CN114925538A - Wind power plant equivalent modeling method, device and medium applied to electromagnetic transient simulation - Google Patents
Wind power plant equivalent modeling method, device and medium applied to electromagnetic transient simulation Download PDFInfo
- Publication number
- CN114925538A CN114925538A CN202210611672.0A CN202210611672A CN114925538A CN 114925538 A CN114925538 A CN 114925538A CN 202210611672 A CN202210611672 A CN 202210611672A CN 114925538 A CN114925538 A CN 114925538A
- Authority
- CN
- China
- Prior art keywords
- wind
- power plant
- wind power
- equivalent
- collection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/06—Wind turbines or wind farms
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Probability & Statistics with Applications (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a wind power plant equivalent modeling method, a device and a medium applied to electromagnetic transient simulation, wherein the method comprises the steps of decoupling a radiation-shaped wind power plant set power grid into a parallel wind power plant set power grid, and calculating equivalent collector impedance of each wind turbine in the wind power plant set power grid; taking the outlet voltage of each wind turbine generator as a clustering index, and classifying and aggregating the wind turbine generators by adopting a K-means clustering algorithm based on a community discovery algorithm to obtain aggregated wind turbine generators and a wind farm aggregation model; aiming at the wind power plant aggregation model, a centralized reactive compensation capacitor is arranged at the outlet of each aggregation wind turbine generator and a centralized reactive compensation capacitance value is calculated; and adjusting the zero sequence gain coefficient of the current collection lines in the wind power plant collection grid to enable the zero sequence current collection grid parameters to be equivalent, and obtaining a wind power plant dynamic equivalent model. The modeling precision of the wind power plant dynamic equivalent model applied to the electromagnetic transient simulation is improved.
Description
Technical Field
The invention relates to the technical field of wind power plant modeling, in particular to a wind power plant equivalent modeling method, device and medium applied to electromagnetic transient simulation.
Background
At present, the capacity of a large-scale wind farm can reach hundreds of megawatts, and each wind farm will contain dozens or even hundreds of wind turbines. If each wind turbine is modeled in detail, the model is too complex, the electromagnetic transient simulation calculated amount is large, the simulation time is long, and the method is not suitable for actual engineering analysis. Therefore, Dynamic Equivalent Modeling (DEM) needs to be performed on the wind power plant, the whole system is simplified on the premise of ensuring accuracy, and the calculation speed is increased.
At present, the dynamic equivalence methods of wind power plants are mainly divided into two types: single machine equivalence and multiple machine equivalence. The method is characterized in that the single machine equivalence equivalently aggregates all wind turbines in a wind power plant into one wind power plant, a capacity weighted average method is mainly adopted, the positions of the wind turbines in the wind power plant and the electrical connection among the wind turbines are not considered, and only the response characteristic of the whole wind power plant is concerned. The single-machine equivalent operation speed is high, but the model accuracy is low. The multi-machine equivalence mainly adopts a parameter identification method, the wind turbines are classified according to the similarity of different operation parameters of the wind turbines, and the wind turbines with the operation parameters close to each other are subjected to equivalence aggregation. The multi-machine equivalent accuracy is high, but the operation time complexity is high due to the fact that the internal operation parameters of the wind turbine need to be acquired and the parameters need to be identified.
In practical engineering, it is necessary to exactly equate the collector networks between the individual wind turbines in a wind farm. Most of the related multi-machine equivalent modeling methods are not started from a wind power plant topological structure, and the current collection network equivalent method is rough. Meanwhile, the accuracy of the equivalent model is lower when the wind power plant has asymmetric faults because the equivalent method of the zero sequence current collection network is not considered. In addition, the dynamic equivalent modeling method for the wind power plant of the full-power converter type wind power generator set is less researched at present. A dynamic equivalent modeling method for a full-power converter wind turbine generator for electromagnetic transient simulation is lacked, and the method cannot be applied to an electromagnetic transient simulation scene of a large-scale wind power plant.
Disclosure of Invention
The invention provides a wind power plant equivalent modeling method, device and medium applied to electromagnetic transient simulation, and the modeling precision of a wind power plant dynamic equivalent model applied to electromagnetic transient simulation is improved.
An embodiment of the invention provides a wind power plant equivalent modeling method applied to electromagnetic transient simulation, which comprises the following steps:
decoupling a radiation-shaped wind power plant power collection grid into a parallel wind power plant power collection grid, and calculating equivalent collector impedance of each wind turbine generator in the wind power plant power collection grid;
taking the outlet voltage of each wind turbine as a clustering index, and classifying and aggregating the wind turbines by adopting a K-means clustering algorithm based on a community discovery algorithm to obtain an aggregated wind turbine and a wind farm aggregation model;
aiming at the wind power plant aggregation model, a centralized reactive compensation capacitor is arranged at an outlet of each aggregation wind turbine generator and is used for calculating a centralized reactive compensation capacitance value, and the centralized reactive compensation capacitor is used for simulating a distributed capacitance effect of a wind power plant power collection grid;
and adjusting the zero sequence gain coefficient of the current collection lines in the wind power plant current collection grid to enable the zero sequence current collection grid parameters to be equivalent and obtain a wind power plant dynamic equivalent model.
