CN106300417A - Wind farm group reactive voltage optimal control method based on Model Predictive Control - Google Patents
Wind farm group reactive voltage optimal control method based on Model Predictive Control Download PDFInfo
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- 238000004088 simulation Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
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- 230000001105 regulatory effect Effects 0.000 claims description 2
- 238000005457 optimization Methods 0.000 abstract description 8
- 238000005096 rolling process Methods 0.000 abstract description 2
- 230000008859 change Effects 0.000 description 5
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Classifications
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- H02J3/386—
-
- 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
- H02J3/48—Controlling the sharing of the in-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
- 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]
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- 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
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
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Abstract
The invention provides a kind of wind farm group reactive voltage optimal control method based on Model Predictive Control, step 1: each wind farm group reactive power and voltage control cycle is arranged M future position, predicts N number of control cycle altogether；Step 2: utilize that the wind energy turbine set of each future position meritorious is exerted oneself by forecast model, reactive source is idle and exert oneself, each busbar voltage of field group is predicted；Step 3: dominating pair of vertices M*N the future position in future in each control cycle is optimized calculating, obtains N number of optimum control instruction, chooses any one optimum control instruction control instruction as this cycle；Until the next control cycle, control time window is elapsed the most backward, repeat above-mentioned optimization and calculate, it is achieved rolling optimization.The present invention considers the dynamic process that wind farm group ran within the control instruction cycle, using the optimum in whole control cycle as the target of optimum control so that it is reliable that wind farm group runs more safety.
Description
Technical field
The present invention relates to technical field of electric automation, in particular it relates to a kind of wind energy turbine set based on Model Predictive Control
Group's reactive voltage optimal control method.
Background technology
At present, extensive new forms of energy cluster typically uses the method for layering and zoning to control reactive voltage.According to three class control
Pattern, is divided into three levels to realize the whole network Reactive power control, as shown in Figure 1.First gathered by three class control center (save and adjust)
The whole network information, calculates backbone point busbar voltage reference value through Load flow calculation, state estimation, optimization, and is responsible for two grades of controls
System (field group control central station) issues control voltage reference value.The voltage reference value that Two-stage control issues according to three class control center
Selecting to control on the spot and distant place control two ways, control our station reactive-load compensation equipment the most on the spot, a distant place controls i.e. to one-level
Control (transformer station, wind energy turbine set etc.) and issue control command.One-level controls the voltage reference value issued according to Two-stage control, directly controls
Control equipment, adjusts voltage.
Wind farm group control main website as the decision-making level of wind farm group reactive power/voltage control, accept grid dispatching center and issue
Critical point, wind farm group region control target, according to wind farm group regional power grid real-time running data, wind farm group region is entered
Row reactive Voltage Optimum, issues control instruction to control sub-station, and the control cycle is generally 5min-15min.Control sub-station is wind-powered electricity generation
The execution level of field group's reactive power/voltage control, including collecting subsystem in substation and wind energy turbine set substation.Control sub-station obtains from controlling main website
Take control instruction, the reactive apparatus in station is adjusted, make critical point busbar voltage reach to control requirement.Collect transformer station's
Station can be used for the reactive apparatus of Voltage Cortrol SVC, SVG etc., and wind energy turbine set substation can be used for the reactive apparatus of Voltage Cortrol to be had
SVC, Wind turbines etc..Two-stage control sends instructions down the crucial skill being to affect wind farm group safety in operation and economy
Art.In existing wind farm group reactive power and voltage control, region voltage stabilization, reduction region network loss is kept to be typically to carry out
The main target of optimal control.Bristle with anger for the cycle under wind energy turbine set however, it is contemplated that new forms of energy control main website with 5min-15min
Order, output of wind electric field within the instruction cycle is it may happen that bigger change, and the regulation that each reactive source receives after reference value is same
Also it is dynamic process, the optimum of discontinuity surface wind farm group when above-mentioned control method based on current time section can only ensure to control
Run, it is impossible to the running optimizatin of wind farm group in the cycle is controlled.
Summary of the invention
For defect of the prior art, it is an object of the invention to provide a kind of wind farm group based on Model Predictive Control
Reactive voltage optimal control method.
