CN109066694A - Multiple target tide optimization method containing the electric system of flow controller between line - Google Patents
Multiple target tide optimization method containing the electric system of flow controller between line 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
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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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
- 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 proposes a kind of multiple target tide optimization method containing the electric system of flow controller between line, it comprises the steps of: S1: Steady state modeling being carried out to the electric system containing flow controller line using the Steady state modeling method based on additional virtual node, obtains steady-state model；S2: load flow regulation domain of the flow controller in practical power systems between the steady-state model solution outlet established by the step S1；S3: operation of the flow controller line in actual electric network is constrained using load flow regulation domain；S4: the objective function comprehensive with Operation of Electric Systems economic indicator and safety index is established, system running state is assessed；S5: tide optimization is carried out between the electric system containing flow controller line.The present invention is convenient for being applied to the iterative calculation of subsequent tide optimization, so that trend convergence is more easier, improves the practical significance of application.
Description
Technical field
The present invention relates to power system stability and control technology fields, more particularly to a kind of containing flow controller between line
The multiple target tide optimization method of electric system.
Background technique
Flow controller (Interline Power Flow Controller, IPFC) and THE UPFC between line
(Unified Power Flow Controller, UPFC) is equally all third generation flexible AC transmitting system (Flexible
AC Transmission Systems, FACTS) representative device.IPFC has powerful power flowcontrol ability, it is not only
It is only capable of directly controlling target line power as UPFC, moreover it is possible to the interaction for realizing power between a plurality of route, by heavy-haul line
Power orientation, quantitative " carrying " can effectively solve electric network swim and are unevenly distributed to light-loaded circuit, balanced passway for transmitting electricity trend is closed on
Transmission bottlenecks problem.IPFC can dynamically control the active and reactive of electric system, voltage, impedance and generator rotor angle, convenient for optimization system
System operation improves power system transient stability, has boundless application prospect.
It there is no independent IPFC practical application engineering, the CSC work that Marcy substation of the U.S. in 2004 puts into operation in the world at present
Cheng Zhong, IPFC are also merely possible to a kind of its specific operational mode.The country is even in the starting stage for the research of IPFC,
In terms of foreign countries are concentrated mainly on Steady state modeling and tide optimization for the research emphasis of IPFC.
However at present for mainly there is also following three in the modeling of power system mesomeric state containing IPFC and the research of tide optimization
A problem:
1) by IPFC model that conventional power injection method is established need based on and line impedance and admittance, the derivation of equation ten
Divide complexity, the single Load flow calculation time is long, it is difficult to be applied to subsequent tide optimization and iterate to calculate；
2) often ignore the shadow of line resistance, admittance and series coupled transformer equivalent impedance to the Steady state modeling of IPFC
It rings, however in trend iterative calculation, often lead to trend convergence difficulties because this tittle is had ignored；
3) electric system tide optimization containing IPFC research in, do not fully consider the related constraint of practical power systems with
And the practical load flow regulation domain of IPFC itself, this, which will lead to optimization acquired results, may run on system infeasible solution, without real
Border meaning.
Summary of the invention
Goal of the invention: the object of the present invention is to provide a kind of trends between containing line for solving defect existing in the prior art
The multiple target tide optimization method of the electric system of controller.
Technical solution: to reach this purpose, the invention adopts the following technical scheme:
Multiple target tide optimization method of the present invention containing the electric system of flow controller between line includes following step
It is rapid:
S1: the electric system containing flow controller line is carried out using the Steady state modeling method based on additional virtual node
Steady state modeling obtains steady-state model；
S2: flow controller is in practical power systems between the steady-state model established by the step S1 solves outlet
Load flow regulation domain；
S3: it establishes using the load flow regulation domain obtained the step S2 and electric system conventional constraint as comprehensive constraint condition
Operation of Electric Systems constraint condition containing flow controller between line, using load flow regulation domain between flow controller line in practical electricity
Operation in net is constrained；
S4: the objective function comprehensive with Operation of Electric Systems economic indicator and safety index is established, to system running state
It is assessed；
S5: the constraint condition established with the step S3 is the constraint condition based on particle swarm algorithm, with the step S4
The objective function of foundation is the evaluation index based on particle swarm algorithm, carries out trend to the electric system containing flow controller line
Optimization.
Further, the step S1 specifically includes the following steps:
S1.1: it is added at the master control circuit series transformer and auxiliary control route series transformer of flow controller between online
Virtual additional node；
S1.2: the control target instruction target word value P of given master control circuit active powerref1With the control of master control circuit reactive power
Target instruction target word value Qref1, according to master control circuit active power, master control circuit reactive power and master control circuit inverter output voltage
Between relationship, acquire the active component V of master control circuit inverter output voltageseijpWith reactive component Vseijq；
S1.3: according to active power conservation and auxiliary control route active power controller target instruction target word inside flow controller between line
Value Pref2, acquire the active component V of auxiliary control route inverter output voltageseikpWith reactive component Vseikq；
S1.4: by master control circuit inverter output voltage VseijWith auxiliary control route inverter output voltage VseikSubstitution is based on
The expression formula that Power-injected method is established, acquires node injecting power；
S1.5: substituting into power flow equation iteration for node injecting power, acquires target line control target value；
S1.6: judge whether target line control target value and trend amount of unbalance converge to instruction value；If so, stable state
Model, which has iterated to calculate, to be finished, and terminates to calculate；If it is not, then return step S1.2.
Further, the load flow regulation domain in the step S2 is obtained by following steps:
S2.1: each inverter capacity of flow controller line is limited；
S2.2: the voltage magnitude and phase angle of master control circuit inverter and the output of auxiliary control route inverter are adjusted, stable state is passed through
Model acquires each node injecting power；
S2.3: each node injecting power is substituted into power flow equation iteration, acquires master control circuit trend and auxiliary control route tide
Stream；
S2.4: being calculated two inverter output powers at this time and checks whether to will exceed beyond inverter capacity limit
The trend of limitation excludes, and obtains load flow regulation domain.
Further, in the step S3, method that operation of the flow controller line in actual electric network is constrained
Are as follows: flow solution substitution power flowcontrol domain is tested: if beyond power flowcontrol domain adjustable range, being carried out by penalty function
Processing；If without departing from without processing.
Further, the step S5 specifically includes the following steps:
S5.1: the Position And Velocity of particle populations in initialization particle swarm algorithm；
S5.2: initial Load flow calculation is carried out to the particle in each particle populations；
S5.3: examine particle whether meet Operation of Electric Systems constraint, and with step S3 establish constraint condition be based on
The constraint condition of particle swarm algorithm: if satisfied, skipping to step S5.4；If not satisfied, skipping to step S5.2 reinitializes particle；
S5.4: the fitness function value of each particle individual is calculated, is updated in the pbest and gbest of particle swarm algorithm
Data；Pbest is that particle undergoes optimum position, and gbest is the optimum position of entire population approval；
S5.5: particle position and velocity information are updated using particle swarm algorithm；
S5.6: it skips back to step S5.3 and re-executes, then check whether to reach maximum number of iterations: if so, output
As a result, terminating to calculate；If it is not, return step S5.5, iterates to calculate and updates particle information.
Further, in the step S1.6, if meeting the condition of formula (1), determine target line control target value and
Trend amount of unbalance converges to instruction value, otherwise, then determines not converge to instruction value:
In formula (1), PijrefFor master control circuit active command value, QijrefFor master control circuit reactive command value, PikrefFor auxiliary control
Route active command value, Δ P are the active power amount of unbalance in trend iterative process, and Δ Q is the nothing in trend iterative process
Function unbalanced power amount, ε1For the first convergence precision, ε2For the second convergence precision, PjmThe wattful power flowed through for m route of jth
Rate, QjmFor the reactive power that m route of jth flows through, PknThe active power flowed through for n route of kth.
The utility model has the advantages that the invention discloses a kind of multiple target tide optimization sides containing the electric system of flow controller between line
Method, compared with prior art, have it is following the utility model has the advantages that
1) present invention does not need meter and line impedance and admittance, and the derivation of equation is simple, and the single Load flow calculation time is short, is convenient for
It is iterated to calculate applied to subsequent tide optimization；
2) present invention does not ignore line resistance, admittance and the equivalent resistance of series coupled transformer to the Steady state modeling of IPFC
Anti- influence, so that trend convergence is more easier；
3) present invention has fully considered the related constraint of practical power systems and the practical load flow regulation domain of IPFC itself,
Improve the practical significance of application.
Detailed description of the invention
Fig. 1 is IPFC simple equivalent circuit schematic diagram；
Fig. 2 is that IPFC inverter output voltage decomposes vectogram；
Fig. 3 is IPFC master control circuit active power iteration convergence figure；
Fig. 4 is IPFC master control circuit reactive power iteration convergence figure；
Fig. 5 is IPFC auxiliary control route active power iteration convergence figure；
Fig. 6 is IPFC load flow regulation domain figure；
Fig. 7 is target function value iteration convergence curve graph；
Fig. 8 is IPFC steady-state model Load flow calculation flow chart；
Fig. 9 is that IPFC load flow regulation domain obtains and optimize constraint flow chart；
Figure 10 is IPFC schematic view of the mounting position in IEEE-39 node.
Specific embodiment
Technical solution of the present invention is further introduced With reference to embodiment.
Fig. 1 is the schematic equivalent circuit of flow controller (english abbreviation IPFC) between line.Wherein, route im is master control line
Road, in are auxiliary control route, and node j and k are the additional virtual node artificially added, in order to subsequent derivation calculating, Vx、θx(x=
I, j, k, m, n) be each node voltage amplitude and phase angle, Vseix、θseix(x=j, k) is connected in series transformer output voltage amplitude
And phase angle, Xseix(x=j, k) is connected in series transformer equivalent impedance, gix、bixAnd bcix/ 2 (x=j, k) are respectively route etc.
Imitate conductance, susceptance and admittance over the ground, Iix、θix(x=j, k) is the current amplitude and phase angle that the side x is flowed by the side i.
It is available by scheming:
Wherein, to avoid formula from repeating to repeat, x=j, k, similarly hereinafter.
Two line powers are then flowed to by node i are as follows:
Six∠θix=Vi∠θi(Iix∠θix)* (2)
Gained power in formula (2) is subtracted after the initial trend of route can be obtained IPFC is added, the additional note of node i, j, k
Enter power are as follows:
In formula, Pis、Qis、Pix、QixActive/reactive power, b are injected for corresponding node is additionalseij、bseikFor connected in series
Transformer equivalent susceptance.
Line power is flowed by j node to the right it is possible to further acquire are as follows:
Wherein Pjm、QjmRight-hand line road active and reactive power is flowed into for j node.Similarly, it can be derived from corresponding k node Pkn、Qkn,
It does not repeat herein.
In addition to this, under the premise of ignoring IPFC own loss, device itself does not have relative to external power system
Active power interaction, therefore have two inverter active power conservations:
Re(Vseij∠θseijIij∠θij)+Re(Vseik∠θseikIik∠θik)=0 (5)
It as shown in Fig. 2, is two inverter output voltage adjustable range of IPFC in dotted line round frame, by output voltage with route
First node i-node voltage direction is that active/idle decomposition is made in reference.Wherein, Vx、θx(x=i, seix) is respectively each corresponding voltage
Amplitude and phase angle, Vseixp、θseixpFor voltage active component amplitude and phase angle, Vseixq、θseixqFor reactive component amplitude and phase angle.
Formula (4) (5) is subjected to active/idle decomposition in the way of Fig. 3, available:
In formula (6), θij=θi-θj；θik=θi-θk, similarly hereinafter.
When carrying out power flowcontrol, auxiliary control route inverter needs to be used to maintain DC voltage stability IPFC, guarantees IPFC
Stable operation, therefore auxiliary control Line Flow is not fully controllable (be only capable of single control auxiliary control route active or reactive power).
It is general to choose that IPFC control master control circuit is active, nothing due to more paying close attention to effective power flow in engineering application when actual motion
The control strategy of function power and auxiliary control route active power.As the active and reactive instruction value P of master control circuitijref、Qijref, auxiliary control line
Road active command value PikrefIt is worth to timing, is substituted into formula (6), (7), V can be solvedseijp、Vseijq、Vseikp、VseikqValue,
Required value, which is substituted into formula (3), again can acquire the secondary power injection of respective nodes, substitute into trend iteration, more new system shape
State variable carries out the trend interative computation of the electric system containing IPFC.Sentence shown in the standard of holding back such as formula (8):
Wherein Δ P, Δ Q are trend amount of unbalance in trend iteration, ε1、ε2For convergence precision, established as a result, based on attached
Add the IPFC steady-state load flow model of dummy node.
Present embodiment discloses a kind of multiple target tide optimization side containing the electric system of flow controller between line
Method comprising the steps of:
S1: the electric system containing flow controller line is carried out using the Steady state modeling method based on additional virtual node
Steady state modeling obtains steady-state model；
S2: flow controller is in practical power systems between the steady-state model established by the step S1 solves outlet
Load flow regulation domain；
S3: it establishes using the load flow regulation domain obtained the step S2 and electric system conventional constraint as comprehensive constraint condition
Operation of Electric Systems constraint condition containing flow controller between line, using load flow regulation domain between flow controller line in practical electricity
Operation in net is constrained；
S4: the objective function comprehensive with Operation of Electric Systems economic indicator and safety index is established, to system running state
It is assessed；
S5: the constraint condition established with the step S3 is the constraint condition based on particle swarm algorithm, with the step S4
The objective function of foundation is the evaluation index based on particle swarm algorithm, carries out trend to the electric system containing flow controller line
Optimization.
IPFC is mounted in IEEE39 node system as shown in Figure 10.Fig. 3~Fig. 5 is the convergence of IPFC load flow regulation
Figure, is set as 250MW for master control circuit active power command value, reactive power is set as 50MW, and auxiliary control route active power is set
It is set to 180MW.It can be obtained from the figure that the steady-state model fast convergence rate that this patent is established, good in convergence effect, convergence precision are high.Fig. 6
What is provided is the major-minor control Line Flow regulatory domain figure of IPFC obtained with above method, is IPFC load flow regulation inside regulatory domain
Range.
Step S1 specifically includes the following steps:
S1.1: it is added at the master control circuit series transformer and auxiliary control route series transformer of flow controller between online
Virtual additional node；
S1.2: the control target instruction target word value P of given master control circuit active powerref1With the control of master control circuit reactive power
Target instruction target word value Qref1, according to master control circuit active power, master control circuit reactive power and master control circuit inverter output voltage
Between relationship, acquire the active component V of master control circuit inverter output voltageseijpWith reactive component Vseijq；
S1.3: according to active power conservation and auxiliary control route active power controller target instruction target word inside flow controller between line
Value Pref2, acquire the active component V of auxiliary control route inverter output voltageseikpWith reactive component Vseikq；；
S1.4: by master control circuit inverter output voltage VseijWith auxiliary control route inverter output voltage VseikSubstitution is based on
The expression formula that Power-injected method is established, acquires node injecting power；
S1.5: substituting into power flow equation iteration for node injecting power, acquires target line control target value；
S1.6: judge whether target line control target value and trend amount of unbalance converge to instruction value；If so, stable state
Model, which has iterated to calculate, to be finished, and terminates to calculate；If it is not, then return step S1.2.
Load flow calculation flow chart is as shown in Fig. 8.
Load flow regulation domain in step S2 is obtained by following steps:
S2.1: each inverter capacity of flow controller line is limited；
S2.2: the voltage magnitude and phase angle of master control circuit inverter and the output of auxiliary control route inverter are adjusted, stable state is passed through
Model acquires each node injecting power；
S2.3: each node injecting power is substituted into power flow equation iteration, acquires master control circuit trend and auxiliary control route tide
Stream；
S2.4: being calculated two inverter output powers at this time and checks whether to will exceed beyond inverter capacity limit
The trend of limitation excludes, and obtains load flow regulation domain.
In step S3, method that operation of the flow controller line in actual electric network is constrained are as follows: by flow solution
It substitutes into power flowcontrol domain to test: if beyond power flowcontrol domain adjustable range, be handled by penalty function；If not
Exceed, then without processing.
Load flow regulation domain acquisition methods and Load Flow Solution is constrained as shown in Fig. 9.
Step S5 specifically includes the following steps:
S5.1: the Position And Velocity of particle populations in initialization particle swarm algorithm；
S5.2: initial Load flow calculation is carried out to the particle in each particle populations；
S5.3: examine particle whether meet Operation of Electric Systems constraint, and with step S3 establish constraint condition be based on
The constraint condition of particle swarm algorithm: if satisfied, skipping to step S5.4；If not satisfied, skipping to step S5.2 reinitializes particle；
S5.4: the fitness function value of each particle individual is calculated, is updated in the pbest and gbest of particle swarm algorithm
Data；Pbest is that particle undergoes optimum position, and gbest is the optimum position of entire population approval；
S5.5: particle position and velocity information are updated using particle swarm algorithm；
S5.6: it skips back to step S5.3 and re-executes, then check whether to reach maximum number of iterations: if so, output
As a result, terminating to calculate；If it is not, return step S5.5, iterates to calculate and updates particle information.
In step S1.6, if meeting the condition of formula (9), target line control target value and trend amount of unbalance are determined
Instruction value is converged to, otherwise, then determines not converge to instruction value:
In formula (9), PijrefFor master control circuit active command value, QijrefFor master control circuit reactive command value, PikrefFor auxiliary control
Route active command value, Δ P are the active power amount of unbalance in trend iterative process, and Δ Q is the nothing in trend iterative process
Function unbalanced power amount, ε1For the first convergence precision, ε2For the second convergence precision, PjmThe wattful power flowed through for m route of jth
Rate, QjmFor the reactive power that m route of jth flows through, PknThe active power flowed through for n route of kth.
By particle swarm algorithm, optimization obtains iteration convergence curve shown in attached drawing 7.Optimization front and back main control parameters result
Comparison is as shown in table 1:
The optimization of table 1 front and back main control parameters Comparative result such as table
The economic security index of optimization front and back system and target function value comparison are as shown in table 2:
The economic security index and target function value contrast table of the optimization of table 2 front and back system
As can be seen from the results, after multiple target tide optimization, the economy of system operation and safety obtain one
Determine the raising of degree.Wherein, systematic running cost reduces 3649/h, if calculating according to annual utilization hours 3400 hours, every year may be used
To reduce about 1.241 × 107 dollars of operating cost；Security margin index promotes about 9.1% before relatively optimizing.In addition to this, excellent
Relative equilibrium before major-minor control route active power distribution relatively optimizes after change, this also greatly improves Area A trend output section
Trend ability to send outside improves ability to transmit electricity between system realm.It can be seen that optimization method proposed in this paper runs system
Economy, safety and power transmitting capability are substantially improved.
Claims (6)
1. the multiple target tide optimization method containing the electric system of flow controller between line, it is characterised in that: comprise the steps of:
S1: stable state is carried out to the electric system containing flow controller line using the Steady state modeling method based on additional virtual node
Modeling, obtains steady-state model；
S2: trend of the flow controller in practical power systems between the steady-state model solution outlet established by the step S1
Regulatory domain；
S3: it establishes and contains line using the load flow regulation domain obtained the step S2 and electric system conventional constraint as comprehensive constraint condition
Between flow controller Operation of Electric Systems constraint condition, using load flow regulation domain between flow controller line in actual electric network
Operation constrained；
S4: the objective function comprehensive with Operation of Electric Systems economic indicator and safety index is established, system running state is carried out
Assessment；
S5: the constraint condition established with the step S3 is the constraint condition based on particle swarm algorithm, with step S4 foundation
Objective function be the evaluation index based on particle swarm algorithm, between containing line flow controller electric system carry out trend it is excellent
Change.
2. the multiple target tide optimization method according to claim 1 containing the electric system of flow controller between line, special
Sign is: the step S1 specifically includes the following steps:
S1.1: addition is virtual at the master control circuit series transformer and auxiliary control route series transformer of flow controller between online
Additional node；
S1.2: the control target instruction target word value P of given master control circuit active powerref1With the control target of master control circuit reactive power
Instruction value Qref1, according between master control circuit active power, master control circuit reactive power and master control circuit inverter output voltage
Relationship, acquire the active component V of master control circuit inverter output voltageseijpWith reactive component Vseijq；
S1.3: according to active power conservation and auxiliary control route active power controller target instruction target word value inside flow controller between line
Pref2, acquire the active component V of auxiliary control route inverter output voltageseikpWith reactive component Vseikq；
S1.4: by master control circuit inverter output voltage VseijWith auxiliary control route inverter output voltage VseikIt substitutes into and is based on power
The expression formula that injection method is established, acquires node injecting power；
S1.5: substituting into power flow equation iteration for node injecting power, acquires target line control target value；
S1.6: judge whether target line control target value and trend amount of unbalance converge to instruction value；If so, steady-state model
It has iterated to calculate and has finished, terminated to calculate；If it is not, then return step S1.2.
3. the multiple target tide optimization method according to claim 1 containing the electric system of flow controller between line, special
Sign is: the load flow regulation domain in the step S2 is obtained by following steps:
S2.1: each inverter capacity of flow controller line is limited；
S2.2: the voltage magnitude and phase angle of master control circuit inverter and the output of auxiliary control route inverter are adjusted, steady-state model is passed through
Acquire each node injecting power；
S2.3: each node injecting power is substituted into power flow equation iteration, master control circuit trend and auxiliary control Line Flow are acquired；
S2.4: being calculated two inverter output powers at this time and checks whether to will exceed limitation beyond inverter capacity limit
Trend exclude, obtain load flow regulation domain.
4. the multiple target tide optimization method according to claim 1 containing the electric system of flow controller between line, special
Sign is: in the step S3, method that operation of the flow controller line in actual electric network is constrained are as follows: by trend
Solution substitutes into power flowcontrol domain and tests: if handled beyond power flowcontrol domain adjustable range by penalty function；If
Without departing from then without processing.
5. the multiple target tide optimization method according to claim 1 containing the electric system of flow controller between line, special
Sign is: the step S5 specifically includes the following steps:
S5.1: the Position And Velocity of particle populations in initialization particle swarm algorithm；
S5.2: initial Load flow calculation is carried out to the particle in each particle populations；
S5.3: examining whether particle meets Operation of Electric Systems constraint, and the constraint condition established with step S3 is based on particle
The constraint condition of group's algorithm: if satisfied, skipping to step S5.4；If not satisfied, skipping to step S5.2 reinitializes particle；
S5.4: the fitness function value of each particle individual is calculated, the data in the pbest and gbest of particle swarm algorithm are updated；
Pbest is that particle undergoes optimum position, and gbest is the optimum position of entire population approval；
S5.5: particle position and velocity information are updated using particle swarm algorithm；
S5.6: skip back to step S5.3 and re-execute, then check whether to reach maximum number of iterations: if so, output as a result,
Terminate to calculate；If it is not, return step S5.5, iterates to calculate and updates particle information.
6. the multiple target tide optimization method according to claim 2 containing the electric system of flow controller between line, special
Sign is: in the step S1.6, if meeting the condition of formula (1), determining that target line control target value and trend are uneven
Measurement converges to instruction value, otherwise, then determines not converge to instruction value:
In formula (1), PijrefFor master control circuit active command value, QijrefFor master control circuit reactive command value, PikrefFor auxiliary control route
Active command value, Δ P are the active power amount of unbalance in trend iterative process, and Δ Q is the idle function in trend iterative process
Rate amount of unbalance, ε1For the first convergence precision, ε2For the second convergence precision, PjmFor the active power that m route of jth flows through, Qjm
For the reactive power that m route of jth flows through, PknThe active power flowed through for n route of kth.
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| CN113595051A (en) * | 2020-04-30 | 2021-11-02 | 南京理工大学 | Stepped addressing and multi-objective optimization constant volume method for current power flow controller |
| CN113642818A (en) * | 2020-05-11 | 2021-11-12 | 中国能源建设集团江苏省电力设计院有限公司 | Method and device for evaluating installation scheme of inter-line moisture controller and electronic equipment |
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| CN111697588A (en) * | 2020-06-08 | 2020-09-22 | 东南大学 | Prevention control method considering IPFC control mode |
| CN111697588B (en) * | 2020-06-08 | 2022-09-09 | 东南大学 | Prevention control method considering IPFC control mode |
| CN112202167A (en) * | 2020-09-28 | 2021-01-08 | 国网江苏省电力有限公司经济技术研究院 | Multi-target coordination control method for interline power flow controllers based on fuzzy logic |
| CN112202167B (en) * | 2020-09-28 | 2022-07-12 | 国网江苏省电力有限公司经济技术研究院 | Multi-target coordination control method for interline power flow controllers based on fuzzy logic |
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| CN112398133B (en) * | 2020-10-27 | 2022-07-19 | 国网江苏省电力有限公司经济技术研究院 | IPFC model with injection power as variable and load flow calculation method thereof |
| CN114024312A (en) * | 2021-11-16 | 2022-02-08 | 国网江苏省电力有限公司电力科学研究院 | Method and system for improving reactive support capability, storage medium and computing equipment |
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