Further, after the radiation-shaped wind power plant power collection grid is decoupled into the parallel wind power plant power collection grid, before the equivalent collector impedance of each wind power unit in the wind power plant power collection grid is calculated, first power flow calculation is carried out on the wind power plant power collection grid to obtain alternating voltage output by each wind power unit before equivalence, injection current, collection point alternating voltage and first reactive power injected into the alternating current power grid by the wind power plant;
and calculating the equivalent collection impedance of each wind turbine in the wind power plant collection grid according to the alternating voltage, the injected current and the collection point alternating voltage.
Further, with the outlet voltage of each wind turbine generator as a clustering index, classifying and aggregating the wind turbine generators by adopting a K-means clustering algorithm based on a community discovery algorithm to obtain a wind power plant aggregation model, specifically comprising the following steps:
after the equivalent collection impedance of each wind turbine in the wind power plant collection grid is calculated, carrying out secondary load flow calculation on the wind power plant collection grid to obtain an alternating voltage amplitude output by each wind turbine after the equivalence and a data set consisting of the alternating voltage amplitudes;
and classifying and aggregating the data set of the alternating voltage amplitude by adopting a K-means clustering algorithm based on a community discovery algorithm, and calculating the outlet equivalent impedance of each aggregated wind turbine generator after classification and aggregation to obtain a wind power plant aggregation model.
Further, when the data set of the alternating voltage amplitude is subjected to classification and aggregation, the optimal classification number of the K-means algorithm is automatically determined by adopting a community discovery algorithm, and then the data set of the alternating voltage amplitude is subjected to classification and aggregation according to the optimal classification number.
Further, the step of calculating the centralized reactive compensation capacitance value comprises the following steps:
performing third power flow calculation on the wind power plant power collection grid to obtain second reactive power injected into the alternating current power grid by the wind power plant;
and calculating a centralized reactive compensation capacitor at the outlet of the wind power plant according to the second reactive power and the first reactive power.
Further, adjusting a zero sequence gain coefficient of a collecting line in the wind power plant power collection network to enable parameters of the zero sequence collecting network to be equivalent, and the method comprises the following steps:
when the first zero-sequence equivalent impedance is larger than the second zero-sequence equivalent impedance, increasing a zero-sequence gain coefficient of a current collecting line in the wind power plant power collecting network; the first zero-sequence equivalent impedance is zero-sequence equivalent impedance of an original wind power plant grid, and the second zero-sequence equivalent impedance is zero-sequence gain coefficient of a collecting line in the wind power plant grid, so that zero-sequence equivalent impedance of the wind power plant grid is obtained after zero-sequence collecting grid parameters are equivalent;
and when the first zero-sequence equivalent impedance is smaller than the second zero-sequence equivalent impedance, reducing the zero-sequence gain coefficient of the current collection line in the wind power plant power collection network.
Furthermore, the zero sequence gain coefficient is adjusted by adopting a heuristic method.
Further, when adjusting the zero sequence gain coefficient of the collecting line in the wind power plant set grid, increasing or decreasing an adjustment step length each time, and calculating the zero sequence deviation coefficient of the current zero sequence gain coefficient of the wind power plant set grid;
obtaining a minimum value of the zero sequence deviation coefficient and an optimal zero sequence gain coefficient corresponding to the minimum value through increasing or decreasing an adjustment step length for multiple times;
and increasing or reducing the zero sequence gain coefficient of the current collection circuit in the wind power plant power collection network according to the optimal zero sequence gain coefficient.
The invention provides a wind power plant equivalent modeling device applied to electromagnetic transient simulation, which comprises a current collection grid decoupling module, a classification aggregation module, a capacitance value calculation module and a parameter equivalent module;
the power collection network decoupling module is used for decoupling a radiation-shaped wind power plant power collection network into a parallel wind power plant power collection network and calculating equivalent power collection impedance of each wind turbine generator in the wind power plant power collection network;
the classification and aggregation module is used for classifying and aggregating the wind turbines by using the outlet voltage of each wind turbine as a clustering index and adopting a K-means clustering algorithm based on a community discovery algorithm to obtain an aggregated wind turbine and a wind farm aggregation model;
the capacitance value calculation module is used for calculating the capacitance value of a centralized reactive compensation capacitor arranged at the outlet of each aggregated wind turbine generator, and the centralized reactive compensation capacitor is used for simulating the distributed capacitance effect of a wind power plant power collection grid;
the parameter equivalence module is used for adjusting a zero sequence gain coefficient of a power collection line in the wind power plant power collection grid to enable parameters of the zero sequence power collection grid to be equivalent and obtain a wind power plant dynamic equivalence model.