The wind farm group reactive voltage optimal control method based on Model Predictive Control provided according to the present invention, including such as
Lower step:
Step 1: each wind farm group reactive power and voltage control cycle is arranged M future position, predicts N number of control week altogether
Phase；
Step 2: utilize that the wind energy turbine set of each future position meritorious is exerted oneself by forecast model respectively, reactive source is idle and exert oneself, field
The each busbar voltage of group is predicted；
Step 3: dominating pair of vertices M*N the future position in future in each control cycle is optimized calculating, obtains N number of cycle
Optimum control instruction, according to MPC Controlling principle, only choose the optimum control instruction control as this cycle in first cycle
Instruction；
Step 4: after current period controls to terminate, control time window is elapsed by next control cycle the most backward.
When entering next cycle, return and perform step 1.
Preferably, the forecast model in described step 2 includes: exert oneself forecast model, SVC of wind energy turbine set active reactive exerts oneself pre-
Survey model and critical busses voltage-prediction model；
Described wind energy turbine set active reactive is exerted oneself forecast model, is gained merit force data by history, slides according to autoregression flat
All the wind energy turbine set of ARMA Forecasting Methodology acquisition future position is meritorious exerts oneself；
Described SVC exerts oneself forecast model, including: first order inertial loop, the setting value issued according to control point, utilize difference
The time constant of device model, simulation this locality controls dynamic process, obtains the reactive power predictive value that each future position is corresponding；
Described critical busses voltage-prediction model, based on meritorious and reactive power predictive value, by different time section
Static Power Flow is all node voltages voltage prediction value on each future position in solving wind farm group.
Preferably, gain merit the exert oneself establishment step of forecast model of described wind energy turbine set is as follows:
By autoregressive moving average ARMA forecast model, utilize history force data of gaining merit that the future of each future position is had
Merit is exerted oneself and is predicted；
In formula, (a, b) represents the b future position in a control,It is that (a, b) moment wind energy turbine set is meritorious for t
Exert oneself predictive value,It it is the meritorious predictive value of exerting oneself of t (a, b-k) moment wind energy turbine set；It is t (a, b) moment one
The normal white noise process of zero-mean,It it is the normal white noise process of t one zero-mean of (a, b-k) moment；P and q divides
Wei the exponent number of autoregression and moving average；And θjIt is respectively the autoregression of kth rank and jth rank moving average parameter.
Preferably, the exert oneself establishment step of forecast model of described wind energy turbine set and SVC is as follows:
Wind energy turbine set and SVC are all operated in the idle instruction mode of acceptance, and Wind turbines and the idle control of SVC are all by electric power electricity
Subset completes, and response speed is very fast, it is contemplated that wind energy turbine set needs again to be allocated after receiving the idle instruction of higher level, and
Wind field intercommunication has time-lag action, and wind energy turbine set reactive response speed compares unit and SVC is the slowest.But due to wind farm group
The control time cycle be that all meeting of a minute level, wind energy turbine set and SVC completes the finger in previous cycle before next cycle arrives
Order regulation, i.e.
In formula:It is that the prediction of t (a+1,0) moment wind energy turbine set or SVC is idle to exert oneself；WithIt is t (a, 0) moment wind
Electric field or the idle command value of SVC；
After wind energy turbine set and SVC receive the idle instruction of higher level, one can be experienced before again reaching new stable operating point
Dynamic process, this process can utilize first order inertial loop to describe:
In formula:Expression t (a, b) moment wind energy turbine set or the predictive value of SVC,Represent t (a, 0) moment wind energy turbine set or
SVC arranges value, Qt(a,0)Represent t (a, 0) moment wind energy turbine set or the actual value of SVC；ta,bAnd ta,0Respectively represent prediction time and
Initial time, Td is the regulating time constant of wind energy turbine set or SVC, and each wind energy turbine set has different Td from SVC.
Preferably, the establishment step of described critical busses voltage-prediction model is as follows:
Meritorious, idle on predicted time point of each bus in forecast model prediction field group obtained by utilizing in step 2
Value, is processed as PQ node by described bus, is obtained in that the busbar voltage of this section according to the Load flow calculation of discontinuity surface time each
Predictive value.
