CN106058937A - Generalized load modeling method for distribution network containing direct-drive permanent magnet wind farm - Google Patents
Generalized load modeling method for distribution network containing direct-drive permanent magnet wind farm 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
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Abstract
The invention discloses a generalized load modeling method for a distribution network containing a direct-drive permanent magnet wind farm. A simplified model of a direct-drive permanent magnet synchronous generator (DDPMSG) is proposed, and the direct-drive permanent magnet synchronous generator is connected in parallel with a traditional load to be used as a distribution network generalized load model structure containing a direct-drive permanent magnet wind farm. Based on a trajectory sensitivity method, the identifiability and identification difficulty of the parameters in the generalized load are analyzed. For parameter identification, global optimization is performed first using a particle swarm optimization (PSO) algorithm, and then, local optimization is performed on the basis of the PSO result using a Levenberg-Marquardt (LM) algorithm. The model structure of the direct-drive permanent magnet synchronous generator is simplified, and the proposed parameter identification method is of high precision. The modeling method can provide reference for generalized load modeling of other renewable energy access distribution networks containing a full-bridge converter.
Description
Technical field
The present invention relates to a kind of power distribution network broad sense load modeling method containing direct-drive permanent-magnetism wind energy turbine set, belong to power train construction in a systematic way
Mould technical field.
Background technology
Distributed power source based on regenerative resource is a large amount of, dispersion accesses power distribution network, and traditional load model cannot be retouched
State the broad sense dynamic load characteristic containing distributed power source, it is therefore desirable to research containing distributed power source generalized load modeling structure and
Parameter identification method.Wind-power electricity generation is the regenerative resource of the most most exploitation prospect, and research is wide containing wind-power electricity generation
Justice load model, its top priority is set up the model of Wind turbines itself, is understood its dynamic characteristic.
The existing broad sense load Equivalent Model containing wind-power electricity generation, the excessively simplification having, it is impossible to moving of reflection broad sense load
Step response, error is bigger.Have is excessively complicated, there is model order number of parameters high, to be identified many, and model parameter may be deposited
Shortcoming in redundancy.
Summary of the invention
For solving the deficiencies in the prior art, it is an object of the invention to provide a kind of power distribution network containing direct-drive permanent-magnetism wind energy turbine set
The modeling method of broad sense load, the model set up can accurately reflect different wind-power electricity generation ratio, different Voltage Drop journey
The dynamic characteristic of the lower broad sense load of degree, has stronger adaptability.
In order to realize above-mentioned target, the present invention adopts the following technical scheme that:
A kind of power distribution network broad sense load modeling method containing direct-drive permanent-magnetism wind energy turbine set, is characterized in that, comprise the steps:
1) simplified model of direct-drive permanent-magnetism Wind turbines (DDPMSG) is set up；
2) three-phase symmetric voltage be set fall in Wind turbines outlet, by the active power under a certain Voltage Drop scene and
The disturbed path of reactive power is as reference, based on step 1) simplified model set up carries out curve fitting, and first uses population
Optimized algorithm (PSO) obtains optimum results, then uses Levenberg Marquardt (LM) algorithm on PSO optimum results basis
Upper further local optimal searching, finally obtains the identifier of each parameter in direct-drive permanent-magnetism Wind turbines simplified model；
On the basis of parameter identification result, analyze under other disturbance scene actual disturbed path with based on parameter identification value
Disturbed path deviation, analyzes the adaptability of model；
3) by step 1) (i.e. induction-motor load static state in parallel is born for the Wind turbines simplified model set up and tradition load
Lotus) in parallel, set up the power distribution network generalized load modeling structure containing direct-drive permanent-magnetism wind energy turbine set；
4) balanced three-phase voltage is set in the outlet of broad sense load and falls disturbance, by active power, the nothing of the outlet of broad sense load
Merit power, as output signal, calculates the trace sensitivity of each parameter in generalized load modeling；Big according to trace sensitivity amplitude
Little, determine the emphasis parameter in generalized load modeling；According to trace sensitivity phase place, determine distinguishing of generalized load modeling parameter
The property known；
5) according to step 4) the emphasis parameter that determines, set its hunting zone stochastic generation initial value；With step 4) set
The Voltage Drop disturbance put, active power based on the outlet of broad sense load, reactive power, as reference signal, use PSO algorithm
Emphasis parameter in identification generalized load modeling；
6) using parameter identification result based on PSO algorithm as the initial value of LM algorithm, LM method is used to carry out further
Local optimal searching, it is thus achieved that the final identification result of parameter；
7) calculate actual locus and the deviation of emulation path based on identification result, choose parameter during Deviation Indices minimum
Identification result, as final parameter identification value.
