power of the direct-drive fan is P, Q, and the maximum value is the active power value and the reactive power value output by the fan.
And secondly, calculating the weight coefficient of each sub-optimization target by an optimization objective function module according to the voltage deviation of the grid-connected point, and finally determining the total optimization objective function.
The optimization target is set as the active network loss F 1 Offset F of voltage 2 Amount of voltage fluctuation F 3 Adjusting time F 4 Is used for weighting. The control strategy of each target is as follows:
active loss: min F 1 ＝P loss
Voltage deviation amount: min F 2 ＝|U i -U ref | 2
Amount of voltage fluctuation:
adjusting time: min F 4 ＝t s
Reference voltage U based on grid connection point ref The weight coefficient of each optimization target is calculated by an optimization objective function module, and the weight coefficient is specifically as follows:
the weight coefficient of the voltage deviation is adaptively adjusted by using a logistic function:
wherein,,
in U i Is the actual measurement value of the voltage of the grid-connected point, is detected by a data acquisition and monitoring control system, U ref For the reference value of the voltage of the power grid, the delta U is given by a data processing module max The maximum deviation range of the fan without off-grid operation is specified for the national standard.
Weight coefficient alpha of voltage fluctuation quantity 3 The method comprises the following steps:
active net loss P loss Weight coefficient alpha of (2) 1 The method comprises the following steps:
α 1 ＝(1-α 2 -α 3 )×0.7
adjusting time t s Weight coefficient alpha of (2) 4 The method comprises the following steps:
α 4 ＝(1-α 2 -α 3 )×0.3
the overall optimization objective function is
min F＝(α 1 F 1 +α 2 F 2 +α 3 F 3 +α 4 F 4 )
Thirdly, calculating and determining constraint conditions of an optimization model by an optimization constraint condition module according to wind farm data processed by a data processing module, wherein the constraint conditions comprise power grid power flow constraint, each control variable constraint, safety constraint conditions and adjustment time constraint and the method comprises the following specific steps:
the power grid tide constraint is as follows:
the control variable constraints are:
1) OLTC tap adjustment range constraints: n (N) min ≤N≤N max
2) Considering the interval between fans and wake effects, the running state of each fan is greatly different, so that the reactive compensation capacity constraint of each fan is determined:
wherein i is the ith fan.
3) Capacity constraint of each reactive source:
Q s_min ≤Q s ≤Q s_max
the safety constraint conditions are the voltage constraint of each node:
V imin ≤V i ≤V imax
the adjustment time constraint is:
0＜t s ＜t max
wherein t is max For a prescribed maximum adjustment time.
And fourthly, solving the optimization model by a particle swarm optimization module according to a particle swarm optimization algorithm to obtain an optimal control strategy. As shown in fig. 3, the particle swarm optimization algorithm solving steps are as follows:
firstly, reading power grid data to be optimized, and determining a reactive power optimization objective function;
secondly, randomly generating a particle swarm, initializing the position and the speed of the particles, and calculating the initial value of an objective function;
step three, updating the particle speed, the individual optimal position and the global optimal position, and recalculating an objective function;
fourth, comparing with the last iteration to ensure that the current search is the optimal position and the minimum value of the objective function, judging whether the termination condition is reached, and if so, ending the whole search process; if not, the third step is re-entered.
And finally, a plurality of optimal solutions can appear in the solution, and according to the actual application scene, a proper optimal solution is selected by the main control device to serve as a control strategy of each reactive power source, so as to support reactive voltage of the power grid.
Claims (6)
1. An automatic control system for reactive voltage of an offshore wind farm with cooperation of multiple reactive power sources, which is characterized by comprising:
and a data processing module: processing the data transmitted by the data acquisition and monitoring control system, calculating the voltage deviation of the point of connection, and transmitting the data to other modules for subsequent calculation;
and (3) an optimization objective function module: the active power loss, the voltage deviation amount optimizing sub-objective can be set, and the voltage fluctuation amount and the adjusting time optimizing sub-objective can be innovatively set. The system is connected with the data processing module, and based on the data of the data processing module, the weight coefficient of each sub-target can be adaptively adjusted to construct a total optimization objective function;
and (3) optimizing constraint condition module: the system is connected with the data processing module to acquire data of the data processing module, and can set power grid power flow constraint, each control variable constraint, safety constraint condition and creative constraint of reactive compensation capacity and adjustment time of each fan to construct constraint conditions of reactive power optimization control of the offshore wind power of various reactive power sources;
particle swarm algorithm module: the particle swarm algorithm module is respectively connected with the optimization objective function module and the optimization constraint condition module; the particle swarm optimization algorithm module is based on an offshore wind power reactive voltage control optimization solving model and a particle swarm algorithm, and an optimization coordination control scheme of multiple reactive sources is obtained through solving.
2. An automatic control method using the multi-reactive-source collaborative offshore wind farm reactive voltage automatic control system according to claim 1, characterized in that it comprises the steps of:
step one, acquiring state parameters and environment parameters of a fan, and processing data by a data processing module, wherein the voltage deviation of a grid-connected point;
step two, based on the data processed by the data processing module, an optimization objective function module is used for determining an optimization objective function of the offshore wind power reactive power control, and an optimization constraint condition module is used for determining constraint conditions of the offshore wind power reactive power optimization control considering various reactive power sources;
and thirdly, solving the optimization solving model based on the particle swarm algorithm module to obtain an optimization coordination control scheme of multiple reactive power sources.