Compared with prior art, the present invention has a following beneficial effect:
The present invention considers the dynamic process that wind farm group ran within the control instruction cycle, abandoned controlled only with
During control, discontinuity surface optimum is the pattern of target, using the optimum in whole control cycle as the target of optimum control, thus enters one
Step improves safety and the economy that wind farm group runs.
Accompanying drawing explanation
By the detailed description non-limiting example made with reference to the following drawings of reading, the further feature of the present invention,
Purpose and advantage will become more apparent upon:
Fig. 1 is wind-powered electricity generation cluster reactive power/voltage control level schematic diagram；
Fig. 2 is existing wind farm group reactive power and voltage control flow chart；
Fig. 3 is wind farm group reactive power and voltage control time shaft schematic diagram based on Model Predictive Control；
Fig. 4 is wind farm group reactive power and voltage control flow chart based on Model Predictive Control.
Detailed description of the invention
Below in conjunction with specific embodiment, the present invention is described in detail.Following example will assist in the technology of this area
Personnel are further appreciated by the present invention, but limit the present invention the most in any form.It should be pointed out that, the ordinary skill to this area
For personnel, without departing from the inventive concept of the premise, it is also possible to make some changes and improvements.These broadly fall into the present invention
Protection domain.
The wind farm group reactive voltage optimal control method based on Model Predictive Control that the present invention provides, be thoughtful of the future N
Control performance in the individual control cycle is optimum, and essence is a kind of optimization problems.The angle become more meticulous from control, is not only concerned about
Provide the control performance at N number of control point of control decision future, be also concerned about the control in thinner time scale in this N number of control cycle
Performance processed.On the one hand, meritorious the exerting oneself of wind energy turbine set can change within the control cycle；On the other hand, when wind energy turbine set, reactive-load compensation dress
Put after control point receives setting value instruction, owing to time constant is different, dynamic process can be controlled along difference and be transitioned into stable state
Value.Idle output distribution and voltage curve in order to make each moment in transient process are reasonable, also need to study thinner time chi
The reactive power action behavior of degree.Therefore, the present invention marks off thinner time scale within the control cycle.Wind energy conversion system is had
Merit is exerted oneself, and can obtain predictive value according to power prediction；Exert oneself for equipment is idle, then the setting value issued according to control point,
Distinct device model, simulation this locality is utilized to control dynamic process, obtain the reactive power predictive value that each future position is corresponding；Finally
Based on meritorious and reactive power predictive value, all node voltages voltage prediction on each future position in solving wind farm group
Value.
The target of optimal control derives deviation between voltage and the reference value for minimizing whole predetermined period maincenter bus
And the overall network loss of whole predetermined period field group.In like manner, the constraints of control derives equally as the pact on future time section
Bundle value.
Specifically, it is illustrated in figure 2 the idle control flow chart of existing wind farm group, there is data acquisition, optimize calculating
And instruction issues function.This control strategy can effectively run control instruction time section optimization field group, but cannot optimize
The operation of control instruction cycle internal field group, it is impossible to realize Precise control.
Fig. 3 show the wind farm group reactive power and voltage control time based on Model Predictive Control proposed by the invention
Axle, the control strategy proposed arranges M future position to each wind farm group reactive power and voltage control cycle, predicts N number of altogether
The control cycle, each in control algolithm, utilize that the wind energy turbine set of each future position meritorious is exerted oneself by forecast model, reactive source is idle
Exert oneself, the field each busbar voltage of group etc. is predicted calculating.Control algolithm is according to controlling optimization aim, in the control in each control cycle
System point is optimized calculating to following M*N future position, obtains N number of optimum control instruction, but only issues wherein first value and make
Instruct for this periodic Control.Until the next control cycle, control time window is elapsed the most backward, repeats above-mentioned excellent
Change and calculate, it is achieved rolling optimization.
Fig. 4 show proposed wind farm group reactive power and voltage control flow chart, and this controls at existing control base
On plinth, with the addition of exert oneself forecast model, SVC of wind energy turbine set active reactive and exert oneself forecast model and critical busses voltage-prediction model.