Further, described step 1) in the simplified model of direct-drive permanent-magnetism Wind turbines include meritorious link and idle ring
Joint；The deviation that input quantity is Wind turbines active power reference value and active power actual value of described meritorious link, output ring
Joint is d shaft current；The input quantity of described idle link is relevant with the control mode of Wind turbines.
Further, described step 2) in curve matching, the method for identification direct-drive permanent-magnetism wind turbine model parameter.Identification
Object function is that Deviation Indices err minimizes, it may be assumed that
Wherein, always counting in n is identification time window；Pest(k) and QestK in () respectively identification time window, kth is based on distinguishing
Know active power and the reactive power of the directly driven wind-powered machine set outlet that result emulation obtains；Preal(k) and QrealK () is respectively distinguished
In knowing time window, kth emulates active power and the reactive power obtaining directly driven wind-powered machine set outlet based on actual value.
Further, described step 4) in the acquisition methods of trace sensitivity as follows:
41) numerical value of parameter θ to be identified is increased Δ θ, power distribution network broad sense based on Matlab/Simulink platform building
Load grid-connected system, emulation obtains the disturbed track y (θ) of observed quantity y, and wherein Δ θ is increment, Δ θ=10% θ0, wherein θ0For
The initial value of parameter θ to be identified；
42) numerical value of parameter θ to be identified reducing Δ θ, emulation obtains the disturbed track y ' (θ) of observed quantity y；
43) according to formulaCalculate trace sensitivity S of parameter θj, in formula, n
Sampled point number for observed quantity.
Further, described step 4) in the system of selection of emphasis parameter be: in the trace sensitivity of all parameters, choosing
Selecting the parameter of more than trace sensitivity minima η times as emphasis parameter, η is empirically worth selection.
Further, it is characterized in that, described η=5.
Further, described step 5) emphasis parameter in identification broad sense loadThe object function of identification is Deviation Indices
Err minimizes, it may be assumed that Wherein
θmaxAnd θminBe respectively the maximum set value of parameter to be identified and minimum setting value, other parameter values and step 2) in the ginseng that relates to
It is identical that numerical value represents implication.
The beneficial effect that the present invention is reached: the simplified model of the direct-drive permanent-magnetism Wind turbines that the present invention proposes, not only ties
Structure is simple, moreover it is possible to reflection Wind turbines dynamic characteristic under different Voltage Drop degree, has stronger adaptability；Using should
The model structure of the direct-drive permanent-magnetism Wind turbines parallel connection tradition load simplified, as the power distribution network broad sense containing direct-drive permanent-magnetism wind energy turbine set
Load model structure, parameter identification first carries out global optimizing with population (PSO) optimized algorithm, and recycling LM algorithm is excellent at PSO
Further local optimal searching on the basis of changing result.Carried modeling method not only simplify the model structure of broad sense load, and propose
Parameter identification method has higher identification precision.