3. The method for automatically controlling reactive voltage of the multi-reactive-source collaborative offshore wind farm according to the characteristic 2, wherein the optimization objective function module sets the optimization objective as an active loss F 1 Offset F of voltage 2 Amount of voltage fluctuation F 3 And adjusting time F 4 The optimization objective function of the automatic control of the reactive voltage of the offshore wind farm is as follows:
min F＝min(α i F i +α 2 F 2 +α 3 F 3 +α 4 F 4 )
adjusting time index F 4 The optimization objective function of (1) is:
F 4 ＝t s
amount of voltage fluctuation F 3 The optimization objective function of (1) is:
4. the multi-reactive-source cooperative offshore wind farm reactive voltage automatic control method according to the characteristic 2 is characterized in that: the optimization objective function software module adaptively adjusts the weight coefficient of the optimization objective function according to the working condition of the power grid, and the weight of the voltage deviation adopts a logistic function to adaptively adjust:
wherein,,
in U i For the actual measurement value of the voltage of the grid-connected point, U ref As the reference value of the power grid voltage, deltaU max The maximum deviation range of the fan without off-grid operation is specified for the national standard.
5. The multi-reactive-source cooperative offshore wind farm reactive voltage automatic control method according to the characteristic 2 is characterized in that: active network loss F in multiple targets of the optimization objective function module 1 Amount of voltage fluctuation F 3 And adjusting time F 4 The weight coefficient of (2) is determined as follows:
α 1 ＝(1-α 2 -α 3 )×0.7
α 4 ＝(1-α 2 -α 3 )×0.3
6. the multi-reactive-source cooperative offshore wind farm reactive voltage automatic control method and system according to claim 1, wherein the method is characterized in that: the constraint conditions of the optimization control to be determined by the optimization constraint condition module comprise: grid tide constraint, each control variable constraint, safety constraint conditions and adjustment time constraint;
wherein, the adjustment time constraint is:
0＜t s ＜t max
wherein t is max Is the maximum adjustment time;
considering the interval and wake effect between fans, the running state of each fan is or has larger difference, and determining the reactive compensation capacity constraint of each fan:
wherein i is the ith fan.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211742919.9A CN116470564A (en) | 2022-12-30 | 2022-12-30 | Multi-reactive-power-source-coordinated automatic control system and method for reactive voltage of offshore wind farm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211742919.9A CN116470564A (en) | 2022-12-30 | 2022-12-30 | Multi-reactive-power-source-coordinated automatic control system and method for reactive voltage of offshore wind farm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116470564A true CN116470564A (en) | 2023-07-21 |
Family
ID=87172361
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211742919.9A Pending CN116470564A (en) | 2022-12-30 | 2022-12-30 | Multi-reactive-power-source-coordinated automatic control system and method for reactive voltage of offshore wind farm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116470564A (en) |
-
2022
- 2022-12-30 CN CN202211742919.9A patent/CN116470564A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110365013B (en) | Capacity optimization method of photo-thermal-photovoltaic-wind power combined power generation system | |
| CN107069829B (en) | Control system, method and application of station-level virtual synchronous machine | |
| CN102684201B (en) | Voltage threshold probability-based reactive power optimizing method for grid containing wind power plant | |
| WO2011112350A2 (en) | Photovoltaic system with managed output an method of managing variability of output from a photovoltaic system | |
| CN108614612B (en) | Method and system for tracking maximum power of solar photovoltaic cell | |
| CN110808608A (en) | Method and system for evaluating frequency modulation and voltage regulation capability of large-scale new energy participating receiving-end power grid | |
| CN110854907B (en) | Collaborative optimization operation method and system for power distribution network wind power plant under communication fault | |
| CN112736973A (en) | Battery energy storage capacity configuration method and system for stabilizing output fluctuation of wind power and photovoltaic power station | |
| CN111509785A (en) | Method, system and storage medium for multi-source optimal cooperative control of power grid | |
| CN108494020B (en) | Wind-powered electricity generation transmitting system reactive voltage Active Control Method under fluctuations in wind speed | |
| CN112332421A (en) | Photovoltaic power station participation power grid voltage regulation method based on adaptive droop control | |
| CN109713734B (en) | Photovoltaic power adjusting method, device, equipment and medium | |
| CN113555908B (en) | Intelligent power distribution network energy storage optimal configuration method | |
| CN113809760A (en) | Control method and device for wind power plant participating in secondary frequency modulation of power grid | |
| CN114336592A (en) | Wind power plant AGC control method based on model predictive control | |
| CN109638886B (en) | CVaR-based wind power day-ahead output declaration optimization method | |
| CN114530848B (en) | Multi-time scale dynamic partitioning method for optical storage virtual power plant | |
| CN116470564A (en) | Multi-reactive-power-source-coordinated automatic control system and method for reactive voltage of offshore wind farm | |
| CN115036995A (en) | Multi-stage time multiplexing reactive power optimization control method | |
| CN114696370A (en) | Primary frequency modulation wind power cluster control system, method and device | |
| Wang et al. | Hybrid solar power system optimization based on multi-objective PSO algorithm | |
| Wang et al. | Global optimization of CSP-PV hybrid system using an artificial fish-swarm algorithm | |
| CN114006421B (en) | Rapid reactive power control method and system for wind turbine group | |
| Huang et al. | Optimization Strategy of Wind-Photovoltaic-Energy Storage Grid Peak Shaving | |
| CN117526299B (en) | Active and reactive power coordination control system and method for micro-grid |
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 |