Another embodiment of the present invention provides a readable storage medium, which includes a stored computer program, and when the computer program is executed, the computer program controls a device on which the readable storage medium is located to execute the wind farm equivalent modeling method applied to electromagnetic transient simulation according to any one of the method embodiments of the present invention.
The embodiment of the invention has the following beneficial effects:
the invention provides a wind power plant equivalent modeling method, a wind power plant equivalent modeling device and a wind power plant equivalent modeling medium applied to electromagnetic transient simulation. Therefore, the modeling precision of the wind power plant dynamic equivalent model applied to the electromagnetic transient simulation is improved, and the accuracy of the wind power plant steady-state analysis and the accuracy of various fault analyses are further improved.
Drawings
FIG. 1 is a schematic flow chart of a wind farm equivalent modeling method applied to electromagnetic transient simulation according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a wind farm equivalent modeling device applied to electromagnetic transient simulation according to an embodiment of the present invention;
FIG. 3 is a schematic view of a wind farm topology structure of a wind farm equivalent modeling method applied to electromagnetic transient simulation according to an embodiment of the present invention;
fig. 4 is an equivalent wind farm topology structure of a wind farm equivalent modeling method applied to electromagnetic transient simulation according to an embodiment of the present invention.
Detailed Description
The technical solutions in the present invention will be described clearly and completely with reference to the accompanying drawings, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
As shown in fig. 1, a wind farm equivalent modeling method applied to electromagnetic transient simulation provided by an embodiment of the present invention includes the following steps:
step S101: decoupling a radiation-shaped wind power plant power collection grid into a parallel wind power plant power collection grid, and calculating equivalent power collection impedance of each wind turbine generator in the wind power plant power collection grid.
As one embodiment, after decoupling a radial wind farm power collection grid into a parallel wind farm power collection grid, performing first power flow calculation on the wind farm power collection grid before calculating equivalent collection impedance of each wind turbine in the wind farm power collection grid to obtain alternating voltage output by each wind turbine before equivalence, injection current, collection point alternating voltage and first reactive power injected into an alternating current power grid by a wind farm;
and calculating the equivalent collection impedance of each wind turbine in the wind power plant collection grid according to the alternating voltage, the injected current and the collection point alternating voltage.
Step S102: and taking the outlet voltage of each wind turbine generator as a clustering index, and classifying and aggregating the wind turbine generators by adopting a K-means clustering algorithm based on a community discovery algorithm to obtain an aggregated wind turbine generator and a wind farm aggregation model.
As one embodiment, after the equivalent collection impedance of each wind turbine in the wind farm collection grid is calculated, the wind farm collection grid is subjected to second power flow calculation to obtain an alternating voltage amplitude output by each wind turbine and a data set consisting of the alternating voltage amplitudes after the equivalence;
and classifying and aggregating the data set of the alternating voltage amplitude by adopting a K-means clustering algorithm based on a community discovery algorithm, and calculating the outlet equivalent impedance of each aggregated wind turbine generator after classification and aggregation to obtain a wind power plant aggregation model. When the data set of the alternating voltage amplitude is subjected to classification and aggregation, the optimal classification number of the K-means algorithm is automatically determined by adopting a community discovery algorithm, and then the data set of the alternating voltage amplitude is subjected to classification and aggregation according to the optimal classification number.
Step S103: and arranging a centralized reactive compensation capacitor at the outlet of each aggregation wind turbine generator and calculating a centralized reactive compensation capacitance value, wherein the centralized reactive compensation capacitor is used for simulating the distributed capacitance effect of a wind power station power collection grid.
As an embodiment, the centralized reactive compensation capacitance value is calculated according to the following steps:
performing third power flow calculation on the wind power plant power collection grid to obtain second reactive power injected into the alternating current power grid by the wind power plant;
and calculating a centralized reactive compensation capacitor at the outlet of the wind power plant according to the second reactive power and the first reactive power.
Step S104: and adjusting the zero sequence gain coefficient of the current collection lines in the wind power plant collection grid to enable the zero sequence current collection grid parameters to be equivalent, and obtaining a wind power plant dynamic equivalent model.