Wind energy turbine set is meritorious exerts oneself is to utilize history to gain merit force data, is obtained by autoregressive moving average ARMA Forecasting Methodology；For
Wind energy turbine set and the idle of SVC are exerted oneself, can be by first order inertial loop as forecast model, the setting issued according to control point
Value, utilizes the time constant of distinct device model, simulation this locality to control dynamic process, obtain the idle merit that each future position is corresponding
Rate predictive value；It is finally based on meritorious and reactive power predictive value, is calculated by the Static Power Flow of different time section, solve wind-powered electricity generation
All node voltages voltage prediction value on each future position in the group of field.Utilize forecast model, when can solve field group future
The running status of discontinuity surface, observes and controls target accordingly, thus the control target of discontinuity surface time each is carried out global optimization control
System.
The concrete principle of control strategy of the present invention is: utilize forecast model, meritorious to future time section wind energy turbine set, idle
Exert oneself and idle the exerting oneself of transformer station SVC is predicted, thus the maincenter busbar voltage of discontinuity surface wind farm group and total when solving each
Body network loss, is comprehensively minimised as with all time control of section targets controlling target, this control problem is converted to multiple target excellent
Change control problem, utilize genetic algorithm that this problem is solved and obtain the control instruction of each reactive source and be handed down to corresponding wind-powered electricity generation
Field and SVC, the field group in the complex optimal controlled strategy cycle runs.
According to the forecast model of the present invention, wind energy turbine set meritorious is exerted oneself, idle exert oneself, reactive power compensator is exerted oneself and each mother
Line voltage is predicted, and solves the process of following field group's state change under different control instruction, and the action of each controlled plant is all examined
System mode in rate a period of time window, thus the effect that in reaching to optimize the whole instruction cycle, wind farm group runs.
Above the specific embodiment of the present invention is described.It is to be appreciated that the invention is not limited in above-mentioned
Particular implementation, those skilled in the art can make a variety of changes within the scope of the claims or revise, this not shadow
Ring the flesh and blood of the present invention.In the case of not conflicting, the feature in embodiments herein and embodiment can any phase
Combination mutually.
Claims (5)
1. a wind farm group reactive voltage optimal control method based on Model Predictive Control, it is characterised in that include as follows
Step:
Step 1: each wind farm group reactive power and voltage control cycle is arranged M future position, predicts N number of control cycle altogether；
Step 2: utilize that the wind energy turbine set of each future position meritorious is exerted oneself by forecast model respectively, reactive source is idle and exert oneself, field group each
Busbar voltage is predicted；
Step 3: dominating pair of vertices M*N the future position in future in each control cycle is optimized calculating, obtains N number of cycle
Excellent control instruction, according to MPC Controlling principle, only chooses the optimum control instruction in first cycle and refers to as the control in this cycle
Order；
Step 4: after current period controls to terminate, control time window is elapsed by next control cycle the most backward.
Wind farm group reactive voltage optimal control method based on Model Predictive Control the most according to claim 1, it is special
Levying and be, the forecast model in described step 2 includes: wind energy turbine set active reactive exert oneself forecast model, SVC exert oneself forecast model with
And critical busses voltage-prediction model；
Described wind energy turbine set active reactive is exerted oneself forecast model, is gained merit force data by history, according to autoregressive moving average
The wind energy turbine set of ARMA Forecasting Methodology acquisition future position is meritorious exerts oneself；
Described SVC exerts oneself forecast model, including: first order inertial loop, the setting value issued according to control point, utilize distinct device
The time constant of model, simulation this locality controls dynamic process, obtains the reactive power predictive value that each future position is corresponding；
Described critical busses voltage-prediction model, based on meritorious and reactive power predictive value, by the static state of different time section
All node voltages voltage prediction value on each future position in Load Flow Solution wind farm group.