Accompanying drawing explanation
Fig. 1 is the workflow diagram of the present invention；
Fig. 2 (a) (b) is the meritorious link of the direct-drive permanent-magnetism wind turbine model simplified and idle link respectively；
Fig. 3 is the analogue system wiring diagram containing direct-drive permanent-magnetism Wind turbines；
Fig. 4 (a) (b) is that three-phase voltage is fallen 10%U by simplified model identification result respectivelyNDegree, Voltage Drop 30%UN
Adaptability；
Fig. 5 is the power distribution network generalized load modeling structure containing direct-drive permanent-magnetism wind energy turbine set；
Fig. 6 is the broad sense load emulation test system containing directly driven wind-powered field；
Fig. 7 is Voltage Drop 20%UNUnder the trace sensitivity of parameter；
Fig. 8 is Voltage Drop 80%UNUnder the trace sensitivity of parameter；
Fig. 9 is Voltage Drop 20%UNUnder disturbed path based on actual parameter Yu identified parameters；
Figure 10 is Voltage Drop 80%UNUnder disturbed path based on actual parameter Yu identified parameters.
Detailed description of the invention
The invention will be further described below in conjunction with the accompanying drawings.Following example are only used for clearly illustrating the present invention
Technical scheme, and can not limit the scope of the invention with this.
The present invention is illustrated the most in conjunction with the embodiments:
Step 1) propose direct-drive permanent-magnetism Wind turbines simplified model, as shown in Figure 2.
In Fig. 2, PrefAnd QrefBeing respectively active power and reactive power setting value, its numerical value is determined by 2 kinds of modes, a kind of
Directly being assigned by wind energy turbine set station level control system, a kind of running status by direct-drive permanent-magnetism Wind turbines determines.P, Q, U and θ
It is respectively the active power of Wind turbines, reactive power, terminal voltage amplitude and power-factor angle.Respectively there is power control
The ratio of link processed and integral coefficient.TPMeasurement time constant for real power control link.UrefFor voltage reference value.
It is respectively the integral coefficient of idle control and voltage-controlled integral coefficient.idFor d axle actual value；idN、iqNIt is respectively d axle and q
The rated value of shaft current；id.cmd、iq.cmdIt is respectively d axle and the controlled quentity controlled variable of q shaft current；Lmt is the amplitude limit value of d shaft current；S1、S2
And S3For switch；S is the Laplace transformation factor.
Switch S1-S3Different conditions, in order to switch the different control modes of direct-drive permanent-magnetism Wind turbines, specific as follows:
1) S is worked as1=0 and S3When=0, correspondence determines Reactive Power Control mode；
2) S is worked as1=1 and S3When=0, correspondence determines power factor controlling mode；
3) S is worked as2=0 and S3When=1, correspondence determines voltage control mode；
4) S is worked as2=1 and S3When=1, corresponding voltage/idle control mode.
Step 2) simplified model of direct-drive permanent-magnetism Wind turbines is carried out Adaptability Analysis.
As a example by Fig. 3 system.Wherein, T1 and T2 is for rising buckling, l1 and l2 is transmission line of electricity；Fault is set to t=1.7s
Time, there is three phase short circuit fault f, now bus B2 Voltage Drop 20%U at bus B1NLeft and right, fault is extensive after continuing 0.2s
Multiple.Emulation obtains Wind turbines active-power P and the disturbed path of reactive power Q under this fault, on the basis of this disturbed path,
The simplified model shown in Fig. 2 is used to be fitted, according to each parameter in curve-fitting results identification simplified model
During curve matching, particle swarm optimization algorithm (PSO) is first used to obtain optimum results, further with Levenberg
Marquardt (LM) algorithm local optimal searching on the basis of PSO optimum results: the object function of identification is that Deviation Indices err reaches
Minimum, i.e.The time window of identification elect as [1.7,
2.3] s, the step-length of curve of output is 0.00005s.
For the adaptability of simplified model shown in check plot 2, it is still at bus B1 arranging three phase short circuit fault, by adjusting
Earth resistance so that bus B2 Voltage Drop is respectively 10%UNAnd 30%UN, based on Voltage Drop 20%UNUnder parameter identification
Result obtains Wind turbines active power and the disturbed path of reactive power.In the case of 2 kinds, reference model and appraising model is subject to
Disturb path and see that Fig. 4, curve bias contribution are respectively err (10%UN)=0.0029, err (30%UN)=0.0115, UNExpression volume
Determine voltage.Illustrate that the model of direct-drive permanent-magnetism Wind turbines has stronger adaptability according to Fig. 4.