As an embodiment, adjusting a zero sequence gain coefficient of a collection line in the wind farm collection grid to equalize zero sequence collection grid parameters includes the following steps:
when the first zero-sequence equivalent impedance is larger than the second zero-sequence equivalent impedance, increasing a zero-sequence gain coefficient of a current collecting line in the wind power plant power collecting network; the first zero-sequence equivalent impedance is zero-sequence equivalent impedance of an original wind power plant grid, and the second zero-sequence equivalent impedance is zero-sequence gain coefficient of a collecting line in the wind power plant grid, so that zero-sequence equivalent impedance of the wind power plant grid is obtained after zero-sequence collecting grid parameters are equivalent;
and when the first zero-sequence equivalent impedance is smaller than the second zero-sequence equivalent impedance, reducing the zero-sequence gain coefficient of the current collection circuit in the wind power plant power collection grid.
As an embodiment, the adjusting the zero sequence gain coefficient by using a heuristic method includes the following steps:
when the zero sequence gain coefficient of a current collecting line in the wind power plant grid set is adjusted, increasing or decreasing an adjusting step length each time, and calculating a corresponding zero sequence deviation coefficient according to the first zero sequence equivalent impedance and the second zero sequence equivalent impedance of the wind power plant grid set;
obtaining a minimum value of the zero sequence deviation coefficient and an optimal zero sequence gain coefficient corresponding to the minimum value through increasing or decreasing an adjustment step length for multiple times;
and increasing or reducing the zero sequence gain coefficient of the current collection circuit in the wind power plant power collection network according to the optimal zero sequence gain coefficient.
As a detailed embodiment, the embodiment of the present invention comprises the following steps (step A01-step A07 are only used as the serial number of the embodiment, and no corresponding figure is shown):
step A01: and decoupling the radiation-shaped wind power plant power collection grid into a parallel wind power plant power collection grid.
Step A02: carrying out first power flow calculation on the wind power plant power collection grid to obtain the alternating voltage output by each wind turbine before equivalence
Injection current
Sink point AC voltage
First reactive power Q injected into an AC grid by a wind farm c0 And calculating the equivalent collection impedance Z of each wind turbine in the wind power plant collection grid according to a formula (1) e ＝R e +jX e Wherein R is e 、X e Respectively, the resistance and reactance components, j represents an imaginary unit:
step A03: calculating equivalent current collection impedance of each wind turbine in the wind power plant power collection grid, performing secondary load flow calculation on the wind power plant power collection grid to obtain an alternating voltage amplitude output by each wind turbine after equivalence and a data set U consisting of the alternating voltage amplitudes eq 。
Step A05: adopting a K-means clustering algorithm based on a community discovery algorithm to perform on the data set U of the alternating voltage amplitude eq And carrying out classification and aggregation, and calculating the outlet equivalent impedance of each aggregated wind turbine generator after classification and aggregation to obtain a wind power plant aggregation model. When the data set of the alternating voltage amplitude is subjected to the classification and the polymerization, the optimal classification number K of the K-means algorithm is automatically determined by adopting a community discovery algorithm opt And then carrying out classification and aggregation on the data set of the alternating voltage amplitude according to the optimal classification number. According to formula M max ＝M(k opt ) Calculating the optimal classification number k opt Calculating corresponding modularity M for the classified result by using community discovery algorithm, and determining the optimal classification number k according to the value of M opt Wherein M refers to modularity. And according to the classification and aggregation result, the rated capacity, the active power output and the reactive power output of each aggregated wind turbine generator set are respectively the capacity weighted average value of each sub-fan. And keeping the per unit values of the internal parameters of the wind turbine generator and the corresponding parameters of the sub-fans of the wind turbine generator equal. Calculating the equivalent impedance Z of the outlet of each polymerization fan after polymerization according to the formula (2) ag ：
Step A06: and arranging a centralized reactive compensation capacitor at the outlet of each aggregation wind turbine generator and calculating a centralized reactive compensation capacitance value, wherein the centralized reactive compensation capacitor is used for simulating the distributed capacitance effect of a wind power station power collection grid.
Calculating a centralized reactive compensation capacitance value according to the following steps:
performing third operation on the wind power plant power collection gridCalculating the secondary power flow to obtain second reactive power Q of the wind power plant injected into the alternating current power grid ag ；
According to the second reactive power Q ag And the first reactive power Q obtained in the step A02 c0 Centralized reactive compensation capacitor for calculating wind power plant outlet
Step A07: and adjusting the zero sequence gain coefficient of the current collection lines in the wind power plant current collection grid to enable the zero sequence current collection grid parameters to be equivalent and obtain a wind power plant dynamic equivalent model.
Specifically, when a fault occurs at the outlet of the wind power plant, a first zero-sequence equivalent impedance Z is calculated 0 And the second zero sequence equivalent impedance Z 0eq And calculating the zero sequence deviation coefficient
The first zero-sequence equivalent impedance is zero-sequence equivalent impedance of an original wind power plant power collection grid, and the first zero-sequence equivalent impedance means that: and according to the original wind power plant topology, the total zero sequence impedance of the whole current collection network is obtained by calculating the zero sequence impedance of each current collection line of the current collection network in series and parallel. The second zero-sequence equivalent impedance is the zero-sequence equivalent impedance of the wind power plant collection power grid obtained through aggregation in the step A05, and the meaning of the second zero-sequence equivalent impedance is as follows: and B, according to the wind power plant topology obtained after the aggregation in the step A05, calculating the zero sequence impedance of each current collection line of the current collection network in series and parallel to obtain the total zero sequence impedance of the whole current collection network.
