CN108306340A - Interconnected electric power system LOAD FREQUENCY Planar clouds control method containing new energy - Google Patents
Interconnected electric power system LOAD FREQUENCY Planar clouds control method containing new energy Download PDFInfo
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- 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
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- 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
- H02J3/02—Circuit arrangements for ac mains or ac distribution networks using a single network for simultaneous distribution of power at different frequencies; using a single network for simultaneous distribution of ac power and of dc 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
- 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]
Abstract
The present invention relates to the interconnected electric power system LOAD FREQUENCY Planar clouds control methods containing new energy, this method is using control deviation e and deviation variation rate ec as Planar clouds former piece generator, with PI parameter tuning variation delta P and Δ I for one-dimensional cloud consequent generator, constitute the double condition single gauges then cloud generator for being suitble to LOAD FREQUENCY control, then by several double condition single gauges then cloud generator group condition more rules cloud generator in pairs.Control system deviation and deviation variation rate are sampled by two-dimension cloud model controller, after Cloud Model Controller reasoning operation, export the setting valve of PI parameters.The real-time optimization of the LOAD FREQUENCY control parameter in the case of being introduced to uncertain loads such as new energy, the sound state quality and robustness of effective increasing productivity FREQUENCY CONTROL are realized using cloud model.
Description
Technical field
The invention belongs to the mains frequency control technology fields of electric system.Propose a kind of interconnecting electric power containing new energy
System loading frequency Planar clouds control method.This method is the LOAD FREQUENCY Planar clouds control based on Clouds theory, is suitable for new energy
Interconnected electric power system LOAD FREQUENCY uncertainty control under the intervention of source (scene), and it is capable of the robust of effective lifting system
Property.
Background technology
Nowadays extensive interconnected network can be such that adjacent area supports mutually, but obtain huge on-road efficiency and returns of investment
While, the power swing of the frequency departure and interconnection in region is also brought along, and power system capacity is bigger, interconnection exchanges work(
Rate is bigger, subjects the risk of generating system sexual behavior event and large-area power-cuts.In addition extensive new energy power generation grid-connection in recent years,
New energy output power has strong uncertain and is difficult to predict, the fluctuation of power causes system active power imbalance to add
Play, power system security, stable operation are faced with continuous challenge, this proposes higher want to LFC (LOAD FREQUENCY control)
It asks.
Power system frequency is an important indicator for reflecting power quality.LOAD FREQUENCY control is used as Automatic Generation Control
Core, mainly undertake the adjusting of system frequency and active power.Therefore, the control strategy that LOAD FREQUENCY control uses can be direct
Influence control performance, Bevrani H. (Bevrani H, Daneshmand P R.Fuzzy Logic-Based Load-
Frequency Control Concerning High Penetration of Wind Turbines[J].IEEE
Systems Journal,2012,6(1):After Self-adaptive fuzzy control structure 173-180.) is applied to wind-powered electricity generation intervention
Interconnected electric power system LFC controls do not have although load disturbance and wind speed can more effectively be inhibited to change the frequency variation brought
Consider caused parameter uncertainty when the variation of system stable operation point, and fuzzy algorithmic approach is come strictly with accurate membership function
It indicates fuzzy concept, has ignored the uncertainty of degree of membership itself, still have limitation to processing uncertain problem.
The achievement in research for solving new energy intervention interconnected electric power system LOAD FREQUENCY control problem at present is relatively fewer, existing
Technology is more still concentrated on based on PREDICTIVE CONTROL and intelligent control other than that is taken from new energy side stabilizes measure to PI
In the design and optimization of parameter.The existing accuracy for the LFC controller heavy dependence load predictions containing new energy, such as poplar
(the interconnected network LOAD FREQUENCY wide area of Yang Deyou, big wind power plants containing the scale/group of Cai state disperse PREDICTIVE CONTROL to moral friend et al.
[J] Proceedings of the CSEEs, 2015,35 (3):The case where 583-591.) considering different wind-powered electricity generation permeabilities, it is pre- based on model
Survey control method establish LOAD FREQUENCY dispersion prediction model, though control effect be better than conventional PI control, Control platform seriously according to
Rely in precision of forecasting model.
But with the increase of generation of electricity by new energy factory rules and regulations mould and capacity, also continuity is doubled and redoubled for decision variable, state variable,
Electric system is increasingly sophisticated, the frequency fluctuation of system is easily caused only with PREDICTIVE CONTROL, and then increase system frequency modulation burden.At present
Control means and algorithm be still difficult to meet the new energy access scale being growing, therefore LFC controller designs also need into
One step research.Therefore, practical power systems running uncertain (system structure, Parameters variation, random load interference are solved
Deng) the LFC controller quality variation problems that cause are the key points and difficulties studied of the present invention.
Invention content
It, should present invention aims at a kind of interconnected electric power system LOAD FREQUENCY Planar clouds control method containing new energy is proposed
Method design is suitable for the Planar clouds PI controllers of LOAD FREQUENCY control, realizes the conversion of the uncertainty relationship of qualitative and quantitative,
To change the reference power operating point of generating set governing system, the active output for increasing or reducing unit maintains frequency to exist
The method of operation under rated condition makes the system equilibrium of supply and demand.