Step 3) propose to contain the model structure of the power distribution network broad sense load of direct-drive permanent-magnetism wind energy turbine set, such as Fig. 5.Wherein, PwgWith
QwgIt is respectively total active power and reactive power that wind energy turbine set sends.PimAnd QimBe respectively motor absorb active power and
Reactive power.PzipAnd QzipIt is respectively active power and reactive power that static load absorbs.PLAnd QLIt is respectively total load to gain merit
Power and reactive power.P and Q is respectively active power and the reactive power of system side supply broad sense load.J is imaginary unit.
Definition wind energy turbine set generated output and induction-motor load power proportions coefficient, be respectively
Wherein, ZIP represents that static load, IM represent induction motor load, and their model expression is as follows.
ZIP static load model:Wherein, ZP、IP、
PP、Zq、Iq、PqBe respectively meritorious constant-impedance coefficient, meritorious constant current coefficient, meritorious invariable power coefficient, idle constant-impedance coefficient,
Idle constant current coefficient and idle invariable power coefficient, and meetLDP、LDQIt is respectively meritorious and idle frequency
Rate characteristic coefficient.PZIPAnd QZIPIt is respectively active power and the reactive power of static load.PZIP0And QZIP0It is respectively static load
Initial active power and reactive power.ULFor load ports voltage.UL0Load ports Initial Voltage Value.f0At the beginning of system frequency
Initial value.Δ f is frequency departure, Δ f=f-f0, f is system frequency.
IM induction-motor load model:In formula, ωrAnd ωsIt is respectively rotor angle
Speed and system angle speed；E ' and E 'fIt is respectively transient potential and excitation electromotive force；X ' is transient state reactance；T ' is time constant；H
For inertia time constant；C is constant；U is motor port voltage magnitude；δ is rotor angle；TmFor machine torque.
For induction-motor load, the parameter that sensitivity is bigger is stator reactance Xs, initial slippage s0, and to transient stability
Calculate motor ratio K that impact is biggerim, it is thus determined that parameter to be identified is θ=[K in broad sense loadPp,KPi,Tp,lmt,Xs,
H,s0,Kwg,Kim], other parameter uses representative value.
Step 4) the power distribution network broad sense load containing direct-drive permanent-magnetism wind energy turbine set is accessed infinitely great electrical network, that analyzes each parameter can
Identification and identification difficulty.As shown in Figure 6, in figure, WG represents Wind turbines to analogue system；Z represents constant-impedance load；l1-l3
For transmission line of electricity；T1 T3 is transformator.This analogue system is built in Matlab 2010b software, direct-drive permanent-magnetism wind in system
Electric field takes from the wind energy turbine set example (power_wind_type_4_avg) that Matlab carries, and Wind turbines uses determines reactive power
Control mode (Qref=0).Direct-drive permanent-magnetism wind energy turbine set and conventional load are connected at the bus B1 of 25kV by liter buckling, bus B1
Boosting is incorporated to the Infinite bus system of 120kV further, and static load part uses constant-impedance load model.
Disturbance is set at bus B1 arrange three phase short circuit fault, by changing earth resistance, voltage at bus B1 is fallen
Fall respectively 20%UNAnd 80%UN, fault initial time is 1.7s, and the persistent period is 0.2s, gathers the voltage at B1, wattful power
Rate, reactive power are as measured data.
Calculating the trace sensitivity of each parameter, method is as follows:
41) numerical value of parameter θ to be identified is increased Δ θ, power distribution network broad sense based on Matlab/Simulink platform building
Load grid-connected system, emulation obtains the disturbed track y (θ) of observed quantity y；
42) numerical value of parameter θ to be identified reducing Δ θ, emulation obtains the disturbed track y ' (θ) of observed quantity y；
43) according to formulaCalculate trace sensitivity S of parameter θj, in formula, n is
The sampled point number of observed quantity, Δ θ=10% θ0, wherein θ0Initial value for parameter θ to be identified.
Voltage Drop is respectively 20%UNAnd 80%UN, and wind-powered electricity generation ratio is Kwg=50%, Kim=50% time each parameter
Trace sensitivity curve, is shown in Fig. 7 and Fig. 8 respectively.
According to Fig. 7 trace sensitivity curve in the case of Voltage Drop is less, it will be seen that each parameter in generalized load modeling
[KPp,KPi,Tp,Xs, H] trace sensitivity curve difference time zero passage, discriminant parameter can unique identification accordingly.Rail according to Fig. 6
Mark level of sensitivity, it is judged that parameter [Tp,Xs, H] trace sensitivity numerical value relatively big, be easily discernible；Parameter [KPp, KPi] track
Sensitivity number is less, is relatively difficult to identification.And due to the initial slippage s of motor0, wind energy turbine set generated output Proportional coefficient Kwg
And induction-motor load power proportions COEFFICIENT KimRelatively big to the dynamic image of broad sense load, therefore when Voltage Drop is less, choosing
Select parameter [KPp,KPi,Tp,Xs,H,s0,Kwg,Kim] as parameter to be identified.
According to Fig. 8 trace sensitivity size in the case of three-phase voltage falls relatively greatly, draw parameter [KPp,KPi,Tp]
Trace sensitivity is less, but parameter [lmt, Xs, H] trace sensitivity bigger.Trace sensitivity curve shape according to Fig. 8 comes
See, parameter [lmt, Xs, H] trace sensitivity curve difference time zero passage, can unique identification.Therefore in the bigger situation of Voltage Drop
Under, Selection parameter [lmt, Xs,H,s0,Kwg,Kim] as parameter to be identified.
Step 5) comprehensive PSO and LM method carries out parameter to the power distribution network generalized load modeling containing direct-drive permanent-magnetism wind energy turbine set and distinguishes
Know.
If operating mode is: Kwg=50%, Kim=50%, detailed model based on Matlab/Simulink platform building, imitative
The true three-phase voltage that obtains falls lower broad sense load outlet active power and the disturbed path of reactive power, and as benchmark, based on
The simplified model of distribution networks containing direct-drive permanent-magnetism wind energy turbine set proposed is fitted, accordingly each parameter in identification simplified model.
First use PSO method to obtain optimum results during identification, then use LM algorithm local on the basis of PSO optimum results to seek
Excellent.
Object function is
At Kwg=50%, KimParameter identification result under=50% operating mode is shown in Table 1.Three-phase voltage falls 20%UNUnder
The parameter identification result of PSO method, is shown in Table 1.
Table 1 Voltage Drop 20%UNThe parameter identification result of lower PSO method
Step 6) using the result of PSO algorithm as the initial point of LM algorithm, carry out local optimal searching, object function and population
Algorithm object function is consistent, and under Voltage Drop 20%UN, LM algorithm identification result, is shown in Table 2.Choose the identification that in table 2, error is minimum
Result is as the final identifier of parameter, the 2nd row parameter being shown in Table in 2.Identification result according to parameter carries out reality and distinguishes
Know the simulation curve contrast of result, see Fig. 9.
Table 2 Voltage Drop 20%UNThe parameter identification result of lower LM method
Use identical method, Voltage Drop 80%U can be obtainedNThe parameter identification result of PSO and LM method, be shown in Table 3.
Parameter identification result under table 3 Voltage Drop 50%UN
Step 7) choose the minimum identification result of error in the table 3 final identifier as parameter, the 3rd be shown in Table in 3
Row parameter.Identification result according to parameter carries out the actual simulation curve contrast with identification result, sees Figure 10.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For Yuan, on the premise of without departing from the technology of the present invention principle, it is also possible to make some improvement and deformation, these improve and deformation
Also should be regarded as protection scope of the present invention.