Adjusting the zero sequence gain coefficient of the current collection lines in the wind power plant current collection grid according to the following steps so as to enable the zero sequence current collection grid parameters to be equivalent:
when the value of | Z 0 |>|Z 0eq When the voltage is lower than the preset voltage, increasing the zero sequence gain coefficient K of the current collecting circuit in the wind power plant power collecting grid 0 To increase the zero sequence impedance of the equivalent system.
When Z 0 |<|Z 0eq When the voltage is lower than the preset voltage, reducing the zero sequence gain coefficient K of the current collecting circuit in the wind power plant power collecting grid 0 So as to reduce the zero sequence impedance of the equivalent system. The zero sequence gain coefficient is adjusted by adopting a heuristic method, and the method comprises the following steps of: setting the adjustment step length to delta K 0 Each time by a delta K when adjusting the zero-sequence gain factor 0 While calculating the corresponding zero sequence deviation coefficient sigma 0 Until σ is encountered 0 Taking the gain coefficient as the optimal gain coefficient K 0opt And is calculated at K 0opt Equivalent zero sequence impedance of lower power collection network
Wherein K 0opt The conditions are satisfied:
the topological structure of the original wind power plant power collection grid adopted by the embodiment of the invention is shown in fig. 3, the wind power plant power collection grid comprises a cluster 1 and a cluster 2, and 37 full-power converter type wind turbines with rated power of 4MW are used in total; the machine group 1 and the machine group 2 are respectively connected to the medium-voltage side of a three-winding transformer of the boosting platform, the high-voltage side of the boosting platform is connected with an equivalent external alternating current system through a 25km cable, the active power and the reactive power output by each machine group are controlled by a single-position power factor, namely, the reference value of the reactive power output of the machine group is set to be 0, and the active power output of all the machine groups is set to be 3 grades, and is set to be 2 MW, 3 MW and 4 MW. The main parameters of the wind power plant grid system are shown in the following table:
according to the embodiment of the invention, for the radiation-shaped wind power plant collection network, the radiation shape is decoupled into a parallel structure, and the equivalent collection impedance of each wind generation set is calculated. According to the first power flow calculation result, obtaining the output alternating voltage of each fan before equivalence
Injection current
Collection point AC voltage
And calculating equivalent collecting impedance Z at the outlet of each fan e . And then, carrying out secondary load flow calculation on the equivalent system to obtain the output alternating voltage amplitude of each fan and generate a data set U eq . Using K-means algorithm to pair U eq Clustering, calculating corresponding modularity M for the classified results by using community discovery algorithm, and for the machine group 1, M max M (3) ═ 0.2858; for fleet 2, M max M (3) ═ 0.3996. Thus, the optimal classification number k is determined according to the value of M opt1 ＝k opt2 And 3, the equivalent wind power plant topological structure obtained according to the optimal classification result is shown in FIG. 4. And setting a centralized reactive compensation capacitor for the aggregated wind power plant, and performing zero sequence collection network equivalence. The initial zero sequence gain coefficient K 0 Setting the value to be 1.50, and carrying out comparison calculation on equivalent zero-sequence impedances of the wind power plants before and after the equivalent aggregation to ensure that the zero-sequence deviation coefficient sigma of the cluster 1 and the cluster 2 0 The optimal zero sequence gain coefficients corresponding to the minimum value are respectively K 01 _ opt ＝1.51,K 02 _ opt ＝1.50。
Under the condition that the wind power plant dynamic equivalent model is established, the simulation time is set to be 0.6s under different fault working conditions, and the electromagnetic transient simulation time comparison between the wind power plant dynamic equivalent model and the wind power plant original model is shown in the following table:
the wind power plant dynamic equivalent model established by the invention can more accurately reflect the transient voltage change trend of the original model. Compared with a traditional single-machine equivalent model, the wind power plant dynamic equivalent model can reflect the instantaneous voltage and power characteristics of an original model more accurately in the fault period of steady-state operation and the single-phase short-circuit fault recovery process. The wind power plant dynamic equivalent model can more accurately reflect the maximum voltage characteristic during the fault recovery period. Therefore, due to the fact that the influence of the capacitance of the collecting line is considered, compared with a traditional single-machine equivalent model, the wind power plant dynamic equivalent model can reflect the reactive power characteristic of an original model more accurately. Meanwhile, after the dynamic equivalent model provided by the invention is adopted, the electromagnetic transient simulation time can be shortened by more than 99.7%, which shows that the wind power plant dynamic equivalent model provided by the invention can obviously reduce the electromagnetic transient simulation calculated amount and improve the electromagnetic transient simulation efficiency.