Wind farm group reactive voltage optimal control method based on Model Predictive Control the most according to claim 2, it is special
Levying and be, gain merit the exert oneself establishment step of forecast model of described wind energy turbine set is as follows:
By autoregressive moving average ARMA forecast model, history force data of gaining merit is utilized to gain merit to the future of each future position
Power is predicted；
In formula, (a, b) represents the b future position in a control,It is that t (a, b) exert oneself by the meritorious of moment wind energy turbine set
Predictive value,It it is the meritorious predictive value of exerting oneself of t (a, b-k) moment wind energy turbine set；It is that (a, b) moment one zero is equal for t
The normal white noise process of value,It it is the normal white noise process of t one zero-mean of (a, b-k) moment；P and q is respectively
Autoregression and the exponent number of moving average；And θjIt is respectively the autoregression of kth rank and jth rank moving average parameter.
Wind farm group reactive voltage optimal control method based on Model Predictive Control the most according to claim 2, it is special
Levying and be, the exert oneself establishment step of forecast model of described wind energy turbine set and SVC is as follows:
Wind energy turbine set and SVC are all operated in the idle instruction mode of acceptance, and Wind turbines and the idle control of SVC are all set by power electronics
For completing；The control time cycle of wind farm group is that all meeting before next cycle arrives of a minute level, wind energy turbine set and SVC is complete
The instruction becoming the previous cycle regulates, i.e.
In formula:It is that the prediction of t (a+1,0) moment wind energy turbine set or SVC is idle to exert oneself；WithIt it is t (a, 0) moment wind energy turbine set
Or the idle command value of SVC；
After wind energy turbine set and SVC receive the idle instruction of higher level, one can be experienced before again reaching new stable operating point dynamically
Process, this process can utilize first order inertial loop to describe:
In formula:Expression t (a, b) moment wind energy turbine set or the predictive value of SVC,Represent t (a, 0) moment wind energy turbine set or SVC
Value, Q are sett(a,0)Represent t (a, 0) moment wind energy turbine set or the actual value of SVC；ta,bWithta,0Respectively represent prediction time and initial time
Carving, Td is the regulating time constant of wind energy turbine set or SVC, and each wind energy turbine set has different Td from SVC.
Wind farm group reactive voltage optimal control method based on Model Predictive Control the most according to claim 2, it is special
Levying and be, the establishment step of described critical busses voltage-prediction model is as follows:
Utilize each bus gaining merit, without work value, general on predicted time point in the forecast model prediction field group obtained by step 2
Described bus is processed as PQ node, is obtained in that the prediction of busbar voltage of this section according to the Load flow calculation of discontinuity surface time each
Value.
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| CN108711866A (en) * | 2018-04-10 | 2018-10-26 | 国网安徽省电力有限公司芜湖供电公司 | A kind of control system for new energy power station reactive voltage |
| CN108923435A (en) * | 2018-07-04 | 2018-11-30 | 山东大学 | A kind of wind-powered electricity generation reactive voltage coordinated control system based on layering MPC |
| CN110198053A (en) * | 2019-04-19 | 2019-09-03 | 山东大学 | It is a kind of to concentrate with the micro-capacitance sensor real-time voltage control method and system combined on the spot |
| CN110957731A (en) * | 2019-11-04 | 2020-04-03 | 天津大学 | Distributed power supply on-site cluster voltage control method based on model predictive control |
| CN112052113A (en) * | 2020-08-26 | 2020-12-08 | 国电南瑞科技股份有限公司 | Communication link layer message single event effect fault tolerance method and device |
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| CN108711866B (en) * | 2018-04-10 | 2023-06-13 | 国网安徽省电力有限公司芜湖供电公司 | Reactive voltage control system for new energy power station |
| CN108923435A (en) * | 2018-07-04 | 2018-11-30 | 山东大学 | A kind of wind-powered electricity generation reactive voltage coordinated control system based on layering MPC |
| CN110198053A (en) * | 2019-04-19 | 2019-09-03 | 山东大学 | It is a kind of to concentrate with the micro-capacitance sensor real-time voltage control method and system combined on the spot |
| CN110957731A (en) * | 2019-11-04 | 2020-04-03 | 天津大学 | Distributed power supply on-site cluster voltage control method based on model predictive control |
| 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 |
| CN112052113A (en) * | 2020-08-26 | 2020-12-08 | 国电南瑞科技股份有限公司 | Communication link layer message single event effect fault tolerance method and device |
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