CN110401205A - A kind of SVC damping controller design method based on improvement drosophila algorithm - Google Patents
A kind of SVC damping controller design method based on improvement drosophila algorithm Download PDFInfo
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Abstract
The present invention relates to automatic control technology fields, more particularly to a kind of based on the SVC damping controller design for improving drosophila algorithm, including wind power plant cluster model and wind power plant cluster low-frequency oscillation analysis method, by considering Multiple Time Scales reactive source operation characteristic, ratio is accessed from reactive power compensator and control strategy is started with, it is influenced using wind power prediction information reduction wind power swing, system oscillation mode under Different Dynamic reactive power compensator strategy and control parameter is analyzed using small signal modal analysis method, induction system low-frequency oscillation may induce factor.It is proposed that a kind of novel improved drosophila optimizing algorithm algorithm of join probability Eigenvalue Sensitivity target solves optimal models, obtain current modal system SVC damping controller optimal value of the parameter, Oscillatory mode shape is avoided to coordinate the System Reactive Power balance of voltage, inhibits low frequency oscillations.Design is configured to system dynamic reactive-load compensation device and proposes guidance and related advisory, guarantees system safe and stable operation.
Description
Technical field
The present invention relates to automatic control technology fields, and in particular to a kind of based on the SVC damping control for improving drosophila algorithm
Device design method.
Background technique
It is growing with power grid scale, existing running example shows to be easy to produce low frequency vibration in Large-Scale Interconnected electric system
It swings, extreme influence system safe and stable operation even results in system crash.Hami Wind-Electric Power Stations access rack weakness,
Wind-powered electricity generation permeability is high, and wind farm grid-connected point (point of common coupling, PCC) reactive voltage sensitivity is higher,
The uncoordinated wind power plant that easily leads to sends out line voltage and the electrical quantity such as idle oscillation between reactive power compensator.Wind-electricity integration system, this area
System repeatedly vibrates, and shows that reactive power compensator is transported during putting into operation by the safety and stability that disturbance will affect this area's power grid
Row, thus there is an urgent need to find the low-frequency oscillation countermeasure for solving wind-powered electricity generation cluster area power grid.
Due to the wind power plant cluster that this area's power grid is only wind power integration, no serial supplementary line, and existing research is to wind-powered electricity generation
Field low-frequency oscillation is mostly based on the valve systems such as DFIG and Series compensation lines, so being no longer appropriate for analysis this area's system.It is another
Aspect, since Wind turbines generally do not inject reactive power toward system, wind power plant booster stations need to install reactive compensation and set
Standby control PCC voltage, to solve the problems, such as System Reactive Power voltage in-situ balancing.Reactive power compensator is studied in above-mentioned analysis to be based on more
Cooperation influences system interaction between device, stresses wind-powered electricity generation cluster regions low-frequency oscillation characteristic and oscillation source positioning, mostly more
System takes the measure of stabilizing after vibrating again.Fall out effect is played still to wind-powered electricity generation cluster low-frequency oscillation as reactive-load compensation equipment
Expansion effect is not furtherd investigate.Reactive power compensator control mode and parameter setting it is unreasonable whether be wind power plant collection
Group's low-frequency oscillation risk factor, existing research is rare to be related to the field.Genetic algorithm and particle swarm algorithm are commonly used to solve electric power
System optimization problem, it is wider to single parameter optimization application, but it is easily trapped into locally optimal solution.Existing drosophila algorithm is more
Element is searched based on the smell in drosophila foraging behavior, it is to be improved that the overall situation searches plain ability.
Summary of the invention
The purpose of the present invention is to propose to a kind of based on the SVC damping controller design method for improving drosophila algorithm, consider more
Time scale reactive source operation characteristic is accessed ratio from reactive power compensator and control strategy is started with, believed using wind power prediction
Breath reduction wind power swing influences, and analyzes Different Dynamic reactive power compensator strategy using small signal modal analysis method and control is joined
Several lower system oscillation mode, induction system low-frequency oscillation may induce factor.It is proposed a kind of join probability Eigenvalue Sensitivity mesh
Target novel improved drosophila optimizing algorithm algorithm solves optimal models, obtains the current optimal ginseng of modal system SVC damping controller
Numerical value inhibits low frequency oscillations so that coordinating the System Reactive Power balance of voltage avoids Oscillatory mode shape.System dynamic reactive-load is compensated
Device configuration design proposes guidance and related advisory, guarantees system safe and stable operation.