The method accurately equates the power collection network, performs centralized equivalence on the distributed capacitance of the power collection network, and equates the zero sequence set power network parameters. The dynamic characteristics of the grid collecting system can be simulated more accurately, so that the method is suitable for steady-state analysis and multiple fault analysis of the wind power plant. In addition, the optimal classification number of the K-means algorithm is automatically determined by adopting a community discovery algorithm, the simulation workload can be reduced, the electromagnetic transient simulation time can be greatly reduced by the provided dynamic equivalent modeling method, and the method has high practical value in actual engineering.
The invention has the following beneficial technical effects:
(1) the dynamic equivalence modeling method for the full-power converter wind power plant applied to the electromagnetic transient simulation is used for accurately equating the power collection network, and can improve the accuracy of equivalence.
(2) The method disclosed by the invention performs equivalence on the distributed capacitance effect of the wind power station power collection grid, and can improve the accuracy of the output reactive power of the wind power station.
(3) The method disclosed by the invention has the advantages that the zero sequence parameters of the wind power station power grid are equivalent, and the accuracy of the wind power station to the reflection of the original model under the asymmetric fault can be improved.
(4) Electromagnetic transient simulation results show that compared with a single-machine equivalent method, the equivalent modeling method provided by the invention has the advantages that when the wind power plant has symmetrical and asymmetrical faults, the instantaneous values of current and voltage are closer to the original model of the wind power plant, and the power waveform is closer to the original model of the wind power plant. Therefore, the invention has strong applicability under various working conditions and has great practical engineering significance.
(5) The electromagnetic transient simulation time is displayed, and by adopting the equivalent modeling method provided by the invention, the simulation time is greatly shortened, the electromagnetic transient simulation calculated amount is reduced, the efficiency is obviously improved, and the practical engineering significance is great.
On the basis of the above embodiment of the invention, the present invention correspondingly provides an embodiment of an apparatus, as shown in fig. 2;
the invention provides a wind power plant equivalent modeling device applied to electromagnetic transient simulation, which comprises a current collection grid decoupling module, a classification aggregation module, a capacitance value calculation module and a parameter equivalent module;
the power collection network decoupling module is used for decoupling a radiation-shaped wind power plant power collection network into a parallel wind power plant power collection network and calculating equivalent power collection impedance of each wind turbine generator in the wind power plant power collection network;
the classification and aggregation module is used for classifying and aggregating the wind turbines by using the outlet voltage of each wind turbine as a clustering index and adopting a K-means clustering algorithm based on a community discovery algorithm to obtain an aggregated wind turbine and a wind farm aggregation model;
the capacitance value calculation module is used for calculating the capacitance value of a centralized reactive compensation capacitor arranged at the outlet of each aggregation wind turbine generator, and the centralized reactive compensation capacitor is used for simulating the distributed capacitance effect of a wind power station power collection grid;
the parameter equivalence module is used for adjusting a zero sequence gain coefficient of a power collection line in the wind power plant power collection grid to enable parameters of the zero sequence power collection grid to be equivalent and obtain a wind power plant dynamic equivalence model.
For convenience and brevity of description, the embodiments of the apparatus of the present invention include all the embodiments of the power scheduling method applied to the incremental power distribution network, and are not described herein again.
On the basis of the embodiment of the invention, the invention correspondingly provides an embodiment of a readable storage medium; another embodiment of the present invention provides a readable storage medium, which includes a stored computer program, and when the computer program is executed, the computer program controls a device on which the readable storage medium is located to execute the wind farm equivalent modeling method applied to electromagnetic transient simulation according to any one of the method embodiments of the present invention.
Illustratively, the computer program may be partitioned into one or more modules that are stored in the memory and executed by the processor to implement the invention. The one or more modules may be a series of computer program instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the terminal device.
The terminal device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The terminal device may include, but is not limited to, a processor, a memory.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal device and connects the various parts of the whole terminal device using various interfaces and lines.