To achieve the above object, the technical solution adopted by the present invention is：Design a kind of interconnected electric power system containing new energy
The step of LOAD FREQUENCY Planar clouds control method, this method is：
Step 1：Establish LOAD FREQUENCY control system model：
LOAD FREQUENCY control system includes the links such as governor, generating set, prime mover and power grid, by establishing generator
Model, load model, governor prime mover model, dominant eigenvalues model obtain the LOAD FREQUENCY control system between each region
Model, each district control deviation ACE of LOAD FREQUENCY control system model is by field frequency deviation delta fi, field frequency deviation system
Number BiAnd the Tie line Power Δ P in regiontiei, according to formula ACE=Δs Ptiei+BiΔfiIt is calculated；Wherein region frequency
Rate deviation delta fiWith the Tie line Power Δ P in regiontieiWith Photovoltaic new energy disturbance, wind-force new energy disturbance, photovoltaic with
The disturbance of wind-force new energy joint, random load disturbance, step load disturbance association；
Step 2：Establish two-dimentional two-output impulse generator Cloud Model Controller：
Step 201：The district control deviation ACE in step 1 is calculated with supervisor control by data acquisition, here
District control deviation ACE is indicated with e again, and data are normalized by formula (1), determine system realm control deviation e,
The data variation range of deviation variation rate ec,
Wherein, yiFor normalization data；xiFor measurement data；xminTo design detection data minimum value；xmaxIt is detected for design
Data maximums；
Then the data variation range of the setting valve Δ P of PI controllers, Δ I are normalized, after normalization, area
Value range, the value range of deviation variation rate ec, the value range of Δ P, the value range of Δ I of domain control deviation e be
[-1,1]；
Step 202：By the setting valve Δ P of district control deviation e, deviation variation rate ec, PI controller after normalization and
Δ I is divided；Wherein, 7 parts are divided into district control deviation e, are denoted as E1,E2,…,E7；Deviation variation rate ec is drawn
It is divided into 7 parts, is denoted as EC1,EC2,…EC7；7 parts are divided into setting valve Δ P, are denoted as Δ P1,ΔP2,…ΔP7；It is right
Setting valve Δ I is divided into 7 parts, is denoted as Δ I1,ΔI2,…ΔI7；Cloud control rule is built upon on the basis of linguistic variable,
It is " negative big " by linguistic variable value, " in negative ", " negative small ", " zero ", " just small ", " center " " honest " seven fuzzy sets；By this
The fuzzy set of seven parts uses the digital character representation of cloud model three respectively；
Step 203：The mapping of E × EC to Δ P, the mapping of E × EC to Δ I are established respectively, regard the two mappings as two
The control rule for tieing up two-output impulse generator Cloud Model Controller, obtains two-dimentional two-output impulse generator Cloud Model Controller；
The two dimension two-output impulse generator Cloud Model Controller includes two dimension X conditions cloud generator, the generation of one-dimensional Y conditions cloud
Device and backward cloud generator；
Step 3：District control deviation e is sampled, to being calculated after the district control deviation e samplings at adjacent two moment
Region difference change rate ec；Using obtained district control deviation e and deviation variation rate ec as the defeated of two dimension X condition cloud generators
Enter data to substitute into the two-dimentional two-output impulse generator Cloud Model Controller that step 2 obtains, generates one group of degree of certainty μ；
Step 4：One group of degree of certainty μ that step 3 generates is inputted into one-dimensional Y conditions cloud generator, generates two groups of water dust drop
(P, μ) and drop (I, μ), wherein P, I respectively refer to the scale parameter in PI controllers and integral parameter；
Step 5：Two groups of water dusts that step 4 generates are input to backward cloud generator, by μiThe point of ＞ 0.9999 is rejected, μi
Indicate that any one μ in two groups of water dusts, backward cloud generator obtain the numerical characteristic of cloud model according to two groups of water dusts of input, and
Take treated it is expected that setting valve of the mean value as PI parameters, two-dimentional two-output impulse generator Cloud Model Controller export setting valve Δ
P and Δ I；
Step 6：Setting valve Δ P and Δ I that step 5 exports are substituted into PI controllers to the original for correcting PI controllers respectively
Beginning PI parameter, revised PI controllers expression formula are
Wherein, u (t) is output valve；KPFor last moment PI controller proportionality coefficient；KIIt is accumulated for last moment PI controller
Divide coefficient；Δ P is PI controller COEFFICIENT KsPPre-corrected value；Δ I is PI controller COEFFICIENT KsIPre-corrected value；E controls for region
Deviation；
And after being corrected PI controllers output quantity, with after amendment PI controllers output quantity control increase or reduce
The active power output of generating set so that the supply and demand power-balance of system, frequency stabilization；
Step 7：Step 3- steps 6 are repeated, the district control deviation of subsequent time is controlled.
Compared with prior art, beneficial effects of the present invention：
Present invention substantive distinguishing features outstanding are：
The application control method is whole with PI parameters using control deviation e and deviation variation rate ec as Planar clouds former piece generator
It is one-dimensional cloud consequent generator to determine variation delta P and Δ I, constitutes the double condition single gauges then cloud generation for being suitble to LOAD FREQUENCY control
Device, then by several double condition single gauges then cloud generator group condition more rules cloud generator in pairs.When a certain input excites each list
When the former piece of Rule Generator, each former piece generator just randomly generates one group of degree of certainty, these degree of certainties and random thorn
Swash each consequent generator and generate one group of water dust, the water dust of all generations is calculated into the cloud model by backward cloud generator
Some numerical characteristic up to output to the end.Aforesaid way constitutes a complete two-dimension cloud model controller.It is logical
It crosses two-dimension cloud model controller to sample control system deviation and deviation variation rate, be transported by Cloud Model Controller reasoning
After calculation, the setting valve of PI parameters is exported.By advantage of the cloud model in uncertain conversion, it is steady to meet system to the greatest extent
Provisioning request.
The present invention marked improvement be：
1. two-dimension cloud model is applied in LOAD FREQUENCY control by the present invention for the first time, cloud model is a kind of two-way cognition of realization
Uncertain transformation model between qualitative and quantitative.Cloud model fuzzy will dexterously be combined with Idea of Probability, by membership function
Accuracy expand be the uncertainty with statistical distribution, can realize to probabilistic controlled device stabilization control
System.The water dust (sample) that cloud model determines some, degree of membership still have uncertainty, can be calculated and be asked by probability distribution
, it is not only not necessarily to artificially give, and sample degree of membership is different everywhere, compensate in the past to handling uncertain problem not
Foot.Based on cloud models theory, the regional internet electric system to introduce new energy devises Planar clouds control for specific object
Device.The real-time excellent of the LOAD FREQUENCY control parameter in the case of being introduced to uncertain loads such as new energy is realized using cloud model
Change, the sound state quality and robustness of effective increasing productivity FREQUENCY CONTROL.
2. the problem of uncertainty that pair new energy is intervened and brought increases, the method for the present invention can also draw a variety of new energy
Enter situation and carry out real-time control well, the load disturbance for simulating different operating modes respectively in Matlab platforms (is situated between containing new energy
Enter), a variety of load disturbances refer to random, step, wind-powered electricity generation, photovoltaic, this honourable 5 kinds of load disturbances, to the application control method and biography
System PI controllers carry out emulation comparison, the results showed that, the application control method disclosure satisfy that the performance indicator of LOAD FREQUENCY control,
And better sound state quality and robustness are shown.
3. the present invention can be used for reducing the deviation that the uncertain problem in LOAD FREQUENCY control system is brought, pass through foundation
Two-dimension cloud model controller, obtains that PI controllers are required to wait for setting parameter.Then, Cloud Model Controller is accessed into two regions
In interconnected electric power system, self-adjusting is carried out to PI setting parameters by two-dimension cloud model controller, to realize electric system
LOAD FREQUENCY controls (LFC).
Description of the drawings
Fig. 1 is the flow chart of the interconnected electric power system LOAD FREQUENCY Planar clouds control method containing new energy in the present invention；
Fig. 2 is representative region i LOAD FREQUENCY control system models；
Fig. 3 is two region LFC system model figures；
Fig. 4 (a) uses the frequency departure analogous diagram of different controller time domains 1 under random signal；
Fig. 4 (b) uses the frequency departure analogous diagram of different controller time domains 2 under random signal；
Fig. 5 (a) uses the frequency departure analogous diagram of different controller time domains 1 under step signal；
Fig. 5 (b) uses the frequency departure analogous diagram of different controller time domains 2 under step signal；
Fig. 6 (a) uses the frequency departure analogous diagram of different controller time domains 1 under photovoltaic signal；
Fig. 6 (b) uses the frequency departure analogous diagram of different controller time domains 2 under photovoltaic signal；
Fig. 7 (a) uses the frequency departure analogous diagram of different controller time domains 1 under wind power signal；
Fig. 7 (b) uses the frequency departure analogous diagram of different controller time domains 2 under wind power signal；
Fig. 8 (a) uses the frequency departure analogous diagram of different controller time domains 1 under honourable signal；
Fig. 8 (b) uses the frequency departure analogous diagram of different controller time domains 2 under honourable signal.
Specific implementation mode
The implementation of the present invention is described further below in conjunction with attached drawing and example.
Interconnected electric power system LOAD FREQUENCY Planar clouds control method (abbreviation method) of the present invention containing new energy, this method
Step is：
Step 1：Establish LOAD FREQUENCY control system model：
LOAD FREQUENCY control system includes the links such as governor, generating set, prime mover and power grid, by establishing generator
Model, load model, governor prime mover model, dominant eigenvalues model obtain the LOAD FREQUENCY control system between each region
Model, each district control deviation ACE of LOAD FREQUENCY control system model is by field frequency deviation delta fi, field frequency deviation system
Number BiAnd the Tie line Power Δ P in regiontiei, according to formula ACE=Δs Ptiei+BiΔfiIt is calculated；Wherein region frequency
Rate deviation delta fiWith the Tie line Power Δ P in regiontieiWith Photovoltaic new energy disturbance, wind-force new energy disturbance, photovoltaic with
The disturbance of wind-force new energy joint, random load disturbance, step load disturbance association；
Step 2：Establish two-dimentional two-output impulse generator Cloud Model Controller (two-dimension cloud model controller or cloud model control
Device)：
Step 201：The district control deviation ACE in step 1 is calculated with supervisor control by data acquisition, here
District control deviation ACE is indicated with e again, and data are normalized by formula (1), determine system realm control deviation e,
The data variation range of deviation variation rate ec,
Wherein, yiFor normalization data；xiFor measurement data；xminTo design detection data minimum value；xmaxIt is detected for design
Data maximums；
Then the data variation range of the setting valve Δ P of PI controllers, Δ I are normalized, after normalization, area
Value range, the value range of deviation variation rate ec, the value range of Δ P, the value range of Δ I of domain control deviation e be
[-1,1]；
Step 202：By the setting valve Δ P of district control deviation e, deviation variation rate ec, PI controller after normalization and
Δ I is divided；Wherein, 7 parts are divided into district control deviation e, are denoted as E1,E2,…,E7；Deviation variation rate ec is drawn
It is divided into 7 parts, is denoted as EC1,EC2,…EC7；7 parts are divided into setting valve Δ P, are denoted as Δ P1,ΔP2,…ΔP7；It is right
Setting valve Δ I is divided into 7 parts, is denoted as Δ I1,ΔI2,…ΔI7；Cloud control rule is built upon on the basis of linguistic variable,
By linguistic variable value it is " negative big ", " in negative ", " negative small ", " zero ", " just small ", " center " " honest " seven for rule of simplification
Fuzzy set；Three numerical characteristics of cloud model are used (it is expected tri- Ex, entropy En, super entropy He numbers respectively the fuzzy set of this seven part
Feature reflects the quantitative performance of qualitativing concept, wherein it is expected that Ex expresses the position of centre of gravity of cloud cluster, to uncertain things one
Determine to have carried out deterministic conversion in degree, is to be best able to represent the point of qualitativing concept；Entropy En is that qualitativing concept is probabilistic
Measurement realizes the unification to not knowing randomness and ambiguity in things.Entropy more major concept is more macroscopical, the distribution of water dust
It is bigger, on the contrary it is smaller.Super entropy He measures the uncertainty of entropy, is codetermined by the randomness and ambiguity of entropy, reflection
The thickness and dispersion of cloud layer.The thicker the super bigger cloud layer of entropy the more discrete, otherwise more thin more concentrate.) indicate { Ei,ECi,ΔPi,
ΔIi| i=1,2,3,4,5,6,7 }；Wherein
E1=EC1=Δ P1=Δs I1=" bearing big NB "=(- 1,0.06,0.005)
E2=EC2=Δ P2=Δs I2=" NM in negative "=(- 0.7,0.06,0.005) E3=EC3=Δ P3=Δs I3
=" bearing small NS "=(- 0.3,0.06,0.005)
E4=EC4=Δ P4=Δs I4=" zero Z "=(0,0.06,0.005) E5=EC5=Δ P5=Δs I5=" is just small
PS "=(0.3,0.06,0.005)
E6=EC6=Δ P6=Δs I6=" center PM "=(0.7,0.06,0.005) E7=EC7=Δ P7=Δs I7="
Honest PB "=(1,0.06,0.005)
Step 203：The mapping of E × EC to Δ P, the mapping of E × EC to Δ I are established respectively, regard the two mappings as two
The control rule for tieing up two-output impulse generator Cloud Model Controller, obtains two-dimentional two-output impulse generator Cloud Model Controller；
The two dimension two-output impulse generator Cloud Model Controller includes two dimension X conditions cloud generator, the generation of one-dimensional Y conditions cloud
Device and backward cloud generator；
Step 3：District control deviation e is sampled, to being calculated after the district control deviation e samplings at adjacent two moment
Region difference change rate ec；Using obtained district control deviation e and deviation variation rate ec as the defeated of two dimension X condition cloud generators
Enter data to substitute into the two-dimentional two-output impulse generator Cloud Model Controller that step 1 obtains, generates one group of degree of certainty μ；
Step 4：One group of degree of certainty μ that step 3 generates is inputted into one-dimensional Y conditions cloud generator, using degree of certainty μ as former piece,
Two groups of water dust drop (P, μ) and drop (I, μ) are generated, wherein P, I respectively refers to the scale parameter in PI controllers and integral parameter；
Step 5：Two groups of water dusts that step 4 generates are input to backward cloud generator, by μiThe point of ＞ 0.9999 is rejected, μi
It indicates any one μ in two groups of water dusts, obtains the numerical characteristic of cloud model, by operation, take treated it is expected mean value conduct
The setting valve of PI parameters, two-dimentional two-output impulse generator Cloud Model Controller export setting valve Δ P and Δ I；
Step 6：Setting valve Δ P and Δ I that step 5 exports are substituted into PI controllers to the original for correcting PI controllers respectively
Beginning PI parameter, revised PI controllers expression formula are
Wherein, u (t) is output valve；KPFor last moment PI controller proportionality coefficient；KIIt is accumulated for last moment PI controller
Divide coefficient；Δ P is PI controller COEFFICIENT KsPPre-corrected value；Δ I is PI controller COEFFICIENT KsIPre-corrected value；E controls for region
Deviation；
And after being corrected PI controllers output quantity, with after amendment PI controllers output quantity control increase or reduce
The active power output of generating set so that the supply and demand power-balance of system, frequency stabilization；
Step 7：Step 3- steps 6 are repeated, the district control deviation of subsequent time is controlled.
The present invention obtains PID setting valve Δs P, the Δ I at current time by two-dimentional two-output impulse generator Cloud Model Controller,
Then setting valve and original PI controllers are subjected to linear operation, PI controllers rationally export control according to the parameter configuration after adjusting
Amount, control increase or reduce the active power output of generating set so that the supply and demand power-balance of system, frequency stabilization.
Two-dimentional Normal Cloud Generator is according to the district control deviation e of input, deviation variation rate ec and regular cloud former piece A and B
One group of degree of certainty μ is generated, any one degree of certainty in this group of degree of certainty is expressed as μi, detailed process include：
Step 301 is according to the numerical characteristic (Ex of cloudA,EnA,HeA) generate to be desired for EnA, standard deviation HeANormal state with
Machine number PAi, according to the numerical characteristic (Ex of cloudB,EnB,HeB) generate to be desired for EnB, standard deviation HeBNormal random number PBi。
Step 302 gives quantitative district control deviation e and deviation variation rate ec, and degree of certainty is calculated according to formula (2),
One-dimensional Y conditions cloud generator is using district control deviation e and deviation variation rate ec as cloud generator former piece, with PI parameters
It is cloud generator consequent to adjust variation delta P and Δ I, according to 3 numerical characteristic value (Ex of input rule cloud consequent CC,EnC,
HeC) and one group of degree of certainty μ, generate water dust drop (P, μ), drop (I, μ).
Backward cloud generator obtains the numerical characteristic of cloud model according to the water dust of input, specifically includes：
Step 501：In calculating process, by μiThe point of ＞ 0.9999 is rejected, and m water dust is left；
Step 502：Using the average value of m water dust as desired estimated value：
Step 503：It calculates
Step 504：Seek ZqArithmetic mean of instantaneous valueAnd variance
Step 505：Calculate one of cloud numerical characteristic entropy
Step 506：Calculate one of the cloud super entropy He estimated values of numerical characteristic：
Two dimension in the application refers to district control deviation e and deviation variation rate ec.
No matter for random disturbance input or step or new energy access disturbance, control method of the present invention is relative to biography
The PI controls of system show faster tracking characteristics and more effectively load disturbance rejection ability, and it is special to show excellent robust
Property.It is limited by the constraint of resource environment, the scale that new energy intervenes power grid is increasing, and the application is specifically for extensive new energy
The adverse effect that the uncertainty in source brings power system load FREQUENCY CONTROL quality, it is proposed that one kind being based on cloud models theory
Interconnected network LOAD FREQUENCY Planar clouds control method.This method devises LFC two dimension dual input lose-lose using two-dimension cloud model
Go out Cloud Model Controller, this controller adaptively adjusts PI controllers ginseng on the basis of conventional PI control device according to system condition
Number, the adverse effect that can effectively overcome the uncertainty of system to bring Control platform.Simulation result shows either to random
Load disturbance under load disturbance, step load disturbance or new energy intervention, cloud PI controller indices are all shown
Preferable dynamic property and robustness, and it is superior to the dynamic quality of conventional PI control device.It controls and provides as the following region LFC
The exploration of new way and new method, the application control method are effective, feasible, can meet the performance of interconnected electric power system LFC
Index request can effectively eliminate the uncertain influence that new-energy grid-connected is brought, improve the permeability of new energy.
Embodiment 1
LFC controllers be accomplished that input area control deviation e and deviation variation rate ec exports controlled quentity controlled variable to controller
Between mapping relations, in the power system by various enchancement factors interfere and influence, mapping relations have uncertainty；
Mapping relations are not known between the be accomplished that qualitative to quantitative of cloud model, therefore can be by two-dimentional two-output impulse generator cloud model
In controller application to LFC, Fig. 1 is the flow of the LOAD FREQUENCY method based on two-dimentional two-output impulse generator Cloud Model Controller
Figure.
By two-dimentional two-output impulse generator Cloud Model Controller to district control deviation e and deviation variation rate ec samplings,
After knowing 3 numerical characteristics of cloud, one group of degree of certainty μ is randomly generated through regular former piece, these degree of certainty random stimulus consequents
Two groups of water dust drop (P, μ), drop (I, μ) are generated, the water dust of all generations is calculated into the cloud mould by backward cloud generator
The numerical characteristic Δ P and Δ I of type, and then the setting valve of PI parameters is obtained, detailed process is as follows：
Step 1：Establish LOAD FREQUENCY control system model：
Electric system is non-linear, parameter uncertainty the dynamical system with height；The delay of its steam control valve
Governor dead time and the uncertainty of parameters the time performance of characteristic, steam turbine are especially prominent.
LOAD FREQUENCY control system includes governor, generating set, prime mover and power grid link, by establishing generator mould
Type, load model, governor prime mover model, dominant eigenvalues model obtain the LOAD FREQUENCY control system mould between each region
Type, each district control deviation ACE of LOAD FREQUENCY control system model is by field frequency deviation delta fi, field frequency deviation factor
BiAnd the Tie line Power Δ P in regiontiei, according to formula ACE=Δs Ptiei+BiΔfiIt is calculated；Wherein field frequency
Deviation delta fiWith the Tie line Power Δ P in regiontieiWith Photovoltaic new energy disturbance, the disturbance of wind-force new energy, photovoltaic and wind
The disturbance of power new energy joint, random load disturbance, step load disturbance association.
The LFC system models of simplified representative region i are as shown in Figure 2.KsijIndicate that the interconnection between region and region is normal
Number, when system has load disturbance Δ PdiWhen, generating region frequency deviation fiWith the Tie line Power Δ P in regiontiei, Δ
fiPass through field frequency deviation factor BiAnd Δ PtieiIt collectively forms district control deviation ACE, ACE and is defined as ACE=Δs Ptiei+Bi
Δfi.Input quantities of the ACE as controller constitutes controlled quentity controlled variable u by control algolithmi, by governor, generator, prime mover etc.
Link is used for regulatory region frequency deviation fiWith the Tie line Power Δ P in regiontiei。
The present embodiment conducts a research by taking two regional internet electric system as an example, by establishing following generator model, bearing
Lotus model, governor prime mover model, dominant eigenvalues model can obtain the two region LFC system model figures of Fig. 3.In Fig. 3,
Ts1、Ts2For the governor inertia time constant in region 1,2, Tt1、Tt2For the reheat-type time constant in region 1,2；D1、D2For area
The load damped coefficient perunit value in domain 1,2；M1、M2For the inertia constant of a set perunit value in region 1,2；R1、R2For region 1,2
Governor regulating constant；B1、B2For the field frequency deviation factor in region 1,2；ACE1, ACE2 are that the region control in region 1,2 is inclined
Difference.Δf1、Δf2For the field frequency deviation in region 1,2；ΔPL1、ΔPL2For the load disturbance in region 1,2；Traditional controller is logical
Frequently with pi controller, output Δ U expression formulas are Δ U=KP·ACE+∫KIK in ACEdt formulasPFor proportional gain,
KIFor storage gain.
1) generator model
Generator is important one of link, and generator is meeting the equilibrium of supply and demand of electric system.
ΔPm-ΔPc=Ms Δs ω
Wherein, Δ PmFor the mechanical output variable quantity of prime mover；ΔPcFor generator electromagnetic power variable quantity；M turns for unit
Dynamic inertia；Δ ω is angular frequency variable quantity.
2) load model
Load is one of important component of electric system, and the order of accuarcy of mathematical model is to electrical power system transient point
The accuracy of analysis structure has a great impact.As follows is indicated to frequency sensitivity load, that is, frequency character of load：
Wherein, Δ PLf=D Δs ω
Electromagnetic power variable quantity is represented by：ΔPe=Δ PL+ΔPLf=Δ PL+DΔω
Wherein, Δ PLFor the load variations amount of frequency insensitive part；ΔPLfFor the load variations amount of frequency sensitive part；
D is the load damped coefficient in region.
The present invention introduces new energy output power, as seen from the figure, the new energy in region 1 by taking new energy as an example in region 1
Output is equivalent to a disturbing source, and coupled region 2 conveys dominant eigenvalues to region 1 and increases, cause region 2 by from
The disturbance in region 1.ACE introduces new energy power swing as disturbance.By the conservation of energy：ΔPe-ΔPnewenergy=Δ PL+D
Δω。ΔPeFor generator electromagnetic power variable quantity；ΔPnew energyFor the active output bias amount of new energy；ΔPLIt is disturbed for load
It is dynamic；D is damped coefficient；Δ ω system angle frequency variations, therefore in the LFC systems for considering new-energy grid-connected, new energy is had
Work(output is handled as the load of " negative ", you can obtains the LOAD FREQUENCY Controlling model containing new-energy grid-connected.
3) governor prime mover model
Governor plays a major role to mains frequency, and the variation of mains frequency is directly reflected into the variation of the unit angle of attack, and one
The negative feedback links of the governing system of secondary fm role will play the role of systems stabilisation frequency.To the transmission function of governor
Expression formula is as follows：
Wherein, Δ PrefFor the reference power of unit；R is difference coefficient；TsFor the time inertia constant of governor；ΔPω
For the corresponding power variation of steam turbine valve.
Using non-reheat turbine model
ΔPω=(1+sTt)ΔPm
Wherein, TtFor reheat turbine time constant.
4) dominant eigenvalues model
It is connected with other units or region by an interconnection, the linearization approximate of dominant eigenvalues is taken to express：
Wherein, Δ PtieFor the variable quantity of dominant eigenvalues；T12For the synchronization factor of dominant eigenvalues；Δω1、Δω2For
The angular frequency variable quantity in region 1,2.
5) district control deviation ACE is one of target of LFC, by the field frequency deviation and dominant eigenvalues of control zone
Deviation obtains：
ACE=Δs Ptie-10BΔf
Wherein, the frequency response coefficient in the areas in order to control B；The control deviation in the areas in order to control Δ f.
6) foundation of new energy model
To further investigate and analyzing the frequency variation characteristic for intervening rear region interacted system containing extensive new energy, and herein
On the basis of design the LFC controllers based on cloud model, need to establish the Power System Interconnection regional model containing new energy first.Wherein
Traditional energy system is introduced new energy with " negative " borne forms still based on microvariations inearized model.The present embodiment
Photovoltaic and the typical grid-connected power generation system model of two kinds of wind-force are established respectively.
The real-time output power module of photovoltaic plant is:
Wherein PbGo out activity of force (kW) in real time for photovoltaic；PsnFor the rated power of photovoltaic；SIt is strong for real-time solar radiation
Degree；SstdReach light radiation value when rated power for photovoltaic generation, its value is set as 1000W/m under normal condition2, ScValue is set as
150W/m2。
Wind turbine output power model is as follows：
In formula, k0、k1、k2It is generator power characteristic curve relevant parameter；vi、vr、vo、PrIt is wind-driven generator respectively
Cut wind speed, rated wind speed, cut-out wind speed and rated power.
Step 2：Establish two-dimentional two-output impulse generator Cloud Model Controller：
Step 201：Determine system realm control deviation e, the setting valve Δ P of deviation variation rate ec and PI controller, Δ I
Data variation range, and pass through formulaIt is normalized；Wherein, yiFor normalization data；xiTo measure
Data；xminTo design detection data minimum value；xmaxTo design detection data maximum value；After normalization, district control deviation e's
Value range, the value range of deviation variation rate ec, the value range of Δ P, the value range of Δ I are [- 1,1].
Step 202：By after normalization region difference e, deviation variation rate ec, PI controller setting Δ P and Δ I carry out
It divides.Wherein, 7 parts are divided into district control deviation e, are denoted as E1,E2,…,E7；7 are divided into deviation variation rate ec
Part is denoted as EC1,EC2,…EC7；7 parts are divided into setting valve Δ P, are denoted as Δ P1,ΔP2,…ΔP7；To setting valve Δ
I1,ΔI2,…ΔI7.Cloud control rule is built upon on the basis of linguistic variable, is rule of simplification, is " negative by linguistic variable value
Greatly ", 7 fuzzy sets such as " negative in ", " negative small ", " zero ", " just small ", " center " " honest ".The fuzzy set of this 7 parts is respective
Distribution within the scope of domain.The fuzzy set of this 7 part is used to the digital character representation of cloud model 3 respectively；That is " negative big ", " negative
In ", " negative small ", " zero ", " just small ", " center ", " honest " the corresponding numerical characteristic value of 7 fuzzy sets be：(- 1,
0.06,0.005), (- 0.7,0.06,0.005), (- 0.3,0.06,0.005), (0,0.06,0.005), (0.3,0.06,
0.005), (0.7,0.06,0.005), (1,0.06,0.005).
Step 203：The mapping of E × EC to Δ P, the mapping of E × EC to Δ I are established respectively, as two-dimentional dual input
The control rule of dual output Cloud Model Controller, each parameter of step 202 is input among more rules controller.
After reasoning operation, PI controller setting Δs P, the Δ I after renormalization are exported.By setting valve Δ P, Δ I
The calculation formula of output quantity is in substitution routine PI controllers：Wherein,
U (t) device output valves in order to control；KPFor last moment PI controller proportionality coefficient；KIFor last moment PI controller integral coefficient；
Δ P is PI controller COEFFICIENT KsPPre-corrected value；Δ I is PI controller COEFFICIENT KsIPre-corrected value；E is district control deviation.
Step 3：District control deviation e is sampled, to being calculated after the district control deviation e samplings at adjacent two moment
Region difference change rate ec；Using obtained district control deviation e and deviation variation rate ec as the defeated of two dimension X condition cloud generators
Enter data to substitute into the two-dimentional two-output impulse generator Cloud Model Controller that step 2 obtains, generates one group of degree of certainty μ；
Two-dimentional cloud generator is according to 3 of the district control deviation e of input, deviation variation rate ec and regular cloud former piece A and B
Numerical characteristic value (ExA,EnA,HeA)、(ExB,EnB,HeB) one group of degree of certainty μ is generated, it specifically includes：
Step 301：According to the numerical characteristic (Ex of cloudA,EnA,HeA) and (ExB,EnB,HeB) generate to be desired for EnAWith
EnB, standard deviation HeAAnd HeBNormal random number PAiAnd PBi；
Step 302：Given quantitative district control deviation and deviation variation rate calculate degree of certainty according to formula (2)
Step 4：One group of degree of certainty μ that step 3 generates is inputted into one-dimensional Y conditions cloud generator, using degree of certainty μ as former piece,
Two groups of water dust drop (P, μ) and drop (I, μ) are generated, wherein P, I respectively refers to the scale parameter in PI controllers and integral parameter；
Step 5：Two groups of water dusts that step 4 generates are input to backward cloud generator, by μiThe point of ＞ 0.9999 is rejected, μi
Indicate that any one μ in two groups of water dusts, backward cloud generator obtain the numerical characteristic of cloud model according to two groups of water dusts of input, and
Take treated it is expected that setting valve of the mean value as PI parameters, two-dimentional two-output impulse generator Cloud Model Controller export setting valve Δ
P and Δ I；
Wherein backward cloud generator obtains the numerical characteristic of cloud model according to two groups of water dusts of input, and method is：
Step 501：In calculating process, by μiThe point of ＞ 0.9999 is rejected, and m water dust is left；
Step 502：Using the average value of m water dust as desired estimated value：
Step 503；It calculates
Step 504：Seek ZqArithmetic mean of instantaneous valueAnd variance
Step 505：Calculate one of cloud numerical characteristic entropy
Step 506：Calculate one of the cloud super entropy He estimated values of numerical characteristic：
Step 6：Setting valve Δ P and Δ I that step 5 exports are substituted into PI controllers to the original for correcting PI controllers respectively
Beginning PI parameter, revised PI controllers expression formula are
Wherein, u (t) is output valve；KPFor last moment PI controller proportionality coefficient；KIIt is accumulated for last moment PI controller
Divide coefficient；Δ P is PI controller COEFFICIENT KsPPre-corrected value；Δ I is PI controller COEFFICIENT KsIPre-corrected value；E controls for region
Deviation；
And after being corrected PI controllers output quantity, with after amendment PI controllers output quantity control increase or reduce
The active power output of generating set so that the supply and demand power-balance of system, frequency stabilization；
Step 7：Step 3- steps 6 are repeated, the district control deviation of subsequent time is controlled.
Emulation module (Cloud Model Controller is write by S-function functions) is built in Matlab/Simulink, to two
Field frequency deviation is emulated, and the simulation result in the case of various disturbances is shown in Fig. 4 (a)-Fig. 8 (b).
Fig. 4-8 shows the interconnected electric power system LOAD FREQUENCY Planar clouds control method containing new energy using the present invention, can
Reach stationary value quickly, and deviation is inhibited to fluctuate, there is good control effect, can effectively overcome the uncertainty of system
The adverse effect that Control platform is brought.Simulation result shows either to disturb random load disturbance, step load, still
Load disturbance under new energy intervention, two-dimension cloud model controller indices all show preferable dynamic property and robust
Property, and it is superior to the dynamic quality of conventional PI control device.The spy that new way and new method are provided is controlled as the following region LFC
Rope, two-dimension cloud model controller are effective, feasible, can meet the performance indicator requirement of interconnected electric power system LFC；Planar clouds mould
Type controller can effectively eliminate the uncertain influence that new-energy grid-connected is brought, and improve the permeability of new energy.
The present invention does not address place and is suitable for the prior art.
Claims (1)
1. a kind of the step of interconnected electric power system LOAD FREQUENCY Planar clouds control method containing new energy, this method, is：
Step 1：Establish LOAD FREQUENCY control system model：
LOAD FREQUENCY control system includes governor, generating set, prime mover and power grid link, by establish generator model,
Load model, governor prime mover model, dominant eigenvalues model obtain the LOAD FREQUENCY control system model between each region,
Each district control deviation ACE of LOAD FREQUENCY control system model is by field frequency deviation delta fi, field frequency deviation factor Bi、
And the Tie line Power Δ P in regiontiei, according to formula ACE=Δs Ptiei+BiΔfiIt is calculated；Wherein field frequency is inclined
Poor Δ fiWith the Tie line Power Δ P in regiontieiWith Photovoltaic new energy disturbance, the disturbance of wind-force new energy, photovoltaic and wind-force
The disturbance of new energy joint, random load disturbance, step load disturbance association；
Step 2：Establish two-dimentional two-output impulse generator Cloud Model Controller：
Step 201：The district control deviation ACE in step 1 is calculated with supervisor control by data acquisition, here region
Control deviation ACE is indicated with e again, and data are normalized by formula (1), determines system realm control deviation e, deviation
The data variation range of change rate ec,
Wherein, yiFor normalization data；xiFor measurement data；xminTo design detection data minimum value；xmaxTo design detection data
Maximum value；
Then the data variation range of the setting valve Δ P of PI controllers, Δ I are normalized, after normalization, region control
Value range, the value range of deviation variation rate ec, the value range of Δ P, the value range of Δ I of deviation e processed be [- 1,
1]；
Step 202：By the setting valve Δ P and Δ I of district control deviation e, deviation variation rate ec, PI controller after normalization into
Row divides；Wherein, 7 parts are divided into district control deviation e, are denoted as E1,E2,…,E7；7 are divided into deviation variation rate ec
A part, is denoted as EC1,EC2,…EC7；7 parts are divided into setting valve Δ P, are denoted as Δ P1,ΔP2,…ΔP7；To setting valve
Δ I is divided into 7 parts, is denoted as Δ I1,ΔI2,…ΔI7；Cloud control rule is built upon on the basis of linguistic variable, by language
Variable-value is " negative big ", " in negative ", " bearing small ", " zero ", " just small ", " center " " honest " seven fuzzy sets；By this seven part
Fuzzy set use the digital character representations of cloud model three respectively；
Step 203：The mapping of E × EC to Δ P, the mapping of E × EC to Δ I are established respectively, the two mappings are double as two dimension
The control rule for inputting dual output Cloud Model Controller, obtains two-dimentional two-output impulse generator Cloud Model Controller；
The two dimension two-output impulse generator Cloud Model Controller include two dimension X conditions cloud generator, one-dimensional Y conditions cloud generator and
Backward cloud generator；
Step 3：District control deviation e is sampled, to calculating region after the district control deviation e samplings at adjacent two moment
Deviation variation rate ec；Using obtained district control deviation e and deviation variation rate ec as the input number of two dimension X condition cloud generators
According to substituting into the two-dimentional two-output impulse generator Cloud Model Controller that step 2 obtains, one group of degree of certainty μ is generated；
Step 4：One group of degree of certainty μ that step 3 generates is inputted into one-dimensional Y conditions cloud generator, using degree of certainty μ as former piece, is generated
Two groups of water dust drop (P, μ) and drop (I, μ), wherein P, I respectively refer to the scale parameter in PI controllers and integral parameter；
Step 5：Two groups of water dusts that step 4 generates are input to backward cloud generator, by μiThe point of ＞ 0.9999 is rejected, μiIt indicates
Any one μ in two groups of water dusts, backward cloud generator obtains the numerical characteristic of cloud model according to two groups of water dusts of input, and takes place
Setting valve of the expectation mean value as PI parameters after reason, two-dimentional two-output impulse generator Cloud Model Controller output setting valve Δ P and
ΔI；
Step 6：Setting valve Δ P and Δ I that step 5 exports are substituted into PI controllers to the original PI for correcting PI controllers respectively
Parameter, revised PI controllers expression formula are
Wherein, u (t) is output valve；KPFor last moment PI controller proportionality coefficient；KIFor last moment PI controller integration system
Number；Δ P is PI controller COEFFICIENT KsPPre-corrected value；Δ I is PI controller COEFFICIENT KsIPre-corrected value；E is that region control is inclined
Difference；
And after being corrected PI controllers output quantity, with after amendment PI controllers output quantity control increase or reduce generate electricity
The active power output of unit so that the supply and demand power-balance of system, frequency stabilization；
Step 7：Step 3- steps 6 are repeated, the district control deviation of subsequent time is controlled.
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