Claims (7)
1. the power distribution network broad sense load modeling method containing direct-drive permanent-magnetism wind energy turbine set, is characterized in that, comprise the steps:
1) simplified model of direct-drive permanent-magnetism Wind turbines is set up；
2) three-phase symmetric voltage is set falls in Wind turbines outlet, by the active power under a certain Voltage Drop scene and idle
The disturbed path of power is as reference, based on step 1) simplified model set up carries out curve fitting, and first uses particle group optimizing
Algorithm obtains optimum results, then uses Levenberg Marquardt algorithm on the basis of particle swarm optimization algorithm optimum results
Local optimal searching, finally obtains the identifier of each parameter in direct-drive permanent-magnetism Wind turbines simplified model further；
On the basis of parameter identification result, analyze under other Voltage Drop scene actual disturbed path with based on parameter identification value
Disturbed path deviation, analyzes the adaptability of model；
3) by step 1) simplified model set up is in parallel with tradition load, sets up the power distribution network broad sense containing direct-drive permanent-magnetism wind energy turbine set
Load model structure；
4) balanced three-phase voltage is set in the outlet of broad sense load and falls disturbance, the active power that broad sense load is exported, idle merit
Rate, as output signal, calculates the trace sensitivity of each parameter in generalized load modeling；According to trace sensitivity amplitude size, really
Determine the emphasis parameter in generalized load modeling；According to trace sensitivity phase place, determine the identifiability of generalized load modeling parameter；
5) according to step 4) the emphasis parameter that determines, set its hunting zone stochastic generation initial value；With step 4) arrange
Voltage Drop disturbance, active power based on the outlet of broad sense load, reactive power, as reference signal, use Particle Swarm Optimization
Emphasis parameter in method identification generalized load modeling；
6) using parameter identification result based on particle swarm optimization algorithm as the initial value of Levenberg Marquardt algorithm,
Levenberg Marquardt algorithm is used to carry out local optimal searching further, it is thus achieved that the final identification result of parameter；
7) calculate actual locus and the deviation of emulation path based on identification result, choose parameter identification during Deviation Indices minimum
As a result, as final parameter identification value.
A kind of power distribution network broad sense load modeling method containing direct-drive permanent-magnetism wind energy turbine set the most according to claim 1, its feature
Be, described step 1) in the simplified model of direct-drive permanent-magnetism Wind turbines include meritorious link and idle link；Described meritorious link
The deviation that input quantity is Wind turbines active power reference value and active power actual value, output element is d shaft current；Described
The input quantity of idle link is relevant with the control mode of Wind turbines.
A kind of power distribution network broad sense load modeling method containing direct-drive permanent-magnetism wind energy turbine set the most according to claim 1, its feature
Be, described step 2) in curve matching identification direct-drive permanent-magnetism wind turbine model parameter, the object function of identification is Deviation Indices
Err minimizes, it may be assumed thatWherein, n is the identification time
Always counting in window；Pest(k) and QestK in () respectively identification time window, kth obtains based on identification result emulation
The active power of directly driven wind-powered machine set outlet and reactive power；Preal(k) and QrealK () is respectively kth in identification time window
Active power and the reactive power of directly driven wind-powered machine set outlet is obtained based on actual value emulation.
A kind of power distribution network broad sense load modeling method containing direct-drive permanent-magnetism wind energy turbine set the most according to claim 1, its feature
Be, described step 4) in the acquisition methods of trace sensitivity as follows:
41) numerical value of parameter θ to be identified is increased Δ θ, power distribution network broad sense load based on Matlab/Simulink platform building
Grid-connected system, emulation obtains the disturbed track y (θ) of observed quantity y, and wherein Δ θ is increment, Δ θ=10% θ0, wherein θ0For waiting to distinguish
Know the initial value of parameter θ；
42) numerical value of parameter θ to be identified reducing Δ θ, emulation obtains the disturbed track y ' (θ) of observed quantity y；
43) according to formulaCalculate trace sensitivity S of parameter θj, in formula, n is observation
The sampled point number of amount.
A kind of power distribution network broad sense load modeling method containing direct-drive permanent-magnetism wind energy turbine set the most according to claim 1, its feature
Be, described step 4) in the system of selection of emphasis parameter be: in the trace sensitivity of all parameters, select trace sensitivity
The parameter that little value is more than η times is as emphasis parameter, and η is empirically worth selection.
A kind of power distribution network broad sense load modeling method containing direct-drive permanent-magnetism wind energy turbine set the most according to claim 4, its feature
It is, described η=5.
A kind of power distribution network broad sense load modeling method containing direct-drive permanent-magnetism wind energy turbine set the most according to claim 1, its feature
It is, described step 5) emphasis parameter in identification broad sense loadTime, the object function of identification is that Deviation Indices err minimizes,
That is: Wherein θmaxAnd θminRespectively
For maximum set value and the minimum setting value of parameter to be identified, n is always counting in identification time window；Pest(k) and Qest(k)
Respectively in identification time window, kth emulates active power and the nothing of the directly driven wind-powered machine set outlet obtained based on identification result
Merit power；Preal(k) and QrealK () is respectively identification time window in, kth obtains directly driven wind-powered unit based on actual value emulation
The active power of outlet and reactive power.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107453401A (en) * | 2017-09-13 | 2017-12-08 | 河海大学 | A kind of double-fed wind power generator parameter identification method |
| CN108460228A (en) * | 2018-03-21 | 2018-08-28 | 电子科技大学 | A method of it is equivalent that wind power plant being carried out based on multi-objective optimization algorithm |
| CN109217336A (en) * | 2018-09-05 | 2019-01-15 | 河海大学 | Synchronous generator damped coefficient on-line identification method based on two-zone model |
| CN110688808A (en) * | 2019-09-24 | 2020-01-14 | 南通大学 | Particle swarm and LM optimization hybrid iterative identification method of power battery model |
| CN111416377A (en) * | 2020-03-16 | 2020-07-14 | 南方电网科学研究院有限责任公司 | Flexible direct current control method and device for improving transient stability of power grid |
| CN111651939A (en) * | 2020-06-08 | 2020-09-11 | 华北电力大学 | Dynamic equivalent modeling method for permanent magnet wind power plant considering converter control parameter difference |
| CN111725840A (en) * | 2020-06-29 | 2020-09-29 | 浙江大学 | Parameter identification method for direct-drive wind generating set controller |
| CN114462218A (en) * | 2022-01-19 | 2022-05-10 | 中国人民解放军陆军工程大学 | Simulation method and simulation device for power consumption of power electronic equipment |
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107453401A (en) * | 2017-09-13 | 2017-12-08 | 河海大学 | A kind of double-fed wind power generator parameter identification method |
| CN108460228A (en) * | 2018-03-21 | 2018-08-28 | 电子科技大学 | A method of it is equivalent that wind power plant being carried out based on multi-objective optimization algorithm |
| CN109217336A (en) * | 2018-09-05 | 2019-01-15 | 河海大学 | Synchronous generator damped coefficient on-line identification method based on two-zone model |
| CN110688808A (en) * | 2019-09-24 | 2020-01-14 | 南通大学 | Particle swarm and LM optimization hybrid iterative identification method of power battery model |
| CN110688808B (en) * | 2019-09-24 | 2022-06-24 | 南通大学 | Particle swarm and LM optimization hybrid iterative identification method of power battery model |
| CN111416377A (en) * | 2020-03-16 | 2020-07-14 | 南方电网科学研究院有限责任公司 | Flexible direct current control method and device for improving transient stability of power grid |
| CN111416377B (en) * | 2020-03-16 | 2021-07-30 | 南方电网科学研究院有限责任公司 | Flexible direct current control method and device for improving transient stability of power grid |
| CN111651939A (en) * | 2020-06-08 | 2020-09-11 | 华北电力大学 | Dynamic equivalent modeling method for permanent magnet wind power plant considering converter control parameter difference |
| CN111651939B (en) * | 2020-06-08 | 2024-02-06 | 华北电力大学 | Permanent magnet wind power plant dynamic equivalent modeling method considering control parameter difference of converter |
| CN111725840A (en) * | 2020-06-29 | 2020-09-29 | 浙江大学 | Parameter identification method for direct-drive wind generating set controller |
| CN114462218A (en) * | 2022-01-19 | 2022-05-10 | 中国人民解放军陆军工程大学 | Simulation method and simulation device for power consumption of power electronic equipment |
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