The memory may be used to store the computer programs and/or modules, and the processor may implement various functions of the terminal device by executing or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Wherein, the terminal device integrated module/unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, can be stored in a computer readable storage medium (i.e. the above readable storage medium). Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
It will be understood by those skilled in the art that all or part of the processes in the above embodiments may be implemented by hardware related to instructions of a computer program, where the computer program may be stored in a computer readable storage medium, and when executed, the computer program may include the processes in the above embodiments. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Claims (10)
1. A wind power plant equivalent modeling method applied to electromagnetic transient simulation is characterized by comprising the following steps:
decoupling a radiation-shaped wind power plant power collection grid into a parallel wind power plant power collection grid, and calculating equivalent collector impedance of each wind turbine generator in the wind power plant power collection grid;
taking the outlet voltage of each wind turbine generator as a clustering index, and classifying and aggregating the wind turbine generators by adopting a K-means clustering algorithm based on a community discovery algorithm to obtain aggregated wind turbine generators and a wind farm aggregation model;
aiming at the wind power plant aggregation model, a centralized reactive compensation capacitor is arranged at an outlet of each aggregation wind turbine generator and is used for calculating a centralized reactive compensation capacitance value, and the centralized reactive compensation capacitor is used for simulating a distributed capacitance effect of a wind power plant power collection grid;
and adjusting the zero sequence gain coefficient of the current collection lines in the wind power plant current collection grid to enable the zero sequence current collection grid parameters to be equivalent and obtain a wind power plant dynamic equivalent model.
2. The wind farm equivalent modeling method applied to the electromagnetic transient simulation of claim 1, characterized in that after a radial wind farm power collection grid is decoupled into a parallel wind farm power collection grid, a first power flow calculation is performed on the wind farm power collection grid before an equivalent collector impedance of each wind turbine in the wind farm power collection grid is calculated, so that an alternating voltage, an injection current, a collection point alternating voltage and a first reactive power of the wind farm injected into the alternating current power grid before equivalence are obtained;
and calculating the equivalent collection impedance of each wind turbine in the wind power plant collection grid according to the alternating voltage, the injection current and the collection point alternating voltage.
3. The wind power plant equivalent modeling method applied to the electromagnetic transient simulation, as claimed in claim 2, is characterized in that the wind power plants are classified and aggregated by using the outlet voltage of each wind power plant as a clustering index and adopting a K-means clustering algorithm based on a community discovery algorithm to obtain a wind power plant aggregation model, and specifically comprises the following steps:
after the equivalent collection impedance of each wind turbine in the wind power plant collection grid is calculated, carrying out secondary load flow calculation on the wind power plant collection grid to obtain an alternating voltage amplitude output by each wind turbine after the equivalence and a data set consisting of the alternating voltage amplitudes;
and classifying and aggregating the data set of the alternating voltage amplitude by adopting a K-means clustering algorithm based on a community discovery algorithm, and calculating the outlet equivalent impedance of each aggregated wind turbine generator after classification and aggregation to obtain a wind power plant aggregation model.
4. The wind farm equivalent modeling method applied to electromagnetic transient simulation of claim 3, wherein when the data set of the alternating voltage amplitude is classified and aggregated, an optimal classification number of a K-means algorithm is automatically determined by adopting a community discovery algorithm, and then the data set of the alternating voltage amplitude is classified and aggregated according to the optimal classification number.
5. The wind farm equivalent modeling method applied to electromagnetic transient simulation as claimed in claim 4, wherein calculating the centralized reactive compensation capacitance value comprises the following steps:
performing third power flow calculation on the wind power plant power collection grid to obtain second reactive power injected into the alternating current power grid by the wind power plant;
and calculating a centralized reactive compensation capacitor at the outlet of the wind power plant according to the second reactive power and the first reactive power.
6. The wind farm equivalent modeling method applied to electromagnetic transient simulation as claimed in claim 5, wherein adjusting zero sequence gain coefficients of collection lines in the wind farm collection grid to make zero sequence collection grid parameters equivalent comprises the following steps:
when the first zero-sequence equivalent impedance is larger than the second zero-sequence equivalent impedance, increasing a zero-sequence gain coefficient of a current collection circuit in the wind power plant collection grid; the first zero-sequence equivalent impedance is zero-sequence equivalent impedance of an original wind power plant grid, and the second zero-sequence equivalent impedance is zero-sequence gain coefficient of a collecting line in the wind power plant grid, so that zero-sequence equivalent impedance of the wind power plant grid is obtained after zero-sequence collecting grid parameters are equivalent;
and when the first zero-sequence equivalent impedance is smaller than the second zero-sequence equivalent impedance, reducing the zero-sequence gain coefficient of the current collection line in the wind power plant power collection network.
7. The wind farm equivalent modeling method applied to electromagnetic transient simulation of claim 6, wherein the zero sequence gain coefficients are adjusted using heuristics.
8. The wind farm equivalent modeling method applied to the electromagnetic transient simulation of any one of claims 1 to 7, characterized in that when adjusting the zero sequence gain coefficient of the collection lines in the wind farm set grid, each time an adjustment step length is increased or decreased, and the zero sequence deviation coefficient of the wind farm set grid of the current zero sequence gain coefficient is calculated;
obtaining a minimum value of the zero sequence deviation coefficient and an optimal zero sequence gain coefficient corresponding to the minimum value through increasing or decreasing an adjustment step length for multiple times;
and increasing or reducing the zero sequence gain coefficient of the current collection circuit in the wind power plant power collection network according to the optimal zero sequence gain coefficient.