A kind of SVC damping controller design method based on improvement drosophila algorithm, it is characterised in that: including wind power plant cluster
Model and wind power plant cluster low-frequency oscillation analysis method；
The wind power plant cluster model, referring to is influenced using wind power prediction information reduction wind power swing, including wind
Electric field shunt capacitor group model and wind power plant SVC model；
The wind power plant cluster low-frequency oscillation analysis method refers to first through Eigenvalue Sensitivity index, reflects SVC
Influence of the control parameter to system features value under different operating conditions, can be used to preferred SVC parameter, asks then in conjunction with drosophila optimizing algorithm
Optimal models are solved, current modal system SVC damping controller optimal value of the parameter is obtained, to finally be applicable to solve SVC ginseng
Number coordination optimization problem.
The wind power swing is to influence one of System Reactive Power voltage stabilization important factor in order, introduce wind power swing because
Sub- λ-description wind power plant cluster air extract, be defined as system critical state wind power and normal condition wind power it
Difference is again the ratio between with wind power integration capacity, due to wind power bi-directional fluctuation out-of-limit, the wind power plant cluster that can cause system voltage
Quiescent voltage restricted stability condition are as follows:
To ensure that group system has sufficient air extract, preventing voltage jump caused by active interference and getting over
Limit sets the upwardly or downwardly static voltage stability that t~t+1 time domestic demand meets according to the wind power prediction information at t+1 moment
Nargin may be expressed as:
λ in formulai hlim、λi llimWind power plant i is respectively indicated t moment is upward, downward static voltage stability threshold value； Wind power plant i is respectively indicated in t and t+1 moment practical power output and prediction power output；Indicate wind power plant i installed capacity；Respectively indicate wind power plant i error upward, downward under a certain confidence level of t+1 moment wind power prediction value.
The shunt capacitor mainly provides unidirectional capacitive reactive power for system, generally uses biggish time constant
Realize grouping switching, therefore Reactive-power control ability is limited to a certain node voltage of control in a certain range, voltage control is patrolled
Volume are as follows:
1) whenCut off a group capacitor；
2) whenPut into a group capacitor；
In formula: UcpFor node voltage actual value,Indicate that node voltage controls reference value,Indicate node voltage control
Dead band value processed.
The wind power plant SVC model is mainly made of SVC device, and SVC device does not have slewing, easy to maintain, can
It quickly and smoothly realizes that reactive power continuously adjusts in perception and honoured both direction, guarantees that PCC voltage is rationally being divided into it
It is interior,
I-th SVC Expression formula of system are as follows:
In formula: BSVCFor SVC equivalent susceptance；KSVC、TSVCFor SVC controller gain and time constant；
The injecting power of SVC control node are as follows:
QSVC=-BSVCVSVC 2 (6)
In formula: VSVCFor SVC both end voltage.
The preferred SVC parameter refers to the novel improved drosophila optimizing for proposing a kind of join probability Eigenvalue Sensitivity target
Algorithm solves optimal models, obtains current modal system SVC damping controller optimal value of the parameter, wherein improving drosophila optimization algorithm
Specific steps are as follows:
1) the random initializtion drosophila group position in [0,100] range:
Rand (0,1) indicates 0 to 1 random number in formula；
2) independent drosophila is set and searches for food random direction and distance with smell, drosophila look for food iteration section be [- 50,
50]:
3) due to food Location-Unknown, initial position distance is estimated with Dis, then odorousness judgment value S calculation formula are as follows:
In formula: Δ=Disi·(0.5-δ),0≤δ≤1；
4) formula (9) is substituted into odorousness discriminant function formula (10), acquires the odorousness letter of drosophila body position
Number:
Smelli=F (Si) (11)
In formula: SiFor objective function independent variable, i.e. i-th SVC controller tuner parameters；
5) the maximum drosophila individual of odorousness in drosophila group is determined:
[best Smell, best Index]=max (Smell) (12)
6) retain maximum odorousness value and x, y, z coordinate value, drosophila group is flown using vision guide to the position at this time
It goes；
7) enter iteration searching process, repeat step 2) -5), and judge once to change before whether odorousness is better than
Generation, if so then execute step 6)；
8) an information probability of acceptance P is set to determine information content that disadvantage drosophila group will be obtained from advantage drosophila group, point
Optimum Operation sequence V Tong Guo not obtainedbest1V is distributed with optimal devicebest2, consider computational efficiency, select a suitable estimation
Value is as termination criterion.
The Optimum Operation sequence Vbest1Obtaining step it is as follows:
Step 1: each drosophila group F in analysis groupiSequence of operation vector v1, select optimal drosophila group Fbest
Sequence of operation vector vbest1；
Step 2: each operation is divided into two nonvoid subset U1, U2；Specifically, for each operation Ci(i=1,
2 ..., n), a random number randn ∈ (0,1) is generated, if randn < P, by CiIt is put into U1；Otherwise, by CiIt is put into U2；Such as
Fruit set U1, U2For empty set, then randomly chooses once-through operation and be put into empty set；
Step 3: by U1In with vbest1There are the calculating operation and v of same position1Middle same position remaining operation group again
It closes, obtains new sequence of operation vector Vbest1。
The optimal device distributes Vbest2Obtaining step it is as follows:
Step 1: each drosophila group F in analysis groupiEquipment allocation vector v2, select optimal drosophila group Fbest's
Equipment allocation vector vbest2；
Step 2: each operation is divided into two nonvoid subset U1, U2；Specifically, for each operation Wij(i=1,
2 ..., n, j=1,2 ..., ni), a uniform random number randn ∈ (0,1) is generated, if randn < P, with vbest2In
Identical equipment distributes Wij；Otherwise, then with v2In identical equipment be allocated, obtain new equipment allocation vector Vbest2。
The invention has the benefit that considering Multiple Time Scales when analyzing reactive power compensator configuration and parameter setting
Reactive source operation characteristic, weakening wind power swing by wind power prediction information in the case of the big hair of wind-powered electricity generation influences, and proposes a kind of knot
The novel improved drosophila optimizing algorithm for closing characteristic value Probability Sensitivity index solves optimal models, optimization SVC damping controller ginseng
Number.System is improved to avoid Oscillatory mode shape to inhibit low frequency oscillations with this method tunable System Reactive Power balance of voltage
Voltage stability.
Detailed description of the invention
Fig. 1 is wind power plant cluster control system schematic diagram of the invention；
Fig. 2 is SVC controller structure chart of the invention；
Fig. 3 is nMFOA optimization SVC parameter block diagram of the invention.
Specific embodiment
Essentiality content for a better understanding of the present invention carries out following explanation:
The present invention considers Multiple Time Scales reactive source operation characteristic, accesses ratio from reactive power compensator and control strategy enters
Hand is influenced using wind power prediction information reduction wind power swing, idle using small signal modal analysis method analysis Different Dynamic
System oscillation mode under compensation device strategy and control parameter, induction system low-frequency oscillation may induce factor.It is proposed a kind of knot
The novel improved drosophila optimizing algorithm algorithm for closing probability characteristics value sensitivity target solves optimal models, obtains current modal system
SVC damping controller optimal value of the parameter inhibits the vibration of system low frequency so that coordinating the System Reactive Power balance of voltage avoids Oscillatory mode shape
It swings.Design is configured to system dynamic reactive-load compensation device and proposes guidance and related advisory, guarantees system safe and stable operation.
1, wind power plant cluster control system
Wind power plant cluster control system of the invention, which refers to, considers that wind power prediction information is conducive to weaken current control week
Influence of the active fluctuation to wind power plant cluster voltage level and voltage stability, can be obtained wind power plant according to predictive information and exists in phase
The upwardly or downwardly air extract that time zone domestic demand meets, is described in detail below.
(1) it is influenced using wind power prediction information reduction wind power swing
Wind power swing is to influence one of System Reactive Power voltage stabilization important factor in order, introduces wind power swing factor lambda
Wind power plant cluster air extract is described, is defined as the difference of system critical state wind power and normal condition wind power again
The ratio between with wind power integration capacity.Since the fluctuation of wind power bi-directional can cause, system voltage is out-of-limit, and wind power plant cluster is static
Voltage restricted stability condition are as follows:
To ensure that group system has sufficient air extract, preventing voltage jump caused by active interference and getting over
Limit sets the upwardly or downwardly static voltage stability that t~t+1 time domestic demand meets according to the wind power prediction information at t+1 moment
Nargin may be expressed as: as shown in Fig. 1 dash area
λ in formulai hlim、λi llimWind power plant i is respectively indicated t moment is upward, downward static voltage stability threshold value； Wind power plant i is respectively indicated in t and t+1 moment practical power output and prediction power output；Indicate wind power plant i installed capacity；Respectively indicate wind power plant i error upward, downward under a certain confidence level of t+1 moment wind power prediction value.
(2) wind power plant shunt capacitor group model
Shunt capacitor (Shunt Capacitor Banks, SCB) mainly provides unidirectional capacitive reactive power function for system
Rate generally realizes grouping switching using biggish time constant, therefore Reactive-power control ability is limited to control in a certain range
A certain node voltage.Its voltage control logic are as follows:
1) whenCut off a group capacitor；
2) whenPut into a group capacitor.
In formula: UcpFor node voltage actual value,Indicate that node voltage controls reference value,Indicate node voltage control
Dead band value processed.
Correlative study shows that SCB has " negative voltage adjustment characteristic ", i.e., system voltage is lower, and the capacitive reactive power that SCB is provided also is got over
It is low.In addition when SCB single group capacity is larger, investment or excision SCB are larger to power grid impact, and voltage, which is easy to produce, significantly to jump
Become.By taking single wind-electricity integration system as an example, it is assumed that switching moment maintains blower active power output constant and all blowers remain permanent in field
Power factor 1 is run, and is shown below by the relationship that the available SCB of Load flow calculation puts into capacity and PCC voltage amplification:
In formula: X indicates that wind-powered electricity generation sends long feeder equivalent reactance, U outsideEIndicate Infinite bus system voltage, UWIndicate wind power plant
PCC voltage, PWIndicate the active power that wind power plant equivalence blower issues.
It can be seen that by formula (18), caused voltage change is worked as with wind power plant active power output and PCC after waiting capacity SCB to put into
Preceding voltage magnitude is related.Therefore the investment biggish SCB of single group capacity would be possible to cause wind when blower power output is close to operational limit
Machine overvoltage off-grid accident.
2, wind power plant SVC model
SVC device does not have slewing, easy to maintain, can quickly and smoothly realize in perception and honoured both direction
Reactive power continuously adjusts, and guarantees PCC voltage within being rationally divided into.
I-th SVC Expression formula of system are as follows:
In formula: BSVCFor SVC equivalent susceptance；KSVC、TSVCFor SVC controller gain and time constant.
The injecting power of SVC control node are as follows:
QSVC=-BSVCVSVC 2 (20)
SVC is issued idle directly proportional to its end voltage squared, if therefore its access point voltage fall because of failure and just can not
Nominal reactive is provided.When closing on busbar voltage decline, it is larger idle to export that SVC issues larger susceptance value.But it is limited at that time
Stagnant characteristic, SVC can not take instantaneous reactive control, the subsequent supervention of failure removal go out it is idle cause network voltage further up,
There is serious overvoltage even high pressure off-grid in system.
SVC is equipped with voltage-stablizer and can provide synchronizing torque and ignore damping torque, and damping controller can provide required additional resistance
Buddhist nun inhibits overvoltage.SVC block diagram and damping controller block diagram are shown in Fig. 2, in figure: Δ USVC, Δ ISVCFor SVC port voltage and electricity
Flow size, Δ BLFor controller output quantity, KSVCFor damping controller gain, T1/T2, T3/T4For lead-lag time constant,
Input signal is the instruction that system acquisition difference wind power swing section information issues after idle coordinated control to booster stations SVC
Signal.
3, nMFOA optimizes SVC parameter
NMFOA optimization SVC parameter of the invention, which refers to, proposes that a kind of the novel of join probability Eigenvalue Sensitivity target changes
Optimal models are solved into drosophila optimizing algorithm, obtain current modal system SVC damping controller optimal value of the parameter, are carried out below detailed
It describes in detail bright:
(1) characteristic value probability density index
Nonlinear power system characteristic value is represented by λi=σi+jωi.Work as σiWhen < 0, system is stablized, and works as ωiIt is when ≠ 0
There is a pair of of conjugation complex eigenvalue, the corresponding oscillation mode of each pair of conjugation complex eigenvalue, σ in systemiGiven damping, ωiGiven oscillation
Angular frequency.σi< 0 corresponds to damped oscillation；σi> 0 corresponds to increasing oscillation.Oscillation damping and damping ratio are respectively as follows:
In formula: An、An+1For adjacent periods amplitude maximum or minimum value.
Damping ratio ξiDetermine amplitude of oscillation attenuation rate, ξiBigger, oscillatory extinction is faster.As the interference being excited as a result,
The absolute contribution that some specific installation oscillation mode is made can be calculated:
In formula:For equipment dominant vector, i.e. SVC damping controller parameter KSVC, T1/T2, T3/T4；λiFor i-th of spy
Value indicative；For its right feature vector；ciAmplitude (when t=0) (depending on interference) is motivated for i-th of oscillation mode of system.
In view of wind-powered electricity generation is contributed and part throttle characteristics, the characteristic value of system can be distributed arbitrarily at random.In order to determine characteristic value
Probability attribute, the nonlinear equation parsing of enabled node voltage U indicates the eigenvalue λ of a certain complexitym:
λm=Fm(U) (24)
Desired value expectation can be obtained by voltage expectation, it may be assumed that
In formula (25), expecting factor is indicated with ().
The probability nature of characteristic value can be described by its expectation and variance, therefore characteristic value Probability Sensitivity also includes expectation spirit
Sensitivity and variance sensitivity.Characteristic value expectation sensitivity may be expressed as:
Formula (26b) is second order eigenvalue deri-vative, and A indicates systematic observation matrix in formula；Wm、UmRespectively eigenvalue λmA left side,
Right feature vector simultaneously meetsηi、ηjIt is herein SVC control parameter for special parameter.For with n
The system of characteristic value, left eigenvectorDerivative be represented by the linear combination of all feature vectors:
Desired Probability Sensitivity index is damped to be represented by
In formulaFor σmStandard deviation；KgFor g-th of SVC control parameter.
For damping ratioIts Probability Sensitivity index may be expressed as:
The Probability Sensitivity index obtained by formula (28), formula (29) can reflect that SVC control parameter is special to system under different operating conditions
The influence of value indicative, therefore can be used to preferred SVC parameter.
(2) the SVC controller parameter optimization based on nMFOA
In controller parameter design, some single parameter usually will affect multiple oscillation modes.It is excellent using nMFOA herein
Change SVC damping controller parameter, thus bucking-out system reactive voltage deviation, effectively inhibition low frequency oscillations.Eigenvalue λiPhase
It hopes are as follows:
In formula:For damping constant σiIt is expected that.
In order to reach optimization purpose, damping constant extension expectation is expressed as to the function of SVC damping controller parameter:
In formula:For damping constant expectation, h is damping controller sum.
Optimization problem objective function is that maximum negative damping it is expected average value:
Constraint condition:
In formula: n is all oscillation mode summations；For λiDamping ratio expectation；λi hlim、λi llimFor meter and different wind power with
Wind power plant i upward, downward air extract threshold value after machine fluctuation；Optimal Parameters value is typical range.
As the new results of nonlinear optimal problem research in recent years, drosophila optimization algorithm (Fruit Fly
Optimization Algorithm, FOA) it is a kind of new colony intelligence optimizing algorithm deduced according to drosophila foraging behavior.
Compared with other optimization algorithms such as genetic algorithm, particle swarm algorithm and ant group algorithm, FOA has very high search precision, algorithm
Structure is simple, only several parameter settings, it is easy to carry out parameter adjustment, and have very strong ability of searching optimum.But
FOA is not suitable for solution independent variable and the case where negative value occurs.Therefore, this paper presents a kind of novel improved drosophila optimization algorithms
(novel modified fruit fly optimization algorithm, nMFOA) Lai Youhua SVC damping controller ginseng
Number, can be limited to region [0 ,+∞], and improve the parallel ability of searching optimum of FOA to avoid function variable value.
FOA specific steps are as follows:
1) the random initializtion drosophila group position in [0,100] range:
Rand (0,1) indicates 0 to 1 random number in formula.
2) independent drosophila is set and searches for food random direction and distance with smell, drosophila look for food iteration section be [- 50,
50]:
3) due to food Location-Unknown, initial position distance is estimated with Dis, then odorousness judgment value S calculation formula are as follows:
In formula: Δ=Disi·(0.5-δ),0≤δ≤1。
4) formula (36) is substituted into odorousness discriminant function formula (37) (fitness function), acquires drosophila body position
Odorousness function:
Smelli=F (Si) (38)
In formula: SiFor objective function independent variable, i.e. i-th SVC controller tuner parameters.
5) determine that the maximum drosophila of odorousness is individual (maximizing) in drosophila group.
[best Smell, best Index]=max (Smell) (39)
6) retain maximum odorousness value and x, y, z coordinate value, drosophila group is flown using vision guide to the position at this time
It goes.
7) enter iteration searching process, repeat step 2) -5), and judge once to change before whether odorousness is better than
Generation, if so then execute step 6).
According to above-mentioned iterative step, required parameter is drosophila Population Size, the random of initial position, drosophila search iteration flies
Line direction and distance areas and the number of iterations.
Concertation behavior when to simulate drosophila forage reinforces FOA using multiple drosophila groups in an iterative process
Parallel ability of searching optimum.Enabling drosophila group sum in nMFOA is N, and all drosophila groups form entire drosophila group, every group of fruit
Fly group is a bivector solution, therefore total group can generate N number of bivector solution.It is raw using random initializtion strategy herein
N number of bivector solution is produced, specifically, selects the required sequence of operation at random from all operations first, the length is corresponding works
It counts the sequence length of expression.Then operation is continuously randomly distributed to by the equipment being capable of handling according to the sequence of operation.
In the search work based on smell, the subgroup of X drosophila composition can be generated around each drosophila group, it is specific next
It says, any two choose the change of the first time operating position of work that can generate a new drosophila in drosophila group sequence.
After change, original equipment distribution is modified if choosing the original equipment in work to have variation.
In the search work of view-based access control model, each drosophila subgroup is analyzed respectively, if some drosophila subgroup arrives
It is most short up to the food position used time, then the total group of drosophila is replaced with it, otherwise the total group of drosophila remains unchanged.
In nature, species improve efficiency of looking for food by information interchange cooperation, and drosophila can sharing position information and adjustment position
It sets, effectively flies to food source, be based on this characteristic, cooperative search enhancing search capability is included in nMFOA.Based on cooperation
Search process in, set an information probability of acceptance P to determine information content that disadvantage drosophila group will be obtained from advantage drosophila group,
Optimum Operation sequence V is obtained by following steps respectivelybest1V is distributed with optimal devicebest2。
Obtain Optimum Operation sequence Vbest1:
Step 1: each drosophila group F in analysis groupiSequence of operation vector v1, select optimal drosophila group Fbest
Sequence of operation vector vbest1。
Step 2: each operation is divided into two nonvoid subset U1, U2.Specifically, for each operation Ci(i=1,
2 ..., n), generate a random number randn ∈ (0,1).If randn < P, by CiIt is put into U1；Otherwise, by CiIt is put into U2.Such as
Fruit set U1, U2For empty set, then randomly chooses once-through operation and be put into empty set.
Step 3: by U1In with vbest1There are the calculating operation and v of same position1Middle same position remaining operation group again
It closes, obtains new sequence of operation vector Vbest1。
It obtains optimal device and distributes Vbest2:
Step 1: each drosophila group F in analysis groupiEquipment allocation vector v2, select optimal drosophila group Fbest's
Equipment allocation vector vbest2。
Step 2: each operation is divided into two nonvoid subset U1, U2.Specifically, for each operation Wij(i=1,
2 ..., n, j=1,2 ..., ni), generate a uniform random number randn ∈ (0,1).If randn < P, with vbest2In
Identical equipment distributes Wij；Otherwise, then with v2In identical equipment be allocated, obtain new equipment allocation vector Vbest2。
By analyzing above, the functional block diagram of nMFOA is represented by Fig. 3
As can be seen that the program includes three main process other than initialization.Search process exploitation office based on smell
Portion's solution space.The search process of view-based access control model has updated drosophila group and optimal drosophila subgroup position relatively.By with a certain true
Determine probability and share the optimal drosophila group information of universe, each drosophila group sequence of operation and equipment may be updated in the search process based on cooperation
Allocation vector.Such cooperative search process helps to explore entire solution space.Since nMFOA emphasizes to explore and develops,
It is applicable to solve SVC parameter coordination optimization problem.Consider computational efficiency, a suitable estimated value can be used to sentence as termination
According to.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included within the present invention.Any reference signs in the claims should not be construed as limiting the involved claims.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped
Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should
It considers the specification as a whole, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art
The other embodiments being understood that.
Claims (7)
1. a kind of based on the SVC damping controller design method for improving drosophila algorithm, it is characterised in that: including wind power plant cluster mould
Type and wind power plant cluster low-frequency oscillation analysis method；
The wind power plant cluster model, referring to is influenced using wind power prediction information reduction wind power swing, including wind power plant
Shunt capacitor group model and wind power plant SVC model；
The wind power plant cluster low-frequency oscillation analysis method refers to first through Eigenvalue Sensitivity index, reflection SVC control
Influence of the parameter to system features value under different operating conditions, can be used to preferred SVC parameter, solve most then in conjunction with drosophila optimizing algorithm
Excellent model obtains current modal system SVC damping controller optimal value of the parameter, to finally be applicable to solve SVC parameter association
Adjust optimization problem.
2. according to claim 1 a kind of based on the SVC damping controller design method for improving drosophila algorithm, feature exists
In: the wind power swing is to influence one of System Reactive Power voltage stabilization important factor in order, introduces wind power swing factor lambda and retouches
State wind power plant cluster air extract, be defined as the difference of system critical state wind power and normal condition wind power again with
The ratio between wind power integration capacity, due to wind power bi-directional fluctuation out-of-limit, the wind power plant cluster Static Electro that can cause system voltage
Press restricted stability condition are as follows:
To ensure that group system has sufficient air extract, voltage jump caused by active interference and out-of-limit is prevented,
According to the wind power prediction information at t+1 moment, it is abundant to set the upwardly or downwardly static voltage stability that t~t+1 time domestic demand meets
Degree, may be expressed as:
λ in formulai hlim、λi llimWind power plant i is respectively indicated t moment is upward, downward static voltage stability threshold value； Respectively
Indicate wind power plant i in t and t+1 moment practical power output and prediction power output；Indicate wind power plant i installed capacity；
Respectively indicate wind power plant i error upward, downward under a certain confidence level of t+1 moment wind power prediction value.
3. according to claim 1 a kind of based on the SVC damping controller design method for improving drosophila algorithm, feature exists
In: the shunt capacitor mainly provides unidirectional capacitive reactive power for system, is generally realized using biggish time constant
Grouping switching, therefore Reactive-power control ability is limited to a certain node voltage of control, voltage control logic in a certain range are as follows:
1) whenCut off a group capacitor；
2) whenPut into a group capacitor；
In formula: UcpFor node voltage actual value,Indicate that node voltage controls reference value,Indicate that node voltage control is dead
Zones values.
4. according to claim 1 a kind of based on the SVC damping controller design method for improving drosophila algorithm, feature exists
In: the wind power plant SVC model is mainly made of SVC device, and SVC device does not have slewing, easy to maintain, being capable of quick flat
Slidingly realize that reactive power continuously adjusts in perception and honoured both direction, guarantee PCC voltage within being rationally divided into,
I-th SVC Expression formula of system are as follows:
In formula: BSVCFor SVC equivalent susceptance；KSVC、TSVCFor SVC controller gain and time constant；
The injecting power of SVC control node are as follows:
QSVC=-BSVCVSVC 2 (6)
In formula: VSVCFor SVC both end voltage.
5. according to claim 1 a kind of based on the SVC damping controller design method for improving drosophila algorithm, feature exists
Refer to the novel improved drosophila optimizing algorithm for proposing a kind of join probability Eigenvalue Sensitivity target in: the preferred SVC parameter
Optimal models are solved, current modal system SVC damping controller optimal value of the parameter is obtained, wherein improving the tool of drosophila optimization algorithm
Body step are as follows:
1) the random initializtion drosophila group position in [0,100] range:
Rand (0,1) indicates 0 to 1 random number in formula；
2) independent drosophila is set and searches for food random direction and distance with smell, drosophila looks for food iteration section as [- 50,50]:
3) due to food Location-Unknown, initial position distance is estimated with Dis, then odorousness judgment value S calculation formula are as follows:
In formula: Δ=Disi·(0.5-δ),0≤δ≤1；
4) formula (9) is substituted into odorousness discriminant function formula (10), acquires the odorousness function of drosophila body position:
Smelli=F (Si) (11)
In formula: SiFor objective function independent variable, i.e. i-th SVC controller tuner parameters；
5) the maximum drosophila individual of odorousness in drosophila group is determined:
[bestSmell, bestIndex]=max (Smell) (12)
6) retain maximum odorousness value and x, y, z coordinate value, drosophila group is flown to using vision guide to the position at this time；
7) enter iteration searching process, repeat step 2) -5), and judge whether odorousness is better than preceding an iteration, if
It is to then follow the steps 6)；
8) an information probability of acceptance P is set to determine information content that disadvantage drosophila group will be obtained from advantage drosophila group, is obtained respectively
Obtain Optimum Operation sequence Vbest1V is distributed with optimal devicebest2, consider computational efficiency, select a suitable estimated value as eventually
Only criterion.
6. according to claim 5 a kind of based on the SVC damping controller design method for improving drosophila algorithm, feature exists
In: the Optimum Operation sequence Vbest1Obtaining step it is as follows:
Step 1: each drosophila group F in analysis groupiSequence of operation vector v1, select optimal drosophila group FbestOperation
Sequence vector vbest1；
Step 2: each operation is divided into two nonvoid subset U1, U2；Specifically, for each operation Ci(i=1,2 ...,
N), a random number randn ∈ (0,1) is generated, if randn < P, by CiIt is put into U1；Otherwise, by CiIt is put into U2；If set
U1, U2For empty set, then randomly chooses once-through operation and be put into empty set；
Step 3: by U1In with vbest1There are the calculating operation and v of same position1Middle same position remaining operation reconfigures, and obtains
To new sequence of operation vector Vbest1。
7. according to claim 5 a kind of based on the SVC damping controller design method for improving drosophila algorithm, feature exists
In: the optimal device distributes Vbest2Obtaining step it is as follows:
Step 1: each drosophila group F in analysis groupiEquipment allocation vector v2, select optimal drosophila group FbestEquipment
Allocation vector vbest2；
Step 2: each operation is divided into two nonvoid subset U1, U2；Specifically, for each operation Wij(i=1,2 ...,
N, j=1,2 ..., ni), a uniform random number randn ∈ (0,1) is generated, if randn < P, with vbest2In it is identical
Equipment distributes Wij；Otherwise, then with v2In identical equipment be allocated, obtain new equipment allocation vector Vbest2。
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110661270A (en) * | 2019-11-06 | 2020-01-07 | 电子科技大学 | Coordination control method for voltage stability of power system containing wind power |
| CN112072648A (en) * | 2020-08-28 | 2020-12-11 | 武汉大学 | Method for judging optimal access point of electric energy quality control device for inhibiting inter-harmonic resonance |
| CN113555895A (en) * | 2021-06-11 | 2021-10-26 | 国网内蒙古东部电力有限公司电力科学研究院 | Cluster wind power plant load flow analysis method and system considering multi-factor coupling influence |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105825293A (en) * | 2016-03-10 | 2016-08-03 | 桂林电子科技大学 | Fruit-fly-optimization-algorithm-based multi-station assembling sequence planning method |
-
2019
- 2019-05-24 CN CN201910437622.3A patent/CN110401205A/en not_active Withdrawn
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105825293A (en) * | 2016-03-10 | 2016-08-03 | 桂林电子科技大学 | Fruit-fly-optimization-algorithm-based multi-station assembling sequence planning method |
Non-Patent Citations (1)
| Title |
|---|
| 史昭娣: "风电集群系统电压稳定分析及无功优化控制研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110661270A (en) * | 2019-11-06 | 2020-01-07 | 电子科技大学 | Coordination control method for voltage stability of power system containing wind power |
| CN110661270B (en) * | 2019-11-06 | 2023-03-24 | 电子科技大学 | Coordination control method for voltage stability of power system containing wind power |
| CN112072648A (en) * | 2020-08-28 | 2020-12-11 | 武汉大学 | Method for judging optimal access point of electric energy quality control device for inhibiting inter-harmonic resonance |
| CN113555895A (en) * | 2021-06-11 | 2021-10-26 | 国网内蒙古东部电力有限公司电力科学研究院 | Cluster wind power plant load flow analysis method and system considering multi-factor coupling influence |
| CN113555895B (en) * | 2021-06-11 | 2022-10-18 | 国网内蒙古东部电力有限公司电力科学研究院 | Cluster wind power plant flow analysis method and system considering multi-factor coupling influence |
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