9. A wind power plant equivalent modeling device applied to electromagnetic transient simulation is characterized by comprising a power collection and grid decoupling module, a classification and aggregation module, a capacitance value calculation module and a parameter equivalent module;
the power collection network decoupling module is used for decoupling a radiation-shaped wind power plant power collection network into a parallel wind power plant power collection network and calculating equivalent power collection impedance of each wind turbine generator in the wind power plant power collection network;
the classification and aggregation module is used for classifying and aggregating the wind turbines by using the outlet voltage of each wind turbine as a clustering index and adopting a K-means clustering algorithm based on a community discovery algorithm to obtain an aggregated wind turbine and a wind farm aggregation model;
the capacitance value calculation module is used for calculating the capacitance value of a centralized reactive compensation capacitor arranged at the outlet of each aggregation wind turbine generator, and the centralized reactive compensation capacitor is used for simulating the distributed capacitance effect of a wind power station power collection grid;
the parameter equivalence module is used for adjusting zero sequence gain coefficients of current collection lines in the wind power plant current collection grid so as to enable the zero sequence current collection grid parameters to be equivalent and obtain a wind power plant dynamic equivalence model.
10. A readable storage medium, characterized in that the readable storage medium comprises a stored computer program which, when executed, controls a device on which the readable storage medium is located to execute the wind farm equivalent modeling method applied to electromagnetic transient simulation as recited in any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210611672.0A CN114925538A (en) | 2022-05-31 | 2022-05-31 | Wind power plant equivalent modeling method, device and medium applied to electromagnetic transient simulation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210611672.0A CN114925538A (en) | 2022-05-31 | 2022-05-31 | Wind power plant equivalent modeling method, device and medium applied to electromagnetic transient simulation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114925538A true CN114925538A (en) | 2022-08-19 |
Family
ID=82813252
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210611672.0A Pending CN114925538A (en) | 2022-05-31 | 2022-05-31 | Wind power plant equivalent modeling method, device and medium applied to electromagnetic transient simulation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114925538A (en) |
-
2022
- 2022-05-31 CN CN202210611672.0A patent/CN114925538A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109670213B (en) | Multi-machine equivalent modeling method and device for wind power plant | |
| CN110571794B (en) | Transient model equivalent calculation method suitable for doubly-fed wind power plant | |
| CN112072692B (en) | Impedance equivalence method and device for new energy power generation station | |
| CN109521693B (en) | Wind power plant electromagnetic transient real-time simulation method and device | |
| CN108460228B (en) | Wind power plant equivalence method based on multi-objective optimization algorithm | |
| CN113852120A (en) | Method and system for determining maximum grid-connected capacity of new energy power generation | |
| CN116488267B (en) | Modeling-based wind farm reactive capacity limit simulation calculation method and device | |
| CN114925538A (en) | Wind power plant equivalent modeling method, device and medium applied to electromagnetic transient simulation | |
| CN115659601A (en) | Parameter identification method and device for single-machine equivalent grid-connected model of double-fed wind power plant | |
| CN110707689B (en) | Stability analysis method and device suitable for full-clean energy power generation power grid | |
| CN113435150B (en) | Equivalent circuit and electromagnetic transient simulation method of double-fed wind driven generator | |
| Zhang et al. | Electromagnetic transient modeling and verification of DFIG unit and wind farm based on RTLAB | |
| CN108448582A (en) | Harmonic detecting system and harmonic wave processing system | |
| CN114465280A (en) | Dynamic equivalent modeling method for new energy grid-connected system | |
| CN114357787A (en) | Equivalent modeling method and system for offshore wind plant | |
| CN111884257A (en) | Direct-drive wind turbine group simulation model and data acquisition method and system thereof | |
| CN108448595B (en) | Photovoltaic-series compensation system time domain simulation small disturbance analysis method and system | |
| CN107069702B (en) | Large wind power plant equivalence method for online safety analysis based on in-station topology | |
| Shen et al. | Research on Equivalent Modeling Method of Multiple New Energy Stations for Sub/Supersynchronous Oscillation Analysis | |
| CN111725834B (en) | Method for establishing simulation model of doubly-fed fan converter of wind power plant | |
| Cao et al. | Equivalence method for wind farm based on clustering of output power time series data | |
| Wang et al. | Equivalent Modeling of PV Cluster Dynamic Process Based on K-medoids Clustering | |
| CN113139259B (en) | Wind power plant clustering modeling method for dynamic equivalence of power grid | |
| CN112084644B (en) | Dynamic equivalence method and device for power system and storage medium | |
| CN113946985B (en) | Method and system for determining new energy station equivalent model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |